Contribution of soil and foliar fertilization of nitrogen and sulfur on physiological and quality assessment of wheat (<i>Triticum aestivum</i>L.)

Nitrogen and sulfur supplies have a strong influence on the physical characteristics of crop as well as on the quality and quantity of wheat storage proteins, which play an important role in bread-making process. In order to evaluate the contribution of soil and foliar fertilization of nitrogen and sulfur on physiological and quality assessment of wheat, a field trail was carried out having randomized complete block design with four replications and eight different treatments of nitrogen and sulfur combinations were allotted to plots at different growth stages. Results indicated that highest protein content (12.82%), maximum moisture content (10.9%), maximum crop growth rate and maximum absolute growth rate were recorded when the wheat crop was fertilized with T8 [Nitrogen @ 60 kg·ha-1 at sowing + 40 kg·ha-1 at tillering + 10 kg·ha-1 at anthesis (spray) + 10 kg·ha-1 after anthesis (spray)] + [Sulfur @ 15 kg·ha-1 at sowing + 10 kg·ha-1 at anthesis (spray) + 5 kg·ha-1 after anthesis (spray)], while control practice resulted low moisture content, low protein, minimum crop growth rate and low absolute growth rate. Among physiological components of wheat cultivars, leaf area index was enhanced when fertilization was done with T5 (Sulfur @ 15 kg·ha-1 at sowing + 10 kg·ha-1 at anthesis + 5 kg·ha-1 after anthesis). In all the recorded observations,concerning experiment wheat cultivar Pirsabaq2005 showed appreciable response as compared with other variety (Khyber-87). Thus it is possible to obtain maximum physiological traits as well as bread-making quality of wheat through soil and foliar application of nitrogen and sulfur.

Integrated Energy-Efficient Distributed Link Stability Algorithm for UAV Networks

Ad hoc networks offer promising applications due to their ease of use,installation,and deployment,as they do not require a centralized control entity.In these networks,nodes function as senders,receivers,and routers.One such network is the Flying Ad hoc Network(FANET),where nodes operate in three dimensions(3D)using Unmanned Aerial Vehicles(UAVs)that are remotely controlled.With the integration of the Internet of Things(IoT),these nodes form an IoT-enabled network called the Internet of UAVs(IoU).However,the airborne nodes in FANET consume high energy due to their payloads and low-power batteries.An optimal routing approach for communication is essential to address the problem of energy consumption and ensure energy-efficient data transmission in FANET.This paper proposes a novel energy-efficient routing protocol named the Integrated Energy-Efficient Distributed Link Stability Algorithm(IEE-DLSA),featuring a relay mechanism to provide optimal routing with energy efficiency in FANET.The energy efficiency of IEE-DLSA is enhanced using the Red-Black(R-B)tree to ensure the fairness of advanced energy-efficient nodes.Maintaining link stability,transmission loss avoidance,delay awareness with defined threshold metrics,and improving the overall performance of the proposed protocol are the core functionalities of IEE-DLSA.The simulations demonstrate that the proposed protocol performs well compared to traditional FANET routing protocols.The evaluation metrics considered in this study include network delay,packet delivery ratio,network throughput,transmission loss,network stability,and energy consumption.

A Traffic-Aware and Cluster-Based Energy Efficient Routing Protocol for IoT-Assisted WSNs

The seamless integration of intelligent Internet of Things devices with conventional wireless sensor networks has revolutionized data communication for different applications,such as remote health monitoring,industrial monitoring,transportation,and smart agriculture.Efficient and reliable data routing is one of the major challenges in the Internet of Things network due to the heterogeneity of nodes.This paper presents a traffic-aware,cluster-based,and energy-efficient routing protocol that employs traffic-aware and cluster-based techniques to improve the data delivery in such networks.The proposed protocol divides the network into clusters where optimal cluster heads are selected among super and normal nodes based on their residual energies.The protocol considers multi-criteria attributes,i.e.,energy,traffic load,and distance parameters to select the next hop for data delivery towards the base station.The performance of the proposed protocol is evaluated through the network simulator NS3.40.For different traffic rates,number of nodes,and different packet sizes,the proposed protocol outperformed LoRaWAN in terms of end-to-end packet delivery ratio,energy consumption,end-to-end delay,and network lifetime.For 100 nodes,the proposed protocol achieved a 13%improvement in packet delivery ratio,10 ms improvement in delay,and 10 mJ improvement in average energy consumption over LoRaWAN.

