Network Of Stations Research Articles

Distributed Peer-to-Peer Coordination of Hierarchical Three-Phase Energy Transactions Among Electric Vehicle Charging Stations in Constrained Power Distribution and Urban Transportation Networks

Distributed Peer-to-Peer Coordination of Hierarchical Three-Phase Energy Transactions Among Electric Vehicle Charging Stations in Constrained Power Distribution and Urban Transportation Networks

Application of Geoid and Mean Dynamic Topography Models as an Alternative to the Mean Sea Level Approach in Determining Vertical Datum (Case Study: Java Island and Surrounding)

Abstract Indonesia’s archipelagic territory provides constraints for determining a seamless vertical datum throughout the region. The main constraint is how to connect the vertical datum between islands separated by sea. To define the physical height system of land areas, it usually refers to the Mean Sea Level (MSL), which has different values in each area, and the ideal MSL information is not yet available throughout regions with a minimum data duration of one cycle of 18.6 years. To overcome this constraint, in this research, another alternative method is used: applying a vertical datum that refers to a physical geoid reference surface, namely Mean Dynamic Topography (MDT) using Altimetry satellite data. The study area is Java Island and its surroundings, using vertical information from Geodetic Control Point stations, which also function as Tidal stations in the Indonesian Geospatial Reference System network. The range of MDT deviation values obtained from the Satellite Altimetry with Geodetic Ground Control data is ± 2 meters, which is a good value for coastal areas facing the open sea. This shows that the application of the geoid and MDT models can be used as an alternative, seamless MSL approach in the Java Island region and its surroundings.

Constructing a Corrected Stations Network Using Some Geomatic Techniques

Abstract The major objective of this study is to establish a network of Ground Control Points-GCPs which can use it as a reference for any engineering project. Total Station (type: Nikon Nivo 5.C), Optical Level and Garmin Navigator GPS were used to perform traversing. Traversing measurement was achieved by using nine points covered the selected area irregularly. Near Civil Engineering Department at Baghdad University Al-jadiriya, an attempt has been made to assess the accuracy of GPS by comparing the data obtained from the Total Station. The average error of this method is 3.326 m with the highest coefficient of determination (R2) is 0.077 m observed in Northing. While in the Easting the average error is 4.634 m with (R2) 0.099 m observed. The average error 5.513 m with (R2) is 0.071 m is observed in Reduced Level. The accuracy assessments of the interpolation methods (Total Station and Level) have been calculated using the average error 0.240 m with (R2) is 0.735 m is observed in Reduced Level. The GIS 10.3 program can automatically transform the saved data to text format and produce digital plans with attribute.

Wavelet-based correlations of the global magnetic field in connection to strongest earthquakes

We consider 3-component records of the magnetic field strength with a time step of 1 min at 153 stations of the INTERMAGNET network for 31 years, 1991–2021. Data analysis is based on the calculation of pairwise correlation coefficients between wavelet coefficients in successive time windows 1 day long (1440 min counts). To describe the state of the magnetic field, the maxima of the average values of all pairwise correlation coefficients between stations were chosen, calculated over all detail levels of the wavelet decomposition and over all components of the magnetic field strength vector. The daily time series of such maxima is called wavelet correlation. The division of the network stations into 7 clusters is considered, and a time series of wavelet correlations is calculated for each cluster. In a sliding time window with a length of 365 days, correlation measures of synchronization of wavelet correlations from different clusters are calculated, which are compared with the strongest earthquakes with a magnitude of at least 8.5. For the global time series of wavelet correlations, the method of influence matrices is used to study the relationship between the maximum correlation responses to a change in the length of the day and a sequence of earthquakes with a magnitude of at least 7. As a result of the analysis, precursor effects are identified, and the important role of the Maule earthquake in Chile on February 27, 2010 in the behavior of the response of magnetic field for the preparation of strong seismic events is shown.

