Mathematical Estimation Research Articles

Evaluating the Enterprise Resource Planning (ERP) System of a Bread Manufacturing Organisation from the Perspectives of Stakeholders

Enterprise Resource Planning (ERP) framework integration is an important and foundational business process preparation and knowledgesharing process. The ERP framework has in the past decade been regarded as a vital enabler for organisational performance and functions as an integral feature in today's diverse and competitive market climate. It normally needs considerable feedback in terms of commitment, time, and resources to incorporate an ERP method.In recent research, numerous contributing factors to the ERP's performance or failure were more specifically demonstrated; still, only the ERP's contributions in developed countries were illuminated. Moreover, many developing countries are interested in ERP deployment in their organizations.As Microsoft Dynamics 365 has not been thoroughly researched to date, this study seeks to penetrate an unexplored territory for Malaysia by concentrating on Malaysia-specific results. The importance of this analysis is a detailed review of the spectrum of Microsoft Dynamics 365 operations in supply chain management. Additionally, this analysis aims to help specific sectors and enterprises further grasp the value of Microsoft Dynamics 365 as an enterprise resource planning (ERP) solution. A reasonable amount of evidence would be supplied that addresses the feasibility, usefulness, and practicality of Microsoft Dynamics 365.This research aimed to discover variables that impact ERP users in an ERP environment, with the intention of gaining a more detailed understanding of the end-user ERP output through a qualitative case study. It is important to recognise all of the influences on the stakeholders in the ERP management and in this study, the Microsoft Dynamic 365 (D365) was the target system. The study utilized a qualitative research framework that combines computational techniques and mathematical estimation for data collection and interpretation. The ERP data obtained are the data required for answering the study questions.The findings of this study show the importance of stakeholders and ERP Framework as well as ERP system such as D365 in promoting individual ERP successes. The researcher also looked at the views of stakeholders on the impact of system quality. This relationship was investigated from the viewpoints of the end-users, stakeholders. Understanding the relative value of the information system factors places an emphasis on the vital performance factors perceived by end consumers by organisations and suppliers. Ingeneral, the conclusion from the analysis is that the management of the supply chain in extended companies should not expect too much of an ERP system as eventually still needs various customizationwithin certain industries.This study has helped to reduce the discrepancy in the theory of stakeholders regarding the criteria for the selection of systems and methods for bread production in Malaysia, although some of the criteria and methods provided for in the theories might not be prominent in the real world. Practitioners tend to respect and comply with the requirements of the organisation over the more sophisticated ERP scheme.

ГЕОГРАФИЧЕСКИЕ ЗАКОНОМЕРНОСТИ РАЗВИТИЯ РЫНКА СКЛАДСКОЙ НЕДВИЖИМОСТИ В МОСКОВСКОЙ АГЛОМЕРАЦИИ

The article analyzes the current trends in the warehouse real estate market development in Moscow and the Moscow region, the geography of large warehouses’ construction, the distribution density of the warehouse real estate in Moscow agglomeration. It is revealed that the most popular logistics destinations are the north-western (Zelenograd, Radumlya), southern (Domodedovo, BelyeStolby) and eastern (Balashikha, Noginsk) with the largest cargo flows and free areas for construction, as well as the Central Ring Road (Bely Rast, Sever-4), due to an increase in the movement speed along which the time of cargo delivery is reduced. Less busy destinations are south-western (Vorsino) and north-eastern (Sofrino, Pushkino). In the western (Istra) and northern (Dmitrov) directions, new storage facilities ap- pear mainly between the MKAD and the Central Ring Road due to relatively low transit cargo flows in these directions and the predominance of water and forest lands in the land use structure. It is noted that the logistical influence of the Moscow agglomeration extends beyond the Moscow region in the south- western (Obninsk), north-eastern (Pereslavl-Zalessky) and western (Gagarin) directions. The main con- clusions are supported by statistical information and mathematical estimates.

