Presidency University, Bangalore

In the field of metal Additive manufacturing (AM), Laser Powder Bed Fusion (LP-BF) is recognized as a cutting-edge technique that has made significant improvements. Yet, notwithstanding significant advancement, its total incorporation into the industrialized scene is still restricted. The extensive papers, which include an in-depth discussion of the materials and production factors, shed light on current developments in LB-PF. The emergence how an alloy with elevated entropy and medium-strength metallics as essential components of LB-PF is a prime example of how materials have evolved through time. The discussion concurrently explores scanner tactics and various uses of multi-laser combinations with the goal of better productivity along with customer satisfaction. The introduction broadens the breadth of the story by navigating the tides of developing study impulses. The thoughtful reuse of powdery substances becomes a focus demonstrate, since it combines ecology and commercial feasibility. This dynamic environment also expands to include shape-storing metals, promoting dynamically reactive parts, and magnetically metals, sparking opportunities within magnet-sensitive surfaces. The work concludes in the final sections with a broad overview of the latest developments in the LB-PF sector. The careful analysis of these developments resulted in the consideration of as-yet unexplored pathways. Hidden possibilities for investigation emerge as the curtain is pulled away from this conversation, advancing the field to unexplored territory. In conclusion, the piece explores LB-PF's previous, current, and potential the not-too building of an intricate web of innovations, tendencies, and threats that together influence the course of this cutting-edge production system.

Biometric recognition systems are essential for secure authentication, leveraging unique physiological and behavioral characteristics to identify individuals. The Biometric recognition systems risk of unauthorized access is heightened. This study presents the development of a multimodal biometric recognition system that incorporates vein recognition, specifically palm and knuckle veins, as a reliable alternative to traditional methods. The proposed system employs a contrast-enhancement technique for pre-processing to improve image quality, while Gray-Level Co-Occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT) are utilized for feature extraction. Feature selection is performed using the Chimp Optimization Algorithm (ChOA) to reduce computational complexity and enhance classification accuracy, with final classification executed by a Deep Neural Network (DNN). The DNN-ChOA model achieves an accuracy of 99.85%, sensitivity of 98.25%, and specificity of 97%, significantly outperforming traditional and other modern methods. These results underscore the effectiveness of the multimodal biometric system in delivering robust security while meeting the demands of identity digitalization and virtualization efficiently.

Piperine reported to have diverse pharmacological potentials has been screened towards a number of anti‐inflammatory molecular targets such as Cox‐2, interleukin‐1β (IL‐1β), IL‐4, IL‐13, matrix metallopeptidase‐3 (MMP‐3), MMP‐8; in‐silico using the various computational tools like variable nearest neighbour absorption, distribution, metabolism, excretion, and toxicity (vNN‐ADMET), SWISS ADME, Spartan‐14, iGemdock, and DS‐Visualizer. The results when compared with commercial drugs exhibited parallel anti‐inflammatory potential with ibuprofen and naproxen. In addition, we here report in‐vitro anti‐cathepsin B and serum protein‐protecting activities. Complete inhibition of cathepsin B was observed at 200 µM of piperine and 250 µM for both indomethacin and naproxen, respectively. And, 100% anti‐denaturation was observed at 10 µM of piperine, and 15 µM of indomethacin and naproxen. The results validated the anti‐inflammatory potential of piperine, with both in silico and in vitro studies targeting previously unreported mechanisms.

