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In recent years, Wireless Sensor Networks (WSN) have become vital because of their versatility in numerous applications. Nevertheless, the attain problems like inherent noise, and limited node computation capabilities, result in reduced sensor node lifespan as well as enhanced power consumption. To tackle such problems, this study develops a Modified-Distributed Arithmetic-Offset Binary Coding-based Adaptive Finite Impulse Response (MDA-OBC based AFIR) framework. By leveraging Modified Distributed Arithmetic (MDA) which optimizes arithmetic operations by replacing the multipliers with lookup tables (LUT) hence minimizing energy consumption as well as computational complexity. Offset Binary Coding (OBC) enhanced the efficiency of data transmission by minimizing the data representation overhead. In addition to this, the adaptive strategy is incorporated with the Adaptive Finite Impulse Response (AFIR) framework permitting the filters to dynamically adjust to varying signal characteristics, thus offering high noise suppression and low distortion rates. Comprehensive simulations and comparative analysis validate the effectiveness of the proposed MDA-OBC-based AFIR method. The proposed method attained a lower energy consumption of 1.5 J and 130 W power consumption than the traditional implementations, resulting in significant energy efficiency and data transmission in signal preprocessing and noise suppression in WSNs. [ABSTRACT FROM AUTHOR]

Slow cooling was allowed for the successful growth of metal–organic single crystal of lithium bis (oxalato) borate (LBOB). The single crystal XRD was used to analysis the lattice cell parameters of grown LBOB crystal. The LBOB single crystal belongs to orthorhombic system with space group Pbca and calculated parameters are found to be a = 6.43 Å, b = 7.58 Å, and c = 13.22 Å. The crystalline perfection of the grown crystal was analyzed by high-resolution X-ray diffraction (HRXRD) rocking curve measurement. UV–Vis spectral analysis was used to determine the grown LBOB crystal’s band gap and transmittance (%) value. From the observed UV–Vis spectrum, the lower cut-off wavelength and band gap values of LBOB crystal are 231 nm and 5.1 eV, respectively. Thermal stability of LBOB compound was studied using TG–DTA analysis. The laser-induced damage threshold study revealed that the grown LBOB crystal possesses excellent resistance to laser radiation with a high laser damage threshold value up to 9.74 GW/cm2. Vickers hardness tester was used to examine the LBOB crystal’s hardness value. Z-scan technique was employed to clarify that the third-order nonlinear susceptibility was found to be (χ3) = 3.0371 X 10–11 esu. [ABSTRACT FROM AUTHOR]

Cutaneous tumours are the most common neoplasms seen in dogs. Time bound diagnosis of these tumours is very important for surgical or medical intervention. Though histopathology is considered as the gold standard for the diagnosis of these tumours, it is a time consuming procedure and it is in such contexts that the use of cytology assumes significance. Cytological diagnoses of 14 cases of superficial masses forms the content of the present communication. Among these 14 cases, 11 were round cell tumours that were predominantly lymphoma (seven cases), followed by one case each of mast cell tumour, histiocytoma, extragenital form of transmissible venereal tumour (TVT) and melanoma. Two epithelial tumours namely squamous cell carcinoma and hepatoid gland tumour and one mesenchymal tumour namely liposarcoma were also included in this cohort. This paper deals with the use of a rapid diagnostic technique like cytology for detecting superficial masses in canines. [ABSTRACT FROM AUTHOR]

This study presents the calculation of stretching vibrational frequencies for the triatomic molecule dichlorine monoxide (Cl2 O), explicitly focusing on the fourth, fifth, and sixth overtones. Using a Lie algebraic framework, a detailed mathematical modeling approach is provided to predict these higher overtone frequencies accurately. The results demonstrate the effectiveness of the Lie algebraic method in capturing the complex vibrational behavior of Cl2 O, offering valuable insights into its vibrational spectra. [ABSTRACT FROM AUTHOR]

The present study demonstrates a thorough study of dichlorine monoxide's combined stretching vibrational frequencies (Cl2O) with particular reference to its fourth, fifth and sixth overtone transitions. The research proposes a systematic method for modelling vibrational dynamics and frequency predictions utilizing a Lie algebraic framework. The methodology accounts for anharmonicity and higher inter-level coupling modulation, which provides a basis for overtone theoretical interpretation. These results are helpful within the framework of molecular spectroscopy for interpreting the results of experiments with this compound. Such an approach can be generalized to other polyatomic molecules possessing similar vibrational modes. [ABSTRACT FROM AUTHOR]

In contemporary machine learning, models often struggle with data distribution variations, severely impacting their out-of-distribution (OOD) generalization and detection capabilities. Current object detection methods, relying on virtual outlier synthesis and class-conditional density estimation, struggle to effectively distinguish OOD samples. They often depend on accurate density estimation and may produce virtual outliers that lack realism, particularly in complex or dynamic environments. Furthermore, previous research has typically addressed covariate and semantic shifts independently, resulting in fragmented solutions that fail to comprehensively tackle OOD generalization. This study introduces a unified approach to enhance OOD generalization in object recognition models, addressing these critical gaps. The strategy involves employing adversarial perturbations on the ID (In-Distribution) dataset to enhance the model’s resilience to distribution shifts, thereby simulating potential real-world scenarios characterized by imperceptible variations. Additionally, the integration of Maximum Mean Discrepancy (MMD) at the object level effectively discriminates between ID and OOD samples by quantifying distributional differences. For precise OOD detection, a K-nearest neighbors (KNN) algorithm is used during inference to measure similarity between samples and their closest neighbors in the training data. Evaluations on benchmark datasets, including PASCAL VOC and BDD100K as ID, with COCO and Open Images subsets as OOD, demonstrate significant improvements in OOD generalization compared to existing methods. These discoveries underscore the framework’s potential to elevate the dependability and flexibility of object recognition systems in practical scenarios, particularly in autonomous vehicles where accurate object detection under diverse conditions is critical for safety. This research contributes to advancing OOD generalization techniques and lays the groundwork for future refinement to address evolving challenges in machine learning applications. The code can be accessed from [ABSTRACT FROM AUTHOR]

