Monte Carlo Simulation Analysis Research Articles

Assessment of care provision integration in a community-based mental health system: balanced care model implementation in Andalusia (Spain)

BackgroundAndalusia is the second largest region in Spain, and it has developed a comprehensive mental health (MH) plan that encourages the consolidation of the balanced care model. However, its geographical and socioeconomic disparity is a great challenge for a community-based MH system. Both the assessment of the implementation of the MH plan and the development of new tools to support decision-making can be considered critical.ObjectivesThe present study aims (i) to assess how the integration of different types of MH care may influence system performance and (ii) to check the performance evolution of the integration process geographically regarding the small MH areas of Andalusia.MethodsThe performance of the Andalusian MH system was assessed by combining Monte Carlo simulation, fuzzy inference and data envelopment analysis. The relative technical efficiency was the main performance indicator.ResultsA correct integration of appropriate types of MH care, according to population needs, increases the performance of the Andalusian MH system both from global and regional perspectives. The spatial representation (based on small MH areas) of the results highlights how the performance depends on specific geographical characteristics. By analyzing the identified spatial clusters, defined by different management patterns depending on user and socioeconomic characteristics, benchmark areas and areas for improvement can be studied to design evidence-informed policies and interventions.ConclusionsA global analysis of MH system performance was carried out, including both the successive integration of different types of care and its spatial evolution. Although an appropriate integration of different types of MH care has a positive effect on the Andalusian MH system, this process has different profiles depending on specific geographically based user and socioeconomic characteristics. The balanced care model can be considered the paradigm for assessing the performance of a large and populated territory such as Andalusia, which has a community-based MH system. This methodological approach (performance assessment and spatial analysis) may be used as a guide for developing future evidence-informed policies and managerial interventions.

Modified Ramos-Louzada-G family with baseline Weibull distribution: Properties, characterizations, regression, and applications

The paper introduced a novel family of distributions, called the Kumaraswamy Ramos-Louzada-G (KumRL-G) class, focusing on the five-parameter Kumaraswamy Ramos-Louzada Weibull (KumRLW) distribution. This new family of distributions, which includes existing and numerous new sub-models, offers improved flexibility and accuracy in modeling and analyzing survival data. Key statistical properties, including quantile function, moments, and entropy measures underlying the distribution have been derived, and characterizations have also been provided based on the ratio of two truncated moments and the hazard rate function. The maximum likelihood estimation (MLE) is employed to estimate the parameters of the proposed probability distribution, and Monte Carlo simulation analysis is performed to demonstrate the effectiveness of this method. The significance and adaptability of the new family of distributions are revealed through applications to COVID-19 and survival rate to age 65 of male cohort datasets from Ghana, Nigeria, and Canada. A new location-scale regression model was subsequently formulated from the new KumRLW distribution. Its practicality was demonstrated using survival data on hypertension from Ghana with gender as a covariate. The regression analysis showed that gender is a significant factor in the length of time before hypertension develops. The new KumRL-G family with baseline Weibull distributions provides more flexibility and improved fit in modeling various shapes and behaviors in the survival datasets surpassing its existing sub-models and other notable distributions.

Improving safety in complex systems: A review of integration of functional resonance analysis method with semi‐quantitative and quantitative approaches

AbstractFunctional resonance analysis method (FRAM) is extensively employed in analyzing and managing performance variabilities. Additionally, semi‐quantitative and quantitative methods have been increasingly integrated with the FRAM to analyze complex socio‐technical systems to improve safety levels. This review article presents a comprehensive and updated survey of current literature focused on semi‐quantitative and quantitative methods employed for quantifying performance variabilities and exploring aggregation/propagation rules. A total of 1659 studies published between 2012 and March 2024 from various scientific databases were systematically examined using preferred reporting items for systematic review and meta‐analysis, identifying 29 studies that met inclusion criteria. The identified studies were categorized into four groups based on the quantitative methods employed: Monte Carlo simulation, fuzzy logic, cognitive reliability and error analysis method, and miscellaneous approaches. While different methodologies had unique strengths, they commonly relied on expert judgment for data collection, whether for defining probability distributions in Monte Carlo simulations, membership functions, and fuzzy rule bases in fuzzy inference systems, or selecting common performance conditions, determining their interrelationships, and assigning scores. Addressing bias from expert judgment in assessing performance variabilities can be achieved by using suitable experts' opinions integration techniques, and leading safety indicators in the analysis.