Identification of Structural Reliability Parameters in Distributed Telecommunication System

The article considers estimation of structural parameters for communication network. The article aims to calculate the key indicators of structural reliability. Proposed method consists of mathematical expectation estimation for number of connections and connectivity probability in different structured networks. The methodology is based on solution to the problem of network optimization structure for reliability view. Classification of equipment is proposed in terms of failure impact on quality management of network traffic. According to the accepted classification, reliability parameters are listed characterizing each of the classified equipment groups. Reliability parameters are divided into single and complex ones. The reliability and quality of the communication network functioning is assessed by the estimated values of these parameters. A probabilistic graph of network model and state diagram of communication lines for one hierarchical level were plotted. When plotting, it was assumed that switching nodes are absolutely reliable. The reliability of communication lines is expressed in terms of availability. The calculation of parameters was carried out on the assumption that communication line failures are statistically independent events. Reliability indicators are estimated, such as the probability of connectivity between each pair of telecommunication nodes and the absolute value of mathematical expectation for number of connections. For convenience, practical application of the proposed method, calculation results of the mathematical expectation of number of connections is expressed as a percentage. The article considers a mathematical model of the functioning of a separate communication line with the absolute reliability of the system for monitoring its technical condition. The mathematical model is presented in the form of a state diagram. It is assumed that the network monitoring system performs the functions of periodic and continuous monitoring. A method for calculating complex indicators of reliability, such as the availability factor and the downtime factor, is proposed. The calculation method is based on the matrix method for analyzing probabilistic systems. The calculation formulas include statistical data from the system for monitoring the technical condition of communication lines. Modelling is used to simulate the process of communication network functioning in experimental research. The process of communication network functioning is simulated. Depending on the incoming load, a search for free algorithmic resources and communication lines is performed. The calculation of the reliability indicators of the selected way of servicing calls is performed, taking into account the selection criterion set by the program. The monitor displays a dependency graph of the reliability indicators on the intensity of the incoming load. The calculation of the relative statistical frequency of the success of servicing requests is carried out, and the frequency of communication lines failure is also calculated. The program output is proposals for communication network reconstruction due to network resources redistribution.

Practical Potential of Grain Impurities in the Processing of Sunflower Oil Raw Materials in the Oil and Fat Industry

Abstract The analysis of statistical data showed that a large amount of plant waste is generated annually in oil and fat production plants, which must be processed and reused. The paper analyzes the problems of reusing sunflower oil production waste, which is characterized by a relatively high energy value: 1 ton of plant waste is equivalent to 0.625 tons of conventional fuel. According to the mathematical estimation, the actual total amount of impurities is 7.29%, in which major impurities constitute 25.7%. Studies have shown a high probability of oil – containing impurities – 37.25%. Therefore, it is recommended to process such impurities into fuel briquettes and technical oil to increase the profitability of sunflower oil production. For example, at the annual load of technological equipment of the Melitopol Oil Extraction Plant, in 250 days, at a daily processing capacity of 550 t˙day-1, an annual profit of 560,000 EUR is obtainable from the sunflower grain impurities processed into fuel and technical oil.

Mathematical estimation for maximum flow of goods within a cross-dock to reduce inventory.

Supply chain management has recently renovated its strategy by implementing a cross-docking scheme. Cross-docking is a calculated logistics strategy where freight emptied from inbound vehicles is handled straightforwardly onto outbound vehicles, eliminating the intermediate storage process. The cross-docking approach thrives on the minimum storage time of goods in the inventory. Most of the cross-docks avail temporary storage docks where items can be stored for up to 24 hours before being packed up for transportation. The storage capacity of the cross-dock varies depending on the nature of ownership. In the rented cross-docks center, the temporary storage docks are considered of infinite capacity. This study believes that the temporary storage facilities owned by the cross-dock center are of finite capacity, which subsequently affects the waiting time of the goods. The flow rate of goods within the cross-docks is expected to be maximum to avoid long waiting for goods in the queue. This paper uses a series of max-flow algorithms, namely Ford Fulkerson, Edmond Karp, and Dinic's, to optimize the flow of goods between the inbound port and the outbound dock and present a logical explanation to reduce the waiting time of the trucks. A numerical example is analyzed to prove the efficacity of the algorithm in finding maximum flow. The result demonstrates that Dinic's algorithm performs better than the Ford Fulkerson and Edmond Karp algorithm at addressing the problem of maximum flow at the cross-dock. The algorithm effectively provided the best result regarding iteration and time complexity. In addition, it also suggested the bottleneck paths of the network in determining the maximum flow.

