Insight into the experiment and extraction mechanism for separating carbazole from anthracene oil with quaternary ammonium-based deep eutectic solvents

Carbazole is an irreplaceable basic organic chemical raw material and intermediate in industry.The separation of carbazole from anthracene oil by environmental benign solvents is important but still a challenge in chemical engineering.Deep eutectic solvents (DESs) as a sustainable green separation solvent have been proposed for the separation of carbazole from model anthracene oil.In this research,three quaternary ammonium-based DESs were prepared using ethylene glycol (EG) as hydrogen bond donor and tetrabutylammonium chloride (TBAC),tetrabutylammonium bromide or choline chloride as hydrogen bond acceptors.To explore their extraction performance of carbazole,the conductor-like screening model for real solvents (COSMO-RS) model was used to predict the activity coefficient at infinite dilution (γ^(∞)) of carbazole in DESs,and the result indicated TBAC:EG (1:2) had the stronger extraction ability for carbazole due to the higher capacity at infinite dilution (C^(∞)) value.Then,the separation performance of these three DESs was evaluated by experiments,and the experimental results were in good agreement with the COSMO-RS prediction results.The TBAC:EG (1:2) was determined as the most promising solvent.Additionally,the extraction conditions of TBAC:EG (1:2) were optimized,and the extraction efficiency,distribution coefficient and selectivity of carbazole could reach up to 85.74%,30.18 and 66.10%,respectively.Moreover,the TBAC:EG (1:2) could be recycled by using environmentally friendly water as antisolvent.In addition,the separation performance of TBAC:EG (1:2) was also evaluated by real crude anthracene,the carbazole was obtained with purity and yield of 85.32%,60.27%,respectively.Lastly,the extraction mechanism was elucidated byσ-profiles and interaction energy analysis.Theoretical calculation results showed that the main driving force for the extraction process was the hydrogen bonding ((N–H...Cl) and van der Waals interactions (C–H...O and C–H...π),which corresponding to the blue and green isosurfaces in IGMH analysis.This work presented a novel method for separating carbazole from crude anthracene oil,and will provide an important reference for the separation of other high value-added products from coal tar.

Study on the green extraction of corncob xylan by deep eutectic solvent

Corn as one of the world's major food crops,its by-product corn cob is also rich in resources.However,the unreasonable utilization of corn cob often causes the environmental pollution,waste of resources and other problems.As one of the most abundant polymers in nature,xylan is widely used in food,medicine,materials and other fields.Corn cob is rich in xylan,which is an ideal raw material for extracting xylan.However,the intractable lignin is covalently linked to xylan,which increases the difficulty of xylan extraction.It has been reported that the deep eutectic solvent(DES)could preferentially dissolve lignin in biomass,thereby dissolving the xylan.Then,the xylan in the extract was separated by ethanol precipitation method.The xylan precipitate was obtained after centrifugation,while the supernatant was retained.The components of the supernatant after ethanol precipitation were separated by the rotary evaporator.The ethanol,water and DES were collected for the subsequent extraction of corn cob xylan.In this study,a novel way was provided for the green production of corn cob xylan.The DES was used to extract xylan from corn cob which was used as the raw material.The effects of solid-liquid ratio,reaction time,reaction temperature and water content of DES on the extraction rate of corn cob xylan were investigated by the single factor test.Furthermore,the orthogonal test was designed to optimize the xylan extraction process.The structure of corn cob xylan was analyzed and verified.The results showed that the optimum extraction conditions of corn cob xylan were as follows:the ratio of corn cob to DES was 1:15(g:mL),the extraction time was 3 h,the extraction temperature was 60℃,and the water content of DES was 70%.Under these conditions,the extraction rate of xylan was 16.46%.The extracted corn cob xylan was distinctive triple helix of polysaccharide,which was similar to the structure of commercially available xylan.Xylan was effectively and workably extracted from corn cob by the DES method.This study provided a new approach for high value conversion of corn cob and the clean production of xylan.

