Cumulative Probability Density Research Articles

Dynamic evolution characteristics and hazard assessment of compound drought/waterlogging and low temperature events for maize

Hazard assessment is fundamental in the field of disaster risk management. With the increase in global warming, compound water and temperature events have become more frequent. Current research lacks risk assessments of low temperatures and their compound events, necessitating relevant hazard assessment work to improve the accuracy and diversity of maize disaster prevention and mitigation strategies. This study comparatively analyzed the dynamic evolution characteristics and hazards of compound drought/waterlogging and low temperature events (CDLEs and CWLEs) for maize in the Songliao Plain during different growth periods from 1981 to 2020. First, composite drought/waterlogging and low temperature magnitude indices (CDLMI and CWLMI) were constructed to quantify the intensity of CDLEs and CWLEs by fitting non-exceedance probabilities. Next, static and dynamic hazard assessment models were developed by fitting probability density and cumulative probability density curves to CDLMI and CWLMI. The results showed that the correlations between SPRI and LTI across different decades were mainly negative during the three growth periods. The hazard ratings for both CDLEs and CWLEs were relatively high in the northern part of the study area, consistent with the higher occurrence, duration, and severity of both CDLEs and CWLEs at higher latitudes. Relative to 2001–2010, the center of gravity of hazard shifted southward for CDLEs and northward for CWLEs in 2011–2020. The mean duration, frequency, and hazard were generally higher for CWLEs, but CDLEs were associated with more severe maize yield reductions. This study provides new insights into compound disaster risk assessment, and the research methodology can be generalized to other agricultural growing areas to promote sustainable development of agricultural systems and food security.

An imprecise probability approach-based determination of over-represented southern African plant genera and families used in ethnopharmacology

Ethnopharmacological relevanceThe analyses of patterns of over-representation of southern African traditional medicinal plants at the genus and family level provide information about the differences in plant use among southern African countries and disease categories. ‘Over-representation’ refers to the phenomenon whereby the proportion of plants belonging to a taxonomic group is higher in ethnobotanical pharmacopoeia than in the total flora. Aim of the studyThis study aimed to use the Imprecise Dirichlet Model (IDM) to analyse data from ten southern African countries to establish how over-represented medicinal plant families compare with over-represented genera, how over-represented medicinal taxa differ among countries in this region of Africa, and how over-represented taxa differ among six major disease categories. Materials and methodsFloral data for the total species composition of each country were obtained from online databases. Medicinal plant species lists were generated from published surveys, inventories, and books. IDM calculations were executed using the inverse of the cumulative beta probability density function in Microsoft Excel™. Python programming language source code was used to calculate Pearson correlation (r) values and Jaccard coefficients (J). ResultsNine of forty-two over-represented medicinal plant families in southern Africa (group 1) do not have over-represented genera. Seven of the forty genera with the highest margins of over-representation belong to under-represented families. Nineteen of the forty-two over-represented families have margins of over-representation smaller than the cumulative margins of their over-represented genera. Groups of countries with similar overall flora (J ≥ 0.333) are Botswana and Namibia (group 2), Malawi, Mozambique, Zambia and Zimbabwe (group 3). The families and genera with the highest margins of over-representation are Loganiaceae and Albizia in group 1, Combretaceae and Vachellia in group 2, Dioscoreaceae and Senna in group 3, and Sapotaceae and Solanum in group 4 (South Africa). The families and genera with the highest margins of over-representation across disease categories are Ebenaceae and Albizia, Canellaceae and Dicoma, Combretaceae and Pterocelastrus, Ebenaceae and Bersama, Francoaceae and Erythrina, and Aristolochiaceae and Strychnos for plants used in the treatment of STIs, febrile and mosquito-vector diseases, microbial infections, pain, skin conditions, and female sexual/reproductive problems, respectively. ConclusionsGenus-level calculations are more efficient in generating taxonomic lists that can be used for ethnopharmacological investigations due to the exclusion of under-represented genera. Limiting the size of geographical areas from which medicinal plant lists are sampled and targeting plants used to treat specific types of disease prevents the underestimation of niche over-represented taxa.

Properties of a Random Bipartite Geometric Associator Graph Inspired by Vehicular Networks.

