cometadata/datacite-rtg-text-other-reclassification-records huggingface dataset:
A PRODUCTION INVENTORY MODEL FOR DETERIORATING ITEMS WITH TIME AND PRICE RELIANT DEMAND USING FLOWER POLLINATION OPTIMIZATION
Creators: name: Kumar, Amit
name: Yadav, Ajay Singh
name: Dharmendra Yadav
Publisher: name: Reliability: Theory & Applications
Year: 2025
Subjects: Production Inventory Model
Deteriorating Items
Two-Storage
Shortages
Time and Selling Price Dependent Demand
Flower Pollination Optimization

description: Effective management of production inventory for deteriorating items with dynamic demand patterns is crucial for businesses operating in today’s competitive markets. This paper proposes a comprehensive model that addresses the complexities arising from the dual storage locations, item deterioration, and demand dependencies on both time and selling price. To optimize the decision variables associated with production and inventory control, we employ the Flower Pollination Optimization (FPO) algorithm, a nature-inspired meta-heuristic known for its ability to efficiently navigate complex search spaces. The two-storage production inventory model integrates the dynamics of item deterioration over time, capturing the real-world challenges faced by supply chain managers. The demand for items is modeled to be sensitive to both temporal variations and changes in selling prices, reflecting the intricate nature of market dynamics. Our approach leverages the FPO algorithm to explore and exploit the solution space, allowing for the identification of optimal or near-optimal strategies for production quantities, order quantities, and inventory levels. The FPO algorithm mimics the pollination process in nature, striking a balance between exploration and exploitation to efficiently search for solutions in a highly dynamic and nonlinear environment. The proposed model and optimization approach are validated through extensive simulations and sensitivity analyses. The results demonstrate the effectiveness of the FPO algorithm in finding robust solutions that enhance inventory management, mitigate deterioration-related losses, and adapt to varying demand scenarios. This research contributes to the field of supply chain optimization by offering a novel perspective on tackling the challenges associated with dual storage, item deterioration, and demand dependencies. The findings provide valuable insights for practitioners seeking advanced strategies for optimizing their production inventory systems in the face of evolving market conditions.
A SIGNIFICANT STUDY ON ROBUST MEASURE OF LOCATION PARAMETERS USING DATA DEPTH APPROACHES
Creators: name: Kalaivani S
Publisher: name: Reliability: Theory & Applications
Year: 2025
Subjects: data depth
robust procedures
inference
outliers

description: Data depth procedures are statistical methods used to measure the centrality or depth of a point within a multivariate dataset. These procedures provide a way to quantify how deep or outlying a point is relative to the overall distribution of the data. This study explores various data depth procedures to find reliable location estimations in cases like with and without outliers. In this paper, various depth procedures, such as Mahalanobis depth, Halfspace depth, Euclidean depth, Simplicial depth, and Projection depth, are studied and compared. The efficiency of these depth functions is evaluated using real datasets and simulation studies with R software.
Supplementary_Tables – Supplemental material for Combining Fluoxetine and rTMS in Poststroke Motor Recovery: A Placebo-Controlled Double-Blind Randomized Phase 2 Clinical Trial
Creators: name: Pinto, Camila Bonin
name: Morales-Quezada, Leon
name: Polyana Vulcano De Toledo Piza
name: Zeng, Dian
name: Faddi Ghassan Saleh Vélez
name: Isadora Santos Ferreira
name: Lucena, Pedro Henrique
name: Duarte, Dante
name: Lopes, Fernanda
name: Mirret M. El-Hagrassy
name: Luiz Vicente Rizzo
name: Camargo, Erica C.
name: Lin, David J.
name: Mazwi, Nicole
name: Wang, Qing Mei
name: Randie Black-Schaffer
name: Fregni, Felipe
Publisher: name: SAGE Journals
Year: 2019
Subjects: subject: 110308 Geriatrics and Gerontology
subject: FOS: Clinical medicine
subject: 110904 Neurology and Neuromuscular Diseases
subject: Neuroscience

description: Supplemental material, Supplementary_Tables for Combining Fluoxetine and rTMS in Poststroke Motor Recovery: A Placebo-Controlled Double-Blind Randomized Phase 2 Clinical Trial by Camila Bonin Pinto, Leon Morales-Quezada, Polyana Vulcano de Toledo Piza, Dian Zeng, Faddi Ghassan Saleh Vélez, Isadora Santos Ferreira, Pedro Henrique Lucena, Dante Duarte, Fernanda Lopes, Mirret M. El-Hagrassy, Luiz Vicente Rizzo, Erica C. Camargo, David J. Lin, Nicole Mazwi, Qing Mei Wang, Randie Black-Schaffer and Felipe Fregni in Neurorehabilitation and Neural Repair
Supplemental material for Cognitive, Emotional, and Behavioral Reactions to Snooping: The Role of Attachment Characteristics
Creators: name: Klettner, Annelise M.
name: Shanhong Luo
name: Tine, Joanna
Publisher: name: SAGE Journals
Year: 2019
Subjects: subject: 170199 Psychology not elsewhere classified
subject: FOS: Psychology

description: Supplemental Material for Cognitive, Emotional, and Behavioral Reactions to Snooping: The Role of Attachment Characteristics by Annelise M. Klettner, Shanhong Luo and Joanna Tine in Psychological Reports
A STUDY ON COMPARISON OF VARIOUS CONTINUOUS SAMPLING AND SKIP-LOT SAMPLING PLAN PROCEDURES
Creators: name: S. Suganya
name: K. Pradeepa Veerakumari
Publisher: name: Reliability: Theory & Applications
Year: 2025
Subjects: Continuous Sampling Plan
Skip Lot Sampling Plan
Skip-lot Sampling Plan of Type SkSP-T
Single Sampling Plan