ConvNeXt-UperNet-Based Deep Learning Model for Road Extraction from High-Resolution Remote Sensing Images

When existing deep learning models are used for road extraction tasks from high-resolution images,they are easily affected by noise factors such as tree and building occlusion and complex backgrounds,resulting in incomplete road extraction and low accuracy.We propose the introduction of spatial and channel attention modules to the convolutional neural network ConvNeXt.Then,ConvNeXt is used as the backbone network,which cooperates with the perceptual analysis network UPerNet,retains the detection head of the semantic segmentation,and builds a new model ConvNeXt-UPerNet to suppress noise interference.Training on the open-source DeepGlobe and CHN6-CUG datasets and introducing the DiceLoss on the basis of CrossEntropyLoss solves the problem of positive and negative sample imbalance.Experimental results show that the new network model can achieve the following performance on the DeepGlobe dataset:79.40%for precision(Pre),97.93% for accuracy(Acc),69.28% for intersection over union(IoU),and 83.56% for mean intersection over union(MIoU).On the CHN6-CUG dataset,the model achieves the respective values of 78.17%for Pre,97.63%for Acc,65.4% for IoU,and 81.46% for MIoU.Compared with other network models,the fused ConvNeXt-UPerNet model can extract road information better when faced with the influence of noise contained in high-resolution remote sensing images.It also achieves multiscale image feature information with unified perception,ultimately improving the generalization ability of deep learning technology in extracting complex roads from high-resolution remote sensing images.

A three-dimensional feature extraction-based method for coal cleat characterization using X-ray μCT and its application to a Bowen Basin coal specimen

Cleats are the dominant micro-fracture network controlling the macro-mechanical behavior of coal.Improved understanding of the spatial characteristics of cleat networks is therefore important to the coal mining industry.Discrete fracture networks(DFNs)are increasingly used in engineering analyses to spatially model fractures at various scales.The reliability of coal DFNs largely depends on the confidence in the input cleat statistics.Estimates of these parameters can be made from image-based three-dimensional(3D)characterization of coal cleats using X-ray micro-computed tomography(m CT).One key step in this process,after cleat extraction,is the separation of individual cleats,without which the cleats are a connected network and statistics for different cleat sets cannot be measured.In this paper,a feature extraction-based image processing method is introduced to identify and separate distinct cleat groups from 3D X-ray m CT images.Kernels(filters)representing explicit cleat features of coal are built and cleat separation is successfully achieved by convolutional operations on 3D coal images.The new method is applied to a coal specimen with 80 mm in diameter and 100 mm in length acquired from an Anglo American Steelmaking Coal mine in the Bowen Basin,Queensland,Australia.It is demonstrated that the new method produces reliable cleat separation capable of defining individual cleats and preserving 3D topology after separation.Bedding-parallel fractures are also identified and separated,which has his-torically been challenging to delineate and rarely reported.A variety of cleat/fracture statistics is measured which not only can quantitatively characterize the cleat/fracture system but also can be used for DFN modeling.Finally,variability and heterogeneity with respect to the core axis are investigated.Significant heterogeneity is observed and suggests that the representative elementary volume(REV)of the cleat groups for engineering purposes may be a complex problem requiring careful consideration.

Extraction, Production and Quality Evaluation of Margarine from Oil Extracted from Waste Biomass Peels of Avocado and Virgin Coconut Oil, Using Chitosan from Reared Shells as Preservative

The production and consumption of avocado pears generates tons of wastes, mainly the pear peels which are usually discarded, although they have been reported to contain important phyto-chemicals with biological activities. The adverse health effect associated with the consumption of saturated lipid based foods has ignited research on reformulation of lipid based foods to eliminate Trans Fatty Acids (TFAs). This study was thus aimed at the extraction and characterization of oil from Avocado Peels (APO) and evaluation of the quality of margarine produced from it. Five verities of pear were used for oil extraction by soxhlet method and physiochemical, oxidative, functional and antioxidant characterization was done. Margarines were formulated using a central composite design using oil blends of APO and Virgin Coconut Oil (VCO) with an oil ratio of 10:90, 40:60, 70:30 respectively, varied blending speed, blending time, and chitosan concentration. Samples were characterized and the effect of process parameters on the physiochemical and functional properties of the margarine studied. Optimized conditions were used to produce samples for sensory evaluation. Color, spreadability, aroma, taste and general acceptability was evaluated using ranking difference test. The results showed that the yield, density, and iodine values of APOs oils ranged from 14.91 ± 0.18 to 11.76 ± 0.46;0.93 ± 0.001 to 0.99 ± 0.1;46.63 ± 1.70 to 52.4 ± 0.63, their acid values, TBA and PV values ranged from 1.42 ± 0.39 to 1.97 ± 0.5;0.11 ± 0.002 to 0.18 ± 0.04;and 2.72 ± 0.14 to 4.43 ± 0.36 respectively, with Brogdon avocado peel variety having the overall best properties prepared blends of trans-free APO margarines showed that increase in APO ratio decreased melting point, increased oxidative stability and reduced moisture content of margarine samples. Chitosan addition leads to decrease moisture content and increase functional properties. VCO lead to increase in phenolic and flavonoid content of the margarines. Samples were spreadable and palatable with R20 being most palatable and the most accepted being R26 with a mean score of 7.07 ± 0.70. Decrease in color intensity increased acceptability. This study therefore demonstrated that avocado peel waste biomass can be valorized by using it as raw material for oil extraction, which can serve as good material for the production of trans-free margarines with good oxidative stability, functional and antioxidant properties.