We consider a point process (PP) generated by superimposing an independent Poisson point process (PPP) on each line of a 2D Poisson line process (PLP). Termed PLP-PPP, this PP is suitable for modeling networks formed on an irregular collection of lines, such as vehicles on a network of roads and sensors deployed along trails in a forest. Inspired by vehicular networks in which vehicles connect with their nearest wireless base stations (BSs), we consider a random bipartite associator graph in which each point of the PLP-PPP is associated with the nearest point of an independent PPP through an edge. This graph is equivalent to the partitioning of PLP-PPP by a Poisson Voronoi tessellation (PVT) formed by an independent PPP. We first characterize the exact distribution of the number of points of PLP-PPP falling inside the ball centered at an arbitrary location in R2 as well as the typical point of PLP-PPP. Using these distributions, we derive cumulative distribution functions (CDFs) and probability density functions (PDFs) of kth contact distance (CD) and the nearest neighbor distance (NND) of PLP-PPP. As intermediate results, we present the empirical distribution of the perimeter and approximate distribution of the length of the typical chord of the zero-cell of this PVT. Using these results, we present two close approximations of the distribution of node degree of the random bipartite associator graph. In a vehicular network setting, this result characterizes the number of vehicles connected to each BS, which models its load. Since each BS has to distribute its limited resources across all the vehicles connected to it, a good statistical understanding of load is important for an efficient system design. Several applications of these new results to different wireless network settings are also discussed.

Cyclic Detectors in the Fraction-of-Time Probability Framework

The signal detection problem for cyclostationary signals is addressed within the fraction-of-time probability framework, where statistical functions are constructed starting from a single time series, without introducing the concept of stochastic process. Single-cycle detectors and quadratic-form detectors based on measurements of the Fourier coefficients of the almost-periodically time-variant cumulative distribution and probability density functions are proposed. The adopted fraction-of-time approach provides both methodological and implementation advantages for the proposed detectors. For single-cycle detectors, the decision statistic is a function of the received signal and the threshold is derived using side data under the null hypothesis. For quadratic-form detectors, the decision statistic can be expressed as a function of the received signal without using side data, at the cost of some performance degradation. The threshold can be derived analytically. Performance analysis is carried out using Monte Carlo simulations in severe noise and interference environments, where the proposed detectors provide better performance with respect to the analogous detectors based on second- and higher-order cyclic statistic measurements.

Statistical analysis of wind load probabilistic models considering wind direction and calculation of reference wind pressure values in Liaoning Province, China

Wind pressure serves as the fundamental base for architectural design, particularly the reference wind pressure value, which affects the reliability and economy of high-rise, towering, and large-span structures. To facilitate the calculation of reference wind pressure values for design reference periods, load codes from various countries offer probabilistic distribution models for wind speed or reference wind pressure. However, the application of a uniform load probabilistic distribution model for calculating the reference wind pressure for the reference period often results in overestimation or underestimation of the reference wind pressure. To this end, this study collected annual maximum wind speed data from 34 meteorological stations in Liaoning Province, a region in Northeast China, and established extreme value type I, extreme value type III, lognormal, and generalized extreme value distribution models for the annual maximum wind speeds using the maximum likelihood method and method of moment, respectively. The goodness-of-fit test was conducted utilizing the Kolmogorov–Smirnov method. The results showed that the extreme value type III model and the method of moment achieved the best goodness-of-fit for the probabilistic distribution of annual maximum wind speeds. Based on the measured wind direction data corresponding to the annual maximum wind speed, joint probability density functions and cumulative probability density functions of wind speed and wind direction were established. Finally, the influence of different load statistical periods on the reference wind pressure was analyzed.

Fault detection with confidence level evaluation for perception module of autonomous vehicles based on long short term memory and Gaussian Mixture Model

The reliability of perception is crucial for developing higher levels of autonomy to enhance the safety and performance of fully autonomous vehicles. As a result, fault detection in the perception module is an essential function for autonomous vehicles. While many approaches rely on hardware redundancy, necessitating additional sensors, achieving analytical redundancy in perception is a pivotal factor in reducing the cost and complexity of the autonomous system. This paper utilizes a motion predictor for surrounding vehicles and a Gaussian Mixture Model (GMM) to evaluate the confidence level of the perception module without requiring additional sensor installations. The motion predictor is designed based on a long short-term memory-based recurrent neural network. The error between the motion prediction and detection results is leveraged to estimate the confidence level of the perception module. In essence, the motion prediction results are treated as redundant sensor measurements. The error distribution is modeled using a GMM, and the cumulative probability density of the GMM is employed to assess the confidence level. The proposed algorithm's effectiveness is evaluated through a driving data-based simulation study with fault injection. This study shows an enhanced sensitivity to injected faults and a reduced time for fault detection. Furthermore, the proposed algorithm provides a quantitative estimate of the performance degradation level of the perception module. This estimation can serve as an indicator of uncertainty that the motion planner should account for.

