Dynamic Environment Research Articles

Exploring Opportunities for Global Retirement in Thailand

This research paper delves into the rapidly growing trend of global retirement to the beautiful country of Thailand to shed light on its undeniable appeal as a highly desirable destination for expatriates in search of a truly fulfilling retirement lifestyle. With the population having grown older, especially in the United States, and the number of retirees within it continuing to become increasingly prominent in the developed world, it should come as no surprise that demand for affordable, yet attractive retirement options abroad has surged to unprecedented levels. Among a host of other attractive attributes, Thailand has a very affordable cost of living, an exquisite warm climate, and high-quality medical services that are readily available. Little surprise it is, then, that Thailand has rightly emerged as a profoundly favorable choice for many retirees in pursuit of spending their golden years in a paradisiacal environment. This paper extends the analysis with a view to closely examining a myriad of influential factors that eventually influence the retirees' decision to relocate to Thailand. These range from considerations of minute details of economic aspects, captivating cultural experiences, and complex legal frameworks that regulate the retirement visa procurement process. From interesting case studies, it provides insightful and valuable glimpses into reasons behind their coming, current living conditions, and integration challenges faced by the expatriate retirees amidst the complex Thai tapestry. While Thailand is a country that offers many advantages to the retirees who come to its land, such as extremely rich cultural heritage to immerse themselves in and a very close-knit and supportive expatriate community to lean on, the paper also prudently acknowledges and confronts various potential problems that may arise in such a transformative journey. These include, but are not limited to, economic fluctuations that may present proximate challenges for retirees to the fundamental issue of healthcare accessibility and the mechanism in place to ensure retirees receive the best medical attention. It concludes with a set of well-thought-out recommendations addressed to future retirees, stressing the absolute importance of deep personal reflection and precise advanced planning in preparation for this great adventure to be living abroad. It ultimately gives a face to Thailand as probably the most unmatched up-and-coming retirement haven, while strongly encouraging further research and exploration in the ever-evolving dynamic environment of this Southeast Asian enchanted country. Put differently, the extended text is over-the-top in emphasizing that Thailand can boast of exceptional potential as an emerging and unstoppable retirement haven, while confident calls are made for further research into the field of expatriate experiences within the grand scale of this exquisite, overwhelming, and utterly captivating environment.

A Survey on Human in the Loop for Self-Adaptive Systems

Adaptive systems possess the remarkable capability to assess and modify their behavior in real-time, particularly when the software deviates from its programmed course or when opportunities for enhanced functionality or performance arise. This adaptability proves invaluable in highly dynamic environments, where rapid changes occur, and human oversight alone falls short in effectively managing applications. However, in certain system types, achieving optimal performance through adaptation may necessitate human input, be it as a sensor providing unique information beyond the system’s reach, an actuator driving adaptation, or a fallback mechanism in contingency scenarios. In this context, the concept of ’human-in-the-loop’ harnesses the innate capabilities of humans to execute tasks and make decisions with greater efficiency and precision, thereby ensuring the security and reliability of these systems. Our primary objective in this study is to present a comprehensive analysis of the key research and contributions in this field. Additionally, we aim to pinpoint potential research avenues and unresolved challenges within the realm of Human-in-the-loop.

A business process management lifecycle framework for continuous improvement towards operational excellence: lessons learned from a longitudinal study in a Brazilian organisation

Purpose The purpose of this study is to offer a business process management (BPM) framework with information and insights on designing, implementing, using and assessing business processes for continuous improvement towards operational excellence. Design/methodology/approach An action research is carried out over two and a half years on the BPM evolution of a Brazilian entertainment organisation. Findings Research provides a novel procedural framework towards improving the understanding of how a complete lifecycle approach for BPM can be implemented for continuous improvement, embracing the critical success factors for each lifecycle phase to achieve operational excellence. Information technology and project management are critical success factors resulting in project barriers. Strategic alignment, top management support, collaborative environment, methods and methodology, and focus on users and culture are acknowledged as main enablers. Findings reinforce the importance of an organisation analysis phase to begin the BPM development and highlight the assessment and improvement phase to respond to organisational environment dynamism. Practical implications Practitioners can benefit from the lessons learnt and the proposed framework, which serves as a rigorous methodology to achieve operational excellence in their real-world settings. Originality/value This paper goes beyond the well-known design and analysis phases of BPM development, generally studied with an individual focus, offering a complete lifecycle approach for continuous improvement, analysing each phase, from the drawing board to its use and evaluation. It counts on an original longitudinal study rather than a single-time assessment study.

