Learning With Imbalanced Data in Smart Manufacturing: A Comparative Analysis

The Internet of Things (IoT) paradigm is revolutionising the world of manufacturing into what is known as Smart Manufacturing or Industry 4.0. The main pillar in smart manufacturing looks at harnessing IoT data and leveraging machine learning (ML) to automate the prediction of faults, thus cutting maintenance time and cost and improving the product quality. However, faults in real industries are overwhelmingly outweighed by instances of good performance (faultless samples); this bias is reflected in the data captured by IoT devices. Imbalanced data limits the success of ML in predicting faults, thus presents a significant hindrance in the progress of smart manufacturing. Although various techniques have been proposed to tackle this challenge in general, this work is the first to present a framework for evaluating the effectiveness of these remedies in the context of manufacturing. We present a comprehensive comparative analysis in which we apply our proposed framework to benchmark the performance of different combinations of algorithm components using a real-world manufacturing dataset. We draw key insights into the effectiveness of each component and inter-relatedness between the dataset, the application context, and the design of the ML algorithm

Quantum three-body system in D dimensions

The independent eigenstates of the total orbital angular momentum operators for a three-body system in an arbitrary D-dimensional space are presented by the method of group theory. The Schr\"{o}dinger equation is reduced to the generalized radial equations satisfied by the generalized radial functions with a given total orbital angular momentum denoted by a Young diagram $[\mu,\nu,0,...,0]$ for the SO(D) group. Only three internal variables are involved in the functions and equations. The number of both the functions and the equations for the given angular momentum is finite and equal to $(\mu-\nu+1)$.Comment: 16 pages, no figure, RevTex, Accepted by J. Math. Phy

Relativistic Aharonov-Casher Phase in Spin One

The Aharonov-Casher (AC) phase is calculated in relativistic wave equations of spin one. The AC phase has previously been calculated from the Dirac-Pauli equation using a gauge-like technique \cite{MK1,MK2}. In the spin-one case, we use Kemmer theory (a Dirac-like particle theory) to calculate the phase in a similar manner. However the vector formalism, the Proca theory, is more widely known and used. In the presence of an electromagnetic field, the two theories are `equivalent' and may be transformed into one another. We adapt these transformations to show that the Kemmer theory results apply to the Proca theory. Then we calculate the Aharonov-Casher phase for spin-one particles directly in the Proca formalism.Comment: 12 page

A shift from cattle to camel and goat farming can sustain milk production with lower inputs and emissions in north sub-Saharan Africa’s drylands

Climate change is increasingly putting milk production from cattle-based dairy systems in north sub-Saharan Africa (NSSA) under stress, threatening livelihoods and food security. Here we combine livestock heat stress frequency, dry matter feed production and water accessibility data to understand where environmental changes in NSSA’s drylands are jeopardizing cattle milk production. We show that environmental conditions worsened for ∼17% of the study area. Increasing goat and camel populations by ∼14% (∼7.7 million) and ∼10% (∼1.2 million), respectively, while reducing the dairy cattle population by ∼24% (∼5.9 million), could result in ∼0.14 Mt (+5.7%) higher milk production, lower water (−1,683.6 million m3, −15.3%) and feed resource (−404.3 Mt, −11.2%) demand—and lower dairy emissions by ∼1,224.6 MtCO2e (−7.9%). Shifting herd composition from cattle towards the inclusion of, or replacement with, goats and camels can secure milk production and support NSSA’s dairy production resilience against climate change

Downregulation of CYP17A1 by 20-hydroxyecdysone: plasma progesterone and its vasodilatory properties

Aim: To investigate the effect of 20-hydroxyecdysone on steroidogenic pathway genes and plasma progesterone, and its potential impact on vascular functions. Methods: Chimeric mice with humanized liver were treated with 20-hydroxyecdysone for 3 days, and hepatic steroidogenic pathway genes and plasma progesterone were measured by transcriptomics and GC–MS/MS, respectively. Direct effects on muscle and mesenteric arterioles were assessed by myography. Results: CYP17A1 was downregulated in 20-hydroxyecdysone-treated mice compared with untreated group (p = 0.04), with an insignificant increase in plasma progesterone. Progesterone caused vasorelaxation which was blocked by 60 mM KCl, but unaffected by nitric oxide synthase inhibition. Conclusion: In the short term, 20-hydroxyecdysone mediates CYP17A1 downregulation without a significant increase in plasma progesterone, which has a vasodilatory effect involving inhibition of voltage-dependent calcium channels, and the potential to enhance 20-hydroxyecdysone vasorelaxation

