How does external debt and governance quality impact renewable energy consumption: novel policy insights from BRICS countries.

Amidst global environmental reforms, the role of energy systems is under scrutiny to promote ecological welfare through low-carbon alternatives. Amongst the solutions, the role of renewable energy as a clean source has become popular to mitigate climate change. However, the impact of debt on renewable energy consumption remains limited in the economic literature. The debt initiatives provide funding for environmental initiatives primarily, while it is also credited as a barrier to limiting the growth of clean energy programs. Within such discussion, the current study extended the dialogue by examining how external debt impacts energy transition in Brazil, Russia, India, China, and South Africa (BRICS) economies in the presence of institutional quality, education expenditures, and banking development. Using the novel CS-ARDL, AMG, and CCEMG tests, the study results showed that external debt decreases renewable energy consumption, while institutional quality, educational expenditures, banking developments, and economic growth are essential elements of green energy developments. Based on these conclusions, this study provides novel policy guidelines to align BRICS energy and economic agendas.

Fast Fractional Fourier Transform-Aided Novel Graphical Approach for EEG Alcoholism Detection.

Given its detrimental effect on the brain, alcoholism is a severe disorder that can produce a variety of cognitive, emotional, and behavioral issues. Alcoholism is typically diagnosed using the CAGE assessment approach, which has drawbacks such as being lengthy, prone to mistakes, and biased. To overcome these issues, this paper introduces a novel paradigm for identifying alcoholism by employing electroencephalogram (EEG) signals. The proposed framework is divided into various steps. To begin, interference and artifacts in the EEG data are removed using a multiscale principal component analysis procedure. This cleaning procedure contributes to information quality improvement. Second, an innovative graphical technique based on fast fractional Fourier transform coefficients is devised to visualize the chaotic character and complexities of the EEG signals. This elucidates the properties of regular and alcoholic EEG signals. Third, thirty-four graphical features are extracted to interpret the EEG signals' haphazard behavior and differentiate between regular and alcoholic trends. Fourth, we propose an ensembled feature selection method for obtaining an effective and reliable feature group. Following that, we study many neural network classifiers to choose the optimal classifier for building an efficient framework. The experimental findings show that the suggested method obtains the best classification performance by employing a recurrent neural network (RNN), with 97.5% accuracy, 96.7% sensitivity, and 98.3% specificity for the sixteen selected features. The proposed framework can aid physicians, businesses, and product designers to develop a real-time system.

Geochemical insights of arsenic mobilization into the aquifers of Punjab, Pakistan.

It is well known that groundwater arsenic (As) contamination affects million(s) of people throughout the Indus flood plain, Pakistan. In this study, groundwater (n = 96) and drilled borehole samples (n = 87 sediments of 12 boreholes) were collected to investigate geochemical proxy-indicators for As release into groundwater across floodplains of the Indus Basin. The mean dissolved (µg/L) and sedimentary As concentrations (mg/kg) showed significant association in all studied areas viz.; lower reaches of Indus flood plain area (71 and 12.7), upper flood plain areas (33.7 and 7.2), and Thal desert areas (5.3 and 4.7) and are indicative of Basin-scale geogenic As contamination. As contamination in aquifer sediments is dependent on various geochemical factors including particle size (3-4-fold higher As levels in fine clay particles than in fine-coarse sand), sediment types (3-fold higher As in Holocene sediments of floodplain areas vs Pleistocene/Quaternary sediments in the Thal desert) with varying proportion of Al-Fe-Mn oxides/hydroxides. The total organic carbon (TOC) of cored aquifer sediments yielded low TOC content (mean = 0.13 %), which indicates that organic carbon is not a major driver (with a few exceptions) of As mobilization in the Indus Basin. Alkaline pH, high dissolved sulfate and other water quality parameters indicate pH-induced As leaching and the dominance of oxidizing conditions in the aquifers of upper flood plain areas of Punjab, Pakistan while at the lower reaches of the Indus flood plain and alluvial pockets along the rivers with elevated flood-driven dissolved organic carbon (exhibiting high dissolved Mn and Fe and a wide range of redox conditions). Furthermore, we also identified that paired dissolved AsMn values (instead of AsFe) may serve as a geochemical marker of a range of redox conditions throughout Indus flood plains.

