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Impact of Restrictive Trade Policies, Channel Conflict and Uncertain Business Environment on Marketing Channel Satisfaction in FMCG Sector
Purpose: This study examines the influence of restrictive trade policies, channel conflict, and uncertain business environments on marketing channel satisfaction in the Fast-Moving Consumer Goods (FMCG) sector. It explores how external policy interventions and environmental volatility shape channel dynamics and satisfaction levels. Design/Methodology: A structured questionnaire was administered to channel members in the FMCG sector in Kerala. Data were analyzed using correlation and regression techniques to assess the direct effects of restrictive trade policies and channel conflicts. Data from 189 wholesalers and 262 retailers in Kerala in the FMCG Sector, was analyzed using Structural Equation Modeling (SEM). Results: The results indicate that restrictive trade policies and channel conflicts significantly and negatively impact channel satisfaction. Additionally, the uncertain business environment exacerbates these negative effects. Practical Implications: The findings provide actionable guidance for both policy-makers and marketing channel managers in the FMCG sector. Conclusions: This study contributes to marketing channel literature by integrating policy, conflict, and environmental perspectives into a single framework. It underscores the importance of understanding how macro-level restrictions and uncertainties interact with micro-level channel dynamics in shaping satisfaction, particularly in emerging market contexts. This study is very relevant in the field of distribution science. The Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://Creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited. -
Technological Interventions in Unpaid Care Work and Gender Dynamics
Unpaid care work, frequently centered on women, is an important yet neglected component of the world economy. This study strives to address the potential and flexibility that technology brings in curbing gender disparities, along with the bodily burden associated with unpaid care work. An examination is made for smart home devices, telehealth solutions, and caregiving applications that will further be evaluated for their effectiveness in minimizing the time and effort invested in unpaid care responsibilities. Through existing theoretical frameworks, empirical evidence, and case studies, this paper aims to determine how technological innovation can more effectively redistribute care work between genders and enhance the economic value of unpaid care to further improve gender equality. For instance, in Japan, smart home appliances such as automated pill dispensers and remote monitoring devices have become crucial solutions to a caregiving burden largely imposed on women. Telemedicine services similar to these have transported rural India from its unfavorable health care situation, thereby significantly shortening the time women spend on activities related to health care. Caregiver applications have assisted in the United States in achieving an equal distribution of caregiver responsibilities between male and female caregivers. Sophisticated robotic assistants in South Korea may fill gaps in the workforce while tending to older populations; thus, potentially minimizing housework hours for women. The education systems operating online across Sub-Saharan Africa enable girls to juggle learning with caring effectively and hence strike long-lasting gender parity. Such socio-economic advantages were purely garnered through wearable health monitors in Europe that eased family members burdens while experiencing economic benefits at a broader level. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Predictive Analytics for Stock Market Trends using Machine Learning
Navigating the intricacies of stock market trends demands a novel approach capable of deciphering the web of financial data and market sentiment. This research embarks on a transformative journey into the realm of machine learning, where we harness the power of data to forecast stock market trends with increased precision and accuracy. Commencing with an exploration of stock market dynamics and the inherent limitations of traditional forecasting techniques, this paper takes a bold step into the future by embracing the potential of machine learning. The study begins with an in-depth analysis of data preprocessing, unraveling the complexity of feature selection and engineering, setting the stage for a data-driven odyssey. As our exploration progresses, we dive into the deployment of diverse machine learning algorithms, including linear regression, decision trees, random forests, and the formidable deep learning models such as recurrent neural networks (RNNs) and long short-term memory networks (LSTMs). These algorithms act as our guiding