Dynamic Environment Research Articles

An In-Depth Analysis of Collision Avoidance Path Planning Algorithms in Autonomous Vehicles

Abstract: Path planning is a way to define the motion of an autonomous surface vehicle (ASV) in any existing obstacle environment to enable the vehicle's movement by setting directions to avoid that can react to the obstacles in the vehicle's path. A good, planned path perceives the environment to the extent of uncertainty and tries to build or adapt its change in the path of motion. Efficient path planning algorithms are needed to alleviate deficiencies, which are to be modified using the deterministic path that leads the ASV to reach a goal or a desired location while finding an optimal solution has become a challenge in the field of optimization along with a collision-free path, making path planning a critical thinker. The traditional algorithms have a lot of training and computation, making it difficult in a realistic environment. This review paper explores the different techniques available in path planning and collision avoidance of ASV in a dynamic environment. The objective of good path planning and collision avoidance for a dynamic environment is compared effectively with the existing obstacle’s movement of different vehicles. Different path planning technical approaches are compared with their performance and collision avoidance for unmanned vehicles in marine environments by early researchers. This paper gives us a clear idea for developing an effective path planning technique to overcome marine accidents in the dynamic ocean environment while choosing the shortest, obstacle-free path for Autonomous Surface Vehicles that can reduce risk and enhance the safety of unmanned vehicle movement in a harsh ocean environment.

Morphosedimentary evolution of the Belgica Mound Drift: Controls on contourite depositional system development in association with cold-water coral mounds

Small-scale contourite drift is an important component of continental margins that can record information about complex oceanographic processes. The Belgica Mound Drift is one example of a small-scale contourite drift. It is formed under the influence of cold-water coral (CWC) mounds and represents one of the most distal contouritic expressions influenced by the Mediterranean Outflow Water (MOW) in the NE Atlantic Ocean. Three distinct evolutionary stages have been identified from new high-resolution pseudo-3D reflection seismic data, each associated with a significant change in paleoceanography, affecting both bottom-current intensity and sediment input. The pre-drift stage (Pliocene–Early Pleistocene) corresponds to the regional RD1 erosive event, which was caused by the reintroduction of the MOW in the Porcupine Seabight, creating a distinct paleotopography that will influence all ensuing sedimentary processes. The second stage (Early Pleistocene–Middle Pleistocene) is the contourite drift inception in two distinct centres of growth, strongly steered by topographic obstacles such as the CWC mounds. During the third and final stage (Middle Pleistocene–present day), the contourite drift is developed under a more stable but less dynamic environment, characterised by more continuous and mounded aggradational stratification. The final stage of the contourite drift is related to the Middle Pleistocene Transition, with a spatially variable reduction in the MOW-related bottom currents and sediment input. The spatial and temporal evolution of this drift shows that its present-day morphology is controlled by the location of initial growth. Evolving moat morphology indicates that the intensity of the bottom currents generally increases during the drift evolution.This research presents a crucial paradigm for advancing our knowledge of elucidating the complexities of smaller-sized contourite systems in diverse oceanic environments.

Optimized Kalman filtering in dynamical environments for thumb robot motion estimation

<span>Stroke, a prevalent nerve disorder in Indonesia, necessitates post-stroke rehabilitation like physical and occupational therapy. Hand and finger muscle training, crucial for restoring movement, often involves innovative solutions like finger prosthetic robotics arms. In particular, the advancement in thumb robotics emphasizes the estimation of thumb motion, where the ensemble Kalman filter square root (EnKF-SR) and H-infinity methods are deemed dependable for both linear and nonlinear models. Simulation results, using 400 ensembles, demonstrated nearly identical accuracy between the methods, exceeding 99%, with a 6-7% increase in accuracy compared to 200 ensembles. These advancements offer promising prospects for effective post-stroke rehabilitation and improved thumb movement restoration.</span>

Comparative analysis of hybrid cognitive radar techniques for enhanced target detection and tracking: A performance evaluation perspective

This paper introduces an innovative hybrid algorithmic approach for cognitive radar systems, by integrating unique machine learning optimization techniques such as, YOLO (You Only Look Once), Mask R-CNN, and Recurrent Neural Networks (RNNs). Through extensive simulations, the integrated approach demonstrates notable enhancements in target detection, instance segmentation, and target tracking within radar systems. Leveraging deep learning models, the framework facilitates adaptive and intelligent processing of radar data, augmenting system performance in dynamic environments. By seamlessly integrating state-of-the-art techniques, the proposed framework showcases a comprehensive solution to the challenges faced by traditional radar systems. This research represents a significant stride forward in radar technology, promising transformative impacts across various domains, including surveillance, remote sensing, and autonomous navigation. As radar systems continue to evolve, the adoption of advanced deep learning techniques offers unprecedented opportunities for enhancing situational awareness and decision-making capabilities in complex operational scenarios.

