Search Results (644)

Bioenergy crops are widely regarded as a promising approach to support renewable energy production, diversify farm income, and enhance land-use efficiency. Despite these potential benefits, adoption rates remain low, and empirical understanding of landowners’ decision-making processes is still emerging. This study examines landowners’ interest in and likelihood of adopting bioenergy crops, explicitly differentiating between early-stage interest and near-term adoption intentions. Survey data from 207 landowners are analyzed using a bivariate probit model to identify key factors influencing both outcomes. The results reveal a marked disparity between expressed interest and adoption likelihood, with a significantly greater proportion of landowners indicating interest than those willing to adopt in the near term. Economic orientation increases adoption interest by 9.5 percentage points, while identity orientation increases adoption likelihood by 6.6 percentage points. Determinants such as increased awareness, land size, experience, and participation in conservation programs exert varying influences across different decision stages. These findings suggest that stated interest and stated near-term adoption likelihood represent related but distinct dimensions of adoption readiness, shaped by different economic, identity-based, and institutional factors. Effective promotion of bioenergy crops requires more than general awareness campaigns. Policies should combine financial incentives, technical assistance, market development support, and outreach strategies that present bioenergy crops as compatible with landowners’ economic goals, stewardship values, recreational uses, and long-term attachment to their land. Full article

This paper proposes a generalized seasonal beta prime autoregressive moving average model with dynamic precision, denoted by BPSARMA, for modeling and forecasting positive-valued seasonal time series. The proposed framework extends the generalized BPARMA model by incorporating stochastic seasonal dynamics in the conditional mean through seasonal autoregressive and moving average components while allowing a flexible autoregressive structure for the conditional precision parameter, thereby accommodating time-varying uncertainty. The model also allows the inclusion of covariates and deterministic seasonal regressors. Parameter estimation is carried out by conditional maximum likelihood, and the main inferential and diagnostic tools are discussed. Monte Carlo simulations are conducted to examine the finite-sample behavior of the estimators and associated inference procedures. The practical usefulness of the proposed approach is illustrated through hydro-environmental time series applications, where its forecasting performance is evaluated using both in-sample and out-of-sample predictive measures. The empirical results indicate that the BPSARMA specification often provides competitive or superior forecasting accuracy relative to competing models, highlighting its usefulness for modeling and prediction in positive seasonal time series. Full article

One of the most widely used regression models for count data is the Conway–Maxwell–Poisson regression model (CMPRM), which often provides a better fit for over- and underdispersed count data than traditional models, such as Poisson regression and negative binomial regression. Parameter estimation in the CMPRM is typically performed using the maximum likelihood estimation (MLE) method. However, when explanatory variables are highly correlated, a phenomenon known as multicollinearity arises, posing a significant challenge to the analysis. Multicollinearity makes it difficult to identify the individual effects of explanatory variables, leading to inflated variances and larger standard errors of the MLEs. To address the issue of multicollinearity, this paper introduces a new class of Liu-type estimators within the CMPRM. The proposed estimators aim to improve the estimation accuracy and reliability of the CMPRM compared with existing biased estimation methods. The efficiency of the proposed estimator is evaluated through theoretical comparisons and Monte Carlo simulation experiments conducted under various conditions. Furthermore, two real-data applications are presented to demonstrate the practical usefulness of the proposed estimation method. The results from the theoretical analysis, simulation study, and empirical applications indicate that the proposed estimators outperform existing methods in terms of achieving more accurate and reliable estimates. Full article