Developing a Quantitative Methods Course for Civil and Construction Engineering Students

The ability to apply quantitative methods to gather, review, analyze and draw conclusions from data to support decision making is an important skillset in the engineering profession. We developed a new three-credit hour undergraduate course to develop a deeper understanding and appreciation for basic quantitative methods for construction engineering students. The course’s main objective is to provide basic skills in quantitative methods, by familiarizing students with the critical steps in an analytical approach to decision-making. The course applies a hands-on approach to problem solving and decision making using various engineering-themed problems that practicing engineers are likely to face. The specific methods explored include; constructing quantitative models to include break-even analysis, decision trees, linear optimization, PERT/CPM schedule development and crashing, as well as Monte Carlo Simulation and sensitivity analysis to generate and interpret problem solutions. Instruction emphasizes a hands-on approach and practical assignments to develop a logical framework for describing and solving problems. This new course emphasizes Excel software rather than hand calculations and introduces use of Excel Solver and other data analysis tools.

Distribution characteristics and risk assessment of fluoride in surface water of urban typical rivers

Recent research on fluoride in water primarily focuses on groundwater; however, the potential environmental risks of fluoride in urban rivers should not be overlooked. In 2023, this study collected 135 surface water samples from the Ershibu River in Hefei, China, during various flood periods. Through descriptive statistical analysis, correlation analysis, principal component analysis-multiple linear regression (PCA-MLR) modeling, hazard quotient (HQ) assessment, and Monte Carlo simulation analysis, the spatial and temporal distribution, potential sources, and health risks of fluoride were investigated. The results showed that fluoride concentrations in the Ershibu River ranged from 0 to 1.38 mg/L. According to the PCA-MLR calculations, industrial pollution (73.92 %) was identified as the main source, followed by hydrogeochemical evolution (16.10 %) and agricultural activities (9.98 %). The HQ analysis revealed that the average exceedance rates of HQ for the five exposed populations were as follows: infants (64.45 %) > young children (2.22 %) = adults (2.22 %) > children (0) = teenagers (0). Therefore, relevant authorities should improve defluoridation facilities to reduce fluoride levels in industrial and agricultural wastewater and implement measures to protect public health. Future research should investigate the migration processes and toxicity mechanisms of fluoride more thoroughly.

Monte Carlo simulation analysis of radiation attenuation properties induced by arsenic accumulation in femur and tibia bones of rats exposed to sodium arsenite diet

PurposeThe study aims to investigate the effect of sodium arsenite (NaAsO2) on radiation permeability in biological tissues of rats, challenging existing literature claims about NaAsO2's influence on the density of bones. The motivation is determining whether exposure alters radiation permeability within a defined range of radiological energy. Should any changes be observed, radiation protection guidelines may necessitate the use of lower energy levels in imaging procedures involving such exposure. This consideration is vital for ensuring adherence to radiation safety standards, particularly in medical imaging, where minimizing patient and operator exposure to radiation is paramount. Materials and methodsIn this study, rats were divided into two groups: a control group and a NaAsO2-treated group. After the treatment period, these animals were euthanized to collect their femurs and tibias for further analysis. The elemental composition of these bones was then meticulously examined using Scanning Electron Microscopy (SEM), enhanced by Energy-dispersive X-ray Spectrometry (EDX). Monte Carlo Simulation codes of MCNP6 and PHITS 3.22 were used for investigate photon induced interactions. These powerful tools allowed us to assess the gamma-ray attenuation properties of the collected samples. For an accurate comparison and analysis, we referred to the NIST database using the WinXCom program. Furthermore, the study explored the effects of charged particles on the bone samples, employing the SRIM code for this purpose. This part of the research was crucial to understanding the interactions at a more detailed level. We calculated the photon attenuation parameters for radiological energies up to 0.5 MeV and also investigated the interactions of charged particles with energies as high as 15 MeV. ResultsIn groups treated with NaAsO2, while there is less reduction in mass attenuation coefficients, larger differences have been observed in linear attenuation coefficients. Notably, a decrease in the linear attenuation coefficient has been observed in the NaAs Tibia group. ConclusionThe application of sodium arsenite (NaAsO2) results in a reduction in the Linear Attenuation Coefficient, necessitating a detailed investigation to determine whether this decrease also affects the Hounsfield unit, in accordance with the ALARA (As Low As Reasonably Achievable) principle that guides radiation safety protocols.

