In Vitro Propagation and Artificial Seed Production of Fritillaria cirrhosa D. Don, an Endangered Medicinal Plant

Fritillaria cirrhosa D.Don(Liliaceae)is an endangered perennial bulbous plant and its dry bulb is a valuable med-icinal material with antitussive and expectorant effects.Nevertheless,lack of resources and expensive prices make it difficult to meet clinical needs.This study presents a regeneration system aimed at overcoming the challenge of inadequate supply in F.cirrhosa,focusing on:(1)callus induction,(2)bulblets and adventitious bud induction,and(3)artificial seed production.Callus development was achieved in 84.93%on Murashige and Skoog(MS)medium fortified with 1.0 mg·L^(-1) picloram.The optimal medium for callus differentiation into regenerated bulb-lets was MS medium supplemented with 1.0 mg·L^(-1)6-benzyladenine(6-BA)and 0.2 mg·L^(-1)α-naphthaleneacetic acid(NAA).Subsequently,bulblets and adventitious buds were induced from regenerated bulblet sections cul-tured on MS medium fortified with 0.3 mg·L^(-1)6-BA+1.0 mg·L^(-1)2,4-dichlorophenoxyacetic acid(2,4-D),2.0 mg·L^(-1)6-BA+0.5 mg·L^(-1),and the induction rates were 88.17%and 84.24%,respectively.The regenerated bulblets were transplanted into a substrate of humus soil,river sand,and pearlite(1:1:1)after low-temperature treatment.The germination rate was 42.80%after culture for 30 days.Regenerated bulblets were used for encap-sulations in liquid MS medium containing 3%sucrose(w/v)+0.5 mg·L^(-1) NAA+2.0 mg·L^(-1)6-BA+3%sodium alginate(w/v)with a 10 min exposure to 2%CaCl_(2).Under non-aseptic conditions,the germination rate reached 81.67%,while the rooting rate was 20.56%after 45 days.The capsule added 1.0 g·L^(-1) carbendazim and 1.0 g·L^(-1) activated carbon was the best component of artificial seeds.This study successfully established an efficient regen-eration system for the rapid propagation of F.cirrhosa,involving in vitro bulblet regeneration and artificial seed production.This method introduces a novel approach for efficient breeding and germplasm preservation,making it suitable for large-scale industrial resource production.

Artificial Intelligence for Maximizing Agricultural Input Use Efficiency: Exploring Nutrient, Water and Weed Management Strategies

Agriculture plays a crucial role in the economy,and there is an increasing global emphasis on automating agri-cultural processes.With the tremendous increase in population,the demand for food and employment has also increased significantly.Agricultural methods traditionally used to meet these requirements are no longer ade-quate,requiring solutions to issues such as excessive herbicide use and the use of chemical fertilizers.Integration of technologies such as the Internet of Things,wireless communication,machine learning,artificial intelligence(AI),and deep learning shows promise in addressing these challenges.However,there is a lack of comprehensive documentation on the application and potential of AI in improving agricultural input efficiency.To address this gap,a desk research approach was used by utilizing peer-reviewed electronic databases like PubMed,Scopus,Goo-gle Scholar,Web of Science,and Science Direct for relevant articles.Out of 327 initially identified articles,180 were deemed pertinent,focusing primarily on AI’s potential in enhancing yield through better management of nutrients,water,and weeds.Taking into account researchfindings worldwide,we found that AI technologies could assist farmers by providing recommendations on the optimal nutrients to enhance soil quality and deter-mine the best time for irrigation or herbicide application.The present status of AI-driven automation in agricul-ture holds significant promise for optimizing agricultural input utilization and reducing resource waste,particularly in the context of three pillars of crop management,i.e.,nutrient,irrigation,and weed management.

Prediction of impedance responses of protonic ceramic cells using artificial neural network tuned with the distribution of relaxation times

A deep-learning-based framework is proposed to predict the impedance response and underlying electrochemical behavior of the reversible protonic ceramic cell(PCC) across a wide variety of different operating conditions.Electrochemical impedance spectra(EIS) of PCCs were first acquired under a variety of opera ting conditions to provide a dataset containing 36 sets of EIS spectra for the model.An artificial neural network(ANN) was then trained to model the relationship between the cell operating condition and EIS response.Finally,ANN model-predicted EIS spectra were analyzed by the distribution of relaxation times(DRT) and compared to DRT spectra obtained from the experimental EIS data,enabling an assessment of the accumulative errors from the predicted EIS data vs the predicted DRT.We show that in certain cases,although the R^(2)of the predicted EIS curve may be> 0.98,the R^(2)of the predicted DRT may be as low as~0.3.This can lead to an inaccurate ANN prediction of the underlying time-resolved electrochemical response,although the apparent accuracy as evaluated from the EIS prediction may seem acceptable.After adjustment of the parameters of the ANN framework,the average R^(2)of the DRTs derived from the predicted EIS can be improved to 0.9667.Thus,we demonstrate that a properly tuned ANN model can be used as an effective tool to predict not only the EIS,but also the DRT of complex electrochemical systems.

