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Quality assessment is a cornerstone of fruit production and distribution, particularly regarding storage conditions and duration. Citrus fruits, a staple in global consumption patterns, are the ultimate example. This study employs a nondestructive analytical technique, X-ray computed tomography (CT) scanning, to meticulously analyze a substantial sample of 300 citrus fruits, specifically satsuma, subjected to both ambient (20–22 °C, 50–60% humidity) and refrigeration conditions (6–8 °C, 65–75% humidity). The experiment was conducted through a methodologically rigorous approach, stratified dataset splitting, allocating 60% of the X-ray datasets for training, with 20% dedicated to validation and testing, respectively. The proposed research introduces a pioneering methodology termed features enhanced vision transformer (FEViT), meticulously designed to augment precision in citrus fruit classification and more precise freshness level prediction via X-ray image analysis. Our empirical findings unequivocally demonstrate the superior efficacy of FEViT models vis-a-vis conventional ViT counterparts across new X-ray citrus fruit datasets. Particularly noteworthy are the marked performance gains exhibited by FEViT-large variants, evidenced by notable increases in precision (5.08%), accuracy (5.47%), recall (4.55%), and F1 scores (5.28%) over original variants. This underscores the distinguishable enhanced discriminatory prowess of FEViT models in assessing citrus fruit quality in terms of freshness. Extensive validation through rigorous experimentation ratifies FEViT's supremacy over traditional deep learning architectures, affirming heightened accuracy (99.25%). The current study heralds the advent of FEViT architecture as a milestone in citrus fruit (satsuma) freshness prediction, promising augmented accuracy and robustness vis-a-vis extant methodologies. This research holds profound implications for the agricultural sector, especially in domains such as citrus and broader fruit classification, where nuanced image analysis is indispensable for quality attribute like freshness evaluation and informed decision-making. [ABSTRACT FROM AUTHOR]

This research explores the incorporation of variable order (VO) fractional calculus into the hyperchaotic Lorenz system and studies various chaotic features and attractors. Initially, we propose a variable fractional order hyperchaotic Lorenz system and numerically solve it. The solutions are obtained for multiple choices of control parameters, and these results serve as reference solutions for exploring chaos with the artificial intelligence tool radial basis function network (RBFN). We rebuild phase spaces and trajectories of system states to exhibit chaotic behavior at various levels. To further assess the sensitivity of chaotic attractors, Lyapunov exponents are calculated. The efficacy of the designed computational RBFN is validated through the RMSE and extensive error analysis. The proposed research on AI capabilities aims to introduce an innovative methodology for modeling and analyzing hyperchaotic dynamical systems with variable orders. [ABSTRACT FROM AUTHOR]

Variability of the Surface Air Temperature (SAT) over the Western South Asia (WSA) region leads to frequent heatwaves during the early summer (May-June) season. The present study uses the European Centre for Medium-Range Weather Forecast's fifth-generation seasonal prediction system, SEAS5, from 1981 to 2022 based on April initial conditions (1-month lead) to assess the SAT predictability during early summer season. The goal is to evaluate the SEAS5's ability to predict the El Niño-Southern Oscillation (ENSO) related interannual variability and predictability of the SAT over WSA, which is mediated through upper-level (200-hPa) geopotential height anomalies. This teleconnection leads to anomalously warm surface conditions over the region during the negative ENSO phase, as observed in the reanalysis and SEAS5. We evaluate SEAS5 prediction skill against two observations and three reanalyses datasets. The SEAS5 SAT prediction skill is higher with high spatial resolution observations and reanalysis datasets compared to the ones with low-resolution. Overall, SEAS5 shows reasonable skill in predicting SAT and its variability over the WSA region. Moreover, the predictability of SAT during La Niña is comparable to El Niño years over the WSA region. [ABSTRACT FROM AUTHOR]

The quaternion linear canonical transform is considered as central piece in different fields like applied mathematics, engineering, computer sciences, and statistics. In this work, we have introduced the one-dimensional quaternion linear canonical transform, which is a generalization of the quaternion Fourier transform. We explore the characteristic function within the framework of quaternion algebra, elucidating its essential properties. Building on this foundation, we derive the expected value and variance, offering deeper insights into the probabilistic structures associated with the quaternions. [ABSTRACT FROM AUTHOR]

Freezing temperatures are a severe issue in North Florida, primarily due to occasional cold snaps and frost events in winter and early spring that cause damage to citrus groves, resulting in reduced fruit yield. The apoplasm is the primary cell component that interacts with environmental stress and is essential for plant tolerance to freezing temperatures. The present study was conducted to gain insight into how the application of silicon (Si) and potassium (K) are involved in the leaf apoplasm contributes to freezing stress tolerance, and regulates carbohydrate metabolism. We used Satsuma mandarin (Citrus unshiu Marc.), the most successful commercially grown citrus cultivar in North Florida, and treated trees with two concentrations of Si and K (50 and 100 ppm) both individually and combined as foliar spray to determine their effect as they relate to improving cold hardiness. Freezing stress (-6 °C) caused a severe reduction in photosynthesis, and modulations in leaf carbohydrate metabolism resulted in inhibited plant growth. The exogenous application of Si and K both improved the photosynthesis rate, soluble sugars, and activities of enzymes of carbohydrate metabolism such as fructokinase, phosphofructokinase, hexokinase, sucrose and phosphate synthase, and acid and neutral invertase. Applying Si (100 ppm) and combined treatment (Si + K-50) showed the best response by inducing the maximum tolerance to freeze stress. Our data demonstrated the ameliorative effect of Si and K under freezing stress in citrus is associated with modification in carbohydrate metabolism in the leaf apoplasm. This study provides direction for future research to investigate the effect of Si and K on the transcriptome and metabolome in citrus plants and their tissues under freezing stress. [ABSTRACT FROM AUTHOR]

Emerging Industry 5.0 designs promote artificial intelligence services and data-driven applications across multiple places with varying ownership that need special data protection and privacy considerations to prevent the disclosure of private information to outsiders. Due to this, federated learning offers a method for improving machine-learning models without accessing the train data at a single manufacturing facility. We provide a self-adaptive framework for federated machine learning of healthcare intelligent systems in this research. Our method takes into account the participating parties at various levels of healthcare ecosystem abstraction. Each hospital trains its local model internally in a self-adaptive style and transmits it to the centralized server for universal model optimization and communication cycle reduction. To represent a multi-task optimization issue, we split the dataset into as many subsets as devices. Each device selects the most advantageous subset for every local iteration of the model. On a training dataset, our initial study demonstrates the algorithm's ability to converge various hospital and device counts. By merging a federated machine-learning approach with advanced deep machine-learning models, we can simply and accurately predict multidisciplinary cancer diseases in the human body. Furthermore, in the smart healthcare industry 5.0, the results of federated machine learning approaches are used to validate multidisciplinary cancer disease prediction. The proposed adaptive federated machine learning methodology achieved 90.0%, while the conventional federated learning approach achieved 87.30%, both of which were higher than the previous state-of-the-art methodologies for cancer disease prediction in the smart healthcare industry 5.0. [ABSTRACT FROM AUTHOR]

