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Nanomaterials have become integral in various aspects of agricultural practices, including the development of nano-fertilizers for optimized crop nutrition. This study explores the application of green-synthesized iron (Fe) and molybdenum (Mo) nanoparticles, as well as their composites, using a guava leaf extract (GLE). The focus is on assessing their impact on nitrogen fixation and growth in tomato plants (Solanum lycopersicum). The nanoparticles were characterized through Fourier Transform Infrared Spectroscopy, Ultraviolet Diffused Reflectance Spectroscopy, Raman Spectroscopy, and X-ray diffraction analysis. The experiment involved two application methods (soil and direct plant spraying) with varying nanoparticle concentrations. Results indicate that the 1% composite nanoparticles applied to the soil and 3% Mo directly on plants yield the most favorable growth and nitrogen uptake in S. lycopersicum. Notably, the 1% composite treatment demonstrated significant enhancement in shoot length, number of branches, and shoot diameter at all three growth stages. Conversely, the 3% Mo treatment when applied directly to plants exhibited optimal results showing substantial shoot length, number of branches, and shoot diameter. Post-experimental soil nutrient analysis further revealed the nuanced effects of nanoparticle applications with 1% composite treatments enhancing nutrient availability compared to control and other concentrations. This research contributes to the evolving field of agri-nanotechnology emphasizing the importance of nanoparticle concentration and application method in influencing plant development and nutrient uptake, paving the way for sustainable agricultural practices.

Using natural deep eutectic solvents (NADESs) as a green reagent is a step toward producing environmentally friendly and sustainable technology. This study screened three natural DESs developed using quaternary ammonium salt and organic acid to analyse their capability to extract nickel ions from contaminated mangrove soil, which are ChCl: Acetic Acid (ChCl-AceA), ChCl: Levulinic Acid (ChCl-LevA), and ChCl: Ethylene Glycol(ChCl-Eg) at molar ratio 1:2. The impact of various operating parameters such as washing agent concentration, pH solution, and contact time on the NADES performance in the dissolution of Ni ions batch experiments were performed. The optimal soil washing conditions for metal removal were 30% and 15% concentration, a 1:5 soil-liquid ratio, and pH 2 of ChCl-LevA and ChCl-AceA, respectively. A single removal washing may remove 70.8% and 70.0% Ni ions from the contaminated soil. The dissolution kinetic of Ni ions extraction onto NADES was explained using the linear kinetic pseudo and intraparticle mass transfer diffusion models. The kinetic validation demonstrates a good fit between the experimental and pseudo-second-order Lagergren data. The model's maximum Ni dissolution capacity, Qe are 51.56 mg g−1 and 52.00 mg g−1 of ChCl-LevA and ChCl-AceA, respectively. The synthesised natural-based DES has the potential to be a cost-effective, efficient, green alternative extractant to conventional solvent extraction of heavy metals. [ABSTRACT FROM AUTHOR]

This study aims to identify the utilization of animals, conduct an ethnozoology quantitative analysis, describe animal conservation by local people on Enggano Island, and apply the research results to empower species literacy in biology student teachers. Data collection in this study is in-depth semistructured interviews, observations, participant observations, questionnaires, and tests. Based on the study results, 64 species of animals are used for ten categories. According to the quantitative analysis, Chelonia mydas has the highest CI index (1.071) and RFC index (1.00). Based on these indices, Chelonia mydas was identified as the most important species for local people in Enggano island. The ethnozoology textbook developed from the results of ethnozoology research on Enggano Island is valid, practical, and effective to can increase species literacy in biology student teachers. In the control class, the adjusted mean of species literacy was 63.91, while the adjusted mean of species literacy in the experimental class was 71.87. [ABSTRACT FROM AUTHOR]

Tropical forests have known ecosystem services, including sequestering atmospheric carbon and fixing it into biomass. Since the revolutionary industrial changes, the atmospheric carbon level has risen during the recent decades, while the global temperature has increased compared to the pre-industrial levels. Furthermore, reducing atmospheric carbon can mitigate global warming and climate change. This study aims to estimate the aboveground biomass and carbon stock in the Pesanggrahan Forest, Malang Regency, East Java, Indonesia, and contribute local information to the vast global literature. This study was conducted in August 2023 and measured all tree species with a diameter >20 cm in a 5-ha area. The results found 208 individuals and 39 species of trees, the aboveground biomass and carbon stock of which were calculated. The total aboveground biomass (AGB) and carbon stock were 4,486 and 2,248 tons C/ha, respectively. Meanwhile, the total forest AGB and carbon stock in the 5-ha sampling area were 224.77 tons/ha and 112.39 tons C/ha, respectively. These findings highlight the cruciality of tropical forest vegetation in carbon storage and their overall role in regional and global ecosystem management in light of climate change. [ABSTRACT FROM AUTHOR]