An efficient Parkinson's disease detection framework: Leveraging time-frequency representation and AlexNet convolutional neural network.

Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting the quality of life of over 10 million individuals worldwide. Early diagnosis is crucial for timely intervention and better patient outcomes. Electroencephalogram (EEG) signals are commonly used for early PD diagnosis due to their potential in monitoring disease progression. But traditional EEG-based methods lack exploration of brain regions that provide essential information about PD, and their performance falls short for real-time applications. To address these limitations, this study proposes a novel approach using a Time-Frequency Representation (TFR) based AlexNet Convolutional Neural Network (CNN) model to explore EEG channel-based analysis and identify critical brain regions efficiently diagnosing PD from EEG data. The Wavelet Scattering Transform (WST) is employed to capture distinct temporal and spectral characteristics, while AlexNet CNN is utilized to detect complex spatial patterns at different scales, accurately identifying intricate EEG patterns associated with PD. The experiment results on two real-time EEG PD datasets: San Diego dataset and the Iowa dataset demonstrate that frontal and central brain regions, including AF4 and AFz electrodes, contribute significantly to providing more representative features compared to other regions for PD detection. The proposed architecture achieves an impressive accuracy of 99.84% for the San Diego dataset and 95.79% for the Iowa dataset, outperforming existing EEG-based PD detection methods. The findings of this research will assist to create an essential technology for efficient PD diagnosis, enhancing patient care and quality of life.

A Phase 1, Randomized, Double-Blind, Placebo-Controlled, Single Ascending Dose Study to Evaluate the Pharmacokinetics, Immunogenicity, Safety, and Tolerability After Subcutaneous Administration of Tozorakimab in Healthy Chinese Participants.

Tozorakimab is a high-affinity human immunoglobulin G1 monoclonal antibody that neutralizes interleukin (IL)-33, an IL-1 family cytokine. This phase 1, single-center, randomized, double-blind, placebo-controlled, single ascending dose study (NCT05070312) evaluated tozorakimab in a healthy Chinese population. Outcomes included the characterization of the pharmacokinetic (PK) profile and immunogenicity of tozorakimab. Safety outcomes included treatment-emergent adverse events (TEAEs) and clinical laboratory, electrocardiogram, and vital sign parameters. Healthy, non-smoking, male, and female Chinese participants aged 18-45 years with a body mass index 19-24 kg/m2 were enrolled. In total, 36 participants across 2 cohorts of 18 participants were randomized 2:1 to receive a single subcutaneous dose of tozorakimab (300 mg [2 mL] or 600 mg [4 mL]) or matching placebo (2 or 4 mL). Tozorakimab showed dose-dependent serum PK concentrations with an approximate monophasic distribution in serum over time and a maximum observed peak concentration of 20.1 and 33.7 µg/mL in the 300- and 600-mg cohorts, respectively. No treatment-emergent anti-drug antibodies for tozorakimab were observed in any of the participants. There were no clinically relevant trends in the occurrence of TEAEs across the treatment groups. There were no clinically relevant trends over time in clinical laboratory (hematology, clinical chemistry, and urinalysis), electrocardiogram, or vital sign parameters in any treatment group. Overall, tozorakimab demonstrated dose-dependent systemic exposure in healthy Chinese participants and was well tolerated, with no safety concerns identified in this study.

The effect of fibroblast growth factor-2 on the outcomes of tooth replantation: A systematic review of animal studies.

Background/Aim: The ideal treatment of tooth avulsion is replantation. However, replanting teeth may lead to root resorption. Fibroblast growth factor-2 (FGF-2) is a cytokine that plays an important role in wound repair and tissue regeneration. Recently, FGF-2 has been studied a potential regenerative agent to prevent root resorption and ankylosis. The aim of this review is to analyze and summarize the currently available literature focusing on using FGF-2 based regenerative modalities to improve the outcomes of tooth replantation. Materials and Methods: An electronic search was conducted via PubMed/Medline, Google Scholar and ISI Web of Knowledge, using the Medical Subject Headings (MeSH) terms "Basic fibroblast growth factor," "Fibroblast growth factor-2," "tooth replantation," and "replantation" for studies published between January 2001 and June 2021. Data was extracted and quality assessment was carried using the ARRIVE guidelines. Results: Nine animal studies were included in this review. In six studies, FGF-2 had a favorable effect on the tissue regeneration around roots of replanted teeth when compared to other treatment groups. However, quality assessment of the studies revealed many sources of bias and deficiencies in the studies. Conclusions: Within the limitations of this study, it may be concluded that FGF-2 may improve the outcomes of delayed replantation of avulsed teeth. However, more long-term animal studies, with improved experimental designs, and clinical trials are required to determine the clinical potential of the growth factor in improving the outcomes of delayed tooth replantation.

