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Roadmap on ionic liquid crystal electrolytes for energy storage devices
The current organic liquid electrolytes used in electrochemical energy systems cause rapid performance degradation and even combustion. The advancement of new electrolytes with exceptional safety and electrochemical performance is crucial in addressing these challenges. Recently developed ionic liquid crystals (ILCs) offer promising opportunities for tailoring ion transport channels through modified nano segregated structures, thereby ensuring excellent operating safety and combining the advantageous properties of ionic liquids and liquid crystals. This review focuses on investigating the ion conductive properties and operational mechanisms of ILC electrolytes for energy storage and conversion devices, which play a pivotal role in the development of superior electrolytes. The review critically analyzes the recent development and fundamental properties electrochemical interaction framework of ILC electrolytes applied in energy storage devices. Particular attention is given to elucidating the mechanism of ILC and phase formation, past decade fabrication of energy storage device with ILC electrolytes, emphasizing their capacity for ion redistribution and exceptional stability. Additionally, the review addresses the drawback, limitation, commercialization, challenges and provides future perspective for the growth of ILC electrolytes in the field of energy storage. 2024 Elsevier B.V. -
Robo-revolution: How automated financial advisors are reshaping global finance
Robo-advisors have the potential to revolutionise the financial service industry by making it more accessible and affordable. This study provides a comprehensive overview of robo-advisors in the arena of financial markets and investments and their gaining popularity in the fintech industry, particularly in emerging markets like India. It also discusses the changing landscape of the financial sector in India, benefits and challenges of fintech, and the legal and ethical implications of roboadvisors. The current study presents a comparative study between India and UK markets in terms of acceptance and penetration of robo-advisors. It highlights leading robo-advisory firms in India. The data visualisation is done with the help of Microsoft Power BI and Microsoft Excel on the statista survey data. The expected results of this study assist several stakeholders, such as academicians, researchers, investors, stock brokers, regulators, and policy makers. 2024, IGI Global. All rights reserved. -
Robot-Assisted Children-Centric Strategies in the Hotel Industry: Enhancing Parental Attraction and Sales Growth
The hotel industry is undergoing a profound transformation, shaped by shifting consumer preferences and technological innovations. An intriguing trend within this transformation is the growing adoption of robotic technology to elevate the guest experience. Of particular interest is the emergence of child-centric robots designed to engage and educate young guests. These robots have the potential to significantly influence parental attraction to hotels, a factor that bears substantial implications for hotel sales and profitability. This research delves into the phenomenon of Robot-Assisted Child-Centric Strategies in the Hotel Industry and its impact on increasing parental attraction and, in turn, driving sales growth. It explores how hotels strategically integrate child-centric robots to create distinctive and engaging experiences for families. These robots offer interactive concierge services, in-room companions, educational support, and entertainment, enriching childrens stays and allowing parents to relax. The study investigates the technological innovations and capabilities of these robots, the strategies hotels employ to seamlessly incorporate them, and their impact on parental attraction. Employing a mixed-methods approach, including surveys, interviews, and data analysis, it uncovers the drivers behind the adoption of child-centric strategies and their correlation with sales growth. Ultimately, this research reveals how child-centric robots can revolutionize the hotel industry by attracting families and enhancing guest experiences. It provides valuable insights for hoteliers seeking to leverage this trend, helping them find the delicate balance between guest satisfaction and profitability in a competitive market. Child-centric robots offer a promising avenue for hotels, providing unforgettable stays for families while boosting sales and profitability. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Robotic dining delight unravelling the key factors driving customer satisfaction in service robot restaurants using PLS-SEM and ML