The rapid and unpredictable expansion of urban areas poses a major challenge to humanity in adapting to climate change in the coming decades. Therefore, the presence of urban forests promotes climate change adaptation through their geographic location in cities. This systematic literature review (SLR) analyzed the existing research on ecosystem services provided by urban forests for climate change adaptation. The bibliographic databases Web of Science and Scopus were used for the study according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. There were 484 publications found that were published between 2011 and 2021 using specific terms in both search engines. However, there were only 59 articles from 26 different countries that met the inclusion criteria. When analyzing these 59 articles, the majority focused on carbon storage, while less than a quarter examined other services such as climate regulation and air purification. Hence, research should focus on the less studied or potential ecosystem services for climate change adaptation. Because the results provide a comprehensive overview of the role of urban forests in climate change adaptation, they should provide insights for policymakers, urban planners, and researchers seeking to improve urban resilience through the sustainable use of urban forest ecosystem services. [ABSTRACT FROM AUTHOR]

A common difficulty in building prediction models with real-world environmental datasets is the skewed distribution of classes. There are significantly more samples for day-to-day classes, while rare events such as polluted classes are uncommon. Consequently, the limited availability of minority outcomes lowers the classifier’s overall reliability. This study assesses the capability of machine learning (ML) algorithms in tackling imbalanced water quality data based on the metrics of precision, recall, and F1 score. It intends to balance the misled accuracy towards the majority of data. Hence, 10 ML algorithms of its performance are compared. The classifiers included are AdaBoost, Support Vector Machine, Linear Discriminant Analysis, k-Nearest Neighbors, Naïve Bayes, Decision Trees, Random Forest, Extra Trees, Bagging, and the Multilayer Perceptron. This study also uses the Easy Ensemble Classifier, Balanced Bagging, and RUSBoost algorithm to evaluate multi-class imbalanced learning methods. The comparison results revealed that a highaccuracy machine learning model is not always good in recall and sensitivity. This paper’s stacked ensemble deep learning (SE-DL) generalization model effectively classifies the water quality index (WQI) based on 23 input variables. The proposed algorithm achieved a remarkable average of 95.69%, 94.96%, 92.92%, and 93.88% for accuracy, precision, recall, and F1 score, respectively. In addition, the proposed model is compared against two state-of-the-art classifiers, the XGBoost (eXtreme Gradient Boosting) and Light Gradient Boosting Machine, where performance metrics of balanced accuracy and g-mean are included. The experimental setup concluded XGBoost with a higher balanced accuracy and G-mean. However, the SE-DL model has a better and more balanced performance in the F1 score. The SE-DL model aligns with the goal of this study to ensure the balance between accuracy and completeness for each water quality class. The proposed algorithm is also capable of higher efficiency at a lower computational time against using the standard Synthetic Minority Oversampling Technique (SMOTE) approach to imbalanced datasets. [ABSTRACT FROM AUTHOR]

Water quality analysis is essential to understand the ecological status of aquatic life. Conventional water quality index (WQI) assessment methods are limited to features such as water acidic or basicity (pH), dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), ammoniacal nitrogen (NH3-N), and suspended solids (SS). These features are often insufficient to represent the water quality of a heavy metal–polluted river. Therefore, this paper aims to explore and analyze novel input features in order to formulate an improved WQI. In this work, prospective insights on the feasibility of alternative water quality input variables as new discriminant features are discussed. The new discriminant features are a step toward formulating adaptive water quality parameters according to the land use activities surrounding the river. The results and analysis obtained from this study have proven the possibility of predicting WQI using new input features. This work analyzes 17 new input features, namely conductivity (COND), salinity (SAL), turbidity (TUR), dissolved solids (DS), nitrate (NO3), chloride (Cl), phosphate (PO4), arsenic (As), chromium (Cr), zinc (Zn), calcium(Ca), iron (Fe), potassium (K), magnesium (Mg), sodium (Na), E. coli, and total coliform, in predicting WQI using machine learning techniques. Five regression algorithms—randomforest (RF), AdaBoost, support vector regression (SVR), decision tree regression (DTR), and multilayer perception (MLP)—are applied for preliminary model selection. The results show that the RF algorithm exhibits better prediction performance, with R² of 0.974. Then, this work proposes a modified RF by incorporating the synthetic minority oversampling technique (SMOTE) into the conventional RF method. The proposed modified RF method is shown to achieve 77.68%, 74%, 69%, and 71% accuracy, precision, recall, and F1-score, respectively. In addition, the sensitivity analysis is included to highlight the importance of the turbidity variable in WQI prediction. The results of sensitivity analysis highlight the importance of certain water quality variables that are not present in the conventional WQI formulation. [ABSTRACT FROM AUTHOR]

