We establish an analogue of the classical Polya–Vinogradov inequality for
G
L
(
2
,
F
p
)
, where
p
is a prime. In the process, we compute the ‘singular’ Gauss sums for
G
L
(
2
,
F
p
)
. As an ...application, we show that the collection of elements in
G
L
(
2
,
Z
)
whose reduction modulo
p
are of maximal order in
G
L
(
2
,
F
p
)
and whose matrix entries are bounded by
x
has the expected size as soon as
x
≫
p
1
/
2
+
ε
for any
ε
>
0
.
•Presenting CSA-BA-ABC as an optimization algorithm to solve CHPED problems.•Eliminating BA’s and ABC’s disadvantages through three search mechanisms.•Reporting the case study simulations on a set of ...23 benchmark functions and on 3 CHPED problems.•Considering valve-point effect, prohibited zones and transmission losses.•Verifying the ability, constraints handling capability and robustness of the proposed algorithm in finding a better cost-effective solution.•Comparing the non-parametrical test results with other algorithms to show significant enhancement of the proposed algorithm.
This paper presents a new algorithm based on hybridizing Bat Algorithm (BA) and Artificial Bee Colony (ABC) with Chaotic based Self-Adaptive (CSA) search strategy (CSA-BA-ABC) to solve the large-scale, highly non-linear, non-convex, non-smooth, non-differential, non-continuous, multi-peak and complex Combined Heat and Power Economic Dispatch (CHPED) problems. The proposed hybrid algorithm has better capability to escape from local optima with faster convergence rate than the standard BA and ABC. The proposed algorithm works based on the three mechanisms. The first one is a novel adaptive search mechanism, in which one of the three search phases (BA phase, directed onlooker bee phase and modified scout bee phase) is selected based on the aging level of the individual’s best solution (pbest). In this regard, ABC’s phases can assist BA phase to search based on deeper exploration /exploitation pattern as an alternative. In periodic intervals, the second mechanism called as CSA updates algorithm control parameters using chaotic system based on prevailing search efficiency in the swarm. Lastly, the third mechanism is enhancing the algorithm performance by incorporating individual’s directional information, habitat selection and self-adaptive compensation. The effectiveness and robustness of the proposed algorithm are tested on a set of 23 benchmark functions and three CHPED problems. The obtained results by the suggested algorithm in terms of quality solution, computational performance and convergence characteristic are compared with various algorithms to show the ability of the proposed approach and its robustness in finding a better cost- effective solution.
Alternative fuels derived from vegetable oil have great potential as diesel fuel replacements in the transportation and manufacturing sectors. The aim of this study is to use cobalt chromite ...nanoparticles as a fuel additive with biodiesel in engine and to experimentally investigate the influence of injection pressure on combustion parameters. As an addition, cobalt chromite nanoparticles are used with biodiesel made from kapok oil, which is blended with mineral diesel at a ratio of 20:80. The engine is operated at various injection pressures (200–260 bar) and with an 80 ppm nanoparticle concentration. The results have shown that the increased injection pressure caused by the use of nanoparticles enhances engine combustion properties, such as the peak pressure and the rate of heat release. The main purpose of this research was to investigate the effects of a CoCr2O4 + KME20 mix on a CI engine, with the hope of improving engine performance characteristics. This investigation examines the effects of varying test fuel injection pressures. The increased injection pressure of CoCr2O4 + KME20 resulted in better performance and combustion. The 240‐bar IP was shown to be superior to lower IPs because of its greater penetration length and more uniform formation. The IP rating of 240 bar represented a significant improvement over competing products with respect to emission controls. In addition to reducing our reliance on fossil fuels, this also prevents harmful chemicals from being released into the air.
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•Metabolites and the gut microbiome have an association with Fatty liver disease.•Diagnosis of human diseases by surgery and biomarkers or metabolites-based methods.•Obesity ...metabolites and gut microenvironmental changes metabolomics profile.•Microbiome- metabolome study used for risk assessment of obesity and liver cancers.
