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The fields of biomedicine and catalysis have both shown a great deal of interest in copper oxide nanoparticles (CuONPs). Green synthesis methods are being frequently employed to develop highly active CuONPs. This research investigates the potential photocatalytic and antibacterial applications of two CuONPs that were synthesized utilizing extracts from Dillenia indica (D‐CuONPs) and Mikania micrantha (M‐CuONPs) leaves. Analysis using electron microscopy revealed that CuONPs are uniformly spherical and average size around 15 nm. The development of single‐phase CuONPs with a monoclinic structure is shown by X‐ray diffraction studies. The FTIR peaks at 695 cm−1 and 860 cm−1 represent a stretching of Cu−O, signifying the formation of CuO nanostructure. The EDX profiles stated that the CuONPs are correlated with compliantly intensified Cu and O signals. Under solar irradiation, around 90 % of congo red (CR) dye was degraded by both CuONPs in 120 min, with the rate constants of 0.0187 and 0.0174 min−1, respectively. Furthermore, CuONPs have excellent antibacterial activity against bacteria belonging to both Gram classes. Overall, CuONPs hold great potential for inhibiting the growth of pathogenic bacteria and for degrading organic dyes in the presence of sunlight. As such, they can be manufactured on a large scale using plant sources. [ABSTRACT FROM AUTHOR]

This study aims to develop a robust and adaptable real-time forecasting system for coastal and estuarine regions, considering the challenges posed by the limited or unavailable forecast data. A real-time forecast system has been designed to handle three distinct scenarios: forecast data from global models are available, unavailable, or intermittently unavailable. To address the challenge of data unavailability, this research proposes the application of a deep learning model (DLM). This study involves the construction of a high-resolution numerical model nested within global models. The performance of the DLM is evaluated by comparing the simulation results of the nested model generated using DLM-predicted data against those obtained using data from global models. The results demonstrate a high correlation and around 90% accuracy for up to the initial 5 days of the forecast. Even in the absence of hindcast data up to 4 days prior to the forecast beginning time, the real-time forecast with DLM can achieve an accuracy exceeding 70% of the forecast data from global models. However, direct application of the developed DLM to an unknown domain results in significantly lower performance, highlighting the importance of retraining the DLM with local data. Overall, this study presents a comprehensive approach to constructing a real-time forecasting system for coastal and estuarine regions that adeptly handles various data availability scenarios through the integration of machine learning techniques. [ABSTRACT FROM AUTHOR]

The real-time detection of harmful gases like NO2 and NH3 is crucial for smart agriculture and the noninvasive diagnosis of kidney, liver, lung, and heart diseases. However, sensing these gases and understanding the primary sensing mechanisms can be challenging since the nitrogen site of NO2 or NH3 is the primary site that binds the sensing crystallite. Herein, we designed and fabricated low-cost sensors for dual NO2 and NH3 sensing using a nanodevice-on-chips sensor (NDCS) with mixed-valence Co3O4 (Co2+ and Co3+). Co3O4–NDCS detects NO2 and NH3 at low power, room temperature, and 70% relative humidity. It also exhibits stable, recoverable, and reproducible sensing performance for NO2 and NH3, comparable to several high-performance sensors. The dual sensing performances are closely related to two distinct Co sites (Co2+ and Co3+) in the Co3O4 crystal. Co2+ could host the nitrogen atom of either NO2 or NH3 to form Co–N bonding, which is preferable to NO2 detection. Meanwhile, the N atom could replace one of the six oxygen atoms coordinating with Co3+ to form Co–N bonding, favorable to NH3 detection. Looking forward, the strategy proposed here makes it possible to fabricate high-performance gas sensors with the desired nanostructures and known sensing mechanisms. [ABSTRACT FROM AUTHOR]