Optimization of Public Transport Networks by Considering Alternative Positions for Network Stations

Optimization of Public Transport Networks by Considering Alternative Positions for Network Stations

Optical turbulence profiling at the Table Mountain Facility with the Laser Communication Relay Demonstration GEO downlink

We report the first measurement of the atmospheric optical turbulence profile using the transmitted beam from a satellite laser communication terminal. A ring image next generation scintillation sensor (RINGSS) instrument for turbulence profiling, as described in Tokovinin [MNRAS 502, 747 (2021)10.1093/mnras/staa4049], was deployed at the NASA/Jet Propulsion Laboratory’s Table Mountain Facility (TMF) in California. The optical turbulence profile was measured with the downlink optical beam from the Laser Communication Relay Demonstration (LCRD) geostationary satellite. LCRD conducts links with the Optical Communication Telescope Laboratory ground station and the RINGSS instrument was co-located at TMF to conduct measurements. Turbulence profiles were measured at day and night and atmospheric coherence lengths were compared with other turbulence monitors such as a solar scintillometer and Polaris motion monitor. RINGSS sensitivity to boundary layer turbulence, a feature not provided by many profilers, is also shown to agree with a boundary layer scintillometer at TMF (R = 0.85). Diurnal evolution of optical turbulence and measured profiles are presented. The correlation of RINGSS with other turbulence monitors (R = 0.75 − 0.86) demonstrates the concept of free-space optical communications turbulence profiling, which could be adopted as a way to support optical ground stations in a future Geostationary feeder link network. These results also provide further evidence that RINGSS, a relatively new instrument concept, correlates well with other instruments in daytime and nighttime turbulence.

元ㆍ安南 간 육상 교통 루트와 驛站

The primary transportation routes of the Yuan Dynasty, known as the postal routes, were developed to facilitate the movement of troops and military supplies. Later, these routes became important for official business and trade between Yuan and neighboring countries. Although land transportation routes connecting the central plains of China with Annam began to be established during the Tang Dynasty, they were not actively used until the Yuan Dynasty. The transportation routes between Yuan and Annam were straightened to shorten the travel time by eliminating detours and to enhance the stability and continuity of the routes by constructing postal stations every 60 li. The primary land transportation routes between the two countries were (1) Chongqing-Mengzi-Thang Long and (2) Chongqing-Yongzhou-Thang Long. The first route, departing from Chongqing in Yunnan Province, went directly through steep mountains to reach Mengzi, and then followed the Hong River into Annam. This was the route used by envoys from both countries before the Southern Song conquest; however, it was time-consuming due to the mountainous terrain. The second route was developed after Yuan took control of Yongzhou, becoming the fastest transportation route between the two countries and mainly used for envoys and trade. This route was established by Toghon, featuring a dense network of postal stations every 60 li within Annam, which intimidated the king of Annam. Since the establishment of these postal stations in 1284, Annamese envoys traveled along this route to Dadu every one to two years to deliver tributes, as well as to transport goods for the Yuan court. Annam’s tributes were used to supply luxuries for the imperial family, while gifts from Yuan were used as ceremonial items to enhance the authority of the king of Annam. During this period, Yongping, a postal station at the entrance to Annam, flourished as a hub for private trade.

A statistical analysis method for significant wave height and spectral peak frequency considering the random and time-varying effects based on copula function and Bayesian inference

Random and time-varying effects are important factors for statistical analysis of wave characteristic variables, including the significant wave height and spectral peak frequency. This paper proposes a statistical analysis method for the accurate statistical analysis of the state of the ocean. Several common distributions are applied as candidates for describing a specific variable, denoted as the marginal distribution. The joint distribution for the wave characteristic variables is constructed using copula functions based on the marginal distributions. The probability and unknown parameters of the marginal distributions are then determined by fully Bayesian inference. The best-fitting marginal distribution is selected based on the posterior probabilities of the candidates. Then, unknown parameters of the candidate copula functions are estimated by maximum likelihood estimation. The best-fitting copula function is selected based on Akaike information criterion, root mean squared error and Nash Sutcliffe efficiency. The proposed method is verified using the National Data Buoy Center dataset for 2019. However, this dataset, collected from a network of almost 100 moored buoys and Coastal-Marine Automated Network (CMAN) stations, contains incomplete data. The results reveal that the best-fitting marginal distribution and copula function may vary with the month. The average and maximum values of the improved RMSE using the proposed method are only 0.0064 and 0.0187, respectively. This indicates the high accuracy of the proposed method for the statistical analysis of wave states even though missing some data.