description: This paper explains the brief review of skip-lot sampling plan procedures followed by continuous sampling plan procedures. Also, various types of skip-lot sampling plans are compared with continuous sampling plans. The efficiency of SkSP-T is tested on comparison with various skip-lot sampling plans using Single Sampling Plan. A new system of skip-lot sampling plan of type SkSPT is compared with other skip-lot sampling plans. Different types of skip-lot sampling plans namely SkSP-2, SkSP-3, SkSP-V and SkSP-R. The tables are constructed for various combinations of various parameters using various numerical methods.
A WEIGHTED THREE-PARAMETER XGAMMA DISTRIBUTION WITH PROPRTIES AND ITS APPLICATION TO REAL LIFE DATA
Creators: name: P. Pandiyan
name: Athira D V
Publisher: name: Reliability: Theory & Applications
Year: 2025
Subjects: weighted three-parameter Xgamma distribution
Reliability analysis
moments
maximum likelihood estimation
order statistics
renyi entropy

description: In this article, a weighted three-parameter Xgamma distribution has been proposed. It is an extension of two-parameter Xgamma distribution. The weighted three-parameter Xgamma distribution designed for modelling real-life data. The density function and cumulative distribution function, moments, hazard and survival function, moment-generating function and characteristic function, Bonferroni and Lorenz curve, renyi entropy of this distribution have all been derived. The parameter of this distribution is estimated by maximum likelihood estimation method. Finally, an application of the model to a real-life data set is presented and compared with some other existing distributions.
BAYESIAN ESTIMATION OF INVERSE AILAMUJIA DISTRIBUTION USING DIFFERENT LOSS FUNCTIONS
Creators: name: Aijaz Ahmad
name: Khanday, Manzoor A.
name: Powar, Sonali Kedar
name: Aafaq A. Rather
name: C. Subramanian
Publisher: name: Reliability: Theory & Applications
Year: 2025
Subjects: Bayesian analysis
priors
maximum likelihood estimator
different loss functions.

description: This paper focuses on the Bayesian estimation of the parameter of the inverse Ailamujia distribution, employing advanced prior structures and diverse loss functions. Specifically, the extended Jeffreys’ prior and gamma prior are utilized to derive the Bayesian estimators. Estimation is performed under various loss functions, including squared error, entropy, precautionary, and Linex loss functions, ensuring a comprehensive analysis. To demonstrate the practical applicability and comparative performance of these estimators, an empirical investigation is conducted using a real dataset. The findings highlight the adaptability and effectiveness of the proposed Bayesian approach across different estimation scenarios.
BAYESIAN GLM: A NON-INFORMATIVE APPROACH FOR PARAMETER ESTIMATION IN EXPONENTIAL DISPERSION REGRESSION MODELS
Creators: name: Sadok, Ibrahim
name: Zribi, Mourad
Publisher: name: Reliability: Theory & Applications
Year: 2025
Subjects: Generalized linear models
Bayesian method
Multivariate exponential dispersion
Non-informative prior distribution
Real-world data analysis

description: This paper proposes a novel Bayesian approach to parameter estimation in exponential dispersion regression models (EDRM). By employing a non-informative prior distribution, we offer a flexible and robust framework that avoids the need for subjective prior specification. To efficiently sample from the posterior distribution, we develop an importance-sampling algorithm tailored to the EDRM. Through a real-world data analysis, we demonstrate the efficacy of our proposed method in providing accurate and reliable parameter estimates. This research contributes to the advancement of Bayesian statistical modeling techniques and offers valuable insights for practitioners in various fields.
Supporting Information – Supplemental material for Hopane-6α,16α,22-triol: A New Hopane Triterpenoid from the Lichen Parmotrema sancti-angelii
Creators: name: Jirapast Sichaem
name: Huu-Hung Nguyen
name: Thuc-Huy Duong
Publisher: name: SAGE Journals
Year: 2019
Subjects: subject: 111599 Pharmacology and Pharmaceutical Sciences not elsewhere classified
subject: FOS: Clinical medicine

description: Supplemental material, Supporting Information, for Hopane-6α,16α,22-triol: A New Hopane Triterpenoid from the Lichen Parmotrema sancti-angelii by Jirapast Sichaem, Huu-Hung Nguyen, and Thuc-Huy Duong in Natural Product Communications
BAYESIAN SPATIAL TEMPORAL TREND ANALYSIS FOR DECISION MAKING AND RISK ASSESSMENT IN DENGUE INCIDENCE STUDIES: A CASE OF TAMILNADU
Creators: name: Jaisankar Ramasamy
name: Ranjani Murugesan
Publisher: name: Reliability: Theory & Applications
Year: 2025
Subjects: Spatial temporal
Bayesian modeling
Bayesian hierarchical modeling
Leroux CAR prior

