Maximum Performance In Terms Research Articles

Integrated dual hyperchaotic and Josephus traversing based 3D confusion-diffusion pattern for image encryption

This paper introduces an enhanced image encryption technique that relies on 3D confusion and diffusion patterns, aiming to achieve maximum performance in terms of confusion and diffusion. The proposed encryption method combines four phases: dual hyperchaotic, improved logistic map (ILM), Josephus traversing, and a hash function. The first and second phases utilize improved Chen and Lorentz systems-based 3D patterns to enhance confusion and diffusion. In the third phase, bit-plane permutations are performed through Josephus traversing in three directions: x, y, and z. Lastly, the fourth phase implements diffusion based on ILM to optimize encryption performance. The integration of a hash function in this method serves to heighten key sensitivity. The test results demonstrate the method's exceptional performance across various analyses, including statistical, differential, brute-force, and NIST test suite assessments. Additionally, the proposed method outperforms previous approaches significantly, as indicated by the majority assessment.

Performance Analysis of Domestic Refrigerators Condenser Coil By Applying Lampblack / Synthetic Black Blend

Abstract: Domestic refrigerators are usually used for cooling the bounded space by absorbing the heat through the evaporator and simultaneously rejecting the heat to the surrounding through the condenser coil. The black color coated condenser coil is the main part that rejects heat to the surrounding in the form of radiations. Herein, to increase rejection of the heat from the condenser coil, to increase its performance, a blend of lampblack/ synthetic black (various w/w ratio) was partially painted on. The three different compositions (w/w) are applied on the condenser coil and accordingly, the heat radiating coefficient and coefficient of performance are measured. Optimized composition of lampblack /synthetic black (4:6) provided maximum performance in terms of coefficient of performance 5.51% and with heat transfer coefficient 16.11%. The results show a costeffective approaches to increase the performance as well as the life time.

Improved Metaheuristic Based Failure Prediction with Migration Optimization in Cloud Environment

Cloud data centers consume high volume of energy for processing and switching the servers among different modes. Virtual Machine (VM) migration enhances the performance of cloud servers in terms of energy efficiency, internal failures and availability. On the other end, energy utilization can be minimized by decreasing the number of active, underutilized sources which conversely reduces the dependability of the system. In VM migration process, the VMs are migrated from underutilized physical resources to other resources to minimize energy utilization and optimize the operations. In this view, the current study develops an Improved Metaheuristic Based Failure Prediction with Virtual Machine Migration Optimization (IMFP-VMMO) model in cloud environment. The major intention of the proposed IMFP-VMMO model is to reduce energy utilization with maximum performance in terms of failure prediction. To accomplish this, IMFP-VMMO model employs Gradient Boosting Decision Tree (GBDT) classification model at initial stage for effectual prediction of VM failures. At the same time, VMs are optimally migrated using Quasi-Oppositional Artificial Fish Swarm Algorithm (QO-AFSA) which in turn reduces the energy consumption. The performance of the proposed IMFP-VMMO technique was validated and the results established the enhanced performance of the proposed model. The comparative study outcomes confirmed the better performance of the proposed IMFP-VMMO model over recent approaches.

Enhanced Optimized Classification Model of Chronic Kidney Disease

Chronic kidney disease (CKD) is one of the leading causes of death across the globe, affecting about 10% of the world's adult population. Kidney disease affects the proper function of the kidneys. As the number of people with chronic kidney disease (CKD) rises, it is becoming increasingly important to have accurate methods for detecting CKD at an early stage. Developing a mechanism for detecting chronic kidney disease is the study's main contribution to knowledge. In this study, preventive interventions for CKD can be explored using machine learning techniques (ML). The Optimized deep belief network (DBN) based on Grasshopper's Optimization Algorithm (GOA) classifier with prior Density-based Feature Selection (DFS) algorithm for chronic kidney disease is described in this study, which is called "DFS-ODBN." Prior to the DBN classifier, whose parameters are optimized using GOA, the proposed method eliminates redundant or irrelevant dimensions using DFS. The proposed DFS-ODBN framework consists of three phases, preprocessing, feature selection, and classification phases. Using CKD datasets, the suggested approach is also tested, and the performance is evaluated using several assessment metrics. Optimized-DBN achieves its maximum performance in terms of sensitivity, accuracy, and specificity, the proposed DFS-ODBN demonstrated accuracy of 99.75 percent using fewer features comparing with other techniques.