Low complexity detection algorithms based on ADMIN for massive MIMO

This paper proposes the alternating direction method of multipliers-based infinity-norm (ADMIN) with threshold (ADMIN-T) and with percentage (ADMIN-P) detection algorithms, which make full use of the distribution of the signal to interference plus noise ratio (SINR) for an uplink massive MIMO system. The ADMIN-T and ADMIN-P detection algorithms are improved visions of the ADMIN detection algorithm, in which an appropriate SINR threshold in the ADMIN-T detection algorithm and a certain percentage in the ADMIN-P detection algorithm are designed to reduce the overall computational complexity. The detected symbols are divided into two parts by the SINR threshold which is based on the cumulative probability density function (CDF) of SINR and a percentage, respectively. The symbols in higher SINR part are detected by MMSE. The interference of these symbols is then cancelled by successive interference cancellation (SIC). Afterwards the remaining symbols with low SINR are iteratively detected by ADMIN. The simulation results show that the ADMIIN-T and the ADMIN-P detection algorithms provide a significant performance gain compared with some recently proposed detection algorithms. In addition, the computational complexity of ADMIN-T and ADMIN-P are significantly reduced. Furthermore, in the case of same number of transceiver antennas, the proposed algorithms have a higher performance compared with the case of asymmetric transceiver antennas.

A First Approach on the Impact of Distributed Generation and Fault Impedance on Studies on domain of frequency of Voltage Sags

An analysis of the distributed generation (DG) and fault impedance on studies of voltage sags caused by system faults is presented on domain of frequency. The simulation of 62 case studies of phase-to-ground faults on 13.8, 69, 138 and 230 kV transmission lines were performed and the voltage of a 380 V sensitive industrial busbar client was monitored. These lines are part of the electrical system of the city of Goiania, Brazil. For each case study, different fault positions and impedance fault were simulated by considering different DG levels connected to the consumer busbar. Long-term simulation scenarios were obtained by the Monte Carlo method and analyzed based on their cumulative distribution functions and probability density curves of voltage sags. This is one major contribution of this work.

Normal-tangent-logarithm-(G_1,G_2 ): a class of probabilistic distributions depending on two baselines

Based on the normal distribution, a new generator of continuous distributions is presented using the monotonic functions and , such that and are the baselines. A study of identifiability of the proposed class is exhibited as well as the series expansions for its cumulative distribution function and probability density function. Additionally, some mathematical properties of the class are discussed, namely, the raw moments, the central moments, the moment generating function, the characteristic function, the derivatives of the log-likelihood function, and a study of the support. A numerical analysis comprising a simulation study and an application to real data is presented. Comparisons between the proposed model and other well-known models evince its potentialities and modeling benefits.

Secrecy Rate of TAS in AF-Relay Systems With Multi-Antenna Relay and User Over Non-Identical Channel Estimation Error

This work evaluates the secrecy rate of transmit antenna selection (TAS) in the amplify and forward (AF)-relay system with multi-antenna relay and user. This paper considers that the receiver of relays, users, and an eavesdropper have non-identical channel estimation error. To assess the secrecy rate, this work derives new analytical expressions for the cumulative distribution function (CDF) and probability density function (PDF) of the received signal to noise ratio (SNR) using infinite series for maximal-ratio combining (MRC) and selection combining (SC) receivers. Using the CDF for the received SNR, we derive new exact analytical expressions for the secrecy rate with non-identical channel estimation error for four scenarios. Further, using the PDF for the received SNR, we derive the asymptotic analytical expressions for the secrecy rate and quantify the high SNR slope and the high SNR power offset for four scenarios. From the numerical results, we observe that the relay and user selection has the high SNR power offset gain compared to the non-selection. The secrecy rate of the proposed system can be improved according to legitimate receiver's parameters such as the number of receive antennas and channel correlation coefficient and can be degraded according to eavesdropper's parameters. The superiority of the secrecy rate of the MRC receiver and the SC receiver depends on the channel estimation accuracy of the MRC receiver.

