Short-period Data Research Articles

RESEARCH OF CHANGES IN THE POWER OF CAR FLOW AT THE SHUTTING STATION DURING THE PERIOD OF MILITARY ACTIONS

It is impossible to solve the problem of improving the system of organizing car flows in trains at the current level of information support without creating an information and management system that will integrate into a single information and computing space the most important optimization tasks covering all levels of managing the process of freight transportation on railway transport, such as such as the calculation of the routing of car flows from loaded and empty cars, the calculation of the plan for the formation of freight trains at the training ground of the main technical stations, the calculation of the plan for the formation of local trains in the areas of local work, and the construction of adjusted operational plans for the operation of stations. The aggression of the Russian Federation against Ukraine fundamentally changed the system of railway transport of Ukraine. The volume of cargo transportation by rail transport during military operations in the east of Ukraine decreased by 37% in 2022, but already in 2023, an increase in the transportation of some types of cargo began, in particular, grain cargo, mineral construction materials, dangerous bulk cargoes, military equipment. Transportation of goods by railways of Ukraine, especially during military operations, is not carried out evenly over a long period of time and has fluctuations in the capacity of car flows. The updated directions of their tracking, which depend on the technical condition of the railway infrastructure for the current period, lead to a change in the direction of tracking of car flows and, as a consequence, their organization into trains according to the formation plan at technical stations. The conducted studies established that the capacities of car flows are random values, therefore it is advisable to use mathematical statistics methods for their description, and modeling methods for forecasting work with them. Those car flows that arrive at the sorting station most of all fall under the geometric law of distribution of a random variable depending on the power of the direction of arrival, which has a significant impact on the accumulation parameter at the station. Fluctuations in the capacity of car flows are quite significant and depend on a sufficiently large number of factors. For the operational planning of the work of sorting stations, it is necessary to take the characteristics of the current state or a sample of data for short periods (a decade, a week), that is, it is necessary to constantly analyze the data from the ASK of the VP UZ - there is, therefore, a study of the capacity of car flows in the technical station during the period of changing their directions is an actual task and can be used to adjust the dispatcher routing and the plan for the formation of single-group and group freight trains.

FRF-based modal testing of the highest timber building in the world in operation using wireless accelerometers

Abstract This paper describes practical application of a novel and oven-controlled crystal oscillator (OCXO) high-precision synchronised wireless data acquisition system for a modal testing of the tallest all-timber building in the world – the 18-storey, 88.8m tall Mjøstårnet (the Mjøsa Tower) in Brumunddal, Norway. The modal testing was challenging as it was conducted in an occupied building in normal operation and was based on measuring both dynamic excitation and response yielding a set of frequency response functions (FRFs) across four different floors of the building. To the best knowledge of the authors, this was the first time that an FRF-based modal test was attempted on such a large building structure in operation. Moreover, although the structure was in operation, the testing was completed in only 48h, including commissioning and de-commissioning of all of the instrumentation, yielding a full set of point- and transfer accelerance FRFs. The paper presents the equipment used, key logistical challenges and quality assurance steps made to assure measurement of good quality FRF data in such short period of time demonstrating feasibility of the whole exercise. A total of seven modes of vibration, including higher order modes of vibration were estimated by curve-fitting the measured FRF data. This includes the 3rd bending and 2nd torsion modes difficult to measure via the standard ambient vibration testing of tall buildings. These modes could be used to investigate a number of uncertain modelling features in tall timber buildings, such as the stiffness of timber joints.

Continual adaptation of federated reservoirs in pervasive environments

When performing learning tasks in pervasive environments, the main challenge arises from the need of combining federated and continual settings. The former comes from the massive distribution of devices with privacy-regulated data. The latter is required by the low resources of the participating devices, which may retain data for short periods of time. In this paper, we propose a setup for learning with Echo State Networks (ESNs) in pervasive environments. Our proposal focuses on the use of Intrinsic Plasticity (IP), a gradient-based method for adapting the reservoir’s non-linearity. First, we extend the objective function of IP to include the uncertainty arising from the distribution of the data over space and time. Then, we propose Federated Intrinsic Plasticity (FedIP), which is intended for client–server federated topologies with stationary data, and adapts the learning scheme provided by Federated Averaging (FedAvg) to include the learning rule of IP. Finally, we further extend this algorithm for learning to Federated Continual Intrinsic Plasticity (FedCLIP) to equip clients with CL strategies for dealing with continuous data streams. We evaluate our approach on an incremental setup built upon real-world datasets from human monitoring, where we tune the complexity of the scenario in terms of the distribution of the data over space and time. Results show that both our algorithms improve the representation capabilities and the performance of the ESN, while being robust to catastrophic forgetting.