Customizing pyfao56 for evapotranspiration estimation and irrigation scheduling at the Limited Irrigation Research Farm (LIRF), Greeley, Colorado

Estimation of evapotranspiration (ET, the water used by soil evaporation and plant transpiration) and soil water depletion (Dr, the amount of water to bring the soil water in the root zone to field capacity) are critical in irrigation water management. The open-source Python-based crop ET and water balance modeling package named “pyfao56” was originally developed based on the dual crop coefficient approach as described in the Food and Agricultural Organization of the United Nations, Irrigation and Drainage Paper No. 56 (FAO-56). The package later expanded on the seminal FAO-56 document to consider various options relevant to ET and water balance modeling, including both short (grass) and tall (alfalfa) reference crops, optional discretization of variable soil layers based on field capacity, and interpretation of readily available water (RAW) and Dr based on both a dynamic (growing) root zone and maximum root zone. The package requires two input data objects; the “Parameters” class defines variables affecting soil water balance and ET and the “Weather” class specifies relevant meteorological data. Other optional input data objects include the “Irrigation” class for irrigation events, the “Soil_Profile: class for defining stratified soil layer data, and the “Update” class for assimilation of measured data. Additional tools are available for estimating and forecasting standardized reference ET (ETref), providing seasonal water balance summaries, computing goodness-of-fit statistics between measured and modeled values, and visualizing time series plots of daily Dr, ET, and crop coefficients. The current pyfao56 release (v1.2.1) was incorporated into a customized workflow for specific use at the USDA-ARS Limited Irrigation Research Farm (LIRF) in Greeley, Colorado, and named the LIRF Implementation of Pyfao56 (LIRFIP). Field data from 2023 full and limited irrigation field trials were used to demonstrate the functionality of LIRFIP and its customizations and integration of pyfao56 into the LIRF workflow. Specific customizations included use of pyfao56 “customload” functions to input data from various sources, including weather from an on-site micrometerological station (using the application programming interface (API) developed by the maintainers of the Colorado Agricultural Meteorological (CoAgMet) weather station network), irrigation events (via a shared Google docs sheet), field capacity values by plot and soil layer (via an Excel spreadsheet), and measured soil water content as well as basal crop coefficient (Kcb) calculated from fractional canopy cover (fc) (via an SQLite database). To evaluate multiple research plots, LIRFIP was designed to iterate simulations from multiple instances of pyfao56 and to produce customized output summary files and interactive hypertext markup language (html) graphs as an aid for irrigation management decisions for LIRF field trials. This study demonstrated pyfao56 as a useful, flexible, customizable, and repeatable ET-based water balance model by showcasing its integration within a specific computational workflow for irrigation management field research at LIRF. The approach serves as an example for pyfao56 integration in other water management tools as conceived by water managers, researchers, and practitioners worldwide.

Assessing Multi‐Source Precipitation Estimates in Nepal: A Benchmark for Sub‐Seasonal Model Assessment