description: This study presents a Bayesian spatial-temporal analysis for studying Dengue incidence in Tamil Nadu, aiming to provide insights into decision-making and risk assessment strategies. Statistical models that allow a more accurate depiction of true disease rates by borrowing information from neighboring regions will help mitigate the effects of sparsely populated regions and deliver better inference. Perhaps the most conspicuous manner of modeling spatial dependence is to introduce spatially associated random effects within a Bayesian hierarchical setting. The Bayesian modeling and inferential framework are flexible and extremely rich in its capabilities to accumulate various scientific hypotheses and assumptions. The spatial and spatial temporal epidemiology is concerned with the description and analysis of spatial and spatial temporal variations in disease risk with respect to risk factors. As the primary aim of this work is to quantify the spatial disease pattern of dengue incidences apart from the mapping of disease modelling the disease and finding spatial clusters/hotpots is one important aspect in epidemiology is to find the temporal trends in or outside of clusters. In this study, a spatial-temporal trends model is fitted using the Leroux CAR prior’s set up for studying the spatial-temporal disease patterns with the estimation of the temporal trends with reference to dengue incidences in Tamil Nadu, India.
BAYESIAN PARAMETER ESTIMATION FOR TRANSMUTED WEIBULL DISTRIBUTION WITH CENSORING RATES AND VARIOUS LOSS FUNCTIONS
Creators: name: Jeslin J
name: A, Radhika
name: Haripriya M
Publisher: name: Reliability: Theory & Applications
Year: 2025
Subjects: Transmuted Weibull distribution
Markov Chain Monte Carlo
Bayesian credible intervals
Bayes estimators
posterior risks
absolute error loss function
precautionary loss function
quadratic loss function
censoring

description: Statistical distributions are essential tools for describing and predicting real-world phenomena, though recent advancements in data collection have made it challenging to fit existing probability models to many practical datasets. While non-parametric models are sometimes recommended, parametric models retain substantial popularity due to their interpretability and flexibility. The quadratic rank transmutation map (QRTM) technique has been used to create new families of nonGaussian distributions, known as transmuted distributions, which allow for modifications in moments, skewness, and kurtosis, thus increasing flexibility. The transmuted Weibull distribution (TWD) has gained attention for applications in reliability, survival analysis, and lifetime data analysis. This article focuses on a Bayesian analysis of the transmuted Weibull distribution, a generalization of the traditional Weibull model that addresses its limitations, particularly for datasets exhibiting non-monotonic failure rates. Bayesian parameter estimation is performed using a Markov Chain Monte Carlo (MCMC) algorithm, with both non-informative and informative priors. We calculate Bayes estimators (BEs) and posterior risks (PRs) under different loss functions, including the Absolute Error Loss Function (AELF), precautionary loss function (PLF), and quadratic loss function (QLF). Simulation studies evaluate the Bayes estimators' performance, investigating the effects of various priors, sample sizes, and censoring rates on estimation accuracy and credible interval width. Real-world data applications highlight the practical utility of the Bayesian approach for the TWD, showing consistent results with increasing sample sizes and underscoring the robustness of the MCMC algorithm for parameter estimation. The article is structured as follows: the TWD’s parameters, including scale, shape, and transmutation, are estimated under different loss functions and priors. Bayesian credible intervals (BCIs) are also computed. Both uncensored and censored data environments are considered, with varying sample sizes and censoring rates. Posterior risks for each estimator are analyzed to assess performance, and two real datasets are used to illustrate the flexibility and applicability of the proposed distribution. This study lays a foundation for future research, such as exploring mixtures of transmuted Weibull distributions or conducting Bayesian analyses for record values.
CHARACTERIZATION OF SOME GENERALIZED DISTRIBUTIONS USING ORDER STATISTICS
Creators: name: Athar, Haseeb
name: Mohd. Amir
Publisher: name: Reliability: Theory & Applications
Year: 2025
Subjects: Order statistics
characterization
conditional expectation
continuous distributions

description: The Lindley distribution has been useful for fitting lifetime data. In recent times, several authors studied the extension of the original Lindley distribution. In this paper, we introduced the two general classes of distributions, which include all earlier versions of Lindley distributions. These general classes are characterized using conditional expectations of order statistics. Further, there results are applied to characterize several known distributions like Lindley, X-Lindley, power Lindley, Lindley-Pareto, Ailamujia, power Ailamujia, Lindley-Weibull, length-biased exponential, inverse Lindley, inverse power Lindley and inverted length biased exponential distributions.
CLASSICAL AND BAYESIAN ESTIMATION OF EXPONENTIATED INVERSE RAYLEIGH DISTRIBUTION BASED ON RECORD VALUES
Creators: name: Iftkhar Khan
name: Anwar, Zaki
name: Ali, Zakir
Publisher: name: Reliability: Theory & Applications
Year: 2025
Subjects: Exponentiated inverse Rayleigh distribution
Maximum likelihood estimators
Bayes estimators
Square error loss function
MCMC
TK
Record values
and Real data

description: In this article explores two approaches for estimating the parameters of the exponentiated inverse Rayleigh distribution (EIRD) using record values: Classical estimation and Bayesian estimation. In classical estimation, maximum likelihood estimators (MLE’s) and the asymptotic confidence intervals are derived based on the observed Fisher information matrix of the parameters. In Bayesian estimation, estimators of the parameters are obtained under the square error loss function. This involves using Tierney-Kadane’s approximation (TK) and Markov chain Monte Carlo (MCMC) methods for Bayesian computation. Additionally, the article constructs the highest posterior credible intervals of the parameters using the MCMC method. To evaluate the performance of these estimators, a Monte Carlo simulation study is conducted to compare their behavior. Finally, a real data analysis is presented to illustrate the application of the methods discussed in the article.
COMPARATIVE BAYESIAN ANALYSIS OF THE INVERSE TOPP-LEONE DISTRIBUTION
Creators: name: Aijaz Ahmad
name: Fathima Bi
name: Mahfooz Alam
name: Aafaq A. Rather
name: Danish Qayoom
name: Asgar Ali
Publisher: name: Reliability: Theory & Applications
Year: 2025
Subjects: Bayesian analysis
Jeffery’s prior
Gamma prior
Maximum likelihood estimation
Loss functions