This study investigates the effects of the gulf stream (GS) on sea-level oscillations across various time scales and assesses the performance of a coastal and estuarine model nested within a global model in simulating these variations. It aims to improve boundary conditions to simulate sea-level oscillations more accurately by considering the influence of GS flow. An inverse correlation is observed between observed sea-level oscillation and GS flow, which becomes more pronounced over longer time scales. Using Delft3D, a high-resolution coastal and estuarine model is developed to simulate circulation dynamics in the central Indian River Lagoon (IRL), FL, and adjacent coastal areas on the Florida east coast. The model is nested into the HYCOM (Hybrid Coordinate Ocean Model), and meteorological forcings are derived from the NARR (North American Regional Reanalysis) model. The model demonstrates satisfactory performance across key parameters, including tide, salinity, water temperature, and currents. However, there remains a noticeable difference between the modeled and observed data. To address this, the model is executed with modified flow boundary conditions at eastern boundary nodes, integrating HYCOM tide, and observing low-frequency sea-level variations. The implementation of the new boundary conditions results in an improved simulation of sea-level oscillations. This study presents the conceptual framework and detailed methodologies employed in the creation of a high-resolution model tailored for estuarine and coastal areas nested into global models capable of satisfactorily simulating sea-level oscillations even when the global model does not represent GS effects. [ABSTRACT FROM AUTHOR]

Transition metal complexes of Ni(II) (1), Co(II) (2) and Cu(II) (3) supported by a Schiff base ligand, namely (E)-N-(4 (dimethylamino)benzylidene)-4H-1,2,4-triazol-4-amine (L) have been synthesized. The ligand has been characterized by various spectral studies (1H NMR, 13C NMR, FTIR, UV–Vis and ESI-MS). The metal complexes have been characterized by FTIR, UV–Vis, ESI-MS and elemental analysis. . Elemental analysis and ESI-MS studies confirmed that metal complexes form at 1:2 (metal:ligand) ratio. The electrochemical behaviors of the metal complexes in acetonitrile have also been studied using cyclic voltammetry and metal complexes have showed by reducing in their metal center. In vitro stable free radical 1,1-diphenyl-2-picrylhydrazyl (DPPH) scavenging assay was used to estimate the antioxidant activity of ligands and its metal complexes. Complex 3 exhibited most potent scavenging property with IC50 value 1.97 ± 0.08 μg/mL among the tested compounds compared to the standard antioxidant ascorbic acid (IC50 value 1.14 ± 0.17 μg/mL). Antibacterial screening investigated by disc diffusion method against gram positive (Staphylococcus aureus, Bacillus subtilis and Staphylococcus epidermidis) and gram negative (Escherichia coli) bacterial strains. The result has exhibited that the complex 2 had prominent inhibitory property against gram positive bacterial strains at the dose of 250 μg/mL and 500 μg/mL. Moreover, the complex 3 have also showed good antibacterial activity against Staphylococcus aureus and Bacillus subtilis at the dose of 500 μg/mL.

This paper reports an investigation of the mechanical and microscopic properties of partially replaced banana leaf ash (BLA) concrete. In this research, the cement was partially replaced by BLA in two phases: Phase A (0%, 5%, 10%, 15%, 20%, 25% and 30%) and Phase B (0%, 10%, 20% and 30%). The consequence of partially replacing cement with BLA in concrete was investigated by the application of a range of tests, namely X-ray fluorescence (XRF), compressive strength, splitting tensile strength, flexure strength, ultrasonic pulse velocity and scanning electron microscopy (SEM) analysis. The properties were then correlated with the properties of a standard 100% Portland cement concrete of similar strength. The XRF result of the BLA identified a composition with 48.93% SiO2 and 3.48% Al2O3, which indicates that the material potentially possesses pozzolanic properties. The mechanical properties of the partially replaced BLA concrete noted minor strength loss, approximately 5% with 20% partial replacement. The nondestructive testing data showed enhanced performance up to 20% partial replacement, with ultrasonic pulse values above 3500 m/s. The scanning electron microscopy analysis illustrated that the morphology of BLA specimens contained increased microcracks compared with the control. The decrease in strength observed is attributed to the fibrous composition of the BLA. The mechanical, nondestructive testing and microscopic results highlight the potential to utilize BLA as a partial replacement for cement as a pozzolanic material in concrete at up to 20% by weight of cement. [ABSTRACT FROM AUTHOR]

