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In recent times, mass timber products have encouraged renewed interest in wood as raw material for more sustainable building and construction. However, durability concerns due to wood susceptibilit...

A system of bio-silica borotellurite glasses was fabricated based on the chemical formula [(TeO2)0.7 (B2O3)0.3]1-x (SiO2)x with x = 0.1, 0.2, 0.3, and 0.4 using the melt-quenching technique using silica (98.548% SiO2, from rice husk), TeO2 (Alfar Aeser, 99.9%) and B2O3 (Alfar Aeser, 99.9%). Measurements and characterizations such as density and molar volume measurements, XRD analysis, FTIR, and UV–Vis spectroscopes were performed on the studied glasses. The objective was to determine the glasses’ applicability in optoelectronics, non-linear optics, and laser technologies through polarizability, linear electric susceptibility, and optical basicity study. Apart from confirming the amorphous nature of the glasses, the XRD analysis identified the presence of a crystalline phase of tellurium oxide (α-TeO2) formed. The FTIR spectral study revealed the presence of TeO3, BO3, and SiO4 structural units in the studied glasses. The refractive index (2.3026 – 2.2651), molar polarizability (8.0696 – 9.4334 A3), oxide ion polarizability (3.2970 – 3.6202 A3), electronic polarizability (0.2296 – 0.2335 A3), dielectric constant (5.1307 – 5.3019), optical basicity (0.6719 – 0.7998), metallization criterion (0.410853 – 0.420714) and electric susceptibility (0.3286 – 0.3422 esu) of the glasses were presented. With the high refractive index and favourable electronic/oxide ion polarizability as well as good electric susceptibility, the glasses have shown great potential for optical fibre and laser applications. Metallization criterion value falls in the range of glasses with great potentials for non-linear optical application. The dielectric value suggests the glasses represent wideband semiconducting glasses believed to be good for application in microelectronic substrates fabrication.

Urban infrastructure development including cycling-inclusive transport planning requires a proper institutional arrangement where stakeholder participation is considered as an important component. In Bangkok’s old town (Rattanakosin area), the bicycle tourist route was transformed in 2008 into a dedicated bicycle lane. Following its completion, the physical environment of the bicycle lane has gradually been altered overtime for more than a decade by the involvement of various groups of stakeholders. The objective of this article is to explain how stakeholder participation affects the quality of Rattanakosin bicycle lane. The data was collected through semi-structured interviews with the stakeholders and triangulated with observation and documented content analysis. Data ordering and data coding are used as a data analysis method to create a frequency distribution. The findings show that stakeholder participation affects the quality of Rattanakosin bicycle lane in terms of safety (segregation from other traffics) and comfort (hindrance or obstacles on the bicycle lane). Bottom-up active participation where cycling-related NGOs involved in the co-production process creates positive changes to the Rattanakosin bicycle lane whereas belated inclusion of operational stakeholders who were directly affected by the project led to conflicts and negative changes to the bicycle lane. Besides, having a professional facilitator and effective conflict resolution accelerates the consensus-building of the participatory process. Therefore, the presence of proper participatory typology and inclusiveness of stakeholders are essential to ensure a positive outcome.

The reservoir rock is made up of different minerals and its surface chemistry is influenced by the reservoir environment. Well operations implemented during the life of a field induce changes in th...

The viscosity of a nanofluid is one of its fundamental thermophysical properties, and it is an important consideration in heat transfer applications. Although the viscosity can be reliably obtained from experimental measurements, the development of models to predict the viscosity is a faster and more convenient approach. This study focuses on creating a machine learning model for the viscosity of different carbon nanomaterials dispersed in diesel oil. The nanomaterials considered here include multi-walled carbon nanotubes, graphene nanoplatelets, and their hybrid combinations. A support vector regression-based model was developed and validated using 120 experimental data points in the temperature range 5–100 °C. The model inputs are the nanoparticle mass fraction, the fluid temperature, and the viscosity of the diesel oil. The developed model yields very good predictive performance on the training and testing datasets. The correlation coefficient and the root mean square error were 99.98% and 0.0076 Pa s, respectively, for the training dataset, and 99.99% and 0.0026 Pa s for the testing dataset. These results indicate that the developed model is extremely accurate for predicting the viscosity of carbon-based nanomaterials in a diesel oil medium, and it was found to outclass all existing models. This model could therefore be extremely useful in the design of heat transfer applications.

In this paper, a stability analysis of a general nonlinear Chikungunya virus dynamics model is presented. Two kinds of infected cells, namely latently infected and actively infected are incorporated into the model. It is assumed that, the incidence rate of infection as well as the production, removal and proliferation rates of all compartments are modeled by general nonlinear functions that satisfy sufficient conditions. The model contains two distributed time delays. The nonnegativity and boundedness of the solutions of the model are investigated. Constructions of suitable Lyapunov functionals are established to prove the global stability of the steady states of the model. Numerical simulations are performed to confirm the validity of the established theoretical results.

SummaryA novel approach to exploit heat and pressure generated from the exothermic reactions of the aqueous solution of thermochemical reactants, in removing emulsion blockage induced by oil-based mud (OBM) has been investigated. The proposed technology essentially concerns raising the temperature and pressure of the formation above the kinetic stability of emulsions using thermochemical fluid (TCF). From the batch experiments, to assess the energetics of the thermochemical reaction, it was observed that the temperature of the system could be raised above 170°C at a pressure of 1,600 psi. The chemical can be effectively applied under different operating temperatures Tr = 20, 40, 55, and 100°C without significant effect on the heat and pressure generation. The specific energy per unit volume of the reaction is equivalent to ≈370 MJ/m3 within the operating conditions. OBM was prepared and used as the damaging fluid. A TCF was injected into the damaged core sample for cleaning. Permeability and porosity change of the treated core was tested using nuclear magnetic resonance (NMR) to monitor the efficiency of the TCF injection. Ultimately, injecting 1 pore volume (PV) of the TCF removed approximately 72% of the OBM-based emulsion from the core sample. In addition, permeability of the core sample increased from 120 to 800 md, while the porosity increased from 20 to 21.5% after treatment. Moreover, the pressure profile, observed during the flooding experiment, showed that no precipitation or damage was induced during the TCF flooding. Therefore, it is envisaged that the in-situ heat generation can mitigate the emulsion blockage problem and offer advantages over the existing methods considering environmental friendliness and damage removal efficiency.

