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Coal gangue (CG) is a residual product from coal mining and washing processes. The reutilization of CG to produce geopolymers is a low-carbon disposal strategy for this material. In this study, the calcined CG (CG700°C) was used as aluminosilicate precursors, and the effects of alkali activators (i.e., Na2SiO3/NaOH, NaOH concentration, and liquid–solid) on the mechanical characteristics and microstructure of CG700°C-based geopolymers were investigated. The findings indicated that the specimens with a liquid–solid ratio of 0.50 (G2.0-10-0.50) exhibited a compact microstructure and attained a compressive strength of 24.75 MPa. Moreover, increasing the Na2SiO3/NaOH mass ratio has shortened the setting times and facilitated geopolymer gel formation, resulting in a denser microstructure and improved compressive strength. The higher NaOH concentrations of alkali activators facilitated the dissolution of CG700°C particles, and the geopolymerization process was more dependent on the condensation of SiO4 and AlO4 ions, which promoted the formation of geopolymer networks. Conversely, an increase in the liquid–solid ratio from 0.50 to 0.65 had a negative impact on compressive strength enhancement, impeding the polycondensation rate. Examination through scanning electron microscopy and mercury intrusion porosimetry revealed that employing a lower Na2SiO3/NaOH mass ratio (G1.2-10-0.55), smaller NaOH concentrations (G2.0-8-0.55), and a higher liquid–solid ratio (G2.0-10-0.65) led to the presence of larger pores, resulting in decreased 28 days compressive strength values (15.87 MPa, 13.25 MPa, and 14.92 MPa, respectively), and a less compact structure. The results suggest that the performance of CG700°C-based geopolymers is significantly influenced by alkali activators.

Bdellovibrio bacteriovorus is a predatory gram-negative delta-proteobacteria that preys on other gram-negative bacterial species. With the surge in the cases of Anti-Microbial Resistance and the demand to replace conventional antibiotics, this predatory attribute of B. bacteriovorus has led to a vast number of studies in the last decade alone. These studies demonstrated various aspects of predation and proposed using B. bacteriovorus as an alternative new approach to conventional antibiotics in humans. Here we review the current progress made in this promising new area of research to develop novel antibacterial approaches.

In this paper, we prove two identities concerning quantum derivatives, quantum integrals, and some parameters. Using the newly proved identities, we prove new Simpson's and Newton's type inequalities for quantum differentiable convex functions with two and three parameters, respectively. We also look at the special cases of our key findings and find some new and old Simpson's type inequalities, Newton's type inequalities, midpoint type inequalities, and trapezoidal type inequalities.

The current study was conducted to assess the potential of ginger rhizome extract (Zingiber officinale) for the synthesis of silver nanoparticles (AgNPs) through the green method and its mitigating activity against pathogenic bacterial strains. AgNPs were synthesized through a simple one-step approach and characterized by UV-Visible (UV-Vis) spectroscopy, powder X-ray diffraction (PXRD), transmission electronic microscopy (TEM), and energy dispersive X-rays spectroscopy (EDS). PXRD and TEM results of AgNPs showed the face central cubic structures and predominantly spherical structures with a size of 6.5 nm. EDS analysis confirms the elemental silver in nanoparticles. Moreover, the impact of the pH, as well as temperature, during the synthesis of AgNPs has also been investigated. At 25°C and pH 5, there was no significant peak for AgNPs in the absorption spectra. However, with an increase in temperature from 25°C to 85°C and pH 5 to pH 11, particles started attaining the spherical shape of different sizes due to an increase in the reduction rate. The AgNPs displayed effective results against selected pathogenic strains, Pseudomonas aeruginosa (MTCC 424), Methicillin-resistant Staphylococcus aureus (ATCC 43300), and fungus Candida albicans (KACC 30003). The prepared AgNPs exhibited excellent antioxidant activity and catalytic reduction of methyl orange with the pseudo-first-order rate constant of 3.9 × 10−3.

Abstract Hexavalent chromium from industrial residues is highly mobile in soil and can lead to the contamination of groundwater through runoff and leaching after rainfall. This paper focuses on the three-dimensional (3D) electrokinetic remediation (EKR) of chromium-contaminated soil from an industrial site. Activated carbon particles coupled with Fe ions (AC-Fe) were used as the third electrode. The optimum dose ratio of the electrode particles and remediation time were selected on the basis of single-factor experiments. X-ray photoelectron spectroscopy (XPS) analysis was carried out to explore the reduction of Cr(VI) on the surface of the electrode particles (AC-Fe). The results showed that AC-Fe had a positive effect on Cr(VI) reduction with a removal rate of 80.2%, which was achieved after 10 d by using a 5% dose of electrode particles. Finally, it was concluded that the removal mechanism combined the processes of electromigration, electrosorption/adsorption and reduction of Cr(VI) in the 3D EKR system.