In the past few years there has been a remarkable surge in demand for robot service restaurants. However, as both the technology and the concept of such restaurants are relatively new, there is a limited understanding of how consumers would react to this new change in the service industry. This study focuses on the key factors influencing customer satisfaction and their intention to repeat the experience by using two staged hybrid PLS-SEM and Machine Learning approaches. The finding confirms that perceived enjoyment, speed, and novelty influence customer satisfaction, whereas perceived usefulness has no influence. Additionally, the study uncovers that customer satisfaction and trust positively mediate the relationship and establish the link with repeat experience. The machine learning models (Artificial Neural Network, Support Vector Machines, Random Forest, K-Nearest Neighbors, Elastic Net) predict the intention to repeat the experience of the service robot with an overall model fit of around 57%. We also discussed several new and useful theoretical and practical implications for enhancing the customer experience during the visit to the restaurants. 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
Robotics, artificial intelligence and service automation (RAISA) A model for smart and sustainable destination management
Technological progress in information and communication technologies (ICTs) has facilitated the implementation of automation and the integration of more advanced technologies in all industries. With the advent of robotics, artificial intelligence and service automation (RAISA) technologies, tourism service providers and market players have redirected their attention from traditional and conventional methods of service delivery to more modern and innovative approaches. Emerging technologies such as big data, mobile internet, the internet of things (IoT), and artificial intelligence (AI) have been fueling a rapid growth in innovations that facilitate a sustainable shift towards social robots. While AI is extensively studied in the field of tourism research, robotics and intelligent service automation have received less comprehensive research attention. The present study endeavors to investigate the prospective landscape of the travel and tourism sector using a rigorous qualitative secondary data analysis approach through web page content analysis on the awareness and usage of RAISA technologies in the global marketplace. The publication proposes four primary research objectives: investigating the advantages of RAISA, evaluating its usage and adaptability, examining the effects and difficulties of implementing the systems, and establishing a sustainable future with RAISA-enabled services. Research in these fields will facilitate the systematic and organized generation of knowledge, enabling the academic community to guarantee the advantageous implementations of intelligent automation in the tourism sector. This work presents a taxonomy for RAISA technologies in tourism and presents a persuasive case for how this interdisciplinary field should be included in conventional tourism research. by the Author,. -
Robotics: challenges and opportunities in healthcare
Today, healthcare services and systems are becoming very complex and include a large number of entities characterized by shared, distributed and heterogeneous devices, sensors, and information and communication technologies. Various artificial intelligence techniques have been implemented in various sectors like smart cities, energy, IT sectors, banking, agriculture, retails, and many more, but it has been always challenging to demonstrate this technique effectively in healthcare sector due to its sophisticated procedure and its handling. Data analytics research on healthcare data has grown significantly over the past 10-12 years, and the execution of data analytics algorithms and systems in healthcare has been progressing more quickly. The data analytics service section has gained considerable attention with the development of technology, especially artificial intelligence robots, in the healthcare sector. Robots can help people with cognitive, sensory, and motor disabilities, help the sick or injured, support caregivers, and assist the clinical workforce. The purpose of this study is to provide historical evolution of robotics in healthcare with an overview of the influence of robots in healthcare like clinical support, patient transfer in hospitals, to handle heavy surgical instruments, to transport medical waste, for drug delivery, patient management etc. Furthermore, this chapter also covered the challenges and opportunities in healthcare and also offers a comprehensive aspect at how robots are incorporate in various healthcare applications. 2025 Elsevier Inc. All rights reserved. -
Robs algorithm