A compact quad-element multiple-input multiple-output (MIMO) antenna design for dual-band operation (38.07 and 42.87 GHz) has been proposed for fifth-generation new radio and mm-wave bands. The overall dimension of the proposed design is $1.90\lambda _{0}\times$ $2.53\lambda _{0}\times$ 0.06 $\lambda _{0}$ (or 15 mm $\times$ 20 mm $\times$ 0.508 mm), where $\lambda _{0}$ represents the wavelength corresponding to 38.07 GHz. The proposed antenna consists of slotted and truncated rectangular patch with H-shaped arms and partial ground structure. Parametric analysis with respect to ground width $(W_g)$ for reflection coefficient and antenna gain has been performed to find the optimum values of $W_g$ which satisfies the required bandwidth and bands of operation (n259 and n260). The bandwidth achieved by the proposed quad-element MIMO antenna is 12.25 GHz (31.85–44.37 GHz). The isolation achieved by this antenna for the entire band of operation is < -20 dB. Moreover, the proposed design results stable radiation patterns along with high gain of 5.33 dBi and radiation efficiency of > 97%. The different MIMO diversity parameters achieved by the quad-element antenna are < 0.025 for envelope correlation coefficient, 9.99 dB for diversity gain, and < 0.4 bits/s/Hz for channel capacity loss in the entire operating band. The achieved channel capacity has also been analyzed for this design which resulted to $\approx$ 18.5 bits/s/Hz. Furthermore, the equivalent circuit for the proposed MIMO antenna has also been presented and validated.

Under complex financial circumstances, individuals are empowered to improve financial decision-making by trusting financial advice and utilizing digital technology and resources. Though the extant research has explored numerous factors impacting financial well-being, the specific influence of financial advice and digital financial literacy remains underexamined in the Indian context. Thus, grounded on Social Cognitive theory, this study aimed to examine how insights gained from financial advice and digital financial literacy integrate into individual’s decision-making and, subsequently, influence their financial well-being. The data were collected using purposive sampling from Southern India, with 508 respondents recruited using social media platforms. The research hypotheses were empirically validated through hierarchical regression and mediation analysis using the Hayes Process Macro. The study’s findings reveal that financial advice positively predicted financial decision-making (β = 0.667; p < .000). Similarly, digital financial literacy has a positive impact on financial decision-making (β = 0.369; p < .000). Additionally, financial decision-making (β = 0.105; p < .065) positively predicted financial wellbeing. Thus, both factors emerged as transformative predictors of an individual’s financial well-being. Moreover, the findings reveal the mediating role of financial decision-making between financial advice, digital financial literacy, and financial well-being. Therefore, the study underscores that by leveraging the cumulative effect of professional financial advice and digital technologies, policymakers and government regulatory bodies can augment the critical ability of informed decision-making. Thus, these factors could navigate overcoming individual financial challenges and benefit the overall well-being of a diverse population.

This paper examines the emerging trends shaping the sustainable future of the food industry within the context of Food Industry 5.0, while also addressing the challenges and opportunities inherent in this transformative landscape. The purpose of this study is to provide a comprehensive analysis of the emerging trends driving sustainability initiatives within the food industry. The objectives of this research are threefold: firstly, to identify and analyse the key trends shaping the sustainable future of the food industry; secondly, to assess the challenges faced by stakeholders in implementing sustainable practices within the food supply chain; and thirdly, to explore the opportunities for technological application by the shift towards sustainability. Methodologically, this study employs a multi-faceted approach that combines comprehensive literature review with qualitative analysis of industry reports, case studies, and expert interviews. Data is gathered from a diverse range of sources to provide a holistic understanding of the dynamics at play within the food industry. The results of this research identified and synthesis the various emerging trends in the context food Industry 5.0 driving sustainability initiatives within the food industry. The trends are artificial and human intelligence, blockchain, Collaborative robots (Cobots), Farm optimization with precision farming, plant-based and alternative protein sources, Circular economy practices, and Sustainability in food industry. Despite significant challenges such as high implementation costs, Data security and privacy, Sustainability and environmental impact, Ethical concerns and consumer perception, there exist ample opportunities for innovation and growth for businesses that embrace sustainable practices. Food producers, ranging from farmers to manufacturers, can gain significant benefits from embracing the emerging trends in the food industry.

Pyridine is one of the significant six‐membered N‐heterocycles that has gained the attention of the scientific community because it is an integral part of various medicinally important natural products. There is a sizable and expanding market for pyridine derivatives due to their many pharmaceutical, medicinal, and agricultural uses. Numerous chemicals are being tested in clinical studies, in which pyridine analogs have occupied the top position. Pyridine scaffolds are also becoming more and more prominent for the use of modern medicine and are anticipated to have several uses in daily life. In this connection, various techniques have been developed to create novel pyridine derivatives, such as multicomponent one‐pot reactions, green catalysts environmentally friendly solvents, solvent‐free synthesizing, ultrasonic production, and microwave‐assisted synthesis. This study unifies the synthesis of various pyridine‐based molecular frameworks using green protocols and the results will support new ideas about creating biologically active compounds.