Artificial Bee Colony with Cuckoo Search for Solving Service Composition

In recent years,cloud computing has provided a Software As A Service(SaaS)platform where the software can be reused and applied to fulfill compli-cated user demands according to specific Quality of Services(QoS)constraints.The user requirements are formulated as a workflow consisting of a set of tasks.However,many services may satisfy the functionality of each task;thus,searching for the composition of the optimal service while maximizing the QoS is formulated as an NP-hard problem.This work will introduce a hybrid Artificial Bee Colony(ABC)with a Cuckoo Search(CS)algorithm to untangle service composition problem.The ABC is a well-known metaheuristic algorithm that can be applied when dealing with different NP-hard problems with an outstanding record of performance.However,the ABC suffers from a slow convergence problem.Therefore,the CS is used to overcome the ABC’s limitations by allowing the abandoned bees to enhance their search and override the local optimum.The proposed hybrid algorithm has been tested on 19 datasets and then compared with two standard algorithms(ABC and CS)and three state-of-the-art swarm-based composition algorithms.In addition,extensive parameter study experiments were conducted to set up the proposed algorithm’s parameters.The results indicate that the proposed algorithm outperforms the standard algorithms in the three comparison criteria(bestfitness value,averagefitness value,and average execution time)overall datasets in 30 different runs.Furthermore,the proposed algorithm also exhibits better performance than the state–of–the–art algorithms in the three comparison criteria over 30 different runs.

Numerical Investigation on Vibration Performance of Flexible Plates Actuated by Pneumatic Artificial Muscle

This paper theoretically introduced the feasibility of changing the vibration characteristics offlexible plates by using bio-inspired,extremely light,and powerful Pneumatic Artificial Muscle(PAM)actuators.Many structural plates or shells are typicallyflexible and show highvibration sensitivity.For this reason,this paper provides a way toachieve active vibrationcontrolfor suppressing the oscillations ofthese structuresto meet strict stability,safety,and comfort requirements.The dynamic behaviors of the designed plates are modeled by using thefinite element(FE)method.As is known,the output force vs.contraction curve of PAM is nonlinear generally.In this presentfinite element model,the maximum forces provided by PAM in different air pressure are adopted as controlling forces for applying for the plate.The non-linearity between the output force and displacement of PAM is avoided in this study.The dynamic behaviors of plates with several independent groups of controlling forces are observed and studied.The results show that the natural frequencies of the plate can be varying and the max amplitude decreases significantly if the controlling forces are applied.The present work also demonstrates the potential of the PAM actuators as valid means for damping out the vibration offlexible systems.

An Artificial Neural Network-Based Model for Effective Software Development Effort Estimation

In project management,effective cost estimation is one of the most cru-cial activities to efficiently manage resources by predicting the required cost to fulfill a given task.However,finding the best estimation results in software devel-opment is challenging.Thus,accurate estimation of software development efforts is always a concern for many companies.In this paper,we proposed a novel soft-ware development effort estimation model based both on constructive cost model II(COCOMO II)and the artificial neural network(ANN).An artificial neural net-work enhances the COCOMO model,and the value of the baseline effort constant A is calibrated to use it in the proposed model equation.Three state-of-the-art publicly available datasets are used for experiments.The backpropagation feed-forward procedure used a training set by iteratively processing and training a neural network.The proposed model is tested on the test set.The estimated effort is compared with the actual effort value.Experimental results show that the effort estimated by the proposed model is very close to the real effort,thus enhanced the reliability and improving the software effort estimation accuracy.

