Nonlinear Methodology Research Articles

Sparse Online Gaussian Process Adaptive Control of Unmanned Aerial Vehicle with Slung Payload

In the past decade, Unmanned Aerial Vehicles (UAVs) have garnered significant attention across diverse applications, including surveillance, cargo shipping, and agricultural spraying. Despite their widespread deployment, concerns about maintaining stability and safety, particularly when carrying payloads, persist. The development of such UAV platforms necessitates the implementation of robust control mechanisms to ensure stable and precise maneuvering capabilities. Numerous UAV operations require the integration of payloads, which introduces substantial stability challenges. Notably, operations involving unstable payloads such as liquid or slung payloads pose a considerable challenge in this regard, falling into the category of mismatched uncertain systems. This study focuses on establishing stability for slung payload-carrying systems. Our approach involves a combination of various algorithms: the incremental backstepping control algorithm (IBKS), integrator backstepping (IBS), Proportional–Integral–Derivative (PID), and the Sparse Online Gaussian Process (SOGP), a machine learning technique that identifies and mitigates disturbances. With a comparison of linear and nonlinear methodologies through different scenarios, an investigation for an effective solution has been performed. Implementation of the machine learning component, employing SOGP, effectively detects and counteracts disturbances. Insights are discussed within the remit of rejecting liquid sloshing disturbance.

Field Study of Asphalt Pavement Texture and Skid Resistance under Traffic Polishing Using 0.01 mm 3D Images

Pavement texture and skid resistance are pivotal surface features of roadway to traffic safety, especially under wet weather. Engineering interventions should be scheduled periodically to restore these features as they deteriorate over time under traffic polishing. While many studies have investigated the effects of traffic polishing on pavement texture and skid resistance through laboratory experiments, the absence of real-world traffic and environmental factors in these studies may limit the generalization of their findings. This study addresses this research gap by conducting a comprehensive field study of pavement texture and skid resistance under traffic polishing in the real world. A total of thirty pairs of pavement texture and friction data were systematically collected from three distinct locations with different levels of traffic polishing (middle, right wheel path, and edge) along an asphalt pavement in Oklahoma, USA. Data acquisition utilized a laser imaging device to reconstruct 0.01 mm 3D images to characterize pavement texture and a Dynamic Friction Tester to evaluate pavement friction at different speeds. Twenty 3D areal parameters were calculated on whole images, macrotexture images, and microtexture images to investigate the effects of traffic polishing on pavement texture from different perspectives. Then, texture parameters and testing speeds were combined to develop friction prediction models via linear and nonlinear methodologies. The results indicate that Random Forest models with identified inputs achieved excellent performance for non-contact friction evaluation. Last, the friction decrease rate was discussed to estimate the timing of future maintenance to restore skid resistance. This study provides more insights into how engineers should plan maintenance to restore pavement texture and friction considering real-world traffic polishing.

Enhancing battery system identification: nonlinear autoregressive modeling for Li-ion batteries

Precisely characterizing Li-ion batteries is essential for optimizing their performance, enhancing safety, and prolonging their lifespan across various applications, such as electric vehicles and renewable energy systems. This article introduces an innovative nonlinear methodology for system identification of a Li-ion battery, employing a nonlinear autoregressive with exogenous inputs (NARX) model. The proposed approach integrates the benefits of nonlinear modeling with the adaptability of the NARX structure, facilitating a more comprehensive representation of the intricate electrochemical processes within the battery. Experimental data collected from a Li-ion battery operating under diverse scenarios are employed to validate the effectiveness of the proposed methodology. The identified NARX model exhibits superior accuracy in predicting the battery's behavior compared to traditional linear models. This study underscores the importance of accounting for nonlinearities in battery modeling, providing insights into the intricate relationships between state-of-charge, voltage, and current under dynamic conditions.

Exploring the Role of Complexity in Health Care Technology Bottom-Up Innovations: Multiple-Case Study Using the Nonadoption, Abandonment, Scale-Up, Spread, and Sustainability Complexity Assessment Tool.