In this study, the response of two homogeneous parallel beams with two-parameter Pasternak elastic foundation subjected to a constant uniform partially distributed moving force is considered. On the basis of Euler-Bernoulli beam theory, the fourth order partial differential equations of motion describing the behavior of the beams when subjected to a moving force were formulated. In order to solve the resulting initial-boundary value problem, finite Fourier sine integral technique and differential transform scheme were employed to obtain the analytical solution. The dynamic responses of the two beams obtained was investigated under moving force conditions using MATLAB. The effects of speed of the moving force, layer parameters such as stiffness (K_0) and shear modulus (G_0 ) have been conducted for the moving force. Various values of speed of the moving load, stiffness parameters and shear modulus were considered. The results obtained indicates that response amplitudes of both the upper and lower beams increases with increase in the speed of the moving load. Increasing the stiffness parameter is observed to cause a decrease in the response amplitudes of the beams. The response amplitudes decreases with increase in the shear modulus of the linear elastic layer.

One major characteristic of data is completeness. Missing data is a significant problem in medical datasets. It leads to incorrect classification of patients and is dangerous to the health management of patients. Many factors lead to the missingness of values in databases in medical datasets. In this paper, we propose the need to examine the causes of missing data in a medical dataset to ensure that the right imputation method is used in solving the problem. The mechanism of missingness in datasets was studied to know the missing pattern of datasets and determine a suitable imputation technique to generate complete datasets. The pattern shows that the missingness of the dataset used in this study is not a monotone missing pattern. Also, single imputation techniques underestimate variance and ignore relationships among the variables; therefore, we used multiple imputations technique that runs in five iterations for the imputation of each missing value. The whole missing values in the dataset were 100% regenerated. The imputed datasets were validated using an extreme learning machine (ELM) classifier. The results show improvement in the accuracy of the imputed datasets. The work can, however, be extended to compare the accuracy of the imputed datasets with the original dataset with different classifiers like support vector machine (SVM), radial basis function (RBF), and ELMs.

In a bid to complement the lost reserves from fossils, recent advances in research are tailored towards producing hydrogen as an alternative source of fuel which is aimed at fostering a globally sustainable and reliable energy-economy. In this work, hydrogen was produced from formic acid (FA) using a new technology that involves the use of copper nanoparticles (CuNPs) supported on triethanolamine. The CuNP-catalysts of variant concentrations (i.e. 0.6–1.2 M) were synthesized using the conventional chemical deposition method. Also, a novel approach that bothers on the application of the Differential Method of Analysis (DMA) was used in determining the kinetic parameters for the FA-dehydrogenation. Based on the results, the volume of H2 produced varied with time, pH, concentration and catalyst-size. At 6 h, the 1 M CuNPs gave the highest volume (815 mL) of hydrogen with corresponding pH, particle size and approximate conversion of 3.19, 1.5 nm and 100% respectively, whereas, over extended periods i.e. over 6 h, the approximate volume-conversions of FA increased insignificantly for all catalysts. According to the investigation, the optimum CuNP-catalyst concentration required to produce 815 mL H2 in 6 h is 1 M. The decomposition was a first-order-type with a rate constant (k-value) of 1.0041 s−1.

This paper presents the modelling of the specific heat capacity (SHC) of CuO/water nanofluids using a support vector regression (SVR) and artificial neural network models (ANN). The models presented were developed from the experimental data of SCH of CuO nanoparticles, the volume fractions of CuO nanoparticles and fluid temperature. The volume fraction of CuO nanoparticles considered ranges from 0.4 to 2% while the temperature range includes 293–338 K. The results obtained revealed that the SVR model exhibits slightly higher accuracy compared to the ANN model. However, both the SVR and ANN models clearly demonstrate better prediction performance for the SHC of CuO/water nanofluids compared to the existing theoretical models. The results obtained in this study proves that machine learning models provide a more accurate estimation of SHC of CuO/water nanofluids and they are recommended for heat transfer calculations due to their superior accuracy.

Thermogravimetric analysis (TGA) provides useful information, which can be used in thermal processing and conversion kinetic modeling of materials, including hydrocarbons. In the present study, a s...

Nucleate boiling on downward facing heating walls such as those found in IVR-ERVC and core catcher systems of advanced LWRs is characterized by bubbles of markedly different scales (small bubbles and vapor slugs). The heat transfer governing mechanism of deformable vapor slugs is different from that of small spherical bubbles. Therefore, the two-fluid wall-boiling model developed based on the characteristic of small-scale nucleate bubbles might not be sufficient for prediction of wall boiling heat transfer on a downward facing heating surface. In this article, a hybrid wall-boiling model is formulated and implemented in the solution framework of the Eulerian-Eulerian model coupled with a large-scale interface model and adaptive interface sharpening scheme. This approach gives a realistic representation of the physical phenomena present in flow boiling on a downward facing heating wall, and the validation against experimental data showed similar performance to the two-fluid model in terms of the prediction of wall boiling heat transfer.

The effect of silica nanoparticles on the rheological characteristics of water-in-heavy oil emulsions has been investigated. Enhanced oil recovery methods for heavy oil production (most especially, thermal fluid injection) usually result in the formation of water-in-oil (W/O) emulsion. In reality, the emulsion produced also contains some fine solid mineral particles such as silica, which, depending on its quantity, may alter the viscosity and/or rheological properties of the fluid. A series of binary-component emulsions were separately prepared by dispersing silica nanoparticles [phase fraction, β s, = 0.5%–5.75% (wt/v)] in heavy oil (S/O suspension) and by dispersing water [water cut, θ w = 10%–53% (v/v)] in heavy oil (W/O emulsion). Ternary-component emulsions comprising heavy oil, water droplets and suspended silica nanoparticles (S/W/O) were also prepared with similar ranges of θ w and β s. The viscosity was measured at different shear rates (5.1–1021.4 s−1) and temperatures (30–70 °C). Both binary-component and ternary-component emulsion systems were observed to exhibit non-Newtonian shear thinning behaviour. The viscosity of the heavy oil and W/O emulsions increased in the presence of silica nanoparticles. The effect was, however, less significant below β s = 2% (wt/v). Moreover, a generalized correlation has been proposed to predict the viscosity of both binary-component and ternary-component emulsions.

Laminar flow of oil-in-water (O/W) emulsion in a horizontal pipe has been simulated to quantify the effect of salinity of aqueous phase and water content on flow characteristics using computational...

Nanofluids are now considered the most essential constituent of solar thermal collector due to their superior thermal performance over conventional fluids. An accurate determination of the thermal efficiency of the solar collector depends on the value of the specific heat capacity of the nanofluid. So far, limited attention has been devoted towards accurate modelling of specific heat capacity of nanofluids C P nf despite their relevance in many solar energy-related applications. Surprisingly, there are only two main analytic models for estimating the C P nf in the literature. In most of the reports, these models have shown considerable inconsistencies for predicting the values of C P nf . Moreover, the modelling performance of these models necessitates the need to develop accurate models for the prediction of C P nf . Herein, a Bayesian support vector regression (BSVR) model is proposed to estimate the specific heat capacity of Al2O3/ethylene glycol nanofluid. The model proposed was trained on eighty-four (84) experimental datasets and its predictive accuracy was validated on seventeen (17) new test set. The BSVR model exhibits high accuracy as measured by the values of Pearson's correlation coefficient and the absolute average relative deviation (AARD) of 99.95% and 0.1888, respectively. Remarkably, the accuracy obtained from the proposed BSVR model is an order of magnitude better than existing theoretical models. The proposed technique and model will be useful towards a more reliable and accurate computation of the efficiency of solar collectors.

