This article presents a detail experimental procedure to perform float-and-sink tests for classifying coal samples according to their densities. Moreover, this article emphasizes obtaining 'partition curves' for three different coal samples (heavy media bath, big barrel and small barrel), which helps in evaluating and demonstrating classifier performance. Calculations of independent variables such as 'Probable Error' and 'Imperfection' are also discussed for partition curve that helps in evaluating the effectiveness of various beneficiation equipment used for the upgradation of quality of coal received from different coal mines. It was observed that there is a tendency for the partition curves to steepen as the density of separation decreases. In other words, separations at lower density is sharper than separations at higher density.

In this short paper, the hyperbola-based analysis of wave interference recently developed by Thomas is employed to study the pattern of concentric circular fringes captured on a distant screen, when its plane is oriented orthogonal to the line joining two coherent point-sources. Newton's rings are another instance of concentric circular fringes, generated with an extended source and a plano-convex lens-glass plate combination. The underlying geometry of the latter scenario, as described in the literature, allows for the estimation of two important physical parameters viz. the wavelength of light and the refractive index of a liquid medium. It is demonstrated here that the geometrical arrangement of the former scenario can in principle, be utilized to reach the same ends as well. Additionally, the hallmark distinguishing features of both types of circular fringe patterns are qualitatively and quantitatively elucidated.

Vulnerability detection and safety of smart contracts are of paramount importance because of their immutable nature. Symbolic tools like OYENTE and MAIAN are typically used for vulnerability prediction in smart contracts. As these tools are computationally expensive, they are typically used to detect vulnerabilities until some predefined invocation depth. These tools require more search time as the invocation depth increases. Since the use of smart contracts increases rapidly, their analysis becomes difficult using these traditional tools. Recently, a machine learning technique called Long Short Term Memory (LSTM) has been used to predict the vulnerability of a smart contract. In the present article, we present how to classify smart contracts into Suicidal, Prodigal, Greedy, or Normal categories using Average Stochastic Gradient Descent Weight-Dropped LSTM (AWD-LSTM), a variant of LSTM. We reduced the class imbalance by considering only distinct opcode combinations for normal contracts and achieved a weighted average F1 score of 90.0%. Such techniques can be utilized in real-time to analyze a large number of smart contracts and to improve their security.

Coal use needs to rapidly decline in the global energy mix in the next few decades in order to meet the Paris climate goals of keeping global warming well below 2-degrees Celsius. In emerging economies such as India (the second largest producer and consumer of coal) this would entail reducing long-term coal dependency. Prior work has focused on a coal transition in India from a techno-economic point of view, yet little attention has been given to the socio-economic dimensions of this transition. This is in part due to lack of availability of datasets required for such analysis. The first step in understanding the socio-economic dimensions of a coal transition in India is to understand the scale of current socio-economic dependency on coal at the sub-national level. We contribute to this literature by creating a novel dataset comprised of all 459 operational coal mines in India, using multiple Right to Information Act applications (India's Freedom of Information Act) and then combining this dataset with coal company wise employment factors to estimate direct job numbers at the district level (a sub-administrative unit). We find that coal is produced in 51 districts in 13 states in India with large variations in employment numbers among these districts. While Korba district in Chhattisgarh state is the highest coal producing district, Dhanbad district in Jharkhand state is home to the highest number of coal mining workers. This is the first attempt at understanding the socio-economic dependency on coal at a district level and future work could focus on quantifying other district level socio-economic indicators such as coal related revenues. The new dataset and the results of this paper will be useful for scholars conducting future work on coal transitions and related topics.

We propose a simple algorithm to locate the 'corner' of an L-curve, a function often used to select the regularisation parameter for the solution of ill-posed inverse problems. The algorithm involves the Menger curvature of a circumcircle and the golden section search method. It efficiently finds the regularisation parameter value corresponding to the maximum positive curvature region of the L-curve. The algorithm is applied to some commonly available test problems and compared to the typical way of locating the l-curve corner by means of its analytical curvature. The application of the algorithm to the data processing of an electrical resistance tomography experiment on thin conductive films is also reported.

