Dynamic Environment Research Articles

Unlocking the buffering role of total quality management: how organisational intangible resources shape innovation performance in dynamic and uncertain environments

Purpose The purpose of this paper is to demonstrate how total quality management (TQM) buffers the adverse effect of market dynamism (MKD) on banking industry innovation performance (INP) using resource-based theory. Design/methodology/approach This paper creates and explores a conceptual framework by evaluating the perspectives of 260 chief executive officers from the different branches of 21 ISO 27001 and PCI-DSS-accredited banks over two time periods. The product indicator approach was used in the moderation analysis, with a path being significant at 5% (t > 1.96). The study used support for innovation, innovative leadership, strategic agility and slack resources as control variables for INP. The analysis was done using SmartPLS version 4.1.0.6. Findings As hypothesised, MKD had a significant and negative influence on the INP (ß = −0.326, t = 5.341). TQM neutralised MKD’s negative impacts on INP in a positive and significant manner (ß = 0.127, t = 2.680). Research limitations/implications This study paves the way for upcoming research to explore how TQM may improve INP in the presence of other adverse environmental dynamics like technological dynamism, competitive intensity and government regulation in the banking industry. External trends may have a different impact on other industries, necessitating additional research in these areas. Practical implications Bank managers can manipulate the TQM strategy to shape INP, thereby revealing TQM’s strategic value. Top management teams may consider prioritising and allocating more resources to TQM initiatives. Banks that successfully use the TQM methodology are able to turn a dynamic and uncertain environment into opportunities for growth and differentiation. Originality/value To the best of the author’s knowledge, this is the first study to display how TQM buffers the detrimental impact of MKD on INP. It offers a first attempt at challenging previous studies that had somewhat assumed that MKD encourages banks to be innovative, proving them otherwise, changing the narrative of the literature and stimulating further debate.

Path Planning Approaches in Multi‐robot System: A Review

ABSTRACTThe essential factor in developing multi‐robot systems is the generation of an optimal path for task completion by multiple robots. To ensure effective path planning, this paper studies the recent publications and provides a detailed review of the path planning approaches to avoid collisions in uncertain environments. In this article, path‐planning approaches for multiple robots are categorized primarily into classical, heuristic, and artificial intelligence‐based methods. Among the heuristic approaches, bio‐inspired approaches are mostly employed to optimize the classical approaches to enhance their adaptability. The articles are analyzed based on static and dynamic scenarios, real‐time experiments, and simulations involving hybrid solutions. The increasing focus on using hybrid approaches in dynamic environments is found mostly in the papers employing heuristic and AI‐based approaches. In real‐time applications, AI‐based approaches are highly implemented in comparison to heuristic and classical approaches. Moreover, the findings from this review, highlighting the strengths and drawbacks of each algorithm, can help researchers select the appropriate approach to overcome the limitations in designing efficient multi‐robot systems.

