A Conceptual Model for Building the Relationship Between Augmented Reality, Experiential Marketing & Brand Equity

Purpose:   This study aims to build a conceptual model based on the S-O-R (Stimulus-Organism-Response) framework to understand how Augmented Reality influences brand equity. The proposed model is intended to look into the influence of AR attributes like interactivity, vividness, modality, novelty, and media richness on consumers’ experiential values and brand equity in e-commerce.   Theoretical framework: This study developed the conceptual model by following the Stimulus-Organism-Response (S-O-R) Model  (Mehrabian & Russell, 1974).   Design/methodology/approach: To advance the conceptual and managerial understanding of AR as an experiential marketing tool, this study followed the systematic literature review approach to build the integrated conceptual model.   Findings: Results of the study has developed a conceptual model for studying the application of AR technology as an experiential marketing tool on customer purchasing experience and brand equity in the e-commerce setting.   Research, Practical & Social implications: This study contributes towards the available literature by studying the AR and its various dimensions for building brand equity. This study will not only assist the e-commerce firms in their decision-making process about adopting this technology but also the marketers to develop the effective marketing strategies for consumer experience of AR uses on e-commerce platforms.   Originality/value: This study introduces modality of AR as a media characteristic in its interface in creating seamless user experience in the proposed model

Machine Learning Approach for Prediction of the Online User Intention for a Product Purchase

The deployment of self-learning computer algorithms that can automatically enhance their performance via experience is referred to as machine learning in ecommerce and is a crucial trend of the retail digital transformation. Machine learning algorithms can be unambiguously trained by analysing big datasets, identifying repeating patterns, relationships, and anomalies among all of this data, and creating mathematical models resembling such associations. These models are improved when the algorithms analyse ever-increasing amounts of data, providing us with useful insights into specific ecommerce-related events and the links between all the variables that underlie them. A tool that has been quite effective in studying current affairs, predicting future trends, and making data-driven decisions. The present work investigates the implementation of machine learning algorithms to predict the user intention for purchasing a product on a specific store's website. An Online Shoppers Purchasing Intention data set from the UC Irvine Machine Learning Repository was used for this investigation. In this study, two classification-based machine learning algorithms i.e. Stochastic Gradient Descent (SGD) algorithm and Random Forest algorithm were used. SGD algorithm was used for first time in prediction of the online user intention. The results showed that the Random Forest resulted in the highest F1-Score of 0.90 in contrast to the Stochastic Gradient Descent algorithm

Nanomechanics of Aerogels

The goal of this project is to study and analyze the nanomechanics of aerogels that are doped with materials like Carbon Nanotubes (CNT) and Graphene. Aerogels provide the framework for nanomaterials to exist in a three dimensional space. Doped Aerogels can potentially be used in applications like enhanced thermal insulation and effective light trapping due to its multi-functionality and superior mechanical properties. These hybrid aerogels are cheaper and easier to fabricate than pure carbon aerogels, which are known to exhibit catalytic properties. By analyzing the physical quantities in the aerogels such as molecular dynamics and mass, thermal fluctuations, electrical conductivity, we can explore new areas of material science with carbon nanotechnology

Solar Applications in Chad

This project uses photovoltaic technologies for the applications of water pumping and lighting in the rural villages of Chad, providing people with greater development opportunities. Research reveals that 70% of the population does not have access to electricity, and most people have to travel at least 2.5 miles to get access to a source of drinking water. Since 80% of the people live below the poverty line, external funding would be necessary to make this project feasible. If funding is provided, this project can be a success in providing affordable lighting and clean water; and can be used as a model for other developing nations

EMS Communication Systems Data Analysis

The exponential growth of the human population requires an increasingly efficient EMS infrastructure and service. The purpose of this project was to examine and analyze various aspects of the EMS communication system within the United States of America, and identify areas that could be improved. Current operation and maintenance of EMS communication system were investigated, including input and retrieval of information, data storage, infrastructure and modes of communication. Areas for improvement were recognized, and feasible solutions that could improve time response, were researched. A medical database was developed to store and retrieve patient information and delay differential equations were formulated to reduce system dynamic delays

An updated review on morpholine derivatives with their pharmacological actions

The invention of newer chemical entities, which have some therapeutically worth is always a great challenge. It is no doubt that it is a lengthier process. We have several drugs in the market for treatment of wide variety of diseases. The marketed drugs available may be heterocyclic or non-heterocyclic derivatives. Always it was found that heterocyclic derivatives have wide variety of pharmacological activity. The intension of this review is to highlight one of the important heterocyclic rings i.e.: Morpholine. Several works have been done on this nucleus, which should be enlighten for more and more applicability

Rise in Mid-Tropospheric Temperature Trend (MSU/AMSU 1978–2022) over the Tibet and Eastern Himalayas