Comprehensive HRV estimation pipeline in Python using Neurokit2: Application to sleep physiology

NeuroKit2 is a Python Toolbox for Neurophysiological Signal Processing. The presented method is an adaptation of NeuroKit2 to simplify and automate computation of the various mathematical estimates of heart rate variability (HRV) or similar time series. By default, the present approach accepts as input electrocardiogram's R-R intervals (RRIs) or peak times, i.e., timestamp of each consecutive R peak, but the RRIs or peak times can also stem from other biosensors such as photoplethysmography (PPGs) or represent more general kinds of biological or non-biological time series oscillations. The data may be derived from a single or several sources such as conventional univariate heart rate time series or intermittently weakly coupled fetal and maternal heart rate data. The method describes preprocessing and computation of an output of 124 HRV measures including measures with a dynamic, time-series-specific optimal time delay-based complexity estimation with a user-definable time window length. I also provide an additional layer of HRV estimation looking at the temporal fluctuations of the HRV estimates themselves, an approach not yet widely used in the field, yet showing promise (doi: 10.3389/fphys.2017.01112). To demonstrate the application of the methodology, I present an approach to studying the dynamic relationships between sleep state architecture and multi-dimensional HRV metrics in 31 subjects. NeuroKit2′s documentation is extensive. Here, I attempted to simplify things summarizing all you need to produce the most extensive HRV estimation output available to date as open source and all in one place. The presented Jupyter notebooks allow the user to run HRV analyses quickly and at scale on univariate or multivariate time-series data. I gratefully acknowledge the excellent support from the NeuroKit team.•Univariate or multivariate time series input; ingestion, preprocessing, and computation of 124 HRV metrics.•Estimation of intra- and inter-individual higher order temporal fluctuations of HRV metrics.•Application to a sleep dataset recorded using Apple Watch and expert sleep labeling.

A hybrid approach for integrated surface and subsurface hydrologic simulation of baseflow with Iterative Ensemble Smoother

Integrated surface and subsurface hydrologic models are particularly well suited for the simulation of baseflow as exchange fluxes does not rely on boundary conditions. Regional scale hydrologic models are of particular interest as they can provide guidance to stakeholders toward sustainable water resources management. However, integrated hydrologic models are rarely considered at the regional scale because their computational costs usually prevent efficient model calibration and predictive uncertainty analysis. Moreover, estimation of baseflow and each water budget component usually requires additional post-processing steps. In this paper, we thus aim here to provide a hybrid approach for the application of an integrated hydrologic model at the regional scale when low-flow processes are of primary concern. A surface water mass balance module has been developed to solve the hydrological mass balance from precipitations and temperature datasets. This module calculates potential infiltration fluxes that are used as input to the integrated modeling platform HydroGeoSphere (HGS). This approach provides a computationally tractable integrated model where low-flow processes are explicitly considered, baseflows generated in an integrated fashion, and each water budget component accessible. The model is applied to a region covering 36 900 km2 in the Southeastern part of the province of Quebec, Canada. Thanks to the computational efficiency of the approach, a rigorous mathematical parameter estimation, the Levenberg–Marquardt form of the Iterative Ensemble Smoother, can be considered to calibrate the model to baseflow of eight main rivers, snow water equivalent and evapotranspiration. An ensemble of 187 equally-probable realizations was used for history matching of observations and for the non-linear uncertainty analysis. An improved representation of baseflow can be linked to an appropriate non-linear uncertainty quantification through an efficient integrated surface and subsurface hydrologic model to support managing water resources at the regional scale.