K-carrageenan and various concentrations of zinc nitrate hexahydrate have been combined to develop a zinc ion-conducting biopolymer electrolyte membrane using the solution casting method. To examine the amorphous nature of the samples, X-ray diffraction (XRD) study has been used. The maximum amorphous nature is observed for 1 g K-carrageenan with 1.1 M. wt % of zinc nitrate hexahydrate biopolymer membrane. Fourier transform infrared spectroscopy (FTIR) investigations have shown that the complexation occurs between the K-carrageenan with zinc nitrate hexahydrate. Surface morphology of Pure K-carrageenan, 1 g K-carrageenan with 1.1 M.wt % of zinc nitrate hexahydrate and 1 g K-carrageenan with 1.2 M.wt % of zinc nitrate hexahydrate have been studied by SEM analysis. Pure K-carrageenan membrane (SEM) has uniform surface with uniform small pores. 1 g K-carrageenan with 1.1 M.wt % of zinc nitrate hexahydrate membrane (SEM) shows rectangular rod-shaped nature along with pores of moderate diameter. 1 g K-carrageenan with 1.2 M.wt % of zinc nitrate hexahydrate membrane SEM shows rectangular rod-shaped nature along with pores of moderate diameter and aggregates. For the zinc-doped biopolymer membrane samples, the differential scanning calorimetry (DSC) is used to determine the glass transition temperature. Pure K-carrageenan has got Tg value at 37.04 °C. When the salt concentration is increased upto 0.9 M.wt % of the Tg value increases. When the salt concentration is further increased upto 1.1 M.wt % of the Tg value decreases. Highest zinc ion conducting membrane has got a Tg value of 75.95 °C. According to electrochemical impedance spectroscopy (EIS), the 1 g K-carrageenan with 1.1 M. wt % of zinc nitrate hexahydrate membrane has highest zinc ion conductivity of 2.9 × 10−3 S cm−1. According to the linear sweep voltammetry (LSV) investigation, the 1 g K-carrageenan with 1.1 M. wt % of zinc nitrate hexahydrate membrane has shown a wide electrochemical stability window of 2.75 V. The Evans polarization method determined that Zn2+ ion has a transference number of 0.42. The cyclic stability of highest conducting biopolymer membrane (Zn2+ ion) is studied by Cyclic Voltammetry. The electrolyte used in the construction of the primary zinc battery is 1 g K-carrageenan with 1.1 M. wt % of zinc nitrate hexahydrate having highest zinc ion conductivity. For this built-in battery, the OCV (Open Circuit Voltage) is found to be 1.43 V. [ABSTRACT FROM AUTHOR]

Pollution by industrial effluent, chemical fertilisers, and drugs has turned into a very relevant problem in the world today that is posing a very great danger to water sources and its users. Effective and popular methods of oxidation processes include advanced oxidation processes (AOP) to remove pollutant concentrations but their executions are methodically power intensive and are generally known to produce secondary toxic products. To overcome these limitations, this work proposes a green oxidation process accompanied by a deep learning (DL) model for the successful identification of pollutants and their efficient degradation. This oxidation technique uses green oxidising agents such as hydrogen peroxide, and plant based catalysts, which lead to minimal production of secondary hazardous pollutants. The DL model acquires essential data including diverse pollutants, reaction conditions, and water chemistries to forecast optimum sets of the operating parameters such as oxidant concentration, pH, temperature, reaction time. This helps in achieving the degradation of pollutants in a more enhanced way depending on the environmental conditions. The deep learning incorporated into the model enables it to learn from the previous performance and provides optimal pollution removal efficiency with low energy complicity and environmental impact as well. Furthermore, the DL model involves a prediction of the best routes by which the production of undesirable by-products can be minimised making the whole process more sustainable. There is experimental confirmation that the proposed method enhances the efficiency of pollutant elimination rates in contrast to the conventional oxidation approaches while requiring less energy and having lower costs of operation. It is evidenced by the model's ability to function under varying environmental conditions, mainly due to its sustainable design, which makes the presented model suitable for large scale application in the remediation of the environment. This work lays the foundation for combining green chemistry and artificial intelligence in developing technologies for reducing the amount of hazardous pollutants in the environment that are eco-friendly and highly efficient. [ABSTRACT FROM AUTHOR]

The food production company and the pharmaceutical industries produce enormous organic compositions of wastewater which are so complex and hence require efficient cleaning techniques to reduce environmental effects. Biochemical oxidation process which includes aerobic and anaerobic digestion is used to achieve these goals. But the methods of optimising these processes to various cases of wastewater composition are still subject to improvement. This work examined the application of Machine Learning (ML) for the probabilistic modelling of biochemical oxidation processes in an endeavor of achieving higher efficiency in treatment processes and lower costs. Statistical algorithms such as Random Forest, Support Vector Machine, and Neural Networks were applied to model the prediction of oxidation processes' efficiency concerning parameters including pH, temperature, organic load, and microbial activity. This method gives scope to making modifications instantly so that maximum degradation of the organic pollutant can be achieved. Accordingly, one with the most effective treatment technique according to the study is the advanced oxidation processes (AOPs) when applied with the ML optimisation. AOPs such as Fenton reaction and photocatalysis have expressed higher efficiency in removal of very stable and complex compounds to enhance the general efficiency of every wastewater treatment system. Machine learning helps in the better management of AOPs since it can forecast the best operational conditions thus less usage of chemicals and energy. This integration also enhances the biodegradability of pharmaceutical and food waste and also supports regulatory compliance. Research findings show that through MLoptimised biochemical oxidation, COD and TOC were reduced by up to 90% depicting the solution as sustainable and cost effective. This work gives guidelines on how to use ML in enhancing efficiency in the aspects of wastewater treatment for enhanced environmental conservation in the pharmaceutical and food industries. [ABSTRACT FROM AUTHOR]

This research aims to enhance the electrochemical oxidation process for the dehydration of pollutants using advanced electrodes. Electrochemical approaches employed in the past suffer from one or more problems including low efficiency and lack of selectivity when treating wastewater. To overcome these limits, this research focuses on the synthesis and use of improved electrode materials which accelerates the reaction kinetics, increases the electrode's stability, and improves the overall efficiency of the process. They also design the novel electrodes in a way that enhances the surface area together with the electrical and catalytic activity, which enhances the degradation of pollutants and dehydration. The performance of experimental outcomes shows enhanced pollutant removal rates than the traditional electrodes such that energy intensiveness and operating costs are relatively low. Also, it discusses the impact of some of the operating conditions including the current density, electrolyte concentration, and flow rate on the efficiency of the electrochemical system. The results also imply that the inclusion of these new materials strengthens the state-of-the-art approaches relating to the electrochemical wastewater treatment thus enhancing its efficiency in environmental management. Such findings are useful in the ongoing advancement of ecological methods of water purification that will be useful for the removal of pollutants from industrial effluent and similar purification processes. [ABSTRACT FROM AUTHOR]