Dysprosium doped Ni0.50Co0.50DyxFe2−xO4 (x = 0.25, 0.50, 0.75) was synthesized via auto combustion of nitrate citrate gel. The prepared composites were given microwave plasma treatment to improve their dielectric properties for constructing high-frequency devices. X-ray diffraction analysis confirmed the production of Dy-doped cubic spinel NiCo ferrites. The average crystallite size for the untreated magnetic composite was 32.10 nm, while for plasma-treated samples was 25.17 nm. A growth in lattice parameters and decreased porosity was observed in plasma-treated composites. VSM findings revealed a decrease in saturation magnetization from 54.15 to 36.75 emu/g with an increase in Dy3+ from 0.25 to 0.50%. Saturation magnetization again raised with increasing Dy3+ content from 0.50 to 0.75%. The lower Dy3+ concentration resulted in low saturation magnetization, while the high Dy3+ concentration showed high saturation magnetization of the composite. Temperature and frequency-dependent dielectric characteristics revealed bulbous enrichment in the dielectric constant and significant Q factor for plasma-treated samples. The perceived deviation in dielectric loss with frequency change is credited to the conduction phenomenon, which agrees with Koop's phenomenological theory. High dielectric constant and low dielectric loss, even at higher temperatures, enable the synthesized ferrites to be strong candidates for high-frequency devices. [ABSTRACT FROM AUTHOR]

In the current study, samples were taken from the sewage treatment plant to isolate rubber-degrading microbial strains. The sludge samples were screened for rubber degradation microbes and later on, each strain was separately characterised for rubber degradation. Initially, strain BA23 showed half the rubber degradation ratio in mineral salt medium. The isolated microbial strains were identified based on 16SrRNA gene sequence analysis, morphological and biochemical characteristics and named Brucella sp. belonging to the genus Bacillus. Later on, the medium conditions for strain BA23 were optimised based on carbon, nitrogen sources, pH, temperature and incubation period. After optimising various environmental and nutritional conditions, the rubber degradation increased twice when inorganic nitrogen sources (aammonium nitrate) used 1 g/L in the medium at pH 7.5. Furthermore, the structural changes that occurred in the rubber polymer associated with the rubber polymer were analysed through Fourier transform infrared spectroscopy (FTIR) and the breakdown of important functional groups such as aldehyde, ketone ester and carboxylic acid was observed. Finally, the gravimetric weight loss of CO2 during rubber degradation was 42.88 g/L, showing CO2 evolution greatly when the rubber degradation occurred in the mineral salt medium. It was concluded that strain BA23 has a maximum potential for rubber degradation when it is used for future solid waste management of rubber and its products for a long period in the environment. [ABSTRACT FROM AUTHOR]

The theory of quaternions has gained firm ground in recent times and is being widely explored, with the field of signal and image processing being no exception. However, many important aspects of quaternionic signals are yet to be explored, particularly the formulation of generalized sampling expansions (GSE). In the present article, our aim is to formulate the GSE in the realm of a one-dimensional quaternion linear canonical transform. To facilitate the intent, we construct a set of quaternionic filter functions which are used to construct a system of equations determining the synthesis functions for the process of reconstruction. Besides, as a special case, another sampling formula involving the derivatives of the quaternionic signal is also obtained in the sequel. The proposed method not only expands our understanding of quaternionic signal processing but also holds promising implications for various applications in the field. As an endorsement of the obtained results, an example with simulations demonstrating the signal reconstruction is presented at the end. [ABSTRACT FROM AUTHOR]

Plastics are one of the best innovations of twenty-first century serving the humanity in all sectors of life such as agriculture, packaging, automotive, construction and healthcare. With ever increasing demand and ease of processability, plastic production is increasing at a rapid pace thus piling up huge amount of waste in the environment. Lack of infrastructure for plastic waste management, poorly managed recycling facilities and technologies, in-efficient waste collection systems, wrong and unrestrained disposal practices and lack of awareness contributes towards making plastic waste management an open challenge and a threat to the global environment. Previously, the best suited available strategies for plastic waste management include incineration, landfilling, and recycling and reuse, but they were not considered sufficient to tackle the large volume of the problem. Due to lack of infrastructure and management about 80% of the globally produced plastic had not been recycled in any form. Furthermore, the high cost associated with the recycling regarding sorting and processing makes recycling economically least feasible and non-sustainable. The resource circularity of plastic waste includes depolymerization of the waste polymer at the end-of-life cycle into the monomers followed by bio-upcycling to another value-added product thus entering into another life cycle. Talking about the economic validity and feasibility of bio-upcycling technology of plastic waste to produce value-added products, bio-upcycling is considered as a promising strategy as it involves transformation of depolymerized monomers from any route, may it be chemical or biological transformation, into the value-added products using endogenous or engineered metabolic pathways of already existing biological systems. In this review, bio-upcycling of plastic waste as a solution to the current limitations of traditional plastic waste management practices was discussed. Moreover, the biotechnological routes for the upcycling of plastic wastes into value-added products and economic circularity of the polymers were also described. Furthermore, the opportunities and challenges for promoting the circularity of plastic waste were also discussed. [ABSTRACT FROM AUTHOR]

A fully-coupled general circulation model of intermediate complexity is documented. The study presents an overview of the model climatology and variability, with particular attention to the phenomenology of processes that are relevant for the predictability of the climate system on seasonal-to-decadal time-scales. It is shown that the model can realistically simulate the general circulation of the atmosphere and the ocean, as well as the major modes of climate variability on the examined time-scales: e.g. El Niño-Southern Oscillation, North Atlantic Oscillation, Tropical Atlantic Variability, Pacific Decadal Variability, Atlantic Multi-decadal Variability. Potential applications of the model are discussed, with emphasis on the possibility of generating sets of low-cost large-ensemble retrospective forecasts. We argue that the presented model is suitable to be employed in traditional and innovative model experiments that can play a significant role in future developments of seasonal-to-decadal climate prediction. [ABSTRACT FROM AUTHOR]

China has changed its focus from traditional high-speed economic growth to high-quality economic development (HQED) and the implementation of environmentally friendly practices. This transition can have parallel or unparallel impacts on energy insecurity (EIS). In this regards, HQED, inter Alia, is crucial in mitigating EIS and combating the energy crisis. Our study explores the impact of economic growth (EG) and HQED on EIS using the provincial panel data of China for the period 2011–2017. From the perspective of comparative analysis, the results reveal that HQED reduces EIS while EG increases it. The robustness checks indicate that industrial structure (IS) has a negative impact on EIS, whereas industrial structure upgrading (ISU) and green innovation (GI) have a positive influence. This implies that IS contributes to an increase in EIS, whereas ISU and GI result in a decrease in EIS. In addition, the analysis reveals that digital financial inclusion (DFI) exhibits a significant positive relation with EIS, albeit occasionally a negative but insignificant link. The policy implication is that the government should stimulate policies to promote HQED which reduces the EIS. [ABSTRACT FROM AUTHOR]

A kidney stone is a solid formation that can lead to kidney failure, severe pain, and reduced quality of life from urinary system blockages. While medical experts can interpret kidney-ureter-bladder (KUB) X-ray images, specific images pose challenges for human detection, requiring significant analysis time. Consequently, developing a detection system becomes crucial for accurately classifying KUB X-ray images. This article applies a transfer learning (TL) model with a pre-trained VGG16 empowered with explainable artificial intelligence (XAI) to establish a system that takes KUB X-ray images and accurately categorizes them as kidney stones or normal cases. The findings demonstrate that the model achieves a testing accuracy of 97.41% in identifying kidney stones or normal KUB X-rays in the dataset used. VGG16 model delivers highly accurate predictions but lacks fairness and explainability in their decision-making process. This study incorporates the Layer-Wise Relevance Propagation (LRP) technique, an explainable artificial intelligence (XAI) technique, to enhance the transparency and effectiveness of the model to address this concern. The XAI technique, specifically LRP, increases the model's fairness and transparency, facilitating human comprehension of the predictions. Consequently, XAI can play an important role in assisting doctors with the accurate identification of kidney stones, thereby facilitating the execution of effective treatment strategies. [ABSTRACT FROM AUTHOR]

