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Dynamic strategies and evolutionary trajectories: A comprehensive review of experiential marketing in the soft drink industry
This comprehensive review explores the evolution of experiential marketing in the soft drink industry from 2005 to 2024. It uses analysis from a diverse set of 62 scholarly articles, Google books, Google Scholar, SSRN, Fig share, and various publishers such as Taylor & Francis, IGI Global, and Springer. The study traces the industry's trajectory from traditional marketing approaches to a digital-centric paradigm. The research captures pivotal moments in the development of experiential marketing strategies, emphasizing the integration of technology, sustainability, and community engagement. Key findings highlight the industry's adaptability to changing consumer preferences, the strategic use of data-driven insights, and the importance of inclusivity in crafting compelling brand narratives. The study identifies overarching trends, challenges, and opportunities that shaped the experiential marketing landscape in the soft drink industry over the past two decades. 2024, IGI Global. All rights reserved. -
Dynamic Scheduling of Scientific Workflows with Budget-Constraints in the Cloud
This research presents a novel approach to dynamic scheduling in cloud computing environments, directing on budget-constrained workflows and towards optimizing makespan, Quality of Service (QoS) metrics, and energy efficiency. Exploiting the Task Duplication Scheduling Algorithm (TDSA) and Salp Swarm Algorithm (SSA) which is influenced by the motion patterns of marine life forms, the proposed Enhanced Salp Swarm Algorithm (ESSA) algorithm dynamically assigns tasks to cloud resources while bearing budget curbs. The algorithm seeks to reduce makespan, guaranteeing efficient completion of workflows, at the same time boosting the QoS metric resource utilization. Moreover, the incorporation of energy-efficient scheduling techniques further donates to the sustainability of cloud environment operations. By constructing a mathematical representation which captures the trade-off between makespan, resource utilization, and budget constraints, the mechanism productively balances competing objectives to reach optimal scheduling outcomes. Through substantial experimentation with 5 Scientific Workflows, the success and efficiency of the suggested methodology are assessed, exhibiting its potential to significantly improve the performance of budget-restricted workflow in cloud environments while boosting workflows makespan (up to 9%) and improving asset usage (up to 5%) and energy efficiency (up to 10.5%). This research presents to advancing the latest in dynamic scheduling techniques for cloud environment, benefitting practical solutions for real-world deployment and operation. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Dynamic route scheduler in vehicular ad hoc network for smart crowd control
Revenue generated by tourism is positively correlated with the development of any city. In recent years, tourism is getting peak focus among the government, local bodies, and researchers. This has led to increase in initiatives to grow tourism in and across the country. Being one of the most flourishing sectors, tourism in India shows bold signals of emerging as a strong participant in the world of tourism. In addition to safeguarding its culture and deep-rooted traditional values, tourism provides a way to increase employment opportunities as well as increase the foreign exchange within the country. There are many open research problems arising in the domain, which need the attention of researchers. City traffic management is one among the major concern for cities around the world. Scheduling dynamic travel plans for tourists with crowd and traffic awareness has high scope for research. In this paper, a system is proposed which connects the vehicles to a centralized sink for getting the optimal routes. Route scheduling is done based on a prediction model. Different parameters were collected from the environment that includes crowd, traffic, and schedule of other vehicles. The system has modules like static nodes, mobile nodes, host nodes, and sink node for the control and management. Selection of path and protocol is a primary strategy to design any VANET systems. Hence, performance analysis of routing protocols for the proposed system is done as a major step in selection of protocols. Packet delivery ratio, jitter, and throughput are common measures used for the comparison of protocols. 2019, Springer-Verlag London Ltd., part of Springer Nature. -
Dynamic response of parabolic reflector antenna subjected to shock load and base excitation considering soil-structure interaction