To discharge the colored effluents from industries there needs to be effective and affordable treatment options. Adsorption using reduced graphene oxide (rGO) as an adsorbent is a prominent one. In this study, green coffee bean extract (GCBE) is utilized as a safe reducing agent for the reduction of graphene oxide (GO) to synthesize rGO. The formation of rGO is confirmed by a new peak in the UV–vis spectra at 275 nm and a diffraction peak in the XRD patterns at 22°. The effective formation of rGO is further substantiated by a change in the GO peak's properties in the FTIR, EDX, and Raman spectra and a weight loss change in TGA. The SEM and TEM analyses demonstrate the effective production of the nano‐sheets of rGO having exfoliated and segregated in a few layers. Furthermore, the obtained rGO exhibited outstanding efficacy in wastewater cleanup, effectively adsorbing MB as a prototype organic dye. The kinetics and isotherm study suggested that the adsorption leads by the chemisorption and monolayer formation on the homogeneous surface of rGO. The maximum adsorption capacity is found to be 89.3 mg g−1. This process offers a fresh opportunity for the economical and safe production of rGO for wastewater treatment. [ABSTRACT FROM AUTHOR]

Silver nanoparticles (AgNPs) prepared by green synthesis have a lot of potentials in various fields. Among them, as an antioxidant, antibacterial agent, and nanoprobe for the colorimetric detection of mercury (Hg2+) ions is thought to be the most important. The antibacterial, antioxidant, and colorimetric sensing potential of the greenly produced AgNPs utilizing Piper chaba stem extract are all predicted in this investigation. By using the disc diffusion method, the antibacterial activity of greenly produced AgNPs are assessed, and the findings are measured from the zone of inhibition (ZOI). It is revealed that the Staphylococcus aureus, Micrococcus spp., Escherichia coli, and Pseudomonas aeruginosa bacterial strains are significantly resisted by the greenly produced AgNPs. The antioxidant activity test of AgNPs reveals a considerable impact on free radical scavenging having the inhibitory concentration (IC50) is 1.13 mL (equivalent to 0.45 mg mL−1). Also, with a low limit of detection of 28 ppm, the resulting AgNPs are used as highly selective and economical colorimetric sensors for Hg2+ detection. The study's findings support the hypothesis that Piper chaba stems can serve as a source for the production of AgNPs with high antibacterial and antioxidant activity and usefulness for simple colorimetric readings of Hg2+. [ABSTRACT FROM AUTHOR]

A numerical model was developed using Delft3D to simulate the circulation dynamics in Port Everglades, FL, and the adjacent coastal area. The model was nested within the HYCOM (Hybrid Coordinate Ocean Model), while meteorological data were obtained from the NARR (North American Regional Reanalysis) model. To evaluate the model, model outputs were compared with observed data from the NOAA. Calibration experiments were conducted on the model parameters, including the bottom friction, wind forcings, and vertical layer specification. These experiments revealed that implementing a 10-layer model slightly improved the vertical stratification, while the utilization of 2-D wind data resulted in more pronounced surface layer characteristics in temperature and velocity profiles and employing moderate values of the Chezy coefficient produced optimal outcomes for the bottom roughness parameter. The model demonstrated satisfactory performance across major parameters, including water level, salinity, temperature, and currents. A real-time forecast system has been constructed with this nested model, providing up to 3-day forecasts that are updated daily. To facilitate automated forecasting without manual intervention, an automation system has been developed using a combination of bash, MATLAB, and Python scripts. This study provides a comprehensive documentation of the concepts and detailed methods involved in developing a real-time forecast model for estuarine and coastal regions. [ABSTRACT FROM AUTHOR]

Alkaloids are the most diversified nitrogen-containing secondary metabolites, having antioxidant and antimicrobial properties, and are extensively used in pharmaceuticals to treat different types of cancer. Nicotiana serves as a reservoir of anti-cancer alkaloids and is also used as a model plant for the de novo synthesis of various anti-cancer molecules through genetic engineering. Up to 4% of the total dry weight of Nicotiana was found to be composed of alkaloids, where nicotine, nornicotine, anatabine, and anabasine are reported as the dominant alkaloids. Additionally, among the alkaloids present in Nicotiana, β-carboline (Harmane and Norharmane) and Kynurenines are found to show anti-tumor effects, especially in the cases of colon and breast cancers. Creating new or shunting of existing biosynthesis pathways in different species of Nicotiana resulted in de novo or increased synthesis of different anti-tumor molecules or their derivatives or precursors including Taxadiane (~22.5 µg/g), Artemisinin (~120 μg/g), Parthenolide (~2.05 ng/g), Costunolide (~60 ng/g), Etoposide (~1 mg/g), Crocin (~400 µg/g), Catharanthine (~60 ng/g), Tabersonine (~10 ng/g), Strictosidine (~0.23 mg/g), etc. Enriching the precursor pool, especially Dimethylallyl Diphosphate (DMAPP), down-regulating other bi-product pathways, compartmentalization or metabolic shunting, or organelle-specific reconstitution of the precursor pool, might trigger the enhanced accumulation of the targeted anti-cancer alkaloid in Nicotiana. [ABSTRACT FROM AUTHOR]