This manuscript describes the synthesis of three new selenium vinyl/allyl adducts (IV–VI), which incorporate imidazolium- N-heterocyclic carbene salts (I–III) are. The characterization was carried out using NMR spectroscopy and elemental analysis. Particularly, for adduct IV, its structural elucidation was further validated through X-ray diffraction. The vinyl/allyl compounds (I–VI) were evaluated toward two different bacterial strains Escherichia coli and staphylococcus aureus. Salt III shows a MIC = 62.5 µg/ mL and its respective adducts VI shows MIC = 31.2 µgmL−1 against Escherichia coli. All the compounds (I–VI) exhibited significant efficacy against S. aureus with MIC value in the range of 31.2–250 µgmL−1. The highest activity is shown by compound VI i-e 31.2 µg/ mL. All the compounds (I–VI) have thrombolytic potential (65.2 ± 0.89–88.2 ± 0.11%), suggesting their viability as effective agents for thrombolysis. Hemolysis showed cytotoxicity in the range of 2.87 ± 0.12 to 5.49 ± 0.28% which demonstrated they have not injurious effect on health and thus safe. [ABSTRACT FROM AUTHOR]

Plant yield and productivity are being impacted significantly by global climate change. Since water scarcity is one of the main risks to agriculture's future, developing plants that can withstand water stress is crucial. The peach is an essential fruit crop for examining how plants respond to drought stress, which lowers crop performance through yield and quality losses. Melatonin, a versatile, beneficial stress-relieving hormone, can be used to alter how plants respond to environmental stresses. The current investigation examined how melatonin may improve early peach seedlings' resistance to drought stress. Our findings revealed a significant decrease in plant biomass and photosynthetic activity and increased oxidative damage under drought stress. Melatonin application promotes plant development and reduces oxidative damage by increasing photosynthetic activity and chlorophyll contents. Melatonin also increases soluble sugars and proline contents. Furthermore, an increase in the activities of superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), and ascorbate peroxidase (APX) was also recorded in peach seedlings. According to the results of our investigation, melatonin applied at a concentration of 100 µM had the highest effect on increasing plant survival under drought stress and contributed to the development of a practical approach for peaches under drought stress conditions. [ABSTRACT FROM AUTHOR]

The brain-derived neurotrophic factor (BDNF) involves stress regulation and psychiatric disorders. The Val66Met polymorphism in the BDNF gene has been linked to altered protein function and susceptibility to stress-related conditions. This in silico analysis aimed to predict and analyze the consequences of the Val66Met mutation in the BDNF gene of stressed individuals. Computational techniques, including ab initio, comparative, and I-TASSER modeling, were used to evaluate the functional and stability effects of the Val66Met mutation in BDNF. The accuracy and reliability of the models were validated. Sequence alignment and secondary structure analysis compared amino acid residues and structural components. The phylogenetic analysis assessed the conservation of the mutation site. Functional and stability prediction analyses provided mixed results, suggesting potential effects on protein function and stability. Structural models revealed the importance of BDNF in key biological processes. Sequence alignment analysis showed the conservation of amino acid residues across species. Secondary structure analysis indicated minor differences between the wild-type and mutant forms. Phylogenetic analysis supported the evolutionary conservation of the mutation site. This computational study suggests that the Val66Met mutation in BDNF may have implications for protein stability, structural conformation, and function. Further experimental validation is needed to confirm these findings and elucidate the precise effects of this mutation on stress-related disorders. [ABSTRACT FROM AUTHOR]

This study proposes a novel, efficient and a secured multiple color images encryption scheme based on the random walk of knight in a virtual 3D chessboard, chaotic system and SHA-256 hashing. First of all, a perfect square number of color images are input. These images are decomposed into their red, green and blue components. These components are stacked together to get a 3D image. Three intensity values from this 3D image are randomly chosen. These values serve as the starting address for the knight to start its scrambling project. This act increases both the key space and plaintext sensitivity. Next, this 3D image is converted into 1D image for the scrambling process. Knight — a chess piece, has been commissioned to walk randomly in the 3D chessboard in all the three coordinate planes for an arbitrary number of times. The movement of knight in the 3D chessboard has been linked with the transferring of pixels from the 1D image to the scrambled 3D image. In this way, the input images are abundantly scrambled. Lastly, an XOR operation has been carried out to throw the diffusion effects. The three chaotic vectors given by the intertwining logistic map have been used in the selection of planes, mode of movement of knight and the diffusion process. SHA-256 hash codes for the given input color images have been used for the plaintext sensitivity. Security analyses and the computer experiments suggest the desirable security effects and prospects for some real world application of the proposed cipher. Information entropy obtained by the proposed algorithm is 7.9999. [ABSTRACT FROM AUTHOR]

The present investigation has centered on examining the imp act of vegetation-based management strategies on the socio-economic, physical, and ecological aspects of the Aghsu watershed in Golestan province, with a particular emphasis on mitigating soil erosion and flooding issues. This study implemented four biological interventions (namely contour furrows with seedling planting, reforestation, channel terraces with tree planting, and agroforestry) and identified 16 management scenarios based on the managerial issues of the area. To establish a hierarchy of management scenarios, the techniques of TOPSIS, SAW, linear assignment, and VIKOR were employed, in conjunction with four distinct weighting methodologies (namely, Shannon Entropy, AHP, ANP, and adjusted weight). The Borda method has been employed to amalgamate the ranking of the suggested technique. Based on the results obtained from all proposed methods for prioritizing scenarios, it has been confirmed that "Scenario 1" holds the lowest priority. The findings indicate that "Scenario 3" (Agroforestry) and "Scenario 6" (Agroforestry and Contour furrows with planting of seedling) represent the optimal scenario groups. The findings indicate that the amalgamation of two scenarios and scenarios that incorporate Agroforestry exhibit superior outcomes. The implementation of agroforestry is a noteworthy endeavor that warrants heightened attention, with other management pursuits being regarded as secondary priorities. Moreover, it can be asserted that the precise weighting of evaluation criteria plays a crucial role in prioritizing management scenarios. The precise computation of weights is imperative. The utilization of a scenario-based methodology in this study facilitates the prediction of the potential outcomes of biologic management interventions. The utilization of this methodology is postulated to enable watershed planners and stakeholders to select the optimal scenario from the proposed set of management scenarios. [ABSTRACT FROM AUTHOR]

The current research was aimed to examine the NLO properties of novel benzodithiophene-based donor–acceptor (D-A)-type compounds (BDTD1–BDTD8) via structural modulation of reference compound (BDTR). Optimization of the reference compound (BDTR) along with eight derivatives was accomplished at M06/6-311G(d,p) level. Subsequently, the optimized geometries were further employed to execute additional analyses: UV–Vis absorption, natural population analysis (NPA), NLO, frontier molecular orbitals (FMOs) and natural bond orbital (NBO) properties at the above-mentioned functional. All the tailored compounds (BDTD1–BDTD8) were reported with less energy difference in comparison with BDTR. The descending order of compounds accordant to Egap values was found to be as BDTR > BDTD1 > BDTD8 > BDTD4 > BDTD2 > BDTD5 > BDTD7 > BDTD6 > BDTD3. Furthermore, assisted by FMOs analysis, density of states (DOS) computations demonstrated significant charge mobility from HOMO towards LUMO in the derivatives. Global reactivity descriptors correspond to Egap values, BDTD3 with least energy difference accompanied with less hardness (1.028 eV) and highest softness (0.486 eV) among all the derivatives. BDTD3 exhibited the highest value of λmax (717.211 nm) in all the designed compounds relative to BDTR (576.161 nm). For all fabricated chromophores, the βtot values are presented in the decreasing order: BDTD3 > BDTD7 > BDTD6 > BDTD5 > BDTD2 > BDTD4 > BDTD1 > BDTD8 > BDTR. Interestingly, the highest values as 2.324 × 10–27 and 4.302 × 10–32 esu of βtot and γtot, respectively, were indicated by BDTD3 compound. Valuable NLO findings explored that our derivatives might be significant candidates for nonlinear optics. [ABSTRACT FROM AUTHOR]