Recent Advances in Visual SLAM Algorithms in Dynamic Environments

This paper reviews the latest research progress of visual SLAM in dynamic scenes. The improved algorithms dependent on deep learning and multi-sensor fusion are discussed. First of all, the challenges of traditional visual SLAM in dynamic environments are stated, especially the interference of high dynamic objects on system feature extraction, pose estimation and mapping. Then, the application of several object detection technologies such as YOLOv5 and YOLOv7 in dynamic visual SLAM was analyzed in detail. These methods significantly improve the positioning precision and robustness of the system by identifying and eliminating dynamic feature points in the scene. In addition, this paper explores the visual SLAM system combining semantic segmentation and geometric constraint optimization, and finally introduces the multi-sensor SLAM method combining IMU data, point cloud data and visual information. Experimental results show that these improved algorithms have a significant improvement in positioning accuracy and system robustness compared with traditional visual SLAM algorithms under TUM and other dynamic data sets.

A Lightweight Deep-Learning Visual SLAM for Indoor Dynamic Environment Using Yolov10

Vision simultaneous localization and mapping (SLAM) technology has become a key research direction in the field of mobile robotics in recent years. However, the accuracy and stability of traditional vision SLAM technology greatly affected by the dynamic environment, and the mainstream dynamic feature point rejection method combining vision and semantic segmentation techniques is not applicable to edge-end devices with limited resources and high real-time requirements. This research suggests a visual SLAM algorithm based on the YOLOv10n lightweight target detection model and the GCNv2 feature point extraction model to accomplish real-time dynamic feature point rejection to address those problems. To compensate for the detection accuracy and stability issues of the YOLOv10n model while leveraging its real-time advantages, the algorithm also employs multi-target Kalman filtering with data association through the Hungarian algorithm, history window smoothing, and potential dynamic feature point recording methods to enhance robustness. The algorithm is validated on the TUM RGB-D Dataset, and the results demonstrate that the method can effectively reject the dynamic feature points in the dynamic environment, and it has a significant improvement in the accuracy and stability of the visual SLAM system.

Survey of Path Planning for Aerial Drone Inspection of Multiple Moving Objects

Recent advancements in autonomous mobile robots (AMRs), such as aerial drones, ground vehicles, and quadrupedal robots, have significantly impacted the fields of infrastructure inspection, emergency response, and surveillance. Many of these settings contain multiple moving elements usually neglected in the planning process. While a large body of work covers topics addressing scenarios with stationary objects, promising work with dynamic points of interest has only recently gained traction due to computational complexity. The nature of the problem brings with it the challenges of motion prediction, real time adaptability, efficient decision-making, and uncertainty. Concerning aerial drones, while significantly constrained computationally, good understanding and the relative simplicity of their platform gives way to more complex prediction and planning algorithms needed to work with multiple moving objects. This paper presents a survey of the current state-of-the-art solutions to the path planning problem for multiple moving object inspection using aerial drones. The presented algorithms and approaches cover the challenges of motion and intention prediction, obstacle avoidance, planning in dynamic environments, as well as scenarios with multiple agents. Potential solutions and future trends were identified primarily in the form of heuristic and learning methods, state-of-the-art probabilistic prediction algorithms, and further specialization in regard to every scenario.

