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Unique helical conformation of the fourth cytoplasmic loop of the CB1 cannabinoid receptor in a negatively charged environment
The proximal portion of the C-terminus of the CB1 cannabinoid receptor is a primary determinant for G-protein activation. A 17 residue proximal C-terminal peptide (rodent CB1 401-417), the intracellular loop 4 (IL4) peptide, mimicked the receptor's G-protein activation domain. Because of the importance of the cationic amino acids to G-protein activation, the three-dimensional structure of the IL4 peptide in a negatively charged sodium dodecyl sulfate (SDS) micellar environment has been studied by two-dimensional proton nuclear magnetic resonance (2D 1H NMR) spectroscopy and distance geometry calculations. Unambiguous proton NMR assignments were carried out with the aid of correlation spectroscopy (DQF-COSY and TOCSY) and nuclear Overhauser effect spectroscopy (NOESY and ROESY) experiments. The distance constraints were used in torsion angle dynamics algorithm for NMR applications (DYANA) to generate a family of structures which were refined using restrained energy minimization and dynamics. In water, the IL4 peptide prefers an extended conformation, whereas in SDS micelles, 310-helical conformation is induced. The predominance of 310-helical domain structure in SDS represents a unique difference compared with structure in alternative environments, which can significantly impact global electrostatic surface potential on the cytoplasmic surface of the CB1 receptor and might influence the signal to the G-proteins. 2007 Elsevier Inc. All rights reserved. -
Biocidal Activity of Barium and Iron-Co-Doped Titanium Dioxide Nanocomposites, Synthesized by Psidium guajava-Mediated Precipitation Method
The emergence of drug-resistant microorganisms and the need for effective anticancer agents necessitate the development of novel nanomaterials with enhanced biomedical performance. This study aims to synthesize barium and iron dual-doped titanium dioxide (TiBaFeO NC) using a green precipitation method with Psidium guajava leaf extract, targeting improved antimicrobial and anticancer efficacy. The synthesized nanocomposite was characterized by various analytical techniques. XRD confirmed the crystalline anatase phase of TiO2 and TiBaFeO NC, with average crystallite sizes of 40 and 37nm, respectively, suitable for biomedical applications. UV-Vis analysis showed a decrease in bandgap from 3.79eV for TiO2 to 3.67eV for TiBaFeO NC, indicating enhanced reactive oxygen species (ROS) generation potential. PL spectra exhibited green emissions at 520nm for TiO2 and 523nm for TiBaFeO NC, reflecting a higher oxygen vacancy defect density in the doped nanocomposite. Biological evaluations demonstrated that TiBaFeO NC exhibited superior antimicrobial activity against Gram-positive bacteria (Staphylococcus aureus, Bacillus subtilis, Bacillus megaterium), Gram-negative bacteria (Shigella dysenteriae, Escherichia coli, Proteus vulgaris), and fungi (Candida albicans). Furthermore, TiBaFeO NC showed enhanced anticancer activity against human breast cancer cells (MDA-MB-231) with an IC50 of 9.8g/mL, outperforming TiO2. These results suggest that TiBaFeO NC is a promising nanocomposite for advanced biomedical applications, combining enhanced antimicrobial and anticancer properties through defect-mediated ROS generation. 2026 International Union of Biochemistry and Molecular Biology, Inc. -
Synthesis and Biological Evaluation of Sr and Co Co-Doped TiO?Folic Acid Nanocomposites: Antibacterial, Antifungal (Candida albicans), Antioxidant (DPPH and Trolox), and In Vitro Anticancer Activity against HepG2 Cells
Liver cancer and multidrug-resistant bacterial infections pose significant health challenges, highlighting the urgent need for multifunctional therapeutics. In this study, a TiO? nanocomposite co-doped with strontium (Sr) and cobalt (Co) and surface-functionalized with folic acid (TiO?SrCoFA) nanocomposite was synthesized via a hydrothermal method followed by post-synthesis FA functionalization. XRD confirmed the anatase phase, with reduced crystallite size for TiO?SrCoFA, while TEM showed spherical, uniformly dispersed nanoparticles (~ 23nm) with no agglomeration. DLS revealed a hydrodynamic diameter of 138.6nm, and XPS/FTIR confirmed Sr, Co, and FA incorporation. Optical studies (UV-Vis and PL) indicated electronic modifications conducive to ROS generation. TiO?SrCoFA exhibited enhanced antimicrobial activity against Gram-positive bacteria (Staphylococcus aureus, Bacillus subtilis, Bacillus megaterium), Gram-negative bacteria (Klebsiella pneumoniae, Proteus vulgaris), and Candida albicans. Antioxidant assays demonstrated concentration-dependent scavenging (2883%) comparable to vitamin C. In HepG2 liver cancer cells, TiO?SrCoFA showed superior cytotoxicity with an IC?? of 6.5g/mL versus 9.8g/mL for TiO?, inducing apoptosis and oxidative stress. The enhanced bioactivity is attributed to nanoscale size, Sr/Co doping, FA-mediated targeting, and ROS generation. TiO?SrCoFA thus represents a promising multifunctional nanotherapeutic platform for simultaneous antimicrobial, antioxidant, and anticancer applications. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