The use of on-line monitors for controlling the oxide phase of oxidation processes occurring in agrochemical industries is critical for the product and process quality. Conventional monitoring methods used in many processes contain significant drawbacks such as failure to identify significant fluctuations in time, which results in inefficiencies and or safety concerns. Based on this background, this research aims at how complex AI algorithms can be incorporated into the sensor networks to monitor gas-phase oxidation in the production of agrochemicals in real-time. The system applies ML models like the neural networks and support vector machines and feeds on data such as temperature, pressure and concentration of the reactants in the different environmental and process parameters. It is also essential to note that the identified AI algorithms are expected to predict possible deviations from the typical operating conditions and subsequently make corrections where necessary in the oxidation process. Furthermore, through optimisation by use of AI, the reaction kinetics are well controlled, hence reducing the formation of unwanted by-products and energy consumption. To build the system, the process data from previous processes are used and the system is tested through simulations and experiments. This point can be regarded as another major advantage of the application of the indicated optimised AI monitoring system as organisational and methodological improvements brought to the process of manufacturing agrochemicals increase its safety, effectiveness, and longevity. Accordingly, the study concludes that real-time AI-based monitoring results in less number of interruptions, increases the robustness of the process, and is in tandem with the shift towards Industry 4.0 paradigm by focusing on such factors as smart manufacturing solutions leading to sustainable agrochemical production. [ABSTRACT FROM AUTHOR]

Lithium ion conducting membrane has been prepared based on cellulose acetate and LiCl using the solution casting method. Amorphous/crystalline nature of the prepared membranes are analyzed using X-ray diffraction. The surface of the prepared membrane is analyzed using SEM. Complexation of cellulose acetate and LiCl is confirmed using Fourier transform infrared spectroscopy. The ionic conductivity of the examined membranes is determined through AC impedance measurement. The membrane, comprising 1 g cellulose acetate with 0.8 weight% (wt%) of LiCl, exhibits the highest Li-ion conductivity of 7.1 × 10−3 S cm−1 among all prepared membranes. Thermal stability of the prepared membranes is analyzed using Differential scanning calorimetry and Thermogravimetric analysis. The electrochemical stability of the highest Li-ion conducting membrane has been analyzed using Linear sweep voltammetry (LSV), and the potential window was found to be 1.76 V. Using the highest Li-ion conducting membrane, the primary Li-ion conducting battery and coin cell is constructed. Primary Li-ion conducting battery shows an Open-circuit voltage (OCV) of 1.8 V and its performance has been studied using various loads. The stability of the coin cell is analyzed using the Galvanostatic charge-discharge. The performance of the highest Li-ion conducting membrane as an electrolyte in an electrochemical storage device is analyzed in the present work. [ABSTRACT FROM AUTHOR]

Calcium ortho phosphates and its derivatives (CamPn) are well known for bio bones and implants. However, still there is lot of scope and necessity of various multi-directional applications such as non-degraded, thermally stable, elastic and radiative capable bio glasses and its derivatives. In this direction, YxSb10−xCa30P60 materials are prepared and characterized by thermoluminescence, elastic and bio degradation techniques. Observed, results and studies suggests that the materials developed were transparent and glassy. Thermoluminiscence, trap-depth analysis and parameters advised that the sample code 'ξ5' found to be useful thermo-luminescent resource. Obtained range of both micro hardness and thermal stability acclaims that the samples prepared were mechanically hard. Additionally, we have reported the results of Y3+ ionic influence on vitro bio-activity studies of present glasses. Degradation results along with vitro bio-activity studies as a function of both Y2O3 and Sb2O3 concentration suggesting that the sample code 'ξ5' might be useful biocompatible resource. The deconvoluted FT-IR spectra of the glasses also studied. The optical absorption studies suggesting the sample code 'ξ5' exhibiting highest optical band gap value. Radiation shielding properties suggesting the sample code 'ξ5' exhibiting highest radiation protection efficiency and mass attenuation coefficient. CamPn and its derivatives are well known for being used in bio-bones and implants. However, there is still a lot of scope for and need for bio glasses and their derivatives that don't break down, are thermally stable, are bendable, and can radiate. [ABSTRACT FROM AUTHOR]

Background: Cytoplasmic T Lymphocyte Antigen − 4 (CTLA-4) gene encodes an immunoregulatory receptor expressed on surface of activated T-cells to mediate peripheral tolerance against self-antigen. It suppresses auto-reactive T-cell proliferation either by inactivation or apoptosis of T-cells. The CTLA-4 mRNA undergoes alternative splicing to synthesize a native soluble form of CTLA-4 (sCTLA-4) protein, which lacks exon 3 that encodes for transmembrane region. As a result, sCTLA-4 circulates as a soluble serum protein and acts as an immunoregulator molecule to maintain homeostasis in the blood. Materials and results: Techniques coupled with quantitative Polymerase Chain Reaction (qPCR) and High-Resolution Melting Analysis (HRMA) were used to screen CTLA-4 3'Untranslated Region (UTR) CT60 (A/G) rs3087243 Single Nucleotide Polymorphism (SNP) and their association with Rheumatoid Arthritis (RA) in the Indian population. In addition, we also evaluated the concentration of sCTLA-4 serum protein in RA patients carrying rs3087243 SNP with different genotypes (A/A, G/A, and G/G). Statistical analysis of Odds Ratio (OR), Confidence Interval (C.I), and Relative Risk (RR) have shown that frequency of CTLA-4 rs3087243 SNP G/G genotype was significantly associated with RA in the Indian population (OR 1.7140; CI = 1.0765 to 2.7290; RR = 1.5434; p = 0.0232). The sCTLA-4 concentration was also significantly lower in RA patients carrying rs3087243 SNP G/G genotype than control group (p < 0.001). Conclusion: Co-inheritance of CTLA-4 signal peptide and 3'UTR SNPs may activate RAPP pathway. Downregulation of CTLA-4 and sCTLA-4 serum protein by rs3087243 SNP can increase the hyperactivation of T-cells, which causes RA. [ABSTRACT FROM AUTHOR]

Introduction: Vitex negundo has a myriad of medicinal uses, such as antioxidant, analgesic and anthelminthic properties, and it is used for dysmenorrhoea. The plant contains various phytochemicals, such as flavonoids, vitamins and casticin. All components of the plant are utilised medicinally due to the minimal occurrence of adverse drug reactions, making it a potential drug target for various chronic diseases. However, the role of this plant in neurodegenerative disorders, such as Alzheimer's Disease (AD), has not yet been extensively studied. Aim: To evaluate the in-silico and in-vitro activity of the anti-Alzheimer properties of the Methanolic Extract of Vitex negundo (MEVN) leaves. Materials and Methods: The present in-silico docking study and in-vitro study were conducted in the Department of Pharmacology at Sri Ramachandra Institute of Higher Education and Research, Porur, Chennai, Tamil Nadu, India, over a period of two months. The in-silico analysis was performed to determine the intermolecular interactions between the ligand and protein of the top five scored molecules using PyMol software. To assess the Acetylcholinesterase (AChE) inhibitory property, the findings were presented as docking scores. Cell viability and cytotoxicity were evaluated using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, with results expressed as percentages for various concentrations: 6.25 µg/mL, 12.5 µg/mL, 25 µg/mL, 50 µg/mL, 75 µg/mL, and 100 µg/mL of MEVN. Results: The MEVN exhibited competitive inhibition of the AChE enzyme. There were active interactions between Agnuside, Isochlorogenic Acid B, Isochlorogenic Acid C, Kaempferol-3-O-rutinoside, and Quercetin found in the MEVN with AChE. The percentage of cell viability for the concentrations of 6.25 µg/mL, 12.5 µg/mL, 25 µg/mL, 50 µg/mL, 75 µg/mL, and 100 µg/mL, as determined by the MTT assay, was 98.62%, 98.86%, 97.19%, 96.66%, 91.86%, and 78.82%, respectively. The results indicated that MEVN does not exhibit any toxicity. Conclusion: The findings demonstrated that MEVN possesses AChE inhibitory properties and maintains cell viability, both of which are indicative of anti-Alzheimer activity. Therefore, MEVN can be further evaluated for its potential in preventing cell cytotoxicity and treating AD. [ABSTRACT FROM AUTHOR]