AbstractSub‐seasonal to seasonal (S2S) prediction provides an extended range of lead time for decision‐makers across multiple sectors. S2S forecast is crucial for a country that are susceptible to hydro‐meteorological disasters like Nepal. However, S2S forecast requires assessment with reliable datasets before its application. Since, Nepal lacks a dense station network, multi‐source precipitation estimates (MPEs) are the obvious alternatives. Therefore, using classical evaluation metrics and extreme precipitation indices, this study assessed eleven high‐resolution datasets against 159‐gauge stations over Nepal for the 2001–2020 period. These datasets are classified as gauge‐interpolated (APHRODITE), merged (TPMFD, MSWEP, CLDAS, and CHIRPS), satellite‐based (IMERGV7, IMERGV6, CMORPH, and PERSIANN), and reanalysis (ERA5‐L and HAR). Satellite datasets (except IMERGV7) failed to capture the spatial pattern of mean annual precipitation, while others broadly captured it. Most MPEs struggled to accurately estimate wet season precipitation compared to dry season. Furthermore, increasing estimation error from light to extreme precipitation and decreasing skill metrics from flat to complex terrain, demonstrate the intensity and terrain‐specific limitations of MPEs. In terms of precipitation extremes, APHRODITE exhibits the highest skill, followed by MSWEP, TPMFD, HAR, ERA5‐L, IMERGV7, CLDAS, IMERGV6, CHIRPS, CMORPH, and PERSIANN. IMERGV7 exhibits increased skill in detecting extreme precipitation and precipitation over orography against IMERGV6. APHRODITE and TPMFD datasets performed consistently well in all scales, including weekly anomaly, with an average anomaly correlation of 0.84 and 0.66 respectively. However, since APHRODITE is not widely available, TPMFD can serve as a benchmark dataset for evaluating high‐resolution S2S forecasts within the study region.

The fireball of November 24, 1970, as the most probable source of the Ischgl meteorite

AbstractThe discovery of the Ischgl meteorite unfolded in a captivating manner. In June 1976, a pristine meteorite stone weighing approximately 1 kg, fully covered with a fresh black fusion crust, was collected on a mountain road in the high‐altitude Alpine environment. The recovery took place while clearing the remnants of a snow avalanche, 2 km northwest of the town of Ischgl in Austria. Subsequent to its retrieval, the specimen remained tucked away in the finder's private residence without undergoing any scientific examination or identification until 2008, when it was brought to the University of Innsbruck. Upon evaluation, the sample was classified as a well‐preserved LL6 chondrite, with a W0 weathering grade, implying a relatively short time between the meteorite fall and its retrieval. To investigate the potential connection between the Ischgl meteorite and a recorded fireball event, we have reviewed all documented fireballs ever photographed by German fireball camera stations. This examination led us to identify the fireball EN241170 observed in Germany by 10 different European Network stations on the night of November 23/24, 1970, as the most likely candidate. We employed state‐of‐the‐art techniques to reconstruct the fireball's trajectory and to reproduce both its luminous and dark flight phases in detail. We find that the determined strewn field and the generated heat map closely align with the recovery location of the Ischgl meteorite. Furthermore, the measured radionuclide data reported here indicate that the pre‐atmospheric size of the Ischgl meteoroid is consistent with the mass estimate inferred from our deceleration analysis along the trajectory. Our findings strongly support the conclusion that the Ischgl meteorite originated from the EN241170 fireball, effectively establishing it as a confirmed meteorite fall. This discovery enables to determine, along with the physical properties, also the heliocentric orbit and cosmic history of the Ischgl meteorite.

The KISS Network in 2015–2016: Catalogs and Comparison of the Processing Results with Operational Estimates from the Permanent Network

As part of the international collaboration of several research groups from Russia, France, and Germany, 77 temporary seismic stations were installed in the summer of 2015 for one-year period to conduct a detailed study of the deep structure of the Earth’s crust and upper mantle in the region of the Klyuchevskoi Volcano Group (KGV) in the Kamchatka Peninsula. One of the results of the KISS experiment (Klyuchevskoi Investigation – Seismic Structure of an extraordinary volcanic system) was the final catalog of the joint data from the temporary stations and the permanent network of the Kamchatka Branch of the Geophysical Survey of the Russian Academy of Sciences (KB GS RAS). The catalog comprises 2136 events, including 560 for which the permanent network catalog lacked sufficient data for correct processing. The catalog in .xlsx format and the station bulletin in .isf format are presented in the supplementary material to the paper. A comparative analysis was conducted on the joint solutions of two catalogs: one obtained solely from the data of the KB GS RAS permanent network stations and another from a denser seismic network integrated with KISS stations.

