巴基斯坦西瓦里克道克派珊组豕脊齿象属(长鼻目)新材料（英文）

褶齿豕脊齿象(Choerolophodon corrugatus)是豕脊齿象属在巴基斯坦西瓦里克群发现的唯一种。其首次发现是在西瓦里克亚群下部的卡米列组(中新世晚期),而后大量见于西瓦里克亚群中段的道克派珊组(中新世晚期至上新世早期)。本次报道的褶齿豕脊齿象新材料发现于道克派珊模式产地及其邻近区域。发现的材料包括象牙、下颌、上颌碎片以及一些乳前臼齿和臼齿。新材料与欧洲的Ch. pentelici的牙齿特征极其相似,但在头骨和下颌特征上有所区别。

Velocity and Structural Modeling of Mesozoic Chiltan Limestone and Goru Formation for Hydrocarbon Evaluation in the Bitrisim Area, Lower Indus Basin, Pakistan

The present study focuses on building a workflow for structural interpretation and velocity modeling and implementing to Jurassic-Cretaceous succession(Chiltan Limestone and Massive sand of the Lower Goru Formation).2D-Migrated seismic sections of the area are used as data set and in order to confirm the presence of hydrocarbons in the study area,P and S-wave seismic velocities are estimated from single-component seismic data.Some specific issues in the use of seismic data for modeling and hydrocarbon evaluation need to deal with including distinguishing the reservoir and cap rocks,and the effects of faults,folds and presence of hydrocarbons on these rocks.This study has carried out the structural interpretation and modeling of the seismic data for the identification of traps.The results demonstrate existence of appropriate structural traps in the form of horst and grabens in the area.2D and 3D velocity modeling of the horizons indicates the presence of high velocity zones in the eastern half of the study while relatively low velocity zones are encountered in the western half of the area.Two wells were drilled in the study area(i.e.Fateh-01 and Ichhri-01)and both are dry.Immature hydrocarbons migration is considered as a failure reason for Fateh-01 and Ichhri-01 well.

A Periglacial Palaeoenvionment in the Upper Carboniferous-Lower Permian Tobra Formation of the Salt Range,Pakistan

The Upper Carboniferous—Lower Permian(Upper Pennsylvanian-Asselian) Tobra Formation is exposed in the Salt and Trans Indus ranges of Pakistan.The formation exhibits an alluvial plain(alluvial fan-piedmont alluvial plain) facies association in the Salt Range and Khisor Range.In addition,a stream flow facies association is restricted to the eastern Salt Range.The alluvial plain facies association is comprised of clast-supported massive conglomerate(Gmc),diamictite(Dm)facies,and massive sandstone(Sm) Hthofacies whereas the stream flow-dominated alluvial plain facies association includes fine-grained sandstone and siltstone(Fss),fining upwards pebbly sandstone(Sf),and massive mudstone(Fm) Hthofacies.The lack of glacial signatures(particularly glacial grooves and striatums) in the deposits in the Tobra Formation,which are,in contrast,present in their timeequivalent and palaeogeographically nearby strata of the Arabian peninsula,e.g.the AI Khlata Formation of Oman and Unayzah B member of the Saudi Arabia,suggests a pro-to periglacial,i.e.glaciofluvial depositional setting for the Tobra Formation.The sedimentology of the Tobra Formation attests that the Salt Range,Pakistan,occupied a palaeogeographic position just beyond the maximum glacial extent during Upper Pennsylvanian-Asselian time.

Understanding the Language of ISIS:An Empirical Approach to Detect Radical Content on Twitter Using Machine Learning

The internet,particularly online social networking platforms have revolutionized the way extremist groups are influencing and radicalizing individuals.Recent research reveals that the process initiates by exposing vast audiences to extremist content and then migrating potential victims to confined platforms for intensive radicalization.Consequently,social networks have evolved as a persuasive tool for extremism aiding as recruitment platform and psychological warfare.Thus,recognizing potential radical text or material is vital to restrict the circulation of the extremist chronicle.The aim of this research work is to identify radical text in social media.Our contributions are as follows:(i)A new dataset to be employed in radicalization detection;(ii)In depth analysis of new and previous datasets so that the variation in extremist group narrative could be identified;(iii)An approach to train classifier employing religious features along with radical features to detect radicalization;(iv)Observing the use of violent and bad words in radical,neutral and random groups by employing violent,terrorism and bad words dictionaries.Our research results clearly indicate that incorporating religious text in model training improves the accuracy,precision,recall,and F1-score of the classifiers.Secondly a variation in extremist narrative has been observed implying that usage of new dataset can have substantial effect on classifier performance.In addition to this,violence and bad words are creating a differentiating factor between radical and random users but for neutral(anti-ISIS)group it needs further investigation.