On-demand beamforming and wide dynamic power range for WPT and EH applications

Abstract This work delves into advancements in wireless power transfer (WPT) and radiofrequency (RF) energy harvesting (EH), focusing on on-demand beamforming and wide-dynamic power range technologies. These innovations promise significant improvements in efficiency and adaptability for wireless energy systems. For transmitting RF power, on-demand beamforming enhances power delivery precision by accurately targeting specific devices, minimizing energy waste, and maximizing received power. This technology is particularly useful in dynamic environments with constantly changing device positions, ensuring stable power levels and effective real-time power transfer, such as for mobile device charging. For receiving RF power, wide-dynamic power range implementation allows EH and WPT systems to adjust power output across a broad spectrum, optimizing energy use and extending device lifespan. This capability supports scalability, accommodating devices with varied power needs, also enabling new applications in consumer electronics, healthcare, smart homes, and cities, and enhancing energy management in smart infrastructures. Additionally, this study explores three-dimensional (3D)-printable antennas and RF circuitries for battery-free applications. The versatility of 3D printing allows the creation of complex, efficient, and customizable RF components, fostering innovative battery-free solutions. Integrating on-demand beamforming and wide-dynamic power range technologies in EH systems promise improved energy transfer efficiencies, reduced losses, and sustainable, cost-effective wireless power systems.

A Review of Harmonic Detection, Suppression, Aggregation, and Estimation Techniques

The rapid growth of power electronics-based devices, such as electric vehicles and renewable energy systems, has introduced nonlinear components into power systems, generating high-frequency harmonics that distort current and voltage waveforms. These distortions pose significant risks to the stability of power grids, potentially leading to equipment malfunctions, reduced efficiency, and even system failures. To address these challenges, advanced harmonic detection, suppression, and estimation techniques are required to ensure the reliable operation of modern power systems. This paper comprehensively reviews the most widely used methods for managing harmonic distortions, focusing on recent harmonic detection, suppression, and estimation advancements. Key techniques, such as Fourier analysis and wavelet transforms, are compared alongside emerging machine learning-based approaches and adaptive filtering methods, which offer enhanced accuracy in real-time and dynamic environments. Additionally, advancements in harmonic suppression technologies, including passive, active, and hybrid filtering, are discussed for their effectiveness in mitigating harmonic impacts. Furthermore, the paper explores harmonic aggregation techniques that assess the cumulative impact of multiple harmonic sources and innovative estimation models that improve harmonic quantification under complex grid conditions. With the growing integration of renewable energy and electric vehicles, this review highlights the importance of advanced harmonic management strategies to ensure the safety, efficiency, and long-term stability of power systems.

Enhancing Real-Time Vision Systems: Integrating Dynamic Vision Sensors with Vision Transformers to Increase Computational Efficiency

Abstract. Optimizing and understanding vision systems' computational accuracy is crucial as they become increasingly integrated into daily life. Dynamic Vision Sensors (DVS) and Vision Transformers (ViTs) lead computer vision technology with efficient object recognition and image processing. However, DVS data's high computational complexity poses a problem for its real-time implementations. Merging these technologies can enhance vision system performance in dynamic environments and optimize real-time DVS processing. In our work, we use a ViT architecture to classify the DVS 128 dataset and compare our results with existing works using SNNs. We analyze how our method affects accuracy and loss, experimenting with different DVS-to-ViT input patch sizes. Our results show that the large patch size of 32x32 pixels has better accuracy and smaller loss than the 4x4 pixel patches as epochs increase. Our method also achieved a high 98.4% accuracy and low 0.22 loss within five epochs, significantly outperforming previous works averaging 93.13% accuracy over more epochs. These results highlight ViTs' large potential for real-time DVS data classification in applications that require high accuracy, like autonomous vehicles and surveillance systems.