Computing Challenges of UAV Networks: A Comprehensive Survey

Devices and networks constantly upgrade,leading to rapid technological evolution.Three-dimensional(3D)point cloud transmission plays a crucial role in aerial computing terminology,facilitating information exchange.Various network types,including sensor networks and 5G mobile networks,support this transmission.Notably,Flying Ad hoc Networks(FANETs)utilize Unmanned Aerial Vehicles(UAVs)as nodes,operating in a 3D environment with Six Degrees of Freedom(6DoF).This study comprehensively surveys UAV networks,focusing on models for Light Detection and Ranging(LiDAR)3D point cloud compression/transmission.Key topics covered include autonomous navigation,challenges in video streaming infrastructure,motivations for Quality of Experience(QoE)enhancement,and avenues for future research.Additionally,the paper conducts an extensive review of UAVs,encompassing current wireless technologies,applications across various sectors,routing protocols,design considerations,security measures,blockchain applications in UAVs,contributions to healthcare systems,and integration with the Internet of Things(IoT),Artificial Intelligence(AI),Machine Learning(ML),and Deep Learning(DL).Furthermore,the paper thoroughly discusses the core contributions of LiDAR 3D point clouds in UAV systems and their future prediction along with mobility models.It also explores the prospects of UAV systems and presents state-of-the-art solutions.

Accelerated Particle Swarm Optimization Algorithm for Efficient Cluster Head Selection in WSN

Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost.One of them is a sensor network with embedded sensors working as the primary nodes,termed Wireless Sensor Networks(WSNs),in which numerous sensors are connected to at least one Base Station(BS).These sensors gather information from the environment and transmit it to a BS or gathering location.WSNs have several challenges,including throughput,energy usage,and network lifetime concerns.Different strategies have been applied to get over these restrictions.Clustering may,therefore,be thought of as the best way to solve such issues.Consequently,it is crucial to analyze effective Cluster Head(CH)selection to maximize efficiency throughput,extend the network lifetime,and minimize energy consumption.This paper proposed an Accelerated Particle Swarm Optimization(APSO)algorithm based on the Low Energy Adaptive Clustering Hierarchy(LEACH),Neighboring Based Energy Efficient Routing(NBEER),Cooperative Energy Efficient Routing(CEER),and Cooperative Relay Neighboring Based Energy Efficient Routing(CR-NBEER)techniques.With the help of APSO in the implementation of the WSN,the main methodology of this article has taken place.The simulation findings in this study demonstrated that the suggested approach uses less energy,with respective energy consumption ranges of 0.1441 to 0.013 for 5 CH,1.003 to 0.0521 for 10 CH,and 0.1734 to 0.0911 for 15 CH.The sending packets ratio was also raised for all three CH selection scenarios,increasing from 659 to 1730.The number of dead nodes likewise dropped for the given combination,falling between 71 and 66.The network lifetime was deemed to have risen based on the results found.A hybrid with a few valuable parameters can further improve the suggested APSO-based protocol.Similar to underwater,WSN can make use of the proposed protocol.The overall results have been evaluated and compared with the existing approaches of sensor networks.