The Influence of Environmental, Social, and Governance (ESG) on Mergers and Acquisitions: Due Diligence and Integration
The article outlines the growing influence of Environmental, Social and Governance (ESG) elements in mergers and acquisitions (M&A). ESG due diligence is now a vital part of evaluating the risks and opportunities linked to target companies during mergers and acquisition transactions. Companies can gain a deeper insight into risks, opportunities and long-term value creation by assessing their environmental impact, social responsibility and governance structures. The study incorporates ESG factors in the entire M&A process, focusing on the significance of early evaluation and ongoing monitoring after the merger. The study also outlines effective methods for ESG due diligence, acknowledges the challenges faced and explores the potential for future research in this developing area. The results highlight how strong ESG practices are essential for effective M&A deals and better financial results in todays corporate strategy. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Importance Of Artificial Intelligence in Improving Human Resource Management For Companies To Find Suitable Candidature
The efficient use of pertinent human resources both inside and outside the company via management structures guided by economic and humanistic principles is known as human resource management. It is a catch-all word for a set of actions that guarantee the accomplishment of group objectives and the optimization of member growth. Employers need the correct recruitment tools to fill available positions since traditional recruiting approaches are not up to par in the global talent battle. First, as the digital tool redesigns business, we look at how talent acquisition has evolved from digital 1.0 to 3.0 (AI-enabled). Artificial intelligence technology has made recruiting more efficient and made recruiters' daily tasks easier. Additionally, the analysis in the paper shows that artificial intelligence (AI) is crucial to every step of the hiring process, including promotion, application, screening, evaluation, and coordination. This study demonstrates how organizations are realizing the value of talent management in gaining a competitive edge as the need for higher-level talent grows. Even though some HR managers are using AI for talent acquisition, our research shows that there is still an opportunity for development. 2024 IEEE. -
Approach Towards Web for Exploring the Suitable Job for Individuals
In light of future work challenges, true human resource management (HRM) must be rebuilt. This involves over time human resource development; it must also contain the concept of sustainability to move from consuming to generating human resources. The labor market is constantly changing, with nontraditional jobs becoming increasingly important, especially in light of the current COVID-19 legislation. A useful teaching strategy in a variety of academic fields, including career development, is experiential learning. Important elements for establishing experiential learning programs at the institutional level are also covered by researchers. Our framework may assist businesses in identifying the type of experiential learning that best fits their objectives and setting for professional training. It can also help ensure that the training is successfully designed and delivered. 2024 IEEE. -
Identification and structure-activity relationship studies of small molecule inhibitors of the human cathepsin D
Cathepsin D, an aspartyl protease, is an attractive therapeutic target for various diseases, primarily cancer and osteoarthritis. However, despite several small molecule cathepsin D inhibitors being developed, that are highly potent, most of them show poor microsomal stability, which in turn limits their clinical translation. Herein, we describe the design, optimization and evaluation of a series of novel non-peptidic acylguanidine based small molecule inhibitors of cathepsin D. Optimization of our hit compound 1a (IC50 = 29 nM) led to the highly potent mono sulphonamide analogue 4b (IC50 = 4 nM), however with poor microsomal stability (HLM: 177 and MLM: 177 ?l/min/mg). To further improve the microsomal stability while retaining the potency, we carried out an extensive structureactivity relationship screen which led to the identification of our optimised lead 24e (IC50 = 45 nM), with an improved microsomal stability (HLM: 59.1 and MLM: 86.8 ?l/min/mg). Our efforts reveal that 24e could be a good starting point or potential candidate for further preclinical studies against diseases where Cathepsin D plays an important role. 2020 Elsevier Ltd -
Reliability, maintainability and sensitivity analysis of physical processing unit of sewage treatment plant