Sparse matrix is a matrix having a relatively large proportion (proportion - a ratio is a comparison of two numbers. We generally separate the two numbers in the ratio with a colon (:)) of zero elements. To store the elements of the matrix in computer memory, linear array concept of storing is used. When a sparse matrix is stored in full-matrix storage mode, all its elements, including its zero elements, are stored in an array, which is a wastage of memory. In order to avoid the memory and processing overhead many alternate forms are used. Each one has separate time and space complexities and performances. In this paper we are suggesting one way of representing the sparse matrix which has both time and space complexity O(2n) only, while all other methods work with complexity more than O(3n) where n is the total number of non-zero elements in the matrix .The implementation of this algorithm in applications may improve the performance especially in the area of adjacency matrix, tree representation, 3D representation to an object, network communication, electronics, mathematical calculations, picture storage/file storage, file compression, bioinformatics, and the computer performance. The proposed algorithm has a large scope not only in computing but also in different branches of science, electronics and graphics. 2006 Elsevier Inc. All rights reserved. -
Robust and imperceptible image watermarking using chaotic map-integrated quantum-inspired multi-objective cuckoo search optimization
With the rapid growth of multimedia data transmission for secure and reliable communication has become critical due to increasing cyber threats. This paper presents a Chaotic Map-integrated Quantum-Inspired Multi-Objective Cuckoo Search (CMQICS)-based watermarking approach to achieve high imperceptibility, robustness, and embedding efficacy. The proposed approach integrates quantum-inspired cuckoo search optimization with chaotic mapping to enhance watermark embedding. A multi-image watermarking scheme is also used to strengthen payload capacity while minimizing visual distortion. The embedding process operates in the frequency domain using a hybrid Discrete Cosine TransformTwo-Dimensional Discrete Wavelet Transform (DCT-2DWT) combined with a zig-zag scanning strategy. This ensures attack resilience. The experimental results show that CMQICS achieves a Peak Signal-to-Noise Ratio (PSNR) of approximately 89 dB, a Structural Similarity Index Measure (SSIM) of 0.99, and an average embedding time of around 1s. Randomness analysis further validates the security of the embedded watermark. The comparative evaluations states that the CMQICS outperforms existing approaches. The Author(s) 2025. -
Robust Bidirectional Long Short-Term Memory-Based Class Imbalance Handling in Dyslexia Prediction at its Early Stage
Dyslexia is a neurological condition that presents difficulties and obstacles in learning, particularly in reading. Early diagnosis of dyslexia is crucial for children, as it allows the implementation of appropriate resources and specialized software to enhance their skills. However, the evaluation process can be expensive, time-consuming, and emotionally challenging. In recent years, researchers have turned to machine learning and deep learning techniques to detect dyslexia using datasets obtained from educational and healthcare institutions. Despite the existence of several deep learning models for dyslexia prediction, the problem of handling class imbalance significantly impacts the accuracy of detection. Therefore, this study proposes a robust deep learning model based on a variant of long short-term memory (LSTM) to address this issue. The advantage of Bidirectional LSTM, which has the ability to traverse both forward and backward, improves the pattern of understanding very effectively. Still, the problem of assigning values to the hyper-parameters in BLSTM is the toughest challenge which has to be assigned in a random manner. To overcome this difficulty, the proposed model induced a behavioral model known as Red Fox Optimization algorithm (RFO). Based on the inspiration of red fox searching behavior, this proposed work utilized the local and the global search in assigning and fine-tuning the values of hyper-parameters to handle the class imbalance in dyslexia dataset. The performance evaluation is conducted using two different dyslexia datasets (i.e., dyslexia 12_14 & real-time dataset). The simulation results explore that the proposed robust Bidirectional Long Short-Term Memory accomplishes the highest detection rate with reduced error rate compared to other deep learning models. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Robust Control of DFIG Based Wind Energy System Using an H? Controller