Parabolic reflector antenna structures are subjected to dynamic loads along with normal loads. Determining the dynamic response of the antenna structure subjected to short-duration loads such as earthquake loads and shock loads considering soil-structure interaction is very important to ensure the safety and functionality of the antenna system resting on soft soil. A 7.2m diameter parabolic reflector antenna with a 90-degree elevation orientation is considered for the study. A triangular pulse of shock load is applied to the antenna at different locations and responses are estimated to understand the coupling effect of soil and structure on frequencies, damping, and response. Transient response analysis is carried out. Earthquake analysis is also carried out as per IS 1893 part 4:2016 considering Zone V site location. The foundation soil below the antenna is considered homogeneous with shear wave velocity (Vs) of 100m/sec. A direct method of analysis considering soil-structure interaction as per ASCE 4-16 is performed. FEM software MSC NASTRAN is used for analysis. The absorbing boundary conditions are used to reflect radiation damping. The depth-wise stress variation in foundation soil is evaluated. The results of free vibration analysis, transient response analysis with fixed base and SSI are compared. 2022 the Author(s). -
Dynamic Pricing in Airline Industry
Asian Journal of Research in Business Economics and Management, Vol. 7, Issue 1, pp. 15-29, ISSN No. 2249-7307 -
Dynamic Performance Analysis of D-STATCOMs for Mitigating Charging Station Impacts in Photovoltaic Distribution Systems Using Enhanced HunterPrey Optimization
This study investigates the dynamic operational performance and optimal allocation of Distribution Static Compensators (D-STATCOMs) in PVEV integrated distribution systems to mitigate the impacts of voltage fluctuations, increased power losses, and reactive power imbalances. An Enhanced HunterPrey Optimization (EHPO) algorithm is proposed in the study, incorporating chaotic initialization, adaptive parameter control, and Cauchy's mutation exploration strategy to improve global search capability and convergence reliability. The proposed method is validated on the IEEE 33, 69, and 118-bus distribution test systems under varying PV generation and EV charging demand scenarios. Results show that the EHPO-based D-STATCOM placement significantly reduces active power losses and enhances voltage stability. The findings highlight the effectiveness of combining advanced metaheuristic optimization with custom power devices to ensure the resilient, reliable, and sustainable operation of future EVPVdominated distribution networks. 2026, TUBITAK. All rights reserved. -
Dynamic optimal network reconfiguration under photovoltaic generation and electric vehicle fleet load variability using self-adaptive butterfly optimization algorithm
Currently, electrical distribution networks (EDNs) have used modern technologies to operate and serve many types of consumers such as renewable energy, energy storage systems, electric vehicles, and demand response programs. Due to the variability and unpredictability of these technologies, all these technologies have brought various challenges to the operation and control of EDNs. In this case, in order to operate effectively, it is inevitable that effective power redistribution is required in the entire network. In this paper, a multi-objective based dynamic optimal network reconfiguration (DONR) problem is formulated using power loss and voltage deviation index considering the hourly variation of load, photovoltaic (PV) power, and electric vehicle (EV) fleet load in the network. This paper introduces recently introduced meta-heuristic butterfly optimization algorithm (BOA) and it's improve variant of self-adaptive method (SABOA) for solving the DONR problem. The simulation study of IEEE 33-bus EDN under different conditions has proved the effectiveness of DONR, and its adoptability for real-time applications. In addition, by comparing different performance indicators (such as mean, standard deviation, variance, and average calculation time) of 50 independently run simulations, the efficiency of SABOA can be evaluated compared with other heuristic methods (HMs). Comparative studies show that SABOA is better than PSO, TLBO, CSA and FPA in the frequent occurrence of global optimal values. 2021 Walter de Gruyter GmbH, Berlin/Boston 2021. -
Dynamic Offloading Technique for Latency-Sensitive Internet of Things Applications using Fog Computing
Internet of Things (IoT) has evolved as a novel paradigm that provides com-putation power to different entities connected to it. IoT offers services to multiple sectors such as home automation, industrial automation, traffic management, healthcare sector, agriculture industry etc. IoT generally relies on cloud data centers for extended analytics, processing and storage support. The cloud offers highly scalable and robust platform for IoT applications. But latency sensitive IoT applications suffer delay issues as the cloud lies in remote location. Edge/fog computing was introduced to overcome the issues faced by delay-sensitive IoT applications. These platforms lie close to the IoT network, reducing the delay and response time. The fog nodes are usually distributed in nature. The data has to be properly offloaded to available fog nodes using efficient strategies to gain benefit from the integration. Differ-ent offloading schemes are available in the literature to overcome this prob-lem This paper proposes a novel offloading approach by combining two effi-cient metaheuristic algorithms, Honey Badger Algorithm (HBA) and Fla-mingo Search Algorithm (FSA) termed as HB-FS algorithm. The HB-FS is executed in an iterative manner optimizing the objective function in each it-eration. The performance evaluation of the proposed approach is done with different existing metaheuristic algorithms and the evaluations show that the proposed work outperforms the existing algorithms in terms of latency, response time and execution time. The methodology also offers better degree of imbalance with proper load balancing under different conditions. 2023 Authors. All rights reserved. -
