Sketch-to-Architecture: Generative AI-aided Architectural Design

Recently, the development of large-scale models has paved the way for various interdisciplinary research, including architecture. By using generative AI, we present a novel workflow that utilizes AI models to generate conceptual floorplans and 3D models from simple sketches, enabling rapid ideation and controlled generation of architectural renderings based on textual descriptions. Our work demonstrates the potential of generative AI in the architectural design process, pointing towards a new direction of computer-aided architectural design. Our project website is available at: https://zrealli.github.io/sketch2arcComment: Pacific Graphics 2023, accepted as Poste

Prediction of Anthracnose Risk in Large-Leaf Tea Trees Based on the Atmospheric Environmental Changes in Yunnan Tea Gardens—Cox Regression Model and Machine Learning Model

Crop diseases pose a major threat to agricultural production, quality, and sustainable development, highlighting the importance of early disease risk prediction for effective disease control. Tea anthracnose can easily occur in Yunnan under high-temperature and high-humidity environments, which seriously affects the ecosystem of tea gardens. Therefore, the establishment of accurate, non-destructive, and rapid prediction models has a positive impact on the conservation of biodiversity in tea plantations. Because of the linear relationship between disease occurrence and environmental conditions, the growing environmental conditions can be effectively used to predict crop diseases. Based on the climate data collected by Internet of Things devices, this study uses LASSO-COX-NOMOGRAM to analyze the expression of tea anthracrum to different degrees through Limma difference analysis, and it combines Cox single-factor analysis to study the influence mechanism of climate and environmental change on tea anthracrum. Modeling factors were screened by LASSO regression, 10-fold cross-validation and Cox multi-factor analysis were used to establish the basis of the model, the nomogram prediction model was constructed, and a Shiny- and DynNOM-visualized prediction system was built. The experimental results showed that the AUC values of the model were 0.745 and 0.731 in the training set and 0.75 and 0.747 in the verification set, respectively, when the predicted change in tea anthracnose disease risk was greater than 30% and 60%, and the calibration curve was in good agreement with the ideal curve. The accuracy of external verification was 83.3% for predicting tea anthracnose of different degrees. At the same time, compared with the traditional prediction method, the method is not affected by the difference in leaf background, which provides research potential for early prevention and phenotypic analysis, and also provides an effective means for tea disease identification and harm analysis

Source of soil water in cold regions based on stable isotope tracers

Soil water plays a key role in vegetation growth and ecological stability in cold regions. The soil water sources in the Qilian Mountains of the northeastern Tibetan Plateau have been quantified based on stable isotope data (δ2H and δ18O) of 1913 samples. The results indicated that δ18O of soil water ranged from ˗14.13–11.21‰, with an average of ˗6.78‰, and on a spatial scale, it increased gradually from the southeast to the northwest, where it became more positive. The stable isotopes of soil water are affected by evaporation, leading to a lower slope and interception of the evaporation line: δ2H = 3.23 δ18O–32.51 (R2 = 0.71, p < 0.01). The relationship between soil water and other water bodies showed that the soil water was mainly replenished by precipitation and ground ice meltwater. End-member mixing analysis (EMMA) showed that precipitation and ground ice accounted for approximately 91 % and 9 % of soil water, respectively, during heavy ablation. In the 0–20 cm, 20–40 cm, 40–60 cm, 60–80 cm, and 80–100 cm soil layers, the contributions of ground ice meltwater to soil water were 0 %, 3 %, 7 %, 13 %, and 18 %, respectively. Notably, the contribution of ground ice meltwater to soil water gradually increased with elevation, whereas that of precipitation decreased. This study has obtained relevant data for the cold region, where it is typically difficult to conduct observations and sampling. More importantly, it analyzes the source and influencing factors of soil water in cold regions using the stable isotope method, which is of great significance for the study of the water cycle in cold regions

PEEK for Oral Applications: Recent Advances in Mechanical and Adhesive Properties

Polyetheretherketone (PEEK) is a thermoplastic material widely used in engineering applications due to its good biomechanical properties and high temperature stability. Compared to traditional metal and ceramic dental materials, PEEK dental implants exhibit less stress shielding, thus better matching the mechanical properties of bone. As a promising medical material, PEEK can be used as implant abutments, removable and fixed prostheses, and maxillofacial prostheses. It can be blended with materials such as fibers and ceramics to improve its mechanical strength for better clinical dental applications. Compared to conventional pressed and CAD/CAM milling fabrication, 3D-printed PEEK exhibits excellent flexural and tensile strength and parameters such as printing temperature and speed can affect its mechanical properties. However, the bioinert nature of PEEK can make adhesive bonding difficult. The bond strength can be improved by roughening or introducing functional groups on the PEEK surface by sandblasting, acid etching, plasma treatment, laser treatment, and adhesive systems. This paper provides a comprehensive overview of the research progress on the mechanical properties of PEEK for dental applications in the context of specific applications, composites, and their preparation processes. In addition, the research on the adhesive properties of PEEK over the past few years is highlighted. Thus, this review aims to build a conceptual and practical toolkit for the study of the mechanical and adhesive properties of PEEK materials. More importantly, it provides a rationale and a general new basis for the application of PEEK in the dental field

