Speaker identification refers to the process of recognizing human voice using artificial intelligence techniques. Speaker identification technologies are widely applied in voice authentication, security and surveillance, electronic voice eavesdropping, and identity verification. In the speaker identification process, extracting discriminative and salient features from speaker utterances is an important task to accurately identify speakers. Various features for speaker identification have been recently proposed by researchers. Most studies on speaker identification have utilized short-time features, such as perceptual linear predictive (PLP) coefficients and Mel frequency cepstral coefficients (MFCC), due to their capability to capture the repetitive nature and efficiency of signals. Various studies have shown the effectiveness of MFCC features in correctly identifying speakers. However, the performances of these features degrade on complex speech datasets, and therefore, these features fail to accurately identify speaker characteristics. To address this problem, this study proposes a novel fusion of MFCC and time-based features (MFCCT), which combines the effectiveness of MFCC and time-domain features to improve the accuracy of text-independent speaker identification (SI) systems. The extracted MFCCT features were fed as input to a deep neural network (DNN) to construct the speaker identification model. Results showed that the proposed MFCCT features coupled with DNN outperformed existing baseline MFCC and time-domain features on the LibriSpeech dataset. In addition, DNN obtained better classification results compared with five machine learning algorithms that were recently utilized in speaker recognition. Moreover, this study evaluated the effectiveness of one-level and two-level classification methods for speaker identification. The experimental results showed that two-level classification presented better results than one-level classification. The proposed features and classification model for identifying a speaker can be widely applied to different types of speaker datasets.

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The challenges of environmental protection are especially prevalent in South and Southeast Asian nations, which adversely affects their sustainable developmental goals. During the last two decades, increased industrialization and urbanization have caused massive air pollution, particularly in the most industrialized and densely populated countries. Due to China’s fast economic expansion and development, the demand for natural resources has increased, resulting in climate change, biodiversity loss, soil degradation, and environmental risks. China’s ecological footprint has been the subject of little investigation on the premises of a circular economy. This study used a literature review methodology on the critical key factors that hinder or facilitate the transition of a linear economy towards a circular economy. Further, based on the literature review, this study used industrial ecology, energy efficiency, and waste recycling technology factors to analyze the role of the circular economy on the country’s environmental sustainability agenda for the period of 1975–2020. The results show that in the short run, the link between ecological footprints and per capita income is monotonically decreasing; however, in the long run, the relationship is U-shaped. In both the short and long run, waste recycling technology and cleaner manufacturing significantly decrease ecological footprints. Renewable energy consumption increases ecological footprints in the short run but decreases them in the long run. The management of natural resources reduces ecological footprints to support the ‘resource blessing’ hypothesis. The Granger causality corroborated the unidirectional relationship between ecological footprints, oil rents, and urbanization and ecological footprints. In addition, economic growth Granger causes industrialization and waste recycling technology while green energy Granger causes economic growth, industrialization, and recycling technology. The two-way link between economic development and urbanization exists within a nation. The variance decomposition analysis (VDA) predicts that in the future, China’s natural resources, green energy demand, and technological spillover will limit its ecological footprint through material and technology efficiency.

Oil price fluctuations have always been controversial and remain significant in how a country's economy develops. It is especially easy for the worldwide price of natural resources to fluctuate, putting developing nations at risk of economic instability. Consider Pakistan's economy, which is very sensitive to changes in oil prices due to its reliance on the commodity. This research analyses the effects of oil prices on several macroeconomic indicators, including inflation, imports, gross savings, domestic lending to the private sector (DCPS), and industrial value-added in Pakistan. The study uses an error-correcting framework known as autoregressive distributed lag (ARDL) modelling to examine long-term connections between variables and their short-term implications. Additionally, data acquired between 1970 and 2020 was analyzed using Granger causality tests, impulse response functions (IRFs), and variance decomposition analyses (VDAs). The study found that inflation and domestic loans to the private sector hampered economic development in the near run. Conversely, imports, gross savings, industrial value added, and oil rents have a positive effect. A long-term connection between these variables was verified using the boundaries test. A unidirectional link was found in the causality tests between economic growth and imports, inflation and economic growth, and gross saving and domestic credit. An inverse link between domestic credit and inflation was found. The effect of oil rents on economic development in Pakistan is expected to rise during the next four years, according to the forecasts, before levelling out. According to the VDA results, the most critical factor influencing Pakistan's economic development over the next decade would be domestic lending to the private sector. Following these empirical results, the study proposes policy adjustments that might help Pakistan's economy expand more quickly and sustainably.

Air pollution, particularly particulate matter (PM2.5) levels, was a significant focus at the COP26 summit. Rampant production practices and changing lifestyles contribute to the issue globally. China's rapid urbanization and reliance on fossil fuels significantly threaten global health sustainability. This study aims to evaluate China's environmental agenda and offer policy recommendations for achieving a green and clean environment. To accomplish this, the study assesses the crucial factors contributing to China's air pollution levels, focusing specifically on fine PM2.5 from 1975 to 2020. By implementing the ARDL bounds testing approach, the study confirmed a non‐linear relationship between per capita income and PM2.5, demonstrating an inverted U‐shaped curve with a turning point observed at a per capita income level of US$3030 in the short run. Furthermore, a positive correlation between these variables was detected in the long run. The study also revealed that rapid urbanization initially leads to increased PM2.5 concentrations, whereas it has a decreasing effect in the long term. To progress towards sustainable production and consumption, China has embraced efficient environmental technologies and increasing clean energy sources in its production mix. Leveraging these strategies, the country strives to achieve its decarbonization agenda and ensure a cleaner future. By conducting an ex ante analysis, this study identified ecological technologies, renewable energy demand and oil resource rents as critical influencers on China's air pollution levels over the next decade. The findings underscore the pressing need to embrace alternative energy sources, eco‐friendly technologies and resource conservation to tackle air pollution effectively and accomplish China's decarbonization objectives. It is imperative to prioritize adopting sustainable practices, ensuring a cleaner environment for current and future generations.