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This study aims to analyze the impact of financial development, foreign direct investment, economic growth, electricity consumption, and trade openness on environmental quality for a panel of 59 Belt and Road Initiative (BRI) countries, over the period of 1980–2016. The presence of the environmental Kuznets curve (EKC) hypothesis is investigated. The cross-sectional augmented Dickey-Fuller (CADF) and cross-sectional Im, Pesaran, and Shin panel unit root test; the Westerlund cointegration test, the dynamic seemingly unrelated regression (DSUR) approach; and the Dumitrescu and Hurlin (Econ Model 29:1450–1460, 2012) panel causality approach are employed. It is found that the analyzed variables are stationary at first differences and are cointegrated. It is also found that an increase in financial development, foreign direct investment, and trade openness enhance environmental quality, while the increase in economic growth and electricity consumption degrade environmental quality. The presence of the EKC hypothesis for the selected panel countries is validated. Furthermore, the Dumitrescu-Hurlin (DH) panel causality test result confirmed the presence of bidirectional causality among economic growth, foreign direct investment, financial development, electricity consumption, and trade openness with environmental quality.

Nanotechnology deals with the synthesis of materials and particles at nanoscale with dimensions of 1–100 nm. Biological synthesis of nanoparticles, using microbes and plants, is the most proficient method in terms of ease of handling and reliability. Core objectives of this study were to synthesize metallic nanoparticles using selenium metal salt from citrus fruit extracts, their characterization and evaluation for antimicrobial activities against pathogenic microbes. In methodology, simple green method was implicated using sodium selenite salt solution and citrus fruit extracts of Grapefruit and Lemon as precursors for synthesizing nanoparticles. Brick red color of the solution indicated towards the synthesis of selenium nanoparticles (SeNPs). Nanoparticle’s initial characterization was done by UV–Vis Spectrophotometry and later FTIR analysis and DLS graphs via Zetasizer were obtained for the confirmation of different physical and chemical parameters of the nanoparticles. Different concentrations of SeNPs were used for antimicrobial testing against E. coli , M. luteus , B. subtilis and K. pneumoniae comparative with the standard antibiotic Ciprofloxacin. SeNPs possessed significant antimicrobial activities against all the bacterial pathogens used. Conclusively, SeNPs made from citrus fruits can act as potent antibacterial candidates.

The study elucidates the potential benefits of incorporating a BiI 3 interfacial layer into perovskite solar cells (PSCs). Using MAPbI 3 and MAGeI 3 as active layers, complemented by the robust TiO 2 and Spiro-OMeTAD as the charge-transport-layers, we employed the SCAPS-1D simulation tool for our investigations. Remarkably, the introduction of the BiI 3 layer at the perovskite-HTL interface significantly enhanced hole extraction and effectively passivated defects. This approach minimized charge recombination and ion migration towards opposite electrodes, thus elevating device performance relative to conventional configurations. The efficiency witnessed a rise from 19.28 to 20.30% for MAPbI 3 and from 11.90 to 15.57% for MAGeI 3 . Additionally, MAGeI 3 based PSCs saw an improved fill-factor from 50.36 to 62.85%, and a better J sc from 13.22 to 14.2 mA/cm 2 , signifying reduced recombination and improved charge extraction. The FF for MAPbI 3 based PSCs saw a minor decline, while the V oc slightly ascended from 1.24 to 1.25 V and J sc from 20.01 to 21.6 mA/cm 2 . A thorough evaluation of layer thickness, doping, and temperature further highlighted the critical role of the BiI 3 layer for both perovskite variants. Our examination of bandgap alignments in devices with the BiI 3 interfacial layer also offers valuable understanding into the mechanisms fueling the observed improvements.

This study intends to examine the impact of ICTs (i.e., internet usage and mobile cellular subscriptions), globalization, electricity consumption, financial development, and economic growth on environmental quality by using 1994–2014 panel data of BRICS economies. This study employed a second-generation panel unit root test accounting for the presence of cross-sectional dependence and indicated that carbon dioxide emissions, electricity consumption, financial development, internet usage, mobile usage, globalization, and economic growth have integration of order one. The results from Westerlund panel co-integration test confirms that the variables are co-integrated and revealed that ICT-finance-globalization-electricity-GDP-CO 2 nexus has long-run equilibrium relationship. The results from dynamic seemingly unrelated regression (DSUR) indicate that internet usage and mobile cellular subscriptions (ICTs) have significant, adverse impact on carbon dioxide emissions. To put it simply, ICT positively contributes towards environmental quality. Similarly, economic growth also has an adverse effect on carbon dioxide emissions. On the other hand, electricity consumption, globalization, and financial development have a significant positive effect on carbon emissions. In addition, Granger causality test results show the presence of a bidirectional causal relationship between internet usage and environmental quality, financial development and electricity consumption, ICT and financial development, mobile cellular subscription and globalization, economic growth and environmental quality, and internet usage and economic growth. A unidirectional causal link is detected running from mobile cellular subscriptions towards environmental quality, ICT towards electricity consumption, financial development towards environmental quality, globalization towards environmental quality, and globalization towards economic growth. Moreover, time series analysis has also been done in this study to analyze the findings for each of BRICS countries which are directed towards important policy implications. For instance, ICT policy can play an integral part in improving environmental quality policy.

