The increased demand for solar renewable energy sources has created recent interest in the economic and technical issues related to the integration of Photovoltaic (PV) into the grid. Solar photovoltaic power generation forecasting is a crucial aspect of ensuring optimum grid control and power solar plant design. Accurate forecasting provides significant information to grid operators and power system designers in generating an optimal solar photovoltaic plant and to manage the power of demand and supply. This paper presents an extensive review on the implementation of Artificial Neural Networks (ANN) on solar power generation forecasting. The instrument used to measure the solar irradiance is analysed and discussed, specifically on studies that were published from February 1st, 2014 to February 1st, 2019. The selected papers were obtained from five major databases, namely, Direct Science, IEEE Xplore, Google Scholar, MDPI, and Scopus. The results of the review demonstrate the increased application of ANN on solar power generation forecasting. The hybrid system of ANN produces accurate results compared to individual models. The review also revealed that improvement forecasting accuracy can be achieved through proper handling and calibration of the solar irradiance instrument. This finding indicates that improvements in solar forecasting accuracy can be increased by reducing instrument errors that measure the weather parameter.
Paper-based analytical devices (PADs) with four new designs could be fabricated using commercially available home-based scan-and-cut printer. They serve for miniaturised platforms for chemical analysis. Replication analysis of a sample together with the calibration (using the analyte standards at different concentrations) can be completed in a single run, by utilising smartphone as the detector. Some new approaches for choosing detection zones were suggested. The four proposed PAD designs here were used as models in microliter scale operation to demonstrate the well-known chemistries of colorimetric determinations of iron, phosphate, and hardness using 1,10-phenanthroline and simple aqueous guava leaf extract; molybdate, and EBT-EDTA complexometric titration, respectively, through calibrations: where Blue (B) value = 88.2log [Fe3+] - 80.8, R2 = 0.989; B value = 1.75 [Fe3+] + 0.198, R2 = 0.999; Grey scale (I) value = 1.77 [Fe3+] - 1.22, R2 = 0.997; Red (R) value = 16.1log [PO43-] + 8.95, R2 = 0.999; Hue (H) value = 43.3log [Ca2+] + 233, R2 = 0.994, respectively. For the hardness, using one of the PAD designs, true titration was also possible. Applications of the proposed devices and procedures were demonstrated for real world samples with validation. Additionally, kinetic study of the molybdenum blue for phosphate was demonstrated using one of the PADs.
Tension stiffening is a characteristic behavior of reinforced concrete (RC) beams which is directly affected by the bond-slip property of steel bar and concrete interfaces. A beam strengthened with a near-surface mounted (NSM) technique would be even more affected by tension stiffening, as the NSM reinforcement also possess a bond-slip property. Yet assessing how much the tension stiffening of NSM contributes to the behavior of RC beams is difficult due to the fact that bond-slip effects cannot be directly incorporated into a strain-based moment-curvature analysis. As such, the tension stiffening is typically incorporated through various empirical formulations, which can require a great deal of testing and calibrations to be done. In this paper a relatively new method, which can be called the mechanics-based segmental approach, is used to directly simulate the tension stiffening effect of NSM reinforcements on RC beams, without the need for empirical formulations to indirectly simulate the tension stiffening. Analysis shows that the tension stiffening of NSM fiber reinforced polymer (FRP) contributes a significant portion to the stiffness and strength of the strengthened RC beam not only during serviceability, but at all load levels.
Short-term streamflow prediction is essential for managing flood early warning and water resources systems. Although numerical models are widely used for this purpose, they require various types of data and experience to operate the model and often tedious calibration processes. Under the digital revolution, the application of data-driven approaches to predict streamflow has increased in recent decades. In this work, multiple linear regression (MLR) and random forest (RF) models with three different input combinations are developed and assessed for multi-step ahead short-term streamflow predictions, using 14 years of hydrological datasets from the Kulim River catchment, Malaysia. Introducing more precedent streamflow events as predictor improves the performance of these data-driven models, especially in predicting peak streamflow during the high-flow event. The RF model (Nash-Sutcliffe efficiency (NSE): 0.599-0.962) outperforms the MLR model (NSE: 0.584-0.963) in terms of overall prediction accuracy. However, with the increasing lead-time length, the models' overall prediction accuracy on the arrival time and magnitude of peak streamflow decrease. These findings demonstrate the potential of decision tree-based models, such as RF, for short-term streamflow prediction and offer insights into enhancing the accuracy of these data-driven models.