A Method of Circular Scleral Buckle Length Calculation in Retinal Detachment Surgery

Purpose. To develop a method of circular buckle length calculation for optimization of circular scleral buckling in retinal detachment surgery.Methods. At the first stage of the study a formula for calculation of optimal circular scleral buckle length for patients with various axial length (AL) of the eyeball was developed (90 eyes). These healthy eyes were divided into three groups, 30 eyes in each: group 1 — AL 19 to 23.5 mm, group 2 — AL 23.6 to 27 mm, group 3 — AL over 27 mm. In all the groups AL and equatorial diameter of the eyeball were measured with ultrasound A- and B- scanning (Тomey UD8000, Tomey AL 3000). Mathematic estimation of equatorial eyeball diameter dependence on AL was performed using correlation and regression analysis and the formula for optimal circular buckle length calculation was derived. At the second stage the derived formula was used in clinic during retinal detachment surgery in 15 eyes of 15 patients aged 28 to 44 years (37.6 ± 2.6) with subtotal retinal detachment occupying 2 to 3 quadrants. Follow-up period was 1 to 4 months (2.3 ± 0.5). For control, in all patient’s intraoperative measurement of the eyeball circumference in equatorial zone was performed.Results. The first step of the study revealed high correlation coefficient (r) between AL and equatorial eyeball diameter in groups 1 and 2, 0.89 and 0.87, respectively. In group 3 correlation coefficient was 0.57 which shows moderate correlation between AL and equatorial eyeball diameter. Group 3 (AL > 27 mm) was not included in deriving the formula for circular buckle length calculation. Finally, regression equation was obtained and the following formula for circular buckle length calculation was derived: L = 0.9π (8.05 + 0.66 AL), where L — circular buckle length, π = 3.14, AL — axial length of the eyeball. At the second stage of the study (eyes with AL less than 27 mm) anatomical attachment of the retina was achieved in all 15 cases (100 %), elevation of the buckle was 1.44 to 1.6 mm (1.5 ± 0.02), circular buckle shortening made 10 % of the initial eyeball diameter. Control measurement of eyeball diameter in equatorial zone coincided with calculated values, that is, equaled the data obtained from presented formula and from preoperative ultrasound measurement of equatorial eyeball diameter.Conclusions. The derived formula for circular buckle length calculation is highly effective for eyes with AL less than 27 mm: provides optimal height of impression roll (approximately 1.5 mm), reduces the risk of postoperative complications, it is simple in use and shortens the operation time. For calculation of circular buckle length for eyes with AL over 27 mm it is reasonable to use the data of preoperative ultrasound A-scan measurement of equatorial diameter of the eyeball.

Mathematical modeling and estimation for next wave of COVID-19 in Poland

We investigate the problem of mathematical modeling of new corona virus (COVID-19) in Poland and tries to predict the upcoming wave. A Gaussian mixture model is proposed to characterize the COVID-19 disease and to predict a new / future wave of COVID-19. This prediction is very much needed to prepare for medical setup and continue with the upcoming program. Specifically, data related to the new confirmed cases of COVID-19 per day are considered, and then we attempt to predict the data and statistical activity. A close match between actual data and analytical data by using the Gaussian mixture model shows that it is a suitable model to present new cases of COVID-19. In addition, it is thought that there are N waves of COVID-19 and that information for each future wave is also present in current and previous waves as well. Using this concept, predictions of a future wave can be made.

Research on control of instantaneous high power pulse energy supply of multi-stage coil transmitter based on deep learning

The high-power multi-stage coil launcher generates electromagnetic force on armature projectile by capacitor energy storage discharge, which drives it to accelerate, the trigger control of multi-stage coil launcher usually makes the external structure of the launcher complex and unstable by adding position sensors and combining the position signals of emitters. Permanent magnet synchronous motor (PMSM)’s no-position detector control mode, which by establishing an estimation model get speed and position according to voltage and current. There are thyristors and diodes in the driving circuit of the multi-stage coil launcher, which makes the dynamic process nonlinear, and multiple circuits are coupled with the launcher armature. Compared with the permanent magnet synchronous motor, the mathematical model is complex and the order is higher. The method of mathematical model estimation is not suitable for coil launcher. In this paper, a method of using deep learning neural network to predict the speed of transmitting device and realize control is proposed. Through sample learning, the neural network for predicting the speed through the voltage and current signals of discharge is obtained, and the predicted speed signal is compared with the actual speed signal, which achieves a good fit.

