改性油菜秸秆生物质炭吸附/解吸Cd2+特征

该研究选用蒸汽爆破油菜秸秆,对其进行羟基磷灰石和KMnO4浸渍处理,再用壳聚糖和NaOH溶液改性所获得的生物质炭改性,以比较表面特性变化和吸附/解吸Cd^2+的特征。结果表明,改性处理可有效地在生物质炭表面负载相应官能团,如羟基磷灰石处理使生物质炭表面磷酸盐增多,比表面积提高至225.68m^2/g;而壳聚糖、KMnO4和NaOH处理,则引入了-NH2和-OH、-COOH等酸性含氧官能团。尽管改性生物质炭表面电荷减少,但Cd^2+吸附容量却提高了13%~315%,其吸附行为可用Langmuir等温吸附式拟合,并符合Pseudo second order吸附动力学方程。改性后,生物质炭对Cd^2+的吸附主要为专性吸附,其初始吸附速率提高了65%~379%,而解吸率降低了17%~91%,表明对Cd2+的吸附更快且更加稳定,具有良好的应用潜力。

基于高光谱成像的牧草粗蛋白含量检测研究

粗蛋白(CP)是评价牧草营养价值和品质参数的关键指标。快速、准确地对牧草中粗蛋白含量进行评估在畜牧业生产研究中具有重要意义。为确定牧草粗蛋白含量的高光谱特征波段及最优检测模型,研究分别于2017年5月至9月间在黑龙江省杜尔伯特自治区的人工牧草场(羊草)内每月随机选取35个样本,5个月共采集175个样本。采样时在样本点处放置1m×1m的样方,将样方内所有牧草全部齐地面收割采集后称重并冷藏保存。将样本带回实验室后,立即进行牧草叶片高光谱图像采集,同时采用凯氏定氮法对采集的牧草样本进行粗蛋白化学值测定,以此建立牧草粗蛋白含量高光谱数据集。研究首先通过Savitzky-Golay卷积平滑(SG)、多元散射校正(MSC)、变量标准化(SNV)、一阶导数(1-Der)和直接正交信号校正(DOSC)方法5种预处理方法对高光谱数据进行处理后分别建立偏最小二乘回归(PLSR)检测模型,从中确定最优预处理方法。利用最优预处理结果,分别采用连续投影算法(SPA)和随机蛙跳算法(RF)进行牧草粗蛋白含量的特征波段选择,并利用选择结果分别进一步建立PLSR模型,以此确定适合粗蛋白含量的特征波段选择方法,确定最优高光谱检测模型。结果表明,在五种高光谱预处理方法中,基于SNV方法预处理后所建立的高光谱PLSR模型表现最优(R2-P=0.929,RMSE-P=6.344mg·g^-1,RPD=4.204)。利用连续投影算法筛选的粗蛋白含量特征波长为30个,分布于530~700和940~1000nm范围内。经随机蛙跳算法确定的粗蛋白含量特征波段为6个,分别为826.544,827.285,828.766,971.012,972.494和973.235nm。因此,该研究中牧草粗蛋白含量最优高光谱检测模型为SNV-RF-PLSR(R2-P=0.933,RMSE-P=6.034mg·g^-1,RPD=4.322),模型精度较高。该研究结果为牧草粗蛋白含量的高光谱检测提供了最优模型和理论基础,同时为指导草业生产开拓了新的技术思路。

基于核密度估计的新浪微博数据地理空间分析:以上海市为例

为了提取和分析中国上海的社交网络位置数据,通过使用KDE作为空间分析技术来探讨LBSN数据的应用,分析用户参与的新浪微博签到数据与城市特征之间的关系,更重要的是调查上海密集地区的人口密度,以便于相关部门更好地观察和管理。通过使用新浪微博API收集了中国上海10个不同地区在2016年1月~3月期间的数据,并利用核密度估计对基于位置社交网络数据集的新浪微博用户的签到频率进行分析。研究结果表明,核密度估计方法为利用地理空间数据集进行空间模式建模提供了有益的见解。此外,与研究区域的副城区相比,中心城区的密度更大。由此得出结论:通过使用核密度估计技术,可以评估个体的签到行为以及更广泛的总体人口模式。该研究对城市功能及其环境影响、城市可持续性发展和基于城市人口密度的应急响应等领域都有一定的借鉴意义。