Optimization of thermal characteristics of axisymmetric synthetic air jet impingement on flat surface

The local heat transfer of axisymmetric synthetic air jet impinging on flat surface is investigated experimentally. Acoustic speaker is used for generation of synthetic air jet with cavity of cylindrical shape.The experiments is conducted for actuator frequency ranging from 50Hz to 400Hz, orifice diameter 2mm to 10mm, orifice plate thickness 2mm to 10mm, cavity diameter 50mm to 75mm, cavity depth 30mm to 60mm, jet to plate distance 16mm to 112mm. A steady heat flow is maintained on the flat surface. Local heat transfer characteristics on flat surface is found by thermal images using IR thermal imaging and thin foil technique.The experimental results reveals that the heat transfer is highly effected by frequency, orifice diameter, orifice plate thickness, jet to plate distance. However, cavity depth and cavity diameter has small influence on thermal performance.The higher cavity volume show more influence on heat transfer characteristics. An optimization of synthetic jet performance parameters is studied for maximum thermal performance interms of heat transfer characteristics.

Impaired Modulation of Motor and Functional Performance in Patients after Total Knee Arthroplasty: A Prospective Observational Study.

Submaximal levels of effort are required for the performance of the most common daily tasks. Inaccuracy in modulating motor outputs during submaximal tasks has been reported as indicator of safety during daily activities in subjects with lower limb musculoskeletal disorders. The study is aimed at investigating performance modulation ability during motor and functional tasks in patients after total knee arthroplasty (TKA). Sixteen patients with end-stage osteoarthritis undergoing TKA and twenty age-matched healthy participants performed isokinetic knee extension, sit-to-stand, and walking tasks at three levels of self-estimated effort (100%, 50%, and 25%) the day before (T0) and 4 days after surgery (T1). Maximum performance in terms of peak torque (PT—knee extension), overshoot (OS—sit-to-stand), and walking speed was evaluated. Subsequently, relative error (RE) between target and observed performance was computed for the submaximal tasks (RE50% and RE25%). Our results showed a decline of maximum performance after surgery, which resulted lower in patients compared to healthy subjects. RE50% and RE25% for knee extension (involved limb) (p < 0.001) and RE25% for sit-to-stand (p < 0.001) increased from pre- to postsurgery. At T0, knee extension RE25% and walking RE50% and RE25% were higher in patients. At T1, RE50% and RE25% were higher in patients for knee extension (involved limb), sit-to-stand, and walking. In conclusion, the ability to modulate motor and functional performance decreased after TKA and resulted impaired when compared to healthy age-matched subjects. Based on relationship between ability to modulate motor outputs and risk of falling, the role of modulation ability as indicator of readiness for discharge and safe return to daily activities deserves further investigations in patients in early phase after TKA.

Assessment of Water Permeability Test on Polypropylene based Self-Compacted Fibre Reinforced Concrete (PSCFRC)

An investigation conducted to study the effect of water permeability and strength characteristics such as compressive strength of Polypropylene self-compacted fibre reinforced concrete (PSCFRC) is presented. Polypropylene fibres of lengths, 35 mm with a diameter of 0.44 mm, were systematically combined in different mix proportions to combinations of 0.2%, 0.4%, and 0.6% Polypropylene fibre volume fraction. For comparison, a concrete mix with no fibres was also mixed. A total of 72 cube specimens of 150 mm were tested, 36 each for compressive strength and water permeability at 28 and 56 days of curing. According to the findings of this study, a fibre combination of SCFRC 0.6 percent is the most acceptable fibre composition to use in Polypropylene self-compacted fibre reinforced concrete (PSCFRC) for maximum performance in terms of compressive strength and water permeability requirements together.

High-temperature 3D printing of polyetheretherketone products: Perspective on industrial manufacturing applications of super engineering plastics

A refined additive manufacturing system of material extrusion type was developed considering the high-temperature conditions of extruder and chamber. With this setup, a super engineering plastic material, named polyetheretherketone (PEEK), could be printed into three-dimensional (3D) parts without any delamination or warpage. To obtain maximum performance in terms of mechanical strength, experimental studies were conducted considering the thermal process parameters such as nozzle and chamber temperatures. These optimal process settings and carefully controlled heating conditions, incorporated with post-processing of additional heat treatment, could provide a high degree of crystallinity and high tensile strength (i.e., more than 90% compared to original material properties). To demonstrate the feasibility of industrial applications, we fabricated and tested several product prototypes, such as a wafer jig, a lathe chuck part, and plastic injection molds, all of which confirmed the excellent performance of PEEK for chemical resistance, high strength, and heat resistance, respectively.