Increased usage of smart devices especially under the internet of things (IoT) have provoked emergence of different security issues as a result of connected devices worldwide. This review article aspires to assess more recent threats on IoT ecosystems and present cutting edge computer science solutions towards these threats. AI, ML, Blockchain, and Quantum Cryptography are discussed for their applicability towards improving the security of the IoT technology. The article also looks at the existing architectures and standard technologies including; Lightweight Machine-Type Communications (LwM2M) as well as Datagram Transport Layer Security (DTLS) as well as the use of cloud and edge computing in the defense against IoT threats. Real-world application examples, smart city and building infrastructure, smart healthcare and industrial IoT use cases provide a use case of these solutions. Finally, the review summarizes future trends and directions of IoT cybersecurity that has presented the significance of continuous research toward standardized, quantized, and privacy-preserving security frameworks. [ABSTRACT FROM AUTHOR]

The green bond has emerged as an important financial instrument to advance environmentally friendly projects. While institutional investors have shown ample interest in green bonds, retail investors have lagged in their adoption. This study intends to examine the determinants of retail investors' attitude and intention toward green bond investment, utilizing the theory of planned behavior as the fundamental model and incorporating several context‐specific factors. Data from 506 Indian retail investors, representing the majority of Indian states and union territories (UTs), were collected through multi‐stage stratified random sampling. The PLS‐SEM method, coupled with artificial neural network (ANN) analysis and fuzzy set qualitative comparative analysis (fsQCA), was employed to test the hypothesized relationships, ensure the robustness of outcomes, and derive important practical insights. The findings suggest that intrinsic factors (perceived behavioral control and attitude) are superior predictors of investors' behavioral intentions relative to external factors (government policy support and social influence). Further, for green bonds to receive favorable evaluations from investors, they should display adequate financial cum environmental performance. Investors' attitude is significantly influenced by issuers' ratings and their willingness to pay premiums. Environmental concerns and perceived risk also influence investors' attitude toward green bonds, albeit with relatively lower strength. This study holds significance as it offers crucial implications for researchers, market participants, policymakers, and regulators involved in the development of the green bond market. [ABSTRACT FROM AUTHOR]

Since 2019, COVID disease threats the world. It originated in China, and due to its easy spread characteristics, it spreads quickly among all the countries. As the world faced a new kind of virus, the government could not control its spread, and the fatality rate had also hugely increased. In 2021, the vaccine for the virus was introduced by various countries, which aids in controlling the spread and mortality rate. The mutant form of this virus exhibits high efficiency. So, this study analyzes the effective of COVID vaccines released after 2020 against all COVID variants through the fuzzy superiority and inferiority ranking (SIR) decision-making method. The vaccines and variants are analyzed by the m × n matrix pattern under the fuzzy SIR-integrated weight method. The disease variants are taken as criteria and that are assigned integrated weight based on subjective, objective weight approaches. The intuitionistic fuzzy set-double parameter (IFS-DP) handles the vagueness by strictly including only membership and non-membership functions with the condition 0 ≤ μ ∗ (x) + ν ∗ (x) ≤ 1. As a result, the vaccines Moderna and Covishield are attained top rank and have a high likelihood to combat all the COVID variants compared to other vaccines. The reached outcomes are corroborated through sensitivity and comparative analysis. The sensitivity and comparative studies are measured by increasing the order of IFS-DP and extant fuzzy MCDM methods, respectively. The comparison of these results with those obtained from the proposed method is similar to the sensitivity study, along with minor variations observed in the comparative study. Therefore, the proposed method generates an efficient outcome. Moreover, the testing hypothesis technique is applied to check the efficiency of proposed method. [ABSTRACT FROM AUTHOR]

The pulse beetle, Callosobruchus spp., poses a threat to legumes by consuming the protein content of the grain, resulting in potential losses in storage ranging from 12 to 30%. Molecular marker technology helps to mitigate the breeding constraint in the development of bruchid resistance in green gram breeding programmes. In the present study, validation of locus-specific STSbr1 and STSbr2 markers linked with bruchid resistance was done using fifteen genotypes viz., VBN (Gg) 2 (susceptible) and Vigna radiata var. sublobata/2 (resistant), F1 hybrid, two susceptible and ten resistant RILs derived from the above parents. The marker STS br1 behaved as a dominant marker and produced an approximate allele size of 225 bp in the resistant parent, F1 hybrid and resistant RILs and absent in susceptible parent and susceptible RILs, which showed cent per cent co-segregation with bruchid resistance locus. Hence, it is concluded that STS br1 is linked with bruchid-resistant genes. The marker STS br2 behaved as a co-dominant marker and produced an approximate allele size of 470 bp in all the 15 genotypes evaluated (monomorphic) and did not differentiate the resistant and susceptible genotypes. The marker STS br1 could be used in marker-assisted selection to develop bruchid-resistant varieties and to screen the germplasm to identify bruchid-resistant donors in green gram. [ABSTRACT FROM AUTHOR]