Face Mask and Social Distance Monitoring via Computer Vision and Deployable System Architecture

The coronavirus(COVID-19)is a lethal virus causing a rapidly infec-tious disease throughout the globe.Spreading awareness,taking preventive mea-sures,imposing strict restrictions on public gatherings,wearing facial masks,and maintaining safe social distancing have become crucial factors in keeping the virus at bay.Even though the world has spent a whole year preventing and curing the disease caused by the COVID-19 virus,the statistics show that the virus can cause an outbreak at any time on a large scale if thorough preventive measures are not maintained accordingly.Tofight the spread of this virus,technologically developed systems have become very useful.However,the implementation of an automatic,robust,continuous,and lightweight monitoring system that can be efficiently deployed on an embedded device still has not become prevalent in the mass community.This paper aims to develop an automatic system to simul-taneously detect social distance and face mask violation in real-time that has been deployed in an embedded system.A modified version of a convolutional neural network,the ResNet50 model,has been utilized to identify masked faces in peo-ple.You Only Look Once(YOLOv3)approach is applied for object detection and the DeepSORT technique is used to measure the social distance.The efficiency of the proposed model is tested on real-time video sequences taken from a video streaming source from an embedded system,Jetson Nano edge computing device,and smartphones,Android and iOS applications.Empirical results show that the implemented model can efficiently detect facial masks and social distance viola-tions with acceptable accuracy and precision scores.

Performance Analysis of Plant Shells/PVC Composites under Corrosion and Aging Conditions

To make full use of plant shellfibers(rice husk,walnut shell,chestnut shell),three kinds of wood-plastic com-posites of plant shellfibers and polyvinyl chloride(PVC)were prepared.X-ray diffraction analysis was carried out on three kinds of plant shellfibers to test their crystallinity.The aging process of the composites was conducted under 2 different conditions.One was artificial seawater immersion and xenon lamp irradiation,and the other one was deionized water spray and xenon lamp irradiation.The mechanical properties(tensile strength,flexural strength,impact strength),changes in color,water absorption,Fourier transform infrared spectroscopy(FTIR),and microstructures of the composites before and after the two aging experiments were analyzed.The results showed that the chestnut shell had the highest crystallinity,which was 42%.The chestnut shell/PVC composites had the strongest interface bonding,the least internal defects,and the best general mechanical properties among the three composites.Its tensile strength,bending strength and impact strength were 23.81 MPa,34.12 MPa,and 4.32 KJ·m^(-2),respectively.Comparing the two aging conditions,artificial seawater immersion and xenon lamp irradiation destroyed the quality of the combination of plant shellfibers and PVC,making the internal defects of the composites increase.This made the water absorption ability and changes in the color of the composites more obvious and led to a great decrease in the mechanical properties.The general mechanical properties of the chestnut shell/PVC composites were the best,but their water absorption ability changed more obviously.

Bridging the Gap:Integration of Artificial Intelligence with Organ-on-Chip(AI-OoC)

.Organ-on-Chip(OoC)has emerged as a revolutionary approach to emulate human organ function-ality in vitro,offering unparalleled insights into physiological processes and disease modeling.The integration of artificial intelligence(AI)with OoC platforms presents a transformative synergy,combining the precision of microscale organ replication with the analytical prowess of intelligent algorithms,is emerging as a transforma-tive force in harnessing the full potential of OoC.This perspective investigates the multifaceted implications of integrating AI with OoC,examining its impact on biomedical research,acknowledging the synergistic po-tential that arises from combining the precision of microscale organ replication with the analytical capabilities of intelligent algorithms,and fostering a future where the intricate workings of the technology and biology.

高原地区试用温室型多介质人工湿地处理污水运行效果分析

从西藏自治区县域生活污水的特点及居民的生活习惯出发,构建处理能力为1 000 m3/d的温室型多介质人工湿地示范工程,通过温室型多介质人工湿地与室外水平潜流人工湿地的组合工艺处理县域生活污水,以实现对该区域生活污水的净化,并用于补充附近水环境的生态用水。监测数据表明,温室内多介质人工湿地整个运行期间COD平均出水浓度为40.92 mg/L、氨氮平均出水浓度为4.28 mg/L、总氮平均出水浓度为8.20 mg/L、总磷平均出水浓度为0.40 mg/L,出水质量高于《城镇污水处理厂污染物排放标准(GB 18918—2002)》(以下简称《标准》)一级A标准。系统有效克服了西藏地区高寒缺氧的气候条件,具有高效、稳定的去污性能。另外,室外潜流人工湿地最终COD(28.02 mg/L)、总磷(0.24 mg/L)出水水质均达到《地表水环境质量标准》(GB 3838—2002)的Ⅳ类标准。鉴于氮磷元素是植物生长的必需元素,工程出水可回用补充湿地。本工程的运行成功对于西藏自治区县域污水的人工湿地处理具有一定的工程示范作用和推广应用价值。