Risk overbounding for ionospheric gradient monitor using geometry-free double differenced carrier phase measurements

Ionosphere anomaly can cause large spatial gradients in the double-differenced carrier phase (DDCP) measurements. Taking advantage of this characteristic, the ionospheric gradient monitor (IGM) is designed with multiple ground reference stations to detect threatening ionospheric gradients for safety of life applications. An optimal IGM should be most sensitive to ionospheric gradients and least sensitive to other errors. However, current IGM suffers from the influence of tropospheric error, which can degrade monitor performance under extreme weather conditions by causing risks of false alarms and missed detections. To address this issue, an alternative IGM is proposed using the geometry-free combination of DDCP as the test statistic. Ambiguity resolution procedure is designed for estimating the ambiguity term in the test statistic. The risk induced by the wrong ambiguity fix and tropospheric error are analyzed and bounded with required averaging period and minimum baseline length. The results show that the proposed theoretical IGM is capable of achieving probability of false alarm of 10–8 and probability of missed detection of 10–6 with a filtering period of 612 s and baseline length of 371.7 m or a filtering period of 544 s and baseline length of 384.4 m. The experimental results using data collected from Hong Kong Satellite Positioning Reference Station Network demonstrate that the performance of the proposed theoretical IGM is comparable to that of the existing detection algorithm.

Deep Learning-Based Automatic River Flow Estimation Using RADARSAT Imagery

Estimating river flow is a key parameter for effective water resource management, flood risk prevention, and hydroelectric facilities planning. Yet, traditional gauging methods are not reliable under very high flows or extreme events. Hydrometric network stations are often sparse, and their spatial distribution is not optimal. Therefore, many river sections cannot be monitored using traditional flow measurements and observations. In the last few decades, satellite sensors have been considered as complementary observation sources to traditional water level and flow measurements. This kind of approach has provided a way to maintain and expand the hydrometric observation network. Remote sensing data can be used to estimate flow from rating curves that relate instantaneous flow (Q) to channel cross-section geometry (effective width or depth of the water surface). Yet, remote sensing has limitations, notably its dependence on rating curves. Due to their empirical nature, rating curves are limited to specific river sections (reaches) and cannot be applied to other watercourses. Recently, deep-learning techniques have been successfully applied to hydrology. The primary goal of this study is to develop a deep-learning approach for estimating river flow in the Boreal Shield ecozone of Eastern Canada using RADARSAT-1 and -2 imagery and convolutional neural networks (CNN). Data from 39 hydrographic sites in this region were used in modeling. A new CNN architecture was developed to provide a straightforward estimation of the instantaneous river flow rate. Our results yielded a coefficient of determination (R2) and a Nash–Sutcliffe value of 0.91 and a root mean square error of 33 m3/s. Notably, the model performs exceptionally well for rivers wider than 40 m, reflecting its capability to adapt to varied hydrological contexts. These results underscore the potential of integrating advanced satellite imagery with deep learning to enhance hydrological monitoring across vast and remote areas.

Implementing robust outlier detection to enhance estimation accuracy of GNSS-IR based seasonal snow depth retrievals