lights, revealing intricate patterns concealed within historical stock price data. Our journey reaches new heights as we recognize the significance of augmenting predictive models with external data sources. Incorporating elements like news sentiment analysis and macroeconomic indicators enriches our understanding of the market landscape, enhancing the predictive capabilities of our models. We also delve into the crucial aspects of model evaluation, guarding against overfitting, and selecting appropriate performance metrics to ensure robust and reliable predictions. The research reaches its zenith with a meticulous analysis of real-world case studies, providing a comparative perspective between machine learning models and traditional forecasting methods. The results underscore the remarkable potential of machine learning in predicting stock market trends more accurately. 2023 IEEE. -
Digital Forensics Investigation for Attacks on Artificial Intelligence
The new research approaches are needed to be adopted to deal with security threats in AI based systems. This research is aimed at investigating the Artificial Intelligence (AI) attacks that are malicious by design. It also deals with conceptualization of the problem and strategies for attacks on Artificial Intelligence (AI) using Digital Forensic tools. A specific class of problems in Adversarial attacks are tampering of Images for computational processing in applications of Digital Photography, Computer Vision, Pattern Recognition (Facial Mapping algorithms). State-of-the-art developments in forensics such as 1. Application of end-to-end Neural Network Training pipeline for image rendering and provenance analysis, 2. Deep-fake image analysis using frequency methods, wavelet analysis & tools like - Amped Authenticate, 3. Capsule networks for detecting forged images 4. Information transformation for Feature extraction via Image Forensic tools such as EXIF-SC, Splice Radar, Noiseprint 5. Application of generative adversarial Networks (GAN) based models as anti-Image Forensics [8], will be studied in great detail and a new research approach will be designed incorporating these advancements for utility of Digital Forensics. The Electrochemical Society -
Pattern Reconfigurable Antennas for Wireless Applications: A Review of Design Techniques and Advances
This literature survey investigates advances in pattern reconfigurable antennas that take advantage of Meta-surface (MS) technology over other techniques. The study explores how these reconfigurable antennas are transforming next-generation communication systems, addressing critical applications such as 5G/6G networks and smart wireless environments. Key research observations include the ability of MS-based designs to achieve dynamic beam steering, crucial to meeting the diverse requirements of future communication systems. The paper also identifies challenges such as design complexity, power efficiency, integration with existing systems, and scalability for practical deployments. By highlighting these advances and addressing open challenges, this survey aims to provide information on the potential of MS-enabled reconfigurable antennas to shape the future of wireless communication technologies. 2025 IEEE. -
Pattern Reconfigurable Antenna Design Using Amc Array for Enhanced 5G Performance
This paper introduces a microstrip patch antenna integrated with a novel artificial magnetic conductor (AMC) to reconfigure the antenna radiation pattern for 5 G sub- 6 GHz applications. By adopting AMC technology, the proposed antenna exhibits beam steering, pattern reconfiguration, and enhancement in gain, suitable for the high data rate demands of 5 G networks. The design process involves the evolution of antenna elements, the design of AMC unit cell (AMC-UC) to operate at the desired 3.75 GHz, and the development of an AMC integrated antenna that is suitable to control the antenna radiation pattern, achieving significant enhancements in signal directionality and minimizing interference. The simulation results demonstrate improved performance parameters, such as return loss and gain, highlighting the potential of AMC-assisted reconfigurable antennas in advancing 5G network coverage and capacity. This work provides valuable information on the achievement of versatile and efficient antenna designs for next-generation wireless communications. 2025 IEEE. -
Enhancing fabric quality with AI-based defect detection systems
In summary, there is a necessity to use AI-based defect detection systems in fabric quality improvement especially in the process of textile production. These sophisticated solutions eliminate the requirement for time-consuming and error-prone traditional manual procedures, and thus not only speed up the inspection but guarantee a higher quality of the products. -
AI- and ML-driven intelligent design of digital twins