The choice of this study has a significant impact on daily life. In various fields such as journalism, academia, business, and more, large amounts of text need to be processed quickly and efficiently. Text summarization is a technique used to generate a precise and shortened summary of spacious texts. The generated summary sustains overall meaning without losing any information and focuses on those parts that contain useful information. The goal is to develop a model that converts lengthy articles into concise versions. The task to be solved is to select an effective procedure to develop the model. Although the present text summarization models give us good results in many recognized datasets such as cnn/daily-mail, newsroom, etc. All the problems can not be resolved by these models. In this paper, a new text summarization method has been proposed: combining the Extractive and Abstractive Text Summarization technique. In the extractive-based method, the model generates a summary using Sentence Ranking Algorithm and passes this generated summary through an abstractive method. When using the sentence ranking algorithm, after rearranging the sentences, the relationship between one sentence and another sentence is destroyed. To overcome this situation, Pronoun to Noun conversion has been proposed with the new system. After generating the extractive summary, the generated summary is passed through the abstractive method. The proposed abstractive model consists of three pre-trained models: google/pegusus-xsum, facebook/bart-large-cnn model, and Yale-LILY/brio-cnndm-uncased, which generates a final summary depending on the maximum final score. The following results were obtained: experimental results on CNN/daily-mail dataset show that the proposed model obtained scores of ROUGE-1, ROUGE-2 and ROUGE-L are respectively 42.67 %, 19.35 %, and 39.57 %. Then, the result has been compared with three state-of-the-art methods: JEANS, DEATS and PGAN-ATSMT. The results outperform state-of-the-art models. Experimental results also show that the proposed model is qualitatively readable and can generate abstract summaries. Conclusion: In terms of ROUGE score, the model outperforms some art-of-the-state models for ROUGE-1 and ROUGE-L, but doesn't achieve good result in ROUGE-2. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to the multi-agent coverage path-planning problem. An efficient multi-robot coverage algorithm yields a coverage path for each robot, such that the union of all paths generates an almost full coverage of the terrain and the total coverage time is minimized. The proposed algorithm enables multiple robots with limited sensor capabilities to perform efficient coverage on a shared territory. Each robot is assigned to an exclusive route which enables it to carry out its tasks simultaneously, e.g., cleaning assigned floor area with minimal path overlapping. It is very difficult to cover all free space without visiting some locations more than once, but the occurrence of such events can be minimized with efficient algorithms. The proposed multi-robot coverage strategy directs a number of simple robots to cover an unknown area in a systematic manner. This is based on footprint data left by the randomized path-planning robots previously operated on that area. The developed path-planning algorithm has been applied to a simulated environment and robots to verify its effectiveness and performance in such an application.

Microalga Chlorella vulgaris was cultured in different concentrations of normal molasses medium (NMM0.5 g/l, NMM1.0 g/l, and NMM1.5 g/l) to evaluate growth performance of the sugar mill byproduct as a low cost culture media and Bold basal medium (BBM) as control. Maximum growth of Chlorella vulgaris was found in NMM1.0 g/l on 8th day of the culture followed by BBM, NMM 0.5 g/l and NMM 1.5 g/l. Similar findings were also observed in determining chlorophyll a content and optical density of C. vulgaris. Maximum cell growths 191.88 (x 105)/mL, chlorophyll ... content 10.60 (mg/l) and optical density 2.15 were recorded in NMM1.0 g/l. Maximum SGR of cell was determined 0.56 (mg/day) grown in NMM1.0 g/l followed by 0.52, 0.52 and 0.48 (mg/day) in BBM, NMM0.5 g/l and NMM1.5 g/l, respectively. Chlorophyll ... content and total biomass of Chlorella vulgaris followed the similar trend. Protein (46.49%) and lipid (14.18%) of C. vulgaris was detected significantly higher (P<0.01) in NMM1.0 g/l than that grown in BBM and other concentrations of NMM. The growth performance of the investigating molasses medium (NMM1.0 g/l) indicates that the molasses may be a good low cost culture medium ingredient source for C. vulgaris or any other microalga species. [ABSTRACT FROM AUTHOR]