The economic and environmental aspects of energy production have become important due to the increasing complexity energy sector and envoirnmental pollution, warranting to test the connection between ...financial imbalances, energy prices and carbon emission. The study aims to test the impact of vertical fiscal imbalances (VFI) on energy prices and carbon emission trends by considering the dual-perspectives of environmental regulation and industrial structure. The empirical outcomes indicated that vertical fiscal imbalances limited the environmental quality of Pakistan. Furthermore, VFI also caused environmental degradation by affecting industrial structure. VFI inhibits the intensity of environmental regulation, promotes the upgrade of industrial structures, both of which cause additional carbon emissions. The study suggest to energy ministries and energy regulation offices to revisit the machinism of energy prices determination and revised machanisim should provide a user-friendly assessment to understand the actual costs associated with the rising concern of environmental pollution. By this, envoirnmental protection maximization and optimal energy conservation is expacted to increase. Based on empirical findings, the study extends the suggestion that vertical fiscal imbalances should be considered an active indicator by the key policy makers and other stakeholders for energy prices determination and environmental quality upgradation.
•Vertical fiscal imbalances can lead to environmental degradation by affecting both environmental regulation and industrial structure.•Vertical fiscal imbalances have a significant impact on carbon emission patterns.•A positively significant coefficient of foreign direct investment supports that it increases the level of carbon emissions.•Energy prices do not reflect the level of environmental damages, significant changes in the environment and fuel-based transportation.
The heart has a high rate of ATP production and turnover that is required to maintain its continuous mechanical work. Perturbations in ATP-generating processes may therefore affect contractile ...function directly. Characterizing cardiac metabolism in heart failure (HF) revealed several metabolic alterations called metabolic remodeling, ranging from changes in substrate use to mitochondrial dysfunction, ultimately resulting in ATP deficiency and impaired contractility. However, ATP depletion is not the only relevant consequence of metabolic remodeling during HF. By providing cellular building blocks and signaling molecules, metabolic pathways control essential processes such as cell growth and regeneration. Thus, alterations in cardiac metabolism may also affect the progression to HF by mechanisms beyond ATP supply. Our aim is therefore to highlight that metabolic remodeling in HF not only results in impaired cardiac energetics but also induces other processes implicated in the development of HF such as structural remodeling and oxidative stress. Accordingly, modulating cardiac metabolism in HF may have significant therapeutic relevance that goes beyond the energetic aspect.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly pathogenic virus that has caused the global COVID-19 pandemic. Tracing the evolution and transmission of the virus is crucial ...to respond to and control the pandemic through appropriate intervention strategies. This paper reports and analyses genomic mutations in the coding regions of SARS-CoV-2 and their probable protein secondary structure and solvent accessibility changes, which are predicted using deep learning models. Prediction results suggest that mutation D614G in the virus spike protein, which has attracted much attention from researchers, is unlikely to make changes in protein secondary structure and relative solvent accessibility. Based on 6324 viral genome sequences, we create a spreadsheet dataset of point mutations that can facilitate the investigation of SARS-CoV-2 in many perspectives, especially in tracing the evolution and worldwide spread of the virus. Our analysis results also show that coding genes E, M, ORF6, ORF7a, ORF7b and ORF10 are most stable, potentially suitable to be targeted for vaccine and drug development.
Automated machine learning pipeline (ML) composition and optimisation aim at automating the process of finding the most promising ML pipelines within allocated resources (i.e., time, CPU and memory). ...Existing methods, such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline composition and optimisation of these methods frequently require a tremendous amount of time that prevents them from exploring complex pipelines to find better predictive models. To further explore this research challenge, we have conducted experiments showing that many of the generated pipelines are invalid in the first place, and attempting to execute them is a waste of time and resources. To address this issue, we propose a novel method to evaluate the validity of ML pipelines, without their execution, using a surrogate model (AVATAR). The AVATAR generates a knowledge base by automatically learning the capabilities and effects of ML algorithms on datasets’ characteristics. This knowledge base is used for a simplified mapping from an original ML pipeline to a surrogate model which is a Petri net based pipeline. Instead of executing the original ML pipeline to evaluate its validity, the AVATAR evaluates its surrogate model constructed by capabilities and effects of the ML pipeline components and input/output simplified mappings. Evaluating this surrogate model is less resource-intensive than the execution of the original pipeline. As a result, the AVATAR enables the pipeline composition and optimisation methods to evaluate more pipelines by quickly rejecting invalid pipelines. We integrate the AVATAR into the sequential model-based algorithm configuration (SMAC). Our experiments show that when SMAC employs AVATAR, it finds better solutions than on its own. This is down to the fact that the AVATAR can evaluate more pipelines within the same time budget and allocated resources.
•We propose the AVATAR to evaluate the pipeline validity using a surrogate model.•Experiments show that invalid pipelines may result in overall bad performance.•Experiments show the efficiency of multiple configuration initialisation of SMAC.