This paper examines the performance of underwater wireless optical communication (UWOC) links using single-input and single-output (SISO) and selection combining (SC) receiver diversity techniques. To accurately model the effects of underwater turbulence on light propagation, the Malaga gamma (MG) distribution is employed for both weak and strong turbulence conditions. Analytical bit error rate (BER) expressions for on–off keying modulated UWOC links are derived for both SISO and SC receivers, utilizing the MG distribution’s power series representation. Monte Carlo simulations are conducted to validate the analytical BER results. The findings of this study provide valuable insights into the design and optimization of UWOC systems operating in various underwater environments.

The current study is investigated the synthesis, structural characterization, and antimicrobial evaluation of a terpolymer and its polychelate. The terpolymer (SPF) was synthesized from p-Semidine (1) and p-Phenylenediamine (2) with Formaldehyde (3) through a polycondensation method using dimethylformamide as a reaction medium. Terpolymer-poly chelate with copper was prepared from SPF ligand using a molar ratio of 1: 2 (Cu²⁺: ligand). The structures of the synthesized ligand and its polychelate were characterized using elemental analysis, Fourier Transform Infrared, UV–visible, and Nuclear Magnetic Resonance (¹H & ¹³C) spectral studies. The average molecular weights of the SPF terpolymeric ligand was determined by gel permeation chromatography. The in-vitro experimental studies of SPF Terpolymeric ligand and its copper complex as antimicrobial agents were tested against Candida albicans, Bacillus subtilis, Pseudomonas aeruginosa, Escherichia coli, and Staphylococcus aureus using the disc diffusion method, with Gentamicin serving as the reference for comparison. The antimicrobial studies revealed that the synthesized compounds exhibited promising results, comparable to a standard drug. Notably, the SPF metal complex demonstrated superior activity against Bacillus Subtilis (30 mm/ml) and Pseudomonas aeruginosa (26 mm/ml) compared to the ligand. The zone of inhibition is a key indicator in antimicrobial testing, reflecting the effectiveness of antimicrobial agents at various concentrations and offering valuable insights into their potential applications in preventing or treating microbial infections. The in-vitro experimental studies have been validated with the DFT, Molecular docking, and ADMET studies and found that the synthesized terpolymer and its polychelate were potent antibacterial and antifungal agents. In addition, the anti-destructive effect of SPF terpolymer resin on mild steel in sulphuric acid was assessed by mass-loss and electrochemical performances. By forecasting the mechanism of adsorption, thermodynamic and kinetic statistics were intended. Based on the collected data, the inhibition efficiency (%) is computed, demonstrating the terpolymer resin’s ability as a corrosion inhibitor, with an optimal concentration of 1 × 10⁻³ M exhibiting an inhibitory efficacy of 92.90%. Absorption and DFT modules are also used to study the molecular level coating of SPF polymer on the mild steel exterior. The result of conceptual module runs illustrated SPF polymer developed π-orbitalis interaction on head and tail fragments with the mild metal, which leads the better corrosion inhibition. The predictable information exposed SPF resin that exhibited good anticorrosive consequences on mild steel in destructive media. Graphical Abstract

This study indicates the selected social media marketing (SMM) dimensions such as influence social media content, engagement and interaction, brand awareness and perception, and influencer marketing that have influence on young consumers and drive their online purchase decisions. This study addresses these factors focusing on the context of young consumers in Bangladesh. For this investigation, a quantitative approach is employed through a structured questionnaire survey, and the data was collected from 412 Bangladeshi young users age limit is between 18 to 30, who purchase their products in online platform. The young population is between the ages of 18 and 30, and these samples were selected purposively. Data was inputted through MS Excel, and the PLS-SEM version 4 software was used to evaluate the hypothesized relationships among the variables. The findings reveal that the influence of social media content, engagement and interaction, brand awareness and perception, and influencer marketing encourage the young customer in social interactions that significantly influence their purchase decisions. This research contributes to a deeper understanding of how this young generation interacts with SMM and how businesses can leverage these SMM dimensions (content, engagement, brand perception) to effectively reach and convert this important online shopping demographic in Bangladesh.