Research on Logistics Carbon Emission Mathematical Estimation and Compensation by Computer Statistics and Data Analysis

Global warming is one of the most serious environmental problems facing human society. Greenhouse gases represented by CO2 are considered to be an important cause of climate change. The development of human society is the main reason for the generation of CO2. Economic development and population growth make the use area of fossil fuels continue to expand, which is an important factor leading to the increase of CO2 emissions. The logistics industry is developing rapidly. Its development not only brings value-added economic benefits, but also increases CO2 emissions, and the carbon emission of the logistics industry has gradually become one of the hotspots of attention. Based on the previous theories on carbon emission, this paper determines the research on carbon emission estimation and carbon compensation in the operation of logistics industry, and estimates the carbon emission of logistics industry around the official statistical yearbook data and survey data. Finally, this paper further improves the carbon compensation mechanism of logistics industry from the perspective of compensation subject, compensation standard, compensation mode, process management and carbon price formulation, and puts forward the corresponding measures of carbon compensation mechanism from the perspectives of planning, management, technology, market and publicity.

Computer Application to Estimate PVT Conditions in Oil Wells in the Ecuadorian Amazon

A reservoir behaviour's characterisation is determined by analysing the fluids' physical properties, reported in Pressure, Volume and Temperature (PVT) tests. These tests are performed in the laboratory or are estimated by mathematical correlations with the well's basic properties. The eastern basin of Ecuador is considered a hydrocarbon zone, and the analysis of the physical properties of the fluid from oil wells is essential. The aim is to develop the PVTTESTSYSTEM software to estimate PVT conditions when there are no laboratory tests. The study methodology is based on (i) Compilation of 10 PVT laboratory tests of oil wells in the eastern basin of Ecuador; (ii) Analysis of mathematical correlations; (iii) Development of the PVTTESTSYSTEM software, with the wells' initial conditions' input, selecting the mathematical correlation and estimation of results, based on the relationship of the properties of oil and gas; iv) Comparison of data obtained by laboratory tests and PVTTESTSYSTEM software reports. The software used with a graphical interface presents a registration and login platform and five modules that allow: inserting company and field data, initial oil well data, selecting correlations, calculating PVT properties and generating a graphic report. The results show that the mathematical correlations that estimate PVT properties were systematised, which approximate the laboratory tests' real results. The approximation of the calculated results with the actual results establishes a high confidence level for the PVTTESTSYSTEM software.

Estimating reaction parameters in mechanism-enabled population balance models of nanoparticle size distributions: A Bayesian inverse problem approach.

In order to quantitatively predict nano- as well as other particle-size distributions, one needs to have both a mathematical model and estimates of the parameters that appear in these models. Here, we show how one can use Bayesian inversion to obtain statistical estimates for the parameters that appear in recently derived mechanism-enabled population balance models (ME-PBM) of nanoparticle growth. The Bayesian approach addresses the question of "how well do we know our parameters, along with their uncertainties?." The results reveal that Bayesian inversion statistical analysis on an example, prototype nanoparticle formation system allows one to estimate not just the most likely rate constants and other parameter values, but also their SDs, confidence intervals, and other statistical information. Moreover, knowing the reliability of the mechanistic model's parameters in turn helps inform one about the reliability of the proposed mechanism, as well as the reliability of its predictions. The paper can also be seen as a tutorial with the additional goal of achieving a "Gold Standard" Bayesian inversion ME-PBM benchmark that others can use as a control to check their own use of this methodology for other systems of interest throughout nature. Overall, the results provide strong support for the hypothesis that there is substantial value in using a Bayesian inversion methodology for parameter estimation in particle formation systems.