Chromium (Cr) contamination in soil, especially Cr(VI), is a serious threat to the environment and human health. The electrokinetic remediation (EKR) is a promising technology to remediate the Cr(VI). Therefore, in this study, EKR coupled with a permeable reactive barrier (PRB) was used to treat the Cr(VI)-contaminated soil. The CTMAB-Z, a modified zeolite (prepared with cetyltrimethyl ammonium bromide) alone and a mixture of CTMAB-Z and Fe(0) were used as PRB-1 and PRB-2 reactive media, respectively. The effect of chemical enhancers/additives, i.e. DL-tartaric acid and Tween 80 on EKR of Cr(VI) was also analysed in the contrasting experiments. While the effects of repair time, voltage gradient and DL-tartaric acid concentration on Cr(VI) remediation were investigated by using the multifactor orthogonal experiment which was based on contrasting experiments. The contrasting experiment results showed that the highest Cr(VI) removal rate (66.27%) and leaching efficiency (71.29%) were observed in the experimental group which had DL-tartaric acid and PRB-2. Furthermore, the multifactor orthogonal experiment results had depicted that the highest Cr(VI) removal rate (80.92%) and leaching efficiency (85.25%) were achieved after treating the samples at a voltage gradient of 2.5 V cm−1 for 8 days in the presence of 0.15 M concentration of DL-tartaric acid. This study demonstrated that Cr(VI) remediation through EKR process could be significantly enhanced by the use of PRB and additives.

Municipal solid waste incineration (MSWI) fly ash has a high concentration of heavy metals (HMs) which are hazardous to the environment. Moreover, it has high pH and buffering capacity which hinders the removal of HMs. Another constraining factor is the considerable fraction of HMs which exist in oxidizable and reducible states. The acid pretreatment of MSWI fly ash is a key solution to this problem. Therefore, the current experiment is carried out to evaluate the effect of acid pretreatment of MSWI fly ash and reaction/proposed time on the removal efficiency of HMs through an electrokinetic experiment. The leaching experiment results show that acid pretreatment has increased the desorption/release of heavy metal ions (Pb2+, Cd2+, Cu2+ and Zn2+). It enhances the migration of HM ions in electrolytic cells which get precipitated at the cathode, thereby increasing the removal efficiency of HMs in the electrokinetic experiment. Moreover, it is found that prolonged proposed time (12 d) has significant effect on the removal efficiency of HMs. Finally, it is concluded that acid pretreatment and prolonged proposed time have enhanced the removal electrokinetic remediation of HMs which is carried out via three processes, i.e. desorption (enhanced by acidification), migration and precipitation.

Peatlands are vital in the global carbon cycle, acting as significant sinks for carbon and releasing methane (CH4) and carbon dioxide (CO2) into the atmosphere. However, the complex interactions between environmental factors and the microbial communities responsible for these greenhouse gas emissions remain insufficiently understood. To address this knowledge gap, a pilot-scale mesocosm study was conducted to assess the impact of different terminal electron acceptors (TEAs), including sulfate (SO42-), humic acid (HA), and goethite, on CH4 and CO2 emissions and microbial community structures in peatlands. Our results revealed that the addition of TEAs significantly altered the CH4 and CO2 emissions. Specifically, the addition of SO42- nearly doubled CO2 production while substantially inhibiting CH4 emissions. The combined addition of SO42- and HA, as well as HA alone, followed a similar pattern, albeit with less pronounced effects on CH4. Goethite addition resulted in the highest inhibition of CH4 among all treatments but did not significantly increase CO2 production. Community composition and network analysis indicated that TEAs primarily determined the structure of microbial communities, with each treatment exhibiting distinct taxa networks. Proteobacteria, Acidobacteria, Chloroflexi, and Bacteroidetes were the most abundant phyla across all mesocosms. The presence of methanotrophs, including Methylomirabilales and Methylococcales, was linked to the inhibition of CH4 emissions in these mesocosms. This study provides novel insights into the spatial variability of microbial diversity and density in peatlands under various TEAs, emphasizing the role of methanogenesis and CO2 fluxes in carbon cycling. Our findings enhance the understanding of carbon cycling in microbe-rich environments exposed to TEAs and highlight the potential for future studies to investigate the long-term effects of TEAs on microbial communities, enzymes, and carbon storage. [ABSTRACT FROM AUTHOR]