Adaptation of Federated Explainable Artificial Intelligence for Efficient and Secure E-Healthcare Systems

Explainable Artificial Intelligence(XAI)has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning(ML)and Deep Learning(DL)based algorithms.In this paper,we chose e-healthcare systems for efficient decision-making and data classification,especially in data security,data handling,diagnostics,laboratories,and decision-making.Federated Machine Learning(FML)is a new and advanced technology that helps to maintain privacy for Personal Health Records(PHR)and handle a large amount of medical data effectively.In this context,XAI,along with FML,increases efficiency and improves the security of e-healthcare systems.The experiments show efficient system performance by implementing a federated averaging algorithm on an open-source Federated Learning(FL)platform.The experimental evaluation demonstrates the accuracy rate by taking epochs size 5,batch size 16,and the number of clients 5,which shows a higher accuracy rate(19,104).We conclude the paper by discussing the existing gaps and future work in an e-healthcare system.

Depression Intensity Classification from Tweets Using Fast Text Based Weighted Soft Voting Ensemble

Predicting depression intensity from microblogs and social media posts has numerous benefits and applications,including predicting early psychological disorders and stress in individuals or the general public.A major challenge in predicting depression using social media posts is that the existing studies do not focus on predicting the intensity of depression in social media texts but rather only perform the binary classification of depression and moreover noisy data makes it difficult to predict the true depression in the social media text.This study intends to begin by collecting relevant Tweets and generating a corpus of 210000 public tweets using Twitter public application programming interfaces(APIs).A strategy is devised to filter out only depression-related tweets by creating a list of relevant hashtags to reduce noise in the corpus.Furthermore,an algorithm is developed to annotate the data into three depression classes:‘Mild,’‘Moderate,’and‘Severe,’based on International Classification of Diseases-10(ICD-10)depression diagnostic criteria.Different baseline classifiers are applied to the annotated dataset to get a preliminary idea of classification performance on the corpus.Further FastText-based model is applied and fine-tuned with different preprocessing techniques and hyperparameter tuning to produce the tuned model,which significantly increases the depression classification performance to an 84%F1 score and 90%accuracy compared to baselines.Finally,a FastText-based weighted soft voting ensemble(WSVE)is proposed to boost the model’s performance by combining several other classifiers and assigning weights to individual models according to their individual performances.The proposed WSVE outperformed all baselines as well as FastText alone,with an F1 of 89%,5%higher than FastText alone,and an accuracy of 93%,3%higher than FastText alone.The proposed model better captures the contextual features of the relatively small sample class and aids in the detection of early depression intensity prediction from tweets with impactful performances.

Steam explosion of crop straws improves the characteristics of biochar as a soil amendment

Five crop straws (wheat, rice, maize, oil-rape, and cotton) were first steam-exploded for 2 min at 210℃, 2.5 MPa and then pyrolyzed at 500℃ for 2 h. Steam explosion (SE) induced 47–95% and 5–16% reduction of hemicellulose and cellulose, respectively, in the crop straws. The biochars derived from SE-treated feedstocks had a lower specific surface area (SSA) and pore volume, compared to those from pristine feedstocks, with one exception that SE enhanced SSA of oil-rape straw biochar by approximately 16 times. After SE, biochars had significant higher anion exchange capacity (AEC) (6.88–11.44 cmol kg–1) and point of zero net charges (PZNC) (pH 3.61–5.32) values. It can thus be speculated that these biochars may have higher potential for anions adsorption. In addition, oil-rape straw might be suitable to SE pretreatment for preparing biochar as a soil amendment and sorbent as well. Further work is required for testing its application in soil.