Navigating the Virtual Environment

In the ever-evolving landscape of higher education, the intersection of learning spaces and technology presents unprecedented opportunities to enhance student experiences. This poster highlights the effective use of 360-degree technology to create immersive digital learning experiences across various STEM disciplines in an Australian University. This poster demonstrates the implementation of Universal Design for Learning (UDL) principles in virtual laboratory tours. Our 360-degree virtual tours embody UDL's core principles (Meyer et al., 2014) by offering multiple means of engagement through immersive experiences, representation via diverse media formats (textual overlays, audible descriptions, graphical elements, and interactive hotspots). By adhering to digital accessibility guidelines (Ismailov & Chiu, 2022), it ensures that our virtual laboratory inductions cater to a wide range of learning preferences and needs, potentially enhancing the effectiveness of STEM education for diverse student populations. Traditional induction processes are often delivered face-to-face, facing challenges like time constraints, safety concerns, and limited accessibility. The COVID-19 pandemic has further highlighted the need for flexible, remote learning solutions (Rapanta et al., 2020). 360-degree virtual tours overcome barriers by immersing students in a digital environment, allowing them to become familiar with procedures before entering physical space (Tan & Tan, 2021). This technology also supports dynamic learning environments, enhancing teaching effectiveness and student engagement (Christopoulos et al., 2018). The effectiveness of 360-degree virtual tours has been demonstrated across various areas, from hospitality education to construction safety training (Pham et al., 2018; Patiar et al., 2017). Virtual tours offer numerous benefits, including cost-effectiveness, accessibility, and enhanced engagement. They also support authentic, experiential learning without time or location constraints, potentially improving knowledge retention and attitudes toward learning (Cardona et al., 2023). A key benefit of 360-degree technology is its web-based implementation, making experiences accessible across a wide range of devices and operating systems. While implementation may require some initial investment in hardware and expertise, the availability of consumer-grade tools and simple workflows makes adoption increasingly feasible for educators (Tan & Tan, 2021). This poster outlines the process of developing, implementing, and evaluating 360-degree virtual tours. The development phase began with the implementation of virtual tours in ten laboratories with large enrollments. Using specialised cameras (Insta360 X3), we captured spherical photographs of these laboratories, allowing students to virtually explore as if physically present. To enhance interactivity and educational value, we integrated various elements such as hotspots, videos, and quizzes using SharePoint Space and H5P platforms. The implementation phase involved incorporating these virtual tours into the online laboratory induction process. We seamlessly integrated the tours with existing course management systems to ensure easy access for students. For evaluation, we focused on qualitative assessment methods to gain insights into student experiences and perceptions on aspects such as ease of navigation, perceived realism, and overall utility of the virtual tours. This assessment was conducted through surveys and informal discussions, offering valuable feedback on the virtual tours' effectiveness in preparing students for physical laboratory work.

Adaptive Random Early Detection Using Deep Reinforcement Learning for Enhanced Congestion Control in Dynamic TCP/IP Networks

AQM is used to meet targets for congestion control and low delay, high throughput in TCP/IP networks. But methods developed for AQM like Random Early Detection (RED) work strictly on the control parameters pre-set on them and thus may not respond very well to changing network conditions. Thus, this paper presents a composite AQM model using DRL based on DQN, and the ability to learn independently regarding the AQM weight parameter of the queue. The fact that DRL is adaptive means the effectiveness of the proposed system is also assured. As it is observed in implementing traditional model-based approach, the stability and performance degrade when tested in different network conditions; however, DRL’s ability to learn independently enables ML to solve network congestion as it happens. Consequently, more stability is achieved, the delay is lesser, more bandwidth is used and the overall packet drop rate is low; the proposed DRL-RED model is ideal for dynamic network environments. The proposed DRL-RED model is compared with the regular RED algorithm for both low density and high-density networks. This proposed model has achieved sustained throughput of up to 49.9 Mbps with 0.949 % reduction on delay and very low PLR of 8.38043%. From the comparative analysis, it is clear that the employment of DRL with RED improves the stock of network throughput, the reduction on packet drop frequency, and the general delay in network situations. There are two main disadvantages of this approach: first, it cannot reach the heavy-traffic, the problem partly solved by model-based approach; second, the values of the congestion-control parameters cannot be reset, an issue eradicated by DRL as a result of its inherent adaptiveness. Consequently, new stability and increased performance can be achieved under various network conditions.

Investigating the e-learning choice under the learners’ perspective using demand driven learning model: insights from Vietnam

Over the past four decades, e-learning has experienced rapid global growth, revolutionizing higher education. In Vietnam, universities have increasingly turned their attention and resources toward e-learning development, adapting to specific contexts and needs. This study investigates the drivers of e-learning choice within the framework of the demand-driven learning model (DDLM), with a primary focus on three core factors: quality content, delivery, and service. Employing a quantitative approach and the PLS-SEM technique, this research uncovers significant findings. The results of the study highlight the pivotal role of quality content, which exerts the most potent influence on e-learning choice, evidenced by a robust path coefficient of 0.400. Service and delivery, with direct path coefficients of 0.183 and 0.173, respectively, also play substantial roles in shaping e-learning decisions. Moreover, mediate role of delivery in the the e-learning choice model has been confirmed. Quality content leads to delivery, and delivery, in turn, leads to e-learning choice. Similar pathway has been found with service. A higher level of service increases delivery, which positively impacts on e-learning choice. These findings hold critical implications for the formulation of e-learning development policies in Vietnam’s higher education institutions. Universities should prioritize the continuous development of high-quality, engaging, and up-to-date educational content that aligns with industry needs and student interests. Additionally, emphasis should be placed on providing a supportive e-learning experience, characterized by responsive customer service, accessible technical support, and efficient issue resolution mechanisms. Moreover, universities should consider the implementation of user-friendly and interactive content delivery platforms and methods that actively engage learners. In essence, this research serves as a guide for universities in Vietnam, enabling them to enhance their e-learning offerings by ensuring the content quality, support services, and delivery methods, ultimately fostering a dynamic and accessible learning environment that meets the evolving needs of their students and the demands of the modern educational landscape.