A partial mitochondrial DNA Cytochrome Oxidase subunit I (mtCOI) gene haplotype variant of the coconut rhinoceros beetle (CRB) Oryctes rhinoceros, classed as 'CRB-G (clade I)', has been the focus of much research since 2007, with reports of invasions into new Pacific Island locations (e.g., Guam, Hawaii, Solomons Islands). For numerous invasive species, inference of invasion biology via whole genome is superior to assessments via the partial mtCOI gene. Here, we explore CRB draft mitochondrial genomes (mitogenomes) from historical and recent collections, with assessment focused on individuals associated within the CRB-G (clade I) classification. We found that all Guam CRB individuals possessed the same mitogenome across all 13 protein-coding genes and differed from individuals collected elsewhere, including 'non-Guam' individuals designated as CRB-G (clade I) by partial mtCOI assessment. Two alternative ATP6 and COIII partial gene primer sets were developed to enable distinction between CRB individuals from Guam that classed within the CRB-G (clade I) haplotype grouping and CRB-G (Clade I) individuals collected elsewhere. Phylogenetic analyses based on concatenated ATP6–COIII genes showed that only Guam CRB-G (clade I) individuals clustered together, and therefore Guam was not the source of the CRB that invaded the other locations in the Pacific assessed in this study. The use of the mtCOI and/or mtCOIII genes for initial molecular diagnosis of CRB remained crucial, and assessment of more native CRB populations will further advance our ability to identify the provenance of CRB invasions being reported within the Pacific and elsewhere. [ABSTRACT FROM AUTHOR]

Timely and accurate detection of diseases in vegetables is crucial for effective management and mitigation strategies before they take a harmful turn. In recent years, convolutional neural networks (CNNs) have emerged as powerful tools for automated disease detection in crops due to their ability to learn intricate patterns from large-scale image datasets and make predictions of samples that are given. The use of CNN algorithms for disease detection in important vegetable crops like potatoes, tomatoes, peppers, cucumbers, bitter gourd, carrot, cabbage, and cauliflower is critically examined in this review paper. This review examines the most recent state-of-the-art techniques, datasets, and difficulties related to these crops' CNN-based disease detection systems. Firstly, we present a summary of CNN architecture and its applicability to classify tasks based on images. Subsequently, we explore CNN applications in the identification of diseases in vegetable crops, emphasizing relevant research, datasets, and performance measures. Also, the benefits and drawbacks of CNN-based methods, covering problems with computational complexity, model generalization, and dataset size, are discussed. This review concludes by highlighting the revolutionary potential of CNN algorithms in transforming crop disease diagnosis and management strategies. Finally, this study provides insights into the current limitations regarding the usage of computer algorithms in the field of vegetable disease detection. [ABSTRACT FROM AUTHOR]

This research presents a machine learning (ML)‐based model that determines the DC and RF characteristics of InGaAs sub‐channel double gate high electron mobility transistors (DG‐HEMTs) to optimize the device structure. We employ technology computer‐aided design (TCAD) simulations to analyze the DC and RF performance of InGaAs sub‐channel DG‐HEMTs, generating a range of datasets by varying the material composition, layer width, and thickness of different layers in the device structure. We then train and optimize support vector regression (SVR) models using 5‐fold cross‐validation, varying the kernel function and degree parameters, and achieve better performance with the radial basis function (RBF) kernel. The simulated results indicate that the ML model predicts physical parameters more effectively than experimental analysis, offering a compact modeling solution that requires fewer computing resources than traditional methods. [ABSTRACT FROM AUTHOR]

Governments have been concerned with balancing economic growth and environmental sustainability. Nevertheless, it has been noted that sustainable development is interconnected with economic variables, the institutional framework, and the efficacy of ecological regulatory measures. This study experimentally examines the correlation of economic policy uncertainty (EPU), financial development (FD), ecological innovation (EI), corruption (IQ), foreign direct investment (FDI), trade openness (TR), natural resource rent (NRR), and CO2 emission. We utilized longitudinal data from the Organization for Economic Cooperation and Development (OECD) countries from 2003 to 2021 to address the existing research void. This study used sequential processes of the linear panel data model (SELPDM) and the SYS-GMM approaches in obtaining consistent and efficient results. The inverse U-shaped relationship between FD and environmental degradation (ED) is confirmed by the long-term elasticity estimates generated by the SELPDM method Elasticity estimates for the long-run show that rigorous ecological regulations, higher renewable energy utilization, higher FD and less corruption, an interaction between FD and rigorous ecological regulations all contribute to reduced ED. Its also being observed that both EPU, FDI and trade openness are positively affecting the ED. It confirms the idea of pollution refuge between the OECD countries. The causality test results show that corruption and FD had reciprocal links with ED, while FDI, trade openness and strict environmental policies were also found to have bidirectional linkage with ED. To achieve sustainable development and prevent environmental degradation in the long term, we propose implementing an institutional financial framework and FD in OECD nations. This may be accomplished by focusing on the effectiveness of environmental regulatory laws and creating a conducive institutional environment. [ABSTRACT FROM AUTHOR]