Spectroscopy in the visible and near-infrared region (Vis-NIR) region has proven to be an effective technique for quantifying the chlorophyll contents of plants, which serves as an important indicator of their photosynthetic rate and health status. However, the Vis-NIR spectroscopy analysis confronts a significant challenge concerning the existence of spectral variations and interferences induced by diverse factors. Hence, the selection of characteristic wavelengths plays a crucial role in Vis-NIR spectroscopy analysis. In this study, a novel wavelength selection approach known as the modified regression coefficient (MRC) selection method was introduced to enhance the diagnostic accuracy of chlorophyll content in sugarcane leaves. Experimental data comprising spectral reflectance measurements (220-1400 nm) were collected from sugarcane leaf samples at different growth stages, including seedling, tillering, and jointing, and the corresponding chlorophyll contents were measured. The proposed MRC method was employed to select optimal wavelengths for analysis, and subsequent partial least squares regression (PLSR) and Gaussian process regression (GPR) models were developed to establish the relationship between the selected wavelengths and the measured chlorophyll contents. In comparison to full-spectrum modelling and other commonly employed wavelength selection techniques, the proposed simplified MRC-GPR model, utilizing a subset of 291 selected wavelengths, demonstrated superior performance. The MRC-GPR model achieved higher coefficient of determination of 0.9665 and 0.8659, and lower root mean squared error of 1.7624 and 3.2029, for calibration set and prediction set, respectively. Results showed that the GPR model, a nonlinear regression approach, outperformed the PLSR model.
The exponential growth of human population and anthropogenic activities have led to the increase of global surface water contamination especially in river, lakes and ocean. Safe and clean surface water sources are crucial to human health and well-being, aquatic ecosystem, environment and economy. Thus, water monitoring is vital to ensure minimal and controllable contamination in the water sources. The conventional surface water monitoring method involves collecting samples on site and then testing them in the laboratory, which is time-consuming and not able to provide real-time water quality data. In addition, it involves many manpower and resources, costly and lack of integration. These make surface water quality monitoring more challenging. The incorporation of Internet of Things (IoT) and smart technology has contributed to the improvement of monitoring system. There are different approaches in the development and implementation of online surface water quality monitoring system to provide real-time data collection with lower operating cost. This paper reviews the sensors and system developed for the online surface water quality monitoring system in the previous studies. The calibration and validation of the sensors, and challenges in the design and development of online surface water quality monitoring system are also discussed.
Stable carbon isotope ratio measurements are used to investigate the provenance of vanillin. In this study, a variety of commercial vanillin samples and vanilla products were analyzed to provide a frame of reference for the variability of carbon isotope delta values in various vanillin samples, with the results ranging from -20.6 to -36.7‰ relative to the Vienna Peedee Belemnite (VPDB). We present information on the development of two synthetic vanillin reference materials, VANA-1 and VANB-1, prepared in 0.75 g units in glass vials, to be used for the calibration of carbon isotope delta measurements of vanillin and other easily combustible organic materials. Characterization of 40 vials each of VANA-1 and VANB-1 was performed by three laboratories over several measurement sequences. The certified carbon isotope delta values are -31.30 ± 0.06‰ (VANA-1) and -25.85 ± 0.05‰ (VANB-1). These uncertainties, for the 95% confidence level, include considerations for measurement uncertainty, coherence of the reference materials used for calibration, batch homogeneity, and stability during storage and transportation. The results are traceable to the VPDB through a set of nine reference materials (IAEA-CH-6, USGS65, IAEA-600, NBS22, USGS61, IAEA-603, IAEA-610, IAEA-611, and IAEA-612). For up to date certified values, users should refer to doi.org/10.4224/crm.2022.vana-1 and doi.org/10.4224/crm.2022.vanb-1.
