Survival-of-the-fittest Research Articles

Intrinsic sustainability assessment at technosphere level by adapting entropy based ecologic indicators. A preliminary analysis for some main waste treatment processes.

The present study was developed on the following evidence: "what is considered to be environmentally sustainable in the technosphere has to be in compliance with the development tendency of the ecosphere". Starting from this hypothesis, two entropy-based indicators were proposed: the specific entropy per amount of exergy gained (SEEG); the specific entropy per amount of exergy exploitable in the technosphere (SEEE). These were retrieved from the specific entropy (SE) indicator largely exploited for assessing the health status and the development tendency of ecosystems. Entropy based indicators SEEG and SEEE were used for assessing the environmental sustainability of incineration, anaerobic digestion and composting of waste. The aim of these indicators was to support already available methodologies for environmental impact assessment for better understating the intrinsic sustainability of specific processes, since the only quantification of the emission appears not enough informative for this goal. Concerning the ecosphere, SE minimization follows the Darwinian principle "survival of the fittest", being the fittest ecosystems those able to make the most efficient and effective use and storage of energy and materials, at the lower relative entropy generation. Consequently, the lower are SEEG and SEEE the fittest is the analysed system (i.e. sustainable). Main findings indicate SEEG ranging from 2.471 to 3.705, for incinerator, from 0.007 to 0.106 for anaerobic digestion whereas resulted constant = 0.266 for composting. The SEEE ranged from 0.433 to 0.484 for incinerator, from <0.001 to 0.008 for anaerobic digestion and resulted constant = 0.014 for composting. These preliminary results indicates that processes and system able to return chemicals and materials exploitable at technosphere level were characterized by higher ability in returning exergy flows at the lower entropy increase compared to hose returning only energy.

Non-neutral clonal selection and its potential role in mammalian germline stem cell dysfunction with advancing age.

The concept of natural selection, or "survival of the fittest", refers to an evolutionary process in nature whereby traits emerge in individuals of a population through random gene alterations that enable those individuals to better adapt to changing environmental conditions. This genetic variance allows certain members of the population to gain an advantage over others in the same population to survive and reproduce in greater numbers under new environmental pressures, with the perpetuation of those advantageous traits in future progeny. Here we present that the behavior of adult stem cells in a tissue over time can, in many respects, be viewed in the same manner as evolution, with each stem cell clone being representative of an individual within a population. As stem cells divide or are subjected to cumulative oxidative damage over the lifespan of the organism, random genetic alterations are introduced into each clone that create variance in the population. These changes may occur in parallel to, or in response to, aging-associated changes in microenvironmental cues perceived by the stem cell population. While many of these alterations will be neutral or silent in terms of affecting cell function, a small fraction of these changes will enable certain clones to respond differently to shifts in microenvironmental conditions that arise with advancing age. In some cases, the same advantageous genetic changes that support survival and expansion of certain clones over others in the population (viz. non-neutral competition) could be detrimental to the downstream function of the differentiated stem cell descendants. In the context of the germline, such a situation would be devastating to successful propagation of the species across generations. However, even within a single generation, the “evolution” of stem cell lineages in the body over time can manifest into aging-related organ dysfunction and failure, as well as lead to chronic inflammation, hyperplasia, and cancer. Increased research efforts to evaluate stem cells within a population as individual entities will improve our understanding of how organisms age and how certain diseases develop, which in turn may open new opportunities for clinical detection and management of diverse pathologies.

Prepayment Model of Supply Chain Financing Based on Internet of Things and Machine Learning Algorithm

Nowadays, the relationship between all kinds of things is constantly strengthened, and the regional restrictions on things are also constantly weakened, especially in the aspect of trade. In the past, people could only buy some basic goods in a relatively small area if they wanted to buy them in a relatively small area, but now the situation is very different. People's dependence on traders is gradually decreasing; that is to say, the traditional advantages of traders are gradually losing. Particularly, in today's fierce competition, survival of the fittest, and limited profits, the entire trade industry is facing a lot of impacts, not to mention the survival of traders relying on this industry. Therefore, traders must think of a new way to adapt to the characteristics of the new era, to continue to make profits and maintain their own survival. In fact, for the whole industry, this is also the most urgent problem to be solved. This study mainly explores the above problems and puts forward some feasible methods to solve the problems, hoping to cause some thinking of the industry personnel, so that they can modify and adjust their working mode. In addition, it should be emphasized that the mode of using Internet of things and machine learning to achieve the purpose of innovation is of great practical significance.