Variations in the anatomical characters of leaf and stem were examined in six species of three genera of Acantheae (Acanthoideae : Acanthaceae) from Yemen, namely, Acanthus L., Blepharis Juss., and Crossandra Salisb. Both light and scanning electron microscopy were used. The paper describes the anatomical variation in the outline of stems, petioles, midribs, lamina and leaf margin. Stem outlines were circular, squarish or squarish with prominent grooves. The adaxial shape of petioles varies from straight, unevenly straight, straight with lateral wings and grooved. Cystoliths were absent in all genera. Raphides cells are present in the petiole and stem of Acanthus arboreus and Crossandra johanninae and absent in Blepharis species. Fibres showed in different part such as margin lamina and surrounding the vascular tissues. The type and position of trichomes were also investigated and both glandular and non-glandular trichomes were found to be present in Acantheae tribe. [ABSTRACT FROM AUTHOR]

A study on foliar epidermis was conducted on nine species belonging to the Moraceae family to investigate diverse micromorphological characteristics that hold taxonomic importance in leaf epidermis. Based on LM (light microscopy) and SEM (scanning electron microscopy), it was found that the shape of epidermal cells in the studied members is rectangular, irregular, polygonal, and pentagonal on both leaf epidermal surfaces. The common shape of the epidermal cells is polygonal in most species. The size of the epidermal cell in length and width varies from species to species. The wall of the epidermal cells is thick in most species, except in Broussonetia papyrifera (L.) L’Hér. ex Vent. and Morus alba L., and Morus laevigata Wall. ex Brandis, where the epidermal cells are thin on both leaf surfaces. It was found that the majority of the plants are hypostomatic, i.e., the adaxial surface of the leaves has no stomata. Most species have anomocytic stomata on the abaxial surface of the leaf. The cyclocytic stomata were found only in Ficus elastica Roxb. ex Hornem. and Ficus benjamina L. The stomata type, which is rare in the studied species, is cyclocytic, laterocytic, and paracytic. We found variations in the shape and size of leaf epidermal cells and stomata on both leaf surfaces in all selected species. The most important element of the leaf is the stomatal index, which serves as a geographic indicator and shows the transpiration rate of the leaf. The stomatal index ranges from (76.7%) in B. papyrifera to (2.13%) in F. benjamina on the adaxial surface, while on the abaxial surface it is highest (61.8%) in Ficus religiosa L. and lowest (3.06%) in Ficus virens Aiton. The identification of the plants at both genus and specific-levels was found to be taxonomically appropriate based on the epidermal architecture of the leaves. Thus, the current study aims to clarify the qualitative and quantitative properties of the leaf epidermis in order to give information for Moraceae family species identification and categorization. [ABSTRACT FROM AUTHOR]

Nanotechnology offers promising opportunities in combating infectious agents, especially multidrug-resistant bacteria (MDR), which is a major concern in modern times. Zinc oxide nanoparticles (ZnO-NPs) are effective in delivering therapeutic agents to living systems due to their biocompatibility and bioactivity, making them effective against infectious microbes and also allowing them to be applied as an effective anticancer instrument. This study aimed to investigate the antimicrobial activity of ZnO-NPs against P. aeruginosa, a multi-drug resistant (MDR) bacterium in combination with Piperacillin-Tazobactam, as well as the in vitro effect of anticancer activity on MCF-7 and HepG2 cell lines. Piperacillin-Tazobactam, are beta-lactam antibiotics highly effective against Gram-positive and Gram-negative bacteria such as Pseudomonas aeruginosa, and recommended for the empirical treatment of Febrile Neutropenia (FN) after chemotherapy. ZnO-NPs of variable size (designated as ZnO-NPs-A, ZnO-NPs-B, and ZnO-NPs-C with 35±2.5 nm, 50±2 nm, and 65±3 nm mean sizes, respectively) and Piperacillin-Tazobactam alone and in combination were used for the study. The extended-spectrum beta-lactamase (ESBLs) producing MDR P. aeruginosa strain sensitivity profiling towards different combinations of ZnO-NPs and Piperacillin-Tazobactam was measured. There was no synergistic effect observed against growth inhibition of P. aeruginosa. The combined dose of ZnO-NPs-A and Piperacillin-Tazobactam showed great antibacterial efficacy even as compared to the pure drug against P. aeruginosa. Similarly, the Piperacillin-Tazobactam with ZnO-NPs-C showed the least cell viability as compared to the drug alone. The study showed a significant decrease in cell viability with the combined application of Piperacillin-Tazobactam and ZnO-Nps in comparison to the individual treatments. [ABSTRACT FROM AUTHOR]

