Advanced Machine Learning Methods for Prediction of Blast-Induced Flyrock Using Hybrid SVR Methods

Blasting in surface mines aims to fragment rock masses to a proper size.However,flyrock is an undesirable effect of blasting that can result in human injuries.In this study,support vector regression(SVR)is combined with four algorithms:gravitational search algorithm(GSA),biogeography-based optimization(BBO),ant colony optimization(ACO),and whale optimization algorithm(WOA)for predicting flyrock in two surface mines in Iran.Additionally,three other methods,including artificial neural network(ANN),kernel extreme learning machine(KELM),and general regression neural network(GRNN),are employed,and their performances are compared to those of four hybrid SVR models.After modeling,the measured and predicted flyrock values are validated with some performance indices,such as root mean squared error(RMSE).The results revealed that the SVR-WOA model has the most optimal accuracy,with an RMSE of 7.218,while the RMSEs of the KELM,GRNN,SVR-GSA,ANN,SVR-BBO,and SVR-ACO models are 10.668,10.867,15.305,15.661,16.239,and 18.228,respectively.Therefore,combining WOA and SVR can be a valuable tool for accurately predicting flyrock distance in surface mines.

Emergence of taurine as a therapeutic agent for neurological disorders

Taurine is a sulfur-containing,semi-essential amino acid that occurs naturally in the body.It alternates between inflammation and oxidative stress-mediated injury in various disease models.As part of its limiting functions,taurine also modulates endoplasmic reticulum stress,Ca^(2+)homeostasis,and neuronal activity at the molecular level.Taurine effectively protects against a number of neurological disorders,including stro ke,epilepsy,cerebral ischemia,memory dysfunction,and spinal cord injury.Although various therapies are available,effective management of these disorders remains a global challenge.Approximately 30 million people are affected worldwide.The design of taurine fo rmation co uld lead to potential drugs/supplements for the health maintenance and treatment of central nervous system disorders.The general neuroprotective effects of taurine and the various possible underlying mechanisms are discussed in this review.This article is a good resource for understanding the general effects of taurine on various diseases.Given the strong evidence for the neuropharmacological efficacy of taurine in various experimental paradigms,it is concluded that this molecule should be considered and further investigated as a potential candidate for neurotherapeutics,with emphasis on mechanism and clinical studies to determine efficacy.

An Improved Steganographic Scheme Using the Contour Principle to Ensure the Privacy of Medical Data on Digital Images

With the improvement of current online communication schemes,it is now possible to successfully distribute and transport secured digital Content via the communication channel at a faster transmission rate.Traditional steganography and cryptography concepts are used to achieve the goal of concealing secret Content on a media and encrypting it before transmission.Both of the techniques mentioned above aid in the confidentiality of feature content.The proposed approach concerns secret content embodiment in selected pixels on digital image layers such as Red,Green,and Blue.The private Content originated from a medical client and was forwarded to a medical practitioner on the server end through the internet.The K-Means clustering principle uses the contouring approach to frame the pixel clusters on the image layers.The content embodiment procedure is performed on the selected pixel groups of all layers of the image using the Least Significant Bit(LSB)substitution technique to build the secret Content embedded image known as the stego image,which is subsequently transmitted across the internet medium to the server end.The experimental results are computed using the inputs from“Open-Access Medical Image Repositories(aylward.org)”and demonstrate the scheme’s impudence as the Content concealing procedure progresses.

A Harmonic Approach to Handwriting Style Synthesis Using Deep Learning

The challenging task of handwriting style synthesis requires capturing the individuality and diversity of human handwriting.The majority of currently available methods use either a generative adversarial network(GAN)or a recurrent neural network(RNN)to generate new handwriting styles.This is why these techniques frequently fall short of producing diverse and realistic text pictures,particularly for terms that are not commonly used.To resolve that,this research proposes a novel deep learning model that consists of a style encoder and a text generator to synthesize different handwriting styles.This network excels in generating conditional text by extracting style vectors from a series of style images.The model performs admirably on a range of handwriting synthesis tasks,including the production of text that is out-of-vocabulary.It works more effectively than previous approaches by displaying lower values on key Generative Adversarial Network evaluation metrics,such Geometric Score(GS)(3.21×10^(-5))and Fréchet Inception Distance(FID)(8.75),as well as text recognition metrics,like Character Error Rate(CER)and Word Error Rate(WER).A thorough component analysis revealed the steady improvement in image production quality,highlighting the importance of specific handwriting styles.Applicable fields include digital forensics,creative writing,and document security.