Dynamic Offloading Technique for Latency Sensitive Iot Applications Using Fog Computing
The Internet of Things (IoT) has evolved as one of the most popular technological newlineinnovations that offers processing power to different types of entities connected to it. IoT has made traditional applications smarter and easier to use. IoT offers reliable service to different sectors such as healthcare, industrial control, agriculture, autonomous vehicles, traffic management etc. IoT nodes are generally energy-constrained and hence depend on cloud platforms for storage and analytics of generated data. The cloud provides required services for the newlineconnected applications based on pay per use policy. But cloud datacenter being at remote location fails to accommodate the time requirements of delay-sensitive IoT newlineapplications. Edge/fog computing was designed to address the demands of timesensitive IoT applications. The IoT-Fog-Cloud architecture reduces the delay and response time incurred by the IoT-Cloud model. The fog layer in the three-tier architecture is distributed in nature. Hence the latency depends on how well the underlying offloading algorithms distribute the tasks among available fog nodes. Different offloading policies are mentioned in the literature to address this issue. This work initially tries to solve the offloading problem using one of the novel newlineoffloading approaches Flamingo Search Algorithm (FSA). Later, the results obtained from FSA are fine-tuned using another metaheuristic algorithm, the Honey Badger Algorithm (HBA). Finally, both FSA and HBA are hybridized to generate the HB-FS algorithm which effectively solves the task offloading problem. The performance evaluation of the proposed approach is done with different existing metaheuristic algorithms and the evaluations show that the newlineproposed work outperforms the existing algorithms in terms of latency, average newlineresponse time and execution time. The methodology also offers a lesser degree of newlineimbalance and standard deviation than the compared approaches. -
Dynamic Load Scheduling Using Clustering for Increasing Efficiency of Warehouse Order Fulfillment Done Through Pick and Place Bots
The domain of warehouse automation has been picking up due to the vast developments in e-commerce owing to growing demand and the need to improve customer satisfaction. The one crucial component that needs to be integrated into large warehouses is automated pick and place of orders from the storage facility using automated vehicles integrated with a forklift (Pick and Place bots). Even with automation being employed, there is a lot of room for improvement with the current technology being used as the loading of the bots is inefficient and not dynamic. This paper discusses a method to dynamically allocate load between the Pick and Place BOTs in a warehouse during order fulfillment. This dynamic allocation is done using clustering,an unsupervised Machine Learning algorithm. This paper discusses using fuzzy C-means clustering to improve the efficiency of warehouse automation. The discussed algorithm improves the efficiency of order fulfillment significantly and is demonstrated in this paper using multiple simulations to see around 35% reduction in order fulfillment time and around 55% increase in efficiency. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Dynamic Load Balancing on Switches of Software Defined Network Managed by OpenDayLight Controller
In recent times, the world is becoming a global village where connectivity is a new norm irrespective of geographical location. Within corporate networks, huge setbacks are faced due to a lack of efficient resource management. Load balancing is inevitable to cater to a reliable, faster, and congestion-free communication experience for exponentially increasing online enterprises. Dynamic network resource management for high performance and low data transmission latency in a network is necessary. The major issue faced by the traditional network is that it relies on static hardware switches. Software-Defined Network approaches paved a way to overcome the limitations of traditional networks. This research proposes the Dynamic Load Balancing Algorithm for Software-Defined Networks to utilize network resources optimally. The major function of the proposed algorithm is to determine alternative paths and further distribute the incoming and outgoing network flows to achieve optimum network resource utilization with faster traffic flow completion. The experiment is performed with the OpenDayLight Controller on the Mininet simulator, which emulates the network with the novel scheme. The results prove that the proposed solution has accomplished the benchmarks of optimum throughput, reduced redundancy, and reduced flow completion time. 2025 IEEE. -