Post-Covid 19 World

As the world struggles to contain the Covid-19 pandemic, one thing is becoming clear: the world after the outbreak will be entirely different politically, socially, economically, and health-wise. No one will emerge from this crisis without suffering some loss. Previous threats to individual and social liberties were ineffective in changing our way of life, but now this vital asset of our civilization is in grave danger. The influential countries will redraw a new international order in the post-Covid-19 world, taking into account the lessons learned in coping with the current pandemic. The position of international organizations will be scrutinized as well. Low GDP growth, high debt, and reliance on oil and basic technology would make it difficult for developing countries to sustain independent foreign policies. In education, e-governance, commerce, and artificial intelligence; IT will play a significant role in all aspects of life. Monitoring of people with tracking devices will run counter to human rights issued in the name of security and protection. The tourism industry will continue to thrive, but visa requirements will be tightened. Covid-19 is teaching a valuable lesson. Survival of the fittest would not work in the march of civilization. Even if we have to bear them on our backs, we must accompany the poor and helpless.

Joint suppression of normal gear mesh component and background noise for early local fault detection based on dynamic evolutionary digital filter

Signal denoising is one of the most essential steps in mechanical fault diagnosis, which can help improve the signal-to-noise ratio (SNR) and find faults as earlier as possible. Many denoising techniques have been developed to suppress both Gaussian white and non-Gaussian background noise. However, most of them achieved poor performance in terms of assessing local gear fault in the early stage. The key problem is that only one or several gear tooth’s stiffness or displacement excitation are aroused in the beginning of local faults and most of the gear teeth or gear pairs are healthy, especially for the multi-stage gearbox. And both the normal gear meshing signals and background noise lead to low SNR of fault characteristics. To overcome this problem, this paper proposes a dynamic evolutionary digital filter (Dyna-EDF) method to realize the optimal suppression of background noise and normal gear meshing component in the acquired signal aiming to enhance the SNR of early fault characteristics. The proposed method belongs to an adaptive noise cancellation (ANC) method, which can find and suppress the relevant component between the acquired signal and reference signal based on an iterative optimization of a digital filter. In the proposed method, a filter parameter population and ANC structure is initialized and established firstly. Then the filter parameters of the population are dynamically adjusted to find the optimal filter based on cloning and mating evolutionary strategies. Furthermore, “survival of the fittest” idea is employed for the optimal filter searching and applied during each iteration in Dyna-EDF method to prevent the premature problem that found in traditional EDF. Finally, the derived optimal filter is applied to suppress the relevant component between the acquired signal and reference signal using an ANC process. Results show that the proposed Dyna-EDF method successfully increase the SNR of fault characteristics, both normal gear mesh component and background noise are suppressed effectively. The proposed Dyna-EDF method seems achieve better denoising performance than traditional EDF method, its improved versions and the traditional denoising methods.

Nurses suffering in silence: Addressing the stigma of mental health in nursing and healthcare.

Nurses suffering in silence: Addressing the stigma of mental health in nursing and healthcare.

SARS-CoV-2 Intermittent Virulence as a Result of Natural Selection

For the first time in history, we have witnessed the origin and development of a pandemic. To handle the accelerated accumulation of viral mutations and to comprehend the virus’ evolutionary adaptation in humans, an unparalleled program of genetic sequencing and monitoring of SARS-CoV-2 variants has been undertaken. Several scientists have theorized that, with the Omicron surge producing a more contagious but less severe disease, the end of COVID-19 is near. However, by analyzing the behavior shown by this virus for 2 years, we have noted that pandemic viruses do not always show decreased virulence. Instead, it appears there is an evolutionary equilibrium between transmissibility and virulence. We have termed this concept “intermittent virulence”. The present work analyzes the temporal and epidemiological behavior of SARS-CoV-2 and suggests that there is a high possibility that new virulent variants will arise in the near future, although it is improbable that SARS-CoV-2’s virulence will be the same as was seen during the alpha or delta waves, due to the fact that the human population has reached a sufficient level of herd immunity through natural infection or due to the vaccination programs. The most recent global mortality data raised a question whether this pandemic is really over. Furthermore, it is uncertain when the endemic phase will begin. Darwin’s words: “the survival of the fittest” are still valid, and the virus will continue killing nonvaccinated old people, vaccinated old people, and those with comorbidities. We have underestimated the SARS-CoV-2 mastery of immune escape and have not yet seen the full adaptive potential this virus can develop through natural selection.