description: This paper focuses on the Bayesian estimation of the shape parameter for the Inverse Topp-Leone (ITL) distribution. To achieve this, we employ both the extended Jeffrey’s prior and the gamma prior, facilitating the derivation of posterior distributions for the shape parameter. The Bayesian estimators are calculated under various loss functions, including the squared error loss function (SELF), entropy loss function (ELF), precautionary loss function (PLF), and Linex loss function (LLF), each chosen to address different practical scenarios and estimator biases. In addition to the Bayesian approach, we also explore maximum likelihood estimation (MLE) to provide a comparative benchmark. The performance of these estimators is assessed and compared based on mean squared error (MSE) across multiple sample sizes, allowing for a detailed evaluation of estimator robustness and accuracy. A real-world dataset is then analyzed to further demonstrate the relative efficiency of each estimator under the different loss functions, providing practical insights into the applicability of each estimation approach for the ITL distribution. This analysis offers a comprehensive perspective on the versatility and precision of Bayesian and classical estimation methods for the ITL model.
Additional file 1 of Estrogen modulation of cortical spreading depression
Creators: name: Kudo, Chiho
name: Harriott, Andrea M.
name: Moskowitz, Michael A.
name: Waeber, Christian
name: Ayata, Cenk
Publisher: name: figshare
Year: 2024
Subjects: subject: Biochemistry
subject: Medicine
subject: Neuroscience
subject: Physiology
subject: FOS: Biological sciences
subject: Pharmacology
subject: Biotechnology
subject: Chemical Sciences not elsewhere classified
subject: Ecology
subject: Cancer

description: Additional file 1: Supplemental Table 1. Specific parameters for the quantitative in vitro receptor labeling protocol for each radioligand.
title: J. Robert Oppenheimer and His Role in the Development of the United States Nuclear Weapons Policy 1945-1953
Creators: name: Harris, Craig M.
Publisher: name: Old Dominion University
publisherIdentifier: https://ror.org/04zjtrb98
publisherIdentifierScheme: ROR
Year: 1991

description: J. Robert Oppenheimer played a highly visible role in the development of nuclear weapons policy for the United States. He was very influential in President Harry S. Truman's Administration after the Second World War. Following the Soviet Union's first atomic bomb blast in August 1949, Oppenheimer became out of step with those who made nuclear weapons policy. He continued to give advice on disarmament issues. In 1953, the first year of Dwight D. Eisenhower's Administration, Oppenheimer questioned the utility of the Superpower's nuclear arms build up, particularly the hydrogen bomb, while leaving the American people ignorant of the impact such an arms race would have on them. The President at first entertained Oppenheimer's advice, but turned against him because of Russian claims that they had exploded a hydrogen bomb. Oppenheimer became seen as a whistle blower to the news media and was seen as a liability because of his vulnerability to congressional demagogues such as Senator Joseph McCarthy.
CONSTRUCTION OF GAMMA ZERO-INFLATED POISSON DOUBLE SAMPLING PLANS
Creators: name: Priyadharshini R
name: K, Shalini
Publisher: name: Reliability: Theory & Applications
Year: 2025
Subjects: Sampling inspection by attributes
Double sampling plan
Prior distribution
Zero-inflated Poisson distribution
Average quality level
Limiting quality level
Operating characteristics function

description: In a well-supervised production framework, non-conformities occur seldom, resulting in a more number of zeros in the count of non-conformities. The zero-inflated Poisson (ZIP) distribution is a suitable model for handling zero inflation. Double sampling plan (DSP) is a precise quality inspection method where a decision on the approval or rejection of a lot is made after reviewing two samples, providing stronger conclusions than single sampling plan (SSP). In practice, decision-making for submitted lots requires a consistent assessment of both within-lot and between-lot variations, which can be addressed using Bayesian methodology. A Bayesian approach integrates prior knowledge and provides more information for making decisions about the approval or rejection of a lot. This article focuses on the designing of Bayesian DSPs; employing a Gamma prior to the parameter in the Poisson component of ZIP distribution the operating characteristic (OC) function is derived. Examples are provided to assess Gamma-ZIP (GZIP) DSPs. The significance of GZIP DSPs over conventional ZIP DSPs is also presented.
COST AND RELIABILITY OPTIMIZATION OF A COMPLEX SYSTEM USING MULTI-OBJECTIVE GREY WOLF OPTIMIZATION TECHNIQUE
Creators: name: Anuj Kumar
name: Negi, Ganga
name: Mangey Ram
name: Pant, Sangeeta
name: Dimri, Sushil Chandra
Publisher: name: Reliability: Theory & Applications
Year: 2025
Subjects: Nature-inspired optimization techniques
Cost minimization
reliability optimization
multi-objective grey wolf optimization

description: Modern engineering systems increasingly focus on multi-objective optimization. Nature-inspired optimization techniques have shown superior efficiency and effectiveness compared to many traditional methods across various parameters. This work demonstrates the reliability and cost optimization of a complex bridge system using the Multi-Objective Grey Wolf Optimization algorithm (MOGWO). The bridge system in question is a seriesparallel system. A key performance highlight is the use of an archive for search agents to generate a Pareto optimal front (PoF) with a minimal number of iterations. Among the various solutions in the PoF, the solution set that best meets the multi-objective criteria is preferred. Additionally, statistical analyses are conducted to further validate the competitiveness of the results.
DECISION SUPPORT SYSTEM OF EVAPORATING SYSTEM OF SUGAR PLANT
Creators: name: Sihmar, Parveen
name: Modgil, Vikas
Publisher: name: Reliability: Theory & Applications
Year: 2025
Subjects: availability analysis
evaporating system
sugar industry
petri nets