India is facing radical change in perspective of inadequate water resources which can reduce by using treated water. In this direction, sewage treatment plants play a key role. Sewage treatment plant comprises three units namely physical processing, chemical processing and biological process. The physical process is the most important part and it has five component arranged in series configuration. It becomes necessary to perform this process with high efficiency and reliability, availability, maintainability, and dependability (RAMD) is the methodology to analyze the performance. The failure and repair rates of the subsystems has been considered exponentially distributed. ChapmanKolmogorov differential equations are derived using Markovian birthdeath process and several measures like mean time between failures, mean time to repair and dependability ratio are derived. The sensitivity analysis of reliability of the plant has also been performed. RAMD investigation shows that: availability of system is 0.952177, reliability of the system after 20days is 0.2018 and after 60days 0.00823, maintainability of the plant is 0.999948, dependability ratio is 0.9541 and raw sewage sump is the most sensitive subsystem of the plant with reliability 0.382893. This work is projected to support as an informative exertion in steering a RAMD analysis of physical processing unit and vary few work is available in literature related to the performance features of physical processing units of the sewage treatment plants. The main findings may be very useful for sewage treatment plants designers. 2019, Springer Nature Switzerland AG. -
Hybrid AODV: An Efficient Routing Protocol for Manet Using MFR and Firefly Optimization Technique
A MANET is a category of ad hoc protocol that could vary positions and track itself on the flutter. It utilizes wireless connections that are attached to several networks. They include wirelessly in a self-configured, self-healing network while not having permanent communication linked in a collection of mobile networks. The network topology of nodes typically varies in MANET, and nodes are free to stir errantly and independently as a router as they accelerate traffic to more nodes within the network. Ad hoc on-demand distance vector (AODV) was employed for node selection to attain the shortest path strategy in existing techniques. In the proposed system, the hybrid AODV (HAODV) technique incorporates the MFR (Most Forward within Radius) technique to detect the shortest path routing algorithm. The MFR method was deployed for selecting the neighbor node, while HAODV was deployed to find the shortest path. To find the shortest path based on the updating equation, the Firefly algorithm is also implemented into the Hybrid AODV. The proposed work's performance is calculated by different network parameters like the end to end delay, average routing overhead, throughput, and packet delivery ratio. After comparing AODV and DSR algorithms, the proposed algorithm (HAODV) shows improvement in packet delivery ratio, end-To-end delay, Routing overhead, and throughput. 2021 World Scientific Publishing Company. -
A Survey on Solution of Imbalanced Data Classification Problem Using SMOTE and Extreme Learning Machine
Imbalanced data are a common classification problem. Since it occurs in most real fields, this trend is increasingly important. It is of particular concern for highly imbalanced datasets (when the class ratio is high). Different techniques have been developed to deal with supervised learning sets. SMOTE is a well-known method for over-sampling that discusses imbalances at the level of the data. In the area, unequal data are widely distributed, and ensemble learning algorithms are a more efficient classifier in classifying imbalances. SMOTE synthetically contrasts two closely connected vectors. The learning algorithm itself, however, is not designed for imbalanced results. The simple ensemble idea, as well as the SMOTE algorithm, works with imbalanced data. There are detailed studies about imbalanced data problems and resolving this problem through several approaches. There are various approaches to overcome this problem, but we mainly focused on SMOTE and extreme learning machine algorithms. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Experimental Approach and CFD Analysis on Flow Devices
This paper deals with the study of experimental approach and investigation by using computational fluid dynamics (CFD) on various flow devices. An orifice meter, venturimeter and a nozzle meter are the most common type of measuring devices used for rate of flow by creating the differences in velocity and pressure. Pressure drop is an important parameter occurring in these flow devices, which is due to restricted passage of flow, properties, diameter ratio, etc. The focus here is to calculate the coefficient of discharge and other flow parameters to analyze theoretically with the application of Bernoullis equation. The main objective of this paper is to analyze the variations across the sections of orifice meter, venturimeter and nozzle meter. Comparison of results by both experimental and computational methods was clearly understood, and also, the flow level was calibrated by calculating the coefficient of discharge in both the methods. 2021, Springer Nature Singapore Pte Ltd. -
