Maejo University (MJU.) (also spelled Maecho University, Thai: มหาวิทยาลัยแม่โจ้) in Chiang Mai Province, Thailand, is the oldest agricultural institution in the country. Founded in 1934 as the Northern Agricultural Teachers Training School, it was restructured and renamed several times until it gained the status of a full-fledged public university in 1996 and since then has been known as Maejo University.Maejo University main campus at Chiang Mai is composed of the faculties of Business, Economics, Liberal Arts, Tourism Development, Information Technology, Fisheries Technology and Aquatic Resources, Animal Sciences and Technology, Renewable Energy, Administrative, Landscape and Environmental Design, Agricultural Production, Science, and Engineering and Agro-Industry. In addition to these, the university has two smaller campuses in Phrae and Chumphon..
Singular fractional Lane–Emden equations with Caputo derivatives arise in radially symmetric models with memory effects, but their numerical treatment is difficult because of the singular coefficient at the origin and the limited convergence range of local series approximations. This paper develops a three-phase hybrid computational scheme that combines an even-indexed fractional power-series expansion for regular initialization near the singular point, a fractional Adams–Bashforth–Moulton predictor–corrector method for long-range continuation, and Richardson extrapolation for accuracy enhancement. The proposed approach avoids direct numerical integration from the singular origin and provides a practical framework in which the local analytical structure is coupled with stable numerical continuation over extended domains. A global error estimate is derived in terms of the series-startup error and the fractional ABM discretization error. Numerical experiments confirm the accuracy and robustness of the method for both classical and fractional Lane–Emden problems. In the classical limit, the method accurately reproduces benchmark first-zero values for near-critical polytropic indices, including n=4.99. In the fractional case, the computations indicate a transition boundary in the (α, n)-plane separating finite-radius profiles from monotone infinite-extent solutions. These results demonstrate that the proposed hybrid scheme is an efficient and reliable tool for high-precision simulation of singular nonlinear fractional models on long computational intervals.
Identifying risk factors that exhibit significant associations with child growth and development is crucial for preventing unhealthy growth and supporting children's overall development. Given that children have a diverse range of growth patterns, it is particularly relevant to evaluate these associations across quantiles rather than simply focusing on the mean or median values. In this paper, we develop a Bayesian variable selection method within quantile regression (QR) and quantile mixed models (QMMs). In particular, these methods are designed to analyse longitudinal data, such as child growth data. This novel methodology combines several key components, including the Bayesian sparse group LASSO method, a likelihood function based on the scale mixture representation of the asymmetric Laplace (AL) distribution. It also incorporates spike-and-slab priors for regression coefficients and utilises linear mixed models based on a decomposition for the covariance matrix of random effects. By combining these elements, our approach offers a comprehensive solution for simultaneous selection and estimation of fixed and random effects in QMMs. We assess the performance of the proposed method through simulation studies, which demonstrate its strong variable selection and predictive capabilities. Furthermore, we illustrate its practical utility by applying it to the Growing Up in Scotland (GUS) dataset, providing practical insights into its real-world applicability.
The Southeast Asian millipede genus Chamberlinius Wang, 1956, has been known only from Taiwan, southern Japan, and recently from central Vietnam. This paper describes a new species, Chamberlinius laoticus sp. nov., from a limestone cave in Khammouane Province, Laos, representing the second known mainland representative from Indochina. The new species is readily distinguished from all congeners by its smaller size, uniformly pale coloration lacking any darker markings, and the absence of ridge-like spiracles flanking the gonopod aperture. This warrants the generic diagnosis to be slightly amended. An updated key to and a distribution map for all seven known species of Chamberlinius are also provided.
This article explores the sliding mode control (SMC) of nonlinear complex dynamical networks affected by random measurement uncertainties and deception attacks, employing an observer-based event-triggered mechanism. The fundamental control signal is susceptible to deception attacks, where adversaries inject false data probabilistically, disrupting system functionality. To counteract these attacks, a secure SMC strategy is proposed. A novel sliding surface is then formulated, leading to the derivation of sliding mode dynamics. Additionally, the observer-based event-triggered mechanism is integrated to filter sampled signals, thereby optimizing network bandwidth usage and minimizing resource transmission rates. The deception attacks, measurement uncertainties, and time-varying coupling delays are modeled as a three-variable process following the Bernoulli distribution. By leveraging the Lyapunov function approach, sufficient conditions are established to ensure both the reachability of the sliding region and the mean square asymptotic stability of the system while maintaining extended dissipative performance. The sliding mode controller gain and event-triggered weighting matrix are designed using the linear matrix inequality (LMI) framework in conjunction with an observer. Finally, the effectiveness and benefits of the proposed approach are demonstrated through two numerical examples, including an application to Chuas circuit model.
An integrative approach based on morphological characteristics and DNA data from the mitochondrial COI gene has revealed five new species of the pill millipede genus Rhopalomeris Verhoeff, 1906 from Thailand: R. muka Sapparojpattana & Likhitrakarn, sp. nov., R. lentiginosa Sapparojpattana & Likhitrakarn, sp. nov., R. dulcia Sapparojpattana & Likhitrakarn, sp. nov., R. punctata Sapparojpattana & Likhitrakarn, sp. nov., and R. verhoeffi Sapparojpattana & Likhitrakarn, sp. nov. The five new species are distinguishable from their congeners by distinctive color patterns and telopod structure. The interspecific genetic divergences between the new species and other Rhopalomeris species ranged from 7.86 to 13.71%. Intraspecific genetic divergences within these five species ranged from 0 to 4.53%, with the highest divergences found in R. carnifex and R. verhoeffi sp. nov. This is consistent with both the wide distribution range and the significant intraspecific morphological variations observed in these latter two species. Furthermore, the taxonomic scope of R. carnifex in Thailand is reviewed, and intraspecific morphological variations within R. carnifex and R. verhoeffi sp. nov. are discussed. A distribution map, morphological illustrations from SEM, and an updated key to all known Rhopalomeris species are also provided.
