驱动大功率LED之道

本文将讨论对驱动新型LFD的恒定电流源（而不是电压源）的要求,并介绍驱动高亮度LED的各种方法本文将介绍LED可采用的不同拓扑和配置,以及用开关稳压器和控制器驱动LED的方法,并讨论降压和升压拓扑以及这两种设计之间的折衷策略,同时还会介绍参考设计以及驱动这些大功率LED的几种解决方案     

Modeling approaches and performance for estimating personal exposure to household air pollution: A case study in Kenya

This study assessed the performance of modeling approaches to estimate personal exposure in Kenyan homes where cooking fuel combustion contributes substantially to household air pollution (HAP). We measured emissions (PM_2.5, black carbon, CO); household air pollution (PM_2.5, CO); personal exposure (PM_2.5, CO); stove use; and behavioral, socioeconomic, and household environmental characteristics (eg, ventilation and kitchen volume). We then applied various modeling approaches: a single-zone model; indirect exposure models, which combine person-location and area-level measurements; and predictive statistical models, including standard linear regression and ensemble machine learning approaches based on a set of predictors such as fuel type, room volume, and others. The single-zone model was reasonably well-correlated with measured kitchen concentrations of PM_2.5 (R² = 0.45) and CO (R² = 0.45), but lacked precision. The best performing regression model used a combination of survey-based data and physical measurements (R² = 0.76) and a root mean-squared error of 85 µg/m³, and the survey-only-based regression model was able to predict PM2.5 exposures with an R² of 0.51. Of the machine learning algorithms evaluated, extreme gradient boosting performed best, with an R² of 0.57 and RMSE of 98 µg/m³.     

Transesterification of β ‐keto esters catalysed by transition metal complexes in a novel heterogeneous way

β‐keto esters have been successfully transesterified with primary, secondary, tertiary, allyl and alkynic alcohols by recyclable Mn(III) salen or vanadyl(IV) acetate catalysts realised while conducting the reaction deliberately for the first time in a novel heterogeneous media to afford good to excellent yields. This revised version was published online in July 2006 with corrections to the Cover Date.     

Catanionic Surfactant Vesicles as a New Platform for Probing Glycan–Protein Interactions

Glycomics lags substantially behind proteomics and genomics in its ability to decipher and synthesize complex glycans. The slow progress in deciphering glycan interactions at a molecular level is in large part due to the absence of a functional system to express, on a large scale, carbohydrates of known structure, in the context of a biologically relevant assay system. Here, the characterization of glycan‐functionalized catanionic surfactant vesicles (CVs) as a platform for glycan synthesis is described, and it is demonstrated that the resulting glycan‐functionalized CVs can serve as a scaffold for the interrogation of protein‐glycan interactions. It is demonstrated that Neisseria gonorrhoeae lipooligosaccharide (LOS) glycosyltransferase LgtE, an enzyme that catalyzes the addition of galactose onto a terminal glucose found on LOS, can be used to biochemically modify LOS or glucose functionalized CVs. CVs are characterized by differential lectin binding using flow cytometry. LgtE activity is measured on whole cells and LOS functionalized vesicles and found to have approximately the same biochemical properties. It is further demonstrated that CVs can be inkjet printed. This paper presents proof‐of‐concept that glycan‐functionalized catanionic vesicles can be used to create a high‐specificity and high‐throughput glycan array that will allow for the investigation of a variety of protein–glycan interactions.     

Plant leaf disease classification and damage detection system using deep learning models

Agriculture is the primary source of livelihood for about 70% of the rural population in India. The crop variety cultivated in India is very diverse. There are more than 500 crop varieties grown in India. Despite the technological advances, the agricultural practices are still manual and involve less automation than western countries. Most of the diseases affecting a plant will reflect the damage in the leaves. The diseases affecting the plant can thus be identified from the leaf images. This paper presents an automatic plant leaf damage detection and disease identification system. The first stage of the proposed method identifies the type of the disease based on the plant leaf image using DenseNet. The DenseNet model is trained on images categorized according to their nature, i.e., healthy and the type of the disease. This model is then used for testing new leaf images. The proposed DenseNet model produced a classification accuracy of 100%, with fewer images used during the training stage. The second stage identifies the damage in the leaf using deep learning-based semantic segmentation. Each RGB pixel value combination in the image is extracted, and supervised training is performed on the pixel values using the 1D Convolutional Neural Network (CNN). The trained model can detect the damage present in the leaves at a pixel level. Evaluation of the proposed semantic segmentation resulted in an accuracy of 97%. The third stage suggests a remedy for the disease based on the disease type and the damage state. The proposed method detects various defects in different plants in the experimental analysis, namely apple, grape, potato, and strawberry. The proposed model is compared with the existing techniques and obtained better performance in comparison with those methods.