Mid-mantle seismic scatterers beneath the Samoan hotspot

We analyze teleseismic array data of deep and intermediate-depth earthquakes in the Fiji-Tonga-Kermadec region recorded at seismic networks at the west coast of US and Alaska with the aim of mapping seismic scatterers in the lower mantle and in the upper mantle transition zone beneath the Samoan hotspot. Short period and broadband waveform data are array-processed in order to detect coherent but anomalous phases in the P coda up to nearly 120 s after direct P waves and locate where they arrive from. Updating the data set reported in our previous study (Kaneshima and Helffrich, 2010; Kaneshima, 2018; Kaneshima, 2019), we examine nine scatterers near the hotspot, seven in the lower mantle and two in the transition zone, with the main focus on three lower mantle scatterers nearest to the hotspot at 1000 to 1200 km depths. We observe S-to-P waves generated from the three lower mantle scatterers for earthquakes with a wide range of hypocenters from 17°S to 24°S, while hardly any signs of corresponding P-to-P scattering waves are observed. The pulse shapes of the S-to-P waves from the three scatterers are simple and their pulse widths do not significantly differ from those of direct P waves when the signal quality is high enough. The geometry of the scatterers suggested by these observations is a steeply dipping sheet-like structure with the thickness less than several kilometers. The material consisting of the sheet needs to have a Vs value at least several percent lower than the surrounding mantle, as required by the amplitudes of S-to-P scattering waves relative to P, which is typically several percent. The low Vs anomaly of basaltic rocks relative to pyrolite under the shallow lower mantle conditions may be consistent with the observations (Wang et al., 2020). Seismic tomography models suggest that the nine scatterers tend to be located around the edge of a plume that ascends from the Pacific LLSVP to the Samoan hotspot. Numerical simulations of ascending plumes from a thermo-chemical pile at the bottom of the mantle show that the plumes tend to rise preferentially in the vicinity of the edge of the pile. Other simulations also show that blobs of recycled former oceanic crust subducting to the bottom of mantle are entrained into a plume and stretched vertically to a significant degree, consistently with the images suggested by geochemical studies of OIB. The scattering observations therefore may elucidate the structure of entrained basaltic blobs on their way of returning back to the surface hotspot.

Threat Hunting Architecture Using a Machine Learning Approach for Critical Infrastructures Protection

The number and the diversity in nature of daily cyber-attacks have increased in the last few years, and trends show that both will grow exponentially in the near future. Critical Infrastructures (CI) operators are not excluded from these issues; therefore, CIs’ Security Departments must have their own group of IT specialists to prevent and respond to cyber-attacks. To introduce more challenges in the existing cyber security landscape, many attacks are unknown until they spawn, even a long time after their initial actions, posing increasing difficulties on their detection and remediation. To be reactive against those cyber-attacks, usually defined as zero-day attacks, organizations must have Threat Hunters at their security departments that must be aware of unusual behaviors and Modus Operandi. Threat Hunters must face vast amounts of data (mainly benign and repetitive, and following predictable patterns) in short periods to detect any anomaly, with the associated cognitive overwhelming. The application of Artificial Intelligence, specifically Machine Learning (ML) techniques, can remarkably impact the real-time analysis of those data. Not only that, but providing the specialists with useful visualizations can significantly increase the Threat Hunters’ understanding of the issues that they are facing. Both of these can help to discriminate between harmless data and malicious data, alleviating analysts from the above-mentioned overload and providing means to enhance their Cyber Situational Awareness (CSA). This work aims to design a system architecture that helps Threat Hunters, using a Machine Learning approach and applying state-of-the-art visualization techniques in order to protect Critical Infrastructures based on a distributed, scalable and online configurable framework of interconnected modular components.