Cost-effective endoscopic screening for gastric cancer in a cohort with low Helicobacter pylori prevalence.

Periodic endoscopic screening for gastric cancer (GC) is widely performed in East Asia; however, the optimal screening strategy remains unclear. This study aimed to determine the most cost-effective endoscopic screening strategy for the detection and treatment of GC in a cohort with a low Helicobacter pylori prevalence. The following data were retrospectively extracted from participants who received screening endoscopy between April 2019 and March 2023: age, H. pylori infection status, presence of intestinal metaplasia, pathological diagnosis of GC, and the interval between the most recent endoscopies. A Markov state transition model was constructed based on the cohort data. The cost-effectiveness of 15 strategies with different starting ages (40/50/60years) and screening intervals (1/2/3/4/5years) was compared. The net monetary benefit (NMB) and incremental cost-effectiveness ratio (ICER) of quality-adjusted life-years gained by treatment were used as outcomes. A simulation model was constructed based on the cohort data of 94 137 participants (mean age 54.5years, males 57.9%; 74.4% H. pylori-naïve, 94.2% intestinal metaplasia-negative). The results of the base-case analysis showed that the screening strategy of 4-year intervals starting at the age of 40years had the highest NMB (97 401 578 yen). In both the Monte Carlo simulation and one-way sensitivity analysis with a varying probability of H. pylori infection status transition, the ICER was superior in the screening strategy every 4years, starting at age 40years. Our simulation showed that endoscopic screening at 4-year intervals starting at the age of 40years was the most cost-effective method.

Methodology for the Selection of Risk Response Actions while Considering Corporate Objectives in the Metalworking Industry

Context: Projects in metalworking companies are affected by risk. Proper risk management depends on the responses provided to improve the project plan. However, multiple potential actions may result in constraints due to multiple factors. The purpose of this article is to propose a hybrid approach to solve the problem of selecting risk response actions while considering strategic objectives, fuzzy logic, and simulation. Method: First, 334 risks were identified through a literature review and a discussion with experts. These were then filtered, resulting in 70 operational risks. Subsequently, the ten critical risks were prioritized using the risk matrix. Then, using Monte Carlo simulation and correlation analysis, the activities most affected by the risks were identified. Finally, potential response actions were designed for each case, and fuzzy logic and quality function deployment were applied to evaluate them. Results: The selected responses were framed within the strategic objectives, i.e., customer satisfaction, business profitability, and implementation of new technologies. This, while considering some corporate attributes that the actions had to meet finishing the project on time, having low costs, and meeting the scope. The selected actions had a better profile than others seeking to minimize time or costs. Conclusions: EPCC projects are complex and often suffer from gaps in scope, time, and cost. Risk analysis and the selection of responses in the planning phase help to improve performance. This study developed a risk response plan for a project executed in Brazil. Risks were identified, classified, and mitigated using simulations, resulting in an 11-day reduction in the project’s estimated duration.

Potential Cost Savings with 60-day Peripheral Nerve Stimulation Treatment in Chronic Axial Low Back Pain.

Chronic axial low back pain (CLBP) that is not responsive to medication management or physical therapy often requires significant clinical intervention. Several interventional pain management options exist, including a 60-day peripheral nerve stimulation (PNS) treatment. This economic evaluation investigated the potential for projected cost savings associated with prioritizing 60-day PNS treatment relative to a 'standard of care' (SOC) approach (where patients do not have access to 60-day PNS). A decision tree (supervised machine learning) model tracked treatment progression across two hypothetical cohorts of US patients with CLBP in whom non-interventional options were ineffective (Cohort A: treatment starting with 60-day PNS followed by any additional interventional and surgical treatments versus Cohort B: standard of care interventional and surgical treatments without access to 60-day PNS). Treatment efficacy estimates were based on published success rates. Conditional on treatment failure, up to two additional interventions were considered within the 12-month time frame in both cohorts. SOC treatment options included epidural injection, radiofrequency ablation (RFA), basivertebral nerve ablation (BVNA), PNS permanent implant (PNS-PI), spinal cord stimulator (SCS) trial/implant, and spinal fusion surgery. Treatment choice probabilities in both cohort algorithms were based on clinician interviews. Costs were based on national Medicare reimbursement levels in the ambulatory surgery center (ASC) setting. Savings reflected the difference in projected costs between cohorts. A Monte Carlo simulation and sensitivity analyses were conducted to generate confidence intervals and identify important inputs. The treatment algorithm which prioritized initial 60-day PNS treatment was projected to save $8056 (95% CI $6112-$9981) per patient during the first year of interventional treatment relative to the SOC approach. Use of the 60-day PNS treatment as an initial interventional treatment in patients with CLBP may result in significant savings for Medicare. Projected savings may be even larger for commercial payers covering non-Medicare patients.