DyPanVO: A Robust Monocular Visual Odometry in Dynamic Environments by Utilizing Multiple Deep Neural Networks

Abstract Traditional monocular Visual Odometry (VO) is typically based on the assumption of a static environment. However, it performs poorly in dynamic scenes, suffering from error accumulation and scale drift. To address these issues, a novel framework, named DyPanVO, was proposed, which incorporates multiple deep neural networks for feature point extraction, panoptic segmentation, and depth estimation. Firstly, learning-based methods are employed, specifically SuperPoint for robust feature extraction and LightGlue for accurate feature matching. Then, outlier points are eliminated by combining dynamic prior results from panoptic segmentation with epipolar geometry constraints to determine the motion state of objects. Additionally, the introduction of depth information ensures that scale is recovered and fundamentally solves the issue of error accumulation. Two types of experiment (outdoor and indoor scenes) are carried out to show the effectiveness of DyPanVO. Moreover, the comparison results demonstrate that it performs comparably with multi-view geometry-based and outperforms learning-based methods.

Multi-Sensor Fusion and Deep Learning: A New Frontier in Stabilization Algorithm Optimization

The rapid development of automation technology has highlighted the importance of stabilization algorithms in ensuring the stability and functionality of smart devices. These algorithms are critical for maintaining balance and precision, especially in applications such as unmanned aerial vehicles (UAVs), handheld devices, and autonomous systems. This research focuses on the application of stabilization algorithms across various domains, analyzing their principles, advantages, limitations, and potential optimization strategies. The study employs a literature review to investigate the integration of multi-sensor fusion and deep learning techniques to improve system stability. It shows that multi-sensor fusion significantly enhances system robustness by mitigating external disturbances, while deep learning improves autonomous decision-making in dynamic environments, particularly in UAV path planning and task execution. The findings confirm the crucial role of stabilization algorithms in modern systems and provide insights into future optimization directions. By combining deep learning with sensor fusion, future systems are expected to achieve greater stability and autonomy, contributing to the advancement of UAVs and other intelligent systems.

A Fast Adaptive AUV Control Policy Based on Progressive Networks with Context Information

Deep reinforcement learning models have the advantage of being able to control nonlinear systems in an end-to-end manner. However, reinforcement learning controllers trained in simulation environments often perform poorly with real robots and are unable to cope with situations where the dynamics of the controlled object change. In this paper, we propose a DRL control algorithm that combines progressive networks and context as a depth tracking controller for AUVs. Firstly, an embedding network that maps interaction history sequence data onto latent variables is connected to the input of the policy network, and the context generated by the network gives the DRL agent the ability to adapt to the environment online. Then, the model can be rapidly adapted to a new dynamic environment, which was represented by the presence of generalized force disturbances and changes in the mass of the AUV, through a two-stage training mechanism based on progressive neural networks. The results showed that the proposed algorithm was able to improve the robustness of the controller to environmental disturbances and achieve fast adaptation when there were differences in the dynamics.

Assessing Environmental Dynamics and Angular Influence on PV Soiling: Employing ANFIS to Mitigate Power Losses

The performance of solar photovoltaic systems is impacted by dust accumulation, raising maintenance concerns and discouraging wider adoption to accelerate decarbonization pathways. This research investigates the influence of environmental dynamics on dust accumulation based on several locations, considering weather conditions, seasonality, and angular installation variations, over a three-month period. Low-iron glass coupons were employed to collect on-site soiling from four different locations: agricultural, residential, industrial, and desert. The samples collected were characterized using scanning electron microscopy (SEM) for morphology, X-ray diffraction (XRD) for mineralogy, energy-dispersive X-ray spectroscopy (EDX) for elemental analysis, spectrophotometry for optical properties, and I–V tracing for efficiency analysis. The data were processed using ANFIS techniques to extract the maximum power point (MPP) and reduce the power losses. The results showed significant differences in the dust properties across the sites, influenced by the topography, weather conditions, and human activity. The measurements revealed a decrease in transmittance of up to 17.98%, resulting in power losses of up to 22.66% after three months. The findings highlight the necessity for tailored maintenance strategies to mitigate the impact of human activities and site-specific factors on performance. This could be employed in developing predictive models providing valuable insights for sustaining solar energy systems.