Mepolizumab improvements in health-related quality of life and disease symptoms in a patient population with very severe chronic rhinosinusitis with nasal polyps: psychometric and efficacy analyses from the SYNAPSE study

BackgroundAlthough the psychometric properties of patient-reported outcome measures (e.g. the 22-item Sino-nasal Outcomes Test [SNOT-22]) in chronic rhinosinusitis with nasal polyps (CRSwNP) have been defined, these definitions have not been extensively studied in patients with very severe CRSwNP, as defined by recurrent disease despite ≥ 1 previous surgery and a current need for further surgery. Therefore, the psychometric properties of the symptoms visual analogue scales (VAS) were evaluated, and meaningful within-patient change thresholds were calculated for VAS and SNOT-22.MethodsSYNAPSE (NCT03085797), a randomized, double-blind, placebo-controlled, 52-week trial, assessed the efficacy and safety of 4-weekly mepolizumab 100 mg subcutaneously added to standard of care in very severe CRSwNP. Enrolled patients (n = 407) completed symptom VAS (six items) daily and SNOT-22 every 4 weeks from baseline until Week 52. Blinded psychometric assessment of individual and composite VAS was performed post hoc, including anchor-based thresholds for meaningful within-patient changes for VAS and SNOT-22, supported by cumulative distribution function and probability density function plots. The effect of mepolizumab versus placebo for 52 weeks on VAS and SNOT-22 scores was then determined using these thresholds using unblinded data.ResultsInternal consistency was acceptable for VAS and SNOT-22 scores (Cronbach’s α-coefficients ≥ 0.70). Test–retest reliability was demonstrated for all symptom VAS (Intra-Class Correlation coefficients > 0.75). Construct validity was acceptable between individual and composite VAS and SNOT-22 total score (r = 0.461–0.598) and between individual symptom VAS and corresponding SNOT-22 items (r = 0.560–0.780), based upon pre-specified ranges. Known-groups validity assessment demonstrated generally acceptable validity based on factors associated with respiratory health, with all VAS responsive to change. Mepolizumab treatment was associated with significantly increased odds of meeting or exceeding meaningful within-patient change thresholds, derived for this very severe cohort using six anchor groups for individual VAS (odds ratio [OR] 2.19–2.68) at Weeks 49–52, and SNOT-22 (OR 1.61–2.96) throughout the study.ConclusionsSymptoms VAS and SNOT-22 had acceptable psychometric properties for use in very severe CRSwNP. Mepolizumab provided meaningful within-patient improvements in symptom severity and health-related quality of life versus placebo, indicating mepolizumab provides substantial clinical benefits in very severe CRSwNP.

Weighted power Maxwell distribution: Statistical inference and COVID-19 applications.

During the course of this research, we came up with a brand new distribution that is superior; we then presented and analysed the mathematical properties of this distribution; finally, we assessed its fuzzy reliability function. Because the novel distribution provides a number of advantages, like the reality that its cumulative distribution function and probability density function both have a closed form, it is very useful in a wide range of disciplines that are related to data science. One of these fields is machine learning, which is a sub field of data science. We used both traditional methods and Bayesian methodologies in order to generate a large number of different estimates. A test setup might have been carried out to assess the effectiveness of both the classical and the Bayesian estimators. At last, three different sets of Covid-19 death analysis were done so that the effectiveness of the new model could be demonstrated.

Optimizing the Expected Maximum of Two Linear Functions Defined on a Multivariate Gaussian Distribution

We study stochastic optimization problems with objective function given by the expectation of the maximum of two linear functions defined on the component random variables of a multivariate Gaussian distribution. We consider random variables that are arbitrarily correlated, and we show that the problem is NP-hard even if the space of feasible solutions is unconstrained. We exploit a closed-form expression for the objective function from the literature to construct a cutting-plane algorithm for a highly nonlinear function, which includes the evaluation of the cumulative distribution function and probability density function of a standard normal random variable with decision variables as part of the arguments. To exhibit the model’s applicability, we consider two featured applications. The first is daily fantasy sports, where the algorithm identifies entries with positive returns during the 2018–2019 National Football League season. The second is a special case of makespan minimization for two parallel machines and jobs with uncertain processing times; for the special case where the jobs are uncorrelated, we prove the equivalence between its deterministic and stochastic versions and show that our algorithm can deliver a constant-factor approximation guarantee for the problem. The results of our computational evaluation involving synthetic and real-world data suggest that our discretization and upper bounding techniques lead to significant computational improvements and that the proposed algorithm outperforms suboptimal solutions approaches. History: Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.1259 .

