A Dynamic Framework for Internet-Based Network Time Protocol.

Time synchronization is vital for accurate data collection and processing in sensor networks. Sensors in these networks often operate under fluctuating conditions. However, an accurate timekeeping mechanism is critical even in varying network conditions. Consequently, a synchronization method is required in sensor networks to ensure reliable timekeeping for correlating data accurately across the network. In this research, we present a novel dynamic NTP (Network Time Protocol) algorithm that significantly enhances the precision and reliability of the generalized NTP protocol. It incorporates a dynamic mechanism to determine the Round-Trip Time (RTT), which allows accurate timekeeping even in varying network conditions. The proposed approach has been implemented on an FPGA and a comprehensive performance analysis has been made, comparing three distinct NTP methods: dynamic NTP (DNTP), static NTP (SNTP), and GPS-based NTP (GNTP). As a result, key performance metrics such as variance, standard deviation, mean, and median accuracy have been evaluated. Our findings demonstrate that DNTP is markedly superior in dynamic network scenarios, a common characteristic in sensor networks. This adaptability is important for sensors installed in time-critical networks, such as real-time industrial IoTs, where precise and reliable time synchronization is necessary.

Hyperparameter Optimization of Bayesian Neural Network Using Bayesian Optimization and Intelligent Feature Engineering for Load Forecasting.

This paper proposes a new hybrid framework for short-term load forecasting (STLF) by combining the Feature Engineering (FE) and Bayesian Optimization (BO) algorithms with a Bayesian Neural Network (BNN). The FE module comprises feature selection and extraction phases. Firstly, by merging the Random Forest (RaF) and Relief-F (ReF) algorithms, we developed a hybrid feature selector based on grey correlation analysis (GCA) to eliminate feature redundancy. Secondly, a radial basis Kernel function and principal component analysis (KPCA) are integrated into the feature-extraction module for dimensional reduction. Thirdly, the Bayesian Optimization (BO) algorithm is used to fine-tune the control parameters of a BNN and provides more accurate results by avoiding the optimal local trapping. The proposed FE-BNN-BO framework works in such a way to ensure stability, convergence, and accuracy. The proposed FE-BNN-BO model is tested on the hourly load data obtained from the PJM, USA, electricity market. In addition, the simulation results are also compared with other benchmark models such as Bi-Level, long short-term memory (LSTM), an accurate and fast convergence-based ANN (ANN-AFC), and a mutual-information-based ANN (ANN-MI). The results show that the proposed model has significantly improved the accuracy with a fast convergence rate and reduced the mean absolute percent error (MAPE).

The Simulated Characterization and Suitability of Semiconductor Detectors for Strontium 90 Assay in Groundwater.

This paper examines the potential deployment of a 10 mm × 10 mm × 1 mm cadmium telluride detector for strontium-90 measurement in groundwater boreholes at nuclear decommissioning sites. Geant4 simulation was used to model the deployment of the detector in a borehole monitoring contaminated groundwater. It was found that the detector was sensitive to strontium-90, yttrium-90, caesium-137, and potassium-40 decay, some of the significant beta emitters found at Sellafield. However, the device showed no sensitivity to carbon-14 decay, due to the inability of the weak beta emission to penetrate both the groundwater and the detector shielding. The limit of detection for such a sensor when looking at solely strontium-90 decay was calculated as 323 BqL-1 after a 1-h measurement and 66 BqL-1 after a 24-h measurement. A gallium-arsenide (GaAs) sensor with twice the surface area, but 0.3% of the thickness was modelled for comparison. Using this sensor, sensitivity was increased, such that the limit of detection for strontium-90 was 91 BqL-1 after 1 h and 18 BqL-1 after 24 h. However, this sensor sacrifices the potential to identify the present radionuclides by their end-point energy. Additionally, the feasibility of using flexible detectors based on solar cell designs to maximise the surface area of detectors has been modelled.

The Simulation of In-Situ Groundwater Detector Response as a Means of Identifying Beta Emitting Radionuclides by Linear Regression Analysis.