Phytogenic Synthesis of Metal/Metal Oxide Nanoparticles for Degradation of Dyes

Now-a-days nanotechnology is one of the booming fields for the researchers.With the increase in industrialization mainly textile,paper,medicine,plastic industry,there is an increase in concentration of organic dyes as pollutant.Release of harmful dyes in water bodies has become a serious issue,as most of the dyes are carcinogenic and mutagenic in nature and causes various diseases.Therefore,there is a requirement to find out new approaches for efficient treatment of effluent containing dyes.Nanoparticles are one of the potential solutions to this problem.They can be synthesized from different methods,however synthesis of nanoparticles from different plant parts(leaf,root or stem extract)is economical as well as ecofriendly.Phytogenic nanoparticles have various environmental applications and one of them is remediation of dyes.The aim of this review is to provide an overview of last five years studies about catalytic and photocatalytic degradation of various harmful dyes by plant synthesized nanoparticles,mechanism of degradation and advantages and disadvantages of phytogenic synthesis.

Adaptive Scheme for Crowd Counting Using off-the-Shelf Wireless Routers

Since the outbreak of the world-wide novel coronavirus pandemic,crowd counting in public areas,such as in shopping centers and in commercial streets,has gained popularity among public health administrations for preventing the crowds from gathering.In this paper,we propose a novel adaptive method for crowd counting based on Wi-Fi channel state information(CSI)by using common commercial wireless routers.Compared with previous researches on device-free crowd counting,our proposed method is more adaptive to the change of environ-ment and can achieve high accuracy of crowd count estimation.Because the dis-tance between access point(AP)and monitor point(MP)is typically non-fixed in real-world applications,the strength of received signals varies and makes the tra-ditional amplitude-related models to perform poorly in different environments.In order to achieve adaptivity of the crowd count estimation model,we used convo-lutional neural network(ConvNet)to extract features from correlation coefficient matrix of subcarriers which are insensitive to the change of received signal strength.We conducted experiments in university classroom settings and our model achieved an overall accuracy of 97.79%in estimating a variable number of participants.

Module-level design and characterization of thermoelectric power generator

Thermoelectric power generation provides us the unique capability to explore the deep space and holds promise for harvesting the waste heat and providing a battery-free power supply for IoTs.The past years have witnessed massive progress in thermoelectric materials,while the module-level development is still lagged behind.We would like to shine some light on the module-level design and characterization of thermoelectric power generators(TEGs).In the module-level design,we review material selection,thermal management,and the determination of structural parameters.We also look into the module-level characterization,with particular attention on the heat flux measurement.Finally,the challenge in the optimal design and reliable characterization of thermoelectric power generators is discussed,together with a calling to establish a standard test procedure.

Mineral Chemistry and Nomenclature of Amphiboles of Garnet Bearing Amphibolites From Thana Bhilwara,Rajasthan,India

Amphiboles are frequently observed in the medium to high grade metamorphic rocks of garnet bearing amphibolites from Thana.In present work,authors have discussed the mineral chemistry and nomenclature of amphiboles.On the basis of mineral chemistry,the amphibole from garnet bearing amphibolite are normally varies from Hastingsite,Ferropargasite to Tschermakite variety.

Robust Vehicle Detection Based on Improved You Look Only Once

Vehicle detection is still challenging for intelligent transportation systems(ITS)to achieve satisfactory performance.The existing methods based on one stage and two-stage have intrinsic weakness in obtaining high vehicle detection performance.Due to advancements in detection technology,deep learning-based methods for vehicle detection have become more popular because of their higher detection accuracy and speed than the existing algorithms.This paper presents a robust vehicle detection technique based on Improved You Look Only Once(RVD-YOLOv5)to enhance vehicle detection accuracy.The proposed method works in three phases;in the first phase,the K-means algorithm performs data clustering on datasets to generate the classes of the objects.Subsequently,in the second phase,the YOLOv5 is applied to create the bounding box,and the Non-Maximum Suppression(NMS)technique is used to eliminate the overlapping of the bounding boxes of the vehicle.Then,the loss function CIoU is employed to obtain the accurate regression bounding box of the vehicle in the third phase.The simulation results show that the proposed method achieves better results when compared with other state-of-art techniques,namely LightweightDilated Convolutional Neural Network(LD-CNN),Single Shot Detector(SSD),YOLOv3 and YOLOv4 on the performance metric like precision,recall,mAP and F1-Score.The simulation and analysis are carried out on PASCAL VOC 2007,2012 and MS COCO 2017 datasets to obtain better performance for vehicle detection.Finally,the RVD-YOLOv5 obtains the results with an mAP of 98.6%and Precision,Recall,and F1-Score are 98%,96.2%and 97.09%,respectively.