Improved Metaheuristic Algorithm Based Finite Element Model Updating of a Hybrid Girder Cable-Stayed Railway Bridge

This study proposes a generally applicable improvement strategy for metaheuristic algorithms, improving the algorithm’s accuracy and local convergence in finite element (FE) model updating. Based on the idea of “survival of the fittest” in biological evolution, the improvement strategy introduces random crossover and mutation operators into metaheuristic algorithms to improve the accuracy and stability of the solution. The effectiveness of the improvement strategy with three typical metaheuristic algorithms was comprehensively tested by benchmark functions and numerical simulations of a space truss structure. Meanwhile, the initial FE model of a railway hybrid girder cable-stayed bridge was updated to examine the effect of the improved metaheuristic algorithm within the FE model, updating for complex engineering structures. The results show that the accuracy and stability of the improved metaheuristic algorithm were improved by this process. After the initial FE model of the hybrid girder cable-stayed bridge was updated, the calculated frequencies and displacements were closer to the measured values, better representing the actual structure, and showing that this process can be used for baseline FE models of bridges.

Keunggulan Bersaing UMKM Berbasis Sumber Daya di Tengah Pandemi Covid-19

This study aims to describe the strategic steps that can be applied by MSMEs, in order to be able to utilize their resources to achieve competitive advantage, especially in conditions of uncertainty, such as the current covid-19 pandemic. The method used in this research is library research. The steps for collecting information and data are carried out in stages, starting from: (i) Design, (ii) Implementation, (iii) Analysis, and (iv) Compiling and writing a review. The results show that in achieving competitive advantage there are many resource variables that influence it. The results showed that the variables of innovation ability and creativity are resources that have a dominant influence on competitive advantage. In addition, another dominant variable that also affects competitive advantage is HR, because HR holds an important key in running the MSE business, including in creating various innovations and creativity. For this reason, the strategic steps that must be taken by MSMEs are to increase innovation and creativity, if they want to survive and be able to compete in any conditions, especially in conditions of uncertainty. Like Darwin's theory of evolution: The Survival of the Fittest, SMEs must be prepared to adapt to changes in the environment that affect business.&#x0D; Keywords: Innovation, Creativity, Competitiveness, Competitive Advantage, Resources, MSME

Mobile Robot Path Planning Based on Improved Coyote Optimization Algorithm

An improved coyote optimization algorithm (ICOA) is proposed to meet the requirements of global search capability, convergence speed, and stability for mobile robot path planning problems. Firstly, the population is divided into the elite group and the general groups after initialization, and the coyote individuals in the general groups are evolved by introducing the strategy of the best leading the poor. Secondly, after returning to the original position, coyotes adopt a new growth mode, and each individual makes a certain contribution to improve the level of the whole group. Thirdly, different from the original greedy algorithm, the survival of the fittest is carried out after each group of coyotes grows up, and the individuals do not affect each other. Finally, ICOA and the other seven optimization algorithms are simulated on four maps for mobile robot path planning. The simulation results show that ICOA can keep the diversity of the population, and has a strong global search ability, better stability, fast convergence speed, which reflects the strong optimization ability.

CRM software as a service and importance of the approach for SMEs

CRM is “old news” in large enterprises. On the other hand, in ever evolving economies where the strongest prevail, causing mergers and survival of the fittest (and the largest), it is increasingly difficult for small and medium enterprises (SMEs) to gain new customers and to retain old ones. Being able to timely understand customer habits, their needs and drivers is substantial for all businesses, large or small. Some of the core CRM software functionalities and reports will help SMEs to get comprehensible picture of their customers, key drivers of their businesses and the path their companies are taking. In this paper, importance of having CRM software, especially under SaaS licensing, understanding data it provides and using both to increase customer satisfaction and retention for SMEs will be discussed. Some of key performance indicators (KPI) will be explained, including how they can influence the increase revenue and enable sustainability and longevity of SMEs.