A Brief Study on Indian Startups- Analytic Research on the Success and Failure Factors of Swiggy, Boat, Doodhwala, And Shopx

India’s startup ecosystem has witnessed unprecedented growth in recent years, emerging as one of the most dynamic and fast-paced environments globally. This research paper examines the primary factors leading to the high failure rate of Indian startups, focusing on familiar challenges such as inadequate business models, poor product-market fit, cash flow problems, regulatory hurdles, and inefficient management. Through case studies of successful ventures like Swiggy, and BoAT, this paper highlights critical success factors that have enabled these companies to thrive, including data-driven decision-making, customer-centric strategies, adaptability, and strong strategic partnerships. The study also explores the pivotal role of venture capitalists (VCs) in shaping startup trajectories, from early seed funding to advanced growth stages. Additionally, the paper delves into the influence of Indian government initiatives, such as Startup India and the Atal Innovation Mission, in fostering a supportive ecosystem that encourages entrepreneurship and innovation. Keywords: Entrepreneur, Start-up, Economy, Future market, Challenges

The role of emotional marketing in enhancing strategic customer behavior through financial knowledge: An analytical study of a sample of shopping malls in Iraq

The research aims to diagnose the logical relationships between emotional marketing and its role in enhancing strategic customer behavior through financial knowledge to reach results in the dynamic environment of shopping malls. The paper was designed in light of adopting the descriptive analytical approach to variables and collecting data and information through the questionnaire tool distributed to the shopping malls of the research sample. Using statistical analysis programs to reach the results, the study sample reached (25) shopping malls in the local environment of the country. The results showed that there is a threat in the local environment in which these malls operate through the entry of foreign investment projects in this field, which led to an increase in supply and competition in terms of quality, prices and delivery services to gain customers. The studied malls had to develop a strategy on how to attract and retain customers by using emotional marketing practices and relying on financial knowledge to influence strategic customer behavior, as the current and future customer has become rapidly changing in tastes, needs, desires and transformation through technological development and the world has become a small village that can be viewed at any time and in short periods of time. The research also concluded that the threat of new investment through the establishment of new malls as new investment projects affects supply and demand in the environment in which these studied malls operate. The research also concluded that there is a correlation and influence relationship between the research variables of the studied malls, which includes a package of results that benefit the research community. The paper aims to bridge the knowledge gap between the variables of the paper in the dynamic environment by using emotional marketing as a competitive tool in influencing strategic customer behavior through financial knowledge to gain and maintain the current customer and gain the potential customer.

Advancements in Cancer Research: 3D Models, Single‐Cell, and Live‐Cell Techniques for Better Insights

AbstractThe present review provides a comprehensive overview of the current state of in vitro cancer studies, focusing on recent advancements and ongoing in cell culture models and analyses techniques. Cancer cells grow in a complex and dynamic environment, interacting with various cellular components, such as stromal cells, cancer‐associated fibroblasts, immune cells, and the extracellular matrix (ECM). The ECM provides structural support and unique characteristics essential for tumorigenesis. Accurately modeling this intricate tumor microenvironment and precisely analyzing cell–cell and cell–ECM interactions are crucial for understanding cancer progression and therapeutic responses. Consequently, oncology research is advancing toward a) three‐dimensional tumor models, b) single‐cell level analyses, and c) live‐cell analyses. This review aims to elucidate current knowledge in this field, emphasizing the benefits these innovative approaches offer over traditional two‐dimensional models, bulk analyses, and endpoint measurements.