A recent generalized Bilal (G-Bilal) model demonstrates remarkable flexibility in capturing a wide spectrum of failure behaviors, including monotonic and non-monotonic (upside-down bathtub-shaped) hazard patterns, outperforming several existing models such as the Weibull, gamma, and exponential families. This paper develops several inferential frameworks for different G-Bilal parameters of life using samples gathered by improved Type-II adaptive progressive censoring. This enhanced design ensures optimal control of test duration while maintaining high inferential precision. Expressions for the model parameters, reliability, and hazard rate functions are derived, followed by the development of maximum likelihood (ML) and maximum product of spacing (MPS) estimators with their asymptotic confidence intervals using the observed Fisher information with the delta approach. Furthermore, Bayesian estimators and two associated credible intervals are proposed under independent gamma priors and computed through Markov iterations, with both ML and MPS posteriors considered. Extensive Monte Carlo experiments confirm the consistency, robustness, and precision of the proposed estimators, with Bayesian spacing-based methods exhibiting superior accuracy and coverage. The model’s practical potential is further verified through two real applications: one involving mechanical system lifetimes and another analyzing health insurance premium data, representing physical and actuarial domains, respectively. Using the introduced censoring, the proposed G-Bilal model outperforms all competing models in terms of goodness-of-fit and reliability estimates in both cases. The results underscore the G-Bilal model’s adaptability, computational stability, and empirical superiority, establishing it as a powerful tool for modern reliability and actuarial risk assessments. Full article

Background: Large language models and other generative artificial intelligence (AI) tools are increasingly being embedded in digital healthcare services, including mobile health applications, telerehabilitation, remote monitoring, and hybrid care pathways. In this review, digital healthcare refers to technology-mediated healthcare services in which digital platforms, mobile applications, wearables, remote communication, and AI-enabled interfaces support health assessment, self-management, rehabilitation, clinical decision support, or service delivery. When AI-generated exercise guidance moves from general education to individualized recommendations about dose, progression, contraindications, or rehabilitation, it may become directly actionable and safety-relevant. Objectives: This review aimed to clarify when AI-generated exercise guidance in digital healthcare may warrant safety-relevant governance attention and to outline implementation considerations for explainability, human oversight, and service-level governance. It addresses a gap in the literature: general AI-governance and exercise-prescription discussions rarely specify how point-of-use explanations, review thresholds, and escalation safeguards can be organized for directly actionable AI exercise guidance. Methods: We conducted a governance-oriented narrative review of peer-reviewed literature and representative regulatory or guidance documents. This review was not designed as a systematic review, scoping review, or exhaustive evidence map; transparent source mapping was used to support conceptual synthesis. Searches and source mapping focused on generative AI, large language models, explainable AI, clinical decision support, digital health, mobile health, exercise prescription, rehabilitation, trust, automation bias, and human oversight. Sources were included when they informed the safety, explainability, governance, or real-world implementation of patient-facing AI-generated exercise guidance. Extracted material was grouped by evidentiary role and synthesized through framework synthesis and governance mapping to distinguish literature-supported observations, author interpretation, and proposed implementation tools. Results: The included sources were first organized into five thematic groups: digital exercise delivery and exercise-prescription evidence; explainability, trust, and automation bias literature; professional responsibility, ethics, and patient disclosure literature; regulatory and policy documents; and digital literacy, patient/clinician attitudes, and equity literature. The synthesis then proceeded from safety relevance to explanation needs, human oversight and escalation needs, and selected regulatory and policy signals before translating these strands into conceptual and implementation-oriented outputs rather than empirically validated instruments. AI-generated exercise guidance was most safety-relevant in scenarios involving individualized dose, progression, contraindication-sensitive action, or rehabilitation strategy. Across the included sources, generic transparency alone was not sufficient to support reviewable use; relevant explanation elements included evidence sources, risk warnings, reasoning paths, and reasonable alternatives. Oversight considerations varied with embodied risk, clinical ambiguity, user vulnerability, and likelihood of direct enactment. Implementation considerations linked interface design, clinical review, escalation, auditability, and post-deployment monitoring. Conclusions: AI-generated exercise guidance in digital healthcare may warrant governance attention as a patient-safety and accountability issue when it influences actionable exercise decisions. The proposed framework offers a conceptual basis for designing more reviewable and accountable mobile and remote exercise-support services. Future work can validate these outputs in patient-facing services, clinician review workflows, usability studies, implementation pilots, and safety evaluations. Full article