Long-Term Monitoring of the Antibody Response to a SARS-CoV-2 Infection.

A group of 110 patients from the West Bohemian region who had been infected with COVID-19 was monitored for the purposes of this study. We focused on cases of mild or moderate COVID-19; statistically the most likely to occur. Day zero was defined as the day on which a positive PCR test was first established. The mean length of observation was 6.5 months, the maximum length 12 months. The first blood samples were taken from a smaller cohort during the 1–3 months following the first positive PCR test. We assumed that SARS-CoV-2 antibodies would be present during this period and therefore a limited number of samples were taken for the purpose of detecting antibodies. More samples were collected, starting 4 months after the first positive PCR test. A subsequent set of blood samples were drawn, mostly 6 months after the first ones. Our study confirmed the presence of total IgG SARS-CoV-2 antibodies up to 1 year after the onset of the disease. The peak of antibody production was observed in the third month after the first positive PCR test. A mathematical estimate of the median duration of antibody positivity was calculated to be 18 months from the onset of the COVID-19 infection.

Modeling and estimation of moisture transport properties of drying of potential Amazon biomass for renewable energy: Application of the two-compartment approach and diffusive models with constant or moisture-dependent coefficient

Mathematical modeling and estimation of moisture transport properties in drying of acai biomass were done in this work. The first approach consisted of applying the diffusive model for three different boundary conditions at the particle's surface and the effective diffusivity considered a constant or variable parameter. It was found that the model's accuracy depended on the drying level and the boundary conditions for the effective diffusivity assumed as a constant parameter. Accordingly, the results from the Arrhenius relationship depended on the predictions of the different approaches of the diffusive model, giving different values of the activation energy for the same experimental condition. The effective diffusivity ranged from 9 × 10−11 to 7 × 10−10 m2 s−1 and the activation energy from 38 to 55 kJ mol−1, indicating a high resistance to moisture evaporation. The predictions were not improved when considering the effective diffusivity as a moisture-dependent parameter, possibly due to the simplifications used for calculations. The two-compartment concept consisted of a set of differential equations that gave the best concordance with experimental data. The kinetics parameters ranged from 0.2 to 17 h−1 and presented a well-defined variation with the operating conditions, encouraging its use in drying optimization for efficient conversion processes.

Estimation of State Variables in the Ćuk Converter Mathematical Model with Partially Unknown Parameters

In this paper the solution was proposed for the state variables estimation problem in the mathematical model of the DC switch-mode power converter built according to the Ćuk scheme. Pulse converters are one of the main components of most modern electrical devices and the circuit proposed by Slobodan Ćuk in the 70s of the 20th century is still relevant and demanded. Traditionally, PI (proportional-integral) controllers or proportional-integral adaptive control algorithm (PI-PBC), based on passification methods and superior to standard PI controllers in accuracy, are used as the control algorithm for power converters. However, you need to know the entire vector of the state variables of the converter to build a PI-PBC controller, and moreover, all its parameters must be accurately known. Unfortunately, in practice, such assumptions are not fulfilled, since parametric drifting is possible, and measurements of the converter’s state require additional sensors, which in some cases does not justify itself. Thus, there is a need to develop additional observers or estimators that allow obtaining data on all variables of the converter, as well as its parameters. The solution is based on the GPEBO method (generalized parameter estimation-based observers). The problem was solved under assumption that only the output signal (the output voltage of the converter) is measurable and some of the mathematical model parameters are unknown. An important aspect of the observer design is the development of an algorithm for unknown parameters and the state vector of a mathematical model estimation that ensures convergence in a finite time. Finite-time convergence is extremely important when designing observers since transients in pulse converters occur very quickly.

