The developing viral illness of cattle known as lumpy skin disease (LSD) has terrible economic consequences. The virus responsible for lumpy skin disease is a member of the Poxviridae family and the Capripoxvirus genus. It is an economically significant transboundary disease that affects cattle, water buffalo, and camels. The symptoms of the disease include the development of lumps or nodules on the skin of the animal. This illness is widespread in Africa and the Middle East and has recently appeared in Asia. This article discusses the Lumpy skin disease outbreak and detection among cattle using Mobilenetv2 and Deep learning techniques. The lumpy skin disease dataset is utilized for this experiment and is balanced using the oversampling technique. MobileNetv2, a pre-trained neural network extracts the features from the images for image processing. Later deep learning model with the combination of a two-dimensional convolution neural network, max pooling, flatten and dense layer is utilized for classification purposes. The proposed model outperforms in terms of lumpy skin disease detection; the performance is compared using confusion metrics parameters with a classification accuracy of 99.88%.
The q-Weibull distribution is a flexible probability distribution that is commonly used in reliability analysis, survival analysis, and extreme value modelling. Obtaining accurate parameter estimates is essential for these applications. This paper investigates several estimation techniques for the q-Weibull distribution, with special emphasis on the Metropolis-Hastings algorithm. Through an extensive simulation study, we evaluate the performance of these methods across different sample sizes. In addition, we apply them to a real dataset to illustrate their practical utility and to highlight situations where the Metropolis-Hastings approach proves particularly advantageous.
This study investigates the thermally stratified magnetohydrodynamic flow of Casson-Williamson hybrid nano liquid around a linearly stretched vertical cylinder in a porous region. The unique aspect of this research is the inclusion of thermal stratification effects on non-Newtonian hybrid nanofluid flow. Computational solutions are derived by employing MATLAB's Bvp4c algorithm, with velocity and thermal profiles illustrated graphically, and distinct non-dimensional factors. Shear and thermal transmission rates are also calculated and displayed in tables. Results expose that thermal stratification reduces the thermal profile, with notable heat stratification, even causing negative temperature values. It's also shown that the Williamson hybrid nanofluid exhibits a 16.5% increase in heat transmission rate over the Williamson nanofluid and a 30.7% higher shear stress rate. The heat transmission rate is enhanced by increasing the thermal buoyancy factor but decreases with higher values of the Casson factor, thermal stratification factor, porosity factor, and Weissenberg number. Additionally, the thermal distribution increases with a higher Weissenberg number and curvature factor. These findings offer substantial potential for enhancing thermal management in various industrial applications, marking a significant contribution to fluid mechanics and nanofluid research. The outcomes align well with prior studies.
Public-Private Partnerships (PPP) are crucial for infrastructure development in developing countries. PPP projects frequently face challenges, sych as inefficiencies in project management and collaboration. Building Information Modeling is capable to address these challenges, by enhancing collaboration and improving project efficiency. This study aims to evaluate the impact of BIM on PPP projects in Jordan and Malaysia. PPPs in devoloping countries play a pivotal role in infrastructure development. The study uses a questionnaire approach to achieve the objectives. A total of 1100 questionnaires were distributed to experts from public, private, and educational institutions, 441 valid responses received, which represents a 40.1% response rate. The questionnaire covered five aspects, starting with demographic data, then followed by the key aspects of BIM adoption, which are administrative, sustainability, management, tools framework, and productivity. The data was analyzed using SPSS software to assess BIM's influence on PPP project performance. The results show that BIM adoption could significantly improve performance in areas such as document management, interface management, energy efficiency, public service quality, and organizational structures, with specific increases in productivity and sustainability metrics. The experts identify that the adoption has positive impact on PPP projects in both Jordan and Malaysia. The study provides an imperical evidence of the potential of success PPP projects if and when BIM is adopted. The results shows improvements in document management, effective interface management, energy efficiency, quality and innovation of public services, and organizational structures. The study also contributes in inriching the body of literature on the context on BIM and PPPs, and showcasing the applications developting countries.