The in-situ characterisation of strontium-90 contamination of groundwater at nuclear decommissioning sites would represent a novel and cost-saving technology for the nuclear industry. However, beta particles are emitted over a continuous spectrum and it is difficult identify radionuclides due to the overlap of their spectra and the lack of characteristic features. This can be resolved by using predictive modelling to perform a maximum-likelihood estimation of the radionuclides present in a beta spectrum obtained with a semiconductor detector. This is achieved using a linear least squares linear regression and relating experimental data with simulated detector response data. In this case, by simulating a groundwater borehole scenario and the deployment of a cadmium telluride detector within it, it is demonstrated that it is possible to identify the presence of 90Sr, 90Y, 137Cs and 235U decay. It is determined that the optimal thickness of the CdTe detector for this technique is in the range of 0.1 to 1 mm. The influence of suspended solids in the groundwater is also investigated. The average and maximum concentrations of suspended particles found at Sellafield do not significantly deteriorate the results. It is found that applying the linear regression over two energy windows improves the estimate of 90Sr activity in a mixed groundwater source. These results provide validation for the ability of in-situ detectors to determine the activity of 90Sr in groundwater in a timely and cost-effective manner.

Integration of Ground- Penetrating Radar and Gamma-Ray Detectors for Nonintrusive Characterisation of Buried Radioactive Objects.

The characterisation of buried radioactive wastes is challenging because they are not readily accessible. Therefore, this study reports on the development of a method for integrating ground-penetrating radar (GPR) and gamma-ray detector measurements for nonintrusive characterisation of buried radioactive objects. The method makes use of the density relationship between soil permittivity models and the flux measured by gamma ray detectors to estimate the soil density, depth and radius of a disk-shaped buried radioactive object simultaneously. The method was validated using numerical simulations with experimentally-validated gamma-ray detector and GPR antenna models. The results showed that the method can simultaneously retrieve the soil density, depth and radius of disk-shaped radioactive objects buried in soil of varying conditions with a relative error of less than 10%. This result will enable the development of an integrated GPR and gamma ray detector tool for rapid characterisation of buried radioactive objects encountered during monitoring and decontamination of nuclear sites and facilities.

A Novel Method for Remote Depth Estimation of Buried Radioactive Contamination.

Existing remote radioactive contamination depth estimation methods for buried radioactive wastes are either limited to less than 2 cm or are based on empirical models that require foreknowledge of the maximum penetrable depth of the contamination. These severely limits their usefulness in some real life subsurface contamination scenarios. Therefore, this work presents a novel remote depth estimation method that is based on an approximate three-dimensional linear attenuation model that exploits the benefits of using multiple measurements obtained from the surface of the material in which the contamination is buried using a radiation detector. Simulation results showed that the proposed method is able to detect the depth of caesium-137 and cobalt-60 contamination buried up to 40 cm in both sand and concrete. Furthermore, results from experiments show that the method is able to detect the depth of caesium-137 contamination buried up to 12 cm in sand. The lower maximum depth recorded in the experiment is due to limitations in the detector and the low activity of the caesium-137 source used. Nevertheless, both results demonstrate the superior capability of the proposed method compared to existing methods.

A Model for Remote Depth Estimation of Buried Radioactive Wastes Using CdZnTe Detector.

This paper presents the results of an attenuation model for remote depth estimation of buried radioactive wastes using a Cadmium Zinc Telluride (CZT) detector. Previous research using an organic liquid scintillator detector system showed that the model is able to estimate the depth of a 329-kBq Cs-137 radioactive source buried up to 12 cm in sand with an average count rate of 100 cps. The results presented in this paper showed that the use of the CZT detector extended the maximum detectable depth of the same radioactive source to 18 cm in sand with a significantly lower average count rate of 14 cps. Furthermore, the model also successfully estimated the depth of a 9-kBq Co-60 source buried up to 3 cm in sand. This confirms that this remote depth estimation method can be used with other radionuclides and wastes with very low activity. Finally, the paper proposes a performance parameter for evaluating radiation detection systems that implement this remote depth estimation method.

The Effect of Gamma and Beta Radiation on a UVTRON Flame Sensor: Assessment of the Impact on Implementation in a Mixed Radiation Field.