description: This paper addresses an analysis methodology for assessing the efficacy of a evaporating system in a sugar industry. A stochastic Petri nets technique is employed to simulate the interactions between the subsystems. A software package, "Petri module," from GRIF, was licensed. The performability of subsystems has been evaluated, and fluctuations in repair and failure rates have been observed. The maintenance order priority was assigned to the subsystems of the evaporating system based on the criticality of failure. Finally, a decision support system is implemented to assist maintenance personnel in making more informed decisions during the development of maintenance policies. It has been noted that the evaporator is an essential component that requires the complete attention of the plant manager.
DESIGNING SINGLE SAMPLING PLANS BASED ON ZERO-INFLATED BINOMIAL DISTRIBUTION
Creators: name: Sangeetha S
name: K, Shalini
name: Hemalatha R
Publisher: name: Reliability: Theory & Applications
Year: 2025
Subjects: Sampling inspection by attributes
Single sampling plan
Zero-inflated Binomial distribution
Operating characteristic function
Producer’s risk
Consumer’s risk

description: Statistical Quality Control (SQC) involves the use of statistical techniques used to assess and monitor the quality of a product. Acceptance sampling is a statistical technique that determines whether to approve or decline a batch of products using a sample instead of inspecting each one individually. This helps to manage quality assurance and reduces the need for exhaustive inspection, balancing the risks of accepting faulty items and rejecting good ones. A single sampling plan (SSP) involves inspecting a fixed number of items from a batch and deciding based on the results whether the entire batch should be accepted or rejected. Now-a-days in the manufacturing processes, due to the evolution in technology the number of nonconforming is very less and this leads to a high frequency of zeros in the count data of nonconforming. If count data contains a significant number of zeros, the Zero-inflated Binomial (ZIB) distribution is the suitable probability distribution for such conditions. The ZIB distribution addresses this issue by combining a binomial distribution with an inflation parameter that accounts for the excessive zeros, making it a more accurate representation of the quality control process in environments where defectives are rare. This article aims to design SSPs when the number of non-conforming units follows ZIB distribution. By adopting the ZIB distribution, the SSPs provide better decision-making in terms of acceptance or rejection of product batches. The operating characteristic (OC) function of the ZIB sampling plan is derived. Furthermore, the parameters of the SSP, specifically the sample size n and acceptance number c, are derived for various sets of values, such as (p1, 𝛼, p2, 𝛽). Numerical illustrations are presented to demonstrate the application of the ZIB SSPs. The risk efficiency of ZIB SSP is compared with Binomial SSP.
DIGITAL INVENTORY: REFORMAT RISK OPTIMIZATION MODEL FOR A LAPTOP
Creators: name: Diwakar Shukla
name: Sahu, Deepti
Publisher: name: Reliability: Theory & Applications
Year: 2025
Subjects: Digital Inventory
Mathematical Model
Memory Space
Digital Space Digital Files
Reformat Risk
Digital devices
Risk Optimization
Users Category
Risk Computation
Random Access Memory(RAM)
Read Only Memory(ROM).

description: In recent times, due to advancements in technologies specially in the computer world, people face problem related to limited digital capacity of a digital devices. Many reasons exist such as unwanted or unnecessary files stored in (a) System digital space (b) ROM space (c) Working space for users and (d) Hard disk space. By the regular use of a laptop, user space and hard disk digital space get occupied because of the creation of new files and new folders at every moment. Such a situation motivates for development of a digital inventory model for digital space. This paper presents a digital inventory model which is a useful tool for laptop reformat risk minimization. Users Categories are defined as per their intensive professional involvements. Several graphs are drawn showing the output analysis and importance of the study. Theoretical findings are supported by the numerical computations. It is found that reformat risk is directly proportional to the growth of file/folder creation in either of categories.
DEVELOPMENT OF NEW METHODS FOR PROTECTING SUBSTATION AND OVERHEAD LINES FROM OVERVOLTAGES
Creators: name: N.M. Piriyeva
name: N.S. Mammadov
name: S.V. Rzayeva
Publisher: name: Reliability: Theory & Applications
Year: 2025
Subjects: overhead lines
substations
overvoltages
surge protection
surge arrester
skin effect
frequency-dependent device

description: This article explores various methods and devices used for protecting overhead lines and substations from surges, particularly those induced by lightning strikes. Traditional surge protection methods such as lightning rods, arresters, and grounding systems are discussed, highlighting their limitations and challenges, especially in long-distribution networks. The study examines the development and implementation of novel surge protection devices, including nonlinear surge arresters, frequency-dependent devices (FDD), and multi-chamber arresters. Special attention is given to FDD, which utilizes ferromagnetic materials to create frequency-dependent resistance, effectively suppressing high-frequency overvoltages. Experimental results demonstrate the efficacy of FDD in reducing the amplitude of lightning-induced overvoltage pulses and enhancing the lightning resistance of overhead lines and substations. However, challenges such as insufficient information on device effectiveness, limited ohmic resistance at high frequencies, and size constraints hinder widespread adoption. The article concludes by emphasizing the need for further research to optimize FDD designs, increase active resistance, and assess operational effectiveness to facilitate broader deployment across different voltage classes.
ENHANCING EMOTION RECOGNITION WITH MULTIMODEL APPROACH USING DEEP NEURAL NETWORKS
Creators: name: Dr. Komal Anadkat
name: Ayush Solanki
name: Dhruva Patel
name: Vraj Thakkar
Publisher: name: Reliability: Theory & Applications
Year: 2025
Subjects: Multimodel Fusion
Emotion recognition
Deep Learning
EEG

