By-example Synthesis of Compactly Stored Textures

AbstractThis paper proposes a novel by-example texture synthesis algorithm. Unlike most present algorithms whose synthesized textures of different sizes must be stored in memory until rendering, our results are compactly stored as paths of the generative graphs. This is achieved by adapting the graph-based synthesis framework to be suitable for arbitrary types of textures. Two synthesis processes are carried out successively to grow the texture strip by strip to the desired horizontal and vertical dimensions, each of which is cast as a constrained shortest path problem that can be solved efficiently by our proposed algorithm. The strips are formed by consecutive cuts which are precomputed in the preprocessing step, and are selected with a redesigned mechanism accounting for both global and local matching errors. The variances make the graph-based synthesis framework also applicable to a wide variety of textures besides architectural textures, and the synthesis quality for them is greatly improved

Practical and Ethical Challenges of Large Language Models in Education: A Systematic Scoping Review

Educational technology innovations leveraging large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content. While various innovations have been developed to automate a range of educational tasks (e.g., question generation, feedback provision, and essay grading), there are concerns regarding the practicality and ethicality of these innovations. Such concerns may hinder future research and the adoption of LLMs-based innovations in authentic educational contexts. To address this, we conducted a systematic scoping review of 118 peer-reviewed papers published since 2017 to pinpoint the current state of research on using LLMs to automate and support educational tasks. The findings revealed 53 use cases for LLMs in automating education tasks, categorised into nine main categories: profiling/labelling, detection, grading, teaching support, prediction, knowledge representation, feedback, content generation, and recommendation. Additionally, we also identified several practical and ethical challenges, including low technological readiness, lack of replicability and transparency, and insufficient privacy and beneficence considerations. The findings were summarised into three recommendations for future studies, including updating existing innovations with state-of-the-art models (e.g., GPT-3/4), embracing the initiative of open-sourcing models/systems, and adopting a human-centred approach throughout the developmental process. As the intersection of AI and education is continuously evolving, the findings of this study can serve as an essential reference point for researchers, allowing them to leverage the strengths, learn from the limitations, and uncover potential research opportunities enabled by ChatGPT and other generative AI models

Separating and characterizing functional alkane degraders from crude-oil-contaminated sites via magnetic nanoparticle-mediated isolation

Uncultivable microorganisms account for over 99% of all species on the planet, but their functions are yet not well characterized. Though many cultivable degraders for n-alkanes have been intensively investigated, the roles of functional n-alkane degraders remain hidden in the natural environment. This study introduces the novel magnetic nanoparticle-mediated isolation (MMI) technology in Nigerian soils and successfully separates functional microbes belonging to the families Oxalobacteraceae and Moraxellaceae, which were dominant and responsible for alkane metabolism in situ. The alkR-type n-alkane monooxygenase genes, instead of alkA- or alkP-type, were the key functional genes involved in the n-alkane degradation process. Further physiological investigation via a BIOLOG PM plate revealed some carbon (Tween 20, Tween 40 and Tween 80) and nitrogen (tyramine, L-glutamine and D-aspartic acid) sources promoting microbial respiration and n-alkane degradation. With further addition of promoter carbon or nitrogen sources, the separated functional alkane degraders significantly improved n-alkane biodegradation rates. This suggests that MMI is a promising technology for separating functional microbes from complex microbiota, with deeper insight into their ecological functions and influencing factors. The technique also broadens the application of the BIOLOG PM plate for physiological research on functional yet uncultivable microorganisms

Modification of PEDOT: PSS to enhance device efficiency and stability of the Quasi-2D perovskite light-emitting diodes

Poly(3,4-ethylenedioxy thiophene): poly(styrene sulfonate) (PEDOT: PSS) is a hole transport layer (HTL) that is often employed in a diverse array of optoelectronic devices, such as perovskite solar cells and perovskite light-emitting diodes (PeLEDs). By simply doping lithium fluoride (LiF) into PEDOT: PSS, we demonstrate that the electrical characteristics of the HTL can be modified. Especially in quasi-2D perovskite LEDs, the crystallization process is regulated by LiF modification, leading to reduced phase impurity defects and improved carrier transport in the perovskite emission layer. Therefore, the luminance and efficiency of the quasi-2D PeLEDs are notably enhanced. The optimized PeLED with LiF modification exhibits a peak luminance of 21517 cd m−2 with 317% higher than the standard PeLED; and a high current efficiency of 39.8 cd A−1 with 237% higher than the standard PeLED. Moreover, the device stability is also improved with a nearly doubled half lifetime due to the reduced phase impurities. The work demonstrates a facile yet effective method for altering PEDOT: PSS hole transport layer, emphasizing the critical role of the underneath layer in the crystallization of quasi-2D perovskites