Due to the short path length of alpha particles in air, a detector that can be used at a distance from any potential radiological contamination reduces the time and hazard that traditional alpha detection methods incur. This would reduce costs and protect personnel in nuclear power generation and decommissioning activities, where alpha detection is crucial to full characterisation and contamination detection. Stand-off alpha detection could potentially be achieved by the detection of alpha-induced radioluminescence, especially in the ultraviolet C (UVC) wavelength range (180⁻280 nm) where natural and artificial background lighting is less likely to interfere with detection. However, such a detector would also have to be effective in the field, potentially in the presence of other radiation sources that could mask the UVC signal. This work exposed a UVC sensor, the UVTRON (Hamamatsu, Japan) and associated electronics (driver circuit, microprocessor) to sources of beta and gamma radiation in order to assess its response to both of these types of radiation, as may be found in the field where a mixed radiation environment is likely. It has been found that the UVTRON is affected by both gamma and beta radiation of a magnitude that would mask any UVC signal being detected. 152Eu generated 0.01 pulses per second per Bq through beta and gamma interactions, compared to 210Po, which generates 4.72 × 10-8 cps per Bq from UVC radioluminescence, at 20 mm separation. This work showed that UVTRON itself is more susceptible to this radiation than the associated electronics. The results of this work have implications for the use of the UVTRON as a sensor in a stand-off detection system, highlighting the necessity for shielding from both potential gamma and beta radiation in any detector design.

Alpha Particle Detection Using Alpha-Induced Air Radioluminescence: A Review and Future Prospects for Preliminary Radiological Characterisation for Nuclear Facilities Decommissioning.

The United Kingdom (UK) has a significant legacy of nuclear installations to be decommissioned over the next 100 years and a thorough characterisation is required prior to the development of a detailed decommissioning plan. Alpha radiation detection is notoriously time consuming and difficult to carry out due to the short range of alpha particles in air. Long-range detection of alpha particles is therefore highly desirable and this has been attempted through the detection of secondary effects from alpha radiation, most notably the air-radioluminescence caused by ionisation. This paper evaluates alpha induced air radioluminescence detectors developed to date and looks at their potential to develop a stand-off, alpha radiation detector which can be used in the nuclear decommissioning field in daylight conditions to detect alpha contaminated materials.

Gas Flow to Enhance the Detection of Alpha-Induced Air Radioluminescence Based on a UVTron Flame Sensor.

In many field applications where alpha-induced radioluminescence (or so-called UV fluorescence) could potentially be used for stand-off detection of alpha-emitting materials, it may not be possible to create a fully purged gas atmosphere. Hence, an alternative gas delivery method to utilise the radioluminescence enhancing properties of gases has been investigated, with the novel results from this presented herewithin. A solar blind ultraviolet C (UVC) sensor (UVTron R9533, Hamamatsu, Japan) has been used to detect changes in the signal in the UVC wavelength range (180⁻280 nm), where gases of Ar, Xe, Ne, N2, Kr, and P-10 were flowed over a 6.95 MBq 210Po source using a narrow diameter pipe close to the source. In comparison with an air atmosphere, there was an increase in signal in all instances, the greatest being the flow of Xe, which in one instance greater than doubled the average counts per second. This increase in signal could prove beneficial in the design of a stand-off alpha detector to detect the very small UVC radioluminescence signals from alpha-emitting materials found in nuclear decommissioning environments.

Ground Penetrating Radar as a Contextual Sensor for Multi-Sensor Radiological Characterisation.

Radioactive sources exist in environments or contexts that influence how they are detected and localised. For instance, the context of a moving source is different from a stationary source because of the effects of motion. The need to incorporate this contextual information in the radiation detection and localisation process has necessitated the integration of radiological and contextual sensors. The benefits of the successful integration of both types of sensors is well known and widely reported in fields such as medical imaging. However, the integration of both types of sensors has also led to innovative solutions to challenges in characterising radioactive sources in non-medical applications. This paper presents a review of such recent applications. It also identifies that these applications mostly use visual sensors as contextual sensors for characterising radiation sources. However, visual sensors cannot retrieve contextual information about radioactive wastes located in opaque environments encountered at nuclear sites, e.g., underground contamination. Consequently, this paper also examines ground-penetrating radar (GPR) as a contextual sensor for characterising this category of wastes and proposes several ways of integrating data from GPR and radiological sensors. Finally, it demonstrates combined GPR and radiation imaging for three-dimensional localisation of contamination in underground pipes using radiation transport and GPR simulations.

First Results of Using a UVTron Flame Sensor to Detect Alpha-Induced Air Fluorescence in the UVC Wavelength Range.