In this note, we contribute a multi-language handwritten digit recognition dataset named MNIST-MIX, which is the largest dataset of the same type in terms of both languages and data samples. With the same data format with MNIST, MNIST-MIX can be seamlessly applied in existing studies for handwritten digit recognition. By introducing digits from 10 different languages, MNIST-MIX becomes a more challenging dataset and its imbalanced classification requires a better design of models. We also present the results of applying a LeNet model which is pre-trained on MNIST as the baseline.

This note uses electricity generator level 2001–2018 US capacity, generation, and heat input data to evaluate trends in same-plant capacity factor (how much plants run) and heat rate (how efficiently plants run) as plants age. Based on compound annual growth rates for capacity factor and, for thermal plants, heat rate, and based on the subset of US plants that have been operating since 2010 or earlier, same-plant capacity factors increased slightly, and heat rates decreased slightly, between 2001–2018 (weighted average based on 2018 plant capacity). Trends vary by region, fuel, and plant age. Notably, US natural gas-fired power plants tended to run more, and more efficiently, as they aged, while coal-fired power plants tended to run less, and less efficiently. Potential drivers include relative plant age, policy, financial competitiveness, and an anticipated tendency for plant operators to react to the effects of equipment aging with maintenance, repair, replacement, and optimization. These observations can inform committed emissions-based research, which requires making assumptions about how plant operational characteristics change (or do not) as they age.

The index of refraction structure constant, characterizing the intensity of optical turbulence, describes the disruption of a propagating electromagnetic beam passing through an inhomogeneously heated turbulent environment. In order to improve predictive models, it is critical to develop a deeper understanding of the relationships between environmental parameters and optical turbulence. To that end, an overwater, 890 m scintillometer link was established along the Chesapeake Bay adjacent to the Severn River in Annapolis, Maryland. Specifically, data from the scintillometer, as well, as numerous meteorological parameters were collected over the period of approximately 15 months to characterize a scintillometer link in the near-maritime environment. The characteristics of this near-maritime link were distinct from those observed in prior over-land and open ocean links. Further, existing macro-meteorological models for predicting from environmental parameters developed for open-ocean links were shown to perform poorly in the near-maritime environment. While the offshore adapted macro-meteorological model demonstrated lower prediction error, this study suggests that new models could be developed to reduce prediction error in the near-maritime environment. The complete data set, including measurements, and to our knowledge, one of the first to extend beyond one year, is available.

A technique based on Archimedes' Principle is described for measuring the volume of small objects (0.5–5 cm³) less dense than water. The volume of 10 small red chillies was measured by pycnometry and an immersion Archimedes technique which involved suspending the chillies in water in a container placed on an electronic balance. A pycnometer, which uses helium gas is able to determine the internal solid volume of the chillies and water immersion the outer volume. The difference between the two volumes gives the volume of internal air cavities in the chillies. The pycnometer and immersion techniques were compared by measuring the volume of wax candles with volumes between 0.3 and 2.5 cm³. A Bland-Altman analysis revealed that the Archimedes volumes were lower than pycnometry volumes by which needs further investigation. A combination of pycnometry and water immersion may be a useful tool for botanical studies.

Fixation and transportation of human fecal samples is often difficult in geographically remote locations due to unavailability of options for immediate freezing. In this study effectiveness of five different chemical fixatives were evaluated on human fecal samples including for supernatant using RNAprotect® Bacteria Reagent (Qiagen), 95% ethanol, acetone, TRIzol® and a mixture of all these fixatives, in addition to immediate freezing. DNA was extracted from the fecal samples using QIAamp® Fast DNA Stool Mini Kit as well as quality and yield of extracted DNA was monitored for a period of 30 days. It was found that except TRIzol®, all other preservatives showed good DNA quality and yield for a period of one month based on agarose gel electrophoresis, Nanodrop and Qubit fluorometric measurements. It was also found that supernatant of fecal sample fixed with RNAprotect Bacteria Reagent gave reliable DNA yield in comparison to other various fixatives. The study also revealed that quality and yield of DNA from fecal samples fixed in acetone were very promising since it is a cost-effective fixative. Overall, the study shows future applicability for downstream DNA analyses of the RNAprotect® Bacteria Reagent, 95% ethanol, acetone, and a mixture of all these fixatives for fixing human fecal samples to be collected from geographically remote locations or in regions where available resources are largely limited.