Design of aperiodic spherical antenna arrays for wideband performance

This paper illustrates a highly nonlinear problem related to the design of aperiodic antenna arrays using spherical geometry for maximum performance. This synthesis of aperiodic spherical antenna arrays considers two design cases: (1) aperiodic arrays with a nonuniform spacing between rings assuming equal element spacings on the same ring (ASANRUE case) and (2) aperiodic arrays with a nonuniform spacing between rings and between antenna elements of the same ring (ASANRNE case). This process is carried out by using the differential evolution (DE) optimization technique with the goal of finding the optimum angular position of each element on the elevation and azimuthal planes to achieve the maximum performance in terms of the side lobe level, directivity, and number of antenna elements used in the array for three design objectives: natural response of the radiation pattern, beam scanning, and the restriction of a minimum distance among elements. The innovative contribution of this paper is the application of the differential evolution algorithm to a nontrivial and highly complex design problem: the aperiodic element distribution over a spherical antenna arrangement. The advantage of this approach with respect to the state of the art on the topic of spherical array design is that a lower side lobe level and maximum directivity for a spherical antenna array can be obtained for aperiodic element distributions with a reduced number of antenna elements and hence a smaller radius, which means less complexity and lower fabrication costs. Simulation results based on CST Microwave Studio are provided to take mutual coupling into account.

Nozzle design influence on the steam-driven ejector

In the present paper, a series of numerical simulations of wet steam flows within ejectors distinguished by four primary nozzle contours have been carried out with the objective to evaluate the overall ejector performance. The studied primary nozzle contours are the following: a standard Laval nozzle (LAVAL); a nozzle that is designed in order to provide Constant Expansion Rate (CER); and two nozzles (MOC_SHORT, MOC_LONG) that are designed by employing axisymmetric Method of Characteristics. At first, the wet steam flow simulation throughout solely nozzles are carried out. Within the CFD simulations routines most valuable flow parameters are compared: expansion rates, boundary layer displacement and momentum thicknesses; entropy generation rate from various thermodynamic forces; thrust and liquid mass fraction across the nozzle exit plane. A comprehensive analysis of solely nozzles revealed that the CER nozzle contour is the most aerodynamically efficient design, which provides the minimum liquid mass fraction by the nozzle exit and possess the minimum entropy generation rate. The CFD results analysis of a full ejector domain revealed that an ejector with a MOC_SHORT primary nozzle contour provides the maximum performance in terms of secondary mass flow rate. At that, the secondary mass flow rate in MOC_SHORT nozzle contour case is almost 4% greater than for the CER nozzle design case.

AlgPred 2.0: an improved method for predicting allergenic proteins and mapping of IgE epitopes.

AlgPred 2.0 is a web server developed for predicting allergenic proteins and allergenic regions in a protein. It is an updated version of AlgPred developed in 2006. The dataset used for training, testing and validation consists of 10075 allergens and 10075 non-allergens. In addition, 10451 experimentally validated immunoglobulin E (IgE) epitopes were used to identify antigenic regions in a protein. All models were trained on 80% of data called training dataset, and the performance of models was evaluated using 5-fold cross-validation technique. The performance of the final model trained on the training dataset was evaluated on 20% of data called validation dataset; no two proteins in any two sets have more than 40% similarity. First, a Basic Local Alignment Search Tool (BLAST) search has been performed against the dataset, and allergens were predicted based on the level of similarity with known allergens. Second, IgE epitopes obtained from the IEDB database were searched in the dataset to predict allergens based on their presence in a protein. Third, motif-based approaches like multiple EM for motif elicitation/motif alignment and search tool have been used to predict allergens. Fourth, allergen prediction models have been developed using a wide range of machine learning techniques. Finally, the ensemble approach has been used for predicting allergenic protein by combining prediction scores of different approaches. Our best model achieved maximum performance in terms of area under receiver operating characteristic curve 0.98 with Matthew's correlation coefficient 0.85 on the validation dataset. A web server AlgPred 2.0 has been developed that allows the prediction of allergens, mapping of IgE epitope, motif search and BLAST search (https://webs.iiitd.edu.in/raghava/algpred2/).