Perceptions of War in a Darwinian Perspective

Representations of war cannot be separated from moral perceptions of human conflicts. Whether war is deemed a normal phenomenon for human nature ‒ “wars have always existed and will always take place” ‒ or on the contrary is regarded as the unleashing of outrageous atrocities that could be avoided, has implications on how conflicts are remembered and commemorated. After the epoch-making publication of Charles Darwin’s On the Origin of Species (1859), and the consequential flood of ill-advised books and articles which constructed what has been termed “social Darwinism”, war was perceived as the legitimate manifestation of “the survival of the fittest” both for nations and individuals. The reason for wars could be found in evolutionary biology (Crook, 1994). Yet other writers chose to read peace into Darwin’s writings, and pointed to the notions of solidarity and philanthropy present in such publications as Descent of Man (1871). Reassessing Darwinism on the basis of the “reversionary effect of evolution” (Tort, 1983) leads to delegitimizing war time violence through the deconstruction of socio-biological arguments and gives a case for such anti-warmongering war memorials as Gentioux-Pigerolles in France which reads ‘Cursed be War!’

Of Battle and Business: Military Language in the Corporate Environment

Abstract As early as the second half of the 20th century, theoreticians and commentators in the field of business and corporate culture have started to increasingly make use of military and war metaphors when describing or analyzing business-related matters. This ongoing tendency can be explained by the fact that both military and business jargons have had similar historical trajectories and focus on similar goals and objectives. Military philosophy can furthermore offer valuable lessons and advice to any aspiring manager. To be aware of the symbiosis between military jargon and the language of business is to have a clearer overview of the impact of business practices and eventually to gain the upper hand in a domain where battle and the survival of the fittest are at the basis of all activities and endeavors. The aim of the present paper is to trace the similar trajectories in the evolution of military and business jargon and to analyze, by resorting to practical examples, the manifold similarities between the language of war and the one of business.

MFRAG: Multi-Fitness RankAggreg Genetic Algorithm for biomarker selection from microarray data

Microarray technology allows the simultaneous study of up to thousands of gene expressions, which is essential for researchers to understand cancer, diagnose disease, and drugs development. However, the large number of irrelevant and redundant genes in the data is a challenge to the performance and efficiency of the feature selection method and the classification algorithms. furthermore, the characteristic of the small data sample size increases the risk of over-fitting. Therefore, an efficient, accurate, and robust feature selection pipeline is necessary for gene selection. This paper proposed a new hybrid feature selection method Multi-Fitness RankAggreg Genetic Algorithm(MFRAG). Considering that a single model tends to overfit on small sample datasets, MFRAG integrates nine feature selection methods that can evaluate feature weights to evaluate individuals and calculates individual fitness by ensemble models. MFRAG more clearly imitates the natural principle of “survival of the fittest.” It improves the selection and mutation processes in genetic algorithms, enhances the stability and reliability of the selection process through fusion mechanisms and integrated models, and guides the evolutionary process through a set of lists generated by a feature fusion model. The experimental section compares MFRAG with six standard feature selection methods on ten publicly available microarray data and 18 published state-of-the-art methods on three datasets that researchers widely use. The results show that MFRAG outperforms all standard methods in classification accuracy and number of features and is ahead of most advanced methods.

Tissue stem cells: survival of the fittest

Barrier epithelial tissues such as the skin, lung and gut are the first line of defense between our body and the outside world. As such, their stem cells must continually generate and rejuvenate their tissue, but also self‐renew to maintain the reservoir of stem cells. When the barrier has been breached, for example by wounding, the stem cells must not only repair the damaged tissue but also call to the immune system to help guard against pathogen entry. All the while the stem cells must protect themselves from a variety of assaults, including not only injury but also mechanical stress, noxious chemicals, ultraviolet radiation, and allergens that stimulate an inflammatory response. How do stem cells equip themselves to respond to these different stresses? My laboratory uses mice as a model organism and the skin epithelium as our tissue to explore the molecular mechanisms involved. We use high throughput genetic and genomic approaches to learn at a molecular level how the stem cells balance growth and differentiation in normal tissue homeostasis, and how they change their behavior and interactions with their neighbors (the stem cell ‘niche’) in response to changes in their environment. Our global objective is to apply our knowledge of the basic science of epithelial stem cells to unfold new avenues for therapeutics to treat inflammatory conditions and cancers.