Incremental high quality probabilistic roadmap construction for robot path planning

A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Doctor of PhilosophyIn robotics, path planning refers to the process of establishing paths for robots to move from initial positions to goal positions without colliding into any obstacle within specified environments. Constructing roadmaps and searching for paths in the roadmaps is one of the most commonly used methodologies adopted in path planning. However, most sampling-based path planners focus on improving the speed of constructing roadmaps without taking into account the quality. Therefore, they often produce poor-quality roadmaps. Poor-quality roadmaps can cause problems, such as time-consuming path searches, poor quality path production, and even failure of the searching. This research aims to develop a novel sampling-based path planning algorithm which is able to incrementally construct high-quality roadmaps while answering path queries for robots with many degrees of freedom. A novel K-order surrounding roadmap (KSR) concept is proposed in this research based on a thorough investigation into the criteria of high-quality roadmaps, including the criteria themselves and the relationships between them. A KSR contains K useful cycles. There exist a value T for which we can say, with confidence, that the KSR is a high quality roadmap when K=T. A new sampling-based path planning algorithm, known as the KSR path planner that is able to construct a roadmap incrementally while answering path queries, is also developed. The KSR path planner can be employed to answer path queries without requiring any pre-processing. The planner grows trees from the initial and goal III configurations of a path query and connects these two trees to obtain a path. The path planner retains useful vertices of the trees and uses these to construct the roadmap and adds useful cycles to the existing roadmap in order to improve the quality. The roadmap constructed can be used to answer further queries. With the KSR path planner algorithm, there is no need to calculate the value of K to construct a high quality roadmap in advance. The quality of the roadmap improves as the KSR path planner answer queries until the roadmap is able to answer any path queries and no further useful cycles can be added into the roadmap. If the number of path queries is infinite, a high quality KSR can be constructed. The novelty of this KSR path planner is twofold. Firstly, it employs a vertex category classifier to understand local environments where roadmap vertices reside. The classifier is developed using a decision tree method. The classifier is able to classify vertices in a roadmap based on the region information stored in the vertices and their neighbours within a certain distance. The region information stored in the vertices is obtained while the edges connecting the vertices are added to the roadmap. Therefore, employing the vertex category classifier does not require much additional execution time. Secondly, the KSR path planner selects suitable developed strategies to prune the existing roadmap and add useful cycles according to the identified local environments where the vertices reside to improve the quality of the existing roadmap. Experimental results show that the KSR path planner can construct a roadmap and improve the quality of the roadmap incrementally while answering path queries until the roadmap can answer all the path queries without any pre-processing stage. The roadmap constructed by the KSR path planner then achieves better quality than the roadmaps constructed by Reconfigurable Random Forest (RRF) path planner and traditional probabilistic roadmap (PRM) path planner

The complete chloroplast genome of sweet tea (Lithocarpus polystachyus)

Lithocarpus polystachyus, also known as the sweet tea, is a plant of the family Fagaceae. It is widely distributed in southern China, India, and Thailand. The chloroplast (cp) genome of L. polystachyus is 161,217 bp in size containing 122 unique genes, including eight rRNA genes, 37 tRNA genes, and 77 protein-coding genes (PCGs). Phylogenetic analysis exhibited that L. polystachyus was most related to L. balansae

Transcriptional Factors Mediating Retinoic Acid Signals in the Control of Energy Metabolism