Analytical QbD for the optimization of a multimode HPLC method for the investigation of hydrochlorothiazide, diltiazem and propranolol release from 3D printed formulation

Since 3D printing technology is an emerging field in pharmaceutical technology, the present study aimed at the development of a mixed-mode liquid chromatographic method for the separation and determination of hydrochlorothiazide, diltiazem, and propranolol to investigate their in-vitro release performance from 3D printed tablets. Due to the unique properties of the mixed-mode stationary phase, the three drugs were separated in less than 8 min under isocratic elution. Method development was accomplished following the Analytical Quality by Design principles and was evaluated using risk assessment and multivariate analysis. The influences of critical method parameters on critical method attributes (were screened using a 2-level fractional factorial design and subsequently optimized through a central composite design. The method operable design region was approved by the establishment of a robust zone using Monte Carlo simulation and capability analysis. The validation of the HPLC method was performed based on the total error concept. The relative bias was varied between ─ 11.6 % and 10.5 % and the RSD values for repeatability and intermediate precision were below 4.4 % in all cases. The limits of detection (LOD) ranged between 0.17 – 0.90 μg/mL and were adequate for the specific application. The developed method was successfully applied to the analysis of the studied drugs in in-vitro drug release samples obtained from 3D-printed tablets combining the above-mentioned active pharmaceutical ingredients (APIs).

Probabilistic method for risk analysis of unintentional islanding of distributed generators

Unintentional islanding can be a harmful event, requiring the fast actuation of the anti-islanding protection to disconnect the distributed generators. Although passive techniques are mostly used, they have non-detection zones and fail to detect islanding under specific conditions. Therefore, knowing in advance such operative conditions and the risks associated with the islanding occurrence can be a strategic information to support grid operators in the decision-making process regarding the effectiveness of distributed generators interconnection protection. In this context, this paper proposes a probabilistic method to assess the risk of energized island formation by combining the probability of breaker (or recloser) opening with the probability of anti-islanding protection failure. The method employs probabilistic short circuit associated with overcurrent protection actuation by using Monte Carlo simulation and time-domain analysis to obtain the cases of unintentional islanding and to calculate the risk of distributed generator islanded operation. The results drove more realistic and less conservative conclusions than previous literature. Additionally, it is shown that faults as the cause of breaker opening do play a relevant role in the performance of the anti-islanding protection.

How does narcissistic leadership influence change-oriented organizational citizenship behavior? Empirical evidence from China

Based on conservation of resources theory and the work–home resources model, this study examines how and when narcissistic leadership influences employees’ change-oriented organizational citizenship behavior. A total of 363 employees from 61 teams across numerous enterprises based in central China were surveyed using a questionnaire. The study hypotheses were tested using structural equation modeling and Monte Carlo simulation analysis. The findings revealed that narcissistic leadership results in the development of a negative team climate, termed “team chaxu climate,” which, in turn, hinders employees’ change-oriented organizational citizenship behavior. Furthermore, this study explored the moderating role of leaders’ family affective support in the relationship between narcissistic leadership and team chaxu climate. This study contributes to our understanding of the relationship between narcissistic leadership and employee organizational citizenship behavior and empirically validates the work–home resources model.

An Analytical Method for Accuracy Analysis of Close Proximity Rendezvous in Space Based on an Improved C-W Equation under Perturbation Effects

In order to address the issue of quantitatively solving the impact of perturbation forces and initial errors on rendezvous accuracy, traditional analytical methods are not suitable, and the calculation steps for statistical methods based on Monte Carlo simulation analysis are complex and computationally time-consuming. To overcome these challenges, this paper proposes a method for analysing rendezvous accuracy based on an improved relative motion dynamics model. Describing the relative motion between a sub-spacecraft and a target spacecraft by an improved Clohessy-Wiltshire (C-W) equation. By employing the derived state transition matrix from the model, it is possible to calculate the error propagation during the rendezvous process and express the rendezvous accuracy analytically. Through simulation examples, the proposed method proves to be both accurate and efficient in analysing the influence of perturbation forces and random errors such as initial position and velocity on rendezvous accuracy. This study verifies the correctness and effectiveness of the approach.