description: Recognizing and extracting different emotions, and then validating those emotions have become important for enhancing human-computer interaction. Emotions play a crucial role in social interactions, facilitating rational decision-making and perception. Previously researched emotion recognition models have typically focused on a single input type like images, text, or audio, where each model can identify the emotion of a person through a single source like facial expressions, voice, social media posts, etc. However, these uni-model approaches are limited because they rely on just one type of data, which often misses the full range of emotional cues. To overcome these limitations, multi-model emotion recognition techniques are proposed which are useful for detecting emotions through a person’s facial expressions, speech, social media status, and then EEG data. Model fusion techniques have been applied to detect the most accurate emotion for a particular person through fusion of all the models. A recognition rate-based weighting approach is proposed for model fusion, wherein models are assigned weights proportional to their individual recognition rates. This approach enhances overall performance by combining the outputs of various models with higher emphasis on those with better accuracy. The decision fusion-based multimodel emotion recognition model is proposed which achieved a maximum of 87%. accuracy using a bi-model approach and 92% accuracy with a tri-model approach. The weighted decision fusion approach assigns more weight to the model which is more accurate and achieved 93% accuracy. The proposed recognition rate-based weighting approach for fusion has provided significant results, achieving approximately 93% accuracy with 0.900 and 0.904 Cohen kappa and Mathew score respectively using facial expression, speech, and social media text modalities on combined dataset. The proposed model achieved 63% accuracy on a real-world collected dataset without considering EEG data and improved to 73% if EEG is also considered.
ENHANCING ENERGY SYSTEM RELIABILITY: MODERN APPROACHES AND SOLUTIONS
Creators: name: Sh.V. Ismayilova
name: Z.A. Isgandarova
name: K.M. Mukhtarova
Publisher: name: Reliability: Theory & Applications
Year: 2025
Subjects: energy system reliability
SAIDI
SAIFI
Automated Distribution Management Systems (ADMS)
redundancy
economic efficiency

description: The article analyzes methods for improving the reliability of energy systems considering the SAIDI and SAIFI indicators, which reflect the duration and frequency of power outages. Approaches are discussed, including the implementation of intelligent monitoring systems, Automated Distribution Management Systems (ADMS), as well as distributed generation and redundancy. The study confirms that the integrated use of these technologies significantly enhances network reliability, reducing SAIDI and SAIFI indices, and evaluates the economic efficiency of these solutions, demonstrating their long-term profitability.
ENHANCING LINDLEY DISTRIBUTION PARAMETER ESTIMATION WITH HYBRID BAYESIAN AVERAGE MODEL FOR FUZZY DATA
Creators: name: Abbarapu Ashok
name: Nadiminti Nagamani
Publisher: name: Reliability: Theory & Applications
Year: 2025
Subjects: MCMC
MH
lindley distribution
gibbs Sampling
bootstrap sampling
bayesian model average technique

description: With the ultimate goal of increasing parameter estimate accuracy, this study will examine and assess a number of estimating techniques used with the Lindley distribution in the context of fuzzy data. Gibbs sampling, Bootstrapping Sampling, MCMC, MH, and a unique hybrid methodology that combines these approaches via Bayesian model averaging were also studied. The research looks at several sample sizes ranging from 15 to 100 and repeats the estimate method 10,000 times for each size. Fuzzy data are created using established fuzzy systems, and the performance of each approach is measured using average values (AV), mean squared errors (MSE), coverage probabilities, and confidence interval lengths. The findings show that the hybrid technique consistently produces estimates closer to the genuine parameter value of one across all sample sizes, with smaller mean squared errors than individual methods. Furthermore, the hybrid method’s confidence intervals preserve coverage probabilities that are consistent with the targeted confidence level, demonstrating the method’s trustworthiness in statistical inference. Overall, the results show that the hybrid technique improves estimate accuracy and reliability, providing a strong foundation for parameter estimation in the Lindley distribution framework using fuzzy data.
Additional file 27 of Efficacy and safety of oral Chinese patent medicines in the treatment of coronary heart disease combined with hyperlipidemia: a systematic review and network meta-analysis of 78 trials
Creators: name: Yang, Zhenyu
name: Li, Jixin
name: Zhou, Bogeng
name: Ji, Xuan
name: Yuan, Jianying
name: Yan, Junchen
name: Nan, Xilei
name: Guo, Dandan
Publisher: name: figshare
Year: 2024
Subjects: Medicine
Pharmacology
Biotechnology
Chemical Sciences not elsewhere classified
Cancer
Science Policy

description: Additional file 27: Table S4. Adverse reaction statistics table.
Additional file 28 of Efficacy and safety of oral Chinese patent medicines in the treatment of coronary heart disease combined with hyperlipidemia: a systematic review and network meta-analysis of 78 trials
Creators: name: Yang, Zhenyu
name: Li, Jixin
name: Zhou, Bogeng
name: Ji, Xuan
name: Yuan, Jianying
name: Yan, Junchen
name: Nan, Xilei
name: Guo, Dandan
Publisher: name: figshare
Year: 2024
Subjects: Medicine
Pharmacology
Biotechnology
Chemical Sciences not elsewhere classified
Cancer
Science Policy