Dynamic Load Balancing in Cloud Computing Using Memetic Algorithm for Better Response Time
Load balancing is a critical aspect of distributed computing systems, involving the distribution of workload across multiple resources to ensure optimal utilization and performance. In today's digital landscape, where the demand for online services continues to grow exponentially, efficient load balancing mechanisms are indispensable for maintaining high availability, scalability, and reliability of web applications, cloud services, and network infrastructures. The paper implements a load balancing solution using a Memetic Algorithm, an amalgamated optimization method that integrates elements of genetic algorithms with local search methods. By leveraging the principles of evolutionary computation and problem-specific knowledge, memetic algorithms offer a promising approach to addressing the complex optimization and the hurdles connected with the load distribution across the systems. The proposed algorithm is compared with the existing algorithms and proved to be slightly better than the traditional algorithm in terms of response time. Experimental results show that the proposed approach reduces response time by 0.26 seconds. 2025 IEEE. -
Dynamic linkage among crude oil, exchange rates and P/E ratio: The case of India /
International Journal of Pure And Applied Mathematics, Vol.119, Issue 18, pp.1-14, ISSN No: 1314-3395. -
Dynamic job sequencing of converging-diverging conveyor system for manufacturing optimization
Some sectors, such as dairy, automobile, pharmaceutical, computer and electronics, require a range of manufacturing steps to produce a component. The goods in these industries are produced in varieties and the output volume varies from low to high. Typically, these types of businesses use a conveyor system that could have a combination of a diverging and converging conveyor system due to a variety of processing phases involved in the development of the commodity. A conceptual model of the of conveyor system is described, which works manually and to illustrate the importance of the sequence using buffer the buffer layout is modeled and compared to the manual layout. The genetic algorithm is used to find the optimal buffer storage. It can be observed that by adapting various sequencing methods there will be reduction in manufacturing time and setup cost. 2022 Elsevier Ltd. All rights reserved. -
Dynamic Financial Portfolio Optimization Using Temporal Convolutional Networks and Real-Time Data Analysis
This paper presents an integrated framework for AI-driven portfolio optimization combining temporal convolutional networks (TCNs) with conditional value-at-risk (CVaR) minimization. Our system processes real-time market data through an automated pipeline implementing volatility-adjusted feature engineering and walk-forward validation. The architecture employs dilated causal convolutions for temporal pattern extraction combined with Ledoit-Wolf shrinkage covariance estimation for robust portfolio optimization. Experimental results demonstrate an 18.7% annualized return with 22.3% volatility, outperforming traditional mean-variance optimization by 14.2% in risk-adjusted returns. The implementation addresses key challenges in numerical stability and computational efficiency through eigenvalue clamping and gradient checkpointing. 2025 IEEE. -
Dynamic Connectedness and Volatility Spillover Effects of Indian Stock Market with International Stock Markets: An Empirical Investigation Using DCC GARCH
This study employs the DCC-GARCH model to investigate the dynamic connectedness between the Indian and significant global stock markets. Specifically, we examine daily log returns data of the National Stock Exchange (NSE) index and several international indices, including the United States, Australia, China, Germany, England, Japan, and Taiwan. Our analysis indicates a significant level of volatility spillover between the Indian stock market and the international stock market. Notably, we observe a significant positive spillover effect from the S&P 500 and FTSE 100 to the Indian stock market, suggesting contagion effects. Additionally, we find bidirectional spillover between the Indian stock market and the Nikkei 225 and Hang Seng, indicating a high level of interdependence between these markets. Our research contributes to the growing literature on the dynamic connectedness of stock markets and has important implications for policymakers and investors in emerging economies such as India. Overall, this study provides valuable insights into the nature and extent of spillover effects between the Indian and international stock markets. 2023 University of Pardubice. All rights reserved. -
Dynamic Channel Allocation in Wireless Personal Area Networks for Industrial IoT Applications