An innovative approach in enhancing oil and gas recovery is the in situ heat generation through exothermic chemical reactions using thermochemical fluids. This study presents the kinetics and energ...

In this study, the specific heat capacity of Alumina (Al2O3)/water nanofluid has been accurately evaluated using genetic algorithm/support vector regression (GA/SVR) model at volume fractions of 3.7–9.3%. The proposed (genetic algorithm/support vector regression) GA/SVR model was formulated using volume fractions and specific heat capacities of the alumina nanoparticles. The developed GA/SVR model is very accurate as determined from 99.998% correlation coefficient with experimentally obtained data and also has a root mean square error of 0.0014. Furthermore, the obtained results from the GA/SVR were compared with existing analytic models. Remarkably, the proposed model achieved an order of magnitude improvement over the model based on thermal equilibrium (Model II) and a two order of magnitude improvement over the model based on simple mixing rule for ideal gases (model I). Given the improvement in the accuracy, the proposed model would be useful for rapid and highly accurate estimation of the specific heat capacity of alumina/water nanofluids.

In this paper, an analytical framework is presented for device detection in an impulse radio (IR) ultra-wide bandwidth (UWB) system and its performance analysis is carried out. The Neyman–Pearson (NP) criteria is employed for this device-free detection. Different from the frequency-based approaches, the proposed detection method utilizes time domain concepts. The characteristic function (CF) is utilized to measure the moments of the presence and absence of the device. Furthermore, this method is easily extendable to existing device-free and device-based techniques. This method can also be applied to different pulse-based UWB systems which use different modulation schemes compared to IR-UWB. In addition, the proposed method does not require training to measure or calibrate the system operating parameters. From the simulation results, it is observed that an optimal threshold can be chosen to improve the ROC for UWB system. It is shown that the probability of false alarm, PFA, has an inverse relationship with the detection threshold and frame length. Particularly, to maintain PFA&lt, 10−5 for a frame length of 300 ns, it is required that the threshold should be greater than 2.2. It is also shown that for a fix PFA, the probability of detection PD increases with an increase in interference-to-noise ratio (INR). Furthermore, PD approaches 1 for INR &gt, −2 dB even for a very low PFA i.e., PFA=1×10−7. It is also shown that a 2 times increase in the interference energy results in a 3 dB improvement in INR for a fixed PFA=0.1 and PD=0.5. Finally, the derived performance expressions are corroborated through simulation.

Point-of-care ultrasound (POCUS) has become the standard of care for many emergency department evaluations. Low-resource settings are ideal for POCUS to help improve access to imaging for a number of indications that are obstetric, disease or trauma related. The following twelve specific tips are aimed toward organizations and individuals who aspire to initiate POCUS training in a low-resource setting.

Shale reservoirs are characterized with very low productivity due to the high capillary pressure and the ultra-low rock permeability. This article presents an effective treatment to improve the hydrocarbon productivity for shale reservoirs by injecting thermochemical fluids. In this study, several measurements were carried out to determine the effectiveness of the presented treatment. Coreflood, rate transient analysis (RTA), and nuclear magnetic resonance (NMR) measurements were performed. The gas productivity was estimated, before and after the treatment, utilizing the gas flowrates and the pressure drop across the treated rocks. The improvement in gas productivity due to thermochemical fluids was estimated by calculating the productivity index (PI) and the absolute open flow (AOF) before and after the chemical injection. Also, the changes in the pore size distribution, due to chemical injection, were studied using NMR measurements. Results showed that thermochemical treatment can improve the gas productivity by 44%, increase the AOF by 450%, and reduce the capillary pressure by 47%. Also, NMR measurements showed that fractures were induced in the shale rocks after the treatment, which will improve the shale productivity. Ultimately, this study introduces, for the first time, the use of thermochemical fluids to improve the hydrocarbon productivity for shale reservoirs.

Elastic moduli contrast between the adjacent layers in a layered formation can lead to various problems in a conventional hydraulic fracturing job such as improper fracture height growth, limited penetration in a weaker layer only, and nonconductive fractures. In this study, the results of thermochemical fracturing experiment are presented. The hydraulic fracturing experiments presented in this study were carried out on four-layered very tight cement block samples. The results revealed that the novel fracturing technique can reduce the required breakdown pressure in a layered rock by 26%, from 1495 psi (reference breakdown pressure recorded in the conventional hydraulic fracturing technique) to 1107 psi (breakdown pressure recorded in the thermochemical fracturing). The posttreatment experimental analysis showed that the thermochemical fracturing approach resulted in deep and long fractures, passing through majority of the layers, while conventional hydraulic fracturing resulted in a thin fracture that affected only the top layer. A productivity analysis was also carried out which suggested that the fracturing with thermochemical fluids can raise the oil flowrate up to 76% when compared to a conventional hydraulic fracturing technique. Thermochemical fluids injection caused the creation of microfractures and reduces the linear elastic parameters of the rocks. The new technique is cost effective, nontoxic, and sustainable in terms of no environmental hazards.

The traditional visual estimation of wood failure percent (WFP) in shear-failed adhesive-bonded joints is often marred by inconsistencies and produces results with large variations between examiners. This method of determining WFP has also been criticized as inaccurate and subjective. Therefore, an alternative method with improved accuracy and more consistent WFP estimations is desired. This study investigated the feasibility of Fiji-ImageJ software-assisted (F-IMJ-A) estimation of WFP in sheared block specimens of three wood species namely Eucalyptus grandis, Fagus sylvatica, and Pinus sylvestri bonded with three different adhesive systems viz. melamine-urea-formaldehyde, phenol-resorcinol-formaldehyde, and polyurethane. The consistency of the F-IMJ-A method was evaluated between three estimators and against the traditional ASTM D5266:2005 practice. The results obtained showed that the F-IMJ-A method improved, in most cases, the consistency of WFP estimations between the estimators. 5–33%, 1–36%, and 5–64% reduced WFP variations between estimators were recorded in E. grandis, F. sylvatica, and P. sylvestri-bonded joints respectively, across the different adhesive systems and wood failure modes. The F-IMJ-A procedures downscaled, by one-third, the 100% human-error-prone ASTM D5266 procedures as could be applicable with partially trained estimators. However, further improvement in the F-IMJ-A estimation is still desirable considering the range of variations between the estimators despite the recorded improvements. This study has demonstrated the suitability of the F-IMJ-A method in estimating WFP in shear-failed specimens of different wood species bonded with different adhesive systems.