Eco-phenotypic feedback loops differ in multistressor environments.

Natural communities are exposed to multiple environmental stressors, which simultaneously impact the population and trait dynamics of the species embedded within these communities. Given that certain traits, such as body size, are known to rapidly respond to environmental change, and given that they can strongly influence the density of populations, this raises the question of whether the strength of the eco-phenotypic feedback loop depends on the environment, and whether stressful environments would enhance or disrupt this feedback or causal linkage. We use two competing freshwater ciliates-Colpidium striatum and Paramecium aurelia-and expose their populations to a full-factorial design of increasing salinity and temperature conditions as well as interspecific competition. We found that salinity, temperature, and competition significantly affected the density and cell size dynamics of both species. Cell size dynamics strongly influenced density dynamics; however, the strength of this eco-phenotypic feedback loop weakened in stressful conditions and with interspecific competition. Our study highlights the importance of studying eco-phenotypic dynamics in different environments comprising stressful abiotic conditions and species interactions.

Application of PID Control Technology in Unmanned Aerial Vehicles

Abstract. The rapid advancement of unmanned aerial vehicle (UAV) technology underscores the essential role of PID (proportional-integral-derivative) control in ensuring flight stability, particularly through precise motor speed adjustments. Thus, this paper systematically analyzes the fundamental principles and limitations of traditional PID control, further highlighting its insufficient adaptability to dynamic external conditions, which includes load fluctuations and wind disturbances. In response to these challenges, it explores adaptive control strategies such as self-tuning PID and fuzzy logic PID, points out how these advanced methods can effectively improve flight stability by dynamically adjusting control parameters, and demonstrates their superiority over traditional PID through comparative analysis. In addition, the paper anticipates future developments in UAV control systems, thereby emphasizing the integration of artificial intelligence and machine learning technologies into PID control. And this integration seeks to optimize control strategies and markedly enhance the autonomous decision-making capabilities and adaptability of UAVs. Meanwhile, the paper also predicts the development trend of hybrid control systems, that is, combining PID with other advanced control methods (such as linear quadratic control) to enhance the overall control capabilities of UAVs. In short, it underscores the necessity for continuous innovation and in-depth research in response to the dynamic flight environment and the diverse mission requirements.

Exploring the Potential of Augmented Reality (AR) and Virtual Reality (VR) in Developing Immersive Experiences for Education, Entertainment, and Training: Innovations, Applications, and Future Prospects

Augmented reality (AR) and virtual reality (VR) have become groundbreaking technologies that are revolutionizing the way we interact with digital environments. These innovations have great potential in education, entertainment, and teaching by providing a fantastic and interactive experience to bridge the gap between the real and virtual worlds. AR and VR in education create engaging learning environments that encourage deeper understanding through simulations and hands-on virtual experiments. The entertainment industry is leveraging technology to create immersive games, videos, and live events that increase user engagement. AR and VR educational tools also enable safe, effective, and realistic simulations, especially in high-risk fields such as medicine, aviation, and defense. This technology often relies on hardware such as headsets, audio sensors, and haptics, supported by advanced software that can create dynamic 3D environments. Challenges include high construction costs, accessibility, and ensuring long-term user comfort. However, ongoing advances in hardware miniaturization, cloud computing, and artificial intelligence continue to overcome these challenges and encourage the integration of AR and VR into real-world applications. This study explores current innovations, applications, and future prospects for AR and VR, and highlights their potential to transform industries and redefine human-machine interactions.