ABSTRACT Monitoring snow depth variations aligned with the seasonal cycle is crucial for studies on climate change and its impacts due to global warming. Especially in terms of snow hydrology, the determination and continuous monitoring of snow depth to determine snow water equivalent is a priority in climate, water science, drought, flood, and inundation prediction studies, particularly in the water cycle. Recently, the so-called Global Navigation Satellite Systems – Interferometric Reflectometry (GNSS-IR) method enables to extract environmental radiometric and geometric characteristics of surface where the signals transmitted from the satellites reflect. The reflected signal follows an extra path than the direct one and causes a major error source for accurate point positioning but a useful tool for sensing the environment. However, outliers remain a challenge in long-term GNSS-IR estimations. In this study, we proposed a median-based robust outlier detection (ROD) approach for identifying outliers in long-term GNSS-IR snow depth estimations. To validate our approach, we analysed 5-year GNSS L1 SNR and L2 SNR data from 1 January 2015 to 31 December 2019 provided by AB33 and AB39 GNSS stations in Alaska, U.S.A. using the GNSS-IR method. We validated GNSS-IR estimations using snow depth measurements from the Coldfoot and Fort Yukon stations in the SNOTEL network. Applying ROD to long-term snow depth estimates increased the highest correlation from 91.58% to 94.93%, and reduced the lowest RMSE from 8.6 cm to 6.7 cm. In addition, the improvement rates calculated to assess the contribution of ROD to the results showed improvements of up to 6.9% in correlation and 26.1% in RMSE. Overall, the results demonstrate that ROD can be effectively used to detect outliers in long-term GNSS-IR snow depth time series.

Introducing ViDA3, An Earthquake Early Warning Algorithm for Offshore Hypocenter Determination Using Onshore Seismic Networks

ABSTRACT The objective functions adopted by earthquake early warning (EEW) location algorithms are inadequate for out-of-network earthquakes. As a result, the real-time locations of these earthquakes are often erroneous. The consequences of mislocating out-of-network earthquakes are that their magnitudes are miscalculated, and the loci of their shaking predictions map are shifted. Given that the largest earthquakes occur in subduction settings, improving real-time out-of-network earthquake location is of great importance. In this study, the Virtual Dynamically Assembled Array Algorithm (ViDA3) is introduced, which addresses the location issue of offshore and off-network earthquakes. The guiding principle underlying the new EEW location algorithm is that standard seismic networks may be viewed as a collection of medium-sized seismic arrays, with each array consisting of three or more network stations. The potential of array seismology for EEW against out-of-network earthquakes stems primarily from the slowness vector, which points at the direction of the epicentral region. Thus, this region may be constrained merely by intersecting two or more such vectors. In addition, the length of the slowness vector depends on the hypocentral distance and depth and is thus vital for addressing an acute problem in a subduction setting—discriminating between upper crust and deep slab earthquakes. Furthermore, when the slowness of the P phase is known, the slowness of the S phase is deduced, and the S-phase arrival is searched for using the shift-and-sum practice. What makes ViDA3 so attractive is that, in locations where a real-time network is already in place, these added values may be achieved without extra hardware or substantial budget requirements. We present the result of ViDA3 real-time operation on a shallow earthquake offshore Vancouver Island and the result of its replay on a deep slab earthquake in northern Chile. ViDA3 performance is further assessed using a dataset of seismograms from the Mendocino Triple Junction area. It is concluded that ViDA3 location scheme outperforms currently available EEW location algorithms for out-of-network earthquakes.

Temporal and spatial variations of terrestrial water storage in the northeastern Tibetan Plateau retrieved by GNSS observations

The variation of solid Earth's hydrologic loading could cause the elastic vertical deformation of the crust, and the Global Navigation Satellite System (GNSS) could effectively monitor the vertical displacement of surface loads. However, the widely used Green's function method does not work well in areas where GNSS sites are sparse. Here, the vertical displacement time series of GNSS stations and the Slepian basis function method have been applied to convert displacement signals into spatial spectrum signals. The elastic mass load theory is used to study the changes in terrestrial water storage on the Northeastern Tibetan Plateau (NETP). The temporal and spatial characteristics of seasonal water changes are well-represented by the GNSS, the Gravity Recovery and Climate Experiment (GRACE), and the Global Land Data Assimilation System (GLDAS). Several data points suggest that the change in water storage shows a gradual increase from the northeast to the southwest. The greatest annual amplitude of water storage retrieved by GNSS is ∼159 mm, which is greater than the ∼47 mm and ∼44 mm obtained by GRACE and GLDAS. These results demonstrate that GNSS is capable of capturing small-scale hydrological changes in this region, whereas GRACE and GLDAS data tend to underestimate seasonal variations in water storage. We also used GNSS to describe the hydrological drought conditions in NETP, showing that GNSS could be used as an independent method to characterize hydrological drought events. The findings suggest it could observe water storage with high spatial and temporal resolution and aid in comprehending regional hydrological trends with a sparse GNSS station network.