Hydroxyapatite (HAP) was prepared from catfish bones at optimized conditions by employing Central Composite Design (CCD) under Response Surface Methodology (RSM). The temperature of preparation ranges between 300 and 1000 °C and time was varied between 1 and 2 h. The software gave thirteen (13) runs experiment within the conditions. 10 g of treated catfish bones inside crucible was calcined in the furnace at the temperatures and times suggested by the software. The yield percentages were determined and a standard method of pore size estimation Methylene Blue Number Method was used to estimate the adsorption capacity of the calcined catfish bones (CFHAP). Hence, the optimized conditions for adsorption were deduced. Characterization of the catfish bones before and after calcinations was studied by using Scanning Electron Microscopy (SEM); Fourier Transform Infrared Spectroscopy (FTIR;) Energy Dispersive X –ray Spectroscopy (EDX) and Brunaver-Emmett-Teller (BET). The results confirmed that the product is hydroxyapatite. Keywords: Catfish bone, Hydroxyapatite, Optimization, Response surface methodology, Characterization

Modern households are becoming more automated thereby delivering convenience and reducing time spent on house chores. While vacuum cleaners have made home cleaning easier, they are largely noisy and bulky for everyday use. It is therefore sine qua non to improve the technology of vacuum cleaning to reduce these deficiencies. Here, we report the development of a compact and efficient vacuum cleaner robot for potential office and home use. The developed robot is disk-shaped, equipped with vacuuming and cleaning technology and controlled by Arduino mega microcontroller. It sucks dirt via a retractable dustbin on top of which a cooling fan is mounted, and two sweepers each driven by a 3 V DC motor. The robot navigates via two motor shield controlled rear wheels and a front caster wheel which also governs its turning. Four ultrasonic sensors, placed at 90° apart, detect obstacles and subsequently help the robot navigate. The robot is powered by 3 batteries (28.8 V DC), rechargeable via an embedded AC-DC adapter. It is 12 cm wide and 9 cm tall making it easy for maneuvering its environment. Because of the light weight battery, cardboard based dustbin and small blower used, its weight is about 1.5 kg. The total current consumed is ∼1102 mA. When fully charged, a 2200 mAh-capacity battery works continuously for two hours and cleans floor efficiently. With this capability, the device will be deployed for office and home use thereby making cleaning a fully autonomous duty. Keywords: Vacuum cleaner, Robot, Control

Heat transfer models in liquid-vapor two-phase flow with wall boiling rely on the wall heat flux partitioning to quantify heat transfer to liquid and vapor separately. Several wall heat flux partitioning models have been proposed over the years based on variety of heat transfer mechanisms, but the three basic mechanisms that form the core of these models are liquid convection, surface quenching and evaporation heat transfer. A key parameter commonly used to determine the relative contribution made by each mechanism is area fraction of influence of bubble which is determined by multiplying maximum bubble projected area fraction with bubble area of influence factor (K). In classic wall heat flux partitioning models, K accounts for the area within which heat is transferred to liquid that moves in towards the heated wall as bubbles lift-off. The value of K has been a subject of controversy over the years with no unanimous conclusion among researchers. Therefore, in this paper, advanced diagnostic approach involving the combination of infrared thermometry and total reflection principle was employed to experimentally study nucleate flow boiling. Rigorous data analyses was performed to partition the wall heat flux into the aforementioned three basic heat transfer mechanisms using different values of K. All three heat transfer mechanisms were significantly sensitive to varying values of K, but setting K = 0.5 with percentage uncertainties of −60%/+50% closely predicted the experimental measurements. In addition, overlapping area of influence due to merging bubbles was observed to be significant in the model at high heat flux condition and must be discounted to get the true bubble area of influence. A correction method for the overlapping area of influence was therefore proposed to enhance accuracy of the predictive model.

Thermal degradation characteristics of a Japanese oil sand at different heating rates (10, 20, and 30 °C/min), and 30 ml/min air flow rate have been investigated. The kinetic parameters have been calculated based on three stages of weight loss and/or the conversion of the sample. These include, stage 1 (SI): volatilization of moisture content and the light hydrocarbon (20–227 °C), stage 2 (SII): combustion of heavy hydrocarbon (227–527 °C), and stage 3 (SIII): oxidative decomposition of carbonaceous organic matter (502–877 °C). The results showed that the rate of change of the oil sand conversion with time dαdt was affected by the heating rate. The time taken by the system to reach 0.99 conversion was observed as 85, 50, and 35 min at the heating rates of 10, 20, and 30 °C/min, respectively. The frequency factor, A, at SI was between 0.09 and 0.54 min−1, while the activation energy, Ea, was 11.2–12.5 KJmol−1 (the percentage weight loss, Wt, was 0–3.6 %w/w; and the conversion, α, was 0–0.2.). At SII, the values of A and Ea were 2.1–5.5 min−1 and 17.6–19 KJmol−1, respectively (Wt = 3.1–15.88 %w/w; α = 0.17–0.86.). The value of A at SIII was 5.5E11–1.1E13 min−1, while Ea was 160–200 KJmol−1 (Wt = 15.33–17.99 %w/w; and α = 0.84–0.99). Keywords: Thermogravimetric and differential thermal analysis (TG-DTA), Thermal degradation, Heating rates, Kinetics parameters

Interfacial velocity is an important parameter in the modeling of momentum transfer for prediction of heat- and mass-transfer during film-wise condensation. In this study, the interfacial velocity is modeled using an empirical power-law velocity profile with the assumption that the interfacial shear stress is negligible compared with the wall shear stress. A non-intrusive infrared particle tracking velocimetry (IR-PTV) measurement technique is used to validate a newly proposed semi-mechanistic model for the interfacial velocity of a gravity-driven laminar wavy film flow. The proposed model predicts measured interfacial velocities reasonably well and could serve as a closure relation in estimating the film-wise condensation heat transfer coefficient.

