2026-03-18
Myunghyun Oh
Cassava mosaic disease (CMD) can spread across agricultural landscapes through whitefly-mediated transmission, creating invasion fronts whose speed is governed by vector movement and survival. Motivated by mild-strain cross-protection as a management tool, we formulated a spatially explicit vector–host model that couples plant infection dynamics with vector advection–diffusion and includes a protected plant class representing partial cross-protection. We derived an explicit invasion threshold R0 and showed that cross-protection enters through an effective susceptible fraction. For linearly determined (pulled) fronts, we characterized the minimal invasion speed and identified effective drift and diffusion weights that aggregate transport across the coupled host–vector system; near threshold, the speed scales with R0 − 1 through the leading-edge growth rate. We computed monotone traveling waves numerically and found close agreement between observed wave speeds and the pulled-speed prediction in the parameter regime considered. The analysis highlights how increasing vector mortality, improving protection efficacy, and strengthening roguing can either prevent invasion (R0 < 1) or slow spatial spread. Here, we also discuss limitations relevant to field deployment, including human-mediated movement of infected cuttings, temporally varying wind conditions, and episodic planting and harvest.
DOI: 10.3389/fams.2026.17916132026-03-17
Lana A. Abullah, Chnoor M Rahman
IntroductionDetecting bearing faults plays a vital role in industrial maintenance since discovering problems early can help avoid unexpected breakdowns and expensive production losses. Yet, spotting these faults in their initial stages is still difficult because vibration signals are often complex and change over time.MethodsIn this study, optimized Mel Frequency Cepstral Coefficients (MFCC) feature extraction approach enhanced through a modified FOX optimization algorithm. The enhancement focuses on fine-tuning MFCC hyperparameters to maximize the discriminative power of extracted features for fault detection tasks. The proposed Enhanced FOX (EFOX) algorithm integrates different random distribution method and improved exploration–exploitation balance, enabling more effective parameter optimization compared to conventional methods.ResultsExperimental evaluations were conducted using benchmark datasets, and the optimized MFCC features were compared against those obtained via standard MFCC settings and other metaheuristic optimization techniques. Results demonstrate that our approach consistently outperforms competing methods in terms of classification accuracy and the robustness of the proposed model was assessed by testing it on two distinct bearing’s datasets with different noise ratios including −3 dB and −6 dB.DiscussionThe analysis highlights the impact of each of hyperparameter’s of MFCC to bearing fault detection.
DOI: 10.3389/fams.2026.17636372026-03-16
Tsuyoshi Nakamura, Tomomi Yamada, Yoshiaki Nose
An event that hinders or changes the possibility of observing the event of interest is called a competing risk. For instance, clinical studies involving patients with multimorbidity or critically severe illnesses often require the evaluation of competing risks, as the occurrence of other events may preclude the primary event of interest. Cause-specific incidence and the Fine–Gray hazard have been widely used and have become the default methodological approaches in competing risk analysis. Nevertheless, some clinicians are unable to correctly interpret the results obtained from the competing risk analysis. Recently, the cause-distinct incidence has been introduced to resolve drawbacks in competing risk analysis, but because this confusion is widespread among biostatisticians, it may take considerable time to resolve. During this period, clinical researchers may continue to publish articles with incorrect interpretations. This study aimed to address these misinterpretations and accelerate clarification of the prevailing drawbacks.
DOI: 10.3389/fams.2026.17770182026-03-02
Amro Salem Alamaren, Ahmad Ali Eyadat, Ashraf Mohammad Alrjoub, Diana Alhajjeah
This study evaluates the duration of Jordanian public debt as an instrument for hedging interest rate risk over the period 2008–2020. It provides an analytical examination of public debt concepts and emphasizes the relevance of debt duration as a measure of maturity structure and interest rate sensitivity. The study covers both domestic and external public debt and situates the analysis within the historical context of Jordan’s debt evolution, including the 1988 debt crisis and subsequent fiscal adjustments. Using a comprehensive dataset of Jordanian government treasury bonds denominated in Jordanian dinars, the study applies Macaulay Duration and Modified Duration models to assess the effective maturity and interest rate exposure of the public debt portfolio. The results indicate that the estimated Macaulay Duration of Jordanian public debt is approximately 2.514 years, reflecting the weighted average timing of debt cash flows and the horizon of interest rate sensitivity. The Modified Duration is estimated at approximately 2.293 years, indicating a moderate degree of price sensitivity to changes in interest rates. Overall, the findings suggest that Jordan’s public debt exhibits a relatively short- to medium-term maturity structure, allowing for refinancing flexibility while maintaining manageable exposure to interest rate risk. The study highlights the usefulness of duration-based indicators as complementary tools for public debt management and risk assessment in emerging economies.
DOI: 10.3389/fams.2026.17366482026-02-23
Nikolay I. Nikolov, Mladen Savov, Dean Palejev
The classical Benjamini–Hochberg (B-H) method, widely used across various disciplines such as genetics, epidemiology, and social sciences, serves as an established procedure for controlling the false discovery rate (FDR) in multiple comparison scenarios. The B-H method assumes independence among tests, which often does not hold in large-scale dependent datasets. The Benjamini–Yekutieli (B-Y) adjustment controls the FDR under arbitrary dependence but is often very conservative and can lead to a reduction in statistical power. This paper investigates the performance of the B-H and B-Y procedures under specific positive block dependence structures. Two parametric forms of block dependence are considered to model the correlation among paired t-test statistics. Estimation algorithms induced by different matrix norms are developed for approximating the value of the unknown parameter. Modifications of existing multiple testing approaches are proposed by incorporating test dependence and enhancing their power through integration of Kolmogorov-Smirnov tests. Simulation studies are performed to demonstrate that the recommended methods preserve FDR control while improving power compared to traditional techniques.
DOI: 10.3389/fams.2026.1748504