Directionality characteristics of horizontal response spectra from the 2022 Mw 6.9 Chihshang, Taiwan earthquake

Horizontal earthquake ground motion intensity, and specifically response spectral ordinates, vary with orientation. This phenomenon is usually referred to as ground motion directionality and can be separated into two aspects: the orientation where the maximum spectral response occurs and the variation of response spectral ordinates as the orientation moves away from the orientation of maximum spectral response. This work studies both aspects using the recent 2022 Mw 6.9 Chihshang, Taiwan earthquake, which was recorded by a dense network of strong motion stations with various geological and topographical settings. The mean variation of response spectral ordinates with orientation is found to be slightly more significant than that of previous shallow crustal earthquakes in active tectonic regimes. Moreover, the orientation of maximum spectral response is found to be close to the transverse orientation, which is perpendicular to the orientation at a given site that points to the earthquake epicenter, confirming prior observations made for strike-slip earthquakes. These results suggest that the location of a site relative to the seismic source could be used to modify the outputs of ground motion models to estimate spectral responses at specific horizontal orientations.

Instrumentation of the Nigeria network of seismological stations

“Living in fear of Nigeria biggest Earthquake” is a sub-heading of the Punch daily newspaper of Nigeria, dated, 21st of August, 2016, reported the earthquake/tremor recently witnessed in the ancient town of Saki, accompanied by a series of aftershock events that lasted for about three (3) months (March/May, 2016) southwest Nigeria[1]. Similarly, roughly five (5) years later, another series of earthquakes/tremors occurred again in Saki town, which was reported by an online news vendor named “Ripples Nigeria” dated September 8, 2021, under the sub-heading of “earth tremor rocks Saki in Oyo state”[2]. However, these events were not captured nor recorded by any of the functional seismological stations in Nigeria. The nearest seismological station located at the Obafemi Awolowo University (OAU) Ile-Ife, also failed to capture the events. We therefore seek to examine the instrumentation of the Nigerian National Network of Seismographic Stations. This is necessary to understand the functionalities and the capabilities of the deployed seismometers in each of the seismic stations. Therefore, we evaluated the bandpass limit of the seismic wave frequency for the respective seismic stations in Nigeria through the computation of the amplitude-frequency response curve, phase response curve, and count to cm/sec.

Dynamic rebalancing optimization for bike-sharing systems: A modeling framework and empirical comparison

Station-based Bike-sharing systems have been implemented in multiple major cities, offering a low-cost and environmentally friendly transportation alternative. As a remedy to unbalanced stations, operators typically rebalance bikes by trucks. The resulting dynamic planning has received significant attention from the Operations Research community. Due to its modeling flexibility, mixed-integer programming remains a popular choice. However, the complex planning problem requires significant simplifications to obtain a computationally tractable model. As a result, existing models have used a large variety of modeling assumptions and techniques regarding decision variables and constraints. Unfortunately, the impact of such assumptions on the solutions’ performance in practice remains generally unexplored.In this paper, we first systematically survey the literature on rebalancing problems and their modeling assumptions. We then propose a general mixed-integer programming model for multi-period rebalancing problems that can be easily adapted to different assumptions, including trip modeling, time discretization, trip distribution, and event sequences. We develop an instance generator to synthesize realistic station networks and customer trips, as well as a realistic fine-grained simulator to evaluate the operational performance of rebalancing strategies. Finally, extensive numerical experiments are carried out, both on the synthetic and real-world data, to analyze the effectiveness of various modeling assumptions and techniques. Based on our results, we identify the assumptions that empirically provide the most effective rebalancing strategies in practice. Specifically, a set of specific trip distribution constraints and event sequences ignored in the previous literature seem to provide particularly good results.