Early-Stage Segmentation and Characterization of Brain Tumor

Gliomas are the most aggressive brain tumors caused by the abnormal growth of brain tissues.The life expectancy of patients diagnosed with gliomas decreases exponentially.Most gliomas are diagnosed in later stages,resulting in imminent death.On average,patients do not survive 14 months after diagnosis.The only way to minimize the impact of this inevitable disease is through early diagnosis.The Magnetic Resonance Imaging(MRI)scans,because of their better tissue contrast,are most frequently used to assess the brain tissues.The manual classification of MRI scans takes a reasonable amount of time to classify brain tumors.Besides this,dealing with MRI scans manually is also cumbersome,thus affects the classification accuracy.To eradicate this problem,researchers have come up with automatic and semiautomatic methods that help in the automation of brain tumor classification task.Although,many techniques have been devised to address this issue,the existing methods still struggle to characterize the enhancing region.This is because of low variance in enhancing region which give poor contrast in MRI scans.In this study,we propose a novel deep learning based method consisting of a series of steps,namely:data pre-processing,patch extraction,patch pre-processing,and a deep learning model with tuned hyper-parameters to classify all types of gliomas with a focus on enhancing region.Our trained model achieved better results for all glioma classes including the enhancing region.The improved performance of our technique can be attributed to several factors.Firstly,the non-local mean filter in the pre-processing step,improved the image detail while removing irrelevant noise.Secondly,the architecture we employ can capture the non-linearity of all classes including the enhancing region.Overall,the segmentation scores achieved on the Dice Similarity Coefficient(DSC)metric for normal,necrosis,edema,enhancing and non-enhancing tumor classes are 0.95,0.97,0.91,0.93,0.95;respectively.

SDN Based DDos Mitigating Approach Using Traffic Entropy for IoT Network

The Internet of Things(IoT)has been widely adopted in various domains including smart cities,healthcare,smart factories,etc.In the last few years,the fitness industry has been reshaped by the introduction of smart fitness solutions for individuals as well as fitness gyms.The IoT fitness devices collect trainee data that is being used for various decision-making.However,it will face numerous security and privacy issues towards its realization.This work focuses on IoT security,especially DoS/DDoS attacks.In this paper,we have proposed a novel blockchain-enabled protocol(BEP)that uses the notion of a self-exposing node(SEN)approach for securing fitness IoT applications.The blockchain and SDN architectures are employed to enhance IoT security by a highly preventive security monitoring,analysis and response system.The proposed approach helps in detecting the DoS/DDoS attacks on the IoT fitness system and then mitigating the attacks.The BEP is used for handling Blockchain-related activities and SEN could be a sensor or actuator node within the fitness IoT system.SEN provides information about the inbound and outbound traffic to the BEP which is used to analyze the DoS/DDoS attacks on the fitness IoT system.The SENcalculates the inbound and outbound traffic features’entropies and transmits them to the Blockchain in the form of transaction blocks.The BEP picks the whole mined blocks’transactions and transfers them to the SDN controller node.The controller node correlates the entropies data of SENs and decides about the DoS or DDoS attack.So,there are two decision points,one is SEN,and another is the controller.To evaluate the performance of our proposed system,several experiments are performed and results concerning the entropy values and attack detection rate are obtained.The proposed approach has outperformed the other two approaches concerning the attack detection rate by an increase of 11%and 18%against Approach 1 and Approach 2 respectively.