Axial compressive behavior of corroded H-shaped steel columns

The unpredictability of corrosion in steel structures, especially in H-section columns, is a topic that has received limited research attention. Using Monte Carlo simulation (MCS) and finite element analysis (FEA), this study investigates the effect of random pitting corrosion on H-section steel columns' ultimate bearing capacity under axial compression. In this study, MCS was employed to address the random distribution of pitting corrosion. The reliability of MCS and FEA was validated through existing literature on compression tests of H-shaped steel columns. To comprehensively assess the detrimental effects of pitting corrosion, a total of 3650 models of H-shaped columns, each exhibiting different pitting corrosion characteristics, were subjected to analysis. These models incorporated three corrosion parameters (volume loss rate, size, and depth) and two geometric parameters (slenderness and section size). A nonlinear buckling analysis was carried out to investigate the ultimate bearing capacity of the columns. The findings indicate that a volume loss rate of 20% can cause a decrease in the column's ultimate bearing capacity by approximately 50%. Furthermore, the study reveals that at equal volume loss rates, corrosion depth and slenderness significantly influence the reduction factor, while corrosion size and section size exert a minimal impact. As a result, this paper introduces a probabilistic assessment method for estimating the reduction factor in ultimate bearing capacity for H-section steel columns. Notably, with every 10% increase in the volume loss rate, the mean reduction factor in the ultimate bearing capacity decreases by approximately 0.12.

Narrowly Banded Spectra with Peak Frequency around 1 GHz of FRB 20201124A: Implications for Energy Function and Radiation Physics

The radiation physics of fast radio bursts (FRBs) remains an open question. Current observations have discovered that narrowly banded bursts of FRB 20201124A are active in 0.4–2 GHz, and their spectral peak frequency ( νpobs ) is mostly toward ∼1 GHz. Utilizing a sample of 1268 bursts of FRB 20201124A detected with the Five-hundred-meter Aperture Spherical radio Telescope (FAST), we show that the 1σ spectral regime of 71.4% of the events (in-band bursts) is within the FAST bandpass. The intrinsic burst energies ( EBWeobs ) and spectral widths ( σsobs ) are well measured by fitting the spectral profile with a Gaussian function. The derived EBWeobs and σsobs distributions are lognormal and centered at logEBWeobs/erg=37.2(σ=0.76) and logσsobs/GHz=−1.16(σ=0.17) . Our Monte Carlo simulation analysis infers its intrinsic ν p distribution as a normal function centered at ν p,c = 1.16 GHz (σ = 0.22) and its intrinsic energy function as Φ(E)∝E−0.60e−E/Ec with E c = 9.49 × 1037 erg. We compare these results with that of typical repeating FRBs 20121102A and 20190520B, which are active over a broad frequency range at several specific frequencies, and discuss possible observational biases on the estimation of the event rate and energy function. Based on these results, we argue that FRB 20201124A likely occurs in a fine-tuned plasma for maser radiation at a narrow frequency range, while FRB 20121102A and FRB 20190520B could involve clumpy plasma conditions that make maser emission around several specific frequencies in a broad range.

Scheduling Analysis with Monte Carlo Simulation and Statistic Method on a Building Construction Project

In the construction of an office building in Indonesia, a delay occurred in the first-floor concrete work. The project was planned to be completed in 161 days but it took 168 days to complete. This study analyzed the issues that arose by doing a scheduling analysis and by comparing the Monte Carlo Simulation and the Statistic method. A comparison between these methods was carried out to determine the project duration scheduling probability level from each method. The data included in the study were the project’s Time Schedule and the interview data from respondents who were directly related to the office building construction project. Monte Carlo Simulation analysis showed that to get a 100% probability level, the duration can only be accelerated by two days, from 168 days to 166 days, while the Statistical method analysis showed that to get a 99.99% probability level the duration can only be accelerated by 4,2 days, from 168 days to 163.8 days. From the results above, it can be concluded that the Monte Carlo Simulation has a more accurate calculation than the Statistic Method because the result from the Monte Carlo Simulation was 166 days with a 100% probability level. It is close to the project realization duration which was 168 days.
 Keywords: construction delay analysis, monte carlo simulation, statistic method comparison