Residues of organochlorine pesticides in near shore waters of LaiZhou Bay and JiaoZhou Bay, Shandong Peninsula, China

The extent of organochlorine pesticides (OCPs) contamination in coastal waters around LaiZhou Bay and JiaoZhou Bay in Shandong Peninsula, northern China, was investigated. The areas around the two bays are both densely populated, thrived with intensive agriculture and industrial activities. Multi-techniques including GC-MS, GC-mu ECD coupled with chemical peak confirmation and strict QC procedures were used for the quantitative determination of 15 OCPs including alpha, beta, gamma and delta isomers of hexachlorocyclohexane (HCH), pentachloronitrobenzene (PCNB), heptachlor, aldrin, endosulfan, p,p '-DDE, dieldrin, endrin, p,p '-DDD, o,p '-DDT, p,p '-DDT and methoxychlor. The survey results show that contaminations by OCP residues remain widespread in the areas, but the averaged concentration levels are all below the regulatory limits, e.g., CMC limits (acute criterion values) specified in US Environmental Protection Agency (USEPA) and China national standards. Average concentration of OCPs in water samples were from undetectable to 3.8 ng l(-1) in LaiZhou Bay and from 0.1 to 3.9 ng l(-1) in JiaoZhou Bay, respectively. A comparison between the current and historical data shows a rapidly decreasing trend of OCPs over the past twenty years in the study areas. (C) 2006 Elsevier Ltd. All rights reserved

Determination of four major saponins in the seeds of Aesculus chinensis Bunge using accelerated solvent extraction followed by high-performance liquid chromatography and electrospray-time of flight mass spectrometry

Anew method based on accelerated solvent extraction (ASE) followed by a reliable high-performance liquid chromatography-diode array detector (HPLC-DAD) and positive ion electrospray-time of flight mass spectrometry (ESI-TOF/MS) analysis has been developed for the characterization and quantification of four major saponins in extracts of the seeds of Aesculus chinensis Bunge (Semen Aesculi). The saponins escin la, escin Ib, isoescin la and isoescin Ib were extracted from seeds of A. chinesis Bunge via ASE, and the operational parameters of ASE were optimized, such as extraction solvent, extraction temperature, static extraction time and extraction cycles. The optimized procedure employed 70% MeOH as extraction solvent, 120 degrees C of extraction temperature, 7 min of static extraction time, 60% flush volume and the extraction recoveries of the four compounds were nearly to 100% for two cycles. The HPLC conditions are as follows: SinoChrom ODS BP C-18 (4.6 mm x 200 mm, 5 mu m) column, acetonitrile and 0.10% phosphoric acid solution as mobile phase, flow rate is 1.0 mL min(-1), detection length of UV is 203 nm, injection volume is 10 mu L. The results indicated that the developed HPLC method is simple, sensitive and reliable for the determination of four major saponins in seeds of A. chinesis Bunge with a good linearity (r(2) > 0.9994), precision (relative standard deviation (R.S.D.) < 1.5%) and the recovery ranges of 95.2-97.3%. The limits of detection (LOD) of the four compounds were in the range of 0.40-0.75 mu g mL(-1). This assay can be readily utilized as a quality control method for Semen Aesculi and other related medicinal plants. (c) 2007 Elsevier B.V. All rights reserved

Assisted Tea Leaf Picking: The Design and Simulation of a 6-DOF Stewart Parallel Lifting Platform

The 6-DOF Stewart parallel elevation platform serves as the platform for mounting the tea-picking robotic arm, significantly impacting the operational scope, velocity, and harvesting precision of the robotic arm. Utilizing the Stewart setup, a parallel elevation platform with automated lifting and leveling capabilities was devised, ensuring precise halts at designated elevations for seamless harvesting operations. The effectiveness of the platform parameter configuration and the reasonableness of the posture changes were verified. Firstly, the planting mode and growth characteristics of Yunnan large-leaf tea trees were analyzed to determine the preset path, posture changes, and mechanism stroke of the Stewart parallel lifting platform, thereby determining the basic design specifications of the platform. Secondly, a 3D model was established using SolidWorks, a robust adaptive PD control model was built using MATLAB for simulation, and dynamic calculations were carried out through data interaction in Simulink and ADAMS. Finally, the rationality of the lifting platform design requirements was determined based on simulation data, a 6-DOF Stewart parallel lifting platform was manufactured, and a motion control system was built for experimental verification according to the design specifications and simulation data. The results showed that the maximum deviation angle around the X, Y, and Z axes was 10°, the maximum lifting distance was 15 cm, the maximum load capacity was 60 kg, the platform response error was within ±0.1 mm, and the stable motion characteristics reached below the millimeter level, which can meet the requirements of automated operation of the auxiliary picking robotic arm

