Zinc and Silicon Nano-Fertilizers Influence Ionomic and Metabolite Profiles in Maize to Overcome Salt Stress.

Salinity stress is a major factor affecting the nutritional and metabolic profiles of crops, thus hindering optimal yield and productivity. Recent advances in nanotechnology propose an avenue for the use of nano-fertilizers as a potential solution for better nutrient management and stress mitigation. This study aimed to evaluate the benefits of conventional and nano-fertilizers (nano-Zn/nano-Si) on maize and subcellular level changes in its ionomic and metabolic profiles under salt stress conditions. Zinc and silicon were applied both in conventional and nano-fertilizer-using farms under stress (100 mM NaCl) and normal conditions. Different ions, sugars, and organic acids (OAs) were determined using ion chromatography and inductively coupled plasma mass spectroscopy (ICP-MS). The results revealed significant improvements in different ions, sugars, OAs, and other metabolic profiles of maize. Nanoparticles boosted sugar metabolism, as evidenced by increased glucose, fructose, and sucrose concentrations, and improved nutrient uptake, indicated by higher nitrate, sulfate, and phosphate levels. Particularly, nano-fertilizers effectively limited Na accumulation under saline conditions and enhanced maize's salt stress tolerance. Furthermore, nano-treatments optimized the potassium-to-sodium ratio, a critical factor in maintaining ionic homeostasis under stress conditions. With the growing threat of salinity stress on global food security, these findings highlight the urgent need for further development and implementation of effective solutions like the application of nano-fertilizers in mitigating the negative impact of salinity on plant growth and productivity. However, this controlled environment limits the direct applicability to field conditions and needs future research, particularly long-term field trials, to confirm such results of nano-fertilizers against salinity stress and their economic viability towards sustainable agriculture.

Exploring genotypic variability and interrelationships among growth, yield, and quality characteristics in diverse tomato genotypes.

Tomato is the most consumed vegetable crop worldwide, with excellent beneficial health properties and high content of vitamins, minerals, carotenoids, total antioxidants, and phenolic compounds. Hence, improving its genotypes is crucial to sustain its production and ensure food security, principally under the fast-growing worldwide population and abrupt global climate change. The present study aimed to explore the genotypic variability associated with specific characteristics in twenty-five diverse tomato genotypes. In addition, the relationships between growth, yield, and quality traits using both univariate (correlation coefficient, path analysis) and multivariate (principal component, principal coordinates, canonical variate) analysis methods were explored. The results indicated that the evaluated genotypes possessed highly significant variation. This is appropriate for future hybridization through tomato breeding programs. All evaluated genotypes demonstrated considerable potential to develop strong hybrid vigour for growth, yield, and quality characteristics. In particular, the genotypes LS009, LS011, and LS014 could be considered promising, high-yielding, and resistant to yellow leaf curl virus infestation (YLCV) disease parents for future breeding schemes. The number of fruits per plant, fruit diameter, and fruit weight proved strong positive relationships with fruit yield. Accordingly, these characteristics demonstrate their importance in improving fruit yield and could be exploited as indirect criteria for selecting high-yielding tomato genotypes through breeding programs.

Green innovation behaviour: Impact of industry 4.0 and open innovation.

Developing nations have motivated contemporary manufacturing companies to embrace green innovation and focus on smart technology that is sustainable to harness the growing economy and uplift the people. Although, scientific research in this direction has been neglected, and at the same time there are challenges to the attainment while meeting the need of the people. This study aims to investigate the impact of Industry 4.0, open innovation and green innovation performance on green innovation behaviour. To this end, a theoretical model was established. A quantitative research approach was applied in which survey data were utilized to capture 247 responses from different manufacturing companies in Malaysia. The model was tested using SmartPLSver3.0 to measure the structural relationship between variables. The findings indicate that Industry 4.0 and green innovation performance positively impact green innovation behaviour, compared to open innovation which has no impact. The impact of green innovation performance is found to be stronger when compared to Industry 4.0 and open innovation. Likewise, green innovation performance exhibits a substantial mediating impact between the exogenous variables and green innovation behaviour. The policy implication and conclusions are further discussed in the last section of the study.

Wildfires in Australia: a bibliometric analysis and a glimpse on 'Black Summer' (2019/2020) disaster.