RSK2基因对肝癌SMMC-7721细胞增殖与凋亡的影响

目的:探讨P90核糖体S6激酶2(ribosomal S6 kinase 2,RSK2)基因对肝癌细胞SMMC-7721的增殖与凋亡的影响。方法:将肝癌细胞随机分为5组,即对照组、pcDNA3.1(+)空质粒组、pcDNA3.1(+)/RSK2组、siRNA-阴性对照组、siRNA-RSK2组。对照组不转染,其余4组分别转染pcDNA3.1(+)空质粒、pcDNA3.1(+)/RSK2、siRNA-阴性对照、siRNA-RSK2。转染48 h后,荧光定量PCR(RT-PCR)、免疫印迹法检测RSK2 mRNA和蛋白表达;CCK8实验检测各组细胞增殖能力;流式细胞术检测各组细胞凋亡和周期。结果:RT-PCR和免疫印迹结果显示,pcDNA3.1(+)/RSK2组RSK2 mRNA和蛋白表达量明显高于其他4组(P<0.01);siRNA-RSK2组表达量明显低于其他4组(P<0.01)。CCK8检测结果表明,RSK2基因过表达促进SMMC-7721细胞增殖(P<0.01)。流式细胞术结果显示,RSK2基因沉默阻断细胞周期在G1期(P<0.01),细胞总增殖能力下降(P<0.01);siRNA-RSK2组细胞凋亡率明显高于其他4组(均P<0.01),而pcDNA3.1(+)/RSK2组细胞凋亡率与对照组比较无明显差别(P>0.05)。结论:RSK2过表达可促进SMMC-7721细胞增殖,但对凋亡无影响;RSK2沉默抑制细胞增殖能力,促进细胞凋亡。

41.7%氟吡菌酰胺悬浮剂对栝楼根结线虫病的防治效果

[目的]探究41.7%氟吡菌酰胺悬浮剂防治栝楼根结线虫病的使用方法和使用剂量。[方法]采集、鉴定并室内繁殖安徽省潜山市供试田中栝楼根结线虫;在室内接种试验和大田试验条件下,评价41.7%氟吡菌酰胺悬浮剂对栝楼根结线虫病的防治效果。[结果]安徽省潜山市供试田块栝楼根结线虫病病原为南方根结线虫(Meloidogyne incognita);分别在栝楼出苗后第10和70天,使用41.7%氟吡菌酰胺悬浮剂80~100μL/株,对水1000 mL后灌根,田间栝楼根部病情指数分别降低79.3%和91.4%。[结论]41.7%氟吡菌酰胺悬浮剂灌根可以有效防治栝楼根结线虫病。

Understanding Research Trends in Android Malware Research Using Information Modelling Techniques

Android has been dominating the smartphone market for more than a decade and has managed to capture 87.8%of the market share.Such popularity of Android has drawn the attention of cybercriminals and malware developers.The malicious applications can steal sensitive information like contacts,read personal messages,record calls,send messages to premium-rate numbers,cause financial loss,gain access to the gallery and can access the user’s geographic location.Numerous surveys on Android security have primarily focused on types of malware attack,their propagation,and techniques to mitigate them.To the best of our knowledge,Android malware literature has never been explored using information modelling techniques.Further,promulgation of contemporary research trends in Android malware research has never been done from semantic point of view.This paper intends to identify intellectual core from Android malware literature using Latent Semantic Analysis(LSA).An extensive corpus of 843 articles on Android malware and security,published during 2009–2019,were processed using LSA.Subsequently,the truncated singular Value Decomposition(SVD)technique was used for dimensionality reduction.Later,machine learning methods were deployed to effectively segregate prominent topic solutions with minimal bias.Apropos to observed term and document loading matrix values,this five core research areas and twenty research trends were identified.Further,potential future research directions have been detailed to offer a quick reference for information scientists.The study concludes to the fact that Android security is crucial for pervasive Android devices.Static analysis is the most widely investigated core area within Android security research and is expected to remain in trend in near future.Research trends indicate the need for a faster yet effective model to detect Android applications causing obfuscation,financial attacks and stealing user information.

