This study explores novel techniques to improve the detection accuracy of skarn iron deposits using advanced image processing methodologies. Leveraging the capabilities of Aster image, Band Ratio (BR) images, and Principal Component Analysis (PCA), alongside the power of 3D Convolutional Neural Networks (3D-CNN), the research aims to enhance the precision and efficiency of ore detection in complex geological environments. The proposed method employs a specific 3D-CNN architecture, accepting input as a 7×7×C image patch, where C represents the combined number of selected Aster image bands, Principal Component (PC) bands, and computed BR images. To evaluate the accuracy of the proposed method, five distinct image bands combinations, including the proposed bands combination, were tested and evaluated based on the overall accuracy (OA), average accuracy (AA), and kappa coefficient. The results demonstrated that while the incorporation of BR images alongside Aster bands initially seemed promising, it introduced significant confusion in certain classifications, leading to unexpected mis-classification rates. Surprisingly, utilizing solely Aster bands as input parameters yielded higher accuracy rates (OA= 93.13%, AA = 91.96%, Kappa = 90.91%) compared to scenarios involving the integration with band ratios (OA = 87.02%, AA = 79.15, and Kappa = 82.60%) or the integration of BR images to PC bands (OA= 87.78%, AA = 82.39%, Kappa = 83.81%). However, the amalgamation of Aster bands with selected PC bands showed slight improvements in accuracy (OA= 94.65%, AA = 92.93%, Kappa = 93.45%), although challenges in accurately classifying certain features persisted. Ultimately, the proposed combination of Aster bands, PC bands, and BR images (proposed bands combination) presented the most visually appealing and statistically accurate results (OA = 96.95%, AA= 94.87%, and Kappa = 95.93%), effectively addressing misclassifications observed in the other combinations. These findings underscore the synergistic contributions of each of the Aster bands, PC bands, and BR images, with Aster bands proving pivotal for optimal skarn classification, PC bands enhancing intrusions classification accuracy, and BR images strengthening wall rock classification accuracy. In conclusion, the proposed combination of input image bands emerges as a robust and comprehensive methodology, demonstrating unparalleled accuracy in the remote sensing detection of skarn iron minerals.

With recent developments in fusion engineering, interest has sparked in development of fusion devices for deterrent. Enormous amount of energy generated by combining two light nuclei could be contained and manipulated at will to trigger and accelerate micro explosions (from shock wave, x-rays or ion beam focusing) which finally result in full scale blast. Materials required to make such device are critical. They must possess high strength, high hardness, ductility, formability, drawability, and anisotropic properties. High entropy alloys (HEA) are new class of materials which nicely fulfils this requirement. Essentially, they are solid solutions of multi principal elements (usually > 5) eliminating the need of base metal as in conventional alloys. This gives them many unique properties which may be tailored at will (heat treatment, cold rolling, precipitation, irradiation). They also exhibit excellent directional properties with formation of distinct bands along certain preferred crystallographic planes even in hexagonal close packed structures. These anisotropic properties are strong function of rolling, working, or forging (swaging) direction and can be utilized to benefit. This study encompasses making outer shell of a typical fusion device selected on the basis of the weight, which is a function of area of pay load bay of carrier aircraft.

Electroencephalography (EEG) is a brain imaging technique in which electrodes are placed on the scalp. EEG signals are commonly decomposed into frequency bands called delta, theta, alpha, and beta.While these bands have been shown to be useful for characterizing various brain states, their utility as a one-size-fits-all analysis tool remains unclear. We present a two-part data-driven methodology for objectively determining the best EEG bands for a given dataset in this paper. First, a decision tree is used to estimate the optimal frequency band boundaries for reproducing the signal’s power spectrum for a predetermined number of bands. The optimal number of bands is then determined using an Akaike Information Criterion (AIC)-inspired quality score that balances goodness-of-fit with a small band count. Data-driven EEG band discovery may aid in objectively capturing key signal components and uncovering new indices of brain activity.