The present work studies the spectral properties of some X-ray point sources in the galaxies NGC 4382 and NGC 1399 as observed by Chandra in different epochs. Five point sources from the galaxy NGC 4382 and twelve point sources from the galaxy NGC 1399 with net source count ≥ 100 were considered for the present work. Spectra of all the seventeen sources were fitted with two empirical models—the absorbed power law and an absorbed disk black body. Most of the sources were found to be equally well explained by both the models except for a few of them which prefers one model over the other. While most of the sources are found to be in the Ultraluminous hard state ( L x ≥ 10 39 erg s - 1 ; powerlaw photon index ( Γ) ∼ 2.0 /kT in > 1.0 keV), for which inverse comptonization of soft photons may be responsible for their emission. Significantly, we found spectral state transitions in two sources - NGC 4382 X-1 & NGC 1399 X-8. Long-term variability in luminosity were also observed in the sources -NGC 4382 X-1, NGC 1399 X-7, NGC 1399 X-8, NGC 1399 X-9 & NGC 1399 X-17. While most of the ULXs here are found to be powered by accretion on to stellar mass black holes, there is one super-soft ULX (NGC 1399 X-9) with k T in ∼ 0.36 keV and bolometric luminosity, L x ∼ 2.69 × 10 39 erg s - 1 , which in one of the epoch, was found to be consistent with harboring Intermediate mass black hole (IMBH) with M BH ∼ 200 - 67 + 62 M ⊙ , accreting at 0.09 times the Eddington limit. However, another two soft ULXs - NGC 1399 X-10 with k T in ∼ 0.66 keV and NGC 1399 X-15 with k T in ∼ 0.53 keV with their corresponding estimated bolometric luminosities were found to be consistent with harboring stellar mass blackholes accreting within their Eddington limit. [ABSTRACT FROM AUTHOR]

Photocatalytic and antibacterial properties of PbS:Ag (PA) and rGO-blended PbS:Ag (rPA) nanoparticles (NPs) have been compared and reported in this paper. Chemical precipitation and one-pot synthesis methods were used to synthesize NPs of PA and rPA. X-ray diffraction (XRD) studies reveal a cubic crystal structure for both samples, with preferential growth along the (200) plane. Crystallite sizes were 55 and 41 nm for PA and rPA, respectively. Scanning electron microscopy (SEM) images of rPA revealed clustered grains. Both samples exhibited Pb–S bands in Fourier-transform infrared spectroscopy (FTIR) studies. Near band edge emissions occurred at 504, 520, 539 and 595 nm for both PA and rPA. The inclusion of rGO into PA led to lattice misfit, crystal growth disorders, and increased grain boundary scattering. This series of structural disruptions contributed to a reduction in the photoluminescence (PL) intensity for the rPA composite. A higher degradation efficiency of 95.4% was achieved for the rPA catalyst against Rhodamine B dye under visible light. The antibacterial activity of PA is increased with rGO inclusion due to increased generation of reactive oxygen species (ROS). [ABSTRACT FROM AUTHOR]

Electronic spectral analysis of a tri-iodide solution in an acetic acid medium revealed the presence of strong absorption bands for tri-iodide at 287.5 and 351.5nm. A notable shift in the absorption band to 310nm indicated the formation of a complex between Chitosan and tri-iodide in an acidic medium solution. Chitosan-tri-iodide complex formation was significant at concentrations >0.5% (w/v). At lower concentrations, weak complexation is evident through the comparatively lower absorption of the iodophors. The introduction of KSCN to the polymer-iodophor system leads to a gradual reduction in absorption over time and is completed within 1 hour. If polymer mixtures of (1:1) ratio were used in iodophor, it persists as complex iodophor for a longer period. Photoluminescence investigations show sharp and narrow emission peaks at 350.5 and 701.5nm, highlighting unique fluorescent species alongside effective radiative recombination. It minimizes energy dissipation and suggests promising fluorescence applications across diverse fields. [ABSTRACT FROM AUTHOR]

We present the spectral and timing study of V404 Cygni from all its available Chandra observations and which recently come up in public domain of Chandra data archive. The data reduction and analysis were done using CIAO 4.14 and HEASOFT 6.30.1. The spectral analysis was done using spectral fitting package XSPEC version 12.12.1, available in the Heasoft package. The spectra of the source is fitted in the energy range 0.3 -8.0 keV using two empirical spectral models the absorbed power law and an absorbed disk-blackbody. The X-ray binary source V404 Cygni is found to be in the quiescent state having the X-ray luminosity in the range with few times 1032 erg s-1. The source is found to be in the hard state and is well explained by power-law model with a powerlaw photon index ~ 2 with ny in the range~ (0.7-1.2) x 1022 cm-2. From timing analysis, Src-1 (V404 Cygni), in all the time bins- 0.5, 1 and 2 ks, the probability for the count rate to be constant is 0.17 x10-33 in all the observations in the year 2021 and 2023 (ObsID 23421, ObsID 23422, ObsID 23423 & ObsID 28927). However, in the year 2017 observation it is found to be less variable. This clearly shows the presence of short-term variability in kilo-seconds time-scales with the currently available Chandra data. So, it is indicative that the binary source V404 Cygni is more likely to be variable source both in long-term (years) as well as short-term (kiloseconds) scales. [ABSTRACT FROM AUTHOR]

Aims to develop web application for information management and sharing which helps educational institutions broadcast information to the students and their faculties. A college generates and uses a large amount of data where it needs to be stored in the centralized repository. This data must be communicated appropriately from the authorities to students, faculty, and HOD. This web application resolves to store, manage, and distribute this information. It is intended for information management and sharing in a way that can manage and manipulate data in a centralized place which helps us reduce the time required for updating, storing, sharing the information a college is generating. And eliminates duplication and maintains consistency in data and using one of the latest and powerful concepts called session which helps us store the user information across multiple web pages. The goal of the of web application for information management and sharing is to achieve centralized data management and accessibility. [ABSTRACT FROM AUTHOR]

stress is a pervasive difficulty with some distance-attaining fitness implications. Early detection of pressure is important for well timed intervention and mitigation. in this research, we discover the feasibility of predicting stress degrees utilizing a rich dataset of coronary heart rate functions. This dataset includes a big range of coronary heart charge parameters, along with mean RR intervals, widespread deviations, spectral additives, and nonlinear dynamics, amongst others. each facts entry is associated with a completely unique affected person identifier, taking into account personalized analysis. The primary objective of this observe is to broaden a predictive model able to accurately discerning whether or not an man or woman is experiencing stress based on their coronary heart charge traits. machine learning strategies will be hired to extract significant patterns and relationships from the dataset. Key capabilities of interest encompass metrics like RMSSD (Root imply rectangular of Successive RR c programming language variations), LF/HF (Ratio of Low Frequency to excessive Frequency), and sample entropy (Sampen), which capture both linear and nonlinear components of coronary heart charge variability. moreover, demographic and contextual records, consisting of affected person circumstance and coronary heart charge, could be integrated into the predictive models to enhance their accuracy and practicality. This examine pursuits to make a contribution to the improvement of non-invasive and sensible stress detection methods that may be included into wearable gadgets or faraway tracking systems. these gear have the potential to assist individuals in managing pressure and healthcare professionals in delivering timely interventions. The effects may additionally pave the way for customized stress management strategies based totally on an person's heart rate profile. in the end, the findings of this research may additionally have broader implications for improving the satisfactory of existence and basic properly-being of individuals via permitting early pressure detection and intervention thru the analysis of heart price statistics. [ABSTRACT FROM AUTHOR]