Climate change indications in short-period rainfall records from coastal India using trend analysis: a proposed framework

Climate change has reportedly resulted in higher magnitude and frequency of extreme weather variables in many parts of India. Such studies pertaining to rainfall were based on long-period records, gridded rainfall data, or climate model projections, which have inherent uncertainties. As long-period records might not be available for many locations, gridded data or climate model analysis could be supplemented with the examination of short-period observations (< 20 years) for climate change effects. Detecting climate change effect from short-period high-resolution data has not yet been reported in the literature. For such exercise, this study proposes a framework based on trend analysis along with a demonstration case at a coastal site in India. Using the proposed analysis, indications of climate change are identified in short-period (January 1997 to December 2013: 17 years) high-resolution (hourly) rainfall data, which are subsequently corroborated with results from long-period (60-year) annual data from the site. This proof-of-concept study opens up an intriguing possibility of detecting climate change indications from short-period data, subject to validation on a larger scale with observations from more weather stations. The results could aid judicious decision-making regarding safety margins required in hydraulic design, to account for climate change effects. The methodology, if corroborated with more studies, might prove useful for detecting climate change effects from other short-period hydro-meteorological data as well.

Forecasting Analysis of Fishermen’s Productivity Data Using Single Exponential Smoothing

One of the reasons why it is vital to forecast fisher production data in coastal regions is to increase fish resource management efficiency. By calculating the number of fishing boats, the amount of fish that must be caught, and the amount of raw materials required for fish processing based on the anticipated amount of fishermen's production in the following period, decision-makers can determine the amount of fish that must be caught and the amount of raw materials required for fish processing. So that the objective of the research is to forecast fishermen's production data using the Single Exponential Smoothing method, this method is effectively used to perform forecasting of time series data with short period data intervals to produce forecasts for the next period, and it can measure the rate of change of fishermen's production data each period. The results of forecasting data on fishermen's production utilizing time series data intervals from October 2022 to January 2023 to make forecasts for February 2023, namely a MAPE error rate of 2.85%, indicate that the forecasting results are within the "good" category.&#x0D;

Uso de un vehículo aéreo no tripulado como alternativa para evaluar el estado nutricional del cultivo de algodón

The use of unmanned aerial vehicles in photogrammetric studies allows obtaining spatial data in short periods of time and with high spatial resolution. In the research, multispectral images were processed for the study of nutritional conditions of the cotton crop (Gossypium hirsutum). An experimental design of the crop was developed, with different doses and nitrogen sources, in a factorial arrangement with 16 treatments and 4 repetitions in plots completely distributed at random. The EBEE SQ agricultural drone, equipped with the Parrot Sequoia camera, was used and a photogrammetric flight was planned, with the Emoticon AG software, which was synchronized with the drone to establish the flight parameters and capture the reflectance information of the visible spectrum, infrared and red border. The captured images were processed with the PIX4D Mapper software to generate the orthophoto and the 4 spectral bands used in the calculation of the chlorophyll index. Using map algebra tools from ArcGIS software on the results obtained, an analysis of variance was performed with the ANOVA model. With the calculated indices it was possible to show differences in the vigor of the crop depending on the treatments. The analysis of the results showed significant differences in the spectral response of the cotton crop fertilized with different sources (urea, pine nut cake, hen manure and bovine manure) and nitrogen doses (50, 100, 150 and 200 N kg.ha-1). Urea treatment at the 150 dose of N kg.ha-1 showed the best spectral response.

Developing COVID-19 vaccine recommendations during the pandemic: The experience of Serbia's Expert Committee on Immunization.

A National Immunization Technical Advisory Group (NITAG) is a multi-disciplinary body of national experts that provide evidence-based recommendations to policy-makers to assist them in making informed immunization policy and programme decisions. During the COVID-19 pandemic, NITAGs faced many challenges in making evidence-based recommendations for COVID-19 vaccines due to the rapidly evolving situation with new vaccine products available in a short time period and limited data on vaccine effectiveness. The authors reviewed the process used by Serbia's NITAG, which is called the Serbian Expert Committee on Immunization, to develop COVID-19 vaccine recommendations during the pandemic. The article examines the challenges and successes faced by the committee. Serbia's expert committee used the best available evidence to develop over forty recommendations on all aspects of COVID-19 vaccination. These expert committee recommendations facilitated the early procurement and successful roll-out of COVID-19 vaccines, guidance for vaccination of individuals at the highest risk, and high COVID-19 vaccination coverage in the country. The availability of five COVID-19 vaccines in Serbia was an advantage for the successful roll-out but posed challenges for the expert committee. Serbia's expert committee plans to use the experience and best practices developed during the pandemic to improve and expand its work moving forward.