A wildfire, an unplanned fire that is mainly uncontrolled and originates in combustible vegetation in rural or urban settings, is one of the most pervasive natural catastrophes in some areas, such as Siberia, California and Australia. Many studies, such as standard reviews, have been undertaken to look into the works of literature on wildfires or forest fires and their effects on aquatic and terrestrial ecosystems. Regrettably, conventional literature reviews failed to identify the important researchers, evolving complexities, emerging research hotspots, trends and opportunities for further research on the ground of wildfire study. The present study employs bibliometric analysis to investigate this study area qualitatively and quantitatively. The Scopus database systems and Web of Science Core Collection yielded 78 qualifying papers, which were then evaluated using Biblioshiny (A bibliometrix tool of R-studio). According to the statistics, the discipline is expanding at a pace that is 13.68% faster than average. So far, three key periods of transformation have been documented: preliminary evolution (8 articles; 1999-2005), gentle evolution (14 articles; 2006-2013) and quick evolution (56 articles; 2014 to 2021). Forest Ecology and Management and Science journals have the highest number of publications, accounting for 7.70% of total wildfire-related articles published from 1999 to 2021. However, recent data indicate that investigators are shifting their focus to wildfires, with the term 'Australia' having the highest frequency (91) and 'wildfire' having the second highest (58) as the most appeared keywords. The present study will provide a foundation for future research on wildfire incidence and management by receiving information by synthesising previously published literature in Australia and around the world.

Electrode-Potential-Driven Dissociation of N-Heterocycle/BF3 Adducts: A Possible Manifestation of the Electro-Inductive Effect.

Recently, non-Faradaic effects were used to modify the electronic structure and reactivity of electrode-bound species. We hypothesize that these electrostatic perturbations could influence the chemical reactivity of electrolyte species near an electrode in the absence of Faradaic electron transfer. A prime example of non-Faradaic effects is acid-base dissociation near an interface. Here, we probed the near-electrode dissociation of N-heterocycle-BF3 Lewis adducts upon electrode polarization, well outside of the redox potential window of the adducts. Using scanning electrochemical microscopy and confocal fluorescence spectroscopy, we detected a potential-dependent depletion of the adduct near the electrode. We propose an electro-inductive effect where a more positive potential leads to electron withdrawal on the N-heterocycle. This study takes a step forward in the use of electrostatics at electrochemical interfaces for field-driven electrocatalytic and electro-synthetic processes.

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues.

Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in this system. However, integrating with AI, the system would be multiple agent collaborators who are capable of communicating with their desired host efficiently. Again, FL is another interesting feature, which works decentralized manner; it maintains the communication based on a model in the preferred system without transferring the raw data. The combination of FL, AI, and XAI techniques can be capable of minimizing several limitations and challenges in the healthcare system. This paper presents a complete analysis of FL using AI for smart healthcare applications. Initially, we discuss contemporary concepts of emerging technologies such as FL, AI, XAI, and the healthcare system. We integrate and classify the FL-AI with healthcare technologies in different domains. Further, we address the existing problems, including security, privacy, stability, and reliability in the healthcare field. In addition, we guide the readers to solving strategies of healthcare using FL and AI. Finally, we address extensive research areas as well as future potential prospects regarding FL-based AI research in the healthcare management system.

Melatonin Modulates Plant Tolerance to Heavy Metal Stress: Morphological Responses to Molecular Mechanisms.

Heavy metal toxicity is one of the most devastating abiotic stresses. Heavy metals cause serious damage to plant growth and productivity, which is a major problem for sustainable agriculture. It adversely affects plant molecular physiology and biochemistry by generating osmotic stress, ionic imbalance, oxidative stress, membrane disorganization, cellular toxicity, and metabolic homeostasis. To improve and stimulate plant tolerance to heavy metal stress, the application of biostimulants can be an effective approach without threatening the ecosystem. Melatonin (N-acetyl-5-methoxytryptamine), a biostimulator, plant growth regulator, and antioxidant, promotes plant tolerance to heavy metal stress by improving redox and nutrient homeostasis, osmotic balance, and primary and secondary metabolism. It is important to perceive the complete and detailed regulatory mechanisms of exogenous and endogenous melatonin-mediated heavy metal-toxicity mitigation in plants to identify potential research gaps that should be addressed in the future. This review provides a novel insight to understand the multifunctional role of melatonin in reducing heavy metal stress and the underlying molecular mechanisms.

