Evolution of tectonic landscapes and deformation in the southeast Kumaun and western Nepal Himalaya

The area of the present investigation’s expanse constitutes the southernmost extent of the southeast Kumaun Himalaya and western Nepal Himalaya.Multidisciplinary approaches have been employed to understand the landforms associated with tectonic deformation,through detailed field investigation supplemented by the geodetic,chronological,and morphometric database.The morphogenic expressions of the Main Boundary Thrust(MBT)are reflected in the form of~25 km long E-W trending north dipping fault scarp.The deformation along the strike length of the Himalayan Frontal Thrust(HFT)is noticed in the form of uplifted and incised fill terraces,and strath terraces.The deformation within the fluvial sequences in the study area can be visualized in the form of sheared boulders and pebbles,tilted and faulted terrace deposits.Furthermore,the chronological data of fluvial landforms in the study area suggests two major phases of tectonic deformations that have occurred around 58.7±10.8 ka and 3.88±0.4 ka.The chronology of late-Quaternary landforms advocates that the initial stage of aggradations in the Himalayan foothills commenced around 75.1±0.58 ka.The aggradational landforms resulted from the diverse depositional regime as evident from the nature of the sediment sequences from clasts dominated to thick mud sequences.The rate of deformation in the southeastern Kumaun and western Nepal Himalaya is±7 mm/yr,as per the data obtained from the Persistent Scatterer Interferometric Synthetic Aperture Radar(PSInSAR).The landform deformation pattern,phase of incision and aggradation,frequent occurrence of landslides,and recent past earthquake activity within the wide zone of the HFT,the MBT,and Ramgarh Thrust suggests that the southernmost front of the Kumaun Himalaya is active and has potential for future geohazard.The foothill zone of Himalayan towns are actively growing in terms of population and infrastructural development.Therefore,such intradisciplinary studies for tectonically active regions are needed for future infrastructural development.

A New Method for Diagnosis of Leukemia Utilizing a Hybrid DL-ML Approach for Binary and Multi-Class Classification on a Limited-Sized Database

Infection of leukemia in humans causes many complications in its later stages.It impairs bone marrow’s ability to produce blood.Morphological diagnosis of human blood cells is a well-known and well-proven technique for diagnosis in this case.The binary classification is employed to distinguish between normal and leukemiainfected cells.In addition,various subtypes of leukemia require different treatments.These sub-classes must also be detected to obtain an accurate diagnosis of the type of leukemia.This entails using multi-class classification to determine the leukemia subtype.This is usually done using a microscopic examination of these blood cells.Due to the requirement of a trained pathologist,the decision process is critical,which leads to the development of an automated software framework for diagnosis.Researchers utilized state-of-the-art machine learning approaches,such as Support Vector Machine(SVM),Random Forest(RF),Na飗e Bayes,K-Nearest Neighbor(KNN),and others,to provide limited accuracies of classification.More advanced deep-learning methods are also utilized.Due to constrained dataset sizes,these approaches result in over-fitting,reducing their outstanding performances.This study introduces a deep learning-machine learning combined approach for leukemia diagnosis.It uses deep transfer learning frameworks to extract and classify features using state-of-the-artmachine learning classifiers.The transfer learning frameworks such as VGGNet,Xception,InceptionResV2,Densenet,and ResNet are employed as feature extractors.The extracted features are given to RF and XGBoost classifiers for the binary and multi-class classification of leukemia cells.For the experimentation,a very popular ALL-IDB dataset is used,approaching a maximum accuracy of 100%.A private real images dataset with three subclasses of leukemia images,including Acute Myloid Leukemia(AML),Chronic Lymphocytic Leukemia(CLL),and Chronic Myloid Leukemia(CML),is also employed to generalize the system.This dataset achieves an impressive multi-class classification accuracy of 97.08%.The proposed approach is robust and generalized by a standardized dataset and the real image dataset with a limited sample size(520 images).Hence,this method can be explored further for leukemia diagnosis having a limited number of dataset samples.