The simplest machining method for eliminating undesired material from a workpiece using a single point cutting tool is turning. Appropriate cooling and lubrication save production lead time, machining costs, and environmental impacts controlled by cutting fluids. The impact of cooling cum lubrication techniques, such as minimum quantity lubrication with typical cutting fluids, minimum quantity lubrication with nanofluids, and minimum quantity lubrication with hybrid nanofluids, on turning performance metrics were thoroughly reviewed. The effect of these cooling solutions on results, such as cutting temperature, cutting force, surface roughness, tool wear, and chip morphology, has been extensively reviewed and addressed in the literature. Moreover, the nanoparticle types, size, concentration, base fluid types, and lubrication for nanofluid and hybrid nanofluids with minimum quantity lubrication have been considered during the study. The review of the relevant literature indicates that outcomes are significant. The impact of these cooling solutions on cutting temperature, cutting force, surface roughness, tool wear, and chip morphology has been thoroughly examined and addressed in the literature. The review of relevant literature indicates that outcomes are significantly improved when minimum quantity lubrication with nanofluids and hybrid nanofluids is contrasted with alternative cooling techniques. It is possible to reduce substantially pressures, cutting temperatures, surface roughness, and tool wear. Different kinds of nanoparticles, particle size, concentration, and nozzle inclination angle are the most critical factors in optimizing heat transfer rate and minimizing tool wear. Aluminium oxide (Al2O3) is the most commonly used nanoparticle in minimum quantity lubrication applications involving nanofluids and hybrid applications because it forms a coating between the workpiece and cutting tool contact, reducing friction. Additionally, the coefficient of friction significantly slowed. In addition, the minimum quantity lubrication with nanofluids also improves the colour and shape of the chip's morphology.
In this paper, a narrowband and highly linear Low Noise Amplifier (LNA) is designed and implemented on 180 nm CMOS technology, operating at a center frequency of 4.5 GHz. This LNA design is particularly suitable for modern communication systems and emerging 5G applications. For simulation and analysis, the 180 nm Generic Process Design Kit (GPDK) in Keysight's Advanced Design System (ADS) was utilized. To improve stability and gain, resistive feedback was chosen, while inductive source degeneration was employed to achieve better noise performance and linearity. The proposed design aims for a simple architecture, and its narrowband specifications are achieved through optimized input and output matching networks. The analysis showed that the designed LNA provides a gain of 14.1 dB, a minimum noise figure of 0.928 dB, an input 1-dB compression point (P1dB) of 0 dBm, and an input third-order intercept point (IIP3) of 4.942 dBm across a 360 MHz band. These results indicate excellent linearity and signal integrity, and show the design is competitive with relative studies. The novelty of this work lies in achieving high linearity and low noise with a notably simplified design, overcoming common trade-offs. Compared to recent state-of-the-art work, the proposed LNA offers superior linearity and a lower noise figure while maintaining a simple circuit topology.
This paper will involve optimization of the parameters of the 3D printing process of ASTM D638 test samples with Polylactic Acid (PLA) to produce fixtures to support a quad bike chassis that supports 155 kg. The experiment measures the effects of layer height, printing rate, nozzle size and infill density on production time, weight, cost and mechanical characteristics, such as hardness, tensile strength and roughness of the surface. The findings indicate that higher layer height, speed and nozzle diameter lead to less time spent on manufacturing with the most significant effect on manufacturing time being the layer height and the speed of printing. The dimensional accuracy of specimen 1 and 6 was close to 100%. The best surface roughness was topographically at a layer height of 0.2 mm, infill density of 100, nozzle diameter of 0.4 mm, and a print speed of 80 mm /s. When it comes to hardness, the most positive results were reached due to a layer height of 0.08 mm and a nozzle diameter of 0.2 mm. The peak tensile strength was obtained using the layer height of 0.4 mm, infill density of 80 percent, nozzle diameter of 0.15 mm, and a printing speed of 120 mm/s. The originality of this assignment consists in its use in automotive engineering where specially designed 3D-printed fixtures are used instead of standard metal parts, which are lightweight and economical. This study is very insightful and can be applied to improve quality and efficiency of 3D printing in manufacturing of quad bike fittings and further include more additive manufacturing.
Renewable energy has a significant share in the world's energy supply today. The need for energy due to growth and development, along with issues such as greenhouse gas emissions, declining fossil reserves and the growing need for energy, have led countries to turn to use renewable energy. As uncertainty is an inseparable part of decision-making process, we assess and prioritize renewable energy technologies using fuzzy multi-criteria decision-making techniques. In this study, sustainability, technical and financial indices are evaluated then four types of renewable systems are ranked, using hybrid Fuzzy Step-wise Weight Assessment Ratio Analysis/ Fuzzy Weighted Aggregated Sum Product Assessment (F-SWARA/F-WASPAS) method. The results show that among the main criteria, the technology criterion with a weight of 0.402 has gained the first rank, the resources criterion with a weight of 0.253 has gained the second rank and the social criterion with a weight of 0.161 has gained the third rank. Among the sub-criteria, system efficiency, capacity factor and material intensity are ranked first to third, respectively. Among the options, solid oxide fuel cells have won first place.