Assessing the Response of Diverse Sesame Genotypes to Waterlogging Durations at Different Plant Growth Stages.

Sesame is sensitive to waterlogging, and its growth is devastatingly impacted under excess moisture conditions. Thus, waterlogging tolerance is crucial to alleviate yield constraints, particularly under expected climate change. In this study, 119 diverse sesame genotypes were screened for their tolerance to 12, 24, 48, and 72 h of waterlogging relative to non-waterlogged conditions. All plants died under 72 h of waterlogging, while 13.45%, 31.93%, and 45.38% of genotypes survived at 48, 24, and 12 h, respectively. Based on the seedling parameters and waterlogging tolerance coefficients, genotypes BD-7008 and BD-6985 exhibited the highest tolerance to waterlogging, while BD-6996 and JP-01811 were the most sensitive ones. The responses of these four genotypes to waterlogged conditions were assessed at different plant growth stages-30, 40, and 50 days after sowing (DAS)-versus normal conditions. Waterlogging, particularly when it occurred within 30 DAS, destructively affected the physiological and morphological characteristics, which was reflected in the growth and yield attributes. Genotype BD-7008, followed by BD-6985, exhibited the highest chlorophyll and proline contents as well as enzymatic antioxidant activities, including superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT). These biochemical and physiological adjustments ameliorated the adverse effects of waterlogging, resulting in higher yields for both genotypes. Conversely, JP-01811 presented the lowest chlorophyll and proline contents as well as enzymatic antioxidant activities, resulting in the poorest growth and seed yield.

Assessment of heavy metals in farmed shrimp, Penaeus monodon sampled from Khulna, Bangladesh: An inimical to food safety aspects.

The analytical experiment was executed to present detailed reports on the concentration of heavy metals (nickel, iron, zinc, manganese, chromium, lead, and cadmium) in farmed shrimp, Penaeus monodon and its concomitant human health risks upon consumption. A total of 147 farms from six sub-districts of Khulna were selected for sample collection and concentration of heavy metals were determined by Atomic Absorption Spectrometry (AAS) method, following electro-thermal heater digestion. Ni and Cr were found considerably below the detectable limit (BDL) in four sub-districts, while Cr found in shrimps from Rupsa and Paikgacha were far higher than the maximum recommended limit defined by FAO and WHO. The average concentrations of Fe and Mn in all sub-districts crossed the recommendations, whereas average concentrations of Zn, Ni, Cd, and Pb were within the recommendations. Regardless of sampling site, target hazard quotients (THQ) of more than 1 contributed by Fe confirmed higher level of hazard index (HI), indicating potential human health risk. Fortunately, no heavy metal or their additive effect found to offer lifetime potency of carcinogenesis upon consumption of these shrimps. Therefore, probabilistic non-carcinogenic human health risk from Fe contamination necessitates stringent monitoring and controlling of this metal from different sources to farms.

Ichthyo-diversity assessment of the Old Brahmaputra river, Bangladesh: present stance and way forward.

The study was carried out to obtain information on the present status and trend of finfish diversity of the Old Brahmaputra river, Bangladesh. Samples were collected directly from a professional fishing boat caught by different nets, traps and hooks from January 2019 to December 2019. Together with 4 exotic species, a total of 49 species under 6 families were recorded. Though a biodiversity index of 3.65854 and a dominance index of 0.030929 represent the richness of ichthyo-diversity within the river, Synbranchiformes and Tetraodontiformes were not reported throughout the study period. Linear regression analysis showed a positive correlation between water height of the river and monthly abundance of the species found. Catch composition of catfishes and snakeheads slumped while barbs showed triumph over previous findings. A majority of fish recorded were within the least concern category according to IUCN (2015) but portions also belonged to critically endangered, endangered, and vulnerable categories as well. Therefore, conservation measures must be infixed in the Old Brahmaputra river to hold the fish diversity in a sustainable state.

A Vision-Based Machine Learning Method for Barrier Access Control Using Vehicle License Plate Authentication.