Experimental study and analysis of a thermoacoustically driven thermoacoustic refrigerator

Experimental investigations are performed on a half-wavelength standing wave type thermoacoustically driven thermoacoustic refrigerator also known as TADTAR. Present TADTAR device conceived to be a quarter wavelength standing wave type thermoacoustic engine (TAE) coupled to a quarter wavelength standing wave thermoacoustic refrigerator (TAR). A TAE generates acoustic work using heat, and this produced acoustic work is directly fed to TAR where a useful cooling effect is developed. The study here aims to project the enhancement in the performance of a TADTAR system by using better geometric choices and operating conditions. In the present work, by keeping the engine part unaltered, parametric variations on the refrigerator side are performed. Two geometric parameters namely resonator length and TAR stack position and one operating parameter, working gas, have been varied at three distinct choices. The performance of TADTAR is examined for three output parameters of TADTAR namely frequency of oscillations, pressure amplitude, and temperature difference across TAR stack. The present study should be useful for assisting select these parameters for starting the designing of a TADTAR. It also helps in concluding in a more generalized way the dependence of the above-said output of TADTAR on the varying parameters. This paper shows that longer resonator and He-Ar mixture as working gas among the choices is better for a TADTAR system for achieving better performance. It also highlights the potential existence of a unique position for a stack length for a TADTAR to attain maximum performance in terms of the temperature difference across the TAR stack. The present paper reports the maximum temperature difference of 16.3 K across the TAR stack.

Optimal Feature Selection-Based Medical Image Classification Using Deep Learning Model in Internet of Medical Things

Internet of Medical Things (IoMT) is the collection of medical devices and related applications which link the healthcare IT systems through online computer networks. In the field of diagnosis, medical image classification plays an important role in prediction and early diagnosis of critical diseases. Medical images form an indispensable part of a patient's health record which can be applied to control, handle and treat the diseases. But, classification of images is a challenging task in computer-based diagnostics. In this research article, we have introduced a improved classifier i.e., Optimal Deep Learning (DL) for classification of lung cancer, brain image, and Alzheimer's disease. The researchers proposed the Optimal Feature Selection based Medical Image Classification using DL model by incorporating preprocessing, feature selection and classification. The main goal of the paper is to derive an optimal feature selection model for effective medical image classification. To enhance the performance of the DL classifier, Opposition-based Crow Search (OCS) algorithm is proposed. The OCS algorithm picks the optimal features from pre-processed images, here Multi-texture, grey level features were selected for the analysis. Finally, the optimal features improved the classification result and increased the accuracy, specificity and sensitivity in the diagnosis of medical images. The proposed results were implemented in MATLAB and compared with existing feature selection models and other classification approaches. The proposed model achieved the maximum performance in terms of accuracy, sensitivity and specificity being 95.22%, 86.45 % and 100% for the applied set of images.

Scalable Data Analysis and Query Processing

Scalable data is the demand of many of the emerging technologies. In order to target scalability, query processing plays an important role. The target is to achieve the maximum performance in terms of less execution time and more output. This could be achieved with any selected data and implementing advanced algorithms in any platform. This paper has worked in ArcMap in order to handle data of maps through different layers. Map reduction in size eases the process of query processing and generates the resultant records much faster. In addition, query category contributes to scalability as well. Classifying a compound query into a simple one adds positive impact on the results. Query payload and cost are controlled by maintaining execution time of the query and enhancing retuned records per command. This is helpful in analyzing different map layers for selected area of interest. Scalable map data is selected, analyzed with different map layers and results are obtained of processed queries that clearly indicates the successful achievement of scalability of the data through controlled process of query handling in smart and efficient way.

A Mathematical Model for Cross Layer Protocol Optimizing Performance of Software-Defined Radios in Tactical Networks

Unlike conventional mobile ad hoc networks, tactical networks, which provide communication of software-defined radios (SDRs) in mission critical and time-sensitive applications, require cognitive functions across the TCP/IP stack to encounter strict constraints while providing smooth incorporation with IP-based applications. The tactical applications are mission-critical and thus pose unique requirements for the network, including decentralized control and mission specific latency bounds for end-to-end data delivery. This paper presents a mathematical model for a cross-layer design, which optimizes trade-offs among different configurations of the SDRs to achieve maximum performance in terms of energy efficiency, reliable packet delivery at an appropriate data rate and within affordable latency bounds in multi-hop tactical networks. The proposed model is used in a number of mission-critical network scenarios to demonstrate enhanced performance, where SDRs effectively adapt to the dynamic environment.

