FinTech revolution in mineral management: Exploring the nexus between technology adoption and sustainable Resource utilization in an industry 4.0 context.

This study investigates the impact of FinTech adoption on sustainable mineral management policies in Australia within the context of Industry 4.0, using quarterly data from 1990Q1 to 2022Q4. Employing the ARDL-Bounds testing approach, Granger causality analysis, and innovation accounting matrix, the research finds a short-term positive association between FinTech adoption, technological readiness, and green mineral extraction. However, both in the short and long run, investment in sustainable mining technologies, government support for FinTech in mining, and environmental compliance exhibit a negative relationship with resource management. Bidirectional causality is observed between regulatory support for mining FinTech, technological finance solutions, and environmentally conscious mineral practices, while unidirectional causality exists from FinTech adoption to sustainable mining practices. Impulse response functions offer insights into the future impact of variables on eco-conscious mining policies, indicating positive influences from FinTech adoption, government support for FinTech in mining, and technological adaptability over the next decade. Conversely, eco-friendly mining investments, environmental conformity, and social license to operate will impact sustainable mineral utilization. Variance decomposition analysis highlights the most significant shocks on eco-friendly resource management over the next ten years, emphasizing the role of sustainable mining technologies, FinTech adoption, and public support for mining endeavours. In the transition to Industry 4.0, this research provides crucial insights for responsibly utilizing Australia's natural resources by leveraging financial technology and technological readiness.

Wavelet-Based Fractal Analysis of rs-fMRI for Classification of Alzheimer's Disease.

The resting-state functional magnetic resonance imaging (rs-fMRI) modality has gained widespread acceptance as a promising method for analyzing a variety of neurological and psychiatric diseases. It is established that resting-state neuroimaging data exhibit fractal behavior, manifested in the form of slow-decaying auto-correlation and power-law scaling of the power spectrum across low-frequency components. With this property, the rs-fMRI signal can be broken down into fractal and nonfractal components. The fractal nature originates from several sources, such as cardiac fluctuations, respiration and system noise, and carries no information on the brain's neuronal activities. As a result, the conventional correlation of rs-fMRI signals may not accurately reflect the functional dynamic of spontaneous neuronal activities. This problem can be solved by using a better representation of neuronal activities provided by the connectivity of nonfractal components. In this work, the nonfractal connectivity of rs-fMRI is used to distinguish Alzheimer's patients from healthy controls. The automated anatomical labeling (AAL) atlas is used to extract the blood-oxygenation-level-dependent time series signals from 116 brain regions, yielding a 116 × 116 nonfractal connectivity matrix. From this matrix, significant connections evaluated using the p-value are selected as an input to a classifier for the classification of Alzheimer's vs. normal controls. The nonfractal-based approach provides a good representation of the brain's neuronal activity. It outperformed the fractal and Pearson-based connectivity approaches by 16.4% and 17.2%, respectively. The classification algorithm developed based on the nonfractal connectivity feature and support vector machine classifier has shown an excellent performance, with an accuracy of 90.3% and 83.3% for the XHSLF dataset and ADNI dataset, respectively. For further validation of our proposed work, we combined the two datasets (XHSLF+ADNI) and still received an accuracy of 90.2%. The proposed work outperformed the recently published work by a margin of 8.18% and 11.2%, respectively.

ECM-LSE: Prediction of Extracellular Matrix Proteins Using Deep Latent Space Encoding of k-Spaced Amino Acid Pairs.

Extracelluar matrix (ECM) proteins create complex networks of macromolecules which fill-in the extracellular spaces of living tissues. They provide structural support and play an important role in maintaining cellular functions. Identification of ECM proteins can play a vital role in studying various types of diseases. Conventional wet lab-based methods are reliable; however, they are expensive and time consuming and are, therefore, not scalable. In this research, we propose a sequence-based novel machine learning approach for the prediction of ECM proteins. In the proposed method, composition of k-spaced amino acid pair (CKSAAP) features are encoded into a classifiable latent space (LS) with the help of deep latent space encoding (LSE). A comprehensive ablation analysis is conducted for performance evaluation of the proposed method. Results are compared with other state-of-the-art methods on the benchmark dataset, and the proposed ECM-LSE approach has shown to comprehensively outperform the contemporary methods.

Per capita income, trade openness, urbanization, energy consumption, and CO2 emissions: an empirical study on the SAARC Region.

The developing world in general is facing so many crucial problems including global warming in recent years. Global warming has multiple consequences on each segment of the society and therefore, its root causes are important to identify. The present study examines the impact of per capita income, trade openness, urbanization, and energy consumption on CO2 emissions. Countries located in South Asian Association for Regional Cooperation (SAARC) are considered in the study. The selection of the SAARC region is motivated by the diverse nature of its members and further lack of available empirical literature on the same relationship. Annual data from 1980 to 2016 are analyzed using appropriate panel data techniques. The results revealed the presence of environmental Kuznets curve (EKC) in the SAARC region. Further, the introduction of cubic function into the model indicated that the shape of the EKC is N shaped. Besides, trade openness has negative while urbanization and energy consumption have impacted CO2 emissions positively. Moreover, the causality exercise explored a bidirectional causality between urbanization, energy consumption, per capita income, and CO2 emissions. Similarly, energy consumption, per capita GDP, and urbanization are also bidirectionally related. Further, a unidirectional causality running from CO2 emissions, urbanization, and energy consumption to trade openness is detected. Lastly, a unidirectional causality is witnessed from per capita income to energy consumption.

RAFP-Pred: Robust Prediction of Antifreeze Proteins Using Localized Analysis of n-Peptide Compositions.

In extreme cold weather, living organisms produce Antifreeze Proteins (AFPs) to counter the otherwise lethal intracellular formation of ice. Structures and sequences of various AFPs exhibit a high degree of heterogeneity, consequently the prediction of the AFPs is considered to be a challenging task. In this research, we propose to handle this arduous manifold learning task using the notion of localized processing. In particular, an AFP sequence is segmented into two sub-segments each of which is analyzed for amino acid and di-peptide compositions. We propose to use only the most significant features using the concept of information gain (IG) followed by a random forest classification approach. The proposed RAFP-Pred achieved an excellent performance on a number of standard datasets. We report a high Youden's index (sensitivity+specificity-1) value of 0.75 on the standard independent test data set outperforming the AFP-PseAAC, AFP_PSSM, AFP-Pred, and iAFP by a margin of 0.05, 0.06, 0.14, and 0.68, respectively. The verification rate on the UniProKB dataset is found to be 83.19 percent which is substantially superior to the 57.18 percent reported for the iAFP method.

Linear regression for face recognition.

In this paper, we present a novel approach of face identification by formulating the pattern recognition problem in terms of linear regression. Using a fundamental concept that patterns from a single-object class lie on a linear subspace, we develop a linear model representing a probe image as a linear combination of class-specific galleries. The inverse problem is solved using the least-squares method and the decision is ruled in favor of the class with the minimum reconstruction error. The proposed Linear Regression Classification (LRC) algorithm falls in the category of nearest subspace classification. The algorithm is extensively evaluated on several standard databases under a number of exemplary evaluation protocols reported in the face recognition literature. A comparative study with state-of-the-art algorithms clearly reflects the efficacy of the proposed approach. For the problem of contiguous occlusion, we propose a Modular LRC approach, introducing a novel Distance-based Evidence Fusion (DEF) algorithm. The proposed methodology achieves the best results ever reported for the challenging problem of scarf occlusion.