The manuscript examines the role of Artificial Intelligence (AI) and telemedicine in Improving healthcare access and services for older adults. The study focuses on digital consultations and early diagnosis and employs a descriptive research design. Data were collected from 120 older adults in rural and urban areas of Dehradun, Uttarakhand. The analysis uses statistical techniques including descriptive statistics, regression analysis, ANOVA, and chi-square tests through SPSS to evaluate telemedicine usage, challenges, and satisfaction levels among older adults.
This paper introduces a new three-parameter lifetime model, called the Sujatha Power Series (SPS) distribution, motivated by the need for more flexible distributions in reliability and survival analysis. The model is constructed by compounding the Sujatha distribution with a family of power-series distributions, allowing it to accommodate various density and hazard rate shapes, including the important bathtub form. Key distributional properties are derived, and parameter estimation is performed using maximum likelihood methods. The practical performance of the SPS distribution is demonstrated through applications to two real lifetime datasets and comparisons with established competing models. The results indicate that the proposed model provides superior or comparable fits, highlighting its usefulness as an effective and flexible tool for modeling lifetime data in reliability and survival studies.
Wheat productivity is greatly influenced by soil nutrient variability, especially in areas with agroclimatic diversity like Jammu. The study analyzed a comprehensive dataset comprising 5,196 soil samples and corresponding wheat yield records from the districts of Jammu, Rajouri, and Kathua in Jammu region. This work predicted wheat yield from soil factors using machine learning (ML) models. The ML models used include Random Forest, Gradient Boosting, Support Vector Regression, and Decision Trees, in conjunction with embedded and wrapper-based feature selection methods. The soil variables analyzed in this study included pH, EC, OC, N, P, K, S, Cu, Zn, Mn, and Fe. Among the tested machine learning models, Random Forest yielded the highest predictive accuracy, with RMSE = 2.6570, MAE = 2.1578, and MAPE = 44.87%. Recursive Feature Elimination identified an optimal subset of 10 soil predictors, with S, Mn, Zn, and EC emerging as the most influential variables for wheat yield estimation. In all models, sulfur (S), manganese (Mn), electrical conductivity (EC), and zinc (Zn) were consistently found to be the most significant predictors. In comparison to other models, Random Forest and Support Vector Machines generated more reliable and broadly applicable predictions, according to stability study using k-fold cross-validation. The study highlights the effectiveness of machine learning techniques, particularly Random Forest, in predicting wheat yield from soil parameters. The consistent importance of micronutrients like S, Mn, and Zn underscores the need for micronutrient-focused soil management strategies. These findings demonstrate the usefulness of data-driven approaches in heterogeneous soil and climatic conditions.
Proportion data is significant in many disciplines, including as economics, finance, reliability engineering, medicine, biology, and chemistry, since it serves as the basis for identifying trends, expediting procedures, and making well-informed conclusions that result in advances and innovations. For the representation and analysis of proportional data, the beta distribution is a standard model. In this paper, we explore the unit omega distribution [15] and their ordered properties such as the exact expressions, as well as recurrence relations, for the single moments of the order statistics (OSs) and record values. Additionally, various L-moment characteristics based on OS moments and record values were analyzed. The applicability of the unit omega distribution over beta distribution was demonstrated through its successful fit to the original IRT temperature dataset. For comparative assessment, the performance of the unit omega distribution was evaluated against the commonly used beta distribution using the Kolmogorov–Smirnov (KS) goodness-of-fit test. The results indicate that the unit omega distribution yields a smaller KS test statistic and a larger associated p-value compared to the Beta distribution, suggesting a superior fit to the observed data. These findings highlight the flexibility and effectiveness of the unit omega distribution in modelling bounded data. Furthermore, the study provides a strong foundation for future research, particularly in extending the analysis to generalized order statistics and progressive censoring schemes associated with the unit omega distribution.
Fisher’s exact test is a fundamental statistical tool for analyzing associations in 2×2 contingency tables, especially when dealing with small samples or sparse data common in preliminary medical studies. Despite its widespread use, misconceptions regarding its assumptions, application criteria, and interpretation persist. This tutorial provides a structured, practical guide to Fisher’s exact test. We begin with the theoretical foundation, contrasting it with the chi-square test and explaining its reliance on the hypergeometric distribution. The core of the tutorial features step-by-step manual calculations for educational clarity, followed by practical implementation guides using R, SPSS, and Stata. We address common pitfalls, including misuse in large samples, confusion between one- and two-tailed p-values, and the need for multiple testing corrections. Extensions to larger tables via the Fisher-Freeman-Halton test and Monte Carlo simulation are also discussed. By integrating theory with actionable examples, this tutorial aims to enhance statistical literacy and ensure the accurate application of Fisher’s exact test in clinical and epidemiological research, thereby improving the reliability and reproducibility of findings.