Optimization of Polysaccharides Extraction from Physalis alkekengi L.Peel and Its Effect on the Expression of Inflammation-Related Proteins in SW620 Cells

Objective:To establish an optimized aqueous extraction process for polysaccharides from Physalis alkekengi L.peel and to preliminarily explore its in vitro anti-inflammatory activity against colorectal cancer SW620 cells.Methods:A single-factor test combined with orthogonal test analysis was used to evaluate the effects of the material-to-liquid ratio,extraction temperature,and extraction time on the yield of polysaccharides from Physalis alkekengi L.peel.The antioxidant activity of the polysaccharides was assessed by analyzing their free radical scavenging ability in vitro,and the anti-inflammatory effect was evaluated using SW620 cells.Results:The optimal extraction conditions were a material-to-liquid ratio of m(g):V(mL)=1:30,an extraction temperature of 100℃,and an extraction time of 40 minutes,with a predicted polysaccharide yield of 25.7%.The polysaccharides from Physalis peruviana peel effectively scavenged DPPH,superoxide anion,and hydroxyl radicals.After treatment with Physalis peruviana polysaccharides,the levels of IL-1β,IL-18,and TNF-αin the cell culture medium were significantly reduced,and the phosphorylation level of P65 protein in SW620 cells was decreased.Conclusion:This extraction method is stable and reliable,and the prepared Physalis alkekengi L.polysaccharides exhibit significant in vitro antioxidant and anti-inflammatory activities.This study provides a theoretical basis for developing drugs for the prevention and treatment of colorectal cancer.

基于J2EE的企业信息管理系统的构建——以鞋服企业为例

对于鞋服企业而言,信息管理系统的构建是打破数据孤岛,为鞋服产品构建起数字营销一体化的重要举措。本文分析了鞋服企业信息管理系统构建的目的和意义,探讨其设计原则,并分析基于J2EE设计模式的鞋服企业信息管理系统构建的层次结构设计及基本模块,希望能够为鞋服企业的信息管理系统构建提供参考。

基于L-DeepLabv3+的轻量化光学遥感图像道路提取

针对DeepLabv3+在进行光学遥感图像道路提取任务时,存在模型参数量大、细节提取效果差等问题,提出一种改进DeepLabv3+的轻量化道路提取模型L-DeepLabv3+。首先通过将主干网络替换为MobileNetv2来减少模型参数量;其次,在编码层中设计一个改进的空洞空间卷积池化金字塔模块。该模块通过嵌入一个通道空间并联注意力模块和YOLOF模块来增强模型特征表达能力,并且将普通卷积替换为深度可分离卷积进一步减少模型参数量;最后组合采用Dice_loss和Focal_loss作为损失函数来解决正负样本不均衡问题。实验结果表明:L-DeepLabv3+在DeepGlobe Road数据集上进行道路提取的交并比达到68.40%,像素准确率达到82.67%,且模型参数量仅为5.63 MB,FPS达到72.3,与其他模型相比具有明显提升,实现了模型精度与轻量化之间更好的平衡。

Profiling the effects of microwave-assisted and soxhlet extraction techniques on the physicochemical attributes of Moringa oleifera seed oil and proteins