Digital twins (DTs), or virtual copies of real-world systems, have changed and improved many industries in terms of monitoring, analysis, and optimization in real time. Artificial intelligence (AI) and machine learning (ML) together have significantly enhanced the functionalities of DTs so that they become more efficient and versatile decision-making and process improvement tools. The production and application of DTs most importantly rely on AI and ML. Such technologies allow integration and analysis of very large amounts of data from various sources and provide an overview of the physical system. The personnel involved in the company may gain deeper insights into overall business processes and identify changes that would remain unknown when applying the traditional methods, based on the employment of the capabilities of AI-based integration and data analysis. An essential example of ML use cases in the framework of DTs is predictive maintenance. Any ML algorithm can resort to historical data and immediate sensor data to predict potential failures of application equipment and propose a repair schedule, significantly reducing operational downtime and refining the distribution of resources. The AI-powered optimization and simulation methods can give organizations the possibility to consider numerous scenarios and identify the most effective ways to resolve complex issues. The DTs are AI-enabled and can detect and decide on the fly, which allows them to react to changing conditions instantly and prevent some of the issues before they happen. In addition, AI-powered predictive analysis and risk management allow the firms to go a step ahead and address the potential problems in advance by developing effective risk reduction strategies. DTs are mainly constructed with AI and ML in various industries. In the context of manufacturing and Industry 4.0, DTs play an important role in optimizing production and increasing the quality control standards. Urban planners use the DTs to strategize building smart cities, while healthcare professionals use them for medical diagnosis and planning. In the aerospace and auto industries, DTs are beneficial in improving the product development, testing, and other maintenance processes. This chapter focuses on the smart creation of DTs with the help of AI and ML technology. The discussion will also dive into the complex mechanism behind building advanced, digital replicas of physical systems, particularly the support of the AI and ML in the advancement of their usefulness and precision. The chapter begins with the discussion of the role of data integration and analysis in the creation of a DT. This section shows how AI and ML algorithms facilitate the seamless combination of different sources of data into one and reach a much more dynamic and detailed similitude of the physical member. The chapter illustrates how these technologies can convert raw information into valuable information, which makes the DT capable of replicating the real-world situations and behaviors quite dramatically. Moreover, the chapter addresses the profound role of AI and ML in the optimization and simulation of DTs. We observe how these advanced technologies are able to give more precise predictions and process the decision-making and testing of even complex scenarios. The chapter focuses on how AI-enabled optimization methodologies and AI-based simulations driven by ML are broadening the opportunities of DTs, thus driving innovation in a number of verticals. 2026 Elsevier Inc. All rights reserved. -
DIGITAL FORENSICS ANALYSIS FOR ADVERSARIAL ATTACKS IN AI-BASED SYSTEMS
Digital forensics investigation is a field of science that focuses primarily on the recovery and examination of devices. The digital forensics or cyber forensics was primarily used for computer-based forensics. Artificial intel ligence (AI for short) is a group of computational models that use the black box-based technology of the neural network (NN) such as classification, prediction, and optimization tasks for various applications, such as computer vision, medical imaging, natural language processing, and autonomous vehicles. Cyber Forensics such as robotics, deals with a class of problems in which computational models are manipulated due to intentional malfunction such as adversarial attacks, back-end kind of attacks. This study is aimed to investigate attacks on AI. It also describes conceptual attack strategies using AI and digital forensics tools. The main purpose of this work is to provide a comprehensive analysis of malicious AI-based attacks and different types of classifiers. Two main factors were considered when testing attacks on AI-based systems: (1) Use AI to create inputs that reveal different attacks, or (2) Adjust attacker-specific attacks from different threat models and parameters. 2026 by Apple Academic Press, Inc. -
A comparative heat transfer analysis of rectangular fin through LTE and LTNE model