Enhanced Clustering Based OSN Privacy Preservation to Ensure k-Anonymity, t-Closeness, l-Diversity, and Balanced Privacy Utility

Online Social Networks (OSN) sites allow end-users to share agreat deal of information, which may also contain sensitive information,that may be subject to commercial or non-commercial privacy attacks. Asa result, guaranteeing various levels of privacy is critical while publishingdata by OSNs. The clustering-based solutions proved an effective mechanismto achieve the privacy notions in OSNs. But fixed clustering limits theperformance and scalability. Data utility degrades with increased privacy,so balancing the privacy utility trade-off is an open research issue. Theresearch has proposed a novel privacy preservation model using the enhancedclustering mechanism to overcome this issue. The proposed model includesphases like pre-processing, enhanced clustering, and ensuring privacy preservation.The enhanced clustering algorithm is the second phase where authorsmodified the existing fixed k-means clustering using the threshold approach.The threshold value is determined based on the supplied OSN data of edges,nodes, and user attributes. Clusters are k-anonymized with multiple graphproperties by a novel one-pass algorithm. After achieving the k-anonymityof clusters, optimization was performed to achieve all privacy models, suchas k-anonymity, t-closeness, and l-diversity. The proposed privacy frameworkachieves privacy of all three network components, i.e., link, node, and userattributes, with improved utility. The authors compare the proposed techniqueto underlying methods using OSN Yelp and Facebook datasets. The proposedapproach outperformed the underlying state of art methods for Degree ofAnonymization, computational efficiency, and information loss.

Iris Liveness Detection Using Fragmental Energy of Haar Transformed Iris Images Using Ensemble of Machine Learning Classifiers

Contactless verification is possible with iris biometric identification,which helps prevent infections like COVID-19 from spreading.Biometric systems have grown unsteady and dangerous as a result of spoofing assaults employing contact lenses,replayed the video,and print attacks.The work demonstrates an iris liveness detection approach by utilizing fragmental coefficients of Haar transformed Iris images as signatures to prevent spoofing attacks for the very first time in the identification of iris liveness.Seven assorted feature creation ways are studied in the presented solutions,and these created features are explored for the training of eight distinct machine learning classifiers and ensembles.The predicted iris liveness identification variants are evaluated using recall,F-measure,precision,accuracy,APCER,BPCER,and ACER.Three standard datasets were used in the investigation.The main contribution of our study is achieving a good accuracy of 99.18%with a smaller feature vector.The fragmental coefficients of Haar transformed iris image of size 8∗8 utilizing random forest algorithm showed superior iris liveness detection with reduced featured vector size(64 features).Random forest gave 99.18%accuracy.Additionally,conduct an extensive experiment on cross datasets for detailed analysis.The results of our experiments showthat the iris biometric template is decreased in size tomake the proposed framework suitable for algorithmic verification in real-time environments and settings.

Challenges and Limitations in Speech Recognition Technology:A Critical Review of Speech Signal Processing Algorithms,Tools and Systems

Speech recognition systems have become a unique human-computer interaction(HCI)family.Speech is one of the most naturally developed human abilities;speech signal processing opens up a transparent and hand-free computation experience.This paper aims to present a retrospective yet modern approach to the world of speech recognition systems.The development journey of ASR(Automatic Speech Recognition)has seen quite a few milestones and breakthrough technologies that have been highlighted in this paper.A step-by-step rundown of the fundamental stages in developing speech recognition systems has been presented,along with a brief discussion of various modern-day developments and applications in this domain.This review paper aims to summarize and provide a beginning point for those starting in the vast field of speech signal processing.Since speech recognition has a vast potential in various industries like telecommunication,emotion recognition,healthcare,etc.,this review would be helpful to researchers who aim at exploring more applications that society can quickly adopt in future years of evolution.