There is a constant search for biomaterials from natural products like plants for food and industrial applications.The work embodied in this report aimed at investigating the effects of microwave-assisted and soxhlet extraction(MAE and SE) techniques on the functional physicochemical quality characteristics of Moringa oleifera seed oil and proteins extracts. M. oleifera seeds were ground to fine powders and oil was extracted by microwave-assisted and soxhlet extraction techniques using petroleum ether. Quality attributes including yield percent, moisture content,iodine, saponification, specific gravity, viscosity, p H, thiobarbituric acid, acid and peroxide values were measured. Mineral and vitamin contents, chemical/functional groups, fatty acid(FA) composition, and reducing power of the oil were evaluated. Metabolomics of protein extracted from the defatted powders were analyzed by nuclear magnetic resonance(NMR). M. oleifera oil from MAE and SE methods had good yield(34.25 ± 0.0%,28.75 ± 0.0%), low moisture content(0.008 ± 0.0%, 0.011 ± 0.0%), non-drying and unsaturated, moderately saponified, less dense(0.91 ± 0.01, 0.92 ± 0.02 g m L^(-1)), had Newtonian flow, were weakly acidic, showed good content of FAs, recorded strong potential for long shelf-life, showed stability against oxidative rancidity and enzymatic hydrolysis, had very rich deposits of micro-and macro-nutrients as well as water-soluble and lipidsoluble vitamins, and functional groups in the oil were reflective of its content of long-and medium-chain triglycerides(LCT and MCT). Monounsaturated and saturated fatty acids(MUFA and SFA) were detected and the oil has excellent ferric ion reducing power. NMR metabolomic assay revealed the presence of nine essential amino acids(EAAs) in the protein extract. MAE technique is a feasible and acceptable alternative for high throughput extraction of M. oleifera oil with high yield and excellent quality attributes. The study revealed that MAE did not impart any remarkable advantage(s) on the physicochemical properties of M. oleifera seed oil and protein compared to SE technique.

Small incision lenticule extraction and femtosecond-assisted laser in situ keratomileusis in patients with deep corneal opacity:case series

AIM:To report the safety,efficacy,and accuracy of small-incision lenticule extraction(SMILE)or femtosecondassisted laser in situ keratomileusis(FS-LASIK)for the correction of myopia or myopic astigmatism in patients with deep corneal opacity denoted by anterior segment optical coherence tomography(AS-OCT).METHODS:Four patients with monocular corneal opacity(3 due to mechanical injury,1 due to a firecracker wound)were recruited and treated with refractive surgery(3 for SMILE,1 for FS-LASIK combined with limbal relaxing incision(LRI).Preoperative ocular manifestations,surgical details,postoperative visual outcomes,corneal opacity parameters,and corneal topography were analyzed.RESULTS:Preoperatively,spherical diopter ranged from-3.0 D to-4.75 D with cylinder ranging from-0.75 to-5.0 D,and corrected distance visual acuity(CDVA)ranging from 20/25 to 20/20.One eye’s corneal opacity was located in the central zone and three were in the mid-peripheral optical zone.Three patients underwent uneventful SMILE in both eyes,whilst one patient underwent FS-LASIK for high astigmatism in both eyes and LRI in the right eye.CDVA of the eye with corneal opacity ranged from 20/22to 20/20 one to six weeks postoperatively.Two patients achieved better CDVA and no patients lost Snellen lines.The postoperative diopter was within±0.75 D for all eyes.Significant edema existed above the corneal opacity in one eye and dissipated soon.No eccentric corneal topography or morphological anomaly of the corneal cap or flap was observed.CONCLUSION:The cases demonstrate that SMILE or FS-LASIK is safe and effective to treat myopic astigmatism combined with deep corneal opacity lesions after comprehensive preoperative evaluation and appropriate candidate selection.FS-LASIK combined with LRI is also sufficient for correcting high astigmatism due to corneal scarring.

Engineering Multi‑field‑coupled Synergistic Ion Transport System Based on the Heterogeneous Nanofluidic Membrane for High‑Efficient Lithium Extraction