Wind Energy Conversion System (WECS) using a Doubly Fed Induction Generator (DFIG) is popular due to its control flexibility and higher conversion efficiency, but maintaining the operational stability and optimal efficiency under dynamic wind conditions is still a control challenge. In this paper, a nonlinear mathematical model for a DFIG based WECS was developed from fundamentals and its characteristics near the operating point were studied. A Proportional Integral (PI) controller and a Linear Quadratic Regulator (LQR) controller were designed to control the system and the behavior of the closed-loop system with these controllers was studied. While the designed PI controller failed to ensure stability, the LQR controller was giving stability but an LQR controller is vulnerable to loss of stability under uncertainties due to parameter variations or changes in operating points. A suboptimal H? controller was then synthesized to obtain robust control. The closed-loop system performance of the DFIG system with the proposed controller was found to be stable and superior to PI and LQR controllers in terms of performance. 2021, The Korean Institute of Electrical Engineers. -
Robust Deep Learning Empowered Real Time Object Detection for Unmanned Aerial Vehicles based Surveillance Applications
Surveillance is a major stream of research in the field of Unmanned Aerial Vehicles (UAV), which focuses on the observation of a person, group of people, buildings, infrastructure, etc. With the integration of real time images and video processing approaches such as machine learning, deep learning, and computer vision, the UAV possesses several advantages such as enhanced safety, cheap, rapid response, and effective coverage facility. In this aspect, this study designs robust deep learning based real time object detection (RDL-RTOD) technique for UAV surveillance applications. The proposed RDL-RTOD technique encompasses a two-stage process namely object detection and objects classification. For detecting objects, YOLO-v2 with ResNet-152 technique is used and generates a bounding box for every object. In addition, the classification of detected objects takes place using optimal kernel extreme learning machine (OKELM). In addition, fruit fly optimization (FFO) algorithm is applied for tuning the weight parameter of the KELM model and thereby boosts the classification performance. A series of simulations were carried out on the benchmark dataset and the results are examined under various aspects. The experimental results highlighted the supremacy of the RDL-RTOD technique over the recent approaches in terms of several performance measures. 2022 River Publishers. -
Robust Estimation for P[Y
Reliability analysis is critical in measuring the effectiveness and detecting the weakness or mode of failure of other systems in various sectors such as engineering, healthcare, and social science. Stress-strength reliability is the measure of the reliability of a component; that is, the likelihood of a given strength being more significant than the applied stress so that the component will perform the intended function satisfactorily. In the present paper, we are concerned with the estimation of stress-strength reliability R=P[Y -
Robust feature selection using rough set-based ant-lion optimizer for data classification
The selection of an algorithm to tackle a certain problem is a vital undertaking that necessitates both time and knowledge. Non-functional needs, such as the size, quality, and nature of the data, must frequently be taken into account. To develop a generalized machine learning model for any domain, the most relevant features must be chosen because noisy and irrelevant characteristics degrade data mining performance. However, the selection of the dominating features is still dependent on the search technique. When there are a high number of input features, stochastic optimization can be applied to the search space. In this research, the authors investigate the ant lion optimization (ALO), a natureinspired algorithm that mimics the hunting process of ant lions and is further stimulated to identify the smallest reducts. They also investigate rough set-based ant lion optimizer for feature selection. The actual results reveal that the ant lion-based rough set reduct selects a better feature subset and classifies them more accurately. 2022 Information Resources Management Association. All rights reserved. -
Robust Regression Approaches for the FamaFrench 5-Factor Model: A Real Data Study
The FamaFrench five-factor model (FF-5) is one of the advancements of capital asset pricing models (CAPM). Along with other FF-Models, it aims to understand companies and portfolios over a period, Analyzing better return capacity over the five factors such as SMBBusiness Size, HML-Spread between high and low book to market ratio, RMW- Robustness in operating profitability and CMA-investment style to be conservative or aggressive. FF-5-factor regression model widely uses Ordinary Least Squares estimator to estimate the parameter. However, due to the volatility of the markets over the years and not-normal periods, OLS estimators face setbacks due to the assumption violations that are a pre-requisite. This article presents an effort made to improve the performance of the FF-5-factor model using the Robust Dawoud-Kibria estimator. The performance of the FF-5-factor model is compared with other robust estimators such as M, MM, and MMS with MSE criteria. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Robust Rice Leaf Disease Detection using Advanced Preprocessing and Deep CNNs for Class Imbalance Resolution