Electro-magneto-hydrodynamic flow of couple stress nanofluids in micro-peristaltic channel with slip and convective conditions

This study explores the effects of electro-magneto-hydrodynamics,Hall currents,and convective and slip boundary conditions on the peristaltic propulsion of nanofluids(considered as couple stress nanofluids)through porous symmetric microchannels.The phenomena of energy and mass transfer are considered under thermal radiation and heat source/sink.The governing equations are modeled and non-dimensionalized under appropriate dimensionless quantities.The resulting system is solved numerically with MATHEMATICA(with an in-built function,namely the Runge-Kutta scheme).Graphical results are presented for various fluid flow quantities,such as the velocity,the nanoparticle temperature,the nanoparticle concentration,the skin friction,the nanoparticle heat transfer coefficient,the nanoparticle concentration coefficient,and the trapping phenomena.The results indicate that the nanoparticle heat transfer coefficient is enhanced for the larger values of thermophoresis parameters.Furthermore,an intriguing phenomenon is observed in trapping:the trapped bolus is expanded with an increase in the Hartmann number.However,the bolus size decreases with the increasing values of both the Darcy number and the electroosmotic parameter.

A Constant Gain and Miniaturized Antipodal Vivaldi Antenna for 5G Communication Applications

This paper proposes a stable gain and a compact Antipodal Vivaldi Antenna(AVA)for a 38GHz band of 5G communication.A novel compact AVA is designed to provide constant gain,high front to back ratio(FBR),and very high efficiency.The performance of the proposed AVA is enhanced with the help of a dielectric lens(DL)and corrugations.A rectangular-shaped DL is incorporated in conventional AVA(CAVA)to enhance its gain up to 1 dBi and the bandwidth by 1.8 GHz.Next,the rectangular corrugations are implemented in CAVA with lens(CAVA-L)to further improve the gain and bandwidth.The proposed AVA with lens and corrugations(AVA-LC)gives a constant and high gain of 8.2 to 9 dBi.The designed AVA-LC operates from 34 to 45GHz frequency which covers 38GHz(37.5 to 43.5 GHz)band of 5G applications.Further,the presented AVA-LC mitigates the back lobe and sidelobe levels,resulting in FBR and efficiency improvement.The FBR is in the range of 12.2 to 22 dB,and efficiency is 99%,almost constant.The AVA-LC is fabricated on Roger’s RT/duroid 5880 substrate,and it is tested to verify the simulated results.The proposed compact AVA-LC with high gain,an improved FBR,excellent efficiency,and stable radiation patterns is suitable for the 38GHz band of 5G devices.

Implications of future climate change on crop and irrigation water requirements in a semi-arid river basin using CMIP6 GCMs

Agriculture faces risks due to increasing stress from climate change,particularly in semi-arid regions.Lack of understanding of crop water requirement(CWR)and irrigation water requirement(IWR)in a changing climate may result in crop failure and socioeconomic problems that can become detrimental to agriculture-based economies in emerging nations worldwide.Previous research in CWR and IWR has largely focused on large river basins and scenarios from the Coupled Model Intercomparison Project Phase 3(CMIP3)and Coupled Model Intercomparison Project Phase 5(CMIP5)to account for the impacts of climate change on crops.Smaller basins,however,are more susceptible to regional climate change,with more significant impacts on crops.This study estimates CWRs and IWRs for five crops(sugarcane,wheat,cotton,sorghum,and soybean)in the Pravara River Basin(area of 6537 km^(2))of India using outputs from the most recent Coupled Model Intercomparison Project Phase 6(CMIP6)General Circulation Models(GCMs)under Shared Socio-economic Pathway(SSP)245 and SSP585 scenarios.An increase in mean annual rainfall is projected under both scenarios in the 2050s and 2080s using ten selected CMIP6 GCMs.CWRs for all crops may decline in almost all of the CMIP6 GCMs in the 2050s and 2080s(with the exceptions of ACCESS-CM-2 and ACCESS-ESM-1.5)under SSP245 and SSP585 scenarios.The availability of increasing soil moisture in the root zone due to increasing rainfall and a decrease in the projected maximum temperature may be responsible for this decline in CWR.Similarly,except for soybean and cotton,the projected IWRs for all other three crops under SSP245 and SSP585 scenarios show a decrease or a small increase in the 2050s and 2080s in most CMIP6 GCMs.These findings are important for agricultural researchers and water resource managers to implement long-term crop planning techniques and to reduce the negative impacts of climate change and associated rainfall variability to avert crop failure and agricultural losses.