Survival of the Fittest: Numerical Modeling of SN 2014C

Initially classified as a Type Ib supernova (SN), ∼100 days after the explosion SN 2014C made a transition to a Type II SN, presenting a gradual increase in the Hα emission. This has been interpreted as evidence of interaction between the SN shock wave and a massive shell previously ejected from the progenitor star. In this paper we present numerical simulations of the propagation of the SN shock through the progenitor star and its wind, as well as the interaction of the SN ejecta with the massive shell. To determine with high precision the structure and location of the shell, we couple a genetic algorithm to a hydrodynamic and a bremsstrahlung radiation transfer code. We iteratively modify the density stratification and location of the shell by minimizing the variance between X-ray observations and synthetic predictions computed from the numerical model, allowing the shell structure to be completely arbitrary. By assuming spherical symmetry, we found that our best-fit model has a shell mass of 2.6 M ⊙; extends from 1.6 × 1016 cm to 1.87 × 1017 cm, implying that it was ejected ∼ 60/(v w /100 km s−1) yr before the SN explosion; and has a density stratification with an average behavior ∼r −3 but presenting density fluctuations larger than one order of magnitude. Finally, we predict that if the density stratification follows the same power-law behavior, the SN will break out from the shell by mid-2022, i.e., 8.5 yr after explosion.

Cognitive mechanism and neural basis of shyness

<p id="p00005">Shyness refers to individuals’ inhibition in a social situation; it can be an obstacle to participation in social interactions. In recent years, there has been an increase in cognitive neuroscience research on shyness. Within the frameworks of the metacognition model of shyness, the social fitness model of shyness, the lateralized brain-body emotion model of shyness, and the differential susceptibility to environmental influences model of shyness, researchers have explored the brain function differences, related brain networks, and cognitive processes underlying shyness. However, current cognitive neuroscience research on shyness is still in its infancy. For example: (1) researchers have mainly defined shyness from the perspective of personality; (2) the theoretical models of shyness are far from perfect; (3) the results of relevant empirical research have been unsystematic and inconsistent. <br/>Based on the above, this study proposes a psychological development model of shyness, which includes the following four perspectives. (1) Timeline of human evolution: the interaction of genetic factors (susceptibility genes), the internal environment (brain), and the external environment (such as school, family, and culture) affects the development of shyness. In the process of human evolution, human beings and their offspring showed different reactivity to the external environment, resulting in the survival of the fittest. The result of this process is that some individuals carry shyness susceptibility genes, which will not only determine their internal environment (brain) but also be affected by their internal environment (brain) and shape their metacognition and cognitive processes under the influences of school, family, culture, and other aspects of the external environment. The brain environment responsible for shyness is an expression of the interaction between the cognitive control system (centered in the prefrontal lobe) and the emotional system (centered in the amygdala-hypothalamus). Specifically, the cognitive bias/emotional arousal of shy individuals is the result of an imbalance in the regulation of the limbic system (centered in the amygdala-hypothalamus) by the prefrontal cortex. In other words, when facing an external stimulus, the amygdala of highly shy individuals is highly activated, which results in a highly emotional interpretation of low-level external stimuli, which is then projected to a wide range of brain regions including the hypothalamus. The hypothalamus then activates emotional expression by regulating the autonomic nervous system. In this process, the prefrontal cortex plays a top-down regulatory role, in which the strong activation of the right prefrontal lobe and the weak activation of the left prefrontal lobe of highly shy individuals leads to the disinhibition of the activated amygdala, hypothalamus, and other brain regions. Thus, the individual’s ability to regulate their emotions becomes weak and they cannot behave in an adaptive manner. The aspects of the internal environment that influence shyness include the differentiation of brain structure, the lateralization of brain function, and the uniqueness of the brain network. Moreover, in specific brain cognitive processing tasks, individuals with different levels of shyness have significant differences in the activity of different frequency bands and different cognitive components. (2) Timeline of individual development: the critical period of individual development affects the development of shyness. Specifically, the key development period of shyness is largely affected by the development of self-consciousness. With the maturity of self-consciousness, the externalized performance of individuals with different levels of shyness can be clearly distinguished. (3) Social situations: in specific social situations, the metacognition of shy individuals guides their cognitive processes, making them show differences in their physical, emotional, and behavioral performance. At the same time, different kinds of social situations will awaken individuals’ shyness to different degrees. <br/>Future research can be carried out from the following perspectives based on the psychological development model: (1) in terms of personality, clarify whether shyness describes a small number of extreme individuals or is instead a dimension that can sort the whole population; (2) explore the internal process of the emotional experience of shyness; (3) develop experimental research paradigms of shyness; (4) with the help of new brain technology, deeply explore the brain mechanism of shyness; (5) verify, expand, and improve the theoretical model related to shyness in the field of cognitive neuroscience.