PROFESSIONAL DEVELOPMENT IN BASIC EDUCATION

This article seeks to explore the perspectives and challenges faced by teachers in the context of basic education in relation to professional development. Based on an analysis of training practices and public policies aimed at teacher training, we will discuss the main trends and innovations that have emerged in the field, in addition to offering reflections on how these initiatives directly impact the quality of teaching and student learning. To this end, a bibliographical research was developed to find academic productions, finding authors who research on the subject, expanding the theoretical repertoire. Professional development in basic education faces challenges due to recent transformations in the educational environment. The search for quality, equitable and inclusive education becomes increasingly complex in view of the realities found in schools. In recent years, the profile of children has changed, requiring new didactic and practical approaches from educators. This evolution requires the creation of more dynamic learning environments and the use of innovative resources that can meet the diverse needs of students. Therefore, it is essential that education professionals reflect on their methodologies and seek alternatives that provide new meanings to the contemporary educational system. These changes not only challenge educators to adapt, but also offer opportunities for the implementation of pedagogical practices that promote more collaborative and student-centered teaching. It was concluded, therefore, that continuing teacher training is essential for us to meet the current and future demands of basic education.

The Diverse Applications of Aerospace Drones: From Defense to Humanitarian and Commercial Uses

This research investigates the extensive applications of aerospace drones across defense, humanitarian, and commercial sectors, showcasing their transformative impact on modern industry. Aerospace drones have become pivotal tools in diverse settings—from defense and tactical operations to humanitarian aid and infrastructure management—offering operational efficiencies, cost savings, enhanced safety, and innovative solutions to complex challenges. This study explores defense applications such as military reconnaissance, where Vertical Take-Off and Landing (VTOL) drones enable effective surveillance in challenging terrains. In humanitarian settings, drones are utilized for disaster response in earthquake and flood zones, providing critical functions like search-and-rescue, real-time damage assessment, and medical supply delivery. Commercially, drones support long-range infrastructure inspections, smart agriculture, and environmental monitoring, driven by advancements in imaging and remote sensing. The project emphasizes the technical challenges and ethical considerations associated with drone deployment, particularly the need for robust AI and autonomous navigation systems in dynamic and unpredictable environments. Collaborations with international aerospace firms and humanitarian organizations have further refined drone integration strategies in both civilian and military contexts. The research concludes with a discussion on regulatory frameworks and environmental impacts, aiming to provide a comprehensive view of thefuture of aerospace drones and their role in addressing global challenges.

When academia met industry: working toward a needle‐free vaccination future in the sunshine state

AbstractLocated in Brisbane's Northshore riverfront precinct, just meters from the iconic Brisbane River, is the new Vaxxas Biomedical Facility. Dr Imogen Bermingham is a Principal Scientist in the Formulation and Analytical Team at Vaxxas, an Australian biotech company focused on developing a needle‐free vaccination technology. Here, we discuss her work at Vaxxas, highlighting the opportunities for translational research within the growing biotech industry landscape in Queensland, Australia. Dr Bermingham also reflects on her transition from academia to industry, leveraging her skill set and expanding her capabilities within the dynamic research environment at Vaxxas.

Leveraging Machine Learning for Cybersecurity: Techniques, Challenges, and Future Directions

This study aims to explore the application of machine learning (ML) in enhancing cybersecurity measures, focusing specifically on intrusion detection, malware detection, fraud detection, and anomaly detection systems. A systematic review of 743 scholarly papers was conducted, from which 115 were selected for in-depth analysis. The review process involved evaluating advancements in ML techniques within the context of cybersecurity. The findings reveal that ML-driven systems significantly enhance the automation of security processes, improve the recognition of novel threats, and reduce human error in cyber threat management. However, challenges such as adversarial attacks and the need for high-quality model training pose significant barriers to the broader adoption of ML in cybersecurity. The study discusses the implications of these findings, emphasizing the necessity for developing robust and adaptive ML models that can withstand adversarial threats while improving integration across various cybersecurity applications. The insights gained from this research provide a comprehensive overview of the potential benefits and challenges of implementing ML in cybersecurity, highlighting the need for continuous innovation in threat detection mechanisms. This review acknowledges limitations such as the predominance of literature from Western contexts, potentially overlooking insights from other regions. Furthermore, the complexity of implementing ML systems in dynamic cyber environments remains a critical challenge. Future research should concentrate on refining ML algorithms to enhance resilience against adversarial threats, exploring the integration of emerging technologies for improved cybersecurity, and addressing gaps in the existing literature regarding the life cycle of ML models in real-world applications.