An Evolutionary Normalization Algorithm for Signed Floating-Point Multiply-Accumulate Operation

In the era of digital signal processing,like graphics and computation systems,multiplication-accumulation is one of the prime operations.A MAC unit is a vital component of a digital system,like different Fast Fourier Transform(FFT)algorithms,convolution,image processing algorithms,etcetera.In the domain of digital signal processing,the use of normalization architecture is very vast.The main objective of using normalization is to performcomparison and shift operations.In this research paper,an evolutionary approach for designing an optimized normalization algorithm is proposed using basic logical blocks such as Multiplexer,Adder etc.The proposed normalization algorithm is further used in designing an 8×8 bit Signed Floating-Point Multiply-Accumulate(SFMAC)architecture.Since the SFMAC can accept an 8-bit significand and a 3-bit exponent,the input to the said architecture can be somewhere between−(7.96872)_(10) to+(7.96872)_(10).The proposed architecture is designed and implemented using the Cadence Virtuoso using 90 and 130 nm technologies(in Generic Process Design Kit(GPDK)and Taiwan Semiconductor Manufacturing Company(TSMC),respectively).To reduce the power consumption of the proposed normalization architecture,techniques such as“block enabling”and“clock gating”are used rigorously.According to the analysis done on Cadence,the proposed architecture uses the least amount of power compared to its current predecessors.

Fuzzy System Design Using Current Amplifier for 20 nm CMOS Technology

In the recent decade,different researchers have performed hardware implementation for different applications covering various areas of experts.In this research paper,a novel analog design and implementation of different steps of fuzzy systems with current differencing buffered amplifier(CDBA)are proposed with a compact structure that can be used in many signal processing applications.The proposed circuits are capable of wide input current range,simple structure,and are highly linear.Different electrical parameters were compared for the proposed fuzzy system when using different membership functions.The novelty of this paper lies in the electronic implementation of different steps for realizing a fuzzy system using current amplifiers.When the power supply voltage of CDBA is 2V,it results in 155mW,power dissipation;4.615KΩ,input resistance;366KΩ,output resistances;and 189.09 dB,common-mode rejection ratio.A 155.519 dB,voltage gain,and 0.76V/μs,the slew rate is analyzed when the power supply voltage of CDBAis 3V.The fuzzy system is realized in 20nm CMOS technology and investigated with an output signal of high precision and high speed,illustrating that it is suitable for realtime applications.In this research paper,a consequence of feedback resistance on the adder circuit and the defuzzified circuit is also analyzed and the best results are obtained using 100K resistance.The structure has a low hardware complexity leading to a low delay and a rather high quality.

Explainable Artificial Intelligence-A New Step towards the Trust in Medical Diagnosis with AI Frameworks:A Review

Machine learning(ML)has emerged as a critical enabling tool in the sciences and industry in recent years.Today’s machine learning algorithms can achieve outstanding performance on an expanding variety of complex tasks-thanks to advancements in technique,the availability of enormous databases,and improved computing power.Deep learning models are at the forefront of this advancement.However,because of their nested nonlinear structure,these strong models are termed as“black boxes,”as they provide no information about how they arrive at their conclusions.Such a lack of transparencies may be unacceptable in many applications,such as the medical domain.A lot of emphasis has recently been paid to the development of methods for visualizing,explaining,and interpreting deep learningmodels.The situation is substantially different in safety-critical applications.The lack of transparency of machine learning techniques may be limiting or even disqualifying issue in this case.Significantly,when single bad decisions can endanger human life and health(e.g.,autonomous driving,medical domain)or result in significant monetary losses(e.g.,algorithmic trading),depending on an unintelligible data-driven system may not be an option.This lack of transparency is one reason why machine learning in sectors like health is more cautious than in the consumer,e-commerce,or entertainment industries.Explainability is the term introduced in the preceding years.The AImodel’s black box nature will become explainable with these frameworks.Especially in the medical domain,diagnosing a particular disease through AI techniques would be less adapted for commercial use.These models’explainable natures will help them commercially in diagnosis decisions in the medical field.This paper explores the different frameworks for the explainability of AI models in the medical field.The available frameworks are compared with other parameters,and their suitability for medical fields is also discussed.