Machine Learning Empowered Security Management and Quality of Service Provision in SDN-NFV Environment

With the rising demand for data access,network service providers face the challenge of growing their capital and operating costs while at the same time enhancing network capacity and meeting the increased demand for access.To increase efficacy of Software Defined Network(SDN)and Network Function Virtualization(NFV)framework,we need to eradicate network security configuration errors that may create vulnerabilities to affect overall efficiency,reduce network performance,and increase maintenance cost.The existing frameworks lack in security,and computer systems face few abnormalities,which prompts the need for different recognition and mitigation methods to keep the system in the operational state proactively.The fundamental concept behind SDN-NFV is the encroachment from specific resource execution to the programming-based structure.This research is around the combination of SDN and NFV for rational decision making to control and monitor traffic in the virtualized environment.The combination is often seen as an extra burden in terms of resources usage in a heterogeneous network environment,but as well as it provides the solution for critical problems specially regarding massive network traffic issues.The attacks have been expanding step by step;therefore,it is hard to recognize and protect by conventional methods.To overcome these issues,there must be an autonomous system to recognize and characterize the network traffic’s abnormal conduct if there is any.Only four types of assaults,including HTTP Flood,UDP Flood,Smurf Flood,and SiDDoS Flood,are considered in the identified dataset,to optimize the stability of the SDN-NFVenvironment and security management,through several machine learning based characterization techniques like Support Vector Machine(SVM),K-Nearest Neighbors(KNN),Logistic Regression(LR)and Isolation Forest(IF).Python is used for simulation purposes,including several valuable utilities like the mine package,the open-source Python ML libraries Scikit-learn,NumPy,SciPy,Matplotlib.Few Flood assaults and Structured Query Language(SQL)injections anomalies are validated and effectively-identified through the anticipated procedure.The classification results are promising and show that overall accuracy lies between 87%to 95%for SVM,LR,KNN,and IF classifiers in the scrutiny of traffic,whether the network traffic is normal or anomalous in the SDN-NFV environment.

Machine Learning Enabled Early Detection of Breast Cancer by Structural Analysis of Mammograms

Clinical image processing plays a signicant role in healthcare systems and is currently a widely used methodology.In carcinogenic diseases,time is crucial;thus,an image’s accurate analysis can help treat disease at an early stage.Ductal carcinoma in situ(DCIS)and lobular carcinoma in situ(LCIS)are common types of malignancies that affect both women and men.The number of cases of DCIS and LCIS has increased every year since 2002,while it still takes a considerable amount of time to recommend a controlling technique.Image processing is a powerful technique to analyze preprocessed images to retrieve useful information by using some remarkable processing operations.In this paper,we used a dataset from the Mammographic Image Analysis Society and MATLAB 2019b software from MathWorks to simulate and extract our results.In this proposed study,mammograms are primarily used to diagnose,more precisely,the breast’s tumor component.The detection of DCIS and LCIS on breast mammograms is done by preprocessing the images using contrast-limited adaptive histogram equalization.The resulting images’tumor portions are then isolated by a segmentation process,such as threshold detection.Furthermore,morphological operations,such as erosion and dilation,are applied to the images,then a gray-level co-occurrence matrix texture features,Harlick texture features,and shape features are extracted from the regions of interest.For classication purposes,a support vector machine(SVM)classier is used to categorize normal and abnormal patterns.Finally,the adaptive neuro-fuzzy inference system is deployed for the amputation of fuzziness due to overlapping features of patterns within the images,and the exact categorization of prior patterns is gained through the SVM.Early detection of DCIS and LCIS can save lives and help physicians and surgeons todiagnose and treat these diseases.Substantial results are obtained through cubic support vector machine(CSVM),respectively,showing 98.95%and 98.01%accuracies for normal and abnormal mammograms.Through ANFIS,promising results of mean square error(MSE)0.01866,0.18397,and 0.19640 for DCIS and LCIS differentiation during the training,testing,and checking phases.

Interference Mitigation in D2D Communication Underlying Cellular Networks:Towards Green Energy

Device to Device(D2D)communication is emerging as a new participant promising technology in 5G cellular networks to promote green energy networks.D2D communication can improve communication delays,spectral efficiency,system capacity,data off-loading,and many other fruitful scenarios where D2D can be implemented.Nevertheless,induction of D2D communication in reuse mode with the conventional cellular network can cause severe interference issues,which can significantly degrade network performance.To reap all the benefits of induction of D2D communication with conventional cellular communication,it is imperative to minimize interference’s detrimental effects.Efficient power control can minimize the negative effects of interference and get benefits promised by D2D communication.In this work,we propose two power control schemes,Power Control Scheme 1(PCS 1)and Power Control Scheme 2(PCS 2),to minimize the interference and provide performance analysis.Simulation results observe improvements with PCS 1 and PCS 2 as compared to without using any power control scheme in terms of data rate in both uplink and downlink communication modes of Cellular User Equipment(CUE).