The objective of this research is to compare the thermal performance of rectangular porous fins through the Local Thermal Equilibrium and the Local Thermal Non-Equilibrium models. The thermal interactions between the solid and fluid phases are represented by two distinct energy equations in the Local Thermal Non-Equilibrium model. Whereas, heat transfer is governed by a single energy equation in the Local Thermal Equilibrium model. The governing equations describing the temperature distribution inside the fin system are developed using basic heat transfer principles. To enhance thermal conductivity and total effectiveness of heat transmission, the fluid phase of water is amalgamated with Al2O3 and TiO2 nanoparticles. The governing nonlinear ordinary differential equations are nondimensionalized, and the RungeKutta Fehlberg fourth-fifth order (RKF45) method is employed to solve these equations numerically. The accuracy and dependability of the obtained solution are confirmed by comparing it with previous findings. The influence of pertinent parameters on the thermal characteristics of the permeable fin is depicted graphically, and the rate of heat transfer is analyzed by Response surface methodology. It has been determined that, for the capturing of phase-wise thermal variations, Local Thermal Non-Equilibrium model performs better, particularly in permeable media with no heat conduction differences. The Author(s), under exclusive licence to SocietItaliana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2025. -
Non-destructive classification of diversely stained capsicum annuum seed specimens of different cultivars using near-infrared imaging based optical intensity detection
The non-destructive classification of plant materials using optical inspection techniques has been gaining much recent attention in the field of agriculture research. Among them, a near-infrared (NIR) imaging method called optical coherence tomography (OCT) has become a well-known agricultural inspection tool since the last decade. Here we investigated the non-destructive identification capability of OCT to classify diversely stained (with various staining agents) Capsicum annuum seed specimens of different cultivars. A swept source (SS-OCT) system with a spectral band of 1310 nm was used to image unstained control C. annuum seeds along with diversely stained Capsicum seeds, belonging to different cultivar varieties, such as C. annuum cv. PR Ppareum, C. annuum cv. PR Yeol, and C. annuum cv. Asia Jeombo. The obtained cross-sectional images were further analyzed for the changes in the intensity of back-scattered light (resulting due to dye pigment material and internal morphological variations) using a depth scan profiling technique to identify the difference among each seed category. The graphically acquired depth scan profiling results revealed that the control specimens exhibit less back-scattered light intensity in depth scan profiles when compared to the stained seed specimens. Furthermore, a significant back-scattered light intensity difference among each different cultivar group can be identified as well. Thus, the potential capability of OCT based depth scan profiling technique for non-destructive classification of diversely stained C. annum seed specimens of different cultivars can be sufficiently confirmed through the proposed scheme. Hence, when compared to conventional seed sorting techniques, OCT can offer multipurpose advantages by performing sorting of seeds in respective to the dye staining and provides internal structural images non-destructively. 2018 by the authors. Licensee MDPI, Basel, Switzerland. -
The Effect of Prediction on Employee Engagement Organizational Commitment and Employee Performance Using Denoised Auto Encoder and SVM Based Model
The purpose of human resources is to ensure that the appropriate people are hired for open positions at appropriate times, that the system receive the necessary training, and that their performance is monitored and their perspective skills are secure through the use of evaluation methods. Despite the importance of this data to decision-makers, it can be difficult to glean useful insights from large datasets. Data mining has made it possible for human resources experts to automate the hitherto tedious task of manually processing enormous data sets. Finding almost perfect outcomes is the main goal of data mining, which is to discover hidden knowledge in data patterns and trends. The proposed method goes as follows: preprocessing is done by data cleaning and data normalization, feature selection using correlation and information theoretic ranking criteria. The last step in training and evaluating the model is using AE-SVM, which stands for Auto Encoder Support Vector Machine. The suggested model is more effective and performs better than two existing models: Support Vector Machine and AE-CNN. The suggested approach attains an accuracy rate of 94%. 2024 IEEE. -