A mathematical estimation of the physical forces driving podocyte detachment

Loss of podocytes, possibly through the detachment of viable cells, is a hallmark of progressive glomerular disease. Podocytes are exposed to considerable physical forces due to pressure and flow resulting in circumferential wall stress and tangential shear stress exerted on the podocyte cell body, which have been proposed to contribute to podocyte depletion. However, estimations of invivo alterations of physical forces in glomerular disease have been hampered by a lack of quantitative functional and morphological data. Here, we used ultra-resolution data and computational analyses in a mouse model of human disease, hereditary late-onset focal segmental glomerular sclerosis, to calculate increased mechanical stress upon podocyte injury. Transversal shear stress on the lateral walls of the foot processes was prominently increased during the initial stages of podocyte detachment. Thus, our study highlights the importance of targeting glomerular hemodynamics to treat glomerular disease.

Effect of Salinity on Evaporation from Water Surface in Bench-Scale Testing

Freshwater and hypersaline lakes in arid and semi-arid environments are crucial from agricultural, industrial, and ecological perspectives. The purpose of this paper was to investigate the effect of salinity on evaporation from water surfaces. The main achievement of this research is the successful capture of simulated climate–surface interactions prevalent in the Canadian Prairies using a custom-built bench-scale atmospheric simulator. Test results indicated that the evaporative flux has a large variation during spring (water/brine: 1452/764 10−4 g·s−1·m−2 and 613/230 × 10−4 g·s−1·m−2 night) and summer (1856/1187 × 10−4 g·s−1·m−2 day and 1059/394 × 10−4g·s−1·m−2 night), and small variation in the fall (1591/915 × 10−4 g·s−1·m−2 and 1790/1048 × 10−4 g·s−1·m−2 night). The primary theoretical contribution of this research is that the evaporation rate from distilled water is twice that of saturated brine. The measured data for water correlated well with mathematical estimates; data scatter was evenly distributed and within one standard deviation of the equality line, whereas the brine data mostly plotted above the equality line. The newly developed 2:1 water–brine correlation for evaporation was found to follow the combination equations with the Monteith model best matching the measurements.

A new emperor penguin optimisation‐based approach for solar photovoltaic parameter estimation

Mathematical model parameter estimation of solar photovoltaic (PV) cell, module and array represent a prodigious challenge in recent researches, where analytical, metaheuristic and hybrid techniques play a vital role for parameter extraction by using manufacturer's datasheet and experimental data, although the precise and highly reliable solution identification is still a complex problem to be solved. In this paper, a new Emperor Penguin Optimisation- (EPO) based parameter estimation approach for a single-diode Rp solar PV model is presented that is cohesively analysed under different sets of temperature and irradiance (G). Validation of the proposed technique is perceived on the analysis of performance characteristics, that is, current-voltage (I-V) and power-voltage (P-V) of KC200GT, PWP201, and STP6 120-36 PV modules under various simulation conditions. Moreover, estimated results are compared with the experimental data and several established estimation techniques in the literature for validation and demonstrate the proposed technique with highly precise outcomes having reduced computational cost for the PV model parameter extraction problem.

A novel oleaginous yeast Saccharomyces cerevisiae CU-TPD4 for lipid and biodiesel production

This study reports on the novel Saccharomyces cerevisiae CU-TPD4 that was isolated from coconut waste residues obtained from a coconut factory in Thailand. The CU-TPD4 isolate was confirmed to be a S. cerevisiae by molecular analysis and to be an oleaginous yeast with more than 20% (w/w) of the cell dry weight (CDW) present in the form of lipids. The lipid content and lipid yield of CU-TPD4 (52.96 ± 1.15% of CDW and 1.78 ± 0.06 g/L, respectively) under optimized growth conditions were much higher than those under normal growth conditions (22.65 ± 1.32% of CDW and 1.24 ± 0.12 g/L, respectively). The major fatty acids produced by CU-TPD4 were oleic (C18:1), palmitoleic (C16:1), stearic (C18:0), and palmitic (C16:0) acids. Mathematical estimation of the physical properties of the biodiesel obtained by transesterification of the extracted lipid suggested it was suitable as biodiesel with respect to the ASTM D6751 and EN 14214 international standards. Consequently, S. cerevisiae CU-TPD4 is expected to emerge as a promising alternative for biodiesel production.