Design of Energy Efficient WSN Using a Noble SMOWA Algorithm

In this paper,the establishment of efficientWireless Sensor Network(WSN)networks has been projected to minimize the consumption of energy using a new Self-adaptive Multi-Objective Weighted Approach(SMOWA)algorithm for solving a multi-objective problem.The Different WSN nodes deployment policies have been proposed and applied in this paper to design an efficientWireless Sensor Network to minimize energy consumption.After that,the cluster head for each cluster has been selected with the help of the duty cycle.After configuring the WSN networks,the SMOWA algorithms have been developed to obtain the minimum energy consumption for the networks.Energy minimization,as well as the amount of day-saving,has been calculated for the differentWSNswhich has been configured through different deployment policies.The major finding of the research paper is to improve the durability of Wireless Sensor Network(i)applying different deployment strategies:(Random,S pattern and nautilus shell pattern),and(ii)using a new Meta-heuristic algorithm(SMOWA Algorithm).In this research,the lifetime of WSN has been increased to a significant level.To choose the best result set from all the obtained results set some constraints such as“equivalent distribution”,“number of repetitions”,“maximum amount energy storage by a node”has been set to an allowable range.

Scenario of solar energy and policies in India

Solar power is energy from the Sun that is converted into thermal or electrical energy.China,the USA,and India have the world’s richest solar energy sources.India receives solar radiation with an average intensity of 200 MW/km^(2) and 250–300 sunny days a year.The solar radiation in India varies from 4 to 7 kWh/m^(2)/day,with 2300–3200 sunshine hours yearly,depending on the region.Badhla Solar Park,Jodhpur,India,is the world’s largest solar plant,with 2.25 GW of installed capacity.Solar energy is the best alternative elec-tricity source used explicitly in the agricultural sector.In the farming sector,the consumption of solar energy has been increasing.The major issues were the installation costs,low availability of technicians,and poor quality of panels.The focus on skill development equips the workforce to drive innovation,efficiency,and sustained growth in the renewable-energy field.With the support of inter-national collaboration and a skilled workforce,India’s progress in solar energy development is a boon for the industry.

Plant trait estimation and classification studies in plant phenotyping using machine vision - A review

Today there is a rapid development taking place in phenotyping of plants using non-destructive image based machine vision techniques.Machine vision based plant phenotyping ranges from single plant trait estimation to broad assessment of crop canopy for thousands of plants in the field.Plant phenotyping systems either use single imaging method or integrative approach signifying simultaneous use of some of the imaging techniques like visible red,green and blue(RGB)imaging,thermal imaging,chlorophyll fluorescence imaging(CFIM),hyperspectral imaging,3-dimensional(3-D)imaging or high resolution volumetric imaging.This paper provides an overview of imaging techniques and their applications in the field of plant phenotyping.This paper presents a comprehensive survey on recent machine vision methods for plant trait estimation and classification.In this paper,information about publicly available datasets is provided for uniform comparison among the state-of-the-art phenotyping methods.This paper also presents future research directions related to the use of deep learning based machine vision algorithms for structural(2-D and 3-D),physiological and temporal trait estimation,and classification studies in plants.

Physicochemical and in Vitro Antibacterial Evaluation of Metronidazole Loaded Eudragit S‑100 Nanofbrous Mats for the Intestinal Drug Delivery

Metronidazole(MTZ)loaded Eudragit S-100(ES-100)nonwoven nanofbrous mats were successfully electrospun and evaluated for intestinal drug delivery.MTZ was varied in the range of 1–15%(w/w)in ES-100 nanofbrous mats,the morphological characterization of nanofbrous mats was carried out using FE-SEM and the average diameter of nanofber was found in the range 150–600 nm.WAXD and DSC demonstrated the amorphous nature of MTZ in ES-100 nanofbrous mats.Their contact angle analysis confrmed the hydrophobic nature.The mechanical strength of ES-100 nanofbrous mats decreased with increasing MTZ concentration.The drug release profles showed 74%MTZ release from ES-100d within 2 h at pH 6.8 which is the colonic environment.Antibacterial activities against gram-positive bacteria(Staphylococcus aureus)and gramnegative bacteria(Escherichia coli)showed that the ES-100 nanofbrous mats loaded with MTZ exhibited good activity.