Automatic vehicle license plate recognition is an essential part of intelligent vehicle access control and monitoring systems. With the increasing number of vehicles, it is important that an effective real-time system for automated license plate recognition is developed. Computer vision techniques are typically used for this task. However, it remains a challenging problem, as both high accuracy and low processing time are required in such a system. Here, we propose a method for license plate recognition that seeks to find a balance between these two requirements. The proposed method consists of two stages: detection and recognition. In the detection stage, the image is processed so that a region of interest is identified. In the recognition stage, features are extracted from the region of interest using the histogram of oriented gradients method. These features are then used to train an artificial neural network to identify characters in the license plate. Experimental results show that the proposed method achieves a high level of accuracy as well as low processing time when compared to existing methods, indicating that it is suitable for real-time applications.

Operando Tracking of Solution-Phase Concentration Profiles in Li-Ion Battery Positive Electrodes Using X-ray Fluorescence.

The trade-off between energy density and power capabilities is a challenge for Li-ion battery design as it highly depends on the complex porous structures that holds the liquid electrolyte. Specifically, mass-transport limitations lead to large concentration gradients in the solution-phase and subsequently to crippling overpotentials. The direct study of these solution-phase concentration profiles in Li-ion battery positive electrodes has been elusive, in part because they are shielded by an opaque and paramagnetic matrix. Herein we present a new methodology employing synchrotron hard X-ray fluorescence to observe the concentration gradient formation within Li-ion battery electrodes in operando. This methodology is substantiated with data collected on a model LiFePO4/Li cell using a 1 M LiAsF6 in 1:1 ethylene carbonate/dimethyl carbonate (EC/DMC) electrolyte under galvanostatic and intermittent charge profiles. As such, the technique holds great promise for optimization of new composite electrodes and for numerical model validation.

The role of relative rate constants in determining surface state phenomena at semiconductor-liquid interfaces.

In this work, we present a theoretical study of surface state occupation statistics at semiconductor-liquid interfaces, as it pertains to the evolution of H2 and O2 through water splitting. Our approach combines semiclassical charge transport and electrostatics at the semiconductor-liquid junction, with a master rate equation describing surface state mediated electron/hole transfer. As a model system we have studied the TiO2-water junction in the absence of illumination, where it is shown that surface states might not always equilibrate with the semiconductor. Non-trivial electrostatics, for example including a shifting of the Mott-Schottky plateau in capacitive measurements, are explored when deep-level surface states partially equilibrate with the liquid. We also endeavor to explain observations of non-linearity present in Mott-Schottky plots, as they pertain to surface state occupation statistics. In general, it is intended that the results of this work will serve to further the use and development of quantitative device modeling techniques in the description of H2 evolution at semiconductor-liquid junctions.

Interfacial Screening in Ultrafast Voltammetry: A Theoretical Study of Redox-Active Monolayers.

The impact of interfacial screening on electron transfer (ET) at ultrashort time scales is theoretically investigated on redox active monolayers by linear sweep voltammetry (LSV). The charging current associated with the nanosecond screening process is an important experimental determinant in finding both the reorganization energy (λ) and electronic coupling (|M|) through ultrafast methods. On the one hand, time dependent decay of the charging current mitigates its impact on the current contribution from faradaic processes, while on the other hand, allowing substantial decay translates into a reduced upper-bound of applicable scan rates, which are crucial for ultrafast characterization. Analysis of the decay in the charging current suggests that the desired screening may be achieved for relatively weakly coupled systems within the charging time constant. For weakly coupled systems, the scan rate corresponding to nanoscale charging time constants appears to be suitable for the ultrafast investigation of ET characteristics. Moreover, the level of screening achieved at nanosecond decay times is shown to change with the coverage of electrode surface by monolayers; which appears to be accompanied by sharp drops in the time constant during successive saturation of interfacial layers by supporting ions (SI). These observations are expected to help design electrochemical device systems with interfaces capable of high faradaic efficiency at ultrafast limits.

Synthesis and characterization of ZnFe2O4 nanoparticles and its biomedical applications.