The structure of pollen has evolved depending on its local environment, competition, and ecology. As pollen grains are generally of size 10–100 microns with nanometre-scale substructure, scanning electron microscopy is an important microscopy technique for imaging and analysis. Here, we use style transfer deep learning to allow exploration of latent w-space of scanning electron microscope images of pollen grains and show the potential for using this technique to understand evolutionary pathways and characteristic structural traits of pollen grains.

Oceanimonas sp. NSJ1 was isolated from macroplastic debris collected previously from Junput, an intertidal beach, facing the northeast coastal Bay of Bengal of the Northern Indian Ocean. The genome of this isolate is closely related to Oceanimonas doudoroffii with a genome size of 3.56 Mbp. The genome annotation confirmed the presence of 5919 total genes, out of which 5809 were CDSs (coding sequences) and all are protein-coding. The genome codes for 110 RNA with 25 rRNA, 84 tRNA (transfer RNA), and one tmRNA (transfer-messenger RNA). Analyses of the annotated genome of Oceanimonas sp. NSJ1 revealed the presence of enzymes involved in the degradation of polycyclic aromatic hydrocarbons. The presence of phthalate 4,5-dioxygenase oxygenase reductase subunit pht2 within the genome also highlights the novelty of this isolate and future functional potential for studying phthalate degradation in marine environment.

The Monkey-pox virus disease (MPXD) is a type of the pox disease similar to the smallpox disease. This disease produces rashes with lesions on the skin. The MPXD is an endemic in some countries of Africa, however, a recent outbreak of this disease started to appear in some countries, such as the United Kingdom, Spain, Greece, Portugal, Australia, Brazil, and the United States. Lots of indicators are employed for forecasting the 2022 outbreak of the MPXD such as the infection period, the recovery period, the force of infection, the incubation period of the disease.....etc. The aim of this study is finding the infection period, and the recovery period of the 2022 outbreak of the monkey-pox virus disease in two countries: the United States, and Spain. We apply the simulation and analytical methods on a simple epidemic model, which is the SIRD infectious disease model, for finding the previous periods. We found that the infection period of the recent outbreak of the MPXD varies from 10 days to 20 days, while we found that the recovery periods of the recent outbreak of the MPXD varies from 15 days to 30 days. Besides, we found that the average value of the infection period of the 2022 outbreak of the MPXD is about two weeks, and the average value of the recovery period of the 2022 outbreak of the MPXD is about three weeks. The analytical solution with the simulation algorithm which we used in this study can be expanded for other forecasting parameters of the MPXD, and also for multiple countries.

High-rate dynamics occur when a system's acceleration is larger than 100 g_n over durations less than 100 ms. Structural health monitoring algorithms must be created for high-rate dynamic systems to maximize safety and minimize economic losses. There is a need to evaluate these algorithms for precision and accuracy prior to real-world implementation. An experimental testbed was created to simulate large-magnitude events while maintaining repeatability to accurately and robustly assess various structural health monitoring algorithms' capability to monitor high-rate dynamic systems. All previous datasets created on the experimental testbed are discussed, examining various sensor setups, excitations, and boundary condition changes to properly simulate near-high-rate events and provide robust experimental data to evaluate structural health monitoring algorithms.

A machine learning algorithm (MLA) has been applied to a Fourier transform infrared spectroscopy (FTIR) dataset previously analysed with a principal component analysis (PCA) linear discriminant analysis (LDA) model. This comparison has confirmed the robustness of FTIR as a prognostic tool for oral epithelial dysplasia (OED). The MLA is able to predict malignancy with a sensitivity of 84 ± 3% and a specificity of 79 ± 3%. It provides key wavenumbers that will be important for the development of devices that can be used for improved prognosis of OED.