Engineered Hybrid Materials with Smart Surfaces for Effective Mitigation of Petroleum-Originated Pollutants

The generation and controlled or uncontrolled release of hydrocarbon-contaminated industrial wastewater effluents to water matrices are a major environmental concern.The contaminated water comes to surface in the form of stable emulsions,which sometimes require different techniques to mitigate or separate effectively.Both the crude emulsions and hydrocarbon-contaminated wastewater effluents contain suspended solids,oil/grease,organic matter,toxic elements,salts,and recalcitrant chemicals.Suitable treatment of crude oil emulsions has been one of the most important challenges due to the complex nature and the substantial amount of generated waste.Moreover,the recovery of oil from waste will help meet the increasing demand for oil and its derivatives.In this context,functional nanostructured materials with smart surfaces and switchable wettability properties have gained increasing attention because of their excellent performance in the separation of oil–water emulsions.Recent improvements in the design,composition,morphology,and fine-tuning of polymeric nanostructured materials have resulted in enhanced demulsification functionalities.Herein,we reviewed the environmental impacts of crude oil emulsions and hydrocarbon-contaminated wastewater effluents.Their effective treatments by smart polymeric nanostructured materials with wettability properties have been stated with suitable examples.The fundamental mechanisms underpinning the efficient separation of oil–water emulsions are discussed with suitable examples along with the future perspectives of smart materials.

Syntheses,Spectral Studies and Bioanalyses of Some Triorganotin(IV) Complexes of Cephlaxine

Five novel triorganotin（IV） complexes have been synthesized by refluxing trimethyl, triethyl, tributyl, triphenyl and tribenzyltin chloride with Cephlaxine. These compounds were characterized by spectroscopic （IR, IH, 13C, 119Sn NMR） techniques and elemental analysis. The results obtained through these techniques are in full agreement with the proposed 1：1 stoichiometry. The synthesized compounds were than tested against various microorganisms and fungi. The results of new products obtained showed that the triphenyltin（IV） complex displayed promising activity against all types of bacteria and fungi used while all other compounds showed significant antibacterial and antifungal activity.

CEER:Cooperative Energy-Efficient Routing Mechanism for Underwater Wireless Sensor Networks Using Clusters

Underwater acoustic sensor networks(UWASNs)aim to find varied offshore ocean monitoring and exploration applications.In most of these applications,the network is composed of several sensor nodes deployed at different depths in the water.Sensor nodes located at depth on the seafloor cannot invariably communicate with nodes close to the surface level;these nodes need multihop communication facilitated by a suitable routing scheme.In this research work,a Cluster-based Cooperative Energy Efficient Routing(CEER)mechanism for UWSNs is proposed to overcome the shortcomings of the Co-UWSN and LEACH mechanisms.The optimal role of clustering and cooperation provides load balancing and improves the network profoundly.The simulation results using MATLAB show better performance of CEER routing protocol in terms of various parameters as compared to Co-UWSN routing protocol,i.e.,the average end-to-end delay of CEER was 17.39,Co-UWSN was 55.819 and LEACH was 70.08.In addition,the average total energy consumption of CEER was 9.273,Co-UWSN was 12.198,and LEACH was 45.33.The packet delivery ratio of CEER was 53.955,CO-UWSN was 42.047,and LEACH was 30.31.The stability period CEER was 130.9,CO-UWSN was 129.3,and LEACH was 119.1.The obtained results maximized the lifetime and improved the overall performance of the CEER routing protocol.

Estimating Fuel-Efficient Air Plane Trajectories Using Machine Lear

Airline industry has witnessed a tremendous growth in the recent past.Percentage of people choosing air travel as first choice to commute is continuously increasing.Highly demanding and congested air routes are resulting in inadvertent delays,additional fuel consumption and high emission of greenhouse gases.Trajectory planning involves creation identification of cost-effective flight plans for optimal utilizationof fuel and time.This situation warrants the need of an intelligent system for dynamic planning of optimized flight trajectories with least human intervention required.In this paper,an algorithm for dynamic planning of optimized flight trajectories has been proposed.The proposed algorithm divides the airspace into four dimensional cubes and calculate a dynamic score for each cube to cumulatively represent estimated weather,aerodynamic drag and air traffic within that virtual cube.There are several constraints like simultaneous flight separation rules,weather conditions like air temperature,pressure,humidity,wind speed and direction that pose a real challenge for calculating optimal flight trajectories.To validate the proposed methodology,a case analysis was undertaken within Indian airspace.The flight routes were simulated for four different air routes within Indian airspace.The experiment results observed a seven percent reduction in drag values on the predicted path,hence indicates reduction in carbon footprint and better fuel economy.