The electronic band structure, density of states and the behaviour of conduction and valence bands in body-centered cubic rubidium and cesium, particularly under varying pressure conditions are studied. This study examines the relationship between the lattice constant, pressure, and reduced volume for rubidium and cesium, offering insights into their structural properties. Additionally, the distribution of electron energy levels is analyzed using the density of states across various energy regions. The conduction and valence band widths are calculated relative to the Fermi level and mapped across key symmetry points in the Brillouin zone. [ABSTRACT FROM AUTHOR]

Periodontal Disease (PD) is one of the most common inflammatory oral diseases, affecting majority of the population who are 30 years and older. Improper treatment of periodontal disease leads to degradation of periodontal tissues, eventually leading to tooth loss. Conventional clinical therapy for PD aims at eliminating infectious sources and reducing inflammation to arrest disease progression, which cannot achieve the regeneration of lost periodontal tissues. Over the past two decades, various regenerative periodontal therapies, such as Guided Tissue Regeneration (GTR), enamel matrix derivative, bone grafts, growth factor delivery, and the combination of cells and growth factors with matrix-based scaffolds, have been developed to target the restoration of lost tooth-supporting tissues, including the periodontal ligament, alveolar bone, and cementum. This review discusses recent progress in periodontal regeneration using tissueengineering and regenerative medicine approaches. Specifically, the focus is on the advances in biomaterials and controlled drug delivery for periodontal regeneration in recent years. Special attention is given to the development of advanced bio-inspired scaffolding biomaterials and temporospatial control of multi-drug delivery for the regeneration of the cementum-periodontal ligament-alveolar bone complex. Challenges and future perspectives are presented to provide inspiration for the design and development of innovative biomaterials and delivery systems for new regenerative periodontal therapy. [ABSTRACT FROM AUTHOR]

The purpose of this research was to develop glasses that are thermally reflective, radiation-resistant, and mechanically robust. We have synthesized the La(20-x)Pb10B70:Eux series of glasses in this regard. Then, characterization, radiation shielding, structural analysis (Raman, FT-IR, DTA, SEM, XRD, and EDX), dielectrics, thermoluminescence are planned. The XRD and SEM analyses of the test samples reveal a vitreous appearance. The chemical constituents are identified by the EDX analysis in terms of their percentage of atomic weight. The density (2.592 g/cm3) and molar volume (45.44 cm3 mol−1) of the fabricated glass were evaluated as physical properties. The glass transition temperature (approximately 273 °C) and thermal stabilities (0.656 percent) of glasses are determined to be solely dependent on Eu3+ ions, according to DTA investigations. FT-IR and Raman analyses were employed to determine the various structural vibrations of the sample glass. By employing the ultrasonic velocities of the test glasses, an assessment was conducted on the micro-hardness of the glass (5.01 GPa). The shielding characteristics of radiation, such as RPE (31.6 percent) and MAC (16.9 cm2/g), indicate that the shielding behavior is solely the result of the Eu3+ ions. It has been observed that TL properties are examined at gamma irradiation intensities ranging from 5 to 25 kGy. An evaluation was conducted on the frequency factor (3.225 × 1021 S−1) and A.E. (1.785 eV) of glasses. The findings indicate that the TL characteristics of glasses are solely influenced by the Eu3+ ions. Additionally, the investigation of the dielectric properties of glasses was ascertained. The analysis focused on the dielectric constant (8.891), dielectric loss (0.026), ac conductivity (0.735 × 10–8 Ω−1 cm−1), and A.E. (1.051 eV) values of the fabricated glass. The findings suggest that the dielectric properties of glasses are entirely determined by the presence of Eu3+ ions. Overall the LBPE-1.5 glass found to be best in the structural, mechanical, optical, dielectric, radiation shielding and thermoluminescence properties. [ABSTRACT FROM AUTHOR]

n this present paper, we classify and explore the geometrical significance of a class of Lorentzian almost paracontact metric manifolds namely Lorentzian para-Kenmotsu (briefly LP-Kenmotsu) mani- folds whenever the manifolds are either conformally flat or conformally symmetric. It was found that a conformally flat LP-Kenmotsu manifold is of constant curvature and a conformally symmetric LP-Kenmotsu manifold is locally isomorphic to a unit sphere. At the end, we obtain the scalar curvature of φ-conformally flat LP-Kenmotsu manifolds [ABSTRACT FROM AUTHOR]

The present study investigated the phytochemical, total phenolic, flavonoid content and in vitro antioxidant of the methanolic extract of the leaf of Ipomoea hederifolia Linn. In vitro antioxidant activity was determined using 2,2-diphenyl-1-picrylhydrazyl, hydroxyl radical, reducing power and phosphomolybdenum method. The total phenolic and total flavonoid contents were also determined using Folin Ciocalteau's and aluminium chloride method respectively. The preliminary phytochemical screening showed the presence of alkaloids, glycosides, steroids, triterpenoids, flavonoids, phenols and tannins. Results indicated that the total phenolic and flavonoid content were found to be 101.28±0.9238 mg of gallic acid equivalents/g and 14.91±0.672 quercetin equivalent/g respectively. The half-maximal inhibitory concentration values of the extract based on the 2,2-diphenyl-1-picrylhydrazyl was 20.33 µg/ml as compared to standard (ascorbic acid) which is 18.30 µg/ml while the half-maximal inhibitory concentration values of the methanolic extract of Ipomoea hederifolia and standard (ascorbic acid) for hydroxyl scavenging activity were found to be 104.9 µg/ml and 16.88 µg/ml respectively. The reducing power of methanolic extract and standard (ascorbic acid) at 25 µg/ml were found to be 0.183±0.002 and 0.396±0.001 respectively and the total antioxidant capacity of methanolic extract and standard (ascorbic acid) at 100 µg/ml and were found to be 0.094±0.0002 and 0.368±0.002 respectively. [ABSTRACT FROM AUTHOR]

Lung cancer, a predominant global health challenge, necessitates advanced diagnostic strategies to improve patient outcomes. This research explored the integration of machine learning algorithms, specifically Faster R-CNN, YOLO, and SSD, with lung imaging to enhance cancer detection and staging. Using a comprehensive dataset encompassing radiographs and CT scans of Atypical Carci- noids, Adenosquamous carcinoma, and Mucoepidermoid carcinoma, the models were trained, validated, and tested. The results spotlighted the potential of this approach, with the algorithms achieving an average accuracy of approximately 88% for cancer presence determination and 83% for stage identification. In comparison to Traditional CNN's results of 82% and 76% respectively, our methodologies showcased a marked improvement. These findings underscore the significance of machine learning in medical imaging, offering avenues for more precise, timely, and informed clinical decisions, ultimately setting a trajectory for improved patient care in the realm of lung cancer. [ABSTRACT FROM AUTHOR]