In this study, a hybrid framework is proposed that combines fuzzy logic with neutrosophic statistics to deal with the issue of parameter uncertainty in stress-strength reliability analysis of systems that are governed by the Ailamujia distribution. The proposed framework is an extension of the conventional binary reliability model in that it incorporates a fuzzy reliability concept that considers the degree of exceedance of strength over stress rather than the conventional binary concept of exceedance/non-exceedance. To deal with the indeterminate scale parameters that arise in practice, the proposed framework is extended to a neutrosophic framework to arrive at interval estimates of reliability that reflect the imprecise nature of real-world problems. The objectives of this study are to arrive at the fuzzy reliability model, extend it to a neutrosophic framework, and finally examine the performance of the proposed framework through extensive simulation studies. Additionally, the practicality of the proposed model is also examined through a case study of component reliability in mechanical systems.
This study explores the novel application of the performance characteristics of sequential probability ratio test (SPRT) for estimation the scale parameter (θ) of the power Rayleigh distribution, focusing on its scale parameters (θ) that are the fundamental to defining its properties and shaping the statistical characteristics. For testing the null hypothesis H0:θ=θ0 versus H1:θ=θ1, a stop rule method is utilized to optimize the sample size. Through this approach, we derive approximations for key performances metrics, including the operating characteristic (OC) function and the average sample number (ASN). The study also confirms the existence of the function L(θ), necessary to meet the ASN requirements in sequential testing. The new method is comprised of the efficiency of the SPRT compared to the classical Neyman-Pearson (N-P) technique. A comprehensive simulation study shows that the proposed method of SPRT significantly reduces the sample size required for the hypothesis testing by 65.99% to 77.49% compared to the Neyman-Pearson (N-P) test without compromising accuracy.
This paper examines how digital transformation affects sustainable business model performance in Chinese listed manufacturing firms from 2019 to 2023. A composite measurement system is built for digital maturity, green business practices, green finance, and sustainable performance. Reliability testing, descriptive statistics, Pearson correlations, two-way fixed-effects panel regression, robustness checks, mediation and moderation models, Sobel and Bootstrap tests, and structural equation modelling are jointly employed. The results show a positive association between digital transformation and sustainable business model performance (β=0.369, p < 0.01). Green business practices partially mediate this relationship and explain 30.89% of the total effect, while green financial support strengthens the link (interaction β=0.087, p < 0.05). The findings provide statistically reliable evidence that digital capability, green operations, and external finance jointly improve sustainable business outcomes.
Social networking sites serves as influential medium for sharing information and communication; however, their mostly unregulated and open frameworks have also turned them into fertile ground for the dissemination of offensive content. The simplicity of sharing content, coupled with user anonymity and vast reach, facilitates the swift circulation of offensive, abusive, and discriminatory remarks. Engagement-driven algorithms may unintentionally promote such harmful content, increasing its visibility and impact. Consequently, offensive content on platforms like Twitter, YouTube, Facebook, and Reddit frequently gains traction, fuelling online hostility, social division, and tangible real-world effects. Offensive content about women is a prevailing subject on social media platforms. Instances of misogyny are disproportionately represented on social media platforms and misogyny is a substantial societal concern which needs to be addressed. While exhaustive research work has been done for offensive language detection in monolingual settings, the domain of misogyny detection in code-mixed texts is relatively underexplored and there is lack of studies that tackle misogyny detection in under-resourced languages. One of the major causes is unavailability of appropriate Hindi-English mixed-coded language dataset. Therefore, in attempt to bridge this research gap our study focuses on developing a dataset and leveraging deep learning techniques on this high-quality curated dataset containing Hindi-English code-mixed comments from multiple social media platforms. This dataset contains 17,234 comments from different social media platforms, annotated manually into misogynistic and non-misogynistic based on the content. Our study also demonstrates a detailed comparison between baseline machine learning, deep learning, and transformer-based approaches utilising our own curated Hinglish dataset. The results indicated that fine-tuned BERT outperformed the deep learning algorithms with highest 0.92 accuracy.
