Facile synthesis of quinoxaline-2-thiol and quinoxaline from α-oxo sulfines and o-aryl diamines

A series of quinoxaline-2-thiol and quinoxaline were prepared in moderate to good yields from various phenacyl sulfoxides bearing 1-methyl-1H-tetrazole and o-aryl diamines. The proposed reaction mechanism involves generation of sulfines from the phenacyl sulfoxides bearing 1-methyl-1H-tetrazole through thermolysis elimination. Then, site-selectively carbophilic addition of sulfines by o-aryl diamines, followed by elimination, intramolecular nucleophilic addition and dehydration condensation. The current method provides a direct and simple strategy for the preparation of quinoxaline-2-thiols and quinoxalines.

Active Classification With Uncertainty Comparison Queries

Noisy pairwise comparison feedback has been incorporated to improve the overall query complexity of interactively learning binary classifiers. The positivity comparison oracle is extensively used to provide feedback on which is more likely to be positive in a pair of data points. Because it is impossible to determine accurate labels using this oracle alone without knowing the classification threshold, existing methods still rely on the traditional explicit labeling oracle, which explicitly answers the label given a data point. The current method conducts sorting on all data points and uses explicit labeling oracle to find the classification threshold. However, it has two drawbacks: (1) it needs unnecessary sorting for label inference and (2) it naively adapts quick sort to noisy feedback. In order to avoid these inefficiencies and acquire information of the classification threshold at the same time, we propose a new pairwise comparison oracle concerning uncertainties. This oracle answers which one has higher uncertainty given a pair of data points. We then propose an efficient adaptive labeling algorithm to take advantage of the proposed oracle. In addition, we address the situation where the labeling budget is insufficient compared to the data set size. Furthermore, we confirm the feasibility of the proposed oracle and the performance of the proposed algorithm theoretically and empirically.

GO WIDE TO GO DEEP: AFFORDABLE, REPLICABLE ROBOTIC MINIRHIZOTRON SAMPLING FOR PHENOLOGY STUDIES

Minirhizotrons (paired camera systems and buried observatories) are the best current method to make repeatable measurements of fine roots in the field. Automating the technique is also the only way to gather high resolution data necessary for comparison with phenology-relevant above-ground remote sensing, and, when appropriately validated, to assess with high temporal resolution belowground biomass, which can support carbon budgets estimates. Minirhizotron technology has been available for half a century but there are many challenges to automating the technique for global change experiments. Instruments must be cheap enough to replicate on field scales given their shallow field of view, and automated analysis must both be robust to changeable soil and root conditions because ultimately, image properties extracted from minirhizotrons must have biological meaning. Both digital photography and computer technology are rapidly evolving, with huge potential for generating belowground data from images using modern technological advantages. Here we demonstrate a homemade automatic minirhizotron scheme, built with off-the-shelf parts and sampling every two hours, which we paired with a neural network-based image analysis method in a proof-of-concept mesocosm study. We show that we are able to produce a robust daily timeseries of root cover dynamics. The method is applied at the same model across multiple instruments demonstrating good reproducibility of the measurements and a good pairing with an above-ground vegetation index and root biomass recovery through time. We found a sensitivity of the root cover we extracted to soil moisture conditions and time of day (potentially relating to soil moisture), which may only be an issue with high resolution automated imagery and not commonly reported as encountered when training neural networks on traditional, time-distinct minirhizotron studies. We discuss potential avenues for dealing with such issues in future field applications of such devices. If such issues are dealt with to a satisfactory manner in the field, automated timeseries of root biomass and traits from replicated instruments could add a new dimension to phenology understanding at ecosystem level by understanding the dynamics of root properties and traits.

MSIS: Multispectral Instance Segmentation Method for Power Equipment

Infrared image of power equipment is widely used in power equipment fault detection, and segmentation of infrared images is an important step in power equipment thermal fault detection. Nevertheless, since the overlap of the equipment, the complex background, and the low contrast of the infrared image, the current method still cannot complete the detection and segmentation of the power equipment well. To better segment the power equipment in the infrared image, in this paper, a multispectral instance segmentation (MSIS) based on SOLOv2 is designed, which is an end-to-end and single-stage network. First, we provide a novel structure of multispectral feature extraction, which can simultaneously obtain rich features in visible images and infrared images. Secondly, a module of feature fusion (MARFN) has been constructed to fully obtain fusion features. Finally, the combination of multispectral feature extraction, the module of feature fusion (MARFN), and instance segmentation (SOLOv2) realize multispectral instance segmentation of power equipment. The experimental results show that the proposed MSIS model has an excellent performance in the instance segmentation of power equipment. The MSIS based on ResNet-50 has 40.06% AP.