Enhancing Stability and Efficiency in Mobile Ad Hoc Networks (MANETs): A Multicriteria Algorithm for Optimal Multipoint Relay Selection

Mobile ad hoc networks (MANETs) are autonomous systems composed of multiple mobile nodes that communicate wirelessly without relying on any pre-established infrastructure. These networks operate in highly dynamic environments, which can compromise their ability to guarantee consistent link lifetimes, security, reliability, and overall stability. Factors such as mobility, energy availability, and security critically influence network performance. Consequently, the selection of paths and relay nodes that ensure stability, security, and extended network lifetimes is fundamental in designing routing protocols for MANETs. This selection is pivotal in maintaining robust network operations and optimizing communication efficiency. This paper introduces a sophisticated algorithm for selecting multipoint relays (MPRs) in MANETs, addressing the challenges posed by node mobility, energy constraints, and security vulnerabilities. By employing a multicriteria-weighted technique that assesses the mobility, energy levels, and trustworthiness of mobile nodes, the proposed approach enhances network stability, reachability, and longevity. The enhanced algorithm is integrated into the Optimized Link State Routing Protocol (OLSR) and validated through NS3 simulations, using the Random Waypoint and ManhattanGrid mobility models. The results indicate superior performance of the enhanced algorithm over traditional OLSR, particularly in terms of packet delivery, delay reduction, and throughput in dynamic network conditions. This study not only advances the design of routing protocols for MANETs but also significantly contributes to the development of robust communication frameworks within the realm of smart mobile communications.

Automatic Tracking Control Based on Bayesian Optimization

Abstract. This paper discusses the control problem of intelligent tracking vehicle in complex dynamic environment, and puts forward a solution combining Bayesian optimization algorithm and PID control. Traditional PID control faces many challenges when dealing with time-varying and nonlinear systems, but the operating environment of intelligent tracking vehicles is complex and changeable, and it is difficult to adjust parameters in real time to adapt to different operating conditions. Therefore, this paper introduces Bayesian optimization algorithm to predict the optimal parameter combination of PID controller by constructing hyperparameter optimization model, thus effectively improving the control performance and robustness of the system.

Comparative Analysis of Business Environment Dynamics in Central and Eastern Europe: A Multi-Criteria Approach

The COVID-19 pandemic has negatively impacted the world economy and global society. However, small- and medium-sized enterprises are among the most vulnerable and affected groups of businesses, and in some cases, life-saving interventions have resulted in serious existential implications. The difficulties of insufficient demand, non-negligible fixed costs, and inadequate financing are unsustainable for many firms. Thus, the main aim of this study is to evaluate the variables influencing business activities, apply macroeconomic variables to compare the business environments in fifteen European countries, and utilize appropriate statistical techniques to confirm the results. Significant differences exist in the business climate across selected European countries, as identified by the TOPSIS method, CPI, and GCI. Low levels of corruption, strong economic stability, and high competitiveness make countries like Germany and Austria attractive for business environments. Estonia is also a leader in technological innovation and low corruption. Conversely, Bulgaria and Romania are struggling with higher levels of corruption and reduced competitiveness, potentially impeding business endeavours. The Visegrad Group countries are in the middle of the spectrum, scoring average to good but with opportunities for improvement in corruption and innovation. Overall, the business climate in these countries is diverse, reflecting their unique economic, political, and social circumstances.

Adaptive denoising quantum state preparation in a dynamic environment

Quantum state preparation (QSP) is an essential step in many quantum information processing tasks and has been deeply studied in a closed system. However, environmental noise inevitably destroys the quantumness of the system, resulting in decreased fidelity. In this work, we investigate the enhancement of fidelity by pulse control for arbitrary single or two-qubit QSP in a noisy environment. The control pulses are designed by leveraging supervised learning. The environment is modeled as a collection of bosons, and we use the quantum state diffusion equation technique to deal with the dynamics of the system. We consider two types of noise: quasistatic noise and dynamic noise. We propose active and adaptive denoising schemes for the quasistatic and dynamic noise, respectively. The environmental parameters are required to construct the dataset when training the neural network (NN) in adaptive denoising, while they are not needed in active denoising. The adaptive denoising scheme is universal in that it can be effectively applied to both quasistatic and dynamic noises. High-fidelity QSP has been obtained by the well-trained NN. Our work demonstrates that machine learning has great potential in optimal pulse design in a dynamic environment. Published by the American Physical Society 2024