Rapid finding and quantification of the major antioxidant in water extracts of three marine drug organisms from China by online HPLC-DAD/MS-DPPH

National Natural Scientific Foundation of China [20905017, 41076108]; Key Project of Commonwealth Programs for Marine Science [201005034-3, 200805039]; Primary Scientific Foundation of FIO [2008T32, 2010G25]; Science Foundation for Youth Scholars of State Oceanic Administration [2010140]; Ministry of Science and Technology of China [2007AA092001-10]; Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of SciencesA method based on high-performance liquid chromatography (HPLC) with diode array detector coupled with electrospray ionisation-mass spectrometry and an online detection system for radical scavenging was established and used to rapidly find and quantify antioxidant compounds in the water extracts of Hippocampus japonicus Kaup, Hippocampus kuda Bleeker and Syngnathus acus Linnaeus. The online screening results revealed the presence of one major radical scavenging compound identified as hypoxanthine by comparison of mass data and retention time with the standard. Subsequently, the developed HPLC method was applied to quantify hypoxanthine in different H. japonicus, H. kuda and S. acus samples. The results indicated that the developed HPLC method is simple and reliable for the quantification of hypoxanthine with a detection limit at 0.002 mu gmL(-1), and a high recovery from 96.3% to 102.1%. This method provides a powerful tool for rapid identification and quantification of free radical scavenging compounds in complex marine natural products

Edge Device Detection of Tea Leaves with One Bud and Two Leaves Based on ShuffleNetv2-YOLOv5-Lite-E

In order to solve the problem of an accurate recognition of tea picking through tea picking robots, an edge device detection method is proposed in this paper based on ShuffleNetv2-YOLOv5-Lite-E for tea with one bud and two leaves. This replaces the original feature extraction network by removing the Focus layer and using the ShuffleNetv2 algorithm, followed by a channel pruning of YOLOv5 at the neck layer head, thus achieving the purpose of reducing the model size. The results show that the size of the improved generated weight file is 27% of that of the original YOLOv5 model, and the mAP value of ShuffleNetv2-YOLOv5-Lite-E is 97.43% and 94.52% on the pc and edge device respectively, which are 1.32% and 1.75% lower compared to that of the original YOLOv5 model. The detection speeds of ShuffleNetv2-YOLOv5-Lite-E, YOLOv5, YOLOv4, and YOLOv3 were 8.6 fps, 2.7 fps, 3.2 fps, and 3.4 fps respectively after importing the models into an edge device, and the improved YOLOv5 detection speed was 3.2 times faster than that of the original YOLOv5 model. Through the detection method, the size of the original YOLOv5 model is effectively reduced while essentially ensuring recognition accuracy. The detection speed is also significantly improved, which is conducive to the realization of intelligent and accurate picking for future tea gardens, laying a solid foundation for the realization of tea picking robots

Prediction Model of Flavonoids Content in Ancient Tree Sun−Dried Green Tea under Abiotic Stress Based on LASSO−Cox

To investigate the variation in flavonoids content in ancient tree sun–dried green tea under abiotic stress environmental conditions, this study determined the flavonoids content in ancient tree sun−dried green tea and analyzed its correlation with corresponding factors such as the age, height, altitude, and soil composition of the tree. This study uses two machine−learning models, Least Absolute Shrinkage and Selection Operator (LASSO) regression and Cox regression, to build a predictive model based on the selection of effective variables. During the process, bootstrap was used to expand the dataset for single−factor and multi−factor comparative analyses, as well as for model validation, and the goodness−of−fit was assessed using the Akaike information criterion (AIC). The results showed that pH, total potassium, nitrate nitrogen, available phosphorus, hydrolytic nitrogen, and ammonium nitrogen have a high accuracy in predicting the flavonoids content of this model and have a synergistic effect on the production of flavonoids in the ancient tree tea. In this prediction model, when the flavonoids content was >6‰, the area under the curve of the training set and validation set were 0.8121 and 0.792 and, when the flavonoids content was >9‰, the area under the curve of the training set and validation set were 0.877 and 0.889, demonstrating good consistency. Compared to modeling with all significantly correlated factors (p AIC decreased by 32.534%. Simultaneously, a visualization system for predicting flavonoids content in ancient tree sun−dried green tea was developed based on a nomogram model. The model was externally validated using actual measurement data and achieved an accuracy rate of 83.33%. Therefore, this study offers a scientific theoretical foundation for explaining the forecast and interference of the quality of ancient tree sun−dried green tea under abiotic stress