Malware growth has accelerated due to the widespread use of Android applications. Android smartphone attacks have increased due to the widespread use of these devices. While deep learning models offer high efficiency and accuracy, training them on large and complex datasets is computationally expensive. Hence, a method that effectively detects new malware variants at a low computational cost is required. A transfer learning method to detect Android malware is proposed in this research. Because of transferring known features from a source model that has been trained to a target model, the transfer learning approach reduces the need for new training data and minimizes the need for huge amounts of computational power. We performed many experiments on 1.2 million Android application samples for performance evaluation. In addition, we evaluated how well our framework performed in comparison with traditional deep learning and standard machine learning models. In comparison with state‐of‐the‐art Android malware detection methods, the proposed framework offers improved classification accuracy of 98.87%, a precision of 99.55%, recall of 97.30%, F1‐measure of 99.42%, and a quicker detection rate of 5.14 ms using the transfer learning strategy. [ABSTRACT FROM AUTHOR]

Online social networks (OSNs) have reduced global boundaries, with Twitter enabling perspective sharing. Bot profile‐propagated false information misuse raises serious concerns. Considering this issue, we present our research on classifying Twitter accounts as "human" or "bot" using deep neural networks and transfer learning. Our proposed approach, TL‐PBot, stands for bot profile detection using transfer learning. The TL‐PBot framework utilizes Twitter account metadata such as follower count. Our TL‐PBot also incorporates text data from the Twitter description field as a feature. Word representation of the text data is achieved using Global Vectors (GloVe), a pre‐trained model. By employing user profile‐based features, we significantly reduce the overhead of feature engineering. The hybrid nature of the model enables it to effectively handle mixed‐type features, including text, binary, and numerical data. We design the network using long‐short‐term memory (LSTM) units. DNN model layers were trained, and the weights of the pre‐trained model layers were frozen to apply the transfer learning, resulting in reduced training time and improved bot profile detection accuracy. The performance of the proposed TL‐PBot is evaluated using publicly available datasets. The proposed approach is trained and tested on the same datasets and further evaluated on the validation datasets that were not used in the training phase, which is also a novelty in our approach. Comparative analysis with state‐of‐the‐art approaches demonstrates that the TL‐PBot approach achieves a higher accuracy of 98.07%, while excelling in precision of 99%, recall of 98%, f measure of 98.32%, and AUC of 0.99. Employing the transfer learning strategy resulted in an accelerated detection rate of 5.04 milliseconds, attesting to the effectiveness of this approach in enhancing computational efficiency. [ABSTRACT FROM AUTHOR]

Background and Aims: Alzheimer's disease (AD) is a degenerative neurological condition that worsens over time and leads to deterioration in cognitive abilities, reduced memory, and, eventually, a decrease in overall functioning. Timely and correct identification of Alzheimer's is essential for effective treatment. The systematic study specifically examines the application of deep learning (DL) algorithms in identifying AD using three‐dimensional (3D) imaging methods. The main goal is to evaluate these methods' current state, efficiency, and potential enhancements, offering valuable insights into how DL could improve AD's rapid and precise diagnosis. Methods: We searched different online repositories, such as IEEE Xplore, Elsevier, MDPI, PubMed Central, Science Direct, ACM, Springer, and others, to thoroughly summarize current research on DL methods to diagnose AD by analyzing 3D imaging data published between 2020 and 2024. We use PRISMA (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses) guidelines to ensure the organization and understandability of the information collection process. We thoroughly analyzed the literature to determine the primary techniques used in these investigations and their findings. Results and Conclusion: The ability of convolutional neural networks (CNNs) and their variations, including 3D CNNs and recurrent neural networks, to detect both temporal and spatial characteristics in volumetric data has led to their widespread use. Methods such as transfer learning, combining multimodal data, and using attention procedures have improved models' precision and reliability. We selected 87 articles for evaluation. Out of these, 31 papers included various concepts, explanations, and elucidations of models and theories, while the other 56 papers primarily concentrated on issues related to practical implementation. This article introduces popular imaging types, 3D imaging for Alzheimer's detection, discusses the benefits and restrictions of the DL‐based approach to AD assessment, and gives a view toward future developments resulting from critical evaluation. [ABSTRACT FROM AUTHOR]