Application of Response Surface Methodology for Analysing and Optimizing the Finned Solar Air Heater

In this research paper,a solar air heater with triangular fins has been experimentally analysed and optimized.Initially,an experimental set-up of a solar air heater having triangular fins has been developed at the location of 28.10°N,78.23°E.The heat transfer rate through fins and fins efficiency has been determined by the Finite Difference Method model equations.The experimental data and modeled data of response parameters have been optimized in MINITAB-17 software by the Response Surface Methodology tool.For creating the response surface design,three input parameters have been selected namely solar intensity,Reynolds number,and fin base-to-height ratio.The range of solar intensity,Reynolds number,and fin base-to-height ratio is 600 to 1000W/m^(2),4000 to 6000,and 0.4 to 0.8 respectively.The response surface design has been analyzed by calculating the outlet temperature,friction factor,Nusselt number,fin efficiency,thermal performance factor,and exergy efficiency.The optimum settings of input parameters:solar intensity is 1000 W/m^(2);Reynolds number is 4969.7,and the fin base to height ratio is 0.6060,on which these response:namely outlet temperature of 92.531℃,friction factor of 0.2350,Nusselt number of 127.761,thermal efficiency of 50.836%,thermal performance factor of 1.4947,and exergy efficiency of 8.762%.

A Secure and Efficient Signature Scheme for IoT in Healthcare

To provide faster access to the treatment of patients,healthcare system can be integrated with Internet of Things to provide prior and timely health services to the patient.There is a huge limitation in the sensing layer as the IoT devices here have low computational power,limited storage and less battery life.So,this huge amount of data needs to be stored on the cloud.The information and the data sensed by these devices is made accessible on the internet from where medical staff,doctors,relatives and family members can access this information.This helps in improving the treatment as well as getting faster medical assistance,tracking of routine activities and health focus of elderly people on frequent basis.However,the data transmission from IoT devices to the cloud faces many security challenges and is vulnerable to different security and privacy threats during the transmission path.The purpose of this research is to design a Certificateless Secured Signature Scheme that will provide a magnificent amount of security during the transmission of data.Certificateless signature,that removes the intricate certificate management and key escrow problem,is one of the practical methods to provide data integrity and identity authentication for the IoT.Experimental result shows that the proposed scheme performs better than the existing certificateless signature schemes in terms of computational cost,encryption and decryption time.This scheme is the best combination of high security and cost efficiency and is further suitable for the resource constrained IoT environment.

Finding Areas at Risk from Floods in a Downpour Using the Lidar-Based Elevation Model

Climate change can impact coastal areas in different ways,including flooding,storm surges,and beach erosion.Of these,flooding has a major impact on the operation of coastal drainage systems.This paper develops a new flood screening model using a LiDAR based digital elevation model(DEM)to improve the implementation of Victorian’s coastal flooding risk assessment and management.Hydrological elevation models are directed towards protection from cloudbursts and applied to rising sea level.The aim is to simulate water flow on the ground and in streams,and the resulting accumulation of water in depressions of the blue spot using DEM.Due to the presence of pipes,watercourses,bridges and channels it was required that the DEM data to be lowered.The reservoirs of rain will prevent seawater from flowing across the stream channel into land.The rain drain will be open during normal sea levels to allow rain water in the river to move and flow in to the sea.Traditionally,geographic information system(GIS)assists with spatial data management,but lacks modelling capability for complex hydrology problems and cannot be relied upon by decision-makers in this sector.Functionality improvements are therefore required to improve the processing or analytical capabilities of GIS in hydrology.This research shows how the spatial data can be primarily processed by GIS adopting the spatial analysis routines associated with hydrology.The objective of this paper is to outline the importance of GIS technology for coastal flood management.Following a definition of the coastal flood,and,short description of its peculiarities and the urgency of its management,this paper describes the use of GIS technology in coastal flood management,its advantages and the consideration for accuracy.This is followed by the information and LiDAR data required for coastal flood management and the application area in coastal flood management.This paper method is presented to conduct a first high-resolution DEM screening to detect the degree and capacities of the sinks in the coastal landscape.When their capacities are established,the rain volumes received during a rainstorm from their coastal catchments are saved as attributes to the pour points.The conclusion emphases the importance of a geographical information system in coastal flood management for efficient data handling and analysis of geographically related data.Local governments at risk of coastal flooding that use the flood screening model can use to determine appropriate land use controls to manage long-term flood risk to human settlements.