Recent advances in modified starch based biodegradable food packaging: A review.

This study reviews the importance of resistant starch (RS) as the polymer of choice for biodegradable food packaging and highlights the RS types and modification methods for developing RS from native starch (NS). NS is used in packaging because of its vast availability, low cost and film forming capacity. However, application of starch is restricted due to its high moisture sensitivity and hydrophilic nature. The modification of NS into RS improves the film forming characteristics and extends the applications of starch into the formulation of packaging. The starch is blended with other bio-based polymers such as guar, konjac glucomannan, carrageenan, chitosan, xanthan gum and gelatin as well as active ingredients such as nanoparticles (NPs), plant extracts and essential oils to develop hybrid biodegradable packaging with reduced water vapor permeability (WVP), low gas transmission, enhanced antimicrobial activity and mechanical properties. Hybrid RS based active packaging is well known for its better film forming properties, crystalline structures, enhanced tensile strength, water resistance and thermal properties. This review concludes that RS, due to its better film forming ability and stability, can be utilized as polymer of choice in the formulation of biodegradable packaging.

A Randomized Phase I Study of the Anti-Interleukin-33 Antibody Tozorakimab in Healthy Adults and Patients With Chronic Obstructive Pulmonary Disease.

Tozorakimab is a human monoclonal antibody that neutralizes interleukin (IL)-33. IL-33 is a broad-acting epithelial "alarmin" cytokine upregulated in lung tissue of patients with chronic obstructive pulmonary disease (COPD). This first-in-human, phase I, randomized, double-blind, placebo-controlled study (NCT03096795) evaluated the safety, tolerability, pharmacokinetics (PKs), immunogenicity, target engagement, and pharmacodynamics (PDs) of tozorakimab. This was a 3-part study. In part 1, 56 healthy participants with a history of mild atopy received single escalating doses of either intravenous or subcutaneous tozorakimab or placebo. In part 2, 24 patients with mild COPD received multiple ascending doses of subcutaneous tozorakimab or placebo. In part 3, 8 healthy Japanese participants received a single intravenous dose of tozorakimab or placebo. The safety data collected included treatment-emergent adverse events (TEAEs), vital signs, and clinical laboratory parameters. Biological samples for PKs, immunogenicity, target engagement, and PD biomarker analyses were collected. No meaningful differences in the frequencies of TEAEs were observed between the tozorakimab and placebo arms. Three tozorakimab-treated participants with COPD experienced treatment-emergent serious adverse events. Subcutaneous or intravenous tozorakimab demonstrated linear, time-independent PKs with a mean half-life of 11.7-17.3 days. Treatment-emergent anti-drug antibody frequency was low. Engagement of tozorakimab with endogenous IL-33 in serum and nasal airways was demonstrated. Tozorakimab significantly reduced serum IL-5 and IL-13 levels in patients with COPD compared with placebo. Overall, tozorakimab was well tolerated, with a linear, time-independent serum PK profile. Additionally, biomarker studies demonstrated proof of mechanism. Overall, these data support the further clinical development of tozorakimab in COPD and other inflammatory diseases.

Pharmacologically active herbal remedies against atherosclerosis, characterization and DoE based marker quantification by densitometry, and cell based assays on THP-1 cell lines.