Brain-Derived Neurotrophic Factor (BDNF) is a neurotrophin gene family gene that encodes proteins vital for the growth, maintenance, and survival of neurons in the nervous system. The study aimed to screen natural compounds against BDNF variant (V66M), which affects memory, cognition, and mood regulation. BDNF variant (V66M) as a target structure was selected, and Vitamin D, Curcumin, Vitamin C, and Quercetin as ligands structures were taken from PubChem database. Multiple tools like AUTODOCK VINA, BIOVIA discovery studio, PyMOL, CB-dock, IMOD server, Swiss ADEMT, and Swiss predict ligands target were used to analyze binding energy, interaction, stability, toxicity, and visualize BDNF-ligand complexes. Compounds Vitamin D3, Curcumin, Vitamin C, and Quercetin with binding energies values of − 5.5, − 6.1, − 4.5, and − 6.7 kj/mol, respectively, were selected. The ligands bind to the active sites of the BDNF variant (V66M) via hydrophobic bonds, hydrogen bonds, and electrostatic interactions. Furthermore, ADMET analysis of the ligands revealed they exhibited sound pharmacokinetic and toxicity profiles. In addition, an MD simulation study showed that the most active ligand bound favorably and dynamically to the target protein, and protein–ligand complex stability was determined. The finding of this research could provide an excellent platform for discovering and rationalizing novel drugs against stress related to BDNF (V66M). Docking, preclinical drug testing and MD simulation results suggest Quercetin as a more potent BDNF variant (V66M) inhibitor and forming a more structurally stable complex. [ABSTRACT FROM AUTHOR]

To meet the rising requirement of photovoltaic compounds for modernized hi-tech purpose, we designed six new molecules (DTPD1-DTPD6) from banana shaped small fullerene free chromophore (DTPR) by structural tailoring at terminal acceptors. Frontier molecular orbitals (FMOs), density of states (DOS), open circuit voltage (Voc), transition density matrix (TDM) analysis, optical properties, reorganization energy value of hole and electron were determined utilizing density function theory (DFT) and time-dependent density function theory (TD-DFT) approaches, to analyze photovoltaic properties of said compounds. Band gap contraction (∆E = 2.717–2.167 eV) accompanied by larger bathochromic shift (λmax = 585.490–709.693 nm) was observed in derivatives contrary to DTPR. The FMOs, DOS and TDMs investigations explored that central acceptor moiety played significant role for charge transformation. The minimum binding energy values for DTPD1-DTPD6 demonstrated the higher exciton dissociation rate with greater charge transferal rate than DTPR, which was further endorsed by TDM and DOS analyses. A comparable Voc (1.49–2.535 V) with respect to the HOMOPBDBT–LUMOacceptor for entitled compounds was investigated. In a nutshell, all the tailored chromophores can be considered as highly efficient compounds for promising OSCs with a good Voc response. [ABSTRACT FROM AUTHOR]

Glycolate oxidase (GLO) is an FMN-containing enzyme localized in peroxisomes and performs in various molecular and biochemical mechanisms. It is a key player in plant glycolate and glyoxylate accumulation pathways. The role of GLO in disease and stress resistance is well-documented in various plant species. Although studies have been conducted regarding the role of GLO genes from spinach on a microbial level, the direct response of GLO genes to various stresses in short-season and leafy plants like lettuce has not been published yet. The genome of Lactuca sativa cultivar 'Salinas' (v8) was used to identify GLO gene members in lettuce by performing various computational analysis. Dual synteny, protein–protein interactions, and targeted miRNA analyses were conducted to understand the function of GLO genes. The identified GLO genes showed further clustering into two groups i.e., glycolate oxidase (GOX) and hydroxyacid oxidase (HAOX). Genes were observed to be distributed unevenly on three chromosomes, and syntenic analysis revealed that segmental duplication was prevalent. Thus, it might be the main reason for GLO gene diversity in lettuce. Almost all LsGLO genes showed syntenic blocks in respective plant genomes under study. Protein–protein interactions of LsGLO genes revealed various functional enrichments, mainly photorespiration, and lactate oxidation, and among biological processes oxidative photosynthetic carbon pathway was highly significant. Results of in-depth analyses disclosed the interaction of GLO genes with other members of the glycolate pathway and the activity of GLO genes in various organs and developmental stages in lettuce. The extensive genome evaluation of GLO gene family in garden lettuce is believed to be a reference for cloning and studying functional analyses of GLO genes and characterizing other members of glycolate/glyoxylate biosynthesis pathway in various plant species. [ABSTRACT FROM AUTHOR]

In this article, we report the synthesis of graphene quantum dots (GQDs) by hydrothermal method and surface modified CdS quantum dots (QDs) via the colloidal method and the fabrication of their dyad. The CdS QDs functionalized by mercaptoacetic acid (MAA) attach to the GQDs via electrostatic interactions. Spectral overlapping between the emission spectrum of GQDs and the absorption spectrum of CdS QDs allows efficient Förster resonance energy transfer (FRET) from GQDs to the CdS QDs in the GQDs-CdS QDs dyads. The magnitude of FRET efficiency (E) and the rate of energy transfer (kE) assessed by the photoluminescence (PL) decay kinetics are ~61.84% and ⁓3.8 × 108 s− 1, respectively. These high values of FRET efficiency and energy transfer rate can be assigned to the existence of strong electrostatic interactions between GQDs and CdS QDs, which arise due to the presence of polar functionalities on the surface of both GQDs and CdS QDs. The understanding of energy transfer in the luminescent donor-acceptor FRET system is of significant importance and the practical implications of such FRET systems could overall improve the efficiency of photovoltaics, sensing, imaging and optoelectronic devices. [ABSTRACT FROM AUTHOR]

Researchers have paid increasing attention towards the usage of scrambled image for the image encryption in recent years. 2D and 3D scrambled images have already been employed to write the different image encryption algorithms. On the other hand, we need speedy and efficient ciphers to meet the rising demands of our society. The current study has used 1D scrambled image to develop a novel and a speedy image encryption algorithm. As the gray scale image is given to the encryption algorithm, its pixels are inserted randomly to the different positions of the scrambled image. This act has been iterated for a number of times. To throw the diffusion effects in the proposed cipher, Exclusive-OR (XOR) operation has been carried out. 2D logistic map has been selected for the chaotic vectors. First vector served the purpose of scrambling and the second one, the purpose of diffusion. The average time taken by the proposed image cipher is 0.0997 seconds, the information entropy of the cipher image came out to be 7.9975 and the number of pixels change rate (NPCR) is 99.6048%. Simulation and an exhaustive performance analyses depict that the proposed scheme has the desirable security effects and has the prospects for some real world application. [ABSTRACT FROM AUTHOR]