New digital technology presents new challenges to health care on multiple levels. There are calls for further research that considers the complex factors related to digital innovations in complex health care settings to bridge the gap when moving from linear, logistic research to embracing and testing the concept of complexity. The nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework was developed to help study complexity in digital innovations. This study aims to investigate the role of complexity in the development and deployment of innovations by retrospectively assessing challenges to 4 digital health care innovations initiated from the bottom up. A multicase retrospective, deductive, and explorative analysis using the NASSS complexity assessment tool LONG was conducted. In total, 4 bottom-up innovations developed in Region Västra Götaland in Sweden were explored and compared to identify unique and shared complexity-related challenges. The analysis resulted in joint insights and individual learning. Overall, the complexity was mostly found outside the actual innovation; more specifically, it related to the organization's readiness to integrate new innovations, how to manage and maintain innovations, and how to finance them. The NASSS framework sheds light on various perspectives that can either facilitate or hinder the adoption, scale-up, and spread of technological innovations. In the domain of condition or diagnosis, a well-informed understanding of the complexity related to the condition or illness (diabetes, cancer, bipolar disorders, and schizophrenia disorders) is of great importance for the innovation. The value proposition needs to be clearly described early to enable an understanding of costs and outcomes. The questions in the NASSS complexity assessment tool LONG were sometimes difficult to comprehend, not only from a language perspective but also due to a lack of understanding of the surrounding organization's system and its setting. Even when bottom-up innovations arise within the same support organization, the complexity can vary based on the developmental phase and the unique characteristics of each project. Identifying, defining, and understanding complexity may not solve the issues but substantially improves the prospects for successful deployment. Successful innovation within complex organizations necessitates an adaptive leadership and structures to surmount cultural resistance and organizational impediments. A rigid, linear, and stepwise approach risks disregarding interconnected variables and dependencies, leading to suboptimal outcomes. Success lies in embracing the complexity with its uncertainty, nurturing creativity, and adopting a nonlinear methodology that accommodates the iterative nature of innovation processes within complex organizations.

Intelligent Deep Adaptive Intuitionistic Fuzzy Classifier Based Dyslexia Prediction Among Children at its Early Stage

The inability of standard linear research to clearly define the role of cognitive abilities in reading difficulties is most likely due to the fact that reading and the components that are related to reading are both part of a system of factors. There are numerous interrelated and mitigating elements that conventional statistical models are unable to deal effectively. This work aims to increase dyslexia prediction accuracy by introducing two stage non-linear methodology which incorporate both deep learning and uncertainty theory. In the first stage, the pattern of dyslexic symptoms is learned by introducing deep adaptive neural network. During the training phase, the hyperparameters weight and learning rate are optimized using a metaheuristic algorithm called the fruit fly optimization algorithm. This algorithm imitates the behavior of fruit flies to search for the best set of values. The acquired knowledge is infused in the uncertainty inference model known as intuitionistic inference classifier. Each characteristic that defines an instance as dyslexic or non-dyslexic is represented in tristate degrees to precisely address the inconsistent or vague instance which neither or nor exhibit dyslexic symptoms by introducing the hesitancy factor. The simulation results support the proposed intuitionistic fuzzy rules prominently improves the accuracy rate of dyslexia prediction compared to the other existing state of arts.

Forecasting the Stock Market Returns Using nonlinear hybrid GARCH-SETAR model

Forecasting stock market returns is a valuable tool for investors seeking to enhance their gains in stock trading. Predicting stock prices proves to be a formidable endeavor due to its substantial volatility, non-linear characteristics trends, and responsiveness to multifaceted variables, including economic conditions, market trends, seasonality, and sentiment. Despite these complexities, non-linear methodologies like threshold time series and conditional heteroscedasticity models are underutilized. This study aims to assess the predictive capabilities of a hybrid GARCH-SETAR model in the context of stock market returns, encompassing both Islamic and conventional stocks listed on the Pakistan Stock Exchange. The Islamic and conventional stock markets in Pakistan represented by KMI-30, KSE-30 and KSE-100 selected contain daily data from January, 2012 till June, 2023. After the confirmation of stationarity, ARCH effect and non-linearity by ADF, PP unit root, ARCH-LM and BDS test respectively the best estimated linear traditional Box-Jenkins ARIMA, non-linear threshold SETAR and ARIMA-GARCH models are selected based on AIC and BIC information criteria’s. The best proposed hybrid GARCH-SETAR model is also selected based on AIC and BIC information criteria’s. It is revealed that the one-step-ahead recursive forecast performance proposed hybrid GARCH-SETAR model outperforms all other selected linear and non-linear models for both Islamic and conventional stock markets based on RMSE, MAE, MAPE and SMAPE forecast evaluation criteria’s.