In this work, a robust stand-off alpha detection method using the secondary effects of alpha radiation has been sought. Alpha particles ionise the surrounding atmosphere as they travel. Fluorescence photons produced as a consequence of this can be used to detect the source of the alpha emissions. This paper details experiments carried out to detect this fluorescence, with the focus on photons in the ultraviolet C (UVC) wavelength range (180-280 nm). A detector, UVTron R9533 (Hamamatsu, 325-6, Sunayama-cho, Naka-ku, Hamamatsu City, Shizuoka Pref., 430-8587, Japan), designed to detect the UVC emissions from flames for fire alarm purposes, was tested in various gas atmospheres with a 210Po alpha source to determine if this could provide an avenue for stand-off alpha detection. The results of the experiments show that this detector is capable of detecting alpha-induced air fluorescence in normal indoor lighting conditions, as the interference from daylight and artificial lighting is less influential on this detection system which operates below the UVA and UVB wavelength ranges (280-315 nm and 315-380 nm respectively). Assuming a standard 1 r 2 drop off in signal, the limit of detection in this configuration can be calculated to be approximately 240 mm, well beyond the range of alpha-particles in air, which indicates that this approach could have potential for stand-off alpha detection. The gas atmospheres tested produced an increase in the detector count, with xenon having the greatest effect with a measured 52% increase in the detector response in comparison to the detector response in an air atmosphere. This type of alpha detection system could be operated at a distance, where it would potentially provide a more cost effective, safer, and faster solution in comparison with traditional alpha detection methods to detect and characterise alpha contamination in nuclear decommissioning and security applications.

The use of artificial neural network for low latency of fault detection and localisation in transmission line.

One of the most critical concerns in power system reliability is the timely and accurate detection of transmission line faults. Therefore, accurate detection and localisation of these faults are necessary to avert system collapse. This paper focuses on using Artificial Neural Networks in faults detection and localisation to attain accuracy, precision and speed of execution. A 330 kV, 500 km three-phase transmission line was modelled to extract faulty current and voltage data from the line. The Artificial Neural Network technique was used to train this data, and an accuracy of 100% was attained for fault detection and about 99.5% for fault localisation at different distances with 0.0017 µs of detection and an average error of 0%-0.5%. This model performs better than Support Vector Machine and Principal Component Analysis with a higher fault detection time. This proposed model serves as the basis for transmission line fault protection and management system.

The Role of Personal Values in Learning Approaches and Student Achievements.

Personal values play a significant role when adopting learning approaches by individuals during their studies. Particularly in higher education, these values significantly influence the character that individuals play within their learning community and ultimately influence their academic achievements. The purpose of this paper is to investigate personal values in their choice of learning approaches and, subsequently, how it impacts one's academic achievements. It also investigates the importance of developing an individual's personal values as a part of their wider studies, while aligning these with graduate attributes and balancing them with knowledge and skills, to produce successful graduates in a society.

A novel approach to neutron dosimetry.

PURPOSE: Having been overlooked for many years, research is now starting to take into account the directional distribution of neutron workplace fields. Existing neutron dosimetry instrumentation does not account for this directional distribution, resulting in conservative estimates of dose in neutron workplace fields (by around a factor of 2, although this is heavily dependent on the type of field). This conservatism could influence epidemiological studies on the health effects of radiation exposure. This paper reports on the development of an instrument which can estimate the effective dose of a neutron field, accounting for both the direction and the energy distribution. METHODS: A 6Li-loaded scintillator was used to perform neutron assays at a number of locations in a 20 × 20 × 17.5 cm3 water phantom. The variation in thermal and fast neutron response to different energies and field directions was exploited. The modeled response of the instrument to various neutron fields was used to train an artificial neural network (ANN) to learn the effective dose and ambient dose equivalent of these fields. All experimental data published in this work were measured at the National Physical Laboratory (UK). RESULTS: Experimental results were obtained for a number of radionuclide source based neutron fields to test the performance of the system. The results of experimental neutron assays at 25 locations in a water phantom were fed into the trained ANN. A correlation between neutron counting rates in the phantom and neutron fluence rates was experimentally found to provide dose rate estimates. A radionuclide source behind shadow cone was used to create a more complex field in terms of energy and direction. For all fields, the resulting estimates of effective dose rate were within 45% or better of their calculated values, regardless of energy distribution or direction for measurement times greater than 25 min. CONCLUSIONS: This work presents a novel, real-time, approach to workplace neutron dosimetry. It is believed that in the research presented in this paper, for the first time, a single instrument has been able to estimate effective dose.