Cybersecurity risk is commonly expressed through impact and likelihood, yet likelihood remains difficult to estimate because cyber incidents are underreported, heterogeneous datasets are weakly comparable, and attacker behavior changes faster than conventional probability baselines. This article proposes a method for operationalizing likelihood through a cyber exposure profile that integrates external cyber knowledge and organization-specific telemetry into a graph-based representation. The contribution is a formally specified artifact chain—from unified data model through organization-specific profiling, metric registry, likelihood scoring, and control prioritization—that operationalizes four constructs grounded in incident evidence: exposure, traceability, motivation, and systems update. The pipeline provides a pathway from heterogeneous source evidence to a bounded likelihood indicator comparable across organizations and observation periods. An evaluation in 15 real organizations shows that those implementing the cyber exposure profile were associated with reduced incident frequency and faster detection and response times, providing preliminary empirical support for the framework’s directional claims. Full article

With the rapid advancement and cost reduction in high-throughput sequencing technologies, the accumulation of large-scale, three-dimensional gene expression data has surged. Consequently, effectively reducing the dimensionality of these complex datasets to extract critical biological information remains a significant challenge. Although various methods for identifying gene expression modules have been developed, most do not explicitly account for the multifactorial interactions among the temporal, spatial, and environmental dimensions. To address this limitation, we propose a novel three-dimensional triclustering technique based on a multivariate Gaussian mixture model (MVGMM) within a maximum likelihood framework. Specifically, our approach incorporates Legendre polynomials to model the temporal dynamics of gene expression and utilizes the Bayesian Information Criterion (BIC) to determine the optimal number of clusters. To further evaluate the model’s robustness against the high background noise typically present in empirical datasets (such as Arabidopsis thaliana), we conducted a rigorous sensitivity analysis by artificially injecting high-intensity Gaussian white noise into the simulated dataset. Despite severe noise interference, the global minimum of the BIC consistently remained at K = 6. Furthermore, the penalty term in the BIC successfully suppressed artificial cluster proliferation, preventing the model from fitting the noise as new functional modules. The MVGMM framework successfully recovered the predefined cluster structure in simulation studies and identified distinct expression modules in empirical Arabidopsis thaliana data. By jointly modeling temporal, spatial, and environmental variation, this study provides a statistical framework for exploring multidimensional gene expression patterns and may facilitate the identification of coordinated regulatory programs in complex biological systems. Full article

The present paper considers an empirical likelihood approach for testing equality of autocorrelation coefficients in two independent stationary time series. In the time domain, a two-sample blockwise empirical likelihood method is constructed for weakly dependent data. In the frequency domain, a two-sample frequency-domain empirical likelihood test is introduced using spectral moment restrictions for autocorrelation. Under suitable regularity conditions, the corresponding profiled empirical likelihood statistics converge to chi-square limits under the null hypothesis. To improve small-sample performance, a bootstrap Bartlett-type calibration is proposed for the profiled two-sample frequency-domain statistic. The finite-sample behavior of the proposed procedures is examined in a Monte Carlo study covering AR, ARMA, and ARFIMA models with both Gaussian and asymmetric heavy-tailed innovations. The results show that the frequency-domain empirical likelihood procedure provides reliable size control in the short-memory models considered and remains competitive in mild long-memory settings, while the benchmark procedures are more sensitive to parametric misspecification or block-length choice. The simulation study shows that the bootstrap Bartlett-type calibration improves performance in smaller samples. An empirical application to squared Nikkei 225 returns provides evidence of higher short-run volatility persistence during the COVID-19 regime than in the pre-pandemic period. Full article

Identifying corrosion-prone conditions early is a major maintenance challenge in closed, hard-to-access structural zones. This paper reports an in-service validation of the first monitoring layer of a multi-sensor data fusion approach for early warning of such conditions in selected closed zones of a medical rescue aircraft. The work covers sensor selection, installation in restricted-access compartments, and analysis of data from helicopter operations. Environmental, conductance, and electrochemical channels are combined to identify persistent conditions favorable to long-term corrosion development and to assign warning levels linked to maintenance actions. The thresholds proposed here are empirical screening criteria from the 82-day campaign, not universal damage thresholds or proof of existing corrosion. PZT and eddy-current sensing are planned as follow-up diagnostic layers in the overall architecture. These technologies have been validated separately under laboratory or controlled conditions but were not installed on the flying helicopter during this initial period. Although persistent severe early-warning episodes were detected, they did not coincide with an approved maintenance-access window suitable for additional PZT/EC hardware installation. The present results therefore characterize the corrosion-prone environment and the likelihood of corrosion initiation, not the type, exact location, pit depth, mass loss, or crack initiation of actual damage. Field inspection evidence of corrosion in hidden zones supports the practical relevance of early warning, while full end-to-end validation of localization and damage-growth monitoring remains future work. Full article