An overview of smart irrigation systems using IoT

Countries are working into making agriculture more sustainable by integrating different technologies to enhance its operation. Implementing improvements in irrigation systems is crucial for the water-use efficiency and works as a contributor to Sustainable Development Goals (SDGs) under the United Nations specifically Goal 6 and Target 6.4. This paper aims to highlight the contribution of SMART irrigation using Internet of Things (IoT) and sensory systems in relation to the SDGs. The study is based on a qualitative design along with focusing on secondary data collection method. Automated irrigation systems are essential for conservation of water, this improvement could have a vital role in minimizing water usage. Agriculture and farming techniques is also linked with IoT and automation, to make the whole processes much more effective and efficient. Sensory systems helped farmers better understand their crops and reduced the environmental impacts and conserve resources. Through these advanced systems effective soil and weather monitoring takes place along with efficient water management. Irrigation systems have been determined as positive contributor toward optimized irrigation systems that could enhance the use of continuous research and development which focus on enhancing the sustainable operations and cost reduction. Lastly, the challenges and benefits for the implementation of sensory based irrigation systems are discussed. This review will assist researchers and farmers to better understand irrigation techniques and provide an adequate approach would be sufficient to carry out irrigation related activities

Investigation the nonlinear optical properties of silver nanoparticles using femtosecond laser

© 2020 Published under licence by IOP Publishing Ltd. In this research, the fabrication of silver nanoparticles and experimental nonlinear response (NLO). The fabrication of the silver nanoparticles has been done using E-Beam evaporation on a glass substrate (Ag-NPs) and investigation of their nonlinear optical response (NLO). The silver nanoparticles was evaluated by optical spectrum (UV-Vis) that shows localized surface Plasmon band at 375 nm. The experiment shows the nonlinear absorption and nonlinear refraction effect of silver nanoparticles, the silver nanoparticles is analysed by Z-Scan technique using a femtoseconds laser with 800 nm wavelength. The result shows the nonlinear absorption (NLA) is at 4.8710-4cmW-1, while (NLR) is at 7.9410-9cmW-1

Partial discharge detection using low cost RTL-SDR model for wideband spectrum sensing

Partial discharge (PD) is one of the predominant factors to be controlled to ensure reliability and undisrupted functions of power generators, motors, Gas Insulated Switchgear (GIS) and grid connected power distribution equipment, especially in the future smart grid. The emergence of wireless technology has provided numerous opportunities to optimise remote monitoring and control facilities that can play a significant role in ensuring swift control and restoration of HV plant equipment. In order to monitor PD, several approaches have been employed, however, the existing schemes do not provide an optimal approach for PD signal analysis, and are very costly. In this paper an RTL-SDR (Software Defined Radio) based spectrum analyser has been proposed in order to provide a potentially low cost solution for PD detection and monitoring. Initially, a portable spectrum analyser has been used for PD detection that was later replaced by an RTL-SDR device. The proposed schemes exhibit promising results for spectral detection within the VHF and UHF band

On two superintegrable nonlinear oscillators in N dimensions

We consider the classical superintegrable Hamiltonian system given by $H=T+U={p^2}/{2(1+\lambda q^2)}+{{\omega}^2 q^2}/{2(1+\lambda q^2)}$, where U is known to be the "intrinsic" oscillator potential on the Darboux spaces of nonconstant curvature determined by the kinetic energy term T and parametrized by {\lambda}. We show that H is Stackel equivalent to the free Euclidean motion, a fact that directly provides a curved Fradkin tensor of constants of motion for H. Furthermore, we analyze in terms of {\lambda} the three different underlying manifolds whose geodesic motion is provided by T. As a consequence, we find that H comprises three different nonlinear physical models that, by constructing their radial effective potentials, are shown to be two different nonlinear oscillators and an infinite barrier potential. The quantization of these two oscillators and its connection with spherical confinement models is briefly discussed.Comment: 11 pages; based on the contribution to the Manolo Gadella Fest-60 years-in-pucelandia, "Recent advances in time-asymmetric quantum mechanics, quantization and related topics" hold in Valladolid (Spain), 14-16th july 201