We generalize the dynamical analog of the Berry geometric phase setup to the quaternionic model of Avron et al. In our dynamical quaternionic system, the fast half-integer spin subsystem interacts with a slow two-degrees-of-freedom subsystem. The model is invariant under the 1:1:2 weighted SO(2) symmetry and spin inversion. There is one formal control parameter in addition to four dynamical variables of the slow subsystem. We demonstrate that the most elementary qualitative phenomenon associated with the rearrangement of the energy super-bands of our model consists of the rearrangement of one energy level between two energy superbands which takes place when the formal control parameter takes the special isolated value associated with the conical degeneracy of the semi-quantum eigenvalues. This qualitative phenomenon is of the topological origin, and is characterized by the second Chern class of the associated semi-quantum system. The correspondence between the number of redistributed energy levels and the second Chern number is confirmed through a series of examples.

The availability of high quality surface observations of precipitation and volume observations by polarimetric operational radars make it possible to constrain, evaluate and validate numerical models with a wide variety of microphysical schemes. In this article, a novel particle-based Monte-Carlo microphysical model (called “McSnow”) is used to simulate the outer rain bands of Hurricane Dorian which traversed the densely instrumented precipitation research facility operated by NASA at Wallops Island, Virginia. The rain bands showed steady stratiform vertical profiles with radar signature of dendritic growth layers near −15 °C and peak reflectivity in the bright band of 55 dBZ along with polarimetric signatures of wet snow with sizes inferred to exceed 15 mm. A 2D-video disdrometer measured frequent occurrences of large drops >5 mm and combined with an optical array probe the drop size distribution was well-documented in spite of uncertainty for drops <0.5 mm due to high wind gusts and turbulence. The 1D McSnow control run and four numerical “experiments” were conducted and compared with observations. One of the main findings is that even at the moderate rain rate of 10 mm/h collisional breakup is essential for the shape of the drop size distribution.

The fifth Generation (5G) mobile networks use millimeter Waves (mmWaves) to offer giga bit data rates. However, unlike microwaves, mmWave links are prone to user and topographic dynamics. They easily get blocked and end up forming irregular cell patterns for 5G. This in turn cause too early, too late, or wrong handoffs (HOs). To mitigate HO challenges, sustain connectivity and avert unnecessary HO, we propose a HO scheme based on Jump Markov Linear System (JMLS) and Deep Reinforcement Learning (DRL). JMLS is widely known to account for abrupt changes in system dynamics. DRL likewise emerges as an artificial intelligence technique for learning highly dimensional and time-varying behaviors. We combine the two techniques to account for time-varying, abrupt, and irregular changes in mmWave link behaviour by predicting likely deterioration patterns of target links. The prediction is optimized by meta training techniques that also reduces training sample size. Thus, the JMLS-DRL platform formulates intelligent and versatile HO policies for 5G. Results show our proposed prediction scheme about target link behavior post HO to be highly reliable. The scheme also averts unnecessary HOs thus ably supports longer dew time.

One of the key challenges of stock trading is the stock prices follow a random walk process, which is a special case of a stochastic process, and are highly sensitive to new information. A random walk process is difficult to predict in the short-term. Many linear process models that are being used to predict financial time series are structural models that provide an important decision boundary, albeit not adequately considering the correlation or causal effect of market sentiment on stock prices. This research seeks to increase the predictive capability of linear process models using the SPDR S\&P 500 ETF (SPY) and the CBOE Volatility (VIX) Index as a proxy for market sentiment. Three econometric models are considered to forecast SPY prices: (i) Auto-Regressive Integrated Moving Average (ARIMA), (ii) Generalized Auto Regressive Conditional Heteroskedasticity (GARCH), and (iii) Vector Autoregression (VAR). These models are integrated into two technical indicators, Bollinger Bands and Moving Average Convergence Divergence (MACD), focusing on forecast performance. The profitability of various algorithmic trading strategies is compared based on a combination of these two indicators. This research finds that linear process models that incorporate the VIX Index do not improve the performance of algorithmic trading strategies.

The visualization of hyperspectral images in display devices, having RGB colour composition channels is quite difficult due to the high dimensionality of these images. Thus, principal component analysis has been used as a dimensionality reduction algorithm to reduce information loss, by creating uncorrelated features. To classify regions in the hyperspectral images, K-means clustering has been used to form clusters/regions. These two algorithms have been implemented on the three datasets imaged by AVIRIS and ROSIS sensors.