Innovation diffusion modeling plays a crucial role in understanding how new technologies, products, or ideas spread through a population over time. Classical approaches such as the Bass model assume smooth and continuous adoption patterns, which often fail to capture abrupt changes caused by market dynamics, technological disruptions, or policy interventions. This study develops a piecewise smooth diffusion framework that extends the Bass innovation diffusion model to incorporate random shifts across different time intervals. The framework introduces modulation functions that allow both gradual transitions and abrupt perturbations in adoption rates, thereby reflecting the non-linear dynamics of real-world diffusion. Stability analysis is conducted to examine the robustness of the system. The model is applied to historical datasets on cassette sales, compact discs, and physical video records. Empirical evaluation demonstrates that the piecewise approach provides superior fitting accuracy compared with standard Bass formulations, while also reducing parameter estimation errors. The findings highlight the value of modeling random shifts in diffusion processes, offering new insights for understanding technology substitution and for designing adaptive marketing and policy strategies.
The number of caesarean deliveries worldwide has been rising, which raises concerns about whether choosing this operation is suitable. Using both bivariate and multivariate logistic regression techniques, the current study aims to investigate the factors that influence the preference for caesarean delivery in India in order to ascertain whether the procedure is elective or emergency. It also looks at the relationship between the risk of caesarean delivery and women's pre-pregnancy obesity, height, delivery complications, preferred place of antenatal care visit as well as place of delivery, desired child, and sociodemographic variables. Results show that the risk of undergoing Cesarean section in the private sector is about four times higher than that in the public sector. The younger and educated women are more likely to prefer Cesarean delivery as compared to their counterparts. It's likely that this medical treatment is being abused for financial gain in the private sector or that women are choosing to forego labour discomfort on purpose.
Batting and bowling performances are crucial to evaluating the overall contribution of cricket players across all international formats. This study applies factor analysis to assess player performance in Test, ODI, and T20I formats. The dataset comprises 192 players from the 2021-2023 ICC World Test analysis reveals that in the limited-overs formats - ODIs and T20Is - batting performance tends to dominate, accounting for 45.66% and 46.77% of the 34.36% in ODIs and 35.61% in T20Is. However, the test format exhibited a near-equal distribution of variance with batting 40.61% and bowling 39.80% of the total variance.
In animal studies where experimental units are influenced by two sources of variation, row-column designs are commonly employed. When there is a large number of treatments but limited experimental resources, Generalized Row-Column (GRC) designs become useful. These designs enable multiple experimental units at each row-column intersection, optimizing resource use. Historically, GRC designs have been focused on supporting all possible pairwise comparisons among treatments. However, in many biomedical or pharmaceutical experiments, the main goal is not to compare all treatments, but rather to evaluate new (test) treatments against a standard (control) treatment. In such situations, the emphasis is placed on estimating the treatment-control contrast as precisely as possible. To meet this need, we introduce a balanced version of GRC designs specifically for treatment-control comparisons, and we propose a class of partially balanced GRC designs. These modifications aim to improve the precision of contrast estimation between test and control treatments, while still ensuring structural balance within rows and columns.
The existing bivariate normal distribution and its related algorithms in classical statistics cannot account for the degree of indeterminacy when applied under uncertainty. To address this gap, the main objective of this manuscript is to introduce bivariate neutrosophic random variables and study their properties through expectation and variance. In this paper, we also propose the neutrosophic bivariate normal distribution along with some of its key properties. Furthermore, we develop an algorithm based on the proposed distribution to generate imprecise data. A detailed simulation is carried out to examine the effect of the degree of indeterminacy on the data. The comparative study reveals that the variates produced by the proposed algorithm differ from those generated by the existing algorithm. To demonstrate its practical use, we provide a numerical example applying the bivariate normal distribution. Based on the simulation, comparative study, and numerical example, we recommend incorporating the degree of indeterminacy when generating data from the bivariate normal distribution under uncertainty.
In this paper, a parallel system consisting of two non-identical units has been studied. These dissimilar units of system are assumed to have different characteristics and different types of failure modes. All types of failures are treated by a single repairman who is made available to the system within no time. Some constraints for repair priorities are introduced for the system, which will vary as per unit undergoing failure. Failure rates of both units are assumed to follow exponential distribution, while repair rates are supposed to have any arbitrary distribution. To evaluate the system's reliability measures, Regenerative Point Technique has been used. Also, graphical and numerical representations are provided to illustrate the variations in these measures with respect to all parameters involved within system.
The primary focus of this paper is to present an estimation of fuzzy system reliability for a stress-strength model that accounts for uncertainty in the parameters of the distribution function. A drawback of existing methods in the literature is that they do not consider data uncertainty or fuzziness when estimating system reliability. To obtain a more realistic estimation, it is necessary to incorporate the uncertainty present in real-world scenarios. In this work, we incorporate both a distortion function and data fuzziness to estimate system reliability using the stress-strength model, resulting in a more practical approach. We estimate reliability using a suitable distortion function with fuzzy parameters. Specifically, Power, Dual Power, and Piece-wise Type II distortion functions are considered in conjunction with a standard exponential lifetime distribution. Additionally, we obtain a system reliability estimate under a dynamic stress-strength model using a power distortion function with a fuzzy parameter. Several numerical examples are computed to illustrate our approach to fuzzy system reliability estimation. To demonstrate practical application, an illustrative example using simulated estimates is presented for a real-life problem, the stress-strength reliability of reinforced concrete roofs. Finally, a discussion compares the proposed method to an existing method using numerical values.