An Adaptive Genetic Algorithm-Based Load Balancing-Aware Task Scheduling Technique for Cloud Computing

Task scheduling in highly elastic and dynamic processing environments such as cloud computing have become the most discussed problem among researchers.Task scheduling algorithms are responsible for the allocation of the tasks among the computing resources for their execution,and an inefficient task scheduling algorithm results in under-or over-utilization of the resources,which in turn leads to degradation of the services.Therefore,in the proposed work,load balancing is considered as an important criterion for task scheduling in a cloud computing environment as it can help in reducing the overhead in the critical decision-oriented process.In this paper,we propose an adaptive genetic algorithm-based load balancing(GALB)-aware task scheduling technique that not only results in better utilization of resources but also helps in optimizing the values of key performance indicators such as makespan,performance improvement ratio,and degree of imbalance.The concept of adaptive crossover and mutation is used in this work which results in better adaptation for the fittest individual of the current generation and prevents them from the elimination.CloudSim simulator has been used to carry out the simulations and obtained results establish that the proposed GALB algorithm performs better for all the key indicators and outperforms its peers which are taken into the consideration.

A Combinatorial Optimized Knapsack Linear Space for Information Retrieval

Key information extraction can reduce the dimensional effects while evaluating the correct preferences of users during semantic data analysis.Currently,the classifiers are used to maximize the performance of web-page recommendation in terms of precision and satisfaction.The recent method disambiguates contextual sentiment using conceptual prediction with robustness,however the conceptual prediction method is not able to yield the optimal solution.Context-dependent terms are primarily evaluated by constructing linear space of context features,presuming that if the terms come together in certain consumerrelated reviews,they are semantically reliant.Moreover,the more frequently they coexist,the greater the semantic dependency is.However,the influence of the terms that coexist with each other can be part of the frequency of the terms of their semantic dependence,as they are non-integrative and their individual meaning cannot be derived.In this work,we consider the strength of a term and the influence of a term as a combinatorial optimization,called Combinatorial Optimized Linear Space Knapsack for Information Retrieval(COLSK-IR).The COLSK-IR is considered as a knapsack problem with the total weight being the“term influence”or“influence of term”and the total value being the“term frequency”or“frequency of term”for semantic data analysis.The method,by which the term influence and the term frequency are considered to identify the optimal solutions,is called combinatorial optimizations.Thus,we choose the knapsack for performing an integer programming problem and perform multiple experiments using the linear space through combinatorial optimization to identify the possible optimum solutions.It is evident from our experimental results that the COLSK-IR provides better results than previous methods to detect strongly dependent snippets with minimum ambiguity that are related to inter-sentential context during semantic data analysis.

废晶硅太阳能电池的回收利用:作为锂离子电池高性能硅基负极材料的应用

从废弃的硅太阳能电池芯片中回收有用的材料,如Ag,Al,Sn,Cu和Si,是一项可持续的项目,可以抵消废弃晶体硅光伏板不断增长的数量。然而,通过酸碱处理从硅片中回收上述材料的成本较高。在此,本文提出了一个新的概念,以废硅片为原料制造硅基负极。以废硅片为原料,采用高能球磨法制备纳米粒子,然后在纳米粒子上加入碳纳米管和氮掺杂碳以提高纳米粒子的电化学性能。结果表明,复合材料中的金属铝对电化学性能有不利影响。去除Al后,由于Ag的存在,复合材料具有明显的持久循环性能,这使得复合材料的性能明显优于同时去除Al和Ag的复合材料。

A Holistic,Proactive and Novel Approach for Pre,During and Post Migration Validation from Subversion to Git