Poisson mixed-effects models are essential for analyzing repeated count data, relying on latent random effects to account for unobserved heterogeneity and longitudinal dependence. However, the validity of likelihood-based inference in these models is highly sensitive to the specification of both the fixed-effects structure and the distributional assumptions of the random effects. While diagnostics based on the information matrix equality (IME) provide a theoretical framework for detecting misspecification, their high dimensionality and reliance on second-order derivatives often result in numerical instability and poor finite-sample performance in nonlinear settings. Here we introduce the Contrast of Information by Volume (CIV) test, a low-dimensional information-based diagnostic test for Poisson generalized linear mixed models (GLMMs). By integrating the scalar CIV statistics with novel graphical diagnostics, our approach facilitates the interpretation of specification errors in the random-effects structure. We derive the asymptotic behaviour of the CIV statistics under local misspecification and evaluate their properties through Monte Carlo simulations. To ensure robust inference in moderate samples, a parametric bootstrap procedure is employed for size calibration. Simulation results demonstrate that the CIV diagnostics maintain accurate Type I error control and achieve competitive power against common misspecification, including heteroskedasticity, correlation, and heavy-tailed random-effect distributions. Compared to traditional IME diagnostics, estimator-comparison tests, and GMM-based procedures, the CIV approach offers a superior balance between finite-sample stability and detection power. Finally, an empirical application illustrates the utility of the CIV framework in diagnosing latent misspecification and guiding the selection of random-effects covariance structures in applied research. Full article

This theoretical essay, inspired in part by the author’s own perplexities during fieldwork and post-conflict reconciliation efforts, draws on transdisciplinary studies of religion and peace science to trace the effects of religious actors and the causes of peace. The reoccurrence and determination of religiously inspired actors in post-conflict settings, where religion has historically influenced both war and peace, is rooted in the enduring effects of local religious systems. Yet the causes of these effects remain open questions. Answering them forces us to move beyond noting, for example, the interpretive association of religious actors and peaceful outcomes to inquiring whether and how religiously inspired actors cause peace. Since so much of contemporary peacebuilding depends on local religious leaders supporting peace efforts or re-traditionalizing rituals, scholars working in post-conflict settings often find themselves pulled into debates about the effects of religion on peace. An empirical task is therefore investigating the causal links between religion and peacebuilding, the effects of religious actors in their cultural context, and how to contribute to effective interreligious dialogue. To reach these ends, however, a theoretical path is needed. This essay offers such a path, firstly, by addressing faulty assumptions and residual problems in current scholarship, and, secondly, providing a pathway for making causal claims. That pathway includes aggregating commensurable data to distinguish sufficient-components in type and token cases, reconstructing the temporal order and likelihood of counterfactuals, and tracing biological universals and cultural knowledge from religious systems to peace events. Full article

Tsagris and Alzeley proposed the generalized circular projected Cauchy (GCPC) distribution, whose special case is the wrapped Cauchy distribution. In this paper we first derive the relationship with the wrapped Cauchy distribution, and then we attempt to characterize the distribution. We establish the conditions under which the distribution exhibits unimodality. We provide non-closed-form expressions for the mean resultant length and the Kullback–Leibler divergence and analytical forms for the cumulative probability function and the entropy of the GCPC distribution. We propose log-likelihood ratio tests for one- and two-location parameters without assuming the equality of the concentration parameters. We revisit maximum likelihood estimation with and without predictors. In the regression setting we briefly discuss the addition of circular and simplicial predictors. Simulation studies illustrate (a) the performance of the log-likelihood ratio test when one falsely assumes that the true distribution is the wrapped Cauchy distribution, and (b) the empirical rate of convergence of the regression coefficients. Using a real data example, we show how to avoid the log-likelihood being trapped in a local maximum, and we correct a mistake in the regression setting. Full article