Adopting a bio-geo-physical perspective, the present study briefly discusses the cumulative effect of weather events on tourism economies. Regional warming is herein regarded as a multidimensional process, whose economic repercussions can be systematically quantified through diverse sets of quantitative indicators. Such measures capture its variegated manifestations across spatial scales, from the regional to the local-ranging from incremental temperature increases and progressive soil aridification to the growing recurrence of droughts, the intensification of extreme meteorological events (e.g. strong winds), and the oscillation between water scarcity and abrupt surges in atmospheric humidity. The synthesis of these effects delineates the principal causal mechanisms through which climate change translates into tangible outcomes for tourism-related activities. This analytical framework is further enriched through the integration of recent statistical evidence released by official statistics estimating the monetary costs of climate change from both historical and prospective vantage points. The incorporation of such indicators within a broader interpretative scheme of local socio-economic systems provides a novel and methodologically robust approach to the assessment of climate-related costs across multiple spatial and temporal dimensions.
This paper presents a stochastic evaluation of a repairable system consisting of two identical operative units in parallel and one cold standby duplicate unit. The system is modeled using a Semi-Markov process framework combined with the regenerative point technique, which enables the treatment of general repair time distributions beyond the exponential assumption common in classical Markovian models. The novelty of the study lies in jointly analyzing reliability and economic measures-including Mean Time to System Failure (MTSF), steady-state availability, busy period of the repair facility, expected number of repairs, and long-run profit-under a unified framework. Instantaneous activation of the standby unit is incorporated without switchover delay, and its independence from the repair queue is explicitly considered. Numerical and graphical illustrations are provided to compare system performance across different redundancy strategies and to highlight the sensitivity of reliability indices to failure and repair rates. The results show that failures of original units exert a stronger impact on system reliability than those of the duplicate unit, while enhancing repair efficiency significantly improves both availability and profitability. The proposed modeling approach provides practical insights for the design of highly reliable and cost-effective systems in applications such as data centers, manufacturing, and safety-critical infrastructures.
The emergence of soybean diseases represents a formidable obstacle to the sustainable progression of the rapidly evolving soybean industry, which endeavors to achieve elevated productivity and enhanced crop excellence. Prompt and accurate disease prognostication is imperative for efficacious management protocol, as it contributes to limiting pathogen proliferation. This investigation examined the impact of various meteorological factors on the incidence of Soybean Yellow Mosaic Virus (SYMV) in Pantnagar, Uttarakhand. Six multivariate frameworks were assessed, encompassing Stepwise Multiple Linear Regression (SMLR), Artificial Neural Network (ANN) and Elastic Net (ELNET), employing both original climatic parameters and calculated weather indices to predict disease severity. For model construction, 80% of the dataset was allocated for training purposes while the remaining 20% was reserved for validation procedures. Among all examined frameworks, the ANN model incorporating weather indices (ANN-WI) exhibited exceptional predictive performance, attaining a Normalized Mean Square Error (nRMSE) of only 3.08% and an R2 coefficient of 0.99 during the calibration phase. Based on performance metrics, the frameworks were ranked as follows: ANN-WI ≈ ANN-W > ELNET-W > ELNET-WI > SMLR-WI > SMLR-W. The results definitively demonstrate that ANN-based frameworks, especially those incorporating weather indices, significantly exceeded alternative modeling techniques within the investigated area.
Road accidents have been among the leading reasons for injury and death globally; this study aims to develop a set of rules that Indian road traffic and safety agencies can specifically utilize to find out the potential reasons leading to accident severity. In this study we used R software to establish classification models -Multi-Layer Perceptron (MLP), Naive Bayes, decision trees, and logistic regression that can accurately predict the severity of injuries. By adopting 2585 road accident records in India from 2013 to 2018, our analysis reveals that the overall accuracy of logistic regression 87.47%, decision tree 91.21%, and MLP 86.05% in predicting injury severity However, Naive Bayes model demonstrated lower accuracy at 75.57% compared to the other algorithms. Finally, to identify the significant factors influencing accident severity, we have further explored the rules by the decision tree algorithm, and based on the findings, highlighted rules and focus areas to reduce the accident severity