The global carbon neutrality strategy brings a wave of rechargeable lithium‐ion batteries technique development and induces an ever-growing consumption and demand for lithium(Li).Among all the Li exploitation,extracting Li from spent LIBs would be a strategic and perspective approach,especially with the low energy consumption and eco-friendly membrane separation method.However,current membrane separation systems mainly focus on monotonous membrane design and structure optimization,and rarely further consider the coordination of inherent structure and applied external field,resulting in limited ion transport.Here,we propose a heterogeneous nanofluidic membrane as a platform for coupling multi-external fields(i.e.,lightinduced heat,electrical,and concentration gradient fields)to construct the multi-field-coupled synergistic ion transport system(MSITS)for Li-ion extraction from spent LIBs.The Li flux of the MSITS reaches 367.4 mmol m^(−2)h^(−1),even higher than the sum flux of those applied individual fields,reflecting synergistic enhancement for ion transport of the multi-field-coupled effect.Benefiting from the adaptation of membrane structure and multi-external fields,the proposed system exhibits ultrahigh selectivity with a Li^(+)/Co^(2+)factor of 216,412,outperforming previous reports.MSITS based on nanofluidic membrane proves to be a promising ion transport strategy,as it could accelerate ion transmembrane transport and alleviate the ion concentration polarization effect.This work demonstrated a collaborative system equipped with an optimized membrane for high-efficient Li extraction,providing an expanded strategy to investigate the other membrane-based applications of their common similarities in core concepts.

基于Cov‑AHP的EES系统指标体系构建及对人兽冲突影响测度

在野生动物及其栖息地保护的进程中,人兽冲突事件时有发生,探究人兽冲突的影响因素并提出相应建议是促进人与自然和谐共生的必然要求。本研究以北京市周边地区2009—2017年经济、生态和社会系统(EES系统)的22个变量入手,基于协方差-层次分析法(Cov‑AHP)建立了EES系统指标评价体系。为了探究EES系统中人兽冲突的影响因素,建立多元线性回归模型,运用AIC准则筛选并分析对人兽冲突具有显著影响的变量。结果显示:北京市周边地区人兽冲突形式主要表现为损毁农田和果园,受损失最小的是平谷区,最大的是门头沟区和延庆区,冲突高峰期为每年的8—9月。经济系统中,第二产业增长率、第二产业占比、政府一般预算收入、粮食总产量的增长会加剧人兽冲突的损失;生态系统中,退耕造林面积越大、距水系距离越近,越容易造成人兽冲突损失,而距林地距离越近,越不容易造成人兽冲突损失;社会系统中,恩格尔系数、公共图书馆总藏量会加剧人兽冲突的损失,而防护措施占比则相反。因此,在发展经济的同时,应当优化当地的产业结构,加强生态文明建设,提高当地居民对野生动物及栖息地保护的认知。在推进退耕还林工程的过程中,应该合理地恢复当地的生态环境,避免矫枉过正,与野生动物和谐相处,并建立健全防护措施,减少野生动物对人类带来的负面影响。本研究侧重在EES复合系统中探究人兽冲突的主要影响因素,为减少人兽冲突的损失,促进人与自然和谐共生提供理论依据和数据支持。

Deep Learning-Based Semantic Feature Extraction:A Literature Review and Future Directions

Semantic communication,as a critical component of artificial intelligence(AI),has gained increasing attention in recent years due to its significant impact on various fields.In this paper,we focus on the applications of semantic feature extraction,a key step in the semantic communication,in several areas of artificial intelligence,including natural language processing,medical imaging,remote sensing,autonomous driving,and other image-related applications.Specifically,we discuss how semantic feature extraction can enhance the accuracy and efficiency of natural language processing tasks,such as text classification,sentiment analysis,and topic modeling.In the medical imaging field,we explore how semantic feature extraction can be used for disease diagnosis,drug development,and treatment planning.In addition,we investigate the applications of semantic feature extraction in remote sensing and autonomous driving,where it can facilitate object detection,scene understanding,and other tasks.By providing an overview of the applications of semantic feature extraction in various fields,this paper aims to provide insights into the potential of this technology to advance the development of artificial intelligence.