LSTDA: Link Stability and Transmission Delay Aware Routing Mechanism for Flying Ad-Hoc Network (FANET)

The paper presents a new protocol called Link Stability and Transmission Delay Aware(LSTDA)for Flying Adhoc Network(FANET)with a focus on network corridors(NC).FANET consists of Unmanned Aerial Vehicles(UAVs)that face challenges in avoiding transmission loss and delay while ensuring stable communication.The proposed protocol introduces a novel link stability with network corridors priority node selection to check and ensure fair communication in the entire network.The protocol uses a Red-Black(R-B)tree to achieve maximum channel utilization and an advanced relay approach.The paper evaluates LSTDA in terms of End-to-End Delay(E2ED),Packet Delivery Ratio(PDR),Network Lifetime(NLT),and Transmission Loss(TL),and compares it with existing methods such as Link Stability Estimation-based Routing(LEPR),Distributed Priority Tree-based Routing(DPTR),and Delay and Link Stability Aware(DLSA)using MATLAB simulations.The results show that LSTDA outperforms the other protocols,with lower average delay,higher average PDR,longer average NLT,and comparable average TL.

A Two-Tier Framework Based on GoogLeNet and YOLOv3 Models for Tumor Detection in MRI

Medical Image Analysis(MIA)is one of the active research areas in computer vision,where brain tumor detection is the most investigated domain among researchers due to its deadly nature.Brain tumor detection in magnetic resonance imaging(MRI)assists radiologists for better analysis about the exact size and location of the tumor.However,the existing systems may not efficiently classify the human brain tumors with significantly higher accuracies.In addition,smart and easily implementable approaches are unavailable in 2D and 3D medical images,which is the main problem in detecting the tumor.In this paper,we investigate various deep learning models for the detection and localization of the tumor in MRI.A novel twotier framework is proposed where the first tire classifies normal and tumor MRI followed by tumor regions localization in the second tire.Furthermore,in this paper,we introduce a well-annotated dataset comprised of tumor and normal images.The experimental results demonstrate the effectiveness of the proposed framework by achieving 97%accuracy using GoogLeNet on the proposed dataset for classification and 83%for localization tasks after finetuning the pre-trained you only look once(YOLO)v3 model.

Impairments Approximations in Assembled mmWave and Radio Over Fiber Network

The fiber nonlinearity and phase noise(PN)are the focused impairments in the optical communication system,induced by high-capacity transmission and high laser input power.The channels include high-capacity transmissions that cannot be achieved at the end side without aliasing because of fiber nonlinearity and PN impairments.Thus,addressing of these distortions is the basic objective for the 5G mobile network.In this paper,the fiber nonlinearity and PN are investigated using the assembled methodology of millimeter-wave and radio over fiber(mmWave-RoF).The analytical model is designed in terms of outage probability for the proposed mmWave-RoF system.The performance of mmWave-RoF against fiber nonlinearity and PN is studied for input power,output power and length using peak to average power ratio(PAPR)and bit error rate(BER)measuring parameters.The simulation outcomes present that the impacts of fiber nonlinearity and PNcan be balanced for a huge capacity mmWave-RoF model applying input power carefully.