Experimental Investigation on Waste Utilization of Steel fiber and SCBA in Concrete with Partially Replacement of Cement

This work presents the determination of the mechanical properties (compression, split tensile and flexural) of the specimens (cubes, cylinders and beams). The test specimens are M60 high strength concrete which includes ground granulated blast furnace slag (0%,10%, 20%, 30% and 40%) and fly ash (0% 10%, 20%, 30% and 40%) to obtain the desired resistances and properties. Finally, we used granulated blast furnace in different percentages as cement and concrete were replaced. We prepared concrete cubes, beams and cylinders and stored them for a 28-day cure. The tests are performed after 7, 21 and 28 days. To achieve the desired strength that cannot be achieved with conventional concrete and the current method, a large number of test mixtures with different percentages of fly ash and different percentages of ground granulated blast furnace slag are needed to select the combination of materials. Keywords: Fly Ash (FA), Ground Granulated Blast Furnace Slag (GGBS), Compressive strength, Tensile strength, Flexural strength, Ordinary Portland Cement (OPC)

Secondary Exfoliation of Electrolytic Graphene Oxide by Ultrasound Assisted Microwave Technique

Scalable production of large size and high quality graphene is an important prerequisite to fully realize its commercial applications. Herein, we propose a high-efficient route for preparing few-layer graphene. The secondary exfoliation of unexfoliated graphite flakes from electrochemical exfoliation was achieved by using ultrasonication assisted microwave exfoliation technique. The results show that the as-prepared sample has a C/O of 15.2, a thickness of about 1 nm and a transverse dimension of over 100 nm, and the Raman spectrogram shows low defects upon reduction of the sample. These results suggest that electrolytic graphene can be exfoliated to form graphene nanosheets under ultrasonic-assisted microwave technology, thus indicating that the current method has great potential for synthesizing high-quality graphene at an industrial-scale.

Experimental Investigation on Filtration and Cooling Effect of Kitchen Hood Ventilation System from Water-Mist Recirculation Spray: Water-Mist Spray Cycle

A Water-mist spray system in several heavy-duty kitchen hood canopies is installed to efficiently control the high heat loads and grease emissions produced from the cooking process and for safety purposes. The main purpose of this study is to reduce water consumption by introducing the water-mist recirculation system to replace the current method water-mist system since it is working as water loss. A standard ASTM 2519 and UL 1046 full-scaled experiment is developed in the laboratory. An existing Halton Europe/Asian water-mist operating system is adopted in this study. Twelve (12) cycles (at 24 hours of water-mist activation) have been studied to determine the maximum water-mist activation cycle. The data are collected at two (2) hours water-mist activation at every water-mist recirculation cycle. The water-mist spray fluids viscosity is 0.7 cP from fresh water until the 4th cycle (8 hours water-mist spray) and increase 14.29% (0.8 cP) at the 5th cycle to the 12th cycle. On average, the difference in gas emissions percentage for CO concentration between fresh water until the 4th cycle is 10.81 – 18.92% while the CO2 concentration is 12.33 – 18.22%. On average, the difference in cooling effects percentage for ducting temperature between fresh water until the 4th cycle is 5.55% while the hood temperature is 2.33%. From the study, the water-mist recirculation system could save up to 611,667 litres per year and 466,798.5 litres per year water for all U.S, European, and Asian kitchen hood designs per hood length. By adopting the new water-mist recirculation system to the current water-mist kitchen hood, the water operational cost for water successfully reduced to RM 4,889.63 per year and RM 6,977.86 per year for U.S design and European or Asian design per hood length respectively. The water-mist recirculation system has great potential to improve the current water-mist system for the commercial kitchen hood.

Anime4You: An Intelligent Analytical Framework for Anime Recommendation and Personalization using AI and Big Data Analysis

In recent years the popularity of anime has steadily grown. Similar to other forms of media consumers often face a pressing issue: “What do I watch next?”. In this study, we thoroughly examined the current method of solving this issue and determined that the learning curve to effectively utilize the current solution is too high. We developed a program to ensure easier answers to the issue. The program uses a Python-based machine learning algorithm from ScikitLearn and data from My Animelist to create an accurate model that delivers what consumers want, good recommendations [9]. We also carried out different experiments with several iterations to study the difference in accuracy when applying different factors. Through these tests, we have successfully created a reliable Support vector machine model with 57% accuracy in recommending users what to watch.