Green5G: Enhancing Capacity and Coverage in Device-to-Device Communication

With the popularity of green computing and the huge usage of networks,there is an acute need for expansion of the 5G network.5G is used where energy efficiency is the highest priority,and it can play a pinnacle role in helping every industry to hit sustainability.While in the 5G network,conventional performance guides,such as network capacity and coverage are still major issues and need improvements.Device to Device communication(D2D)communication technology plays an important role to improve the capacity and coverage of 5G technology using different techniques.The issue of energy utilization in the IoT based system is a significant exploration center.Energy optimizationin D2D communication is an important point.We need to resolve this issue for increasing system performance.Green IoT speaks to the issue of lessening energy utilization of IoT gadgets which accomplishes a supportable climate for IoT systems.In this paper,we improve the capacity and coverage of 5G technology using Multiple Inputs Multiple Outputs(MU-MIMO).MUMIMO increases the capacity of 5G in D2D communication.We also present all the problems faced by 5G technology and proposed architecture to enhance system performance.

A Hybrid Approach for Network Intrusion Detection

Due to the widespread use of the internet and smart devices,various attacks like intrusion,zero-day,Malware,and security breaches are a constant threat to any organization’s network infrastructure.Thus,a Network Intrusion Detection System(NIDS)is required to detect attacks in network traffic.This paper proposes a new hybrid method for intrusion detection and attack categorization.The proposed approach comprises three steps to address high false and low false-negative rates for intrusion detection and attack categorization.In the first step,the dataset is preprocessed through the data transformation technique and min-max method.Secondly,the random forest recursive feature elimination method is applied to identify optimal features that positively impact the model’s performance.Next,we use various Support Vector Machine(SVM)types to detect intrusion and the Adaptive Neuro-Fuzzy System(ANFIS)to categorize probe,U2R,R2U,and DDOS attacks.The validation of the proposed method is calculated through Fine Gaussian SVM(FGSVM),which is 99.3%for the binary class.Mean Square Error(MSE)is reported as 0.084964 for training data,0.0855203 for testing,and 0.084964 to validate multiclass categorization.

Classification and Categorization of COVID-19 Outbreak in Pakistan

Coronavirus is a potentially fatal disease that normally occurs in mammals and birds.Generally,in humans,the virus spreads through aerial droplets of any type of fluid secreted from the body of an infected person.Coronavirus is a family of viruses that is more lethal than other unpremeditated viruses.In December 2019,a new variant,i.e.,a novel coronavirus(COVID-19)developed in Wuhan province,China.Since January 23,2020,the number of infected individuals has increased rapidly,affecting the health and economies of many countries,including Pakistan.The objective of this research is to provide a system to classify and categorize the COVID-19 outbreak in Pakistan based on the data collected every day from different regions of Pakistan.This research also compares the performance of machine learning classifiers(i.e.,Decision Tree(DT),Naive Bayes(NB),Support Vector Machine,and Logistic Regression)on the COVID-19 dataset collected in Pakistan.According to the experimental results,DT and NB classifiers outperformed the other classifiers.In addition,the classified data is categorized by implementing a Bayesian Regularization Artificial Neural Network(BRANN)classifier.The results demonstrate that the BRANN classifier outperforms state-of-the-art classifiers.

Integration of geoelectric and hydrochemical approaches for delineation of groundwater potential zones in alluvial aquifer