Abstract Earth’s climate experienced an extreme transition going from the Eocene hothouse into Oligocene icehouse conditions. The Middle Eocene Climatic Optimum (MECO ~41Ma) briefly interrupted this cooling trend, initiated in the middle Lutetian (~44 Ma). The tropical position of the Indian subcontinent during the MECO provides an ideal setting to investigate low-latitude seasonality under hot climate conditions, considering the modern minimal seasonality in the tropics. To better understand the seasonality and its effect on shallow marine fauna, a high-resolution multiproxy study is carried out on oyster specimens of the type Flemingostrea pseudoflemingi from the early Bartonian palaeo-tropical Kutch Basin. Clumped isotope (Δ47) thermometry combined with δ18O analyses from excellently preserved shell specimens suggests low seasonal temperature fluctuations (Δ47-T: ~3°C) with a mean temperature of 34.4±2.0°C (95% CI) in the warmer season and 31.4±2.4°C in the months of highest precipitation. Oyster populations from distinct regions within the basin display varying sizes. Smaller variants, inhabiting a zone influenced by marine conditions (δ18Ow: -0.2±0.5‰ to 0.4±1.0‰) experienced slower growth rates (~18 µm/day) only during the warmer months. In contrast, larger variants, probably residing at shallower environment prone to more seasonal changes due to evaporation and freshwater input (δ18Ow: -1.0±0.8‰ to 1±0.7‰), maintained a steady growth rate (~58 µm/day) throughout the year. Although the observed seasonality is similar to modern tropical settings of the Indian and Pacific Ocean, the significantly warmer temperature range created a stressful environment for oysters, where seasonally diverse habitat and nutrient-rich freshwater input were critical to their growth.

The miniaturized dual‐element triple broadband Multiple‐Input‐Multiple‐Output (MIMO) antenna is suggested. By creating a partial ground plane beneath the triple snake‐head‐shaped patch, three wide bandwidths are achieved. The investigated −10 dB impedance bandwidths are 10.2–18.4 GHz, 23.6–29.4 GHz, and 33.4–59.4 GHz, with the fractional bandwidth (FBW) 57.34%, 21.88%, and 56%, respectively. The isolation is improved by joining the microstrip line between the antennas in partial ground plane. The diversity performance of MIMO antenna is examined by the computational analysis of mean effective gain (MEG), diversity gain (DG), total active reflection coefficient (TARC), envelope correlation coefficient (ECC), ergodic channel capacity (CC), and channel capacity loss (CCL). Prototyping of the suggested design is carried out on FR‐4 dielectric substrate with electrical dimensions 0.524λ0 × 0.715λ0 mm² (where λ0 is free space wavelength at center frequency of lowest operating band), dielectric constant 4.4, and loss tangent 0.02. The isolation, ECC, peak gain, average total efficiency, and average CC over the operating bands 10.2–18.4 GHz, 23.6–29.4 GHz, and 33.4–59.4 GHz are (−18.8 dB, 0.027, 4.50 dB, 50.81%, 9.46 bps/Hz), (21.4 dB, 0.057, 4.92 dB, 57.03%, 9.74 bps/Hz), and (−31.8 dB, 0.0082, 5.79 dB, 45.9%, 9.22 bps/Hz), respectively. The proposed design covers X (40%), Ku, K (37.7%), Ka (69.2%), and V (55.4%) frequency bands. A good agreement was found between the measurement and simulation.