Understanding the emerging integrated marketing communication strategies used in marketing Tamil films /
IOSR Journal Of Humanities And Social Science, Vol.21, Issue 2, Ver. V, pp.33-37, ISSN: e-ISSN: 2279-0837, p-ISSN: 2279-0845. -
An analogical study of the narrative techniques used in the film Paradesi (2013) an adaptation of Tamil translation (Yerium Panikkadu) of the novel 'Red Tea' /
International Journal Of Humanities and Social Science Invention, Vol.5, Issue 3, pp.1-6, ISSN: 2319-7722 (Online) 2319-7714 (Print). -
Review on Emerging Internet of Things Technologies to Fight the COVID-19
The Internet of Things (IoT) has been gaining attention in various disciplines ranging from agriculture, health, industries and home automation. When a pandemic first breaks out early detection, isolating the infected, and tracing the contacts are the most important challenges. IoT protocols like Radio-frequency identification (RFID), Wireless Fidelity (WiFi), Global Positioning System (GPS) are gaining popularity for providing solutions to these challenges. IoT based applications in the health sector are benefitting COVID-19 (coronavirus disease of 2019) patients during this pandemic situation. This article explores and reviews the various Internet of Things enabled technologies and applications used in screening, contact tracing, and surveillance. IoT based telemedicine processes are very useful during the pandemic COVID-19. The purpose of this paper is to deliver an overall understanding of the existing and proposed technologies of IoT based solutions to make the situations better during COVID-19. 2020 IEEE. -
Quasar catalogue for the astrometric calibration of the forthcoming ILMT survey
Quasars are ideal targets to use for astrometric calibration of large scale astronomical surveys as they have negligible proper motion and parallax.The forthcoming 4-m International Liquid Mirror Telescope (ILMT) will survey the sky that covers a width of about 27?. To carry out astrometric calibration of the ILMT observations, we aimed to compile a list of quasars with accurate equatorial coordinates and falling in the ILMT stripe. Towards this, we cross-correlated all the quasars that are known till the present date with the sources in the Gaia-DR2 catalogue, as the Gaia-DR2 sources have position uncertainties as small as a few milli arcsec (mas). We present here the results of this cross-correlation which is a catalogue of 6738 quasars that is suitable for astrometric calibration of the ILMT fields. In this work, we present this quasar catalogue. This catalogue of quasars can also be used to study quasar variability over diverse time scales when the ILMT starts its observations. While preparing this catalogue, we also confirmed that quasars in the ILMT stripe have proper motion and parallax lesser than 20 masyr- 1 and 10 mas, respectively. 2020, Indian Academy of Sciences. -
Determination of the size of the dust torus in H0507+164 through optical and infrared monitoring
The time delay between flux variations in different wavelength bands can be used to probe the inner regions of active galactic nuclei (AGNs). Here, we present the first measurements of the time delay between optical and near-infrared (NIR) flux variations in H0507+164, a nearby Seyfert 1.5 galaxy at z = 0.018. The observations in the optical V-band and NIR J, H, and Ks bands carried over 35 epochs during the period 2016 October to 2017 April were used to estimate the inner radius of the dusty torus. From a careful reduction and analysis of the data using cross-correlation techniques, we found delayed responses of the J, H, and Ks light curves to the V-band light curve. In the rest frame of the source, the lags between optical and NIR bands are found to be 27.1-12.0 +13.5 d (V versus J), 30.4-12.0 +13.9 d (V versus H) and 34.6-9.6 +12.1 d (V versus Ks). The lags between the optical and different NIR bands are thus consistent with each other. The measured lags indicate that the inner edge of dust torus is located at a distance of 0.029 pc from the central ultraviolet/optical AGN continuum. This is larger than the radius of the broad line region of this object determined from spectroscopic monitoring observations thereby supporting the unification model of AGN. The location of H0507+164 in the ?-MV plane indicates that our results are in excellent agreement with the now known lag-luminosity scaling relationship for dust in AGN. 2018 The Author(s). -
REMAP: Determination of the inner edge of the dust torus in AGN by measuring time delays