Geoelectric and hydrochemical approaches are employed to delineate the groundwater potential zones in District Okara,a part of Bari Doab,Punjab,Pakistan.Sixty-seven VES surveys are conducted with the Electrical Resistivity Meter.The resultant resistivity verses depth model for each site is estimated using computer-based software IX1D.Aquifer thickness maps and interpreted resistivity maps were generated from interpreted VES results.Dar-Zarrouk parameters,transverse resistance(TR),longitudinal conductance(SL)and anisotropy(λ)were also calculated from resistivity data to delineate the potential zones of aquifer.70%of SL value is≤3S,30%of SL value is>3S.According to SL and TR values,the whole area is divided into three potential zones,high,medium and low potential zones.The spatial distribution maps show that north,south and central parts of study area are marked as good potential aquifer zones.Longitudinal conductance values are further utilized to determine aquifer protective capacity of area.The whole area is characterized by moderate to good and up to some extent very good aquifer protective area on the basis of SL values.The groundwater samples from sixty-seven installed tube wells are collected for hydro-chemical analysis.The electrical conductivity values are determined.Correlation is then developed between the EC(μS/cm)of groundwater samples vs.interpreted aquifer resistivity showing R2 value 0.90.

Appearance Based Dynamic Hand Gesture Recognition Using 3D Separable Convolutional Neural Network

Appearance-based dynamic Hand Gesture Recognition(HGR)remains a prominent area of research in Human-Computer Interaction(HCI).Numerous environmental and computational constraints limit its real-time deployment.In addition,the performance of a model decreases as the subject’s distance from the camera increases.This study proposes a 3D separable Convolutional Neural Network(CNN),considering the model’s computa-tional complexity and recognition accuracy.The 20BN-Jester dataset was used to train the model for six gesture classes.After achieving the best offline recognition accuracy of 94.39%,the model was deployed in real-time while considering the subject’s attention,the instant of performing a gesture,and the subject’s distance from the camera.Despite being discussed in numerous research articles,the distance factor remains unresolved in real-time deployment,which leads to degraded recognition results.In the proposed approach,the distance calculation substantially improves the classification performance by reducing the impact of the subject’s distance from the camera.Additionally,the capability of feature extraction,degree of relevance,and statistical significance of the proposed model against other state-of-the-art models were validated using t-distributed Stochastic Neighbor Embedding(t-SNE),Mathew’s Correlation Coefficient(MCC),and the McNemar test,respectively.We observed that the proposed model exhibits state-of-the-art outcomes and a comparatively high significance level.

A Potential Approach to Enhance the Seebeck Coefficient of UHMWPE by Using the Graphene Oxide

Thermoelectric materials have been a competent source for the production of energy in the present decade.The most important and potential parameter required for the material to have better thermoelectric characteristics is the Seebeck coefficient.In this work,ultra high molecular weight polyethylene(UHMWPE)and graphene oxide(GO)nanocomposites were prepared by mechanical mixing by containing 10000ppm,50000ppm,70000ppm,100000ppm,150000ppm,and 200000ppm loadings of graphene oxide.Due to the intrinsic insulating nature of UHMWPE,the value of Seebeck for pristine UHMWPE and its nanocomposites with 10000ppm&50000ppm of GO concentration was too low to be detected.However,the Seebeck coefficient for composites with 70000ppm,100000ppm,150000ppm,and 200000ppm loadings of GO was found to be 180,206,230,and 235μV/K,respectively.These higher values of Seebeck coefficients were attributed to the superior thermal insulating nature of UHMWPE and the conductive network induced by the GO within the UHMWPE insulating matrix.Although,the values of the figure of merit and power factor were negligibly small due to the lower concentration of charge carriers in UHMWPE/GO nanocomposites but still reported,results are extremely hopeful for considering the composite as the potential candidate for thermoelectric applications.

Research and Implementation of Time Synchronous Dynamic Password Based on SM3 Hash Algorithm

With the rapid development of information technology, demand of network & information security has increased. People enjoy many benefits by virtue of information technology. At the same time network security has become the important challenge, but network information security has become a top priority. In the field of authentication, dynamic password technology has gained users’ trust and favor because of its safety and ease of operation. Dynamic password, SHA (Secure Hash Algorithm) is widely used globally and acts as information security mechanism against potential threat. The cryptographic algorithm is an open research area, and development of these state-owned technology products helps secure encryption product and provides safeguard against threats. Dynamic password authentication technology is based on time synchronization, using the state-owned password algorithm. SM3 hash algorithm can meet the security needs of a variety of cryptographic applications for commercial cryptographic applications and verification of digital signatures, generation and verification of message authentication code. Dynamic password basically generates an unpredictable random numbers based on a combination of specialized algorithms. Each password can only be used once, and help provide high safety. Therefore, the dynamic password technology for network information security issues is of great significance. In our proposed algorithm, dynamic password is generated by SM3 Hash Algorithm using current time and the identity ID and it varies with time and changes randomly. Coupled with the SM3 hash algorithm security, dynamic password security properties can be further improved, thus it effectively improves network authentication security.