Nonlinearity analysis of sit-to-stand and its application

The examination of human biomechanics, particularly the sit-to-stand transition, has been a focal point of research for numerous years, utilizing mathematical models of the musculoskeletal structure and motion analysis. However, researchers and scientists have encountered substantial challenges attributable to the distributed, nonlinear, and time-varying nature of this phenomenon, characterized by numerous degrees of freedom and redundancy at various levels. Conventional biomechanical assessments of human movement typically rely on linear mathematical approaches, which, while advantageous in various scenarios, often inadequately capture the predominantly nonlinear characteristics inherent in human systems. As a consequence, there has been a growing recognition of the limitations of linear methods, leading to an increased adoption of nonlinear analytical techniques rooted in a dynamical systems approach in contemporary research. Notwithstanding this trend, there exists a conspicuous dearth of a comprehensive review paper that meticulously scrutinizes these nonlinear methods and their applications across the spectrum from modelling to rehabilitation. This mini-review aims to address this gap by highlighting recent advancements in nonlinear methodologies. These methodologies have demonstrated the potential to enhance the efficacy of interventions for individuals with sit-to-stand disorders, encompassing the design of intelligent rehabilitation devices, mitigating fall risks, and facilitating early patient classification.

Nonlinear Heart Rate Variability Analysis for Sleep Stage Classification Using Integration of Ballistocardiogram and Apple Watch.

Wearable or non-contact, non-intrusive devices present a practical alternative to traditional polysomnography (PSG) for daily assessment of sleep quality. Physiological signals have been known to be nonlinear and nonstationary as the body adapts to states of rest or activity. By integrating more sophisticated nonlinear methodologies, the accuracy of sleep stage identification using such devices can be improved. This advancement enables individuals to monitor and adjust their sleep patterns more effectively without visiting sleep clinics. Six participants slept for three cycles of at least three hours each, wearing PSG as a reference, along with an Apple Watch, an actigraphy device, and a ballistocardiography (BCG) bed sensor. The physiological signals were processed with nonlinear methods and trained with a long short-term memory (LSTM) model to classify sleep stages. Nonlinear methods, such as return maps with advanced techniques to analyze the shape and asymmetry in physiological signals, were used to relate these signals to the autonomic nervous system (ANS). The changing dynamics of cardiac signals in restful or active states, regulated by the ANS, were associated with sleep stages and quality, which were measurable. Approximately 73% agreement was obtained by comparing the combination of the BCG and Apple Watch signals against a PSG reference system to classify rapid eye movement (REM) and non-REM sleep stages. Utilizing nonlinear methods to evaluate cardiac dynamics showed an improved sleep quality detection with the non-intrusive devices in this study. A system of non-intrusive devices can provide a comprehensive outlook on health by regularly measuring sleeping patterns and quality over time, offering a relatively accessible method for participants. Additionally, a non-intrusive system can be integrated into a user's or clinic's bedroom environment to measure and evaluate sleep quality without negatively impacting sleep. Devices placed around the bedroom could measure user vitals over longer periods with minimal interaction from the user, representing their natural sleeping trends for more accurate health and sleep disorder diagnosis.

Common errors on using convex modelling and linear matrix inequalities for nonlinear control

This note discusses common errors on using convex modelling and linear matrix inequalities for nonlinear control, a methodology that has become increasingly popular due to its systematicness and numerical implementability. Illustrations on common problems are made from existing literature: they are classified and discussed; advices are given to prevent them. Convex modelling is employed in linear parameter varying, Takagi-Sugeno models, and other convex structures in order to subsume or rewrite a nonlinear system for analysis or design via the direct Lyapunov method. Convexity plays a central role in allowing a finite set of vertex conditions in the form of linear matrix inequalities to be sufficient for the corresponding task. In contrast with other nonlinear methodologies, this one produces expressions resembling linear results, which makes it easier to grasp while often inducing subtle mistakes.