description: Additional file 28: Table S5. Table of subgroup analysis.
ENHANCING INTRUSION DETECTION SYSTEM RELIABILITY USING GWO-SOMNN (GREY WOLF OPTIMIZATION WITH SELF-ORGANIZING MAP NEURAL NETWORK)
Creators: name: Ms. Archana Gondalia
name: Dr. Apurva Shah
Publisher: name: Reliability: Theory & Applications
Year: 2025
Subjects: Grey Wolf Optimization
Neural Networks
Self-Organizing Maps
Classification
Intrusion Detection
reliability

description: In today’s fast-changing technological environment, the number of Internet-connected devices has grown significantly, raising the risk of cybersecurity threats for both individuals and organizations. Network Intrusion Detection Systems (NIDS) have become vital tools for protecting networks from these increasing threats. This paper presents a GWO-SOMNN approach (Gray Wolf Optimization with Self-Organizing Map Neural Network) that combines Grey Wolf Optimization (GWO), Self-Organizing Maps (SOM) and Neural Networks (NN) for feature selection and classification on the UNSW-NB15 dataset. The proposed method leverages GWO to optimize feature selection, reducing the dataset’s dimensionality and computational load, while SOM is employed for clustering and visualizing high-dimensional data. Neural Networks are then used for effective classification of network attacks. The GWO-SOMNN approach is evaluated on the UNSW-NB15 dataset, and its performance is measured in terms of 97.18% accuracy and 97.15% F1-score for binary classification and 82.41% accuracy and 78.92% F1-score for multiclass classification. The results demonstrate significant improvements over traditional methods, particularly in enhancing the classification of both binary and multi-class network attacks. These findings highlight the potential of this integrated approach in developing more efficient and accurate network intrusion detection systems.
ENHANCING PATTERN SEQUENCE-BASED FORECASTS: A MODIFIED STRATEGY RELATIVE TO ELECTRICAL LOAD
Creators: name: Suseelatha Annamareddi
name: Sudheer Gopinathan
Publisher: name: Reliability: Theory & Applications
Year: 2025
Subjects: short-term load forecasting
hierarchical clustering
pattern sequence
time series
weighted average

description: A precise forecast of the one-day-ahead load is essential for the efficient management of modern power system operations. This paper proposes a univariate model for short term load forecasting (STLF) that improves the precision of the Pattern sequence forecasting (PSF) algorithm. An analysis was conducted to identify the underlying patterns in the electrical load data using Kmeans clustering and hierarchical clustering algorithms. The results demonstrate the efficacy of hierarchical clustering. The limitations of the original PSF algorithm, particularly in its clustering and prediction phases are addressed using hierarchical clustering and a new weighted average formula. The proposed method was validated using real-time series datasets and its performance was compared with those of three pattern sequence-based forecasting models. The performance is further evaluated on two electricity demand data sets and compared with bench mark models. The uncertainty and reliability of the forecast model was assessed using an error variance metric. The results show the superior forecast accuracy of the model.
ESTIMATING THE POPULATION MEAN USING STRATIFIED DOUBLE UNIFIED RANKED SET SAMPLING FOR ASYMMETRIC DISTRIBUTIONS
Creators: name: Alomair, Mohammed Ahmed
name: Chainarong Peanpailoon
name: Roohul Andrabi
name: Tundo Ul Islam Rather
name: Rather, Khalid Ul Islam
Publisher: name: Reliability: Theory & Applications
Year: 2025
Subjects: Simple random sampling
Ranked set sampling
Median Ranked Set Sampling
Unified ranked set sampling
Double Unified ranked set sampling
Stratified Double Unified ranked set sampling

description: In this study, we propose a novel sampling technique known as Stratified Unified Ranked Set Sampling (SDURSS) and evaluate its efficiency for estimating population means. SDURSS is designed to enhance the estimation accuracy by integrating concepts from ranked set sampling with stratified sampling. Our results demonstrate that the SDURSS estimator generally exhibits superior efficiency compared to SRS, particularly in complex distribution scenarios. While SDURSS often performs more efficiently than SSRS and SRSS, its performance relative to these methods varies depending on the specific distribution and sample size. In several cases, SDURSS outperforms SSRS and SRSS, highlighting its potential benefits in practical applications. The findings suggest that SDURSS is a promising alternative to traditional sampling methods, offering improved efficiency and potentially more accurate estimates of population means. This research underscores the value of exploring advanced sampling techniques to enhance statistical estimation, particularly in scenarios involving asymmetric distributions where traditional methods may be less effective.
EVALUATION OF PARAMETRIC ESTIMATION METHODS FOR THE GAMMA DISTRIBUTION USING MAXIMUM LIKELIHOOD AND BAYESIAN APPROACHES IN A CENSORED LIFE-TESTING STRATEGY WITH MARKOV CHAIN MONTE CARLO SIMULATIONS
Creators: name: Hesse, Christian Akrong
name: Boyetey, Dominic Buer
name: Kpeglo, Emmanuel Dodzi
name: Ashiagbor, Albert Ayi
Publisher: name: Reliability: Theory & Applications
Year: 2025
Subjects: bayesian inference
gamma distribution
maximum likelihood estimation
maximum a-posteriori
reliability analysis