The siphonous green algal family Caulerpaceae includes the monotypic genus Caulerpella and the species-rich genus Caulerpa. A molecular phylogeny was inferred from chloroplast tufA and rbcL DNA sequences analyzed together with a five marker dataset of non-caulerpacean siphonous green algae. Six Caulerpaceae lineages were revealed, but relationships between them remained largely unresolved. A Caulerpella clade representing multiple cryptic species was nested within the genus Caulerpa. Therefore, that genus is subsumed and Caulerpa ambigua Okamura is reinstated. Caulerpa subgenus status is proposed for the six lineages substantiated by morphological characters, viz., three monotypic subgenera Cliftonii, Hedleyi, and Caulerpella, subgenus Araucarioideae exhibiting stolons covered with scale-like appendages, subgenus Charoideae characterized by a verticillate branching mode, and subgenus Caulerpa for a clade regarded as the Caulerpa core clade. The latter subgenus is subdivided in two sections, i.e., Sedoideae for species with pyrenoids and a species-rich section Caulerpa. A single section with the same name is proposed for each of the other five subgenera. In addition, species status is proposed for Caulerpa filicoides var. andamanensis (W.R. Taylor). All Caulerpa species without sequence data were examined (or data were taken from species descriptions) and classified in the new classification scheme. A temporal framework of Caulerpa diversification is provided by calibrating the phylogeny in geological time. The chronogram suggests that Caulerpa diversified into subgenera and sections after the Triassic-Jurassic mass extinction and that infra-section species radiation happened after the Cretaceous-Tertiary mass extinction.
This work investigates a new interrogation method of a fiber Bragg grating (FBG) sensor based on longer and shorter wavelengths to distinguish between transversal forces and temperature variations. Calibration experiments were carried out to examine the sensor's repeatability in response to the transversal forces and temperature changes. An automated calibration system was developed for the sensor's characterization, calibration, and repeatability testing. Experimental results showed that the FBG sensor can provide sensor repeatability of 13.21 pm and 17.015 pm for longer and shorter wavelengths, respectively. The obtained calibration coefficients expressed in the linear model using the matrix enabled the sensor to provide accurate predictions for both measurements. Analysis of the calibration and experiment results implied improvements for future work. Overall, the new interrogation method demonstrated the potential to employ the FBG sensing technique where discrimination between two/three measurands is needed.
The present conventional (destructive) method used in determining LAI is laborious, difficult and time consuming. Thus, an image-based measurement using camera system with fish eye lens offers an alternative means for an accurate indirect measurement of LAI in oil palm. In this study, a methodology was developed to improve the leaf area index of the oil palm determination using hemispherical photography as an indirect method. A set of true LAI data, collected using the destructive method, were used as a reference to calibrate the LAI measurements obtained by the hemispherical photography. A good relationship (r = 0.85) was found between age of palm and hemispherical photographic LAI. However, the estimated LAI obtained by the hemispherical photographic method was underestimated as compared to the destructive method. Some means of calibration was necessary to determine the relationship between the actual LAI and the hemispherical photographic LAI. It was necessary to multiply the LAI value from 5 years to 16 years, by a clumping factor of 2.14 for 5 to 9 year old palms, 2.33 for 10 to 14-year old palms and 2.37 for above 15- year old palms to calculate the accurate LAI values. For palms which are less than 5 year old (i.e. 2 to 3 years in this study), the photography LAI value was equal to the calculated LAI value. This proposed that correction factors would solve this underestimation effect. In addition, two equations were also proposed to estimate the true LAI from the Photographic LAI for immature and mature oil palm plantation.