养殖黄鳝“出血病”病原的研究

黄鳝出血病是近年来养鳝业的主要疾病之一。从通过对养殖黄鳝的肝、肾 分 离的细菌进行生理生化鉴定、药物敏感及感染试验等。结果表明：经细菌学生化生理鉴定 三株病原菌 ReB00521、 ReK00521、 ReL99712均为嗜水气单胞菌 Aeromonas hydrophila， 对黄鳝的半数致死量 LD50分别为 2.84× 106个 /条、 6.12× 106个 /条、 2.13× 106 个 /条，细菌对药物的敏感性有明显差异。

频域合成房间频率响应的人工混响方法

给出了一种频域合成房间频率响应的方法用于卷积法人工混响,基于频域内房间频率响应的后期部分为高斯随机过程的假设,用自回归滑动平均模型为其自协方差函数和功率谱密度进行参数化描述,在对自回归滑动平均模型中的参数求解后,通过逆滤波得到了房间频率响应后期部分,与房间频率响应前期部分组合后经过傅里叶反变换得到完整的房间脉冲响应。仿真结果表明该方法的混响效果与镜像源法接近,明显优于反馈延迟网络法,但其计算复杂度比镜像源法低,便于实时应用。

基于人工智能的穿戴式颈椎病预防系统

伴随现代工作、生活方式的改变,颈椎病的发病率逐年上升。鉴于颈椎病的主要成因之一为头、颈部长期保持固定姿态,该研究团队研发了一套穿戴式颈椎病预防系统。系统主要包含基于加速度传感器的头颈部运动采集模块以及基于人工智能的头颈部运动识别模块。实验结果表明,系统可以准确识别头颈部姿态的长期保持,并在运动识别模块的监督下指导使用者完成有效的运动疗法。系统的使用有利于预防颈椎病的发病。

静电纺丝制备聚己内酯血管支架及其性能

采用聚己内酯(PCL)为纺丝原料,利用自制的静电纺丝设备制备了人工血管支架.研究了溶液浓度对血管支架的微观形貌、纤维直径及分布、力学性能、孔隙率以及接触角的影响.研究结果表明,在温度为30℃、相对湿度50%、纺丝电压22.5 kV,接收距离17.5 cm、二氯甲烷与N,N– 二甲基甲酰胺体积比为7:3的纺丝条件下,PCL溶液浓度为0.2 g/mL时,其力学性能和孔隙率最佳,轴向拉伸强度为(4.35±0.05)MPa,径向拉伸强度为(4.66±0.07)MPa,轴向拉伸弹性模量为(23.70±0.01)MPa,孔隙率为(74.4±0.1)%,而接触角为105.6°,纤维直径为(509.9±68.3)nm,提高了血管支架的力学性能和孔隙率.通过冷等离子体处理120 s,最终得到轴向拉伸强度为(4.10±0.05)MPa,径向拉伸强度为(4.39±0.05)MPa,轴向拉伸弹性模量为(21.20±0.15)MPa,孔隙率为(74.6±0.2)%,接触角为(60.7±2.3)° 的PCL血管支架,处理后的力学性能未发生大幅度下降,孔隙率基本没有变化,提高了支架的亲水性.

智能医学发展战略思考

随着我国科技创新与人工智能的快速发展,人工智能在医学中的应用越来越广,时代提出了智能医学的概念.本文通过文献分析,调研与咨询,从大数据、互联网、人工智能与智能医学、智慧医疗,以及学科建设、人才培养、相关法规制度、伦理建设等方面提出智能医学发展战略相关建议.

基于层次分析和神经网络的机载LiDAR点云分类

在机载LiDAR点云数据处理中,由于机载雷达点云数据的离散性、不确定性等原因,导致点云分类上很难得到准确的结果。针对机载LiDAR点云数据分类问题,提出了基于层次分析和神经网络的机载LiDAR点云分类方法。根据机载LiDAR点云的数据特征以及不同地物的属性,采用层次分析法赋予每个点云一个二进制信号,然后采用后向传播神经网络(BP-ANN)对机载LiDAR点云数据分类。实验表明:这种方法能够从机载LiDAR独立数据源中分类出房屋、高大的树、低矮的树、道路等地物点云。

Intelligent Deep Learning Enabled Human Activity Recognition for Improved Medical Services

