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Three new species of Polycarpaea from Vietnam, Polycarpaea vanphongensis V.T. Tran, H.T. Truong & N.V. Binh, Polycarpaea chungana V.T. Tran, H.T. Truong & N.V. Binh, Polycarpaea phuquocensis V.T. Tran, H.T. Truong & N.V. Binh are described and illustrated based on evidence of molecular sequence data from two markers (ITS1-5.8S-ITS2 and rps16) and combined morphological characteristics. Polycarpaea vanphongensis is closely related to Polycarpaea gaudichaudi Gagnep., Polycarpaea arenaria (Lour.) Gagnep., Polycarpaea phuquocensis V.T. Tran, H.T. Truong & N.V. Binh but differs by its stem glabrous, leaf ovate to elliptic, glabrous, ovary oblong ovoid, base obtuse, apex attenuate, capsule oblong void, 3.8 mm long. Polycarpaea phuquocensis V.T. Tran, H.T. Truong & N.V. Binh differs from the three species mentioned above by its stem being densely villous, leaf spathulate, ciliate, ovary ovoid, base acute, apex obtuse, capsule ovoid, 1.2 mm long. Polycarpaea chungana V.T. Tran, H.T. Truong & N.V. Binh is most similar to Polycarpaea lignosa Gagnep., but differs in having leaf oblong or linear, sparse ciliate, sepal and petal apex deeply concaved or slightly bifid, ovary ovoid, ovoid, 0.8–1.0 mm long. Furthermore, the achievements of analysis using molecular data on the systematic positions of 7 other species are results that have not been in previous molecular analyses. [ABSTRACT FROM AUTHOR]

Biodiversity is a characteristic of ecosystems that plays a crucial role in the study of their evolution, and to estimate it, the species of all plants need to be determined. In this study, we used Unmanned Aerial Vehicles to gather RGB images of mid-to-high-altitude ecosystems in the Zao mountains (Japan). All the data-collection missions took place in autumn so the plants present distinctive seasonal coloration. Patches from single trees and bushes were manually extracted from the collected orthomosaics. Subsequently, Deep Learning image-classification networks were used to automatically determine the species of each tree or bush and estimate biodiversity. Both Convolutional Neural Networks (CNNs) and Transformer-based models were considered (ResNet, RegNet, ConvNeXt, and SwinTransformer). To measure and estimate biodiversity, we relied on the Gini–Simpson Index, the Shannon–Wiener Index, and Species Richness. We present two separate scenarios for evaluating the readiness of the technology for practical use: the first scenario uses a subset of the data with five species and a testing set that has a very similar percentage of each species to those present in the training set. The models studied reach very high performances with over 99 Accuracy and 98 F1 Score (the harmonic mean of Precision and Recall) for image classification and biodiversity estimates under 1% error. The second scenario uses the full dataset with nine species and large variations in class balance between the training and testing datasets, which is often the case in practical use situations. The results in this case remained fairly high for Accuracy at 90.64% but dropped to 51.77% for F1 Score. The relatively low F1 Score value is partly due to a small number of misclassifications having a disproportionate impact in the final measure, but still, the large difference between the Accuracy and F1 Score highlights the complexity of finely evaluating the classification results of Deep Learning Networks. Even in this very challenging scenario, the biodiversity estimation remained with relatively small (6–14%) errors for the most detailed indices, showcasing the readiness of the technology for practical use. [ABSTRACT FROM AUTHOR]

A binary porous material of SiO2 and SiO2–modified expanded graphite (MEGR) was simultaneously prepared based on a low-cost and template-free approach in which a commercially abundant sodium silicate was used as a SiO2 precursor in the presence of expanded graphite (EGR). The polycondensation and excessive aggregation of SiO2 on the surfaces of EGR enabled the formation of highly interconnected porous networks. Besides, the hydrophilicity of MEGR was improved due to the presence of multiple silanol groups from anchored SiO2. The SiO2/MEGR was then used as a binary porous support for the adsorption and stabilization of polyethylene glycol (PEG), forming composite phase change materials (CPCMs) for thermal energy storage. The prepared PEG/SiO2/MEGR CPCMs were investigated for thermal properties with varying PEG content (50–90%) and a fixed SiO2:MEGR ratio of 75:25. The presence of MEGR with desirable porous structure and improved hydrophilicity contributed to the PEG adsorption capacity of SiO2/MEGR. Indeed, the loading of PEG was improved from 60% of pristine SiO2 to 80% of SiO2/MEGR. In addition, the thermal conductivity of the 80% PEG/SiO2/MEGR composite was enhanced to 2.865 W/m K, which was 11.2 times greater than that of pure PEG. Furthermore, the PEG/SiO2/MEGR composite retained high thermal stability and excellent thermal reliability after 500 melting/crystallization cycles. The facile preparation and high thermal performance make the PEG/SiO2/MEGR composite competitive for thermal energy storage applications. [ABSTRACT FROM AUTHOR]

The focus of this study was to examine antisolvent effects, which hold significance in particulate processes, such as crystallization and precipitation. In the first section, an experimental investigation revealed that C1–C4 primary alcohols significantly reduced the solubility of potassium dihydrogen phosphate (KDP) in water. The solid–liquid equilibria of KDP solutions were determined using an innovative polythermal method, demonstrating time and labor efficiency compared to the traditional isothermal method while maintaining solubility determination quality. This achievement established an efficient tool for high-throughput solvent screening, a crucial aspect of particulate process development. In addition to the experimental approach, in the second part, the influence of these alcohols on KDP solubility was analyzed using the eNRTL thermodynamics model. The model's estimated parameters confirmed that the addition of these alcohols induced strong non-ideal behavior in the solutions, altered interactions between solute species and solvent components, and reduced KDP solubility. Under the effects of these alcohols, KDP solubility generally increased with the length of the alkyl chain in the added alcohols, although methanol deviated from this observation. Furthermore, the present work also discussed the limitation of the well-known Bromley's equation, particularly when applied for KDP in alcohol–water mixed solvents. Consequently, binary and ternary systems consisting of KDP, water, and C1–C4 primary alcohols were successfully modeled using eNRTL. Furthermore, it was determined that the obtained model was insufficient for quaternary systems with a higher alcohol content, particularly when high-order interactions were neglected as in the cases of binary and ternary systems. In short, these investigated alcohols have potential for future applications in the design of particulate processes, with a particular emphasis on antisolvent crystallization. [ABSTRACT FROM AUTHOR]

This research is based on expectancy theory to investigate "internal attraction" and "external attraction" and integrate "belief" into the research framework that motivates lecturers to publish their research. Findings of regression analysis from a sample of 475 lecturers in business and economics have revealed that lecturers are motivated by both "internal attraction" and "external attraction"; however, the impact of "internal attraction" is stronger than that of "external attraction" on international publication but weaker on national publication. Belief is found not to influence motivation for national research publications, but negatively influences motivation for international research publications. The research results also reveal that the motivation for international research publications of professors and associate professors is higher than the motivation of other lecturers, irrespective of age and gender. Based on the research results, proper implications for policies to foster motivation for international and national research publications have been suggested for developing countries with constant changes in science and education. [ABSTRACT FROM AUTHOR]