Deep Learning-Driven Anomaly Detection for IoMT-Based Smart Healthcare Systems

The Internet of Medical Things(IoMT)is an emerging technology that combines the Internet of Things(IoT)into the healthcare sector,which brings remarkable benefits to facilitate remote patient monitoring and reduce treatment costs.As IoMT devices become more scalable,Smart Healthcare Systems(SHS)have become increasingly vulnerable to cyberattacks.Intrusion Detection Systems(IDS)play a crucial role in maintaining network security.An IDS monitors systems or networks for suspicious activities or potential threats,safeguarding internal networks.This paper presents the development of an IDS based on deep learning techniques utilizing benchmark datasets.We propose a multilayer perceptron-based framework for intrusion detection within the smart healthcare domain.The primary objective of our work is to protect smart healthcare devices and networks from malicious attacks and security risks.We employ the NSL-KDD and UNSW-NB15 intrusion detection datasets to evaluate our proposed security framework.The proposed framework achieved an accuracy of 95.0674%,surpassing that of comparable deep learning models in smart healthcare while also reducing the false positive rate.Experimental results indicate the feasibility of using a multilayer perceptron,achieving superior performance against cybersecurity threats in the smart healthcare domain.

Microstructure Evolution and Mechanical Behavior of Laser Melting Deposited TA15 Alloy at 500℃ under In-Situ Tension in SEM

TA15 alloy fabricated by laser melting deposition was investigated at 500℃ under tensile deformation. The damage behavior of microstructure was analyzed by the real time observation of the microstructure evolution, microcracks initiation and propagation using in-situ tensile equipment fitted in the SEM chamber. Finally, the mechanism of fracture was discussed. The result showed anisotropic mechanical properties in X-and Z-direction. The existence of columnar β grains and its orientation to the tensile direction were the major factors inducing the anisotropic mechanical properties. As compared to Z-direction specimen, high tensile strength was observed in X-direction specimen due to the resistance in slips propagation provided by the prior-β grain boundaries( β GBs). Accumulation of the cracks at prior β GB caused the shear fracture. In case of Z-direction specimen, parallel orientation of prior β GB and GB α with the tensile direction resulted in a homogeneous deformation. The high reduction of cross section showed the enhanced ductile characteristics at high temperature.

黄腐酸减轻制革废水灌溉引起的铬污染对小麦植株形态、光合作用和氧化性能的改变

巴基斯坦因水资源短缺,农民一直使用制革废水进行灌溉,制革废水含有的大量铬(Cr)及其他污染物对植物生长造成了影响。本文研究了制革废水对小麦植株的影响以及黄腐酸(FA)在减轻Cr毒性中的作用。结果表明:使用制革废水灌溉减缓了植物生长、降低了生物量和光合色素积累。

Antioxidant,phytotoxic and cytotoxic activity of methanolic extract of Trigonella foenum-graecum

Objective:To analyze the methanol extract of Trigonella foenum-graecum(T.foenum-graecum)for antioxidant,phytotoxic and cytotoxic activity.Methods:The powder of T.foenum-graecum was extracted in diluted methanol with the help of random shaking method.All extracts of the plant were measured for cytotoxic activity(beside brine shrimp and antioxidant activity vs.1,1-diphenyl-2-picrylhydrazyl free radical).Results:Various concentrations of methanolic extract of T.foenum-graecum were observed as 36.16%to 54.12%with rising concentrations of 50 to 1000μg/mL.Significantly phytotoxic activity(100 and 1000μg/mL)reduced the growth of roots(radicals)and shoots(hypocotyls)of rice when compared to control after 3 and 7 days’treatment.At a concentration of 10μg/mL,the survival rate of cytotoxic activity of brine shrimp was maximum and at a concentration of 250μg/mL,the death rate of brine shrimp was maximum.Conclusions:T.foenum-graecum has potential activity against free radical mediated sickness and thus it is possible to treat cancer.