Studies have been carried out to determine the chemical (soluble solid content) and physical (firmness) parameters of locally grown Cavendish banana by near infrared (NIR) spectroscopy. NIR spectra in the wavelength region of 680-2500 nm were obtained from a total of 408 Cavendish bananas of different ripeness indices. Chemometrics using multiple linear regression (MLR) was applied to develop calibration models for prediction of firmness and soluble solid content (SSC) of Cavendish banana. Results showed that NIR spectroscopy had the feasibility for non-destructive determination of the quality of Cavendish banana. The coefficient of determination (R2) for firmness and SSC calibration models at different ripeness indices ranged from 0.78 to 0.86 and 0.75 to 0.96, respectively. The calibration models were validated using independent sets of data and prediction models developed with the root mean square error of prediction (RMSEP) ranged from 0.01 to 0.26 kgf and 0.039 to 0.788 Brix for firmness and SSC, respectively. The multi-index models showed considerable robustness but higher prediction error with RMSEP of 0.336 kgf for firmness and 0.937% Brix for SSC compared to index specific model.
A systematic study to assess the concentration of radionuclides in primary coolant and associated water samples from the operation of a TRIGA Mark II reactor has been carried out. The samples were transferred into appropriate counting container and were counted by efficiency-calibrated gamma spectrometer systems for several hours to obtain statistically adequate data for qualitative and quantitative evaluation of the radioactive materials presence. The primary coolant was found to contain various gamma emitting radionuclides including 24Na, 41Ar, 42K, 51Cr, , 54Mn, 56Mn, 60Co, 99mTc, 122Sb, 124Sb and 187W. Most of the detected radionuclides were inferred to be originated from activation products of (n,) nuclear reactions of elements of reactor components such as stainless steel and aluminium alloy used in the reactor system. The study confirms the integrity of the reactor system with no apparent release of any fission products radionuclide into the coolant water system.
Paper recycling plants usually buy their raw material from suppliers. More than often, bulk used paper supplied to the plant contains some significant quantity of water in its internal voids. It may be included intentionally or unintentionally. The price of used paper depends on its weight, thus adding water will help to increase weight and consequently increase the price. In this way, plant owner who purchase the used paper suffers a significant of financial lost. The objectives of our experiment are to establish a calibration curve that correlate between the amount of neutron backscattered and water content, and finally to develop a correction factor that need to be introduced to the measured values of water content. A fast neutron source (Am-Be 241) and a portable backscattering neutron detector were used for water measurement. The experiments were carried out by measuring neutron backscattering from used paper that has been added with different amount of water. As a result, a neutron calibration curve that provides a correlation between neutron backscattering and water content was established.
The paper looks into the possibility of using standard addition method to analyse uranium and thorium in tin slag. Tin slag samples obtained from Butterworth was grind to 180 ȝm and injected with different concentrations of uranium and thorium. Linear calibration graphs were obtained for both these samples with R 2 values for uranium and thorium as 0.9989 and 0.9915 respectively. Based on this graphs, the initial uranium and thorium in the tin slag sample was established as 60 ppm for uranium and 160 ppm for thorium.
Even though EDXRF analysis has major advantages in the analysis of stainless steel samples such as simultaneous determination of the minor elements, analysis can be done without sample preparation and non-destructive analysis, the matrix issue arised from the inter element interaction can make the the final quantitative result to be in accurate. The paper relates a comparative quantitative analysis using standard and standardless methods in the determination of these elements. Standard method was done by plotting regression calibration graphs of the interested elements using BCS certified stainless steel standards. Different calibration plots were developed based on the available certified standards and these stainless steel grades include low alloy steel, austentic, ferritic and high speed. The standardless method on the other hand uses a mathematical modelling with matrix effect correction derived from Lucas-Tooth and Price model. Further
improvement on the accuracy of the standardless method was done by inclusion of pure elements into the development of the model. Discrepancy tests were then carried out for these quantitative methods on different certified samples and the results show that the high speed method is most reliable for determining of Ni and the standardless method for Mn.