Picture fuzzy set (PFS) is an important tool for handling uncertainty and vagueness, particularly in situations that require more answers of the type “yes,” “no,” “abstain” and “refusal.” Correlation coefficient of picture fuzzy sets (PFSs) is an essential measure in picture fuzzy set theory and has a lot of applications in many areas, such as “decision-making,” “medical diagnosis,” “pattern recognition” and “clustering analysis”. In this article, two correlation coefficients of PFSs are introduced along with some of their properties. These correlation coefficients of PFSs are better than existing ones and effective in expressing the nature of correlation (positive or negative correlation). We also show the applications and advantages of the proposed picture fuzzy correlation coefficients over some existing methods in pattern recognition, medical diagnosis and clustering with the help of illustrative examples.

Picture fuzzy set (PFS) is a direct generalization of the fuzzy sets (FSs) and intuitionistic fuzzy sets (IFSs) and is quite powerful in modelling the situations that involve more answers of the type yes, no, abstain, and refuse. In this study, we introduce a novel picture fuzzy (PF) similarity measure on the basis of direct operation on the function of membership, the function of non-membership, the function of neutrality, the function of refusal, and the upper bound of the function of membership of two PFSs instead on the basis of distance measure or the association between the functions of membership, non-membership, and neutrality. The comparison of the proposed PF similarity measure with the existing PF similarity measures reveals that it does not give unreasonable results and also overcomes the drawbacks of the existing PF similarity measures. The application of the proposed measure in pattern recognition is also discussed. Moreover, we also introduce a new multi-attribute decision-making (MADM) method using the proposed PF similarity measure that overcomes a major drawback of the technique for order preference by similarity to ideal solution (TOPSIS). Finally, we contrast the performance of the proposed MADM method with several existing MADM methods in the PF environment.

Cartilage is essential throughout vertebrate life. It starts developing in embryos when osteochondroprogenitor cells commit to chondrogenesis, activate a pancartilaginous program to form cartilaginous skeletal primordia, and also embrace a growth-plate program to drive skeletal growth or an articular program to build permanent joint cartilage. Various forms of cartilage malformation and degeneration diseases afflict humans, but underlying mechanisms are still incompletely understood and treatment options suboptimal. The transcription factor SOX9 is required for embryonic chondrogenesis, but its postnatal roles remain unclear, despite evidence that it is down-regulated in osteoarthritis and heterozygously inactivated in campomelic dysplasia, a severe skeletal dysplasia characterized postnatally by small stature and kyphoscoliosis. Using conditional knockout mice and high-throughput sequencing assays, we show here that SOX9 is required postnatally to prevent growth-plate closure and preosteoarthritic deterioration of articular cartilage. Its deficiency prompts growth-plate chondrocytes at all stages to swiftly reach a terminal/dedifferentiated stage marked by expression of chondrocyte-specific (Mgp) and progenitor-specific (Nt5e and Sox4) genes. Up-regulation of osteogenic genes (Runx2, Sp7, and Postn) and overt osteoblastogenesis quickly ensue. SOX9 deficiency does not perturb the articular program, except in load-bearing regions, where it also provokes chondrocyte-to-osteoblast conversion via a progenitor stage. Pathway analyses support roles for SOX9 in controlling TGFβ and BMP signaling activities during this cell lineage transition. Altogether, these findings deepen our current understanding of the cellular and molecular mechanisms that specifically ensure lifelong growth-plate and articular cartilage vigor by identifying osteogenic plasticity of growth-plate and articular chondrocytes and a SOX9-countered chondrocyte dedifferentiation/osteoblast redifferentiation process.

The Pythagorean fuzzy sets are more robust than fuzzy sets and intuitionistic fuzzy sets in dealing with the problems involving uncertainty. To compare two Pythagorean fuzzy sets, distance measures play a crucial role. In this paper, we have proposed some novel distance measures for Pythagorean fuzzy sets using t-conorms. We have also discussed their various desirable properties. With the help of suggested distance measures, we have introduced some new knowledge measures for Pythagorean fuzzy sets. Through numerical comparison and linguistic hedges, we have established the effectiveness of the suggested distance measures and knowledge measures, respectively, over the existing measures in the Pythagorean fuzzy setting. At last, we have demonstrated the application of the suggested measures in pattern analysis and multi-attribute decision-making.

Having a large number of device connections provides attackers with multiple ways to attack a network. This situation can lead to distributed denial-of-service (DDoS) attacks, which can cause fiscal harm and corrupt data. Thus, irregularity detection in traffic data is crucial in detecting malicious behavior in a network, which is essential for network security and the integrity of modern Cyber–Physical Systems (CPS). Nevertheless, studies have shown that current techniques are ineffective at detecting DDoS attacks on networks, especially in the case of high-speed networks (HSN), as detecting attacks on the latter is very complex due to their fast packet processing. This review aims to study and compare different approaches to detecting DDoS attacks, using machine learning (ML) techniques such as k-means, K-Nearest Neighbors (KNN), and Naive Bayes (NB) used in intrusion detection systems (IDSs) and flow-based IDSs, and expresses data paths for packet filtering for HSN performance. This review highlights the high-speed network accuracy evaluation factors, provides a detailed DDoS attack taxonomy, and classifies detection techniques. Moreover, the existing literature is inspected through a qualitative analysis, with respect to the factors extracted from the presented taxonomy of irregular traffic pattern detection. Different research directions are suggested to support researchers in identifying and designing the optimal solution by highlighting the issues and challenges of DDoS attacks on high-speed networks.