Seafloor depth controls seismograph orientation uncertainty

SUMMARY This study evaluates the seafloor ambient noise environment that varies with the water depth based on a correction analysis of the horizontal sensor orientation for ocean-bottom seismographs. As ocean-bottom seismographs are mainly deployed as ‘free-fall’ installations, we have no information on which direction a horizontal sensor faces at the seafloor. An accurate sensor orientation is crucial for data processing based on seismic wavefields. Among several seismological approaches that use passive sources to correct the horizontal sensor azimuth, the particle motion of teleseismic Rayleigh waves is widely used for broad-band ocean-bottom seismographs. We performed seafloor seismic observations in the Hyuga-nada region at the western end of the Nankai subduction zone and deployed broad-band and short-period seismographs. However, studies have yet to investigate whether orientation correction via the Rayleigh-wave polarization method is valid for short-period data. The results of the Rayleigh wave method from our campaign observation data showed that the estimation uncertainty of short-period sensor orientations increased with a decreasing water depth; we observed a transition depth for the uncertainty at 2200–2600 m. The measurement quality, or the cross-correlation coefficient between the radial and Hilbert-transformed vertical components, also decreased at depths shallower than 2000 m. Moreover, an analysis of the noise power spectral densities showed that ambient noise levels during long periods (&amp;gt;10 s) increased with decreasing depth. Infragravity waves controlled vertical long-period noise levels, while ocean currents dominated horizontal long-period noise; both of these reduced the Rayleigh-wave signals as a function of environmental noise. Infragravity waves also likely distorted the Rayleigh waveforms. Both mechanisms contributed to the sudden rise in orientation uncertainty and low measurement quality at shallow stations (i.e. &amp;lt;2000 m). We confirmed that the variation in orientation uncertainty with the water depth can be used as an index for the ambient noise environment of the seafloor.

Applying the CROPGRO Perennial Forage Model for long-term estimates of Marandu palisadegrass production in livestock management scenarios in Brazil

The use of long-term forage production simulation is important to studies of strategic scenarios, yield-gap predictions and production risk analysis. However, forage models have only been tested using short period data. Therefore, this study aimed to apply the CROPGRO Perennial Forage Model (CROPGRO-PFM), a mechanistic model from Decision Support System for Agrotechnology Transfer (DSSAT) platform, to simulate long-term forage production of Marandu palisadegrass {Urochloa brizantha (Hochst. ex A. Rich.) R.D. Webster [syn. Brachiaria brizantha (Hochst. ex A. Rich.) Stapf]} in different locations of Central Brazil, under different fertilization levels. We chose nine locations distributed in Amazon, Cerrado, and Atlantic Forest Biomes to simulate Marandu palisadegrass production over 36 years (1980–2016). The simulations were carried out using prior calibrations for DSSAT’s CROPGRO-PFM and, as a reference point of growth and herbage simulation, model results were initially contrasted against an agrometeorological model, the Integrated Agrometeorological Model (IAM), to check the consistency between these two different models. Then the CROPGRO-PFM was applied to simulated scenarios represented three beef cattle livestock production systems: (1) Potential production water-not-limiting and highly fertilized, (2) rainfed with high N fertilization, and (3) rainfed with low N maintenance fertilization. In general, the long-term simulations of annual forage production and seasonality were similar for the CROPGRO-PFM and IAM models. As expected for the Central Brazil region, historical averages of monthly herbage accumulation rate of potential yield scenarios were relatively constant throughout the year, declining in the winter period for most locations due to mild temperature reduction and decline of solar radiation. In general, rainfed scenarios simulations showed a seasonal cycle following the rainfall pattern, with reduction in growth rates in the dry season. During the winter season climate is the major limiting growth factor, simulated growth rates were low and similar regardless of N fertilization. Although the CROPGRO-PFM presented consistent results for long term grazing systems, further model changes, calibration, and validations are recommended to improve the process-based model as well as the knowledge about these systems.