We apply the Lagrangian-based moisture back trajectory method to two reanalysis datasets to determine the moisture sources for wet season precipitation over the Arabian Peninsula, defined as land on the Asian continent to the south of the Turkish border and west of Iran. To accomplish this, we make use of the evaporative source region between 65°W–120°E and 30°S–60°N, which is divided into twelve sub-regions. Our comparison of reanalyses and multiple observations allows us to validate datasets and highlight broad-scale similarities in characteristics, notwithstanding some inconsistencies in the southwest AP. The results indicate north-to-south spatiotemporal heterogeneity in the characteristics of dominant moisture sources. In the north, moisture for precipitation is mainly sourced from midlatitude land and water bodies, such as the Mediterranean and Caspian Seas. Areas further south are dependent on moisture transport from the Western Indian Ocean and parts of the African continent. The El Niño-Southern Oscillation (ENSO) exhibits an overall positive but sub-seasonally varying influence on the precipitation variability over the region, with noticeable moisture anomalies from all major source regions. A significant drying trend exists over parts of the Peninsula, which both reanalyses partially attribute to anomalies in the moisture advection from the Congo Basin and South Atlantic Ocean. However, considerable uncertainty in evaporation trends over the terrestrial evaporative sources in observations warrants additional modeling studies to further our understanding of key processes contributing to the negative trends. [ABSTRACT FROM AUTHOR]

Over the satellite era, Antarctic sea ice exhibited an overall long-term increasing trend, contrary to the Arctic reduction under global warming. However, the drastic decline of Antarctic sea ice in 2014–2018 raises questions about its interannual and decadal-scale variabilities, which are poorly understood and predicted. Here, we identify an Antarctic sea ice decadal oscillation, exhibiting a quasi-period of 8–16 years, that is anticorrelated with the Pacific Quasi-Decadal Oscillation (r = −0.90). By combining observations, Coupled Model Intercomparison Project historical simulations, and pacemaker climate model experiments, we find evidence that the synchrony between the sea ice decadal oscillation and Pacific Quasi-Decadal Oscillation is linked to atmospheric poleward-propagating Rossby wave trains excited by heating in the central tropical Pacific. These waves weaken the Amundsen Sea Low, melting sea ice due to enhanced shortwave radiation and warm advection. A Pacific Quasi-Decadal Oscillation-based regression model shows that this tropical-polar teleconnection carries multi-year predictability.A decadal-scale oscillatory pattern is identified that is a dominant mode of Antarctic sea ice variability. This mode is primarily driven by tropical-polar connections, offering insights into the multi-year predictability of Antarctic sea ice. [ABSTRACT FROM AUTHOR]

The monsoons in Pakistan have been exceptionally harsh in recent decades, resulting in extraordinary drought conditions and record flooding events. The changing characteristics of extreme events are widely attributed to climate change. However, given this region's long history of floods and droughts, the role of natural climate variability cannot be rejected without a careful diagnosis. Here, we examine how oceanic and atmospheric variability has contributed to unusual precipitation distributions in West South Asia. Variations in sea surface temperatures in the tropical Pacific and northern Arabian Sea, and internal atmospheric variability related to the circumglobal teleconnection pattern and the subtropical westerly jet stream, explain more than 70% of monthly summer precipitation variability in the 21st century. Several of these forcings have co-occurred with record strength during episodes of extreme monsoons, which have exacerbated the overall effect. Climate change may have contributed to increased variability and the in-phase co-occurrences of the identified mechanisms, but further research is required to confirm any such connection. [ABSTRACT FROM AUTHOR]

Indole-3-acetic acid (IAA) is an important plant hormone required for various plant growth and developmental activities. YUCCA proteins are the enzymes for IAA synthesis that catalyze the rate-limiting step. However, the YUCCA gene family in Japanese apricot has not been thoroughly characterized. Here, we investigated the effect of rootstocks on scion growth of Japanease apricot and genes involved in auxin biosynthesis (YUCCA). Growth characteristics revealed that the height of 'Longyan' [Prunus mume Sieb. et Zucc] plants with P. mume rootstock were significantly shorter than 'Longyan' plants with P. persica rootstock. This study isolated 13 PmYUCCA genes from the Japanese apricot genome. Bioinformatics and expression studies were undertaken to investigate the functions and characteristics of YUCCA genes. The physicochemical properties, gene structure, conserved domains, conserved motif, and phylogenetic relationships of YUCCA genes were determined using bioinformatics analysis. Likewise, we analyzed the expression of PmYUCCA genes in Japanese apricot's leaf and stem-bark (scion). The expression levels of PmYUCCA3 and PmYUCCA5 genes were noticeably lower in the leaves of 'Longyan'/P. mume graft combination than that of the 'Longyan'/P. persica graft combinations, suggesting their distinct roles in regulating growth vigor. This research explores the genome-wide identification, characterization, and possible relationship between growth vigor and expression profile analysis of the YUCCA gene family in Japanese apricot. [ABSTRACT FROM AUTHOR]

Insects are incredibly successful and diverse organisms, but they also pose a significant threat to agricultural crops, causing potential losses of up to US$470 billion. Among these pests, Plutella xylostella (Linnaeus), a devastating insect that attacks cruciferous vegetables, alone results in monetary losses of around US$4–5 billion worldwide. While insecticides have effectively protected plants under field conditions, their use comes with various environmental and mammalian hazards. Additionally, insects are developing resistance to commonly used insecticides, rendering management strategies less effective. Arthropods employ a range of behavioral and biochemical mechanisms to cope with harmful chemicals, which contribute to the development of resistance. Understanding these mechanisms is crucial for addressing the issue of resistance. It is imperative to integrate strategies that can delay the development of resistance and enhance the efficiency of insecticides. Therefore, we present an overview of insecticide resistance in insects, focusing on P. xylostella, to provide insights into the current resistance status of this pest and propose tactics that can improve the effectiveness of insecticides. [ABSTRACT FROM AUTHOR]