Prediction of psychopathology during the COVID-19 pandemic using linear and non-linear methodologies: Importance of COVID-19 threat perception, emotional competencies and resilience

The presence of a mental or physical illness prior to the pandemic, the perceived threat from COVID-19, resilience or emotional intelligence may influence the onset or increase of psychopathology during the COVID-19 lockdown. The aim was to assess predictors of psychopathology by comparing two statistical methodologies (one linear and one non-linear). MethodA total of 802 Spanish participants (65.50% female) completed the questionnaires independently after signing informed consent. Psychopathology, perceived threat, resilience and emotional intelligence were assessed. Descriptive statistics, hierarchical regression models (HRM) and fuzzy set qualitative comparative analysis (fsQCA) were conducted. ResultsThe data obtained through the HRM showed that the presence of a previous mental illness, low resilience and emotional clarity, high emotional attention and repair, and COVID-19 threat perception predicted 51% of the variance in psychopathology. Results obtained from QCA showed that different combinations of these variables explained 37% of high levels of psychopathology and 86% of low levels of psychopathology, highlighting how the presence of prior mental illness, high emotional clarity, high resilience, low emotional attention and low perceived COVID-19 threat play a key role in explaining psychopathology. ConclusionsThese aspects will help promote personal resources to buffer psychopathology in lockdown situations.

Methodology of nonlinearity in the research of the phenomenon of the innovation space of higher school expansion

The article relates to the formation of a methodology of nonlinearity for nonlinear cognition of the spatial expansion of higher education in the innovation sphere, which manifests itself as the implementation of its innovative activities and the spread of innovations not only inside, but also outside its own national states. This trend of embedding higher education in the global innovation process has a stable and long-lasting character, due to its all-round innovatization and globalization.The article offers a rationale of the relevance and presentation of the methodology of nonlinearity as the most effective tool for understanding the expansion of the innovation space of higher education and its integration into the worldwide innovation process. The proposed methodology of nonlinear research is opposed to the so-called linear logic, a linear method often used in economics that considers objects and phenomena with predictable behavior and is not able to give a complete picture of the development of open nonequilibrium systems. The methodology of nonlinearity includes the principles of the theory of systems self-organization and the theory of evolutionary economics. It allows combining both stationary and dynamic components of the nonlinear process under study in one model, revealing the nature of changes in both spatial and temporal properties accompanying the modification of higher education integrated into the global innovation space.The study’s results are presented as the parameters of the nonlinearity of the university innovation space expansion. Theirs disclosed using a nonlinear methodology. There are: the openness of higher education to the import-export of information, energy and material flows; the state of instability, disequilibrium under the influence of external and internal factors; updating the structure through self-organization, leading to the replacement of obsolete parts of the system with new ones; the ability to make qualitative leaps, once at the bifurcation points.The article offers a rationale of the relevance and presentation of the methodology of nonlinearity as the most effective tool for understanding the expansion of the innovation space of higher education and its integration into the worldwide innovation process. The proposed methodology of nonlinear research is opposed to the so-called linear logic, a linear method often used in economics that considers objects and phenomena with predictable behavior and is not able to give a complete picture of the development of open nonequilibrium systems.The methodology of nonlinearity includes the principles of the theory of systems self-organization and the theory of evolutionary economics. It allows combining both stationary and dynamic components of the nonlinear process under study in one model, revealing the nature of changes in both spatial and temporal properties accompanying the modification of higher education integrated into the global innovation space.The study’s results are presented as the parameters of the nonlinearity of the university innovation space expansion. Theirs disclosed using a nonlinear methodology. There are: the openness of higher education to the import-export of information, energy and material flows; the state of instability, disequilibrium under the influence of external and internal factors; updating the structure through self-organization, leading to the replacement of obsolete parts of the system with new ones; the ability to make qualitative leaps, once at the bifurcation points.

Attitudes towards Communication in Nursing Students and Nurses: Are Social Skills and Emotional Intelligence Important?

The communication attitude (ACO) of nurses can significantly influence patient health outcomes. This work aims to evaluate predictor variables of communication attitude (emotional intelligence and social skills) in nurses and nursing students separately by comparing linear and non-linear methodologies. Two samples participated in this study: 312 nursing professionals and 1369 nursing students. In total, 75.60% of the professionals and 83.80% of the students were women. After signing the informed consent form, their emotional intelligence (TMMS-24), social skills (IHS) and ACO (ACO) were assessed. It was found that ACO through linear regression models in professionals was predicted by emotional repair and, in students, by attention and emotional repair, as well as by low exposure to new situations, low social skills in the academic or work area and high empathy. Overall, the comparative qualitative models show how the combination of different skills related to emotional intelligence and social skills lead to high levels of ACO. Conversely, their low levels result in an absence of ACO. Our results highlight the importance of emotional intelligence, especially emotional repair and empathy, as well as the need to consider ways to encourage the learning of these skills in a formal way.