Characteristics of Horizontal to Vertical Spectral Ratio of InSight Seismic Data From Mars

AbstractSince February 2019, the InSight mission has returned seismic and meteorological data from Mars. We conducted horizontal to vertical spectral ratio (HVSR) analysis to the InSight seismic data recorded both on the lander and on the ground. Horizontal to vertical spectral ratio curves of ground data show multi‐peak characteristics at frequencies between 1 and 30 Hz with the highest peak appearing at around 11.7 Hz. The HVSR results of the Very Broad Band (VBB) and Short Period (SP) data differ greatly for frequencies over 10 Hz due to the difference in vertical component. The onboard SP data have the same highest H/V peak as the ground SP data. We conducted numerical modeling of HVSR and confirmed that the wind‐induced lander vibrations are the major sources to the InSight seismic ground motion in high frequencies. The HVSR curves of the continuous ground SEIS data show diurnal variation and are highly correlated with local meteorological conditions. The particle motions are investigated through the polarization analysis and the random decrement method. Generally, the ground motions above the self‐noise level are linearly polarized in the horizontal plane around the highest H/V peak. The damping analysis demonstrated the mechanical origins of the highest HVSR peak, while the 2.4 Hz mode is the most likely one having natural origin. Finally, most marsquake signals analyzed in this study generally highlight the 2.4 Hz trough but cause no significant change on the shape of the H/V curves. Our study explores the possible existence of natural sources in relatively high frequencies of Mars' background noise.

702-P: Short-Period CGM Data Are More Useful than HbA1c for Treatment Adjustment in Type 2 Diabetes Patients

Current recommendations for type 2 diabetes treatment are based on targeting HbA1c depending on different factors. HbA1c reflects recent 8-12 weeks glucose control, but amount of data show HbA1c insufficiencies. Continuous glucose monitoring (CGM) is used for insulin dose adjustment but also for control assessment and treatment determination. In our study we compare seven-day non-real time CGM data and HbA1c to find out which is more appropriate for determining drug prescription in patients with type 2 diabetes. Our study includes 85 patients with type 2 diabetes (35 women, 50 men; age 43.93±10.87 years, disease duration 21.91±6.years) , on different treatment regimens (31 on oral therapy, 33 treated with premixed insulin, 21 on multiple insulin injections) . Patients performed Non-Real Time Continuous Glucose Monitoring by using iProTM for seven days and HbA1c was measured at the end of this period. No difference in HbA1c level was found in three observed groups. HbA1c showed moderate negative correlation to percentage of time within the limits (r1=-0.48; r2= -0.654; r3= -0.674) . Based on HbA1c assessment 34.84% of type 2 patients on oral medications, 36.36% of patients on premixed insulin and 19.05% of type 2 patients on multiple daily injection are with good control. CGM defined 54.84% of type 2 patients on oral medication, 18.27% of patients on premixed insulin and 17.80% of type 2 patients on intensified regimen as well controlled. CGM was more precise than HbA1c in defining patients with good control (p&amp;lt;0.05) . HbA1c shows bad correlation with overall glucose excursion (r1=-0.096, r2=0.200, r3=0.377) . No difference was found between studied groups in regard to CGM percentage of time spent above and below the limits and to AUC above upper limit. We conclude that CGM data give more precise information than HbA1c about glucose control and are more useful for defining treatment adjustment. Even performing CGM for short period helps physician to choose the most appropriate drug. Disclosure T.Totomirova: n/a. M.Arnaudova: None.

Resistance Switching Effect of Memory Device Based on All-Inorganic Cspbbri2 Perovskite.

In this study, the CsPbBrI2 perovskite film was prepared by the preparation of the sol-gel and the spin-coating method, and the cubic lattice was stabilized by introducing Br+ into the CsPbI3 film, which solved the problem of instability of the traditional perovskite phase. Based on the CsPbBrI2 perovskite film, the Ag/CsPbBrI2/ITO memory device with a resistance switching effect was prepared. The morphology and phase compositions of the film were analyzed by scanning electron microscope and X-ray diffraction. The non-volatile and repeatable resistance switching effect of the Ag/CsPbBrI2/ITO memory device was measured under open-air conditions. The experimental results show that the surface of the CsPbBrI2 perovskite film is uniform and dense, and the Ag/CsPbBrI2/ITO memory device has an order of magnitude resistance-on-off ratio after 500 cycles of cyclic voltage. This study shows that Ag/CsPbBrI2/ITO memory devices based on CsPbBrI2 perovskite films have potential applications in the field of non-volatile memory devices. At the same time, the transient properties of the CsPbBrI2 film that can quickly dissolve in deionized water make it potentially useful in short-period data storage units and implantable electronic devices with human or environmental sensors.