A Comprehensive Review of Artificial Intelligence and Machine Learning in Control Theory

Abstract: Traditional control methods, such as proportional-integral-derivative (PID) controllers and linear-quadratic regulators (LQRs), have proven effective for linear and well-modeled systems. However, these methods often perform poorly in nonlinear, complex and dynamic environments. The paper aims to investigate the modern control systems by integrating artificial intelligence (AI) techniques, such as machine learning (ML), reinforcement learning (RL), deep learning, and fuzzy logic, to enhance their adaptive, robust, and predictive capabilities. And it reviews the literature and analyzes AI integration in control systems. The proposed strategies include supervised learning for trajectory optimization and fault detection, reinforcement learning for optimal control in dynamic environments, neural networks for complex nonlinear function approximation, and fuzzy logic for handling uncertainty and imprecise inputs. AI techniques significantly enhance the ability to tackle nonlinear problems and dynamic changes, demonstrating superior performance in applications like self-driving cars adapting to various road conditions and optimal energy distribution in smart grids. Despite the challenges of computational complexity, scalability, and the safety and reliability in the implementation of interpretable AI models, this paper suggests that hybrid approaches combining traditional control and AI techniques, along with the evolution of interpretable AI and convergence with quantum control, hold great promise for advancing AI-driven control systems.

Object detection and tracking for drones: A system design using dynamic visual SLAM

Abstract. Drones can play a quite crucial role in many walks of life today. Enhancing the visual perception ability of drones is crucial to their intelligence level. Among them, it is necessary to focus on strengthening the detection, tracking and mapping capabilities of drones for dynamic objects. However, the existing visual SLAM systems carried by drones do not perform well in dynamic environments. This project designs a monocular visual SLAM system specifically for drones, aiming to achieve efficient three-dimensional mapping and target tracking, surpassing the limitations of simple static mapping and positioning. Besides, this project constructs a drone dynamic SLAM system developed on the ORB-SLAM3 structure, uses drone images to detect, track and map object motion models, and reconstructs environmental maps to obtain motion parameters with real physical scales. This project strives to optimize the input pre-processing module, improve the validity of data and output environmental maps and raster maps. The outcomes demonstrate the system's strong accuracy and adaptability in dynamic installation procedures.

Management Accounting Practices and Financial Performance of Hotel Business in Thailand: Are Contingency Theory Perspectives Still Relevant During the Covid-19 Pandemic?

This study examines the impact of perceived uncertainty on management accounting practices (MAPs) and financial performance in Thai hotels during the COVID-19 pandemic. Using Generalized Structured Composite Analysis (GSCA), results show that uncertainty significantly influences MAP implementation, enhancing financial performance. MAPs partially mediate the relationship between uncertainty and performance, indicating their role in addressing uncertainty but not fully explaining performance outcomes. This research highlights that MAPs should be seen not just as reactive measures but as proactive tools for building resilience in dynamic environments, offering insights into contingency theory application.

Proposed Explainable Interference Control Technique in 6G Networks Using Large Language Models (LLMs)

After the advent of 5th generation (5G) and 6th generation (6G) cellular networks, the complexity of managing real-time signal interference has increased in dense and dynamic environments. Traditional interference techniques, such as frequency reuse and allocation, while effective, lack robust adaptability and transparency needed to reduce interference in advanced communication networks. This paper introduces a novel approach that fuses large language models (LLMs) and Explainable Artificial Intelligence (XAI) to mitigate interference and enhance interference management in the mathematical foundations of 6G networks. The proposed approach provides accurate interference predictions, which the LLM balances with its complex architecture, necessary to meet the demands of beyond 5G and 6G networks, along with interpretable explanations to ensure transparency in decision-making. The proposed framework has been evaluated across various performance metrics. Interference latency consistently achieves lower rates of 0.95 s, compared to traditional techniques, which average around 1 s. Furthermore, the confidence score of the LLM shows a stable value of 0.87 throughout the system, compared to 0.85 in techniques without LLMs. Overall, the XAI-driven LLM demonstrates the potential of incorporating LLMs and XAI into wireless networks to improve resilience in next-generation networks. This proof of concept introduces a novel framework that offers new dimensions in wireless communication, particularly for interference management, prediction, and mitigation.