Atherosclerosis is a complex condition that develops at varying rates in multiple configurations and blood vessels. The primary cause of morbidity and mortality worldwide, particularly in the industrialized nations, continues to be atherosclerosis. Ayurveda, Siddha, and Unani systems of medicine, among other traditional medical systems, utilize polyherbal compositions. The treatment of atherosclerosis has been improved with a novel multibotanical combination. In this study, we sought to formulate, characterize, and standardize a polyherbal formulation based on design of experiments (DoE), densitometric studies and to predict for antioxidant activity using molecular docking analysis based on LC- MS identified phytomarkers. In addition we have assessed its cell viability by MTT assay along with Ao/EtBr staining technique and intracellular ROS assay using THP-1 cell lines. Reported findings showed that the HPTLC based quantified components of selected multiherbals has the ability to treat for atherosclerosis. This document could be used to quickly authenticate the formulation as the method optimized was based on CCD design which shows desirability of 0.962 and 0.839. Cell based assays scientifically proves that the formulation was not toxic based on MTT assay along with AO/EtBr staining technique and has excellent antioxidant activities based on intracellular ROS assay using THP-1 cell lines. The observed findings would be crucial for future clinical aspects since the bioactive molecules contained in the extracts may have anticipated effects with other compounds and show a superior therapeutic potential. As a result, this study offers standardized and potentially therapeutic information about effective polyherbal formulation for atherosclerosis.

Reactive oxygen species induced oxidative damage to DNA, lipids, and proteins of antibiotic-resistant bacteria by plant-based silver nanoparticles.

This study assesses the mechanism of action of plant-based silver nanoparticles (AgNPs) against antibiotic-resistant bacteria. We compared AgNPs synthesized through Salvia moorcroftiana and Origanum vulgare extracts and their conjugates with the antibiotic Ceftriaxone for their capacity to cause oxidative damage through reactive oxygen species (ROS). We quantified ROS in the cells of two bacterial strains after treating them with all AgNP types and observed that AgNPs were most effective in K. pneumoniae as they resulted in the highest ChS1 count (44,675), while in P. aeruginosa, Cfx-AgNPs induced the highest levels of ROS with ChS1 count of 56,865. DNA analysis showed that both plant-based AgNPs (O-AgNPs = 0.192 and S-AgNPs = 0.152) were most effective in K. pneumoniae and S-AgNPs (abs = 0.174) and O-Cfx-AgNPs (abs = 0.261) in P. aeruginosa. We observed a significant increase in the levels of conjugated dienes (86.4 µM) and malondialdehyde (172.25 nM) in the bacterial strains after treatment with AgNPs, compared to the control (71.65 µM and 18.064 nM, respectively, in K. pneumoniae and P. aeruginosa). These results indicate lipid peroxidation. AgNPs also increased the levels of protein thiols (0.672 nM) compared to the control (0.441 nM) in K. pneumoniae, except for Chem-AgNPs (0.21 nM). These results suggest that plant-based AgNPs are more effective in oxidizing bacterial DNA, protein, and lipids than Chem-AgNPs. Furthermore, protein oxidation varied between AgNPs alone and AgNPs-antibiotic conjugates. The highest levels of protein thiols were found in the samples treated with O-Cfx-AgNPs (0.672 nM and 0.525 nM in K. pneumoniae and P. aeruginosa, respectively). The results demonstrated that AgNPs kill bacteria by altering bacterial macromolecules such as DNA, lipids, and proteins.

Role of renewable and non-renewable energy consumption in environmental quality and their subsequent effects on average temperature: an assessment of sustainable development goals in South Korea.

Environmental quality is a global concern, and an accurate assessment of environmental progress has emerged as a key element in achieving sustainable development goals. The study offers a two-pronged contribution by analyzing the interplay between renewable energy consumption, traditional energy consumption, and urbanization on environmental quality while simultaneously testing the impact of environmental quality on average temperature. Distinguishing itself from prior research, the study employs both carbon emissions and ecological footprint to ensure an intricate and reliable analysis. Current study analyzes data from 1970 to 2022 from South Korea to indicate that energy-environment association is influenced by energy consumption, urbanization, and economic growth, while renewable energy consumption is key in long-term environmental sustainability. Our extensive econometric analysis allows us to suggest that South Korea need to prioritize renewable energy, and green growth is prioritized so that economic growth and green agenda are synchronized to overcome environmental challenges. Addictingly, there is need to devise long-term environmental policies to strengthen the role of environmental innovation in energy productivity and boost renewable energy consumption.

Extraction and characterization of starch from low-grade potatoes and formulation of gluten-free cookies containing modified potato starch.