Background and objectives The refractive index of hemoglobin plays important role in hematology due to its strong correlation with the pathophysiology of different diseases. Measurement of the real part of the refractive index remains a challenge due to strong absorption of the hemoglobin especially at relevant high physiological concentrations. So far, only a few studies on direct measurement of refractive index have been reported and there are no firm agreements on the reported values of refractive index of hemoglobin due to measurement artifacts. In addition, it is time consuming, laborious and expensive to perform several experiments to obtain the refractive index of hemoglobin. In this work, we proposed a very rapid and accurate computational intelligent approach using Genetic Algorithm/Support Vector Regression models to estimate the real part of the refractive index for oxygenated and deoxygenated hemoglobin samples. Methods These models utilized experimental data of wavelengths and hemoglobin concentrations in building highly accurate Genetic Algorithm/Support Vector Regression model (GA-SVR). Results The developed methodology showed high accuracy as indicated by the low root mean square error values of 4.65 × 10−4 and 4.62 × 10−4 for oxygenated and deoxygenated hemoglobin, respectively. In addition, the models exhibited 99.85 and 99.84% correlation coefficients (r) for the oxygenated and deoxygenated hemoglobin, thus, validating the strong agreement between the predicted and the experimental results Conclusions Due to the accuracy and relative simplicity of the proposed models, we envisage that these models would serve as important references for future studies on optical properties of blood.

Synthesised vanadium-coordinated N,N-bis(o-hydroxybenzaldehyde)phenylene diamine catalyst, [VO(sal-HBPD)] and supported catalyst, p[VO(sal-HBPD)] were employed for the oxidation of quinoline. The use of [VO(sal-HBPD)] and p[VO(sal-HBPD)] for the oxidation of quinoline, (Quinoline-to-H2O2 ratio 1:7) showed oxidation selectivity as quinoline-N-oxide (100%) was recorded as the oxidation product. Quinoline-N-oxide was confirmed as the oxidation product through GC–MS. Density functional theory (DFT) revealed hydroxylperoxido-species [VOO(sal-HBPD)] (II) as the reactive oxidized oxidovanadium specie responsible for the oxidation. Ionic liquids, 1-butyl-3-methylimidazolium chloride and 1-butyl-3-methylimidazolium nitrate extracted 96% and 87% quinoline-N-oxide respectively. Keywords: Vanadium(IV) catalyst, Hydrogen peroxide (H2O2), Quinoline, Oxidation, Ionic liquids

Grate firing is one of the main competing technologies in biomass combustion for steam and electricity generation. Ash generated in the furnace during combustion process would greatly reduce the boiler thermal performance and may lead to unscheduled shutdown. The focus of this study is to optimize the combustion characteristics of the mixture of palm kernel shell (PKS) and selected additives (Al2O3, MgO and CaO) to develop a fuel mixture of low ash yield and higher heating value (HHV). D-Optimal Design under Cross Methodology of Design Expert (6.08) was employed to mix the components alongside various particle sizes. The mixed samples were ashed in a muffle furnace (848 K) to a constant weight and their HHV were determined using Ballistic Bomb Calorimeter. Combustion test based on optimized PKS additive mixture was conducted with a 5 kW grate furnace from which the effects of varying the ratio of primary to secondary air flow rate on temperatures and flue gas compositions from the furnace were measured. The ash obtained after combustion process was characterized using X-ray diffraction (XRD) for the purpose of identifying the mineral phase compounds that are present in PKS and PKS-additive ash. The optimum composition obtained for the process was 2.5, 0.0, 5.0, 92.5% and 5.50 mm for additives (Al2O3, MgO, CaO), PKS and particle size, respectively. The composition resulted in lowest ash yield (0.56%) and HHV (20.64 kJ/g). The coefficient of determination (R 2) (0.7951 and 0.7344) and least-square errors (0.19 and 0.024) of the prediction model indicated a close fitness to the experiment results obtained for ash yield and HHV. Primary to secondary air ratio of (40:60) recorded maximum temperature (1058 K), minimum level of CO (285 ppm) and 6% oxygen. XRD results showed excellent interaction between PKS and additives. The appearance of potassium-alumino silicate (KAlSiO4) in the PKS-additive ash prevented the release of potassium chloride which has the ability to increase ash deposition and corrosion.

This work reports the preparation of reduced graphene-based bismuth oxybromide (G-BiOBr) nanocomposite using a facile synthesis procedure. The synthesized nanocomposite material was characterized using a powder X-ray diffractometer, transmission electron microscope, X-ray photoelectron spectroscopy and UV–vis diffuse reflectance spectroscopy. The photocatalytic efficiency of the as-prepared BiOBr and G-BiOBr nanocomposite were examined through photodegradation of rhodamine-B dye (RhB) under a visible light irradiation at 420 nm. Experimental factors (such as contact time, the dye concentration and dosage of the catalyst) were investigated using a Central Composite Design. The results revealed that G-BiOBr nanocomposite exhibits improved photocatalytic activity, ≈100% degradation, far above the as-prepared BiOBr material. The improvement in photocatalytic activity may be ascribed to improved light absorption and charge separation. Also, a mechanism was proposed to describe the improved photocatalytic degradation of RhB dye using G-BiOBr where the graphene plays a key role in electron transfer and in reducing the rate of electron-hole recombination.

Enhancing thermal conductivity of nanofluids is an important objective in heat transfer applications. Experimental measurement of thermal conductivity is time consuming, laborious and expensive. One of the common ways to address these limitations involves developing theoretical models to study thermo-physical properties of nanofluid. However, most classical and empirical models fail in predicting experimental results with good precision. In this study, we developed support vector regression (SVR) models that are capable of predicting the thermal conductivity enhancement for metallic and metallic-oxide nanofluids. The accuracy and reliability of the developed models were assessed using statistical parameters such as correlation coefficient (R2), root mean square error (RMSE) and mean absolute error (MAE). The models were characterized with very high correlation coefficients of 99.3 and 96.3% for the metallic and metallic oxide nanofluids, respectively. While the RMSE obtained were 1.11 and 1.33 for the metallic and metallic oxide nanofluids, respectively. In addition, the results of the models were compared with Hamilton-Crosser (HC) model and other empirical models. The SVR models performed much better than all the models examined. Furthermore, the effects of temperature, volume fractions, nanoparticle size and type, and basefluids types were correlated with experimental data in order to assess the performance of the developed models. The results indicate that SVR predictions were accurate and better than common theoretical models.