Here, we studied Bianchi type-III string cosmological models generated using a cloud of strings with particles connected to them in the framework of Lyra geometry considering five-dimensional spacetime. We assume that the shear scalar and scalar expansion are proportional to obtain the exact solutions of the survival field equations here. Secondly, we adopt the assumption considering the Reddy string condition. From the different cases obtained here, the first case gives the Bianchi type-III string cosmology in Lyra geometry in five-dimensional spacetime, and the second case gives the five-dimensional vacuum model in general relativity. The various parameters of the model universe, which are very important for the description of the Universe, are obtained, and their properties are studied and compared with the recent observational findings. The model universe obtained here starts with the Big Bang, and as time progresses, both particle density and energy density decrease with the expansion of our Universe. [ABSTRACT FROM AUTHOR]

This paper emphasizes on detection and classification of Fluoro-Deoxy-Glucose (FDG) radioactivity uptakes in fused Positron Emission Tomography / Computerized Tomography (PET/CT) images automatically. The deep learning technique using Convolutional Neural Network (CNN) is proposed to reduce the complexity in observation, to solve the problem of low accurateness and the time-consuming process of traditional classification methods. The CNN layers are designed and proposed for the FDG uptakes classification problem in fused PET/CT images. The proposed modified CNN model is trained using different optimizers such as Stochastic Gradient Descent Momentum (SGDM), Adaptive Moment Estimation (ADAM), and Root Mean Square propagation (RMSprop). The deep features extracted from the proposed CNN are classified using different classifiers such as K-Nearest Neighbor (KNN), Decision Trees (DT), Ensemble, Naive Bayes (NB), and multi-class Support Vector Machine (SVM) the results of which are compared. The multi-class SVM classifier trained using SGDM optimizer attains the maximum test accuracy of 98.18% and was found to be superior to pre-trained deep models such as AlexNet, ResNet, and GoogleNet. [ABSTRACT FROM AUTHOR]

Autism Spectrum Disorder (ASD) is neurodevelopment-based impact on interactive communication and social skills. Diagnosing ASD is one of serious issues that start manifesting at low ages, and is difficult to diagnose at early stages. Autism is characterized by both environmental and genetic factors. Lack of communication issues, social interaction, and limited interest behaviors are possible individuality of autism noticed in children, along other symptoms. This paper aims at ASD detection by Deep Quantum Neural Network (DQNN), wherein this network is trained by proposed Fractional Social Driving Training-Based Optimization (FSDTBO). The initial stage of this processing starts with acquisition of image from dataset, and further pre-processing is carried out using Gaussian filter, and this filtered image is suspended for Regions of Interest (ROI) extraction. Also, extraction of nub region is done by proposed Social Driving Training-Based Optimization (SDTBO), from which classification process is done by considering extracted features too. Here, proposed FSDTBO is integration process among Fractional Calculus (FC) and SDTBO, wherein SDTBO is collaboration between Social Optimization Algorithm (SOA) and Driving Training-Based Optimization (DTBO). Moreover, classification performance of ASD is found based on three metrics, like accuracy, specificity, and sensitivity with superior values of 0.90, 0.94, and 0.96. [ABSTRACT FROM AUTHOR]

To better understand disease susceptibility and prevention, computational genetic epidemiology is leading research. This paper introduces "GenomeMinds," a breakthrough method for scaling large-scale AI models for disease risk prediction. HPC was used to develop the method. GenomeMinds is compared to six standard methods to demonstrate its benefits. GenomeMinds' incredible potential is shown by real-world performance assessments. These measures evaluate data processing speed, forecast accuracy, scalability, computer efficiency, privacy, and ethics. GenomeMinds benefits are shown via scatter plots, which visually compare data. According to the data, GenomeMinds may revolutionize computational genetic epidemiology by doing well across all criteria. GenomeMinds has faster data processing, better prediction accuracy, stronger scalability, higher computational efficiency, enhanced privacy and security, and a comprehensive ethical awareness. [ABSTRACT FROM AUTHOR]

The current study was conducted with the objective of determining the cause for a significant increase in mortality rates in poultry farms situated in and around Thrissur. The study material consisted of 100 birds aged between two and four months, which were brought for postmortem to the Department of Veterinary Pathology, College of Veterinary and Animal Sciences, Mannuthy. The birds with a history of respiratory illness were included in the study. On detailed postmortem examination, catarrhal or caseous exudate in the trachea/nasal passages, pulmonary congestion, swollen and pale kidneys, splenomegaly with congestion were noticed in cases suspected for infectious bronchitis (IB) infection alone. Enlarged and congested kidneys; enlarged bursa with cheesy material inside the lumen and splenomegaly with subcapsular haemorrhages were noticed in carcasses suspected for concurrent infection with infectious bursal disease (IBD). On microscopic examination, bursa of IB infection alone revealed densely populated lymphocytes with necrotic changes in the medulla and bursa of concurrent infection with IBD revealed extensive haemorrhage in the cortical region and large cyst in the medulla of the plicae. The cases were confirmed using a reverse transcriptase polymerase chain reaction (RT-PCR). Out of 100 samples, 33 cases tested positive for IB. Upon further investigation of these 33 samples, 25 samples were found to be concurrently infected with IBD. The condition was confirmed as infectious bronchitis with concurrent infection with infectious bursal disease based on both pathological findings and molecular analysis. [ABSTRACT FROM AUTHOR]

Wireless body sensor networks have gained significant importance across diverse fields, including environmental monitoring, healthcare, and sports. This research is concentrated on sports applications, specifically exploring the viability of a wireless body area network tailored for high-performing athletes. The paper is divided into three sections. First, the design of the node location that is used for real-time monitoring of a sportsperson in which the node position, such as the human thigh, foot, arm, wrist, and chest, was estimated and the best position was selected. Second, the accuracy of an application when related to the other schemes such as TDMA with ZigBee and RA-TDMA & PA-TDMA was done. The reliability using RA-TDMA performed well and showed approximately 98% reliability. Finally, the features of wireless communiqués that affect the presentation of the network for RA-TDMA were estimated, such as delay and jitter. These findings collectively contribute to advancing the understanding of optimizing wireless body sensor networks for sports applications, with notable achievements including the identification of the arm as the optimal sensor placement, achieving a 98% success rate, and surpassing alternative techniques in network performance parameters like packet delivery rate. [ABSTRACT FROM AUTHOR]