This study aims to analyze the phytochemical and antimicrobial-related assays including antibacterial, antifungal, antipellicle, and antibiofilm potential of the root of an alpine medicinal plant (Arnebia euchroma (Royle) I.M.Johnst). Ethanolic, methyl acetate, chloroform, and aqueous extracts of root parts of the A. euchroma plant were prepared. The active metabolites of the root part of the plant were evaluated both qualitatively and quantitatively by GC-MS and HPLC techniques, using ethanolic extract. The antimicrobial potential of A. euchroma was investigated using crude extract, fractions, and AgNPs of the A. euchroma root against two Gram-positive bacteria (Enterococcus, Staphylococcus aureus) four Gram-negative bacteria (Salmonella typhi, Pseudomonas aeruginosa, Escherichia coli, Proteus vulgaris) and five fungal strains (Rhizoctonia, Cuneate fasciculus, Aspergillus niger, Fusarium oxysporum, and Candida auris). All the bacterial strains showed sensitivity toward all the fractions (ethanolic crude, chloroform, aqueous, and methyl acetate) and AgNPs. While S. typhi was found the most susceptible, P. vulgaris was most resistant to all extracts and AgNPs. The case of anti-biofilm potential, the S. typhi and Enterococcus were shown to be most susceptible, these bacteria formed biofilm which was disrupted completely by the extracts. Among the fungal strains F. oxysporum and A. niger were the most susceptible and C. fasciculus was the most resistant fungi. All extracts, including ethanolic extract, chloroform, aqueous, and methyl acetate, at concentrations of 12.5 µg/mL and 16 µg/mL and AgNPs showed anti-pellicle activity against S. typhi, Rhizoctonia, and A. niger. Dose-dependent trend in all bioassays was noted. The ethanolic crude and chloroform extracts were the most active followed by methyl acetate and then aqueous extracts. The phytochemical analysis revealed that carbohydrates, alkaloids, saponins, phenols, tannins, proteins, and flavonoids were found in the plant while terpenoids were absent. The present study highlights that root of the A. euchroma is rich in various types of bioactive phytochemicals, which may be responsible for anti-microbial activities toward the tested pathogenic of bacterial and fungal strains. Our results suggest and confirm A. euchroma extracts could be used economically as a valuable bioproduct with new functional properties in the further drug development and pharmaceutical industries. [ABSTRACT FROM AUTHOR]

Background: The environment has been significantly impacted by rapid urbanization, leading to a need for changes in climate change and pollution indicators. The 4IR offers a potential solution to efficiently manage these impacts. Smart city ecosystems can provide well-designed, sustainable, and safe cities that enable holistic climate change and global warming solutions through various community-centred initiatives. These include smart planning techniques, smart environment monitoring, and smart governance. An air quality intelligence platform, which operates as a complete measurement site for monitoring and governing air quality, has shown promising results in providing actionable insights. This article aims to highlight the potential of machine learning models in predicting air quality, providing data-driven strategic and sustainable solutions for smart cities., Methods: This study proposed an end-to-end air quality predictive model for smart city applications, utilizing four machine learning techniques and two deep learning techniques. These include Ada Boost, SVR, RF, KNN, MLP regressor and LSTM. The study was conducted in four different urban cities in Selangor, Malaysia, including Petaling Jaya, Banting, Klang, and Shah Alam. The model considered the air quality data of various pollution markers such as PM 2.5 , PM 10 , O 3 , and CO. Additionally, meteorological data including wind speed and wind direction were also considered, and their interactions with the pollutant markers were quantified. The study aimed to determine the correlation variance of the dependent variable in predicting air pollution and proposed a feature optimization process to reduce dimensionality and remove irrelevant features to enhance the prediction of PM 2.5 , improving the existing LSTM model. The study estimates the concentration of pollutants in the air based on training and highlights the contribution of feature optimization in air quality predictions through feature dimension reductions., Results: In this section, the results of predicting the concentration of pollutants (PM 2.5 , PM 10 , O 3 , and CO) in the air are presented in R 2 and RMSE. In predicting the PM 10 and PM 2.5 concentration, LSTM performed the best overall high R 2 values in the four study areas with the R 2 values of 0.998, 0.995, 0.918, and 0.993 in Banting, Petaling, Klang and Shah Alam stations, respectively. The study indicated that among the studied pollution markers, PM 2.5, PM 10 , NO 2 , wind speed and humidity are the most important elements to monitor. By reducing the number of features used in the model the proposed feature optimization process can make the model more interpretable and provide insights into the most critical factor affecting air quality. Findings from this study can aid policymakers in understanding the underlying causes of air pollution and develop more effective smart strategies for reducing pollution levels., Competing Interests: The authors declare there are no competing interests., (©2023 Neo et al.)