The process of well cleanup involves the removal of an impermeable layer of filter cake from the face of the formation. The inefficient removal of the filter cake imposes difficulty on fracturing operations. Filter cake’s impermeable features increase the required pressure to fracture the formation. In this study, a novel method is introduced to reduce the required breakdown pressure to fracture the formation containing the water-based drilling fluid filter cake. The breakdown pressure was tested for five samples of similar properties using different solutions. A simulated borehole was drilled in the core samples. An impermeable filter cake using barite-weighted drilling fluid was built on the face of the drilled hole of each sample. The breakdown pressure for the virgin sample without damage (filter cake) was 6.9 MPa. The breakdown pressure increased to 26.7 MPa after the formation of an impermeable filter cake. Partial removal of filter cake by chelating agent reduced the breakdown pressure to 17.9 MPa. Complete dissolution of the filter cake with chelating agents resulted in the breakdown pressure approximately equivalent to the virgin rock breakdown pressure, i.e., 6.8 MPa. The combined thermochemical and chelating agent solution removed the filter cake and reduced the breakdown pressure to 3.8 MPa. Post-treatment analysis was carried out using nuclear magnetic resonance (NMR) and scratch test. NMR showed the pore size redistributions with good communication between different pores after the thermochemical removal of filter cake. At the same time, there was no communication between the different pores due to permeability impairment after filter cake formation. The diffusion coupling through NMR scans confirmed the higher interconnectivity between different pores systems after the combined thermochemical and chelating agent treatment. Compressive strength was measured from the scratch test, confirming that filter cake formation caused added strength to the rock that impacts the rock breakdown pressure. The average compressive strength of the original specimen was 44.5 MPa that increased to 73.5 MPa after the formation of filter cake. When the filter cake was partially removed, the strength was reduced to 61.7 MPa. Complete removal with chelating agents removed the extra strength that was added due to the filter cake presence. Thermochemical and chelating agents resulted in a significantly lower compressive strength of 25.3 MPa. A numerical model was created to observe the reduction in breakdown pressure due to the thermochemical treatment of the filter cake. The result presented in this study showed the engineering applications of thermochemical treatment for filter cake removal.

Understanding the role of depletion region, charge transport behaviour, and energy band modulations are essential to improve the performance of different sensors including the accurate acetone sensor for the diagnosis of diabetes via the exhaled breath. Herein, we report a new approach to amplify the performance of the acetone gas sensor by engineering the depletion layer. A novel Au decorated ZnO/Ag core-shell films (Au/ZnO/Ag) with different contents of Au, ZnO, and Ag are successfully fabricated via a sequential DC sputtering and post-heat treatment for the detection of acetone in low concentration. The optimum thickness of Ag, ZnO, and Au, to achieve the highest acetone response, are determined to be 11, 13, and 12 nm, respectively. The acetone sensing performance of the Au/ZnO/Ag is four and two times higher than that of ZnO and ZnO/Ag core-shell films. In addition, the Au/ZnO/Ag core-shell sensor exhibits excellent repeatability, high selectivity, and quick response at 150 °C operating temperature. The high performance of the developed sensor is attributed to the formation of a thick depletion layer, and the homogeneous distribution of Au and Ag nanoparticles. The findings demonstrate that the controlled engineering of the depletion layer is an efficacious approach for designing high-performance metal oxide-based gas sensors.

Following the recent advances in drone technologies, various algorithmic optimization problems related to the effective operation of drones are drawing lots of attentions. This paper considers two interesting multiple-drone-assisted search-and-reconnaissance scenarios, in each of which, the trajectory optimization of multiple drones is of great significance to minimize the latency in the system. In the first scenario, multiple drones, whose moments of mobilization are not necessarily the same, are trying to urgently collect intelligence from a given point of interest, and we would like to minimize the task completion time, i.e., the time period between the moment that the first drone commences its operation to the moment that the intelligence from all of the points are collected, by optimizing their trajectories. In the second scenario, multiple drones with different speeds, are hovering around the same routes to regularly collect intelligence from highly geographically-diversified points of interest over an extended time period, and we would like to minimize the worst-case data refreshment rate, the largest time gap between two consecutive observations over the same point of interest. In this paper, we formally define each problem, prove its NP-hardness, and propose an approximation algorithm for it. We also conduct a simulation to study the performance of our result.

Two-phase heat exchanger immersed in a passive containment cooling water storage tank (PCCWST) is a key component of the passive containment cooling system (PCCS) in light water reactors. Condensation and boiling heat transfer phenomena taking place on the inside and outside walls are crucial to the performance of the heat exchanger tube. The performances of such phenomena can be readily affected by the inclination angle of the heat exchanger tube under buoyancy-driven convection conditions in a PCCWST. However, to date no systematic examination of the effects of inclination angle on pool heat exchanger performance has been reported. This paper presents the results of numerical and experimental analyses of how inclination angle affects the performance of a single steam condensation tube immersed in a saturated water pool. To concurrently predict the two-phase heat transfer processes inside and outside the heat exchanger tube, an explicit heat structure coupling of two thermal-hydraulic codes was implemented using open media models. An experimental facility was also constructed to test a single-tube heat exchanger under the same conditions as the simulation. A comparison of simulation data and experimental results obtained for the reference case (tube inclination of 30°) confirmed that the coupled code could predict the heat transfer rate in the pool heat exchanger within the error limits of the experimental measurements. In light of this, additional simulations and experiments were conducted at varying inclination angles, from 3° to 90°. The results of simulations and experimental studies revealed that the heat transfer rate of a heat exchanger tube in a saturated pool is hardly influenced by tube inclination. Although an increase in inclination angle caused the vapor slug to spread around the heat exchanger tube surface, preventing liquid from getting to the surface of the tube, thereby decreasing boiling heat transfer coefficients, this was compensated for by an increase in condensation heat transfer coefficients as the inclination angle increased as a result of accelerated condensate liquid film in the tube. Thus, the overall effect of inclination angle on the thermal performance of a single-tube pool heat exchanger is inconsequential.

Recently, the concept of barrier-coverage of wireless sensor network has been introduced for various civilian and military defense applications. This paper studies the problem of how to organize hybrid sensor network, which consists of a number of energy-scarce ground sensors with homogenous initial battery level and energy-plentiful mobile sensors, to maximum the lifetime of barrier-coverage. Two key observations are (a) as the lifetime of each mobile sensor is much longer than that of the static ground sensors, each mobile sensor is capable of contributing multiple sensor barrier formations, and (b) no mobile sensor node can join two hybrid barriers which will be successively used to continuously protect the area of interest due to the moving delay. Based on these, we introduce a new maximum lifetime barrier-coverage problem in hybrid sensor network. We first propose a simple heuristic algorithm by combining existing ideas along with our own. Then, we design another efficient algorithm for the problem and prove that the lifetime of hybrid barrier constructed by this algorithm is at least three times greater than the existing one on average. Our simulation result shows that the second algorithm outperforms the first algorithm at least 33 percent and up to 100 percent.

The papers in this special section focus on the use of fuzzy techniques and logic for use in financial modeling and simulation. Computational intelligence has attracted a significant and increasing interest from the financial engineering community, and an emerging interest from analytical economics groups. The bar has been raised with the revision of regulations, and the required compliance and risk management. The new rules should be implemented through new processes and supported by developing new computational tools. Computational systems, capturing sentiments, preferences, behavior, and beliefs, are becoming indispensable in financial applications and desirable in economic analysis. They address problems in the classification of credit worthiness and fraud detection, contribute to the analysis and pricing of financial instruments, and effectively support portfolio optimization and investment analysis. They are instrumental in the design of market mechanisms and contagion mechanisms, and are contributing to the simulation of micro- and macro-economic processes. The armory of fuzzy techniques is capable of addressing challenges encountered in financial engineering and analytical economics. Fuzzy logic can effectively describe and incorporate expertsź intuition, market participantsź preferences, and economic agentsź behavior, thus reaching beyond the capabilities of probabilistic models. The objective of this special issue is to bring together the most recent advances in the design and application of fuzzy approaches to real problems in financial engineering and analytical economics.