Data-driven yaw misalignment correction for utility-scale wind turbines

In recent years, wind turbine yaw misalignment that tends to degrade the turbine power production and impact the blade fatigue loads raises more attention along with the rapid development of large-scale wind turbines. The state-of-the-art correction methods require additional instruments such as laser imaging detection and ranging to provide the ground truths and are not suitable for long-term operation and large-scale implementation due to the high costs. In the present study, we propose a framework that enables the effective and efficient detection and correction of static and dynamic yaw errors by using only turbine supervisory control and data acquisition data, suitable for a low-cost regular inspection for large-scale wind farms in onshore, coastal, and offshore sites. This framework includes a short-period data collection of the turbine operating under multiple static yaw errors, a data mining correction for the static yaw error, and ultra-short-term dynamic yaw error forecasts with machine learning algorithms. Three regression algorithms, i.e., linear, support vector machine, and random forest, and a hybrid model based on the average prediction of the three, have been tested for dynamic yaw error prediction and compared using the field measurement data from a 2.5 MW turbine. For the data collected in the present study, the hybrid method shows the best performance and can reduce the total yaw error by up to 85% (on average of 71%) compared to the cases without static and dynamic yaw error corrections. In addition, we have tested the transferability of the proposed method in the application of detecting other static and dynamic yaw errors.

Ambient Noise Tomography with Short-Period Stations: Case Study in the Borborema Province

The analysis of seismic ambient noise has been recently proven a viable alternative to the analysis of seismic waves generated by earthquakes. Although seismic ambient noise had traditionally been discarded from earthquake records, it has now been shown that the cross-correlation of the seismic ambient noise allows for the recovery of the Green’s function between the receivers. Furthermore, seismic noise has the ability to propagate continuously and independently of the occurrence of earthquakes, allowing for high-resolution tomographic studies in regions of low seismicity. Over the last two decades, the cross-correlation of continuous seismic noise recordings between pairs of stations has been widely utilized in surface-wave tomography studies. For the Northeast Brazil region, tomographic studies have been exclusively performed with seismic data from broadband stations; however, in addition to those stations, there exists a large volume of short-period data that might potentially improve the resolution of existing ambient noise tomographies. Thus, the goal of this work is to utilize, in addition to broadband data, short-period data recorded by 22 short-period stations in the Borborema Province. Through cross-correlation and stacking of ambient seismic noise, the emergence of the fundamental mode of the Rayleigh waves and their dispersive character was observed. Once the empirical Green's functions were retrieved, the accuracy of (phase and group) dispersion curves for periods up to 10 s and its tomographic inversion were assessed. Our results demonstrate that short-period dispersion measurements can be successfully integrated in regional tomographic studies for improved resolution.

Fault detection and diagnosis for multiple faults of VAV terminals using self-adaptive model and layered random forest

Fault detection and diagnosis (FDD) can reduce a large amount of energy wasted by faults in variable air volume (VAV) systems. This paper presents a FDD method for detecting and diagnosing multiple faults of VAV terminals using a hybrid method. A self-adaptive zone air-temperature model is presented and used to detect faults of the VAV terminals by comparing the residuals between predicted and measured values with corresponding fault detection thresholds. The model parameters, which are normally constant and determined according to experience from short-period data, are periodically adjusted to improve the model prediction accuracy on the basis of operating data by using a chaos particle swarm optimization algorithm. Moreover, a two-layered random forest-based fault diagnosis method is presented to isolate multiple faults of VAV terminals according to the complexity of the fault symptoms. An expert rule-based fault diagnosis layer is used to isolate VAV terminal faults with simple fault symptoms. A random forest-based fault diagnosis layer is utilized to diagnose VAV terminal faults with complex fault symptoms by adopting a binary decision tree-based multi-label technique. The proposed FDD method is validated using operating data from real VAV systems involving 13 single-fault cases and 6 simultaneous-fault cases. The validation results indicate that the proposed FDD method can accurately detect and diagnose simultaneous multiple faults with only a few independent fault samples and a minimal number of simultaneous-fault samples.