Human Activity Recognition(HAR)has been made simple in recent years,thanks to recent advancements made in Artificial Intelligence(AI)techni-ques.These techniques are applied in several areas like security,surveillance,healthcare,human-robot interaction,and entertainment.Since wearable sensor-based HAR system includes in-built sensors,human activities can be categorized based on sensor values.Further,it can also be employed in other applications such as gait diagnosis,observation of children/adult’s cognitive nature,stroke-patient hospital direction,Epilepsy and Parkinson’s disease examination,etc.Recently-developed Artificial Intelligence(AI)techniques,especially Deep Learning(DL)models can be deployed to accomplish effective outcomes on HAR process.With this motivation,the current research paper focuses on designing Intelligent Hyperparameter Tuned Deep Learning-based HAR(IHPTDL-HAR)technique in healthcare environment.The proposed IHPTDL-HAR technique aims at recogniz-ing the human actions in healthcare environment and helps the patients in mana-ging their healthcare service.In addition,the presented model makes use of Hierarchical Clustering(HC)-based outlier detection technique to remove the out-liers.IHPTDL-HAR technique incorporates DL-based Deep Belief Network(DBN)model to recognize the activities of users.Moreover,Harris Hawks Opti-mization(HHO)algorithm is used for hyperparameter tuning of DBN model.Finally,a comprehensive experimental analysis was conducted upon benchmark dataset and the results were examined under different aspects.The experimental results demonstrate that the proposed IHPTDL-HAR technique is a superior per-former compared to other recent techniques under different measures.

基于水文过程的城市湖泊雨水利用系统的构建方法研究——以武汉梦泽湖为例

城市开发使天然湖泊的供水源大量受阻,导致湖泊在数量与面积上的大幅减少。而湖泊的减少,又加剧了城市内涝的发生。为了改变这种状况,众多城市开始营建人工湖,而兴建的城市人工湖又需要大量的市政补水来填充,实际上城市内涝也未得到有效缓解。本文在研究城市水文过程的基础上,以武汉市梦泽湖的应用研究为例,提出湖泊雨水利用系统的构建方法,予以最大效率地利用雨水来补充城市人工湖水源,同时减少城市内涝的发生。

COVID-19 Pandemic Data Predict the Stock Market

Unlike the 2007–2008 market crash,which was caused by a banking failure and led to an economic recession,the 1918 influenza pandemic triggered a worldwidefinancial depression.Pandemics usually affect the global economy,and the COVID-19 pandemic is no exception.Many stock markets have fallen over 40%,and companies are shutting down,ending contracts,and issuing volun-tary and involuntary leaves for thousands of employees.These economic effects have led to an increase in unemployment rates,crime,and instability.Studying pandemics’economic effects,especially on the stock market,has not been urgent or feasible until recently.However,with advances in artificial intelligence(AI)and the inter-connectivity that social media provides,such research has become possible.In this paper,we propose a COVID-19-based stock market prediction system(C19-SM2)that utilizes social media.Our AI system enables economists to study how COVID-19 pandemic data influence social media and,hence,the stock market.C19-SM2 gathers COVID-19 infection and death cases reported by the authorities and social media data from a geographic area and extracts the sentiments and events that occur in that area.The information is then fed to the support vector machine(SVM)and random forest and random tree classifiers along with current stock market values.Then,the system produces a projection of the stock market’s movement during the next day.We tested the system with the Dow Jones Industrial Average(DJI)and the Tadawul All Share Index(TASI).Our system achieved a stock market prediction accuracy of 99.71%,substantially higher than the 89.93%accuracy reported in the related literature;the inclusion of COVID-19 data improved accuracy by 9.78%.

Development of calibration models for rapid determination of moisture content in rubber sheets using portable near-infrared spectrometers

Rubber sheets are one of the primary products of natural rubber and are the main raw material in various rubber industries.The quality of a rubber sheet can be visually examined by holding it against clear light to inspect for any specks and impurities inside,but its moisture content is difficult to evaluate based on a visual inspection and this might lead to unfair trading.Herein,we developed a rapid,robust and nondestructive near-infrared spectroscopy(NIRS)-based method for moisture content determination in rubber sheets.A set of 300 rubber sheets were divided into a calibration(200 samples)and prediction groups(100 samples).The calibration set was used to develop NIRS calibration equation using different calibration models,Partial Least Square Regression(PLSR),Least Square Support Vector Machine(LS-SVM)and Articial Neural Network(ANN).Among the models investigated,the ANN model with therst derivative of spectral preprocessing presented the best prediction with a coe±cient of determination(R^(2)_(P))of 0.993,root mean square error of calibration(RMSEC)of 0.126%and root mean square error of prediction(RMSEP)of 0.179%.The results indicated that the proposed NIRS-ANN model will be able to reduce human error and provide a highly accurate estimate of the moisture content in a rubber sheet compared to traditional wet chemistry estimation methods according to AOAC standards.