Fullerene free organic chromophores are widely utilized to improve the efficacy of photovoltaic materials. Herein, we designed D-π-A-π-D form chromophores (TAZD1-TAZD5) via end-capped redistribution of donor moieties by keeping the same π-bridge and central acceptor unit for organic solar cells (OSCs). To analyze the photovoltaic characteristics of these derivatives, DFT estimations were accomplished at B3LYP/6–311 G (d,p) functional. Different investigations like frontier molecular orbital (FMO), absorption spectra (UV–Vis), density of states (DOS), binding energy (Eb), open circuit voltage (Voc), and transition density matrix (TDMs) were performed to examine the optical, photophysical and electronic characteristics of afore-mentioned chromophores. A suitable band gap (∆E = 2.723–2.659 eV) with larger bathochromic shift (λmax = 554.218–543.261 nm in acetonitrile) was seen in TAZD1-TAZD5. An effective charge transference from donor to acceptor via spacer was observed by FMO analysis which further supported by DOS and TDM. Further, lower binding energy values also supported the higher exciton dissociation and greater CT in TAZD1-TAZD5. Among all the designed chromophores, TAZD5 exhibited the narrowest Egap (2.659 eV) and maximum red-shifted absorption in solvent as well as gas phase i.e. 554.218 nm and 533.219 nm, respectively which perhaps as a result of the phenothiazine-based donor group (MPT). In a nutshell, all the tailored chromophores can be considered as efficient compounds for promising OSCs with a good Voc response, interestingly, TAZD5 is found to be excellent chromophores as compared to all these designed compounds. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the mass change, mechanical (tensile bond strength, shore A hardness), antifungal properties, and biocompatibility of lignin-modified denture soft liner. Lignin was mixed in concentrations of 0.5, 1, and 2% with a silicone based denture liner (SBDSL), and was labeled as groups L1, L2, and L3 respectively while commercial SBDSL was rendered as control group C. To calculate the tensile bond strength, bar-shaped specimens were placed in a Universal Testing Machine under a tensile force. Tensile bond strength was calculated in MPa using the formula F/A. For Shore, A hardness, disc-shaped specimens were analyzed using a durometer. The mass change (%) of specimens (n = 5) was calculated by subtracting the initial weight of the samples (w1) from the weight of samples after water immersion (w2). The antifungal potential of lignin and the test specimens was measured using agar well and disc diffusion methods. Cell viability assay of SBDSL and their composite was performed on NIH 3T3 fibroblasts. The mean tensile bond strength values of control group C samples were significantly higher (p ≤ 0.05) than groups L1, L2, and L3. The mean shore A hardness values were significantly lower (p ≤ 0.05) for samples in groups L2 and L3 as compared to control group C. After 10 weeks, the control group C samples showed a significantly lower (p ≤ 0.05) mass change as compared to groups L2 and L3. Agar well diffusion methods demonstrated a maximum zone of inhibition for 2% lignin (22.3 ± 1.1 mm), after 24 h. Disc diffusion methods showed a significantly higher (p ≤ 0.05) antifungal potential of groups L2 and L3 as compared to control group C. The addition of a lower concentration of lignin increased the cell viability of SBDSL. In general, the addition of lignin in SBDSL improve the antifungal potential, and cell viability while hardness, tensile bond strength and percent mass change were decreased. [ABSTRACT FROM AUTHOR]

In the last two decades, environmental degradation has been a topic of concern. The rising level of CO2 emissions (CO2E) has adversely affected life in the E7 countries, which comprise of Brazil, China, India, Indonesia, Mexico, Russia, and Turkey. The increased in CO2E is the cause of rising sea levels in the E7 countries. Visibly, E7 nations which are considered as the largest emitters of CO2 are facing the most severe environmental challenges. This study investigates the impact of eco-innovation, economic growth (EG), renewable energy consumption (REC), economic risk (ERI), and globalization on the CO2E, using the Feasible Generalized Lease Squares (FGLS) and Panel Corrected Standard Errors (PCSE) techniques for the period 1995 to 2018. The results indicate an inverted N-shaped relationship between eco-innovation and CO2E. Also, eco-innovation, REC, and economic risk are observed to be significant factors in abating CO2 emissions. On the contrary, globalization and GDP are responsible for rising CO2E in E7 countries. According to empirical estimates, eco-innovation improves the efficiency of carbon emissions, which lowers CO2E. In addition, because they are immune to changes in the price of oil and gas and disruptions brought about by geopolitical events, renewable energy sources can offer countries a more secure energy source than fossil fuels. Alternative energy sources can reasonably cut CO2E while offering a more reliable and secure energy source. Therefore, it is crucial that policies be put in place to cut CO2E by giving priority to environmental innovative policies. [ABSTRACT FROM AUTHOR]

This study aimed to develop a new dental adhesive based on the nanocrystalline cellulose (NCC) along with silica in the resin matrix. Both filler components, including NCC and silica, were synthesized and characterized using various analytical techniques such as Fourier transform infrared spectroscopy, scanning electron microscope, X-ray diffraction, thermographic analysis, and cell viability. The fillers showed structural stability and cytocompatibility was achieved. After characterization, the fillers were added to the resin matrix in different concentrations (0–5 wt%) to prepare the dental adhesive. The prepared dental adhesives were evaluated by Vicker's microhardness, biaxial flexural strength, flexural modulus, and shear bond strength. By adding NCC in the dental adhesives, the mechanical properties increased substantially. Nanocrystalline cellulose alone and in combination with silica filler has been investigated first time to improve the properties of dental adhesive. [ABSTRACT FROM AUTHOR]

The first aim of study was to quantify the viral load in the wastewater samples by RT-qPCR testing in Lahore population to estimate the number of patients affected and predict the next resurgence of COVID-19 wave in the city. The second aim of the study was to determine the hotspot areas of Lahore which remained positive more often for virus with high viral load. In this study, n = 420 sewage samples were collected on an average of two weeks intervals from 30 different sewage water disposal stations (14 sampling events) from Sept 2020 to March 2021. RNA was extracted and quantified by RT-qPCR without concentrating the virus in samples. Number of positive disposal sites (7–93%), viral load from sewage samples (100.296 to 103.034), and estimated patients (660–17,030) ranged from low to high according to the surge and restrain of 2nd and 3rd COVID-19 waves in the country. The viral load and estimated patients were reported high in January 2021 and March 2021 which were similar to the peak of 2nd and 3rd waves in Pakistan. Site 18 (Niaz Baig village DS) showed the highest viral load among all sites. Findings of the present study helped to estimate the number of patients and track the resurgence in COVID-19 waves in Lahore particularly, and in Punjab generally. Furthermore, it emphasizes the role of wastewater-based epidemiology to help policymakers strengthen the quarantine measures along with immunization to overcome enteric viral diseases. Local and national stake holders should work in collaboration to improve the environmental hygiene to control the disease. [ABSTRACT FROM AUTHOR]

Economic development has brought about global greenhouse gas emissions and, thus, global climate change, a common challenge worldwide and urgently needs to be addressed. Accurate carbon price forecasting plays a pivotal role in providing a reasonable basis for carbon pricing and ensuring the healthy development of carbon markets. Therefore, this paper proposes a two-stage interval-valued carbon price combination forecasting model based on bivariate empirical mode decomposition (BEMD) and error correction. In Stage I, the raw carbon price and multiple influencing factors are decomposed into several interval sub-modes by BEMD. Then, we select artificial intelligence-based multiple neural network methods such as IMLP, LSTM, GRU, and CNN to conduct combination forecasting for interval sub-modes. In Stage II, the error generated in Stage I is calculated, and LSTM is used to predict the error; then, the error forecasting result is added to the first stage result to obtain the error-corrected forecasting result. Taking the carbon trading prices of Hubei, Guangdong, and the national carbon market, China, as the research object, the empirical analysis proves that the combination forecasting of interval sub-modes of Stage I outperforms the single forecasting method. In addition, the error correction technique in Stage II can further improve the forecasting accuracy and stability, which is an effective model for interval-valued carbon price forecasting. This study can help policymakers formulate regulatory policies to reduce carbon emissions and help investors avoid risks. [ABSTRACT FROM AUTHOR]

Worldwide population growth and pollution have limited the availability of clean water. Therefore, it is imperative to develop rapid water purification techniques that can eliminate all primary water pollutants, such as organic compounds and synthetic dyes. Pollutant disposal using a photocatalytic technique is efficient and safe for the environment. In this context, the ZnO/MgFe2O4 heterostructure photocatalyst is produced via a sol–gel technique to degrade methylene blue (MB) as a benchmark dye. Synthesized samples are characterized by numerous analytical techniques, including X-ray diffraction analysis, scanning electron microscopy, scanning tunneling electron microscopy, vibrating sample magnetometer, Fourier transform infrared spectroscopy, I-V characteristics curves, and photoluminescence spectroscopy. Photocatalytic degradation efficiency is witnessed at 40.01%, 55.05%, and 71.02% for ZnO, MgFe2O4, and ZnO/MgFe2O4, respectively. Among all the samples, composite heterojunction demonstrates the highest photocatalytic degradation efficiency. The obtained results reveal that the presence of magnetic ferrite in heterojunction promotes the absorption of radiation, reduces electron–hole recombination, and improves charge transfer. High stability over multiple cycles and easy removal character are the added benefits of the tested heterojunction photocatalyst. [ABSTRACT FROM AUTHOR]