Synthesis of Yttria-Stabilized Zirconia Nano Powders for Plasma Sprayed Nano Coatings
Plasma sprayed Yttria Stabilized Zirconia (YSZ) coatings, with few microns sized microstructure/grain morphology has been well researched, reported and established as an industrial Thermal Barrier Coatings (TBC) material/system. However, nano structured YSZ coatings possess improved characteristics when compared with their micron sized counterparts. However, due to their nano sizes, light weight, and low density, plasma spray coating process of nano powders suffers from flowability issues due to lack of nano powder inertia/momentum, leading to poor deposition/uneven coating thickness. In this research work, nano structured YSZ coatings were synthesized by using an Atmospheric Spray Coating (APS) facility. Nano powders of YSZ were used as the starting materials to prepare micron sized plasma sprayable powders. 80?m thick NiCrAlY bond coat (commercial) and 200?m thick YSZ top coat with nano microstructure (lab synthesized) were built on steel substrates. The starting nano crystalline (YSZ) powders, measuring 30-70 nanometers (nm) were synthesized in the laboratory via chemical method (sol-gel) by employing zirconium oxy chloride hexa-hydrate and yttrium nitrate as precursors, citric acid as chelating agent and ethylene glycol for the diversification reaction followed by calcination @ 1000C. They were then re-constituted into micron sized (53-106 ?m) plasma sprayable powders by agglomerating with polyvinyl alcohol (PVA) binders. The nano crystallite morphology of powders and coatings were analyzed by Scanning Electron Microscope (SEM), chemical composition by Energy Dispersive spectroscopy (EDS) and crystal structural phase by X-ray diffraction (XRD). The influence of calcination temperature of 1150C on nano crystallite morphology was also studied. 2019 Elsevier Ltd. -
Plasma sprayed nano refractory coatings
Nano powders may be reconstituted into micron sized plasma sprayable powders either by using a spray drier or a manual process by employing organic binders to agglomerate them. This paper deals with the synthesis of nano sized alumino-silicate plasma sprayable powders and plasma sprayed coatings prepared from them. Nano sized raw materials involving kyanite and andalusite refractory powders were converted into plasma sprayable powders by using polyvinyl alcohol (PVA) binders. The preparation methodology involved obtaining free flowing, micron sized agglomerated nano-alumino-silicates particles which could be plasma spray coated by using an Atmospheric Spray Coating Facility. About 220 microns thick nano-alumino silicate coatings were deposited on 75 microns thick commercial NiCrAlY bond coat on stainless steel substrates. The challenges involved in plasma spray coating the nano material with low density was in obtaining good deposition efficiency, retaining the nano micro structures and the structural phase composition of the coating. The coatings were evaluated for materials characteristics such as crystal structural phase via XRD, microstructure via SEM and chemical composition via EDS. The microstructure depicted fine grained nano-sized surface morphologies, kyanite and andalusite phase structure, with high potential for application as refractory coatings. Published under licence by IOP Publishing Ltd. -
Process development to synthesize plasma sprayable powders from nano alumina ceramic powders
Nano sized (?100 nm) alumina powders were converted into micron sized (30-75 mm) plasma sprayable powders by employing synthetic polymers to agglomerate them. The agglomeration process was carried out (a) in a spray dryer and (b) through systematic manual granulation procedure. The importance of process parameters that govern the plasma spray powder synthesis and thereby the characteristics were being suitable for being plasma spray coated have been brought out in this research paper. A comparative study has been made between the two synthesis methods by testing the powders synthesized under different processing conditions for their flowability characteristics. Micro-structural features related with the shape morphology and powder grain sizes were studied by Scanning Electron Microscope and the elemental composition characterization was carried out by Energy Dispersive Spectroscopy. The most suitable plasma sprayable powders were further coated onto metal substrates by using an Atmospheric Plasma Spray coating unit. The plasma sprayable powders were developed with a goal to explore their potential for their applications as wear resistant nano coatings. 2019 Elsevier Ltd. All rights reserved. -
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An approach for document pre-processing and K Means algorithm implementation