An extensive conformable fractional grey model and its application

In order to expand the applicability of the conventional conformable fractional grey model, an extensive conformable fractional grey model, abbreviated as ECFGM(1,1), is proposed by the introduction of the extensive conformable fractional accumulation. Specifically, the improvements of the proposed model can be outlined as follows. First, the extensive fractional accumulation and difference are designed for accumulated generating operation and its reverse calculation, respectively. Second, based on the extensive form, the parameter estimation and time response function of the ECFGM(1,1) model are deduced, thereinto, the particle swarm optimization algorithm is employed to determine the optimal fractional order for the newly-designed model. It is worthy noting that an algorithm framework by the Monte Carlo simulation and parameter sensitivity analysis is conducted to assess the robustness of the proposed model. To validate this model’s efficacy, the novel technique is adopted to forecast China’s primary energy consumption compared with a series of competitive models. The numerical results indicate the newly-proposed model is superior to all competitors in terms of MAPE and RMSE values, thus, the proposed ECFGM(1,1) model is considered a powerful and promising method for enhancing the existing fractional grey models.

Production risk analysis and prioritization in manufacturing industry: A hybrid approach using Monte Carlo simulation and DEMATEL analysis

Risk identification and prioritization in production sector is critical if an enterprise wishes to compete successfully. To achieve this, Delphi technique was utilized to finalize 36 identified risks in a ‘metal plate production’ stage prior to risk analysis through risk probability and its impact. Risk prioritization is performed by Monte Carlo Simulation (MCS) and Decision Making and Trial Evaluation Laboratory (DEMATEL) analysis. The novelty of this study is to develop an effective hybrid approach by utilizing strengths of MCS (with iterations in thousands on the simulation) as well as DEMATEL analysis (to assess the causal relationship among risk factors). A more accurate risk identification by developing a consolidated list of risks in one of the important production stages followed by an efficient prioritization of interacting (process and product related) risks will help the shop floor operator making better decisions to enhance the quality by minimizing production time and cost.

Health risk assessment of soil heavy metals in a typical mining town in north China based on Monte Carlo simulation coupled with Positive matrix factorization model

The accumulation of heavy metals (HMs) in soil caused by mineral resource exploitation and its ancillary industrial processes poses a threat to ecology and public health. Effective risk control measures require a quantification of the impacts and contributions to health risks from individual sources of soil HMs. Based on high-density sampling, soil contamination risk indexes, positive matrix factorization (PMF) model, Monte Carlo simulation and human health risk analysis model were applied to investigate the risk of HMs in a typical mining town in North China. The results showed that As was the most dominant soil pollutant factor, Cd and Hg were the most dominant soil ecological risk factors, and Cr and Ni were the most dominant health risk factors in the study area. Overall, both pollution and ecological risks were at low levels, while there were still some higher hazard areas located in the central and south-central part of the region. According to the probabilistic health risk assessment (HRA), children suffered greater health risks than adults, with 21.63% of non-carcinogenic risks and 53.24% of carcinogenic risks exceeding the prescribed thresholds (HI > 1 and TCR>1E-4). The PMF model identified five potential sources: fuel combustion (FC), processing of building materials with limestone as raw materials (PBML), industry source (IS), iron ore mining combined with garbage (IOG), and agriculture source (AS). PBML is the primary source of soil HM contamination, as well as the major anthropogenic source of carcinogenic risk for all populations. Agricultural inputs associated with As are the major source of non-carcinogenic risk. This study offers a good example of probabilistic HRA using specific sources, which can provide a valuable reference for strategy establishment of pollution remediation and risk prevention and control.

A novel extension of half-logistic distribution with statistical inference, estimation and applications

In the present study, we develop and investigate the odd Frechet Half-Logistic (OFHL) distribution that was developed by incorporating the half-logistic and odd Frechet-G family. The OFHL model has very adaptable probability functions: decreasing, increasing, bathtub and inverted U shapes are shown for the hazard rate functions, illustrating the model’s capacity for flexibility. A comprehensive account of the mathematical and statistical properties of the proposed model is presented. In estimation viewpoint, six distinct estimation methodologies are used to estimate the unknown parameters of the OFHL model. Furthermore, an extensive Monte Carlo simulation analysis is used to evaluate the effectiveness of these estimators. Finally, two applications to real data are used to demonstrate the versatility of the suggested method, and the comparison is made with the half-logistic and some of its well-known extensions. The actual implementation shows that the suggested model performs better than competing models.