Fatty liver disease (FLD) is one of the largest burdens to human health worldwide and is associated with gut microbiome and metabolite stability. Engineered liver tissues have shown promise in restoring liver functions in non-alcoholic FLD (NAFLD), hepatitis and cirrhosis. Fatty liver, largely noted in obesity and hepatic cancer, is highly fatal and has led to a global increase in death rates. It is associated with complex metabolic reprogramming too. A standard approach to therapy in the newly diagnosed setting includes surgery or identification of biomarkers/ metabolites for therapeutic purposes, which ultimately focus on improvement of liver health in patients. As such there are no standard procedures for patient care, but depending on the severity, systemic therapy with either genomic, proteomic or metabolomic profiling form potential options. Better comparisons and study of underlying mechanisms in gut microbiome-based metabolic functions in obesity are urgently required. Today, an emerging field, focusing on metabolomic approaches and metabolic phenotyping, involved in high-throughput identification of metabolome in obesity and gut disorders, is involved in biomarker and metabolite identification. There are supporting technologies and approaches in NAFLD that throw light on the metabolites and gut microbiome, and also on the understanding of the risk factors of obesity along with liver cancer metabolic reaction networks. We discuss the current state of NAFLD metabolites, gut micro-environmental changes, and the further challenges in digital metabolomics profiling. Innovative clinical trial designs, with biomarker-enrichment strategies that are required to improve the outcome of NAFLD in patients are also discussed.
In recent times, security in cloud computing has become a significant part in healthcare services specifically in medical data storage and disease prediction. A large volume of data are produced in ...the healthcare environment day by day due to the development in the medical devices. Thus, cloud computing technology is utilised for storing, processing, and handling these large volumes of data in a highly secured manner from various attacks. This paper focuses on disease classification by utilising image processing with secured cloud computing environment using an extended zigzag image encryption scheme possessing a greater tolerance to different data attacks. Secondly, a fuzzy convolutional neural network (FCNN) algorithm is proposed for effective classification of images. The decrypted images are used for classification of cancer levels with different layers of training. After classification, the results are transferred to the concern doctors and patients for further treatment process. Here, the experimental process is carried out by utilising the standard dataset. The results from the experiment concluded that the proposed algorithm shows better performance than the other existing algorithms and can be effectively utilised for the medical image diagnosis.
•We developed new hybrid evolutionary algorithm for solving generator maintenance scheduling problem.•Hybrid optimization method balance overall reliability and economy.•A case study of 32 thermal ...generating units reveal the effectiveness of the hybrid method.
This paper presents a Hybrid Particle Swarm Optimization based Genetic Algorithm and Hybrid Particle Swarm Optimization based Shuffled Frog Leaping Algorithm for solving long-term generation maintenance scheduling problem. In power system, maintenance scheduling is being done upon the technical requirements of power plants and preserving the grid reliability. The objective function is to sell electricity as much as possible according to the market clearing price forecast. While in power system, technical viewpoints and system reliability are taken into consideration in maintenance scheduling with respect to the economical viewpoint. It will consider security constrained model for preventive Maintenance scheduling such as generation capacity, duration of maintenance, maintenance continuity, spinning reserve and reliability index are being taken into account. The proposed hybrid methods are applied to an IEEE test system consist of 24 buses with 32 thermal generating units.