A simulation-based approach to modeling component interactions during design of flapping wing aerial vehicles

A new flapping wing aerial vehicle (FWAV) simulation methodology is presented that combines models of the key subsystems: (1) the actuator, (2) the battery, and (3) the wings. This approach captures component interactions that are inherently coupled in order to realize system-level designs for optimal system performance. The approach demonstrates that coupling between wing sizing, flapping motions, and loading conditions propagate into the motor–battery model to alter system-level performance properties. For the actuator subsystem model, a generalized servo motor using empirically derived coefficients to describe torque and angular velocity bandwidth in terms of voltage and current. This model is coupled with a lithium polymer battery model accounting for the nonlinear voltage drop and capacity derating effects associated with loading conditions. For aerodynamic predictions of the wing subsystem, a blade element model for predicting aerodynamic forces is coupled with an elastic wing deformation model that accounts for bending and twisting of the blade elements. System-level performance is then modeled in a design case study by coupling all of the subsystem models to account for relevant interactions, which generates a design trade space spanning a range of wing sizes, airspeeds, and flapping condition. The results from the simulation offer insight into vehicle configuration settings that provide maximum performance in terms of lift and endurance for the Robo Raven II flapping wing aerial vehicle. Experimental validation of the modeling approach shows good predictive accuracy. In addition, the presented framework offers a generalized approach for coupling interacting subsystems to improve overall predictive accuracy and identify areas where component-level improvements may offer system-level performance gains.

A Cost-Effective Method for Identifying Nutrient Media Combinations Producing Plants with Maximum Bioactive Substances

The aim is to find a cost-effective method of identifying nutrient media producing identical plants with maximum performance in terms of the bioactive substances contained in them. An adaptation of QSAR is used. The "spatial structure of the chemical component" is replaced with "the multidimensional structure of the nutrient medium" or with the treated day schemes. For each process, a separate forecast is made. All nutrition media produced in silico are based on the ranges of phytonutrient hormones in biotechnological experiments. We found 43 theoretical combinations of media with more than 80% success under conditions of limited resources in the price range of [0-1,5] euro/liter. The obtained results can be used as: a theoretical guideline for determining the optimal nutrient media and combinations; to the study of other medicinal plants in order to establish effective biotechnological schemes for growth and rooting that are also cost-effective; using ANN, taking into account the species and the ecotype.

Design of circular antenna arrays of circular subarrays exploiting rotational symmetry

This paper presents a design of fractal antenna arrays based on circular arrays of circular subarrays for scannable pattern using rotational symmetry properties of the complex feeding excitations for a maximum performance in terms of side lobe level (SLL) and directivity (DIR). This fractal array design considers the optimization of the amplitude and phase excitations exploiting the fractal characteristics of the geometry (rotational symmetry) in order to generate a scannable pattern. The synthesis process is carried out by the method of Differential Evolution (DE). The main advantages of using rotational symmetry are that the optimal excitations for a particular beam steering angle could be applied to other beam steering angles remaining in the same array factor characteristics (SLL and directivity) for every beam steering angle. This design of fractal antenna arrays provides a complete scanning of the azimuth plane with optimal characteristics of the radiation pattern using just one optimal design vector of amplitude and phase excitations. Furthermore, the performance of the fractal antenna array optimized with DE is compared with respect to the cases of Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and the conventional progressive phase for the same array geometry.

Ballistic Impact Studies on Carbon and E-glass Fibre Based Hybrid Composite Laminates

The present study is aimed at developing Carbon and E- glass based hybrid composite lamiantes for armour applications in order to get advantages of both the types of fibers. Three hybrid composite laminates based on carbon and E-glass in the weight ratio of 75:25, 50:50 and 25:75 with epoxy resin matrix were fabricated. For comparison purpose all carbon (C 100) and all E-glass (E 100) composites were also fabricated. High velocity impact tests were performed on laminates keeping carbon as strike face against 7.62mm mild steel projectile. Present study revealed that the composite laminates comprising carbon and E-glass in the ratio of 50:50 (CE 50-50) has shown maximum performance in terms of energy absorption. It showed 17% & 30% higher energy absorption than C 100 and E 100 composites respectively. Damage areas at front and rear sides as well as damage volume are determined and found that its increase with increase in thickness.

Design of UAVs-Based 3D Antenna Arrays for a Maximum Performance in Terms of Directivity and SLL

This paper presents a design of UAVs-based 3D antenna arrays for a maximum performance in terms of directivity and side lobe level (SLL). This paper illustrates how to model the UAVs formation flight using 3D nonuniform antenna arrays. This design of 3D antenna arrays considers the optimization of the positions of the antenna elements to model the UAVs formation flight. In this case, a disk patch antenna is chosen to be used as element in each UAV. The disk patch antenna is formulated by the well-known cavity model. The synthesis process is carried out by the method of Differential Evolution for Multiobjective Optimization (DEMO). Furthermore, a comparison of the performance of 3D nonuniform antenna arrays is provided with respect to the most conventional arrays (circular, planar, linear, and the cubic) for UAVs formation flight.