Recently, discriminative correlation filter based trackers have achieved extremely successful results in many competitions and benchmarks. These methods utilise a periodic assumption of the training samples to efficiently learn a classifier. However, this assumption will produce unwanted boundary effects which severely degrade the tracking performance. Correlation filters with limited boundaries and spatially regularised discriminative correlation filters were proposed to reduce boundary effects. However, their methods use the fixed scale mask or pre-designed weights function, respectively, which are unsuitable for large-scale variation. In this study, the authors proposed multiscale spatially regularised correlation filters (MSRCF) for visual tracking. The authors' augmented objective can reduce the boundary effect even in large-scale variation, leading to more discriminative model. The proposed multiscale regularisation matrix makes MSRCF fast convergence. The authors' online tracking algorithm performs favourably against state-of-the-art trackers on OTB-2013 and OTB-2015 Benchmark in terms of efficiency, accuracy and robustness.

The deleterious effects of refractory polyaromatic hydrocarbons found in fuels such as organo-sulfur compounds are such that they emit SOx to the environment when combusted, thereby reducing air quality. Herein, oxidative desulfurization (ODS) which is a complementary step to hydrodesulfurization (HDS) was carried out in an attempt to eliminate sulfur compounds in fuels. Refractory organosulfur compounds were oxidized using tert-butyl hydroperoxide as an oxidant and a poly[VO(allylSB-co-EGDMA)], (vanadium(IV) functionalized polymer of 6,6′-(1E,1′E)-(1,2-phenylenebis(azan-1-yl-1-ylidene))bis(methan-1-yl-1-ylidene)bis(2-allylphenol) crosslinked with ethyleneglycol dimethacrylate) as a catalyst to convert sulfur compounds to polar sulfones. Some of the organosulfones were adsorbed via the use of molecularly imprinted polybenzimidazole nanofibers. The sulfur in heavy fuel oil after the oxidation/adsorption method fell below 8900 ± 200 ppmw S from the initial value of 17 920 ± 100 ppmw S.

In the petroleum industry, deposition of hydrocarbon wax is one the critical problems. Wax can deposit and accumulate inside the well completion, surface facilities and transportation pipelines. Wax deposition can lead to significant pressure drop in the production system and may result in stopping the hydrocarbon production. Several treatments are used to remove the deposited wax and improve the hydrocarbon flow. This paper presents a new and cost-effective technique for removing the wax deposition from the production system. In this work, inexpensive and environmentally-friendly fluids have been used for wax removal. Chemicals that able to generate heat and pressure at certain condition were used. In this study, thermochemical fluids were utilized to remove the accumulate wax in the production tubing. Actual wax from Arabian oil field was used to mimic the real condition of wax deposition. Thermochemical solutions that consist of two chemical reagents were used to remove the accumulated wax in a production tubing. The used chemical can react at certain condition and generate significant amount of heat and pressure. Temperature up to 500°F and pressure up to 2000 psi can be in-situ generated due to the thermochemical reaction. The chemical reaction can be triggered using acetic acid as an activating agent, to reduce the operational time for wax removal. The results showed that, more than 95% of the deposited wax can be removed using thermochemical solutions. The in-situ generated heat is able to liquefy the precipitated wax, then, the induced pressure due to the chemical reaction can flush the wax out of the production tubing. The used chemicals did not result in any damage in the pipeline, no corrosion or precipitation was observed in the production tubing. Also, the generated pressure due to thermochemical treatment did not reduce the pipe integrity, no pipe enlargement or damage was induced in the treated samples. This study presents a novel and high-performance treatment for wax removal using thermochemical fluids. The used chemicals can remove the wax from production tubing, surface facilities, and transportation pipelines; without affecting the integrity of the production system. The thermochemical fluids can be used at harsh situation of high temperature and high salinity condition. The obtained results show that there is a good potential for field application of this work in the next few months.

To bring fifth generation (5G) mobile communication\ud capabilities to indoor users, small cells and distributed\ud antenna systems are two promising technologies being considered. This paper investigates and compares the performance of indoor deployed small cell base station (SBS) and indoor distributed antenna systems (DAS) in an isolated multi-storey building. Each floor of the building is equipped with either an indoor SBS or an indoor DAS where geographically distributed remote radio heads (RRHs) are connected to a central unit (CU), and frequency reuse is employed among floors. Signal\ud propagation characteristics within multi-storey buildings and the impact of inter floor interference on performance is analysed and compared for both systems. The effect of different reuse distances, pathloss exponents, penetration loss and co-channel interference on achievable rate is analysed over a wide range of potential mobile equipment (ME) locations.

The viscosity data of two heavy oil samples X and Y, with asphaltene contents 24.8% w/w and 18.5% w/w, respectively, were correlated with temperature and pressure using empirical models and the artificial neural network (ANN) approach. The viscosities of the samples were measured over a range of temperatures between 70 &deg, C and 150 &deg, C, and from atmospheric pressure to 7 MPa. It was found that the viscosity of sample X, at 85 &deg, C and atmospheric pressure (0.1 MPa), was 1894 cP and that it increased to 2787 cP at 7 MPa. At 150 &deg, C, the viscosity increased from 28 cP (at 0.1 MPa) to 33 cP at 7 MPa. For sample Y, the viscosity at 70 &deg, C and 0.1 MPa increased from 2260 cP to 3022 cP at 7 MPa. At 120 &deg, C, the viscosity increased from 65 cP (0.1 MPa) to 71 cP at 7 MPa. Notably, using the three-parameter empirical models (Mehrotra and Svrcek, 1986 and 1987), the correlation constants obtained in this study are very close to those that were previously obtained for the Canadian heavy oil samples. Moreover, compared to other empirical models, statistical analysis shows that the ANN model has a better predictive accuracy (R2 &asymp, 1) for the viscosity data of the heavy oil samples used in this study.