Mixed THz/FSO Relaying Systems: Statistical Analysis and Performance Evaluation

In this paper, the performance of a mixed Terahertz/free-space optical (THz/FSO) wireless transmission system is studied, where the joint effects of channel fading and pointing errors are considered for both THz and FSO links. Employing the semi-blind amplify-and-forward protocol, we derive the cumulative distribution function and probability density function of the end-to-end signal-to-noise ratio. By applying the derived statistics, exact expressions for the outage probability (OP), average bit error rate (BER), and average channel capacity are obtained. In order to attain useful physical insights, asymptotic OP and average BER expressions are also presented. Based on them, the diversity gain is determined, which is shown to depend on channel fading and pointing errors of both links. In addition, by taking into account the hardware impairments, the OP of the non-ideal hardware system is derived. Moreover, the analysis is extended to multi-antenna scenarios and the asymptotic OP is obtained. Finally, illustrative numerical results are plotted and it can be observed that the path loss, channel fading, pointing errors and hardware impairments lead to a considerable degradation in system performance.

Contrast-Controllable Image Enhancement Based on Limited Histogram

To address the technical shortcomings of conventional histogram equalization (HE), such as over-enhancement and artifacts, we propose a histogram-constrained and contrast-tunable HE technique for digital image enhancement. Firstly, the input image histogram is partitioned into two parts, the main histogram and the constrained histogram, by a cumulative probability density threshold; second, the main histogram is redistributed equally in the whole grayscale range; and finally, the nonlinearity of the constrained histogram is mapped to the main histogram. The experimental averages show that the values of the two metrics, information entropy and MS-SSIM, processed by the algorithms in this paper, are more accurate compared to the other six excellent algorithms.

Joint Impact of Phase Error, Transceiver Hardware Impairments, and Mobile Interferers on RIS-Aided Wireless System Over κ-μ Fading Channels

Reconfigurable intelligent surface (RIS) has recently emerged as a promising technology that can potentially benefit the existing wireless communication technologies in addition to being able to fulfill the more stringent requirements of beyond- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5^{th}$ </tex-math></inline-formula> generation/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6^{th}$ </tex-math></inline-formula> generation wireless networks. Motivated by the numerous benefits of RIS technology for improving the performance of wireless communication systems, in this letter, a RIS-aided wireless system is considered in which the destination node is surrounded by the mobile co-channel interferers (CCIs). Each mobile CCI follows the random waypoint (RWP) mobility pattern within a circular region centered around the destination node. Source-destination, source-RIS, RIS-destination, and each interferer-destination links follow the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\kappa - \mu $ </tex-math></inline-formula> distribution. Additionally, a more realistic system model is considered that also incorporates the impact of transceiver (transmitter as well as receiver) hardware distortions. The system’s performance is evaluated by deriving novel closed-form expressions for the coverage probability (CP) and ergodic capacity (EC) based on the cumulative distribution function and probability density function (PDF) of the received signal-to-interference-plus-distortion-plus-noise ratio (SIDNR).

Probability model of the exponentially rising transient response of a failed RC circuit

An electric circuit may be analyzed by means of a number of available theorems and techniques. However, the exact value of a circuit element parameter may not be known sometimes, and only partial information, such as range of values in which the parameter lies, may be available. This situation may arise when a circuit component develops a fault while it is deployed in a harsh environment. In this work, we exploit probability methods to explore such an electric circuit where only partial information about the range of values in which a circuit element parameter lies is available. We treat the circuit element parameter lying in a range of values as a random variable. Thereafter, any circuit parameter, such as a voltage or current, is analyzed using probability theory. Probability model of a desired parameter is derived in terms of the cumulative distribution function and probability density function. We demonstrate this technique by using the example of a resistor capacitor electric circuit whose transient response is an exponential rise. The capacitance value of the capacitor is known to lie in a range of values and is treated as a random variable. Probability distributions of the voltage across the capacitor and the voltage at the output terminals of the electric circuit are derived. Use of the probability distributions for the calculation of probabilities is also demonstrated.