To elucidate dynamic developmental processes in plants, live tissues and organs have to be visualized frequently and for long time periods. The development of roots is studied in depth at a cellular resolution not only to comprehend the basic processes fundamental to maintenance and pattern formation but also study stress tolerance adaptation in plants. Despite technological advancements, maintaining continuous access to samples and simultaneously preserving their morphological structures and physiological conditions without causing damage presents hindrances in the measurement, visualization and analyses of growing organs including plant roots. We propose a preliminary system which integrates the optical real-time visualization through light microscopy with a liquid culture which enables us to image at the tissue and cellular level horizontally growing Brachypodium roots every few minutes and up to 24 hours. We describe a simple setup which can be used to track the growth of the root as it grows including the root tip growth and osmotic stress dynamics. We demonstrate the system’s capability to scale down the PEG-mediated osmotic stress analysis and collected data on gene expression under osmotic stress.

Since the advent of the OBORI, it was subjected to numerous studies. However, most previous studies investigated only the potential impact of the OBORI on the Chinese economy and geopolitics. Therefore, its real effect on Chinese international commerce in OBORI countries is not evaluated yet. Accordingly, this study intends to model the OBORI effect on Chinese product brand purchases across country members. The assessment is made on 18362 purchases of the International Online Consumers (IOCs) from a Chinese international online selling platform. The Data was obtained from a programming language and the octopus software. The OBORI policy's effect on Chinese brands' purchases was examined through a Different In Different Model (DIDM). Results show that the impact of OBORI is weak in the real market. However, it could be significant if OBORI includes more developed and economically strong countries. To Chinese brands and policymakers, we show how the inclusion in the OBORI project of developed countries could contribute more to Chinese product brands' purchases. Thus, the study enables decision-makers to understand the current impact of OBORI on the real market and its potential effect if more developed and economically strong countries are included.

Generative models have recently gained popularity in Remote Sensing, offering substantial benefits for interpreting and utilizing satellite imagery across diverse applications such as climate monitoring, urban planning, and wildfire detection. These models are particularly adept at addressing the challenges posed by satellite images, which often exhibit domain variability due to seasonal changes, sensor characteristics, and especially variations in spectral bands. Such variability can significantly impact model performance across various tasks. In response to these challenges, our work introduces a novel approach that harnesses the capabilities of Generative Adversarial Networks (GANs), augmented with Contrastive Learning, to generate target domain images that account for multi-spectral band variations effectively. By maximizing mutual information between corresponding patches and leveraging the power of GANs, our model aims to generate realistic-looking images across different multi-spectral domains. We present a comparative analysis of our model against other well-established generative models, demonstrating its efficacy in generating high-quality satellite images while effectively managing domain variations inherent to multi-spectral diversity.

This paper presents a design and performance analysis of a 10-element 5G massive MIMO antenna array for sub-6GHz mobile handsets, specifically for LTE bands 42 (3400-3600 MHz), 43 (3600-3800 MHz), 48/49 (3550-3700 MHz) applications. The proposed antenna array consists of 10 closely spaced linearly polarized inverted-F antennas with a compact size of 20 × 9 mm2 of a single element. The proposed antenna array provides high gain, high efficiency, and low correlation between the antenna elements, which results in improved channel capacity, increased data rate, and enhanced signal quality. The performance of the antenna array is evaluated in terms of the radiation pattern, gain, efficiency, and correlation coefficient. The simulation and measured results show that the proposed antenna array achieves an approximate peak gain of 3.1 dBi at the resonance frequency, a total efficiency of 65 %, and a low correlation coefficient of 0.06 between the antenna elements. Therefore, the proposed 5G massive MIMO antenna array is a promising candidate for sub-6 GHz mobile handsets, particularly for LTE bands 42/43/48/49 applications.