The web mining is a cutting edge technology, which includes information gathering and classification of information over web. This paper puts forth the concepts of document pre-processing, which is achieved by extraction of keywords from the documents fetched from the web, processing it and generating a term-document matrix, TF-IDF and the different approaches of TF-IDF (term frequency Inverse document frequency) for each respective document. The last step is the clustering of these results through K Means algorithm, by comparing the performance of each approach used. The algorithm is realized on an X64 architecture and coded on Java and Matlab platform. The results are tabulated. 2014 IEEE. -
Enhanced Security in Payment Gateways Through Face Detection: An Advanced Approach Using DenseNet 121- BiLSTM Models
Because it is one of the most promising applications of image analysis, face recognition has been the subject of intense research and development for many decades. Many modern identification and verification requirements have found a potential new home with the introduction of face recognition (FR) technology. Facial recognition is just one of numerous uses for biometric pattern recognition algorithms. Sequencing is essential for many tasks, including as feature extraction, model training, and preprocessing. Eliminating background noise and obtaining dense vertical edges are part of the preprocessing procedures. Facial feature extraction will be employed to extract features after feature extraction. Use attributes cautiously when training a Desnet121-BiLSTM model. In every respect, the suggested method outperforms two state-of-the-art algorithms, Desnet121 and BiLSTM. An accuracy rating of 97.19% was indicative of a considerable improvement in the figures. 2024 IEEE. -
Robust Statistical Depth Methods for Medical Data: A Focus on Location Estimation and Classification
In robust statistics, data depth functions are extremely powerful and can provide measures of central tendency beyond the ordinary means and medians. These functions provide a sense of depth to points in multivariate space, providing by default a center-outward ranking of observations, which is resistant to outliers and which can be applied to complex and high-dimensional data. Various data depth processes are considered to determine the most optimal location measure with real and simulated data. The performance of Mahalanobis Depth (MD), Half-space Depth (HSD), Zonoid Depth (ZD), Projection Depth (PD), and Spatial Depth (SPD) are compared on some health datasets including the Pima Indians Diabetes Dataset and the Wisconsin Breast Cancer (WBCD) Dataset. The results of these procedures are studied based on calculated depth values and error rates in the discriminant analysis. The findings suggest that the highest depth values are always exhibited by Spatial Depth (SPD), with better robustness and stability without losing accuracy, thus making it the best option. Nevertheless, Mahalanobis Depth (MD) also performs well, which is why it is highly applicable to the robust statistical modelling. Moreover, a new Generalized Mahalanobis Depth (GMD) has been proposed, based on robust location and scatter estimators to eliminate the weaknesses of classical MD. The GMD is more robust to contamination and is valid with singular or ill-conditioned covariance structures, and to high-dimensional data of relevance to real-world data, achieving lower misclassification rates. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Performance Investigation of the High Strength Concrete Using Natural Zeolite with Industrial Waste Materials
Concrete is used in the construction of various structural elements. High Strength newlineConcrete (HSC) production for huge infrastructure projects is challenging. The newlinemanufacture of cement significantly causes global carbon dioxide (CO2) emissions. newlineModifications have been made to cement concrete problems to minimize CO2 emissions and Ordinary Portland Cement (OPC) consumption. This research focuses on developing HSC blended with Natural Zeolite (NZ) and industrial by-products like newlineSilica Fume (SF), Fly Ash (FA), and Metakaolin (MK) to enhance concrete quality, newlinesustainability, and performance. Partial replacement of OPC with 5% NZ and industrial waste materials in 5%, 10%, and 15% amounts to produce M60 grade HSC mixes. In the laboratory, 1,200 concrete specimens were tested for mechanical properties for 3, 7, 28, 60, and 90 days, as well as durability tests such as the Rapid Chloride Penetration Test (RCPT) for 28 days and the acid attack test for 60 days. Mix M3 (85% OPC + 5% NZ + 10% MK) exhibited the highest compressive strength at 72 MPa, split tensile strength at 5.3 MPa, and flexural strength at 9.4 MPa for 90 days curing period, attributed to its low porosity. The reactive silica (SiO2) and alumina (Al2O3) in the mix transformed calcium hydroxide (Ca(OH)2) into calcium silicate hydrate (C-S-H) gel and aluminate compounds. This process improved the newlinemicrostructure of the hardened concrete, resulting in increased imperviousness. The newlinestudy also includes the effect of these industrial waste materials on Zeolite concrete by microstructure analysis. The mathematical models were developed using SPSS software to predict the durability and mechanical properties of all the concrete mixes based on the laboratory data, considering parameters like mix proportions and curing days.