Antimicrobial resistome in wastewater treatment plants was investigated via shotgun metagenomic analysis over a variety of geographical locations, seasons, and biological treatment configurations. The results revealed that the transition of the antimicrobial resistome occurred at two locations during wastewater treatment, which resulted in a distinctive antimicrobial resistome in influent wastewater, activated sludge, and treated effluent. The antimicrobial resistome in influent wastewater was characterized by a high abundance of antibiotic resistance genes (ARGs) on clinically important drugs, whereas sludge retained a higher abundance of multidrug ARGs associated with efflux pump. Seasonality was the primary factor affecting antimicrobial resistome in influent wastewater, which partially succeeded to the subsequent resistome of activated sludge and treated effluent. Importantly, some ARGs on clinically important drugs in influent wastewater passed through the biological treatment to be discharged in the treated effluent, except in the membrane bioreactor process. [ABSTRACT FROM AUTHOR]

In this manuscript, we introduce the notion of interval-valued picture (S, T)-fuzzy graphs (IVP-(S, T)-fuzzy graphs) which is the interplay between (S, T)-norms and picture fuzzy graphs. Interval-valued picture (S, T)-fuzzy graphs (IVP-(S, T)-fuzzy graphs) is also the extension of the picture fuzzy graphs. Since interval-valued picture fuzzy sets (IVPFSs) is the most extended form of the fuzzy sets to deal uncertainties, interval-valued picture (S, T)-fuzzy graphs (IVP-(S, T)-fuzzy graphs) would be more efficient to deal with the problems containing vagueness. For the sake of investigation, firstly we introduce and apply various operations like union, join, cartesian product, direct product, lexicographic product, ring sum, complement etc to interval-valued picture (S, T)-fuzzy graphs. Then, we study the structural properties of interval-valued picture (S, T)-fuzzy graphs (IVP-(S, T)-fuzzy graphs) through homomorphism, co-weak homomorphism, isomorphism etc. Afterwards, we initiate different types of interval-valued picture (S, T)-fuzzy graphs such as regular, total regular, constant etc. Moreover, we also explore some relationships among different types of interval-valued picture (S, T)-fuzzy graphs. Finally, we provide an application of interval-valued picture (S, T)-fuzzy graphs (IVP-(S, T)-fuzzy graphs) towards multiple attribute decision-making (MADM). [ABSTRACT FROM AUTHOR]

Inherent hazards such as landslides pose a threat to human life and may inflict significant harm on the surrounding ecosystem. For planning, controlling, and avoiding landslide situations to minimize damages, a landslide susceptibility map is necessary. As a consequence of this, the current research makes use of a methodical approach and upgraded algorithms to identify and forecast locations that are susceptible to landslides. When it comes to problems associated with landslides, standard optimization techniques have been used quite a bit. This study presents a novel approach to the development of an artificial neural network (ANN) in the Iranian region of Kurdistan by using the cuckoo optimization algorithm (COA) and the SailFish optimizer (SFO) as metaheuristic approaches. In order to maximize the computational properties of these algorithms and depict a new kind of swarm intelligence, a multi-layer perceptron (MLP) neural network is used in the synthesis process. The findings of the landslide hazard maps were checked and compared using actual landslide sites. There were 1072 landslides shown on the inventory map. There was a 70:30 split between training and testing locations at random. Model input was narrowed down to 16 different landslide qualifying variables, namely elevation, slope aspect, slope angle, NDVI, distance to fault, plan curvature, profile curvature, rainfall, distance from river, distance to road, SPI, STI, TRI, TWI, land use, and geology. All of these parameters were considered to be important in determining the likelihood of a landslide occurring. The area under the curve (AUC) criterion was used to evaluate the accuracy of the probabilistic models that were put into use. Incidentally, the calculated comparable AUCs were as follows: 0.797, 0.789, 0.784, 0.779, 0.763, 0.758, 0.749, 0.740, 0.725, and 0.716 for COA-MLP, and 0.719, 0.695, 0.682, 0.675, 0.671, 0.670, 0.662, and 0.650 for SFO-MLP. The greatest hybrid model for forecasting landslide detection corresponds to the COA-MLP model, and it has a swarm size of four hundred people. As a consequence, the findings demonstrated that these two models had an effective performance for ANN-MLP optimization. Taking into consideration this evaluation, the hybrid models that were provided are trustworthy for the modeling of landslide susceptibility. As a result, the map of vulnerability that was developed can be utilized for hazardous design and increased planners' knowledge of dangerous locations. [ABSTRACT FROM AUTHOR]

Fall armyworm (Spodoptera frugiperda) is a serious pest of various agricultural crops, causing heavy economic losses. Mostly, chemical pesticides are used for its management, but it has developed resistance against pesticides and other commonly used control methods. Studies have shown that the exogenous application of silicon has the potential to make plants more resistant to insect herbivores without causing any negative effects on the efficiency of natural enemies. Therefore, a laboratory experiment was conducted to test the impact of silicon dioxide (SiO2) and potassium silicate (K2SiO3) applied by foliar spray and soil drenching on its accumulation in maize and its impact on demographical traits of S. frugiperda to check its most susceptible age stage. Results of the current study revealed a significant effect of all silicon applications compared to control on survival rate, developmental duration, the reproduction rate of S. frugiperda, and silicon accumulation in maize. Furthermore, results indicated that S. frugiperda fed on the maize plants treated with foliar spray or SiO2 showed a lower intrinsic rate of growth (r), finite rate of growth (λ), and net reproduction rate (R0) compared to all other treatments. All the treatments showed a significant effect on the adult pre-oviposition period (APOP), total oviposition period (TPOP), pre-adult survival rate, and oviposition days compared to the control. Age stage-specific life expectancy (exj) and Age stage-specific reproductive values (vxj) of S. frugiperda were significantly affected in all silicon treatments. From current results, it is concluded that silicon can be used as an alternate method for the management of this destructive pest. [ABSTRACT FROM AUTHOR]

Accurate streamflow estimation is crucial for proper water management for irrigation, hydropower, drinking and industrial purposes. The main aim of this study to adopt new data preprocessing techniques (e.g., EMD, EEMD and EWT) to capture the data noise and to enhance the prediction accuracy of machine learning methods for streamflow estimation which is a challenging task in high-altitude basins due to the influence of many external climatic and geographical parameters. The prediction accuracy of support vector regression (SVR), twin support vector machine (T), extreme learning machine (ELM), asymmetric Huber loss function-based ELM (AHELM) and ε-insensitive Huber loss function-based ELM (ε-AHELM) methods are investigated in monthly streamflow prediction. Among the standalone methods, the ε-AHELM performs superior to the SVR, TSVR, ELM, and AHELM in streamflow prediction; improvements in root mean square error are 6.9%, 4.9%, 6% and 4.2%, respectively. The study outcomes reveal that the preprocessing methods considerably improve the prediction accuracy of the implemented standalone models. Among the data preprocessing techniques, it is found that the EWT outperforms the EMD and EEMD techniques by reducing the prediction errors in the best ε-AHELM, EMD-ε-AHELM and EEMD-ε-AHELM models by 68–61.3%, 64.7–63.4% and 59.4–58.6%, respectively. The overall results of the study recommend the use of EWT-ε-AHELM in streamflow estimation. [ABSTRACT FROM AUTHOR]