In this paper we use a definition of the fractional stochastic integral given by Carmona et al. (2003) in 19 and develop a simple approximation method to study quasi-linear stochastic differential ...equations by fractional Brownian motion. We also propose a stochastic process, namely fractional semimartingale, to model for the noise driving in some financial models.
Deep learning has been successfully applied to solve various complex problems ranging from big data analytics to computer vision and human-level control. Deep learning advances however have also been ...employed to create software that can cause threats to privacy, democracy and national security. One of those deep learning-powered applications recently emerged is deepfake. Deepfake algorithms can create fake images and videos that humans cannot distinguish them from authentic ones. The proposal of technologies that can automatically detect and assess the integrity of digital visual media is therefore indispensable. This paper presents a survey of algorithms used to create deepfakes and, more importantly, methods proposed to detect deepfakes in the literature to date. We present extensive discussions on challenges, research trends and directions related to deepfake technologies. By reviewing the background of deepfakes and state-of-the-art deepfake detection methods, this study provides a comprehensive overview of deepfake techniques and facilitates the development of new and more robust methods to deal with the increasingly challenging deepfakes.
•Technologies for creating deepfakes are increasingly approachable.•Automatic assessment of the integrity of digital visual media is indispensable.•Present a survey of algorithms used to create and detect deepfakes.•Extensively discuss the challenges and research directions related to deepfakes.•Facilitate the development of effective methods to deal with challenging deepfakes.
Soy lecithin liposomes (SLP) were prepared and partially surface modified with methoxy polyethylene glycol‐cholesterol conjugate (mPEG‐Chol) to improve its poorly‐soluble‐water‐anticancer‐drugs ...delivery efficiency. Paclitaxel (PTX) was used as the model drug and the PTX/SLP@mPEG was successfully developed with the optimal mass ratio of mPEG‐Chol determined at 4% in the SLP@mPEG formulation. The optimal SLP@mPEG formulation had a particle size range of 161.80 ± 1.51 nm and a negative surface charge of −54.30 ± 1.40 mV. Besides, a sustained drug release profile of 72 h and an encapsulation efficiency of 87.48 ± 0.70% was recorded. Moreover, in vitro cytotoxicity assays demonstrated that SLP@mPEG is nontoxic and cytocompatible. Overall, these obtained results provide insights into the potential of SLP@mPEG as a platform for the development of more effective therapies against cancers.
Methoxy polyethylene glycol–cholesterol conjugate was synthesized by a simplistic two‐step chemical reaction involving two intermediates, which was hypothesized to aid in the anti‐cancer‐drug‐ paclitaxel‐ delivering process of soy lecithin liposomal system. Evaluation of the successful synthesized mPEG‐Chol was confirmed by 1H‐NMR and the final product, PTX/SLP@mPEG shown enhanced poorly water‐soluble anticancer drug delivery with its properties characterized and examined by in vitro drug release as well as cellular cytotoxicity.
The increasing release of nutrients to aquatic environments has led to great concern regarding eutrophication and the risk of unwanted algal blooms. Based on observational data of 20 water quality ...parameters measured on a monthly basis at 40 stations from 2011 to 2020, this study applied different Machine Learning (ML) algorithms to suggest the best option for algal bloom prediction in the Han River, a large river in South Korea. Eight different ML algorithms were categorized into several groups of statistical learning, regression family, and deep learning, and were then compared for their suitability to predict the chlorophyll-derived trophic index (TSI-Chla). ML algorithms helped identify the most important water quality parameters contributing to algal bloom prediction. The ML results confirmed that eutrophication and algal proliferation were governed by the complex interplay between nutrients (nitrogen and phosphorus), organic contaminants, and environmental factors. Of the models tested, the adaptive neuro-fuzzy inference system (ANFIS) exhibited the best performance owing to its consistent and outperforming prediction both quantitatively (i.e., via regression) and qualitatively (i.e., via classification), which was evidenced by the lowest value of mean absolute error (MAE) of 0.09, and the highest F1-score, Recall and Precision of 0.97, 0.98 and 0.96, respectively. In a further step, a representative web application was constructed to assist common users to predict the trophic status of the Han River. This study demonstrated that ML techniques are not only promising for highly accurate water quality modeling of urban rivers, but also reduce time and labor intensity for experiments, which decreases the number of monitored water quality parameters, providing further insights into the driving factors of water quality deterioration. They ultimately help devise proactive strategies for sustainable water management.
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•Comprehensive dataset of 20 parameters at 40 stations of a river for 10 years was used.•ML models of statistical learning, regression family, and deep learning were compared.•DTP, DTN, pH, DO, BOD, Temp, precipitation, and flowrate control algal bloom prediction.•ANFIS showed the best TSI-Chla prediction for regression and classification.•A user-friendly platform of a web application was developed for common users.
In this paper, based on a known formula, we use a simple idea to get a new representation for the density of Malliavin differentiable random variables. This new representation is particularly useful ...for finding lower bounds for the density.
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