Adaptive Runtime Monitoring of Service Level Agreement Violations in Cloud Computing

The cloud service level agreement(SLA)manage the relationship between service providers and consumers in cloud computing.SLA is an integral and critical part of modern era IT vendors and communication contracts.Due to low cost and flexibility more and more consumers delegate their tasks to cloud providers,the SLA emerges as a key aspect between the consumers and providers.Continuous monitoring of Quality of Service(QoS)attributes is required to implement SLAs because of the complex nature of cloud communication.Many other factors,such as user reliability,satisfaction,and penalty on violations are also taken into account.Currently,there is no such policy of cloud SLA monitoring to minimize SLA violations.In this work,we have proposed a cloud SLA monitoring policy by dividing a monitoring session into two parts,for critical and non-critical parameters.The critical and non-critical parameters will be decided on the interest of the consumer during SLA negotiation.This will help to shape a new comprehensive SLA based Proactive Resource Allocation Approach(RPAA)which will monitor SLA at runtime,analyze the SLA parameters and try to find the possibility of SLA violations.We also have implemented an adaptive system for allocating cloud IT resources based on SLA violations and detection.We have defined two main components of SLA-PRAA i.e.,(a)Handler and(b)Accounting and Billing Manager.We have also described the function of both components through algorithms.The experimental results validate the performance of our proposed method in comparison with state-of-the-art cloud SLA policies.

A New Class of L-Moments Based Calibration Variance Estimators

Variance is one of themost important measures of descriptive statistics and commonly used for statistical analysis.The traditional second-order central moment based variance estimation is a widely utilized methodology.However,traditional variance estimator is highly affected in the presence of extreme values.So this paper initially,proposes two classes of calibration estimators based on an adaptation of the estimators recently proposed by Koyuncu and then presents a new class of L-Moments based calibration variance estimators utilizing L-Moments characteristics(L-location,Lscale,L-CV)and auxiliary information.It is demonstrated that the proposed L-Moments based calibration variance estimators are more efficient than adapted ones.Artificial data is considered for assessing the performance of the proposed estimators.We also demonstrated an application related to apple fruit for purposes of the article.Using artificial and real data sets,percentage relative efficiency(PRE)of the proposed class of estimators with respect to adapted ones are calculated.The PRE results indicate to the superiority of the proposed class over adapted ones in the presence of extreme values.In this manner,the proposed class of estimators could be applied over an expansive range of survey sampling whenever auxiliary information is available in the presence of extreme values.

An Efficient Impersonation Attack Detection Method in Fog Computing

Fog computing paradigm extends computing,communication,storage,and network resources to the network’s edge.As the fog layer is located between cloud and end-users,it can provide more convenience and timely services to end-users.However,in fog computing(FC),attackers can behave as real fog nodes or end-users to provide malicious services in the network.The attacker acts as an impersonator to impersonate other legitimate users.Therefore,in this work,we present a detection technique to secure the FC environment.First,we model a physical layer key generation based on wireless channel characteristics.To generate the secret keys between the legitimate users and avoid impersonators,we then consider a Double Sarsa technique to identify the impersonators at the receiver end.We compare our proposed Double Sarsa technique with the other two methods to validate our work,i.e.,Sarsa and Q-learning.The simulation results demonstrate that the method based on Double Sarsa outperforms Sarsa and Q-learning approaches in terms of false alarm rate(FAR),miss detection rate(MDR),and average error rate(AER).

A Regression Type Estimator with Two Auxiliary Variables for Two-Phase Sampling

This paper is an extension of Hanif, Hamad and Shahbaz estimator [1] for two-phase sampling. The aim of this paper is to develop a regression type estimator with two auxiliary variables for two-phase sampling when we don’t have any type of information about auxiliary variables at population level. To avoid multi-collinearity, it is assumed that both auxiliary variables have minimum correlation. Mean square error and bias of proposed estimator in two-phase sampling is derived. Mean square error of proposed estimator shows an improvement over other well known estimators under the same case.