     

Comparative Studies of Microwave and Sol–Gel-Assisted Combustion Methods of NiFe<Subscript>2</Subscript>O<Subscript>4</Subscript> Nanostructures: Synthesis,Structural, Morphological,Opto-magnetic,and Antimicrobial Activity

Nickel ferrite NiFe₂O₄ nanoparticles (NPs) were successfully synthesized by using nickel nitrate, ferric nitrate, citric acid, and ethyl cellulose as a surfactant by a simple sol–gel-assisted combustion method (SACM) and microwave-assisted combustion method (MACM). Structural, morphological, optical, and magnetic properties of the obtained powder were characterized by powder X-ray diffraction (XRD), Fourier transform infrared (FT-IR) spectroscopy, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), photoluminescence (PL) spectroscopy, and vibrating sample magnetometry (VSM). XRD results show that the resultant powder was pure crystalline with cubic structure. The average crystalline size was found to be 18.8 and 10.2 nm synthesized by SACM and MACM, respectively. FT-IR spectra indicate the type of bonds between Ni–O and Fe–O (metal and oxygen). SEM images show that the morphology of the powder consists of well-defined structure. VSM results showed a ferromagnetic behavior of the sample. Antimicrobial activity of NiFe₂O₄ nanoparticles was performed. Both sample 1 (SACM) and sample 2 (MACM) show good inhibition in the zone 100 μg/ml. While comparing, sample 2 shows high inhibition than sample 1.     

Using Personas to Support Augmentative Alternative Communication Device Design: A Validation and Evaluation Study

Interactive computing devices are increasingly being deployed to support individuals in improving and managing health, aging in place, and as assistive devices for those with functional limitations. Augmentative Alternative Communication (AAC) aids are devices that help individuals with speech impairments communicate. They are typically prescribed by clinicians (speech–language pathologists) based on a specific individual’s physical needs and disease classification. While personas have been used in the design of other consumer technologies, they have not been frequently deployed in the design of clinically oriented technologies, including AAC devices. Instead, a clinical/rehabilitation engineering approach to AAC design is typically used, focusing on the physical and symptomatic needs of a diverse group of individuals, and involving the consumer on the level of making feature recommendations and/or testing the technology near the end of the design and production process. To address this challenge, three personas of individuals with amyotrophic lateral sclerosis were developed, intended for use in AAC device design. This article presents results from two studies which evaluated the personas. In the first study, personas were validated using an online survey of eight speech–language pathologists, and interviews of seven individuals with ALS that were not involved in the development of personas. In the second study, AAC device designers were asked to assess the potential utility of the personas for AAC device design. Results showed that developed personas had validity in representing individuals with ALS and that the personas would be useful for AAC device designers. The methods used can be extended for validation and assessment of personas developed for other purposes.     

Heterogeneous Catalytic Sulfimidation using Immobilized Cu(acac)2

The heterogeneous sulfimidation of various sulfides by microencapsulated copper(II) acetylacetonate, [MC‐Cu(acac)₂], and Cu(acac)₂ immobilized in ionic liquids using [N‐(p‐tolylsulfonyl)imino]phenyliodinane (PhINTs) as nitrene donor has been developed to afford the corresponding sulfimides in good to excellent yields. In the presence of a chiral bis(oxazoline) as ligand, asymmetric induction occurs to afford the chiral sulfimides (up to 50% ee). The ionic liquid containing the immobilized bis(oxazoline)‐copper catalyst can be reused for several cycles with consistent activity and enantioselectivity.     

Cu(acac)2 Immobilized in Ionic Liquids: A Recoverable and Reusable Catalytic System for Aza‐Michael Reactions

Copper(II) acetylacetonate immobilized in ionic liquids efficiently catalyzes the aza‐Michael reaction of amines with α,β‐unsaturated carbonyl compounds to produce the corresponding β‐amino carbonyl compounds with great alacrity in excellent yields. The reactions are far more facile than those reported earlier. The recovered ionic liquid phase containing the copper catalyst can be reused for several cycles with consistent activity.