Biomedical applications of ZnFe2O4 nanoparticle are preferable among all kinds of ferrites due to the compatibility of Zn2+ ions for human bodies. We have followed the soft chemical route to synthesize chitosan and PEG coated ZnFe2O4 nanoparticles and also the chitosan-coated-nanoparticles encapsulated with liposome. X-ray diffraction studies by the Mo Kα target, showed the formation of single phase spinel structure. The lattice parameter turned out to be 8.48Å and grain size ~ 4.8 nm (± 0.1 nm). Similar particle size was observed by transmission electron microscope analysis. HRTEM studies showed the distinct lattice fringes thus confirming the good crystallinity of the synthesized nanoparticles. M-H curve at room temperature showed the prepared sample was superparamagnetic in nature, which is also confirmed by the doublets of Mössbauer spectroscopy. Relaxivity values (r2) of Chitosan and PEG coated ZnFe2O4 nanoparticles are 68 and 76 mM-1s-1 respectively. In order to achieve further biocompatibility the chitosan-coated-nanoparticles were encapsulated with liposome. The r2 relaxivity was found as 54mM-1s-1. MR images obtained from the in vitro experiments based on phantoms demonstrated good contrast enhancement. Induction heating of bare and coated particles was investigated to reveal the self heating temperature rising properties of ZnFe2O4 nanoparticles.

Determination of Elemental Composition of Malabar spinach, Lettuce, Spinach, Hyacinth Bean, and Cauliflower Vegetables Using Proton Induced X-Ray Emission Technique at Savar Subdistrict in Bangladesh.

The concentrations of 18 different elements (K, Ca, Fe, Cl, P, Zn, S, Mn, Ti, Cr, Rb, Co, Br, Sr, Ru, Si, Ni, and Cu) were analyzed in five selected vegetables through Proton Induced X-ray Emission (PIXE) technique. The objective of this study was to provide updated information on concentrations of elements in vegetables available in the local markets at Savar subdistrict in Bangladesh. These elements were found in varying concentrations in the studied vegetables. The results also indicated that P, Cl, K, Ca, Mn, Fe, and Zn were found in all vegetables. Overall, K and Ca exhibited the highest concentrations. Cu and Ni exhibited the lowest concentrations in vegetables. The necessity of these elements was also evaluated, based on the established limits of regulatory standards. The findings of this study suggest that the consumption of these vegetables is not completely free of health risks.

Study of microscopic origin of segregation for Fe(x)Cu(1-x) and Cu(x)Co(1-x) liquid binary alloys.

The segregating properties for FexCu1-x and CuxCo1-x liquid-liquid binary alloys are investigated theoretically. Here, the free energy of mixing is calculated by using the electronic theory of metals within the framework of the perturbative approach. The calculated results such as the critical temperature and the critical concentration agree well with the available experimental data. Most importantly, the present work confirms our previous finding [M. Mehedi Faruk and G. M. Bhuiyan, Physica B 422, 56 (2013)] that the volume dependent part of the energy of mixing is mostly responsible for segregation of metallic alloys.

Analgesic and anti-inflammatory activities of leaf extract of Mallotus repandus (Willd.) Muell. Arg.

In folk medicine Mallotus repandus (Willd.) Muell. Arg. is used to treat muscle pain, itching, fever, rheumatic arthritis, snake bite, hepatitis, and liver cirrhosis. This study aimed to evaluate the antinociceptive as well as the anti-inflammatory activities of the methanol extract of leaf. The leaves were extracted with methanol following hot extraction and tested for the presence of phytochemical constituents. Analgesic and anti-inflammatory activities were evaluated using acetic acid induced writhing test, xylene induced ear edema, cotton pellet induced granuloma, and tail immersion methods at doses of 500, 1000, and 2000 mg/kg body weight. The presence of flavonoids, saponins, and tannins was identified in the extract. The extract exhibited considerable antinociceptive and anti-inflammatory activities against four classical models of pain. In acetic acid induced writhing, xylene induced ear edema, and cotton pellet granuloma models, the extract revealed dose dependent activity. Additionally, it increased latency time in tail immersion model. It can be concluded that M. repandus possesses significant antinociceptive potential. These findings suggest that this plant can be used as a potential source of new antinociceptive and anti-inflammatory candidates. The activity of methanol extract is most likely mediated through central and peripheral inhibitory mechanisms. This study justified the traditional use of leaf part of this plant.