Magnetoencephalography (MEG) has been extensively used to measure small-scale neuronal brain activity. Although it is widely acknowledged as a sensitive tool for deciphering brain activity and source localisation, the accuracy of the MEG system must be critically evaluated. Typically, on-site calibration with the provided phantom (Local phantom) is used. However, this method is still questionable due to the uncertainty that may originate from the phantom itself. Ideally, the validation of MEG data measurements would require cross-site comparability.
Vanadium(IV) and vanadium(V) can be determined by using differential pulse cathodic stripping voltammetry technique (DPCSV). Cupferron (ammonium N-nitrosophenylhydroxylamine) was used as ligand to form complex compounds with vanadium ions in Britton-Robinson buffer (BRB) solution. At concentration lower than 1.0×10(-6) M, both V(IV) and V(V) cupferron complexes showed a single cathodic peak at -0.576 V in BRB of pH 4; thus V(IV) and V(V) ions cannot be differentiated at low concentration. However, the ionic species of vanadium can be differentiated at high concentration in the presence of cupferron. Parameters including pH of BRB solution, initial potential and accumulation potential were optimized. Under the optimized parameters, the limit of detection (LOD) was 0.09 nM, and the peak current was linear in the concentration range 0.01-0.9 µM total vanadium ions. The determination of V(IV) and V(V) ions was carried out at higher concentration in the sample using calibration plot method. At higher concentration range of 10-60 µM V(IV) and V(V) ions were determined with LOD of 1.2 and 1.1 µM, respectively. The developed method was successfully applied to 10,00,000 fold diluted Benfield sample and 0.6227 M total vanadium ions were determined. The determination of V(IV) and V(V) ions were also successfully carried out in artificial sample as well as Benfield sample (dilution factor, 10,000). The concentration of V(IV) and V(V) ions was 22.52 µM and 38.91 µM, respectively, giving total vanadium concentration of 0.6143 M in Benfield sample.
This study presents a novel application of near infrared (NIR) spectral linearisation for measuring the soluble solids content (SSC) of carambola fruits. NIR spectra were measured using reflectance and interactance methods. In this study, only the interactance measurement technique successfully generated a reliable measurement result with a coefficient of determination of (R2) = 0.724 and a root mean square error of prediction for (RMSEP) = 0.461° Brix. The results from this technique produced a highly accurate and stable prediction model compared with multiple linear regression techniques.
This paper reports a computational approach for analysis of FTIR spectra where peaks are detected, assigned and matched across samples to produce a peak table with rows corresponding to samples and columns to variables. The algorithm is applied on a dataset of 103 spectra of a broad range of edible oils for exploratory analysis and variable selection using Self Organising Maps (SOMs) and t-statistics, respectively. Analysis on the resultant peak table allows the underlying patterns and the discriminatory variables to be revealed. The algorithm is user-friendly; it involves a minimal number of tunable parameters and would be useful for analysis of a large and complicated FTIR dataset.
Stratified sampling procedure was employed to collect a total of 40 samples; 2 from each stratum, measuring an approximate dimension of 3.25 km(2) of the actual sample site. Appropriate volumes were then evaporated and transferred into clean stainless steel planchets (ISO 9696 and ISO 9697). An eight channel gas-flow proportional counting system connected to a microprocessor loaded with a spreadsheet programme (Quarttro-Pro) and graphic programme (Multiplan) initially calibrated for efficiency was employed to count the background and the prepared samples. A mean efficiency of 33.44 and 41.24 % for the respective alpha and beta sources was obtained. A low background activity was also observed with a mean of 0.165 Bq for alpha and 1.119 Bq for beta. The gross alpha and beta activity concentrations in the water were found to range from 80 +/- 0.05 to 2300 +/- 0.41 Bq m(-3) and 120 +/- 0.08 to 4970 +/- 0.78 Bq m(-3), respectively. This clearly indicate areas of elevated alpha and beta activity concentrations of 37.5 and 47.5 %, respectively when compared with the International Commission for Radiological Protection (1991) maximum acceptable values of 500 Bq m(-3) for alpha and 1000 Bq m(-3) for beta.