Microbial cells as biological factory for nanoparticle synthesis

In present scenario,nanoscience and nanotechnology have drawn the attention of scientist and researchers as leading source of technology with a wide range of useful applications like pharmaceuticals,electronics,biotechnology,biomedicine,wastewater treatment,space technology,industries and medical sciences[1].The physico-chemical characteristics of nanomaterial could make them an ideal material for various applications.Viewing this revolutionary effect,Norio Taniguchi,a famous scientist in 1974 coined the term‘nanotechnology’[2].

Power enhancement using improved maximum power point tracking for solar photovoltaic systems under partial shading

One of the key reasons contributing to a reduction in the performance of a solar photovoltaic(SPV)system is the presence of partial shade on the solar panels.It is necessary to use maximum power point tracking(MPPT)in SPV systems to get around the non-linear behaviour of photovoltaic panels.This research analyses the reduction in power output of SPV systems caused by partial shading.In the latter part of the paper,the improved dynamic behaviour-based ant colony optimization MPPT approach under the effect of module-level partial shading is implemented in MATLAB®/Simulink®.Various partial shading levels were applied to SPV modules to evaluate the system performance.The proposed system follows the maximum power point with 99.9%accuracy and a response time of 2 ms.The addition of a weight coefficient accelerates the convergence of the algorithm.

Stress-related ecophysiology of members of the genus Rhodanobacter isolated from a mixed waste contaminated subsurface

This work examines the physiologic basis of stress tolerance in bacterial strains of the genus Rhodanobacter that dominate in the acidic and highly metal contaminated near-source subsurface zone of the Oak Ridge Integrated Field Research Challenge(ORIFRC)site.Tolerance of R.denitrificans to levels of different stresses were studied in synthetic groundwater medium and R2A broth.Two strains of R.denitrificans,strains 2APBS1T and 116-2,tolerate low to circumneutral pH(4–8),high Uranium(1 mmol/L),elevated levels of nitrate(400 mmol/L)and high NaCl(2.5%).A combination of physiologic traits,such as growth at low pH,increased growth in the presence of high organics concentration,and tolerance of high concentrations of nitrate,NaCl and heavy metals is likely responsible for dominance of Rhodanobacter at the ORIFRC site.Furthermore,extended incubation times and use of low carbon media,better approximating site groundwater conditions,are critical for accurate determination of stress responses.This study expands knowledge of the ecophysiology of bacteria from the genus Rhodanobacter and identifies methodological approaches necessary for acquiring accurate tolerance data.

Simultaneous effects of MHD and Joule heating on the fundamental flows of a Casson liquid with slip

Magnetic field plays a significant role in innumerable fields like mechanical,chemical,biological and medical research.Few examples include power generation systems,magnetohydrodynamic(MHD)accelerators,liquid metal fluids,cooling of nuclear reactors,hightemperature plasmas,chemical processing equipment and electronic components.In view of these facts,the current investigation discusses the fundamental flows of a Casson fluid in horizontal parallel plates(three fundamental situations considered such as the plate walls progresses in the conflicting directions,the progress of lower plate in the flow direction and other is in fixed position,and the progress of the plates in the flow direction respectively).Influences of radiation,velocity slip,porous medium and Joule heating are considered into account.The flow situation is assumed in Cartesian coordinate system.The momentum and energy equations are transformed into non-dimensional system utilizing appropriate nondimensional parameters.The exact solutions of the non-dimensional differential equations have been obtained.The pictorial representations are provided for the velocity as well as temperature characteristics in all the mentioned cases.It is concluded by graphical approach that velocity is enhanced in the case of increasing Darcy’s parameter and boundary slip while reduced for the large magnetic field.On the other hand,it is found that slip factor is reducing the heat transfer rate while magnetic field is giving rise to it.