Uncertainty Quantification of Compressor Map Using the Monte Carlo Approach Accelerated by an Adjoint-Based Nonlinear Method

Precise and inexpensive uncertainty quantification (UQ) is crucial for robust optimization of compressor blades and to control manufacturing tolerances. This study looks into the suitability of MC−adj−nonlinear, a nonlinear adjoint-based approach, to precisely and rapidly assess the performance discrepancies of a transonic compressor blade section, arising from geometric alterations, and building upon previous research. In order to assess the practicality and illustrate the benefits of the adjoint-based nonlinear approach, its proficiency and precision are gauged against two other methodologies, the adjoint-based linear approach (MC−adj−linear) and the high-fidelity nonlinear Computational Fluid Dynamics (MC−CFD) method. The MC−adj−nonlinear methodology exhibits impressive generalization capabilities. The MC−adj−nonlinear method offers a great balance between precision and time efficiency, since it is more precise than the MC−adj−linear method in both design and near-stall conditions, yet requires approximately a thirtieth of the time of the MC−CFD method. Finally, the MC−adj−nonlinear method was utilized to conduct fast UQ analyses of the section at four distinct speeds to quantify the performance uncertainty for the compressor map. It is found that aerodynamic performance is more sensitive to geometric deviations at high speeds than at low speeds. The impact of the geometric deviations is generally detrimental to the mean efficiency.

Revisiting the finance-growth nexus in Hong Kong: fresh insights from nonparametric analysis

PurposeSince financial sector plays a critical economic role in Hong Kong, the current research aims to comprehensively analyze the association between financial development and economic growth in the country to draw correct conclusions about the impact that financial sector's development has on the growth of the economy. This requires both using of more comprehensive data that includes all or nearly all elements of the country's financial sector and utilizing advanced econometrics techniques to provide more reliable evidence based on the findings. In the study, both issues have been addressed more academically to aid the relevant authorities better.Design/methodology/approachThis study empirically examines the financial development-economic growth nexus in Hong Kong employing data covering 1980–2019. The quantile-on-quantile (QQ) approach of Sim and Zhou (2015) is utilized to investigate certain subtle aspects of the association linking financial development and economic growth. In addition, the authors benefit from applying the nonlinear Granger causality test of Diks and Panchenko (2006) to assess the variables' nexus in a nonlinear manner.FindingsIn contrast to the evidence of a unidirectional linkage documented in many related studies, the empirical findings suggest that a bi-directional relationship exists between financial development and economic growth for Hong Kong. This is a helpful input for the relevant policymakers and implies that they can set appropriate policies and regulations to balance financial development and economic growth in this country.Originality/valueThe originality of this study can be divided into two parts. Methodologically, unlike past studies that utilized mostly linear and parametric methods, the paper contributes to the literature by applying the more robust nonparametric and nonlinear methodologies. Theoretically, most researchers have used various financial development indicators, which led to very different conclusions. Therefore, this study attempts to resolve this deficiency in the literature by using a more comprehensive index for financial development.

Application of Dispersion Entropy for the Detection of Emotions With Electroencephalographic Signals

The interest in the application of nonlinear methodologies for the analysis of electroencephalography signals for automatic recognition of emotions has increased notably. A vast number of studies in the research field of emotions detection have focused on the identification of the four quadrants of the valence-arousal emotional model. In this work, the recently introduced dispersion entropy (DispEn) has been applied for the first time to discern between the four groups of emotions corresponding to the four quadrants. This entropy-based index has demonstrated a considerable performance when dealing with this problem. Concretely, frontal and parieto-occipital brain regions reported the most relevant results when identifying the four emotional groups. Furthermore, the implementation of a classification model based on a sequential forward selection (SFS) approach and a support vector machine (SVM) classifier provided an average accuracy of 89.54%. This result is comparable to similar works in the scientific literature, with the advantage that the controlled selection of a reduced number of input features by means of the SFS scheme allowed to give a clinical interpretation of the outcomes. Therefore, it has been possible to reveal new insights about the performance of the most relevant brain regions involved in the processing of emotional information.

A parallel methodology using radial basis functions versus machine learning approaches applied to environmental modelling

Parallel nonlinear models using radial kernels on local mesh support have been designed and implemented for application to real-world problems. Although this recently developed approach reduces the memory requirements compared with other methodologies suggested over the last few years, its computational cost makes parallelisation necessary, especially for big datasets with many instances or attributes. In this work, several strategies for the parallelisation of this methodology are proposed and compared. The MPI communication protocol and the OpenMP application programming interface are used to implement the algorithm. The performance of this methodology is compared with various machine learning methods, with particular consideration of techniques using radial basis functions (RBF). Different methods are applied to model the daily maximum air temperature from real meteorological data collected from the Agroclimatic Station Network of the Phytosanitary Alert and Information Network of Andalusia, an autonomous community of southern Spain. The obtained goodness-of-fit measures illustrate the effectiveness of this nonlinear methodology, and its training process is shown to be simpler than those of other powerful machine learning methods.