Most of the developed countries across the globe have targeted to attain sustainable economic growth. With this focus, the current study evaluated 29 OECD countries over the time period of 1990 to 2018 to analyze the influence of economic and environmental indicators, i.e., export diversification, institutional quality, macrocosmic variables on carbon dioxide, and greenhouse gas emissions. The current study used the quantile regression and generalized method of moments approach on the selected panel. Our comprehensive econometric approach allows us to reveal that export diversification negatively affects carbon emissions but promotes greenhouse gas emissions. Similarly, institutional quality, economic growth, financial development, and economic growth helps to reduce carbon emissions but increase greenhouse emissions. In comparison, trade openness exhibits a positive influence on carbon emissions but a negative on greenhouse gas emissions. Besides, urbanization is found one of the major reasons for environmental degradation. In light of empirical fact findings, this study commends some innovative policy insights for scholars, governors, and policymakers. [ABSTRACT FROM AUTHOR]

The Tropical Western-Central Indian Ocean (WCIO) precipitation anomalies play an important role in modulating the anomalous climate conditions in the North Atlantic and European (NAE) region during the early boreal winter (November–December; ND) season. In this study, we analyzed the forcing mechanism and predictability for the early winter tropical WCIO precipitation anomalies and its teleconnections to the North Atlantic region. The two main forcing mechanisms emerging are the autumn Indian Ocean Dipole (IOD) and a direct atmospheric teleconnection from El Niño-Southern Oscillation (ENSO). Since the autumn IOD is partially forced by ENSO, their independent contributions are also investigated. We found the IOD dominates over the ENSO contribution. The ECMWF-SEAS5 seasonal re-forecast reproduces these forcing mechanisms well and shows a substantial prediction skill for early winter WCIO precipitation. Moreover, the North Atlantic response to the positive WCIO phase projects spatially onto the positive North Atlantic Oscillation (NAO) phase through atmospheric teleconnections and leads to warming in central and western Europe. This teleconnection is reproduced by ECMWF-SEAS5, but with weaker amplitude. Moreover, a significant prediction skill for the NAO as well as for the central and western European temperature anomalies is noted, which is mostly induced by the Indian Ocean precipitation anomalies during early winter. [ABSTRACT FROM AUTHOR]

Climate change and global warming have been driven by a rise in carbon dioxide (CO2) concentrations in recent decades, posing a danger to environmental sustainability. Thus, this research scrutinizes the effects of two types of energy (coal and geothermal) and natural resources on CO2 emissions in 10 newly industrialized countries (NICs). The study also considers the role of financial globalization using a data between 1990 and 2019. This research applied a fresh nonparametric econometric technique termed "method of moments quantile regression (MMQR)." This approach is resistant to outliers and produces an asymmetric connection between variables. Furthermore, the long-run estimators (AMG and CCEMG) are employed as a robustness check. The findings reveal that natural resources, coal, and economic growth contribute to the degradation of the environment in the NICs in all quantiles (0.1–0.90). However, geothermal energy aids in enhancing environmental sustainability at all quantile distributions (0.1–0.90). Our findings are robust with alternative methods (AMG and CCEMG). The research's outcomes have the potential to help NICs nations design policies. Finally, based on the research results, a policy framework is proposed to solve the objectives of SDGs 7 and 13. [ABSTRACT FROM AUTHOR]

Nanotechnology is one of the promising techniques and shares wide ranges of applications almost in every field of life. Nanomaterials are getting continuous attractions due to specific physical and chemical properties and being applied as multifunctional material. The use of nanomaterials/nanoparticles in agriculture sector for crop improvement and protection against various environmental threats have attained greater significance. Size and nature of nanoparticles, mode of application, environmental conditions, rhizospheric and phyllospheric environment, and plant species are major factors that influence the action of nanoparticles. The mode or method of nanoparticle applications to plants is attaining greater attentions. Recently, different methods for nanoparticle applications (seed priming, foliar, and root application) are being used to improve crop growth. It is of quite worth that which method is suitable for nanoparticle application, and how nanoparticles can possibly translocate to various plant tissues from root to shoot or vice versa. These information's are poorly understood and need more investigations to explore the comprehensive mechanism by which nanoparticles make their possible entry through different plant organs and how they transport to regulate various physiological and molecular functions in plant cells. Therefore, this study comprehensively provides the knowledge of nanoparticles uptake via seed priming, foliar exposure, and root application, and their possible translocation mechanism within plants influenced by various factors that has not clearly presented. This study will provide new insights to find out an actual uptake and translocation mechanism of nanoparticles that may help researchers to develop nanoparticle-based new strategies for plants to cope with various environmental challenges. This study also focuses on different soil factors or above ground factors that are involved in nanoparticles uptake and translocation and ultimately their functioning in plants. [ABSTRACT FROM AUTHOR]

In recent decades, global crop production is being threatened by contamination of arable lands with vanadium (V). Among many stress-relief substances, melatonin (ME) is a widely studied biomolecule acting as an antioxidant under stress conditions. The current study was aimed to investigate the response of tomato seedlings towards vanadium stress, along with the circumventing role of ME by promoting V stress tolerance in tomato seedlings. Our results revealed that accentuated inhibition of growth and biomass were caused by V (40 mg/L) stress, mainly due to impairments of photosynthetic systems, root traits, and mineral homeostasis. Conversely, notable reinforcement of plant growth parameters was seen with ME (100 µM) application, with improved chlorophyll content, root morphology, mineral nutrient homeostasis, and gas exchange parameters, along with reduced V accumulation. Further, ME efficiently triggered the antioxidant enzymes activities, by restoring cellular integrity [reduced electrolyte leakage (EL) and malondialdehyde] and restricted production of superoxide (O2•−) and hydrogen peroxide (H2O2) radicals, mainly through regulation of antioxidant enzymes. The present study highlighted the potential role of ME in tomato, for circumventing V-induced phytotoxicity, mainly by boosting photosynthesis, biomass production, redox balance, nutrient uptake, and root traits. In conclusion, ME application restricted the V availability in plant, improved plant growth and, thus, provided an improved V stress tolerance. [ABSTRACT FROM AUTHOR]

Avian coccidiosis is caused by genus Eimeria (E.) i.e. E. maxima, E. necatrix, E. tenella, E. acervulina, E. brunette and E. mitis and lead to three billion US dollar per year economic loss in poultry industry and reduces the growth performance of birds. To purge undesirable foreign agents, immune system produces a variety of molecules and cells that ultimately neutralize target particles in healthy organisms. However; when this particular system compromises, infection develops and the load of pathogens along with their virulence factors overcome both; innate and adaptive immune systems. Livestock and poultry sectors are important part of agriculture industry worldwide. Due to excessive use of chemotherapeutic agents, pathogens have developed resistance against these agents leading to the great economic losses. Numerous therapeutic approaches are in routine process for the treatment and prevention of various ailments but irrational use of antibiotics/chemicals has raised alarming concerns, like the development of drug resistant strains, residual effects in ultimate users and environmental pollution. These problems have led to the development of alternatives. In this regard, anticoccidial vaccine can be used as an alternative but due to high cost of production, plant derived biological response modifiers and antioxidants compounds are considered as a promising alternative. This review summarizes the immunotherapeutic effects of different compounds particularly with reference to avian coccidiosis. [ABSTRACT FROM AUTHOR]