This study was conducted to test the suitability of using nine types of millets namely finger millet, pearl millet, white and yellow sorghum, little millet, barnyard millet, proso millet, kodo millet, and browntop millet in seven popular Indian meal preparations based on sensory characteristics and nutrient value. The popular Indian meal preparations tested were boiled grain, dosa, idli, bisi belle bath, pulao, puttu, and pongal. In total, 53 variations in meal preparations were developed using the millets and seven polished white rice-based meal preparations were developed as control. The main findings indicated that meal preparation crafted from various millets garnered overall sensory scores closely resembling to those derived from polished white rice. Notably, little millet exhibited high scores in pongal and dosa, and achieved elevated overall sensory scores compared to meal preparation from polished white rice. Bisi belle bath made of barnyard millet scored higher in overall sensory score than polished white rice. Moreover, there was significant association between some types of millets' overall sensory characteristics (p<0.005) with polished white rice-based meal preparations. In terms of nutrient value, all the millet-based meal preparations had significantly high nutritional value compared to those made with polished white rice (p<0.05). Especially calcium content of the meal prepared with finger millet was significantly higher compared to polished white rice-based meals (p<0.05). Puttu, idli, and dosa prepared with finger millet had calcium content of 59.4, 10.8, and 70.9mg/100g compared to those prepared with the polished white rice which had only 1.3, 6.3, and 9.2mg/100g. The magnesium content of all millet-based meal preparations was generally several-folds higher compared to the polished white rice-based meal preparations (p<0.05). There is a significant difference in the fiber content of the meals prepared with millets compared to the meals prepared with polished white rice (p<0.05). This study was conducted using millets that are locally available and does not represent all the millet varieties available globally, as each type of millet has a wide range of varieties. Therefore, it is important to understand and choose the type and variety of millet while enhancing the nutritional value of diets. [ABSTRACT FROM AUTHOR]

Energy efficiency is treated as a challenging problem in Wireless Sensor Networks (WSNs) which involves limited non-replaceable and non-rechargeable inbuilt batteries. Optimal utilization of available energy in the sensor nodes is an effective way to improve the lifetime of the WSN with assured quality of service (QoS). Clustering can be employed as an efficient approach for enhancing network lifetime and scalability. Since clustering is considered an NP-hard problem, several metaheuristic algorithms are utilized for accomplishing energy efficiency. With this motivation, this study proposes an energy-aware clustering protocol utilizing a chaotic gorilla troops optimization algorithm (EACP-CGTOA) for WSN. The proposed EACP-CGTOA model derives a CGTOA by replacing the population initiation with circle chaotic mapping to explore the solutions with a high convergence rate and sensitivity. The CGTOA helps to increase the population diversity and overall performance of the optimization algorithm. Besides, the EACP-CGTOA model derives a fitness function involving three input parameters namely distance to neighbours (DTN), distance to base station (DBS), and energy ratio (ER). To ensure the enhanced performance of the EACP-CGTOA technique, a wide range of simulations were carried out and the outcomes are examined under several aspects. The experimental results ensured that the network lifetime and energy efficiency are considerably improved by the EACP-CGTOA model over the existing methods. [ABSTRACT FROM AUTHOR]

In this paper, CdS (CS), graphene oxide (GO) and reduced graphene oxide (rGO)-blended CdS (GCS and rCS) NPs are studied for their photocatalytic, electrochemical, and antimicrobial properties. CS and GCS were synthesized by chemical precipitation method. Centella asiatica leaf extract was used to produce rCS in one pot. X-ray diffraction (XRD) studies revealed hexagonal crystal structure for all the samples. All samples had Cd-S bond vibrations confirmed by Fourier transform infrared (FT-IR) spectra. Photoluminescence (PL) intensities of CdS quenched with GO and rGO decoration. GCS and rCS NPs exhibited strong optical absorption with reduced band gaps. Photodegradation efficiency of CS against CR dye increased from 84 to 90 % for GCS and 96 % for rCS. Specific capacitance of CdS increased with GO, rGO incorporation and the rGO-blended CdS NPs showed better electrochemical properties. Antibacterial tests performed with different concentrations (20, 40, 60 and 80 mg) of CS, GCS and rCS NPs confirmed that GCS and rCS resisted the tested B. subtilis and E. coli bacteria effectively better than CS, with rCS showing more resistance. These outcomes show that rGO blended PbS/NiO NC might be used as antibiotics in the future due to its high effectiveness against the pathogenic microorganisms. [ABSTRACT FROM AUTHOR]

The present investigation, aimed to prepare lead silicate glasses doped with samarium ions and to study it's structure, elastic, spectroscopic and thermoluminiscence properties with Al(10−x)Sbx composite influence. The objective of the investigation is to develop TL sustained good laser active resource by means of studying photo and thermoluminiscence properties. In this direction, we have prepared the glass series of Al(10−x)SbxSm0.1Pb30Si59.9 materials (where, 0 ≤ x ≤ 10, with steps of 0, 2, 4, 6, 8 and 10 mol%) after that planned for structural (XRD, FT-IR, DTA, EDS & SEM), Optical (UV & PL) and thermoluminescence characterization. The XRD and SEM studies reveal the glassy behavior of test samples. The EDX analysis signifies the chemical constitutes with atomic weight %. The physical properties such as density and molar volume of glasses were evaluated. The DTA studies of glasses reveal the glass transition and crystallization temperature points. The values of thermal stabilities of glasses found to be purely function of Sb3+ ions. Various structural vibrations of test glass conformed using FT-IR studies. Using ultrasonic velocities of the test glasses, the glasses' micro-hardness was evaluated. From the absorption spectra, the Judd–Ofelt intensity parameters (Ω2, Ω4, Ω6) have been calculated. The PL spectra under stimulation at 402 nm show a prominent reddish–orange emission at 602 nm. The experimental lifetimes (τr) of the 4G5/2 → 6H7/2 (602 nm) luminescence transition were measured using the decay spectral profiles of this transition. The evaluated emission cross-sections (σse), branching ratios (βr), and quantum efficiency (η) allow us to speculate that the lead silicate glasses doped with Sm3+ ions are ideally suitable as potential gain mediums for visible reddish orange lasers pumped directly by readily available 405 nm laser diodes. Quantum efficiencies of the produced glasses are obtained using a decay curve analysis. The emission spectra used to establish the developed glasses' reddish orange emission are used to compute the CIE chromaticity coordinates. It is evident from the stated fluorescent properties that the developed glasses are suited for reddish–orange laser applications, particularly in opto-electronic devices. From all of the values obtained, it can be observed that SAS6 glass code is ideally suited for reddish–orange laser applications. The TL properties are studied at 30 kGy gamma irradiation dose range. The frequency factor and A.E. of glasses were evaluated. This reveals that the TL behavior of glasses is a purely function of Sb3+ ions. Overall, the Al(10−x)SbxSm0.1Pb30Si59.9 glasses prepared are mechanical hard, TL effective, and optically useful resources. [ABSTRACT FROM AUTHOR]