Characteristics and Growing Stocks Volume of Forest Stand in Dry Temperate Forest of Chilas Gilgit-Baltistan

Chilas forest sub division in Diamer district, of Gilgit-Baltistan is located at northern regions of Pakistan. We estimated tree density, diameter, height and volume of the dominant tree species in four blocks (Thore, Chilas, Thak Niat and Gunar) of Chilas forest sub division. The tree density of deodar was maximum with average 26 tree·ha-1 and minimum was of Chalgoza 4 trees·ha-1. The maximum average height showed by the dominant species (Fir, Kail, Deodar, and Chilgoza) of the study area to be 20.40, 16.06, 12.24 and 12.12 m respectively. Moreover the average maximum volume attained by the Kail, Fir, Deodar and Chalgoza trees was 1.92, 1.57, 0.46 and 0.291 m3·tree-1 respectively. Regression analysis was carried out to determine the relationship between diameter (cm), height (m), tree density (trees·ha-1) and volume (m3·ha-1). The findings of the study will help the future scientific management of the forest for sustained yield. The study also provides information about the unexplored growing stock and structure of the forests. Additionally, this study will help to understand the patterns of tree species composition and diversity in the northern part of Pakistan with dry temperate climate.

Geochemical investigation of low latitude black shale intervals of the Lower to Middle Jurassic succession, Indus Basin, Pakistan

The Lower to Middle Jurassic sedimentary succession is dominated by siliciclastics with a significant amount of black shales in the Indus Basin,Pakistan.Several outcrop samples have been studied using an integrated approach to interpret the conceptual depositional setting from carbon and oxygen isotopes(δ^(13)C&δ18O),organic geochemistry,and palynofacies with major and trace element analysis.For interpretation of trace element data,various single and elemental ratios have been used in this research to unlock the geological history of the studied strata.Ti/Al is 1.96 for high-potential source rock and 7.82 for non-potential source rock,and Cr(less than 1)indicates low clastic input with low oxygen for stratified and stagnant water.The ratios of V/(V+Cr),V/(V+Ni),V/Mo,V/Ni,(Cu+Mo)/Zn,Mo/Al,isotopic values ofδ^(13)C andδ18O and besides the V/Cr elemental ratio,all proxies indicate that there are oxygen-depleted anoxic conditions at high potentials,while in non-potential source rock,these ratios show oxic to sub-oxic settings.In addition to the trace element correlation with total organic carbon,the influx of organic matter is determined by the palynoafacies analysis,which indicates mixed terrestrial and marine organic influx in high-potential source rock and vice versa.Furthermore,the studies of palynofaceis DFPF A-D and SFPF A-B suggest that the depositional setting of black shale occurred in the anoxic proximal to distal shelf.The results suggest that the regional and local occurrence of black shale during the Lower to Middle Jurassic and its geological condition were addressed,and these play an important role in its depositional and paleooceanographic setting in the Eastern Tethys.

Inhibitive efficacy of Nymphoides indica rhizome extract onα-glucosidase, and cross-link formation of advanced glycation end products

OBJECTIVE: To investigate the inhibitive efficacy of Nymphoides Indica(L.) Kuntze rhizome extract onα-glucosidase and on cross-link formation of advanced glycation end products(AGEs).METHODS: The plant extracts were prepared by cold maceration and fractionated in solvents of diverse polarity. The in vitro α-glucosidase inhibition assay, fluorescence spectrometry and SDS-PAGE analysis was performed for antiglycation assays.RESULTS: During α-glucosidase inhibition assay significant inhibition by chloroform(0.43 mg/mL)and methanol fractions(0.66 mg/mL) was noticed.During the AGEs inhibition assay, both oxidative(BSA-MGO) and non-oxidative(BSA-glucose)modes were employed. The inhibition of AGEs by total extract was considered moderate(IC_(50)0.10 mg/mL) as a result of non-oxidative mode, whereas in case of oxidative mode(BASA-MGO) no activity was recorded. Among fractions the methanolic fraction presented significant results both in oxidative(IC_(50)0.01 mg/mL) and non-oxidative modes(IC_(50) 0.3 mg/mL). Likewise the ethyl acetate fraction was more active in non-oxidative mode(IC_(50)0.04 mg/mL) compared to oxidative mode(IC_(50)0.32 mg/mL).During assay for inhibition of cross-link formation,the chloroform fraction significantly inhibited cross-link formation in a dose dependent mode.CONCLUSION: It was finally concluded that N. Indica rhizome extract possesses significant properties that inhibit α-glucosidase, and AGEs cross-link formation.