改进DeepLab v3+模型下的梯田遥感提取研究

[目的与意义]梯田作为农业生产的关键要素之一,其面积估算对于农业政策制定、土地规划和资源管理至关重要。为解决复杂的地形条件、种植环境导致传统遥感数据和监测方法难以开展梯田自动化提取问题,探索一种利用深度学习技术在高分辨率遥感影像中精准提取梯田面积的方法。[方法]以休耕期梯田高分六号影像构建语义分割数据集,同时提出一种改进的DeepLab v3+模型。该模型使用轻量级网络MobileNet v2作为骨干网络,为了同时兼顾局部细节和全局语境,使用多尺度特征融合(Multi-scale Feature Fusion module,MSFF)模块代替空洞空间金字塔池化(Atrous Spatial Pyramid Pooling,ASPP)模块,利用扩张率依次增大的空洞卷积级联模式改善信息丢失的问题。此外,对浅层特征和深层特征使用坐标注意力机制以加强网络对于目标的学习。[结果与讨论]利用红、绿和近红外波段组合方式在梯田提取的精度和效果上表现最佳。相比于原始DeepLab v3+网络,精确率、召回率、F_(1)评分和交并比指标分别提升4.62%、2.61%、3.81%和2.81%。此外,与UNet和原始DeepLab v3+相比,改进的DeepLab v3+在参数量上和浮点运算数有着更为优越的性能,其参数量仅为UNet的28.6%和原始DeepLab v3+的19.5%,同时浮点运算数仅为UNet和DeepLab v3+的1/5。这不仅提高了计算效率,也使得改进后的模型更适用于资源有限或计算能力较低的环境中。[结论]深度学习在高分辨率遥感影像梯田识别中具有较高的精度,有利于为梯田精细化监测和管理提供参考依据。

EEMD与LSTM在轴承剩余寿命预测中的应用

剩余使用寿命(RUL)预测是实现装备健康管理与预测性维护的最主要技术手段之一,为了准确预测轴承的剩余使用寿命,提出了一种基于集合经验模态分解(EEMD)和长短时记忆网络(LSTM)的轴承剩余寿命预测方法。首先,对采集到的振动信号做时域、频域及时频分析,同时记录相应特征;进而,筛选特征,通过EEMD对振动信号予以分解并重构;最后,通过LSTM结合经过处理的信号构建健康特征指标。通过实验证明了该方法能有效的预测出轴承的剩余寿命,且有较高的预测精度。

基于J2EE架构的岩土试验数据管理平台的设计与应用

为解决岩土试验数据采集和应用数据分散、人工处理效率低、非标数据难以建模等问题,运用流程重组、物联网、数据挖掘等多种现代信息技术,对岩土试验数字化工程进行了整体规划和实施,对岩土试验数据管理平台的业务架构和功能架构进行了设计,基于J2EE(Java 2 Platform Enterprise Edition)架构研发了岩土试验数据管理平台和试验数据中心。岩土试验中通过物联网二维码的应用,样本识别准确率和效率得到了提升,岩土试验的总体周期大幅缩短。通过压力试验机升级改造和软件集成,单轴抗压强度试验效率提升了50%以上。岩土试验数据管理平台的建立和升级改造,提升了岩土试验工作效率、岩土试验数据的利用率和测试数据管理水平,获得了良好效益。

Extraction of Cinnamomum longepaniculatum deciduous leaves essential oil using solvent-free microwave extraction: Process optimization and quality evaluation

The optimal process conditions for solvent-free microwave extraction(SFME)of essential oils from Cinnamomum longepaniculatum deciduous leaves after moisture conditioning were established by response surface methodology(RSM).A Box-Behnken design(BBD)was applied to evaluate the effects of three independent variables:moisture content(A:54%–74%),microwave power(B:300–500 W)and microwave time(C:20–40 min),on the extraction yield of essential oil.The compounds of the essential oils obtained by SFME,hydro-distillation and microwaveassisted hydro-distillation(MADE)were identified by gas chromatography-mass spectrometry(GC-MS),and the total lipids of C.longepaniculatum fresh leaves and deciduous leaves were analyzed.The correlation analysis of the response regression model indicated that quadratic polynomial model could be employed to optimize the extraction of essential oil.The optimal extraction condition was A:58%,B:400 W and C:28 min.In the optimal condition,the maximum extraction yield was 4.475 mL/100 g dw and higher than that by MADE.The main compound of the essential oil was eucalyptol(63.54%),and total oxygenated compounds was 78.95%,mainly caused by SFME and the metabolism of endophytic bacteria with decreasing content of phospholipids and fatty acids.Analysis of variance under the extraction condition illustrated high fitness of the model and the success of RSM for optimizing and reflecting the expected process condition.SFME combined with moisture regulation was an effective method for extracting essential oil from C.longepaniculatum deciduous leaves.