Hydro-meteorological characteristics of Chitral River basin at the peak of the Hindukush range

This paper presents the impact of mean maximum temperature on Chitral river basin situated at Chitral district and high altitude (>6000 m) peaks of the Hindukush range under changing climate in Pakistan. The analysis of Chitral River as one of the tributary of Kabul River—the second largest river of Pakistan—revealed that change in temperature has a profound influence on the snow/glacial melt in comparison to the mean monthly rainfall. This is because the studied river is faded by the snow and glacial melt and receives a lot of snowfall from winter (DecFeb) to pre-monsoon (April-May). In monsoon period (Jul-Sep), 30% of the time the discharge rate remains above the mean while 60% of the time the discharge is less than the mean in the pre-monsoon (April-May) period. It means that 10% of the time the discharge is in reach of 300% to 900% of the mean flow, showing a rise in water yield and river discharge rate due to increase in mean monthly maximum temperature. Due to this significant increase (p < 0.05), the glaciers start melting faster and disappear in early summer, hence, reducing their residency period to convert into ice. This shows the signals of changing climate transfer into hydrological changes in Pakistan. Our findings are important for agriculture, hydropower and water management sectors for future planning especially in dry season for sustainable food security and for operation of ydrological installations in the country.

不同药剂拌种对小麦根腐病和孢囊线虫病的防效

在田间条件下研究了150 g·L^(-1)苯醚·阿维·吡等10种药剂拌种对小麦根腐病和孢囊线虫病的防治效果。结果表明:30 g·L^(-1)苯醚甲环唑和60 g·L^(-1)戊唑醇拌种处理对小麦根腐病防治效果分别为73.9%和66.2%,但二者对小麦孢囊线虫无防治效果; 50 g·L^(-1)阿维菌素和140 g·L^(-1)阿维·吡拌种处理后,土壤中校正孢囊减退率分别为58.4%和69.2%;使用600 g·L^(-1)吡虫啉拌种对孢囊线虫未见明显防治效果,但吡虫啉能对阿维菌素的杀线作用有一定增效作用;复配剂150 g·L^(-1)苯醚·阿维·吡和160 g·L^(-1)戊·阿维·吡对小麦根腐病和小麦孢囊线虫病防治效果较好,二者对小麦根腐病防治效果分别为75.6%和63.8%,校正孢囊减退率分别为71.3%和74.1%;从增产效果来看,150 g·L^(-1)苯醚·阿维·吡拌种后小麦增产效果最好,平均产量为7 869.9 kg·hm^(-2),增产率为8.6%。综合药剂安全性、防病效果和增产效果,150 g·L^(-1)苯醚·阿维·吡适合在生产上防治小麦根腐病和孢囊线虫病。

piEnPred:a bi-layered discriminative model for enhancers and their subtypes via novel cascade multi-level subset feature selection algorithm

Enhancers are short DNA cis-elements that can be bound by proteins(activators)to increase the possibility that transcription of a particular gene will occur.The Enhancers perform a significant role in the formation of proteins and regulating the gene transcription process.Human diseases such as cancer,inflammatory bowel disease,Parkinson’s,addiction,and schizophrenia are due to genetic variation in enhancers.In the current study,we havemade an effort by building,amore robust and novel computational a bi-layered model.The representative feature vector was constructed over a linear combination of six features.The optimum Hybrid feature vector was obtained via the Novel Cascade Multi-Level Subset Feature selection(CMSFS)algorithm.The first layer predicts the enhancer,and the secondary layer carries the prediction of their subtypes.The baseline model obtained 87.88%of accuracy,95.29%of sensitivity,80.47%of specificity,0.766 of MCC,and 0.9603 of a roc value on Layer-1.Similarly,the model obtained 68.24%,65.54%,70.95%,0.3654,and 0.7568 as an Accuracy,sensitivity,specificity,MCC,and ROC values on layer-2 respectively.Over an independent dataset on layer-1,the piEnPred secured 80.4%accuracy,82.5%of sensitivity,78.4%of specificity,and 0.6099 as MCC,respectively.Subsequently,the proposed predictor obtained 72.5%of accuracy,70.0%of sensitivity,75%of specificity,and 0.4506 of MCC on layer-2,respectively.The proposed method remarkably performed in contrast to other state-of-the-art predictors.For the convenience of most experimental scientists,a user-friendly and publicly freely accessible web server@/bienhancer dot pythonanywhere dot com has been developed.