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..
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.
In this study, we examined the effect of different dewaxing methods after acid pretreatment on biohydrogen production from cinnamon leaves. Cinnamon leaves, which are a little-used lignocellulosic waste, were treated with hexane, ethanol, and boiling water before being subjected to acid hydrolysis to determine their effect on chemical structure and hydrogen production in the dark under anaerobic conditions. Ethanol dewaxing resulted in the highest cellulose (36.57 %) and hemicellulose (16.40 %) content, whereas boiling water treatment produced the most hydrogen (232.68 mL/L) after 72 h. A significant amount of hydrogen (124.10 mL/L) was produced by the undewaxed sample, while hexane-treated cells only yielded a small amount (1.00 mL/L), directly related to their high cellulose and hemicellulose content. Pretreatment methods played a major role in changing VFA concentration, especially for acetic, propionic, and butyric acids, which form the bulk of the hydrogen produced. Ethanol and boiling water both yielded beneficial VFA profiles for biohydrogen development, featuring abundant acetate and butyrate. Data shows that using boiling water or ethanol for dewaxing increases the extent to which the substrate can be digested and steers fermentation towards hydrogen production. This study highlights that using proper dewaxing methods along with acid pretreatment greatly increases the biohydrogen yield from cinnamon leaves.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused of COVID-19 has posed a major global health crisis in 2019. Herein, six stilbene-coumarin derivatives were synthesized from the natural chalcone dimethylcardamonin (DMC, 5) isolated from Syzygium nervosum. Their antiviral potential against COVID-19 was evaluated by measuring the inhibition of the SARS-CoV-2 main protease (3CLpro), a biological macromolecule, using FRET-based enzymatic assay. The results demonstrated that this scaffold effectively suppressed the proteolytic activity of 3CLpro. Structure-activity relationship analysis indicated that an optimal alkyl substitution length at C-3 position of the coumarin ring comprises six carbon atoms. Among the synthesized derivatives, compound 7c exhibited the strongest inhibitory activity, with an IC50 value of 20.73 mu M, approximately four times more potent than the reference flavonoid baicalein. Furthermore, molecular dynamics simulation provided atomistic insights into the interactions between the stilbene-coumarin derivatives and 3CLpro, revealing plausible binding orientations within the active site. Complementary, Molecular Mechanics Generalized Born Surface Area (MM-GBSA) and fragment molecular orbital (FMO) calculations highlighted key residues that play critical roles in stabilizing the binding of these compounds.