In order to better promote the use of intelligent agricultural machinery, enhance the efficiency of grain production, optimize resource utilization, and effectively address the practical problem of the reduction in the rural labor force, while theoretically clarifying the mechanism that affects the adoption of intelligent agricultural machinery by farmers in Changsha County. Based on a questionnaire survey of farmers in Changsha County, Hunan Province, the ordered logit model was used to identify the significant factors influencing farmers’ adoption of intelligent agricultural machinery. The empirical results show that male farmers, farmers with a non-agricultural occupation, and farmers with a lower education level (below high school) have a lower willingness to adopt intelligent agricultural machinery. As the risk of purchasing intelligent agricultural machinery decreases, market demand increases, and the number of agricultural services provided by the government increases, the likelihood of farmers adopting intelligent agricultural machinery also increases. Based on these findings, this paper proposes targeted suggestions aimed at increasing the adoption of intelligent agricultural machinery by farmers in Changsha County, Hunan Province. Full article

Detecting structural changes or change points in statistical models is a fundamental component of data analysis, as it plays a crucial role in understanding the dynamic behavior of real-world data, particularly in financial markets and cryptocurrency returns. In this study, we developed a procedure based on the likelihood ratio test (LRT) to identify change points in the parameters of the exponentiated exponential logistic (EEL) distribution. Furthermore, the binary segmentation technique was employed to efficiently detect multiple change points and determine their locations. The proposed methodology was derived under the null and alternative hypotheses, and its statistical properties were examined in depth. To evaluate its performance, extensive Monte Carlo simulations were conducted to assess the empirical power of the test under various sample sizes and significance levels. Furthermore, the method was applied to real cryptocurrency return data to demonstrate its practical ability to detect change points and illustrate its effectiveness at identifying structural breaks. The empirical results indicate that the LRT-based procedure exhibits strong capability in detecting significant distributional changes in cryptocurrency data over time, confirming its effectiveness as an analytical tool in statistical and financial studies. Full article

The Poisson exponentially weighted moving average (PEWMA) control chart rests upon the equidispersion assumption of the pure Poisson distribution, a structural symmetry condition stipulating that the process mean and variance are equal. In manufacturing environments characterized by latent process heterogeneity, this assumption is systematically violated: the resulting distributions are inherently asymmetric, heavily right-skewed, and overdispersed. This structural asymmetry renders standard PEWMA control limits artificially narrow, inducing a substantial inflation of false alarm rates. This paper introduces the Poisson mixture EWMA (PM-EWMA) control chart, which models the latent heterogeneous structure of count data as a finite Poisson mixture distribution, with parameters estimated via the Expectation–Maximization (EM) algorithm without requiring prior labeling of process states. The optimal number of components is determined via the Bayesian Information Criterion (BIC) as the primary criterion, supplemented by the Akaike Information Criterion (AIC), its bias-corrected variant (AICc), and the log-likelihood ratio diagnostic. The PM-EWMA chart incorporates the exact mixture variance, accounting for both within-component and between-component variability, into the EWMA control limit structure, thereby providing a theoretically justified correction under the fitted Poisson mixture assumption. A Monte Carlo simulation study comprising 495 factorial configurations benchmarks the PM-EWMA chart against both the standard PEWMA chart and the negative binomial EWMA (NB-EWMA) chart with oracle dispersion calibration, confirming stable in-control ARL performance and demonstrating improved discrimination relative to the misspecified PEWMA baseline. Empirical validation using fabric defect count data from two textile manufacturers in Türkiye, with Overdispersion Indices of 6.01 and 2.74, respectively, demonstrates false alarm reductions ranging from 40.9% to 89.2% relative to the standard PEWMA chart, depending on the smoothing parameter and degree of overdispersion. Full article