Potatoes are among the leading staple crops due to nutritional value and high demand. The undersized and damaged potatoes are considered low grade and mainly dumped as a waste or used in animal feed. The study aimed to extract starch from low grade potatoes, its modification to improve the starch properties and formulation of gluten free cookies using modified starch (MS). The starch was extracted from low-grade potatoes of three varieties known as Asterix, Kruda and Mosaic, using the water steeping method. The native starch (NS) was modified using lintnerization and repetitive autoclaving. MS contains high amylose content which is associated with health benefits. NS and MS were characterized for amylose content, color attributes, granular morphology, water solubility index (WSI), water absorption index (WAI), thermogravimetric analysis (TGA) and Fourier transform infrared spectrometer (FTIR) analysis. Gluten-free cookies were formulated by adding potato NS and MS. The cookies were characterized by sensory evaluation, proximate and textural analysis. The starch yield extracted from three different varieties of potatoes i.e. Asterix, Kruda, Mosaic was 11.53%, 11.32% and 11.24%, respectively. The amylose content of potato starch was significantly (p < 0.05) increased for all varieties (33.61-37.74%) after modification of NS, which was in the range of 25.71-26.60% for different potato varieties. The granules of MS were observed as amorphous structures in comparison to NS granules with smooth surfaces. The addition of MS significantly (p < 0.05) decreased the hardness of the cookies in comparison to NS. Overall, no significant difference was observed in the sensory attributes of control, NS and MS containing cookies. Therefore, in comparison to other dietary fibers, MS can be used as a functional ingredient in food products without compromising the texture and sensory attributes.

Photoelectrocatalytic Detection and Degradation Studies of a Hazardous Textile Dye Safranin T.

Herein, we report an electrochemical scaffold consisting of functionalized multiwalled carbon nanotubes (COOH-fMWCNTs) and iron-doped zinc oxide nanoparticles (Fe-ZnO) for the detection of a hazardous textile dye safranin T (ST) and monitoring of its photocatalytic degradation. Prior to the detection and degradation analysis, Fe-ZnO NPs were synthesized by the sol-gel method and characterized by a number of structural and morphological techniques. The carboxyl moiety of COOH-fMWCNTs possessing a strong affinity for the amino functionality of ST led to significant enhancement of the current response at the designed electrochemical platform, whereas the electrocatalytic role, surface area enhancement, and the provision of binding sites of Fe-ZnO led to a further increase in the peak current intensity of ST. Electrochemical impedance spectroscopy showed that the sensing scaffold made of the glassy carbon electrode modified with COOH-fMWCNTs and Fe-ZnO efficiently transfers charge between the transducer and the redox probe. Under optimized conditions, the developed sensor showed a 2.3 nM limit of detection for ST. Moreover, recovery experiments and anti-interference tests qualified the sensing platform for practical applications. The dye was photocatalytically degraded using Fe-ZnO NPs up to 99% in 60 min with a rate constant of 0.068 min-1. The designed sensor was used to probe the degradation kinetics of the target dye, and the results were found consistent with the findings obtained from electronic absorption method. To the best of our knowledge, the present work is the first approach for the efficient detection and almost absolute degradation of ST.

Multi-Layered Filtration Framework for Efficient Detection of Network Attacks Using Machine Learning.

The advancements and reliance on digital data necessitates dependence on information technology. The growing amount of digital data and their availability over the Internet have given rise to the problem of information security. With the increase in connectivity among devices and networks, maintaining the information security of an asset has now become essential for an organization. Intrusion detection systems (IDS) are widely used in networks for protection against different network attacks. Several machine-learning-based techniques have been used among researchers for the implementation of anomaly-based IDS (AIDS). In the past, the focus primarily remained on the improvement of the accuracy of the system. Efficiency with respect to time is an important aspect of an IDS, which most of the research has thus far somewhat overlooked. For this purpose, we propose a multi-layered filtration framework (MLFF) for feature reduction using a statistical approach. The proposed framework helps reduce the detection time without affecting the accuracy. We use the CIC-IDS2017 dataset for experiments. The proposed framework contains three filters and is connected in sequential order. The accuracy, precision, recall and F1 score are calculated against the selected machine learning models. In addition, the training time and the detection time are also calculated because these parameters are considered important in measuring the performance of a detection system. Generally, decision tree models, random forest methods, and artificial neural networks show better results in the detection of network attacks with minimum detection time.