In this study, we evaluated chlorophyll fluorescence (CF) under two extreme illumination conditions at plant scale with a passive spectroradiometer. Fluorescence (F) was estimated by reading directly from radiance spectra of a variety of plants illuminated with light-emitting diode (LED) grow lights in the laboratory. Solar-induced fluorescence (SIF) was estimated from spectral measurements of the same plants under sunlight using the Fraunhofer Line Depth (FLD) method. Chlorophyll fluorescence yield (F_yield) and solar-induced fluorescence yield (SIF_yield) were calculated by normalizing F and SIF with absorbed photosynthetically active radiation (APAR). Two approaches to estimating APAR were compared: utilizing white reference spectra and reflected spectra versus white reference spectra combined with the fraction of absorbed photosynthetically active radiation (fPAR) derived from literature. Average F and SIF were different by a factor of approximately twenty-four (F = 0.110 ± 0.038 Wm⁻²μm⁻¹sr⁻¹ versus SIF = 2.60 ± 1.87 Wm⁻²μm⁻¹sr⁻¹). In contrast, the average normalized values F_yield and SIF_yield were within the margin of error of one another (F_yield = 0.022 ± 0.008 μm⁻¹sr⁻¹ and SIF_yield = 0.030 ± 0.020 μm⁻¹sr⁻¹). This study highlights the influence of APAR on CF and the importance of properly accounting for it when estimating yield and demonstrates the ability of two simple and portable experimental setups with a passive instrument to obtain fluorescence metrics.

Laser-induced breakdown spectroscopy (LIBS) of pharmaceutical drugs that contain paracetamol is investigated in air and argon atmospheres. Characteristic neutral and ionic spectral lines of various elements and molecular signatures of CN violet and C₂ Swan band systems are observed. The relative hardness of all drug samples is measured as well. Principal component analysis, a multivariate method, is applied in data analysis for demarcation purposes of the drug samples. The CN violet and C₂ Swan spectral radiances are investigated for evaluation of possible correlation of the chemical and molecular structures of the pharmaceuticals. Complementary Raman and Fourier-transform-infra-red spectroscopies are used record molecular spectra of the drug samples. The applicationof the above techniques for the drug screening are important for identification and mitigation of drugs that reveal additives that may cause adverse side-effects.

Molecule radiation can be used as a tool to study colder regions in switching arc plasmas like arc fringes in contact to walls and ranges around current zero (CZ). This is demonstrated in the present study for the first time for the case of ablation-dominated high–current arcs as key elements of self–blast circuit breakers. The arc in a model circuit breaker (MCB) in CO2 with and an arc in a long nozzle under ambient conditions with peak currents between 5 and 10 kA were studied by emission and absorption spectroscopy in the visible spectral range. The nozzle material was polytetrafluoroethylene (PTFE) in both cases. Imaging spectroscopy was carried out either with high-speed cameras or with intensified CCD cameras. A pulsed high-intensity Xe lamp was applied as background radiator for the broad-band absorption spectroscopy. Emission of Swan bands from carbon dimers was observed at the edge of nozzles only or across the whole nozzle radius with highest intensity in the arc center, depending on current and nozzle geometry. Furthermore, absorption of C2 Swan bands and CuF bands were found with the arc plasma serving as background radiator. After CZ, only CuF was detected in absorption experiments.

This article presents analysis of carbon Swan, C$_2$, laser-plasma emission records using line strength data, C$_2$-Swan-lsf, and the ExoMol astrophysical database. Line-strength data fitting of 0.25-nanometer spectral resolution ExoMol-computed spectra for a 6,000-Kelvin temperature C$_2$ Swan system, ${\rm d}\ ^3\,\Pi_{\rm g} \longrightarrow {\rm a} \ ^3\,\Pi_{\rm u}$, $\Delta {\rm v} = 0, \pm 1$, reveals a temperature of 5,640 Kelvin. The six per cent lower temperature is associated primarily with the accuracy of the transition wavelengths in the ExoMol vs. C$_2$-Swan-lsf data. The analysis of experiment data examines spectra that are recorded following laser-induced optical breakdown in carbon monoxide. The laser plasma data are recorded with 0.35-nm spectral resolution. The temperature inferences are elaborated when using non-linear fitting with both databases. The results show temperatures in excess of 6,000 Kelvin for the $\Delta {\rm v} = -1$ sequence, and for a time delay of 30\,$\upmu$s from laser plasma initiation. The accuracy of the C$_2$ Swan bands line strength data is of the order of 1 picometer. These line strength data are also utilized for analysis of laser-induced fluorescence experiments that employ a spectral resolution of 5.5 picometer, and a temperature of 2,716 Kelvin is inferred. Accurate C$_2$ databases show many applications in laboratory diagnosis and interpretation of astrophysical plasma records.