240 nm AlGaN-based deep ultraviolet micro-LEDs:size effect versus edge effect

240 nm AlGaN-based micro-LEDs with different sizes are designed and fabricated.Then,the external quantum efficiency(EQE)and light extraction efficiency(LEE)are systematically investigated by comparing size and edge effects.Here,it is revealed that the peak optical output power increases by 81.83%with the size shrinking from 50.0 to 25.0μm.Thereinto,the LEE increases by 26.21%and the LEE enhancement mainly comes from the sidewall light extraction.Most notably,transversemagnetic(TM)mode light intensifies faster as the size shrinks due to the tilted mesa side-wall and Al reflector design.However,when it turns to 12.5μm sized micro-LEDs,the output power is lower than 25.0μm sized ones.The underlying mechanism is that even though protected by SiO2 passivation,the edge effect which leads to current leakage and Shockley-Read-Hall(SRH)recombination deteriorates rapidly with the size further shrinking.Moreover,the ratio of the p-contact area to mesa area is much lower,which deteriorates the p-type current spreading at the mesa edge.These findings show a role of thumb for the design of high efficiency micro-LEDs with wavelength below 250 nm,which will pave the way for wide applications of deep ultraviolet(DUV)micro-LEDs.

Peak extraction and classification from digital elevation models based on the relationship between morphological characteristics and spatial position

A peak is an important topographic feature crucial in quantitative geomorphic feature analysis,digital geomorphological mapping,and other fields.Most peak extraction methods are based on the maximum elevation in a local area but ignore the morphological characteristics of the peak area.This paper proposes three indices based on the morphological characteristics of peaks and their spatial relationship with ridge lines:convexity mean index(CM-index),convexity standard deviation(CSD-index),and convexity imbalance index(CIBindex).We develop computation methods to extract peaks from digital elevation model(DEM).Subsequently,the initial peaks extracted by neighborhood statistics are classified using the proposed indices.The method is evaluated in the Qinghai Tibet Plateau and the Loess Plateau in China.An ASTER Global DEM(ASTGTM2 DEM)with a grid size of 30 m is chosen to assess the suitability of the proposed mountain peak extraction and classification method in different geomorphic regions.DEM data with grid sizes of 30 m and 5 m are used for the Loess Plateau.The mountain peak extraction and classification results obtained from the different resolution DEM are compared.The experimental results show that:(1)The CM-index and the CSDindex accurately reflect the concave or convex morphology of the surface and can be used as supplements to existing surface morphological indices.(2)The three indices can identify pseudo mountain peaks and classify the remaining peaks into single ridge peak(SR-Peak)and multiple ridge intersection peak(MRI-Peak).The visual inspection results show that the classification accuracy in the different study areas exceeds 75%.(3)The number of peaks is significantly higher for the 5 m DEM than for the 30 m DEM because more peaks can be detected at a finer resolution.

Improved Speech Emotion Recognition Focusing on High-Level Data Representations and Swift Feature Extraction Calculation

The performance of a speech emotion recognition(SER)system is heavily influenced by the efficacy of its feature extraction techniques.The study was designed to advance the field of SER by optimizing feature extraction tech-niques,specifically through the incorporation of high-resolution Mel-spectrograms and the expedited calculation of Mel Frequency Cepstral Coefficients(MFCC).This initiative aimed to refine the system’s accuracy by identifying and mitigating the shortcomings commonly found in current approaches.Ultimately,the primary objective was to elevate both the intricacy and effectiveness of our SER model,with a focus on augmenting its proficiency in the accurate identification of emotions in spoken language.The research employed a dual-strategy approach for feature extraction.Firstly,a rapid computation technique for MFCC was implemented and integrated with a Bi-LSTM layer to optimize the encoding of MFCC features.Secondly,a pretrained ResNet model was utilized in conjunction with feature Stats pooling and dense layers for the effective encoding of Mel-spectrogram attributes.These two sets of features underwent separate processing before being combined in a Convolutional Neural Network(CNN)outfitted with a dense layer,with the aim of enhancing their representational richness.The model was rigorously evaluated using two prominent databases:CMU-MOSEI and RAVDESS.Notable findings include an accuracy rate of 93.2%on the CMU-MOSEI database and 95.3%on the RAVDESS database.Such exceptional performance underscores the efficacy of this innovative approach,which not only meets but also exceeds the accuracy benchmarks established by traditional models in the field of speech emotion recognition.