4D-based Robot Navigation Using Relativistic Image Processing

Machine perception is an important prerequisite for safe interaction and locomotion in dynamic environments. This requires not only the timely perception of surrounding geometries and distances but also the ability to react to changing situations through predefined, learned but also reusable skill endings of a robot so that physical damage or bodily harm can be avoided. In this context, 4D perception offers the possibility of predicting one’s own position and changes in the environment over time. In this paper, we present a 4D-based approach to robot navigation using relativistic image processing. Relativistic image processing handles the temporalrelated sensor information in a tensor model within a constructive 4D space. 4D-based navigation expands the causal understanding and the resulting interaction radius of a robot through the use of visual and sensory 4D information.

A Comprehensive Review of Mobile Robot Navigation Using Deep Reinforcement Learning Algorithms in Crowded Environments

Navigation is a crucial challenge for mobile robots. Currently, deep reinforcement learning has attracted considerable attention and has witnessed substantial development owing to its robust performance and learning capabilities in real-world scenarios. Scientists leverage the advantages of deep neural networks, such as long short-term memory, recurrent neural networks, and convolutional neural networks, to integrate them into mobile robot navigation based on deep reinforcement learning. This integration aims to enhance the robot's motion control performance in both static and dynamic environments. This paper illustrates a comprehensive survey of deep reinforcement learning methods applied to mobile robot navigation systems in crowded environments, exploring various navigation frameworks based on deep reinforcement learning and their benefits over traditional simultaneous localization and mapping-based frameworks. Subsequently, we comprehensively compare and analyze the relationships and differences among three types of navigation: autonomous-based navigation, navigation based on simultaneous localization and mapping, and planning-based navigation. Moreover, the crowded environment includes static, dynamic, and a combination of obstacles in different typical application scenarios. Finally, we offer insights into the evolution of navigation based on deep reinforcement learning, addressing the problems and providing potential solutions associated with this emerging field.

Green human resource management and corporate social responsibility as drivers of organizational performance: mediating effects of employee engagement and corporate reputation in Vietnam

Purpose This study aims to investigate the relationship between green human resource management (GHRM), corporate social responsibility (CSR) and organizational performance (OP) of small- and medium-sized enterprises (SMEs) by examining corporate reputation (CR) and employee engagement (EE) as mediators in Vietnam. Design/methodology/approach This study used the quantitative method with 458 valid responses from mid- to senior-level managers and chief executive officers (CEOs) were obtained and used for data analysis. To analyze and explore the relationships between constructs and mediators, SmartPLS version 4.0 structural equation modeling was used to analyze the data of the SMEs of Vietnam in 2023. Findings The results showed strong and positive relationships between GHRM, CSR and OP in SMEs, with CR and EE serving as significant mediators. Originality/value This research provides new insights into how GHRM and CSR enhance OP by using an integrated model that examines how CR and EE mediate this relationship. It demonstrates that businesses implementing GHRM and CSR can promote sustainable development among stakeholders, leading to improved OP in Vietnam’s dynamic economic environment. The findings have practical implications for executives and managers, emphasizing the need to balance stakeholder, environmental and social interests to optimize GHRM and CSR initiatives and foster sustainable growth. This study’s insights could significantly influence SMEs in the Vietnamese economy.

Towards a Verifiable Toolchain for Robotics

There is a growing need for autonomous robots to complete complex tasks robustly in dynamic and unstructured environments. However, current robot performance is limited to simple tasks in controlled environments. To improve robot autonomy in complex environments, the robot's deliberation system must be able to synthesise correct plans for a task and generate contingency plans for handling anomalous scenarios that were not expected at design time. The robustness of such a system can be quantified using techniques for formal verification and validation. This paper outlines the progress of EU project CONVINCE (CONtext-aware Verifiable and adaptIve dyNamiC dEliberation), which aims to develop a software toolchain that aids developers in designing, developing, and deploying robot deliberation systems that are fully verified. We describe our modelling approach, each of the toolchain components, and how they interact. We also discuss survey results which demonstrate the demand for a verifiable toolchain among the robotics community.