Active galactic nuclei (AGN) are high luminosity sources powered by accretion of matter onto super-massive black holes (SMBHs) located at the centres of galaxies. According to the Unification model of AGN, the SMBH is surrounded by a broad emission line region (BLR) and a dusty torus. It is difficult to study the extent of the dusty torus as the central region of AGN is not resolvable using any conventional imaging techniques available today. Though, current IR interferometric techniques could in principle resolve the torus in nearby AGN, it is very expensive and limited to few bright and nearby AGN. A more feasible alternative to the interferometric technique to find the extent of the dusty torus in AGN is the technique of reverberation mapping (RM). REMAP (REverberation Mapping of AGN Program) is a long term photometric monitoring program being carried out using the 2 m Himalayan Chandra Telescope (HCT) operated by the Indian Institute of Astrophysics, Bangalore, aimed at measuring the torus size in many AGN using the technique of RM. It involves accumulation of suitably long and well sampled light curves in the optical and near-infrared bands to measure the time delays between the light curves in different wavebands. These delays are used to determine the radius of the inner edge of the dust torus. REMAP was initiated in the year 2016 and since then about one hour of observing time once every five days (weather permitting) has been allocated at the HCT. Our initial sample carefully selected for this program consists of a total of 8 sources observable using the HCT. REMAP has resulted in the determination of the extent of the inner edge of the dusty torus in one AGN namely H0507+164. Data accumulation for the second source is completed and observations on the third source are going on. We will outline the motivation of this observational program, the observational strategy that is followed, the analysis procedures adopted for this work and the results obtained from this program till now. 2019 Societe Royale des Sciences de Liege. All rights reserved. -
Estimation of the size and structure of the broad line region using Bayesian approach
Understanding the geometry and kinematics of the broad line region (BLR) of active galactic nuclei (AGN) is important to estimate black hole masses in AGN and study the accretion process. The technique of reverberation mapping (RM) has provided estimates of BLR size for more than 100 AGN now; however, the structure of the BLR has been studied for only a handful number of objects. Towards this, we investigated the geometry of the BLR for a large sample of 57 AGN using archival RM data. We performed systematic modelling of the continuum and emission line light curves using a Markov chain Monte Carlo method based on Bayesian statistics implemented in PBMAP (Parallel Bayesian code for reverberation-MAPping data) code to constrain BLR geometrical parameters and recover velocity integrated transfer function. We found that the recovered transfer functions have various shapes such as single-peaked, double-peaked, and top-hat suggesting that AGN have very different BLR geometries. Our model lags are in general consistent with that estimated using the conventional cross-correlation methods. The BLR sizes obtained from our modelling approach is related to the luminosity with a slope of 0.583 0.026 and 0.471 0.084 based on H ? and H ? lines, respectively. We found a non-linear response of emission line fluxes to the ionizing optical continuum for 93 per cent objects. The estimated virial factors for the AGN studied in this work range from 0.79 to 4.94 having a mean at 1.78 1.77 consistent with the values found in the literature. 2021 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
Dust reverberation mapping of Z229-15
We report results of the dust reverberation mapping (DRM) on the Seyfert 1 galaxy Z229-15 at z = 0.0273. Quasi-simultaneous photometric observations for a total of 48 epochs were acquired during the period 2017 July to 2018 December in B, V, J, H and Ks bands. The calculated spectral index (?) between B and V bands for each epoch was used to correct for the accretion disc (AD) component present in the infrared light curves. The observed ? ranges between -0.99 and 1.03. Using cross-correlation function analysis we found significant time delays between the optical V and the AD corrected J, H and Ks light curves. The lags in the rest frame of the source are 12.52+10.00 -9.55 d (between V and J), 15.63+5.05 -5.11 d (between V and H) and 20.36+5.82 -5.68 d (between V and Ks). Given the large error bars, these lags are consistent with each other. However, considering the lag between V and Ks bands to represent the inner edge of the dust torus, the torus in Z229-15 lies at a distance of 0.017 pc from the central ionizing continuum. This is smaller than that expected from the radius luminosity (R-L) relationship known from DRM. Using a constant ? = 0.1 to account for theADcomponent, as is normally done in DRM, the deduced radius (0.025 pc) lies close to the expected R-L relation. However, usage of constant ? in DRM is disfavoured as the ? of the ionizing continuum changes with the flux of the source. 2021 Oxford University Press. All rights reserved.