The high-altitude Hindu Kush-Himalayan region (HKH, average ~5 km from msl) and the adjacent Indo-Gangetic plains (IG plains, ~0–250 m msl), due to their geographical location and complex topography, are reported to be highly sensitive to climatic changes. Recent studies show that the impacts of climate change and associated changes in water resources (glacial/snow melt water and rainfall) in this region are multifaceted, thereby affecting ecosystems, agriculture, industries, and inhabitants. In this study, 45 years of Microwave Sounding Unit/Advanced Microwave Sounding Unit (MSU/AMSU)-derived mid-tropospheric temperature (TMT, 3–7 km altitude) and lower tropospheric temperature (TLT, 0–3 km altitude) data from the Remote Sensing Systems (RSS Version 4.0) were utilized to analyze the overall changes in tropospheric temperature in terms of annual/monthly trends and anomalies. The current study shows that the mid-tropospheric temperature (0–3 km altitude over the HKH region) has already alarmingly increased (statistically significant) in Tibet, the western Himalayas, and the eastern Himalayas by 1.49 °K, 1.30 °K, and 1.35 °K, respectively, over the last 45 years (1978–2022). As compared to a previous report (TMT trend for 30 years, 1979–2008), the present study of TMT trends for 45 years (1978–2022) exhibits a rise in percent change in the trend component in the high-altitude regions of Tibet, the western Himalayas, and the eastern Himalayas by approximately 310%, 80%, and 170%, respectively. In contrast, the same for adjacent plains (the western and eastern IG plains) shows a negligible or much lower percent change (0% and 40%, respectively) over the last 14 years. Similarly, dust source regions in Africa, Arabia, the Middle East, Iran, and Pakistan show only a 130% change in warming trends over the past 14 years. In the monthly breakup, the ‘November to March’ period usually shows a higher TMT trend (with peaks in December, February, and March) compared to the rest of the months, except in the western Himalayas, where the peak is observed in May, which can be attributed to the peak dust storm activity (March to May). Snow cover over the HKH region, where the growing season is known to be from September to February, is also reported to show the highest snow cover in February (with the peak in January, February, or March), which coincides with the warmest period in terms of anomaly and trend observed in the long-term mid-tropospheric temperature data (1978–2022). Thus, the current study highlights that the statistically significant and positive TMT warming trend (95% CI) and its observed acceleration over the high-altitude region (since 2008) can be attributed to being one of the major factors causing an acceleration in the rate of melting of snow cover and glaciers, particularly in Tibet and the Eastern Himalayas

Performance evaluation and multivariate analysis of maize accessions against drought stress in Lamjung district, Nepal

Abstract The selection of drought-tolerant genotypes from the existing gene pool is a preliminary step for breeding for drought tolerance. Research efforts aimed at exploring the ability of crops to withstand drought stress have not yet touched the realm of reproductive drought performance of local genotypes. To examine how local crop accessions demonstrate varying reproductive performance under drought conditions, twenty accessions of maize (Zea mays L.) were evaluated for six quantitative traits in a two-factor factorial completely randomized design with two replications between February to June of 2023. All the traits under study showed significant differences among the genotypes (p < 0.05). Owing to their grain yield, ear weight, and hundred grain weight, accessions NGRC05592, NGRC05582, NGRC05564, NGRC05565, NGRC05571, and NGRC05569 performed better under drought condition than other tested accessions. Accession NGRC05592 showed the highest yield under drought condition, whereas, NGRC05573 and NGRC05576 showed poor performance. GGE Biplot analysis for grain yield revealed that NGRC05571 and NGRC05592 had the highest mean yield, with the accession NGRC05592 standing out as the stable variety under changing soil moisture levels and performing best among all the tested accessions under drought condition. The possibility of accession NGRC05592 being used as a potential genetic resource for drought breeding programs has been observed, but further studies aiming at its stability under field conditions in diverse agro-climatic regions across different years are encouraged to assure its prospect for developing cultivars suited to drought-affected regions

A novel multi-model estimation of phosphorus in coal and its ash using FTIR spectroscopy

Abstract The level of phosphorus must be carefully monitored for proper and effective utilization of coal and coal ash. The phosphorus content needs to be assessed to optimize combustion efficiency and maintenance costs of power plants, ensure quality, and minimize the environmental impact of coal and coal ash. The detection of low levels of phosphorus in coal and coal ash is a significant challenge due to its complex chemical composition and low concentration levels. Effective monitoring requires accurate and sensitive equipment for the detection of phosphorus in coal and coal ash. X-ray fluorescence (XRF) is a commonly used analytical technique for the determination of phosphorus content in coal and coal ash samples but proves challenging due to their comparatively weak fluorescence intensity. Fourier Transform Infrared spectroscopy (FTIR) emerges as a promising alternative that is simple, rapid, and cost-effective. However, research in this area has been limited. Until now, only a limited number of research studies have outlined the estimation of major elements in coal, predominantly relying on FTIR spectroscopy. In this article, we explore the potential of FTIR spectroscopy combined with machine learning models (piecewise linear regression—PLR, partial least square regression—PLSR, random forest—RF, and support vector regression—SVR) for quantifying the phosphorus content in coal and coal ash. For model development, the methodology employs the mid-infrared absorption peak intensity levels of phosphorus-specific functional groups and anionic groups of phosphate minerals at various working concentration ranges of coal and coal ash. This paper proposes a multi-model estimation (using PLR, PLSR, and RF) approach based on FTIR spectral data to detect and rapidly estimate low levels of phosphorus in coal and its ash (R $$^2$$ 2 of 0.836, RMSE of 0.735 ppm, RMSE (%) of 34.801, MBE of − 0.077 ppm, MBE (%) of 5.499, and MAE of 0.528 ppm in coal samples and R $$^2$$ 2 of 0.803, RMSE of 0.676 ppm, RMSE (%) of 38.050, MBE of − 0.118 ppm, MBE (%) of 4.501, and MAE of 0.474 ppm in coal ash samples). Our findings suggest that FTIR combined with the multi-model approach combining PLR, PLSR, and RF regression models is a reliable tool for rapid and near-real-time measurement of phosphorus in coal and coal ash and can be suitably modified to model phosphorus content in other natural samples such as soil, shale, etc