HIV-1 is a retrovirus that affects the human immune system and consequently leads to the development of AIDS. The high mutation rate in HIV-1 produces different subtypes which underscores the development of new therapeutics against it. This study aims to develop a novel small molecule that can be used as a potential inhibitor against the Vpr protein of all the subtypes of HIV-1. The druggable pockets of the Vpr protein of each subtype were identified and the conformational stability of these pockets was studied. The structure-based Drug Design method was used to design small molecules against the high-scoring pocket from each subtype individually using AutoGrow4 software. Molecules with strong binding affinity were selected from each subtype individually and binding affinity was checked for all the subtypes. Considering druggability and ADMET properties, we have identified two novel molecules that act as potential Vpr protein inhibitors. Both the molecules were shown to form stable complexes with the Vpr proteins of all the subtypes. The biological activity of both molecules was examined using DFT calculation. This study may provide some insight into developing of new therapies in HIV-1 treatment by interrupting protein–protein interaction.

Bacterial respiration, a fundamental biological process, plays a crucial role in ecological systems. The Fairen–Velarde model provides a theoretical framework to study the interplay between oxygen and nutrient concentrations in bacterial populations, representing a system of coupled nonlinear differential equations. In this work, how the introduction of noise affects the stability and behavior of bacterial respiration is investigated. Biological systems are inherently stochastic, with noise arising from environmental fluctuations and molecular‐level randomness. Through numerical simulations, how random fluctuations in oxygen and nutrient concentrations influence the system's stability is analyzed, particularly, the transition between limit cycles and fixed points. These results demonstrate that noise can induce a reduction in time scales, pushing the system toward a domain of fixed points, which contrasts with the noiseless case where the system exhibits a stable limit cycle. By employing statistical analysis across varying noise intensities, the likelihood of reaching the fixed domain is quantified and the area of this domain is examined under different noise conditions. These insights contribute to the broader understanding of how stochastic factors affect bacterial population dynamics, offering implications for microbial ecology and the management of bacterial processes in natural and engineered environments.

The study employed an Artificial Neural Network (ANN) to predict the performance and emissions of a single-cylinder SI engine using blends of Gasoline, Ethanol, and Methanol (GEM) ranging from E10 to E50 equivalence, achieving less than 5% error compared to experimental values. Furthermore, Response Surface Methodology (RSM) was utilized to optimize the engine’s performance, identifying the optimal operating conditions of 2992.9 rpm engine speed and an E20-equivalent GEM blend. Under these conditions, the engine exhibited a brake thermal efficiency (B_The) of 34.63%, a brake specific fuel consumption (BSFC) of 243.7 g/kW-hr, and minimal emissions of 1.5% CO, 108.13 ppm HC, and 1211.8 ppm NOx, with an overall desirability of 0.820, indicating a highly favorable combination of performance and emissions characteristics.

Geopolitical tensions, including the Russia-Ukraine conflict, ongoing Middle-Eastern wars, and the post-Cold War dynamics between the USA and Russia, have contributed to significant global political instability. These risks disrupt economic growth, destabilize energy supply chains, and foster economic uncertainty, often prioritizing energy security over environmental sustainability. Existing literature inadequately addresses how geopolitical risks interact with environmental sustainability, particularly within developed economies like Canada. To bridge this gap, this study examines the role of per capita income on environmental outcomes under the Environmental Kuznets Curve (EKC) framework, explicitly incorporating geopolitical risks as a critical determinant. Using Canadian time series data spanning from 1980 to 2022, this research employs the autoregressive distributed lag (ARDL) estimation technique to explore short- and long-term cointegrating relationships among key variables, including economic growth, energy consumption, trade openness, foreign direct investment (FDI), ICT development, and financial development. The findings confirm the inverted U-shaped EKC hypothesis for Canada, indicating that economic growth initially exacerbates carbon emissions (CO2) before leading to environmental improvements at higher income levels. Geopolitical risks are found to positively contribute to CO2 emissions, emphasizing their role as a barrier to achieving environmental sustainability. To validate robustness, the Kernel Regularized Least Squares (KRLS) machine learning approach is employed, confirming the consistency of results. Additionally, the Toda-Yamamoto causality test identifies directional causal relationships among the variables. Policy recommendations emphasize the need for Canada to implement targeted strategies that mitigate the impact of geopolitical risks on environmental outcomes. Specifically, the study advocates for: (1) diversifying energy sources to reduce reliance on geopolitically sensitive regions, (2) investing in renewable energy technologies to ensure sustainable economic growth, and (3) enhancing trade policies to prioritize low-carbon technologies.