Causality between Service Quality and Customers’ Satisfaction Across Star Hotels

Background: To win the race of hares, one has to become a leopard performing in faster and pro-active manners than others to win the competition. But to come first and remain ahead of others for longer durations, demands quality initiatives in business processes and therefore the topic on quality in service operations beards paramount importance. Objectives: On this note, we have tried to assess the prevailing service quality of various star category hotels in the state of Odisha in India where the renowned SERVQUAL scale is employed to map the difference of opinions between the guests visiting the hotels. Materials &amp; Methods: A total of 395 numbers of interviews were conducted through convenient sampling process and the data was captured through the use of a structured questionnaire. The collected data were assessed through the use of descriptive statistics that includes percentage calculations, cross tabulations, t-test, analysis of variance, correlation coefficient, and multiple regression analysis, etc. Results: The results revealed sorry state of affairs at the selected hotels where positive gap scores were obtained between the expectations and perceptions of the customers. Based upon the findings, curtain corrective measures are proposed in the study for enhancing the qualitative parameters of the hotels. Epilogue: Survival of the fittest is the word that describes the market scenario. The entities that remain fit, agile, anticipatory, proactive, caring, and most importantly quality conscious can only win the race with flying colours. Originality: This study analyzes the quality of various services initiatives prevailing at the star hotels in the state of Odisha in India for which our study can certainly help in generating different guidelines to ensure the optimal levels of quality as desired by the guests at the hotels which will help in development of a long term and sustainable quality initiative in the sector compared to international standards.

Prophesying the Short-Term Dynamics of the Crude Oil Future Price by Adopting the Survival of the Fittest Principle of Improved Grey Optimization and Extreme Learning Machine

Crude oil market analysis has become one of the emerging financial markets and the volatility effect of the market is paramount and has been considered as an issue of utmost importance. This study examines the dynamics of this volatile market of crude oil by employing a hybrid approach based on an extreme learning machine (ELM) as a regressor and the improved grey wolf optimizer (IGWO) for prophesying the crude oil rate for West Texas Intermediate (WTI) and Brent crude oil datasets. The datasets are augmented using technical indicators (TIs) and statistical measures (SMs) to obtain better insight into the forecasting ability of this proposed model. The differential evolution (DE) strategy has been used for evolution and the survival of the fittest (SOF) principle has been used for elimination while implementing the GWO to achieve better convergence rate and accuracy. Whereas, the algorithmic simplicity, use of less parameters, and easy implementation of DE efficiently decide the evolutionary patterns of wolves in GWO and the SOF principle updates the wolf pack based on the fitness value of each wolf, thereby ensuring the algorithm does not fall into local optimum. Furthermore, the comparison and analysis of the proposed model with other models, such as ELM–DE, ELM–Particle Swarm Optimization (ELM–PSO), and ELM–GWO shows that the predictability evidence obtained substantially achieves better performance for ELM–IGWO with respect to faster error convergence rate and mean square error (MSE) during training and testing phases. The sensitivity study of the proposed ELM–IGWO provides better results in terms of the performance measures, such as Theil’s U, mean absolute error (MAE), average relative variance (ARV), mean average percentage error (MAPE), and minimal computational time.

From restaurant to cloud kitchen: Survival of the fittest during COVID-19 An empirical examination

The restaurant industry is experiencing a financial crisis at the moment of COVID-19. The purpose of this paper is to explore and examine the impact of various factors responsible for purchase decisions for generation Z in the context of cloud kitchen. In this endeviour, the researchers used a self-reported survey questionnaire to collect the data. The quality responses were collected using convenience sampling and analysed using SmartPLS. As almost all businesses are hit badly due to the COVID-19 pandemic this paper explored the possibilities for the restaurant business i.e. food and beverage industry in the form of ‘cloud kitchen’. In this endeavour, the impact of green aspects, memorable food experience (experiential food under experience economy) along with certain other factors was examined. This study extends the food choice process model by Furst et al. (1996) that in the recent time green aspect, memorable experience are being preferred by consumers. Moreover, the restaurant may choose to work as ‘cloud kitchen until the situation (COVID-19) normalizes. The research work will contribute to new theoretical knowledge in the form of an extended food choice process model exploring generation Z purchase decision towards cloud kitchen.