Software development is getting a transition from centralized version control systems(CVCSs)like Subversion to decentralized version control systems(DVCDs)like Git due to lesser efficiency of former in terms of branching,fusion,time,space,merging,offline commits&builds and repository,etc.Git is having a share of 77%of total VCS,followed by Subversion with a share of 13.5%.The majority of software industries are getting a migration from Subversion to Git.Only a few migration tools are available in the software industry.Still,these too lack in many features like lack of identifying the empty directories as premigration check,failover capabilities during migration due to network failure or disk space issue,and detailed report generation as post-migration steps.In this work,a holistic,proactive and novel approach has been presented for pre/during/post-migration validation from Subversion to Git.Many scripts have been developed and executed run-time over various projects for overcoming the limitations of existing migration software tools for a Subversion to Git migration.During premigration,none of the available migration tools has the capability to fetch empty directories of Subversion,which results in an incomplete migration from Subversion to Git.Many Scripts have been developed and executed for pre-migration validation and migration preparation,which overcomes the problem of incomplete migration.Experimentation was conducted in SRLC Software Research Lab,Chicago,USA.During the migration process,in case of loss of network connection or due to any other reason,if migration stops or breaks,available migration tools do not have capabilities to start over from the same point where it left.Various Scripts have been developed and executed to keep the migration revision history in the cache(elastic cache)to start from the same point where it was left due to connection failure.During post-migration,none of the available version control migration tools generate a detailed report giving information about the total size of source Subversion repositories, the total volume of data migrated todestination repositories in Git, total number of pools migrated, time taken formigration, number of Subversion users with email notification, etc. VariousScripts have been developed and executed for the above purpose during thepost-migration process.

Analysis of Slope Stability with Dynamic Overloading from Earthquake

The analysis of slope earthquake stability is one of the most important research subjects in geotechnical engineering and earthquake engineering.Two different concepts of slope earthquake stability are put forward：strength reserve stability and dynamic overloading stability.The first concept of slope earthquake stability has been widely accepted,and relative analysis methods are also well de-veloped;the second one,however,is seldom mentioned until now,and the failure criterion and the analysis method based on this concept are yet to be explored.What are researched are just the failure criterion and the analysis method of dynamic overloading earthquake stability.The criterion of critical earthquake peak acceleration for the dynamic overloading stability of a slope and its analysis method,the load increasing method（LIM）,are put forward.The dynamic overloading earthquake stability of a loess slope at Changshougou（长寿沟） in Baoji（宝鸡） City,Shaanxi（陕西） Province,China,is analyzed with LIM.The analysis result reveals that the dynamic overloading earthquake stability of the slope is quite high to the action of the earthquake ground motion,with exceeding probability of 10% in the next 50 years.

Biobanking: A foundation of life-science research and advancement

As a foundation of life-science research and advancement,biobanking has played a critical role and made tremendous contributions to healthcare,biotechnology,disease control and prevention,as well as bio-conservation for the benefit of all humankind.This paper starts with a brief introduction of basic concepts,history,classification,and significance of biobanking,followed by a discussion on cryobiology fundamentals and key challenges faced by cryopreservation in biobanking.A special case discussion on the cryopreservation and biobanking of pathogenic microorganisms to meet both the unmet needs for biomedical research and the urgent demand for developing countermeasures against the ongoing coronavirus disease 2019(COVID-19)pandemic is highlighted with insightful recommendations for future studies.

Optimal load frequency control through combined state and control gain estimation for noisy measurements

Combined estimation of state and feed-back gain for optimal load frequency control is proposed.Load frequency control(LFC)addresses the problem of controlling system frequency in response to disturbance,and is one of main research areas in power system operation.A well acknowledged solution to this problem is feedback stabilization,where the Linear Quadratic Regulator(LQR)based controller computes the feedback gain K from the known system parameters and implements the control,assuming the availability of all the state variables.However,this approach restricts control to cases where the state variables are readily available and the system parameters are steady.Alternatively,by estimating the states continuously from available measurements of some of the states,it can accommodate dynamic changes in the system parameters.The paper proposes the technique of augmenting the state variables with controller gains.This introduces a non-linearity to the augmented system and thereby the estimation is performed using an Extended Kalman Filter.This results in producing controller gains that are capable of controlling the system in response to changes in load demand,system parameter variation and measurement noise.

Human-like stereo sensors using plasmonic antenna embedded MZI with space–time modulation control[Invited]

A micro stereo sensor system is proposed for human sensors,where eyes,ears,tongue,nose,body,and brain are applied by six panda rings embedded in a Mach–Zehnder interferometer(MZI).The input power is applied to the upper branch of MZI and propagates within the system.The six antennas(sensors)are formed by the whispering gallery modes of the panda rings.The space–time modulation signal is applied to the MZI lower branch.The modulated stereo signals can be configured as the plasmon(electron)spin orientations,which can be identified and applied for quantum codes and quantum consciousness.