This study addresses the growing challenges posed by plant diseases, particularly in the rice industry, which is vital for many communities. The research propose a robust framework that integrates Deep Convolutional Neural Networks (Deep CNN) with advanced preprocessing techniques to identify rice leaf diseases, including Brown Spot, Leaf Blast, Hispa, and healthy leaves. Our approach employs normalization to enhance convergence during training and data augmentation to improve model generalizability. Additionally, implement the Synthetic Minority Over-sampling Technique (SMOTE) to create synthetic samples for under-represented classes, addressing class imbalance within the dataset. Experimental results demonstrate the model's impressive accuracy, achieving 98.2% for Brown Spot, 97.5% for Leaf Blast, 94.3% for Hispa, and 96.8% for healthy leaves. Furthermore, our method outperforms established CNN architectures such as AlexNet, VGG16, and ResNet50, showcasing the effectiveness of sophisticated preprocessing in enhancing plant disease detection systems and supporting food security initiatives. 2025 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. -
Rock abrading in South India /
Encyclopedia of Global Archaeology, pp.1-10 -
Role of Additive Manufacturing and Thermal Spray Processed Materials in Electric Vehicle (EV) and Hybrid Electric Vehicle (HEV) Applications
Additive manufacturing (AM) significantly contributes to the development of electric vehicles (EVs) and hybrid electric vehicles (HEVs), providing lightweight, complex, and customized components. This study explores AMs role in advancing EV and HEV technology, with a special focus on integrating thermal spray coatings (TSCs) to enhance component performance. By employing TSCs in AM-fabricated components, manufacturers can improve surface characteristics, wear resistance, and corrosion protection critical factors for long-lasting EV/HEV systems. The synergy between AM and TSC enhances key parts such as battery enclosures, thermal management systems, and structural frameworks by optimizing their thermal insulation, durability, and energy efficiency. Additionally, AM enables efficient material use and lightweighting, which reduces vehicle weight and enhances energy conservation, addressing industry needs for sustainable solutions. This chapter reviews the current applications and future potential of TSC in AM components, highlighting its role in meeting the rigorous demands of the automotive sector. Findings suggest that combining AM and TSC opens pathways for advanced, sustainable EV and HEV designs, aligning with the global shift toward cleaner energy and resource-efficient manufacturing. 2026 selection and editorial matter, R. Suresh, C. Durga Prasad, Satish Kumar, K.N. Bharath, and Ajith G. Joshi; individual chapters, the contributors. -
Role of AI in Computational Risk Modeling of Financial Stability and Portfolio Risk: A New Perspective
The need to assess climate change-related risks and their impact on the financial stability of banks is imperative. Innovations in technology, especially AI andML algorithms, have improved the efficiency and accuracy of risk analysis models. The obstacle for banks is assessing the climate risk exposure due to their lending portfolio. The climate data are uncertain and unavailable, and the granularity of the data is questionable. To overcome these issues, in this chapter, a hybrid risk predictive model is proposed. It uses a combination of ResNet-50 (to analyze and quantify spatial image data) and CoViaR (risk prediction) models. Using the ResNet-50 model, a climate change risk score is developed from images and feature extraction, which is correlated with the emission volume of the borrower firms. Then, using the proposed model, the impact of climate change-related risk on the lending portfolio is evaluated to understand the financial stability of banks through capital. 2025, Bentham Books imprint. -
Role of AI in Enhancing Customer Experience in Online Shopping
AI-powered tools and applications may provide customers with a positive, effective, and customized purchasing experience. By studying client preferences and behaviours, AI systems can anticipate future customer needs, improving and personalizing the shopping experience. The main aim of this study is to examine the role of artificial intelligence (AI) on enhancing customer experience. The results of this study revealed that there is a positive significant relationship between AI features like perceived convenience, personalization and AI-enabled service quality and Customer experience. A total of 416 responses were analysed using a structured questionnaire. The findings indicate significant role of trust as factor, mediating the effects of independent variables on customer experience. Data was analysed using T-test, ANOVA and regression. 2024 IEEE.