Generation of New Julia Sets and Mandelbrot Sets for Tangent Function

The generation of fractals and study of the dynamics of transcendental function is one of emerging andinteresting field of research nowadays. We introduce in this paper the complex dynamics of tangentfunction. Our results are entirely different from thoseexisting in the literature of transcendental function.Keywords: Complex dynamics,Relative Superior Julia set, Relative Superior Mandelbrot set

Solution of the Multi-objective Economic and Emission Load Dispatch Problem Using Adaptive Real Quantum Inspired Evolutionary Algorithm

Economic load dispatch is a complex and significant problem in power generation. The inclusion of emission with economic operation makes it a Multi-objective economic emission load dispatch (MOEELD) problem. So it is a tough task to resolve a constrained MOEELD problem with antagonistic multiple objectives of emission and cost. Evolutionary Algorithms (EA) have been widely used for solving such complex multi-objective problems. However, the performance of EAs on such problems is dependent on the choice of the operators and their parameters, which becomes a complex issue to solve in itself. The present work is carried out to solve a Multi-objective economic emission load dispatch problem using a Multi-objective adaptive real coded quantum-inspired evolutionary algorithm (MO-ARQIEA) with gratifying all the constraints of unit and system. A repair-based constraint handling and adaptive quantum crossover operator (ACO) are used to satisfy the constraints and preserve the diversity of the suggested approach. The suggested approach is evaluated on the IEEE 30-Bus system consisting of six generating units. These results obtained for different test cases are compared with other reputed and well-known techniques

Quantum Inspired Evolutionary Algorithm with a Novel Elitist Local Search Method for Scheduling of Thermal Units

The unit commitment problem is a complex and essential problem in the power generation field, which is solved to obtain the schedule of a large number of generating units to minimize the operating cost and the fulfillment of consumer load demand. The present work solves the unit commitment problem using quantum-inspired evolutionary algorithms with a novel elitist local search method (QIEA-ELS). The proposed algorithm solves the unit commitment problem efficiently and its applicability is verified on various unit test systems. The constraints are satisfied efficiently to find a feasible solution, the novel elitist search method is used to locally explore the search area around the fittest individual to find a better solution in its vicinity in genotype space represent by qubits. The solution of the unit commitment is carried out considering two small population sizes as suggested in earlier work by other authors using QIEA, though it can be extended using larger population size also. The computational time is also reduced by using the suggested method with a novel elitist local search (ELS) method. The results obtained after applying the proposed algorithm are found to better as compared to other well-known solution techniques

Otorhinolaryngological myiasis: the problem and its presentations in the weak and forgotten

Introduction: Myiasis is common in tropical regions, but now increasing incidence is seen in the west due to international travel. Otorhinolaryngological myiasis is uncommon and is seen in diabetics, alcoholics or patients unable in self-care.Objectives: To study presentations of otorhinolaryngological myiasis, identify associated risk factors and species of flies causing myiasis.Methods: Clinical findings and co-morbidities of 67 myiasis cases were noted. Maggots were identified, manually removed, and patients were managed with topical treatment, systemic ivermectin and antibiotics.Findings: Thirty-three nasal myiasis, 13 aural myiasis and 5 patients with oral myiasis were noted. Seven patients with head neck wounds myiasis and nine patients of tracheostome myiasis were recorded.Discussion: Warm humid climate of tropical regions is a major concern along with co-existing conditions like poor sanitation, alcoholism, psychiatric diseases and neuropathies. Hesitancy is seen in attendants and health care professionals to deal with myiasis.Conclusion: Awareness about risk factors is important in avoiding myiasis along with prompt treatment which reduces morbidity. Tracheostome myiasis is an under-documented entity rather than a rare presentation.Keywords: Myiasis, Ivermectin, screwworm, Chrysomya bezziana, Musca domestica, Lucilia sericata.Funding: Non

Clinical Utility of Vitamin D3 As Potent Biomarker in Cardiovascular and Liver Disorders