description: The goal of this study was to address the computational challenges associated with parametric estimation of the gamma distribution by evaluating the performance of the maximum likelihood and maximum a-posteriori estimation methods within the framework of Markov Chain Monte Carlo simulations. This was done by first assuming a censored life-testing strategy that terminates on the rth failure from a given sample of n electronic devices. Second, we obtained the joint distribution function of the first r-order statistic by arranging the r values in order of magnitude. Finally, we explored through the Markov Chain Monte Carlo framework using the maximum likelihood and maximum a-posteriori to estimate the gamma distribution parameters. The findings of this study suggest that both estimation methods were not significantly different from the actual hypothesized parameter values. Further, we observed that irrespective of the prior distribution used for the Bayesian maximum a-posteriori Markov Chain Monte Carlo estimation, the resulting parametric estimates of the gamma distribution remain the same, confirming the assertion that the Bayesian maximum a-posteriori Markov Chain Monte Carlo approach is a valuable tool for informative posterior analysis. The study’s uniqueness lies in adopting a censored life-testing strategy centered on the joint distribution function of the first r-order statistic.
EXPLORING AN EXTENDED RAYLEIGH DISTRIBUTION: MODELING AND APPLICATIONS IN REAL LIFE SCENARIOS
Creators: name: Aadil Ahmad Mir
name: S.P. Ahmad
Publisher: name: Reliability: Theory & Applications
Year: 2025
Subjects: Ratio transformation
Rayleigh distribution
Moments
Conditional moments
Renyi entropy
Maximum Likelihood estimation

description: In this manuscript, we propose a new extension of the Rayleigh distribution, named as Ratio Transformation Rayleigh Distribution (RTRD), which offers superior fits compared to the Rayleigh distribution and several of its known generalizations. We derive various properties of the proposed distribution, including moments, moment generating function, hazard rate, conditional moments, Bonferroni and Lorenz curves, mean residual life, mean waiting time, Renyi entropy and order statistics. The unknown parameters are estimated using the maximum likelihood estimation procedure. An extensive simulation study is conducted to illustrate the behavior of the maximum likelihood estimators (MLEs) based on Mean Square Errors. The flexibility of the new distribution is demonstrated by applying it to two real data sets. Comparative analysis with the Rayleigh distribution, Weighted Rayleigh distribution, Exponentiated Rayleigh distribution and Transmuted Rayleigh distribution reveals that RTRD outperforms these competing distributions based on Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Akaike Information Criterion Corrected (AICC) and other goodness of fit measures.
EXPONENTIATED POISSON-G FAMILY OF DISTRIBUTION: SUB-MODELS, PROPERTIES, ESTIMATION WITH REAL-LIFE APPLICATION
Creators: name: Habibah Rahman
name: Tanusree Deb Roy
Publisher: name: Reliability: Theory & Applications
Year: 2025
Subjects: G-family
Exponentiated model
percentage points
real-life data

description: This study proposes a new family of distributions. A study is done on some of its basic characteristics, such as quantile, skewness, kurtosis, hazard rate function, moments, mean deviations, availability and reliability function of successive linear and circular systems, mean time to failure, mean time between failure, and availability, Bonferroni and Lorenz curves, and entropies. Two unique models of the new family are studied in depth once the general class is introduced. The special basis models have been taken from the exponential and Fréchet distributions. The parameters of the model are estimated using maximum likelihood techniques. There is a thorough analysis of percentage points. Three unique real data sets are used to demonstrate the significance of the new family. A comparison is drawn between the suggested distribution family and well-known two-, three-, and four-parameter components. To model actual data, it can be used as an alternative model to various lifetime distributions found in the statistical literature.
EXPLORING QUADRASOPHIC FUZZY SET: APPLICATIONS IN ASSESSING STRESS LEVELS AND SELF-ESTEEM CONNECTIONS
Creators: name: G. Aruna
name: J. Jesintha Rosline
name: A. Anthoni Amali
Publisher: name: Reliability: Theory & Applications
Year: 2025
Subjects: Quadrasophic Fuzzy set
Quadrasophic Fuzzy Relation
Max-Min Composition
Decision making

description: The ambiguous environment has been addressed with a variety of fuzzy sets and their extensions. The Quadrasophic Fuzzy Set is one of the generalization of Fuzzy set to handle imprecise information efficiently. It is defined with two new parameters. In this artifact, we defined the operations, theorems, and relations of the Quadrasophic Fuzzy Set with pertinent examples. We also established a comparison study with other existing models. Additionally, the integration of Quadrasophic Fuzzy data with the TOPSIS approach to solve the Multi Criteria Decision Making problem is proposed and illustrated by examining the relationship between employee stress levels and their self-esteem, which can trigger obsessive-compulsive disorder, using real-life data. The results are analyzed with SPSS software.
Additional file 30 of Efficacy and safety of oral Chinese patent medicines in the treatment of coronary heart disease combined with hyperlipidemia: a systematic review and network meta-analysis of 78 trials
Creators: name: Yang, Zhenyu
name: Li, Jixin
name: Zhou, Bogeng
name: Ji, Xuan
name: Yuan, Jianying
name: Yan, Junchen
name: Nan, Xilei
name: Guo, Dandan
Publisher: name: figshare
Year: 2024
Subjects: Medicine
Pharmacology
Biotechnology
Chemical Sciences not elsewhere classified
Cancer
Science Policy

description: Additional file 30. Abbreviations.
Additional file 31 of Efficacy and safety of oral Chinese patent medicines in the treatment of coronary heart disease combined with hyperlipidemia: a systematic review and network meta-analysis of 78 trials
Creators: name: Yang, Zhenyu
name: Li, Jixin
name: Zhou, Bogeng
name: Ji, Xuan
name: Yuan, Jianying
name: Yan, Junchen
name: Nan, Xilei
name: Guo, Dandan
Publisher: name: figshare
Year: 2024
Subjects: Medicine
Pharmacology
Biotechnology
Chemical Sciences not elsewhere classified
Cancer
Science Policy

description: Additional file 31. Drug approval number.