Applications of oil-in-water emulsion (O/W) emulsification technology in enhanced recovery and pipeline transportation of heavy oil can be limited by several factors including salinity of the reservoir or process water, process temperature, and water cut. In this investigation, laminar flow of O/W was simulated in a pipeline to investigate the effect of salinity of aqueous phase (NaCl) and water cut on flow characteristics of the fluid. The case was simplified by considering the O/W as a stable, pseudo-homogeneous, single-phase fluid within the conditions operated. Pertinent to the objective of the study, at flow reference temperature, Tref = 30oC, the pressure drop at 30% water cut was 931Pa compared to 84.6 Pa at water cut of 50% (reference working fluid without NaCl). In contrast, the pressure drop was 239Pa, 142Pa, 124Pa, and 82.9Pa at 70000ppm, 40000ppm, 20000ppm, and 10000ppm salinity in the aqueous phase, respectively. In addition, the maximum dynamic viscosity imposed by the fluid, was ≈81000cP at 30% water cut compared to ≈14000cP from the reference fluid. The dynamic viscosity obtained from 70000ppm salinity content was ≈34000cP. Moreover, the results confirm facile application of emulsification technology for pipeline transportation of bitumen from large reduction in pressure drop (99%) regardless of the water cut and salinity.

In petroleum industry, great challenges are associated with producing hydrocarbon from unconventional reservoirs. Tight reservoirs are characterized with low permeability which reduces the hydrocarbon flow into the wellbore. Water blockage is considered as a potential damage issue in tight reservoirs due to increasing the water saturation around wellbore region and eventually decreasing the relative permeability of hydrocarbons. Acid fracturing or hydraulic fracturing are required to remove the damage and enhance the formation conductivity. The objective of this paper is to propose a new technique to remove the water blockage from tight formations using thermochemical treatment. Chemicals that generate pressure and heat at reservoir conditions are used to remove the water bank from tight core samples. Coreflooding experiments, capillary pressure and NMR measurements were conducted as well as routine core analysis. The impact of thermochemical treatment on improving the formation productivity was quantified. The effect of thermochemical injection on rock integrity was analyzed by evaluating the pore geometry before and after the chemical treatment. Thermochemical treatment resulted in a significant improvement in the core conductivity. NMR indicated that, tiny fractures were created in the core samples due the thermochemical flooding. Capillary pressure measurements showed that, the capillary pressure was reduced by 55.6% after the chemical treatment. The results of this study highlight that water blockage is great challenge in tight gas reservoirs. Injecting thermochemical fluids into tight samples reduces the capillary forces significantly, which leads to remove the water accumulation. Therefore, considerable enhancement was observed in the rock conductivity. This study provides a novel approach for removing the water blockage from tight formations using environmentally friendly chemicals. Chemicals that generate heat and pressure at downhole conditions were used to create tiny fractures. This treatment was able to remove the water blockage from tight sandstone cores and improve the productivity index by reducing the capillary forces.

In the event of a core meltdown in a high-power reactor, the integrity of the reactor pressure vessel is presumably protected by severe accident mitigation systems such as in-vessel retention external reactor vessel cooling (IVR-ERVC). However, in the late phase of the accident, two possible locations on the RPV are prone to failure: the location of the focusing effect and location of in-core instrument penetration. These two potential points of damage in the RPV are investigated in this study. A numerical model for the prediction of the natural convection, melting, and solidification processes for IVR-ERVC is presented. The model is based on the enthalpy-porosity approach with an extension for continuous liquid fraction function. The model is implemented in open-source field operation and manipulation (OpenFOAM) computational fluid dynamic code to produce a new solver which is based on the combination of conjugate heat transfer solver and buoyant-driven natural convection solver and the new solver is validated against the melting Gallium experimental test, in-core instrumentation failure experimental test, and BALI experimental test. This numerical model is applied for the investigation of the RPV rupture at the location of the focusing effect and in-core instrumentation penetrations. Severe ablations of the cladding and the weld materials are observed at a heat load of about ~1800 K which is expected to lead to the ejection of the penetration tubes if the force holding the penetration tube in place is lower than the force exerted by the system pressure. Subsequently, a two-layer IVR configuration is assessed and the integrity of the RPV is found not to be compromised under external reactor vessel cooling. However, in the case of a boiling crisis, the temperature of the ex-vessel wall is expected to rise quickly and this is simulated by increasing the ex-vessel wall temperature. The RPV is found to fail near the beltline due to a phenomenon known as focusing effect when the ex-vessel wall temperature rises above 1200 K.

The commercial operation of the AP1000 in China’s Sanmen nuclear power plant demonstrates the feasibility of reactors with advanced passive safety systems. However, being the first-of-a-kind, there is a need for robust, diverse, and independent safety assessment of the plant’s components and systems. This study presents a critical review of AP1000 transients, categorizes and evaluates current safety assessment codes, and discusses their application to safety analysis of AP1000. We extensively discuss the AP1000 safety assessment methods and enumerate sources of uncertainties in codes that have been used to assess a wide spectrum of AP1000 transients. In addition, we identified basic developmental issues in different system codes and crucial problems with their applications to AP1000. Furthermore, we give insights into optimized simulation techniques and advanced modeling approaches for high fidelity computation. As unique contributions, key issues such as the reliability of the passive safety systems, scaling, verification and validation experiments necessary to enhance the safety of AP1000 are discussed. The significant research gaps, future research direction, and current safety issues presented in this work also serve as an important body of knowledge towards a safe and reliable operation of future AP1000 fleets.

Er2O3 doped silica borotellurite glasses were synthesized using the melt-quenching technique with a chemical compositional formula (1 – x){(SiO2)0.2 [(B2O3)0.3 (TeO2)0.7]0.8} (Er2O3)x. Using the XRD, FTIR, UV–Vis, and photoluminescence spectroscopies, the structural, optical and spectroscopic properties of the glasses were studied. The absorption spectra and the refractive index, penetration depth, extinction coefficient, absorption coefficient, dielectric constant, optical conductivity for the glasses were presented. The absorption and emission cross-sections, cross-sectional gain, effective bandwidth, and bandwidth quality factor were also calculated using the UV–Vis data. Photoluminescence and the upconversion luminescence of the glasses were presented for pump wavelengths of 350 nm and 540 nm respectively. The studied glasses presented a high refractive index with values between 2.452 and 2.5153. The emission cross-section, effective bandwidth, bandwidth quality factor increased respectively from 3.53 × 10–22 to 1.3637 × 10–20 cm2, 66.3–103 nm, and 3.636 × 1028–91.095 × 1028 cm3 with increase in the Er3+ ions concentration in the glass system. The synthesized glasses exhibits a high-quality up-conversion emissions around 397.5 nm, 426 nm, and 475 nm from an excitation due to a pump of 540 nm wavelength. Furthermore, a high-quality luminescence emission around 543 nm, 500 nm, 475 nm and 425 nm from an excitation due to a pump of 350 nm wavelength was observed. Considering the values of the glasses refractive index, dielectric behaviour, absorption and emission cross-sections, effective bandwidths and other parameters presented, the synthesized glasses possess great potential for optoelectronic, laser and Erbium dope fibre amplifier (EDFA) applications.