Summary
Radio frequency identification device (RFID) has emerged as one of the most potential building blocks for future IoT‐enabled technologies. Various applications like logistic monitoring use ...the RFID system to deal with the tagged objects. RFID‐based tracking approach is extremely solicited for appropriate logistic distribution because of the frequent tagged‐objects rearrangements. Nevertheless, with an RFID system, one of the most significant issues is resolving collisions between tags as they transfer data to the reader at the same time. This work investigates the core issue of locating all lost tags in RFID systems. The most significant factor in missing tag recognition is to reduce the time required for execution. A problem needs to be formulated to differentiate the tagged objects' motion state, that is, static or dynamic, to handle this issue. It tracks the moving objects with various existing localization approaches. Finally, a tag detection known as constructive Steiner graph matching (CSGM) detection is proposed to achieve time efficiency by utilizing RFID collision signals. Specifically, the physical‐layer features are considered for differentiating the positions. Experimental outcomes demonstrate that the anticipated model can attain better prediction accuracy by reducing inventory time compared to other approaches. Simulation findings show that the suggested approach performs exceptionally well in giving a significant performance boost in an RFID system. It minimizes the complexity of tracking the objects. The simulation outcomes illustrate that the algorithm's identification speed is substantially enhanced and ensuring high system efficiency. The simulation results of the proposed algorithm have been improved in terms of system efficiency (9.5%), success rate (6.3%), and identification speed (4%) compared to the conventional algorithm. The suggested CSGM technique's average waiting time is decreased by more than 45.372%, and its detection speed is increased by at least 38.219% compared to other existing algorithms.
This work investigates the core issue of locating all lost tags in RFID systems. It tracks the moving objects with various existing localization approaches. Finally, a tag detection known as Constructive Steiner‐Graph Matching (CSGM) detection is proposed to achieve time efficiency by utilizing RFID collision signals. The suggested CSGM technique's average waiting time is decreased by more than 45.372%, and its detection speed is increased by at least 38.219% compared to other existing algorithms.
Colonoscopy is currently the best technique available for the detection of colon cancer or colorectal polyps or other precursor lesions. Computer aided detection (CAD) is based on very complex ...pattern recognition. Local binary patterns (LBPs) are strong illumination invariant texture primitives. Histograms of binary patterns computed across regions are used to describe textures. Every pixel is contrasted relative to gray levels of neighbourhood pixels. In this study, colorectal polyp detection was performed with colonoscopy video frames, with classification via J48 and Fuzzy. Features such as color, discrete cosine transform (DCT) and LBP were used in confirming the superiority of the proposed method in colorectal polyp detection. The performance was better than with other current methods.
In recent decades, the Internet of Things (IoT)-enabled Wireless Sensor Network (IWSN) facilitates to development of numerous real-time applications. IWSN has become significant expertise in ...acquiring a better quality of service, long-term consistency, and low-cost management. Nevertheless, the sensor nodes of IWSN typically have restricted battery energy and are vulnerable to several intrusion attacks. To address the constraints of IWSN, an energy-efficient clustering and rapid intrusion detection system have been proposed. A novel MapDiminution-based Training-Discovering Optimization method is employed in the proposed framework to obtain optimal cluster routing path from each cluster to sink. Once the route is determined, the MapDiminution model invokes the task scheduling process in which each cluster member is managed with the queuing framework. This optimum path and scheduling process reduces the energy consumption in IWSN. Afterward, the Hybrid classifier can be formulated by integrating Artificial Neural Network (ANN) with Simulated Annealing (SA). The weights of ANN are optimized through the SA where the different types of intrusion attacks are then classified based on received information from the cluster nodes. The simulation results expose that the proposed framework achieves a lesser energy intake of 0.01 J and a higher detection accuracy of 97.57% as compared to the existing methods.
Globally, climate change has increased various environmental concerns, and there is a very high and still ever greater penetration of renewable energy power into energy grids. The intermittent nature ...of these energy sources has demanded a strong power electronic interface to maintain an uninterrupted power flow to the external load. This paper deals with a novel dual input quasi Z source inverter (qZSI) that can operate with two intermittent sources and perform a single stage power conversion. The novelty of the proposed methodology is the implementation of qZSI with a reduced number of switches, increased voltage gain, and a high boosting factor which results in increased efficiency. A simple boost control-based pulse width modulation (PWM) technique has been adopted, which has been observed after analysis, to have reduced the stress on the switches and simultaneously increased system efficiency. Perturb and observe method-based maximum power point tracking (MPPT) was integrated into the study with the control technique, and its performance was observed using MATLAB/Simulink and further validated with a scale down model.
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