Industrial wireless networks gain a substantial growth in size in the global market. In the congested scenarios of the industrial IoT application instances of wireless personal area networks, it should have a medium access strategy that is efficient and works autonomously to provide a reliable channel by reducing packet collisions. Medium access protocols must consider properties of the links between devices before a node is allowed to access the shared medium. Characteristic metrics of the channel like link quality indicator, received signal strength indicator, and path loss distance have to be considered in the contention resolution process between the nodes. A fuzzy-based channel allocation algorithm is proposed with dynamic adaptation of contention window in channel access strategy of the MAC layer standard. As per the simulation results, the algorithm proposed showed better results in terms of network throughput and packet delivery rate. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Dynamic Carboxylic Acid Arms Enable Proton Shuttling in Iron-Based Hydrogen Catalysis
Devising artificial electrocatalyst with smartly installed proton relay motifs, as present in natural hydrogenase enzyme has long been proved to be an effective strategy. Proton responsive groups properly positioned near the active center act as a proton shuttle site and facilitate H2 generation via an easy hydride/proton coupling step. Herein we investigated a series of Fe (III) complexes of substituted picolinic acid which undergoes reversible dechelation and chelation in presence of acids and base respectively, making free carboxylic acid arms available near the metal center in acid conditions (CF3COOH/HBF4). Investigations on the electrocatalytic activity of these complexes showcased the involvement of these free carboxylic acid arms in electrocatalytic hydrogen evolution reaction (HER). Further, detailed mechanistic analysis reveals sequential two-electron reductions followed by protonation, enabled by pendant carboxylic acid arms, allow the formation of a metal hydride intermediate which facilitates efficient H2 generation. The catalyst showed efficient HER activity achieving maximum turnover frequency (TOFmax) of 10,000 s?1 with faradaic efficiency above 90%. These findings underscore the importance of tailored secondary-sphere interactions in designing efficient, earth-abundant electrocatalysts and these insights could be useful for future design principles of catalyst for several small molecule activation reactions. 2026 Wiley-VCH GmbH. -
Dynamic Behaviour Analysis of Multi-Cell Battery Packs: A Simulation Study
In the era of IoT understanding the dynamic behavior of a Lithium-ion Battery Management System (BMS) has become gradually more important. This research investigates the dynamic behaviour of a six-cell Lithium-ion Battery Management System (BMS) through simulation. The study employs a comprehensive model encompassing key battery parameters, including cell capacity, voltage limits, temperature thresholds, and charge/discharge characteristics. Additionally, state variables such as State of Charge (SOC), State of Health, and State of Function are integrated to capture the battery's internal dynamics. The simulation incorporates a sinusoidal current profile to emulate realistic operating conditions. Notably, Coulomb counting is employed for SOC estimation, and protective measures against overvoltage, undervoltage, and overcurrent are implemented. The study also addresses balancing strategies and communication interfaces within the BMS. The results reveal nuanced interactions between voltage, temperature, SOC, and current, offering insights into the intricate behaviour of the battery system under dynamic conditions. This research not only advances our understanding of BMS functionality but also lays a crucial foundation for the evolution of battery technology and energy management systems in the IoT landscape. The Institution of Engineering & Technology 2023. -
Dust reverberation mapping of Z229-15
We report results of the dust reverberation mapping (DRM) on the Seyfert 1 galaxy Z229-15 at z = 0.0273. Quasi-simultaneous photometric observations for a total of 48 epochs were acquired during the period 2017 July to 2018 December in B, V, J, H and Ks bands. The calculated spectral index (?) between B and V bands for each epoch was used to correct for the accretion disc (AD) component present in the infrared light curves. The observed ? ranges between -0.99 and 1.03. Using cross-correlation function analysis we found significant time delays between the optical V and the AD corrected J, H and Ks light curves. The lags in the rest frame of the source are 12.52+10.00 -9.55 d (between V and J), 15.63+5.05 -5.11 d (between V and H) and 20.36+5.82 -5.68 d (between V and Ks). Given the large error bars, these lags are consistent with each other. However, considering the lag between V and Ks bands to represent the inner edge of the dust torus, the torus in Z229-15 lies at a distance of 0.017 pc from the central ionizing continuum. This is smaller than that expected from the radius luminosity (R-L) relationship known from DRM. Using a constant ? = 0.1 to account for theADcomponent, as is normally done in DRM, the deduced radius (0.025 pc) lies close to the expected R-L relation. However, usage of constant ? in DRM is disfavoured as the ? of the ionizing continuum changes with the flux of the source. 2021 Oxford University Press. All rights reserved.