Conceptualization of smart-philosophy as a post-modern project of non-linear pattern development of the XXI century

The aim of the study is to analyze smart-philosophy, which refers to the priority research based on the new interaction between man and society. The methodological basis of the study is the synergetic approach, in the context of which new realities are “smart” with innovative potential. The informational approach to the analysis of society is based on the synergetic, nonlinear methodology, the analysis of which is applicable to the global transformation’s characteristic of autoreflexive societies of high complexity. It also uses methods for measuring the “smart society” - anthropological and socio-axiological, based on people, education, the movement towards the “society of innovation” and knowledge. It is concluded that the breakthrough in the system of information and communication technologies has determined profound meaningful changes in all spheres of social activity, which is the theoretical autoreflexion of modernity and its dominant direction. The result of the study is the conceptualization of the smart-philosophy of the XXI century as the highest stage of civilizational development of mankind.

Comprehensive comparison of linear and non-linear methodologies for lithium quantification in geological samples using LIBS

Laser-induced breakdown spectroscopy allows fast chemical analysis of light elements without significant sample preparation, turning it into a promising technique for on-site mining operations. Still, the performance for quantification purposes remains its major caveat, obstructing a broader application of the technique. In this work, we present an extensive comparison of the performances of distinct algorithms for quantification of Lithium in a mining prospection stage, using spectra acquired with both a commercial handheld device and a laboratory prototype. Covering both linear and non-linear methodologies, the results show that, when covering a wide range of concentrations typical on a mining operation, non-linear methodologies manage to achieve errors compatible with a semi-quantitative performance, offering performances better than those obtained with linear methods, which are more affected by saturation and matrix effects. The findings enclosed offer support for future applications in the field and may possibly be generalized for other elements of interest in similar mining environments.

Are Coping Strategies, Emotional Abilities, and Resilience Predictors of Well-Being? Comparison of Linear and Non-Linear Methodologies.

Emotional intelligence (EI), problem-oriented coping, and resilience have been deeply studied as psychological predictors of wellbeing in stressful daily situations. The aim was to find out whether coping, EI, and resilience are predictors of well-being, using two statistical methodologies (hierarchical regression models and comparative qualitative models). With this objective in mind, we built an online evaluation protocol and administered it to 427 Spanish people, exploring these variables through a selection of validated tests. The extracted data were studied using linear predictive tests (hierarchical regression models), as well as fuzzy set qualitative comparative analysis. We found that EI variables had important associations with coping, positive affect, negative affect, and life satisfaction, and also acted as relevant predictors for all of them, together with resilience and problem-oriented coping. The fuzzy set qualitative comparative analysis showed a series of logical combinations of conditional causes and results of each potential configuration for these variables. The interaction between the presence of EI, resilience, and coping resulted in high levels of well-being. On the other hand, the presence of high emotional attention in interaction with low resilience and low coping abilities resulted in low well-being. These results increase knowledge about protective factors and allow for the creation of intervention programmes to enhance them.

Impact of Covid-19 pandemic on Remittance Inflow-Economic Growth Nexus in India: Lessons from an Asymmetric Analysis

The relentless spread of the Covid-19 pandemic from the first quarter of 2020 and the emergence of new variants since early 2021 have caused a steep decline in economic activities in all countries. The forecast by international agencies including World Bank is that remittances (REM) on a global scale would decline by 20 percent in 2021; and to South Asia in particular would decrease by 25 percent. India, a low-middle-income country, which has been among the top ten REM recipient countries, is expected to be adversely affected much more. This study examines the asymmetric effects of REM on India’s per capita GDP by using a nonlinear methodology. Additionally, factors like ICT and the financial sector are considered in an extended version of the remittance-growth relationship. The study findings reveal that the impact of the negative partial sum of decomposition of REM is higher than the impact of positive partial sum decomposition of REM, implying a greater adverse effect of declining remittances on per capita GDP growth. The findings are expected to be of substantial importance in designing appropriate policies that affect economic growth through the interaction of remittances with ICT, and financial markets.