A tool for the ages: The Probabilistic Cosmogenic Age Analysis Tool (P-CAAT)

While revolutionary to the geomorphic community, the application of terrestrial cosmogenic nuclide (TCN) dating is complicated by geological uncertainties, which often lead to skewed or poorly clustered TCN age distributions. Although a range of statistical approaches are typically used to detect and remove outliers, few are optimized for analysis of TCN datasets. Many are mean- or median-based and therefore explicitly assume a single probability distribution (e.g., Mean Squared Weighted Deviates, Chauvenet's Criterion, etc.). Given the ubiquity of pre- and post-depositional modification of rock surfaces, which occur at different rates in different geomorphic settings, these approaches struggle with multimodal distributions which often characterize TCN datasets. In addition, most statistical approaches do not propagate measurement or production rate uncertainties, which become increasingly important as dataset size or clustering increases. Finally, most approaches provide arithmetic single solutions, irrespective of geologic context. To address these limitations, we present the Probabilistic Cosmogenic Age Analysis Tool (P-CAAT), a new approach for outlier detection and landform age analysis. This tool incorporates both sample age and geologic uncertainties and uses Monte Carlo simulations to eliminate dataset skewness by isolating component normal distributions from a cumulative probability density estimate for datasets with three or more samples. This approach allows geologic context to inform post-analysis interpretations, as researchers can assign landform ages based upon statistically distinct subpopulations, informed by the characteristics of geomorphic systems (e.g., exhumation of boulders as moraines degrade through time). To evaluate the effectiveness of P-CAAT, we analyzed a range of synthetic TCN datasets and compared the results to commonly used statistical approaches for outlier detection. Irrespective of dataset size or clustering, P-CAAT outperformed other approaches and returned accurate solutions that improve in precision as sample size increases. To enable more comprehensive utilization of our approach, P-CAAT is packaged with a GUI interface and is available for download at kgs. uky.edu/anorthite/PCAAT.

Performance Analysis of Dual-Hop THz Transmission Systems Over α-μ Fading Channels With Pointing Errors

In this article, the performance of a dual-hop relaying terahertz (THz) wireless communication system is investigated. In particular, the behaviors of the two THz hops are determined by three factors, such as the deterministic path loss, fading effects, and pointing errors. Assuming that both THz links are subject to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> fading with pointing errors, we derive exact expressions for the cumulative distribution function (CDF) and probability density function (PDF) of the end-to-end signal-to-noise ratio (SNR). With these statistical expressions, some important performance metrics are evaluated, such as the outage probability (OP), average bit error rate (BER), and average channel capacity (ACC). Moreover, the asymptotic analyses are presented to obtain more insights. The results show that the dual-hop relaying scheme has better performance than the single THz link. Furthermore, the system diversity order depends on the fading parameters and pointing errors of both THz links. In addition, we extend the analysis to a multirelay cooperative diversity system and derive the asymptotic symbol error rate (SER) expressions. Finally, the derived analytical expressions are verified by the Monte Carlo simulation.

COMPARATIVE COST ANALYSIS OF DIFFERENT THERMAL POWER PLANT PROJECTS

Realistic budgeting for complex power plant projects is important for informed investment decisions and avoiding frequent cost overruns. Comparative cost analysis thus provides significant insights for future projects’ investment decisions. This study analyzed the project costs using a regression analysis and Monte Carlo Simulation (MCS) guided by the error analysis for Combined Cycle Power Plants (CCPP) and Natural Gas (NG)-based power plants. The regression analysis shows the cost trend lines with the prediction equation and its R2 value against production capacity in Mega-Watts (MW) of power plant projects in Bangladesh. The MCS presents the cumulative probability function (CDF) and probability density function (PDF) of unit cost in Million U.S. Dollars (MUSD)/MW of both types of projects. The results show that the NG project costs are higher than CCPP power plant projects, where the most frequent project development cost is in a range of 1.01-1.25 MUSD/MW, comparing 0.95-1.15 MUSD/MW of CCPP projects. Among the models, the MCS performs better than the regression model predicting project costs. Finally, MCS is demonstrated in predicting the cost of both types of power plant projects. The study outcomes will assist decision-makers and policy planners in further investment decisions in the power plant and similar infrastructure projects in Bangladesh and economically similar countries.