Retinoic acid (RA), an active metabolite of vitamin A (VA), is important for many physiological processes including energy metabolism. This is mainly achieved through RA-regulated gene expression in metabolically active cells. RA regulates gene expression mainly through the activation of two subfamilies in the nuclear receptor superfamily, retinoic acid receptors (RARs) and retinoid X receptors (RXRs). RAR/RXR heterodimers or RXR/RXR homodimers bind to RA response element in the promoters of RA target genes and regulate their expressions upon ligand binding. The development of metabolic diseases such as obesity and type 2 diabetes is often associated with profound changes in the expressions of genes involved in glucose and lipid metabolism in metabolically active cells. RA regulates some of these gene expressions. Recently, in vivo and in vitro studies have demonstrated that status and metabolism of VA regulate macronutrient metabolism. Some studies have shown that, in addition to RARs and RXRs, hepatocyte nuclear factor 4α, chicken ovalbumin upstream promoter-transcription factor II, and peroxisome proliferator activated receptor β/δ may function as transcriptional factors mediating RA response. Herein, we summarize current progresses regarding the VA metabolism and the role of nuclear receptors in mediating RA signals, with an emphasis on their implication in energy metabolism

Elevated CO2 and O3 Levels Influence the Uptake and Leaf Concentration of Mineral N, P, K in Phyllostachys edulis (Carrière) J.Houz. and Oligostachyum lubricum (wen) King f.

Rising CO2 and O3 concentrations significantly affect plant growth and can alter nutrient cycles. However, the effects of elevated CO2 and O3 concentrations on the nutrient dynamics of bamboo species are not well understood. In this study, using open top chambers (OTCs), we examined the effects of elevated CO2 and O3 concentrations on leaf biomass and nutrient (N, P, and K) dynamics in two bamboo species, Phyllostachys edulis (Carrière) J.Houz. and Oligostachyum lubricum (wen) King f. Elevated O3 significantly decreased leaf biomass and nutrient uptake of both bamboo species, with the exception of no observed change in K uptake by O. lubricum. Elevated CO2 increased leaf biomass, N and K uptake of both bamboo species. Elevated CO2 and O3 simultaneously had no significant influence on leaf biomass of either species but decreased P and N uptake in P. edulis and O. lubricum, respectively, and increased K uptake in O. lubricum. The results indicate that elevated CO2 alleviated the damage caused by elevated O3 in the two bamboo species by altering the uptake of certain nutrients, which further highlights the potential interactive effects between the two gases on nutrient uptake. In addition, we found differential responses of nutrient dynamics in the two bamboo species to the two elevated gases, alone or in combination. These findings will facilitate the development of effective nutrient management strategies for sustainable management of P. edulis and O. lubricum under global change scenarios

The prediction of the evolution of grain size of land-gear forging during the die-forging process

The land-gear forgings are the most important structure parts, made of high strength steel 300M. Because of the bad service environment, the microstructure and performance of the part are very strict requirements. In this article the evolution of grain size during the die-forging process is predicted, the volume fraction of dynamic recrystallization, grain refinement and development of grain size in-homogeneity, and the affection of billet shape on the grain size distribution are analyzed. The simulated results show that the grain size differences on the different billet positions are very large at the deformation beginning. But in final forging stage, the difference of the average grain size is smaller. At some center zones of the part the maximum difference of grain size is bigger than 100 μm

pH-Dependent Adsorption of Peptides on Montmorillonite for Resisting UV Irradiation

Ultraviolet (UV) irradiation is considered an energy source for the prebiotic chemical synthesis of life’s building blocks. However, it also results in photodegradation of biology-related organic compounds on early Earth. Thus, it is important to find a process to protect these compounds from decomposition by UV irradiation. Herein, pH effects on both the adsorption of peptides on montmorillonite (MMT) and the abilities of peptides to resist UV irradiation due to this adsorption were systematically studied. We found that montmorillonite (MMT) can adsorb peptides effectively under acidic conditions, while MMT-adsorbed peptides can be released under basic conditions. Peptide adsorption is positively correlated with the length of the peptide chains. MMT’s adsorption of peptides and MMT-adsorbed peptide desorption are both rapid-equilibrium, and it takes less than 30 min to reach the equilibrium in both cases. Furthermore, compared to free peptides, MMT-adsorbed peptides under acidic conditions are well protected from UV degradation even after prolonged irradiation. These results indicate amino acid/peptides are able to concentrate from aqueous solution by MMT adsorption under low-pH conditions (concentration step). The MMT-adsorbed peptides survive under UV irradiation among other unprotected species (storage step). Then, the MMT-adsorbed peptides can be released to the aqueous solution if the environment becomes more basic (releasing step), and these free peptides are ready for polymerization to polypeptides. Hence, a plausible prebiotic concentration–storage–release cycle of amino acids/peptides for further polypeptide synthesis is established