ANN modeling of the complex discharge-sediment concentration relationship in Bhagirathi river basin of the Himalaya

A simple technique for prediction of suspended sediment concentration (SSC) or total suspended matter in the Himalayan Bhagirathi river is presented. Artificial neural network models have been developed using short time period data of discharge (Q) and SSC during the high activity monsoon period of June to October 2004, when variations are maximum. Two modeling approaches have been employed, a daily approach and a three hourly approach. Although the time period considered is the same in both the approaches, the modeling performance is marginally better in the three hourly approaches where there is a sixfold increase in the data set. The Levenberg–Marquardt optimization algorithm has been improved with NARX [nonlinear autoregressive with exogenous input] architecture and high values of coefficient of determination have been obtained [0.89–0.97]. This study shows that short duration time series data can be used for successfully predicting geo-hydrological variables in the highly complex Himalayan river scenario.

Magnetotelluric imaging of the Iguatu Basin in NE Brazil: From shallow aquifers to mid- crustal conductors

In this work, the Iguatu basin in Northeastern Brazil was imaged by 49 broadband (~0.01–1,000s) magnetotelluric (MT) and transient electromagnetic (TEM) soundings displaced on a 3D arrangement effected by a NE-SW along-basin major axis profile and three NW-SE transversally oriented profiles. The Iguatu basin is an intracratonic sedimentary basin, with genesis related to the opening of the South Atlantic Ocean (160-115 Ma). The basin was implanted over Precambrian basement of the Borborema Province. The sedimentary rocks deposited from Early Cretaceous to Quaternary include medium to coarse sandstones with conglomerate levels at the bottom, siltstones, mudstones, followed by Tertiary similar sedimentary rocks and Quaternary unconsolidated alluvium coverings. The MT responses (impedance and tipper) were robustly estimated and static-shift corrected using transient electromagnetic (TEM) measurements. Both rotational invariant and phase tensor dimensionality analyses revealed a complex subsurface structure, characterized by 2D/1D structures for short period data (<1 s), associated to sedimentary rocks within the basin, and a 3D behavior for longer periods, related to crustal depths. Three-dimensional inversion of full impedance was performed yielding a preferred 3D resistivity model with nRMS error of 1.99, which recovered important geoelectrical structures within the basin and crust. Two remarkably low resistivity anomalies (<1 Ω m) were observed in the crust of the Borborema Province, a feature also observed other MT studies elsewhere in the province. They can be ascribed to either the Mesozoic reactivation of the Aiuaba-Orós-Jaguaribe shear zone or the percolation of aqueous fluids between the zones of weakness presented in the fractured basement beneath the Iguatu basin; or even be a combination of both mechanisms. The resistivity model varies appreciably within the basin alternating low resistivity (<1 Ω m) graben-like and resistive (>300 Ω m) horst-like features, reflecting a structurally complex rift tectonic system of this ~800 m deep pull apart basin. A good correlation between resistivity values and shallow unconfined aquifers were spotted.

Exploring incentive mechanisms for mobile crowdsourcing: sense of safety in a Thai city

ABSTRACTThe rapid adoption of mobile devices enables capture and transmission of a variety of sensor and user-contributed data, creating a new data collection paradigm and a wide range of services, often termed as mobile crowdsourcing. However, we are still investigating factors contributing towards the effectiveness of such systems. One key factor to succeed in mobile crowdsourcing applications is the incentive mechanism, which motivates people to contribute to a crowdsourcing effort. In this research, we conducted field experiments that compared the effectiveness of non-monetary and monetary incentive mechanisms, using both quantitative and qualitative methods. The focus was on an exploration of how these mechanisms motivate users’ performance in a mobile crowdsourcing environment. In the experiment, we developed a smart zoning application that allows users to share areas of the cities they perceived as safe or unsafe. The results from this study contribute to research in mobile crowdsourcing for urban understanding. Taken together, these results suggest that payment-related incentives can not only engage people’s interest in project participation but also help improve their work performance in terms of productivity and quality. Notably, a fixed-price financial reward mechanism is best suited for short period data collection and achieving data quality. Also, referral incentive mechanisms, if properly designed, have the potential to extend user coverage, both spatially and temporally. These results can be helpful with regard to the formulation of guidelines on how to create and organize effective payment-based incentives for crowd involvement in cities.