Vitamin D is lipophilic substance needed for calcium and phosphate balance and the regulation of the osteo-metabolic system.The purpose of the study was to look at the possible significance of Vitamin D3 as a biomarker for liver enzymes and cholesterol. 249 cases were analysed, with different combinations of liver enzymes, vitamin D3, and cholesterol.75 clinical samples were processed over the course of the 3year study.46(61.3%) were females and 29 (38.6%) were males.Males were more affected than females.In respect to Vitamin D3, the age group of 41-60 years had a large range of cholesterol levels and liver enzyme values. 11% of cases had high SGOT levels, while 13% had aberrant cholesterol values.Above the age of 60, there was a linear connection between cholesterol and liver enzymes.There was seasonal variations in serum 25-OHD levels.Winter (November-March) indicated a Vitamin D3 deficiency in the blood serum, accounting for 74 cases(66%).Autumn and summer had the best range, with only 0 and 16 cases (14.2%), respectively.Despite wide variability in serum vitamin D levels, the differences were not statistically significant.Vitamin D3 can be an important biomarker in clinical practice since it can aid in the early detection of potential hazards linked with cardiovascular disease and liver dysfunction

Modeling of Dry Conditioned Sliding Wear And Friction Behavior of Heat-Treated Silicon Nitride Strengthened Al Metal Matrix Nanocomposites

In the presented work, the sliding wear under dry conditions and friction behaviour of Si3N4 reinforced high-strength Aluminum alloy (AA)7068 nanocomposites have been investigated under various loads, sliding velocity, and rubbing distances. The fabrication of nanocomposites has been done by using the stir casting technique with the advancement of ultrasonication. Scanning electron microscope (SEM), Elemental mapping, and energy dispersive spectroscopy (EDS) are used to analyze the microstructure of prepared nanocomposites and worn surfaces. The wear resistance improves with the incorporation of Si3N4 particles in Al 7068 alloy and further increases by increasing the weight % of reinforcement. The reinforcement is done by 0.5, 1, and 1.5 % Si3N4 by weight. ANOVA reveals that sliding distance is the most dominating factor in the wear loss of samples, and load became the most influential parameter in the coefficient of friction (COF). Microstructure reveals grain boundaries become discontinued after T6 heat treatment. AMNCs containing 1.5wt.% Si3N4 shows minimum wear loss compared to other nanocomposites and alloys

An edge-cloud infrastructure for weed detection in precision agriculture

Accurate identification of weeds plays a crucial role in helping farmers achieve efficient agricultural practices. An edge-cloud infrastructure can provide efficient resources for weed detection in resource-constrained rural areas. However, deployed applications in these areas often face challenges such as connectivity failures and network issues that affect their quality of service (QoS). We introduce a signal quality-aware framework for precision agriculture that allocates weed inference tasks to resource nodes based on the current network connectivity and quality. Two Machine Learning (ML) models based on ResNet-50 and MobileNetV2 are trained using the publicly available DeepWeeds image classification dataset. A rule-based approximation algorithm is formulated to execute tasks on resource-constrained computational nodes. We also designed a testbed setup consisting of Raspberry Pi (RPi), personal laptop, cloud server and Parsl environment for evaluating the framework. Reliability of the framework is tested in a controlled setting, under various dynamically injected faults. Experimental results demonstrate that the proposed setup can accurately identify weeds while ensuring high fault tolerance and low completion time, making it a promising solution for weed management in rural agriculture

Assesment of electricity excess in an isolated hybrid energy system: A case study of a Dangiwada village in rural Nepal

The increasing demand of power can be fulfilled through different architectures and electricity supply models by utilizing the available local resources. But most of the isolated energy system suffers from high energy cost and unreliable energy supply. This study identifies different electricity supply models to fulfill the dynamic demand of power in a remote area, which is analyzed in terms of cost of energy and causes for the high cost of energy. Among different factors, the presence of unusable energy (Electricity Excess) produced by the energy system during fulfillment of the demand is found to be major one cause for the high cost of energy. Further, the importance of energy storage system in isolated energy system is discussed. In this case, up to 83.4 % of electricity excess is observed, which can be utilized in different manners to reduce the total energy cost. Electricity excess profile for different energy model, their impacts and possible techniques of the solution with open views are discussed

Performance analysis of Apache openwhisk across the edge-cloud continuum

Serverless computing offers opportunities for auto-scaling, a pay-for-use cost model, quicker deployment and faster updates to support computing services. Apache OpenWhisk is one such open-source, distributed serverless platform that can be used to execute user functions in a stateless manner. We conduct a performance analysis of OpenWhisk on an edge-cloud continuum, using a function chain of video analysis applications. We consider a combination of Raspberry Pi and cloud nodes to deploy OpenWhisk, modifying a number of parameters, such as maximum memory limit and runtime, to investigate application behaviours. The five main factors considered are: cold and warm activation, memory and input size, CPU architecture, runtime packages used, and concurrent invocations. The results have been evaluated using initialization, and execution time, minimum memory requirement, inference time and accuracy