The comprehensive analysis of magnetohydrodynamic Casson fluid flow with rectangular porous medium through expanding/contracting channel

PurposeThis study examines fluid flow within a rectangular porous medium bounded by walls capable of expansion or contraction. It focuses on a non-Newtonian fluid with Casson characteristics, incompressibility, and electrical conductivity, demonstrating temperature-dependent impacts on viscosity.Design/methodology/approachThe flow is two-dimensional, unsteady, and laminar, influenced by a small electromagnetic force and electrical conductivity. The Hybrid Analytical and Numerical Method (HAN method) resolves the constitutive differential equations.FindingsThe fluid’s velocity is influenced by the Casson parameter, viscosity variation parameter, and resistive force, while the fluid’s temperature is affected by the radiation parameter, Prandtl number, and power-law index. Increasing the Casson parameter from 0.1 to 50 results in a 4.699% increase in maximum fluid velocity and a 0.123% increase in average velocity. Viscosity variation from 0 to 15 decreases average velocity by 1.42%. Wall expansion (a from −4 to 4) increases maximum velocity by 19.07% and average velocity by 1.09%. The average fluid temperature increases by 100.92% with wall expansion and decreases by 51.47% with a Prandtl number change from 0 to 7.Originality/valueUnderstanding fluid dynamics in various environments is crucial for engineering and natural systems. This research emphasizes the critical role of wall movements in fluid dynamics and offers valuable insights for designing systems requiring fluid flow and heat transfer. The study presents new findings on heat transfer and fluid flow in a rectangular channel with two parallel, porous walls capable of expansion and contraction, which have not been previously reported.

Optimization of Hybrid Energy Systems Based on MPC-LSTM-KAN: A Case Study of a High-Altitude Wind Energy Work Umbrella Control System

This paper presents an optimization method for hybrid energy systems based on Model Predictive Control (MPC), Long Short-Term Memory (LSTM) networks, and Kolmogorov–Arnold Networks (KANs). The proposed method is applied to a high-altitude wind energy work umbrella control system, where it aims to enhance the stability and efficiency of energy utilization. The work umbrella system integrates wind and solar energy sources, with energy stored in a battery and used to control the umbrella’s operations. The MPC framework is employed to optimize control actions by solving a finite-horizon optimization problem, ensuring the battery State of Charge (SOC) remains within an optimal range. The LSTM network provides accurate predictions of environmental conditions, including wind speed and solar irradiance, which are essential for MPC’s decision-making process. To address complex nonlinearities in the system, the KAN is utilized to model and approximate these dynamics, refining the LSTM predictions. The integration of these advanced control strategies enables the system to handle varying operational conditions and maintain optimal performance. The case study demonstrates the effectiveness of the MPC-LSTM-KAN approach, revealing improvements in the SOC stability, energy efficiency, and operational endurance of the high-altitude wind energy work umbrella system. The results indicate that this hybrid optimization method offers a robust solution for managing hybrid energy systems in dynamic environments.

9.Y.2. Leveraging GenAI and Deep Learning for the Analysis of Public Health Data: A Global Perspective

Abstract The evolution from Artificial Intelligence (AI) to Generative Artificial Intelligence (Gen AI) represents a significant advancement in technological capabilities and applications. Advancements in computing power, algorithmic complexity, and data availability have enabled Gen AI to exhibit broader cognitive abilities such as reasoning, problem-solving, and learning across diverse domains. Private sector investment and innovation is advancing capability in this space at a rapid pace, trending toward a complete paradigm shift towards machines capable of autonomously adapting and learning in dynamic environments, promising profound impacts across industries from education and finance to healthcare and beyond. Yet harnessing the power of GenAI remains out of reach for most public health entities. Questions like “How to start” and “What to do”, let alone concerns regarding cost, security, and ethics, often thwart an agency from endeavoring to even begin their journey to the program improvement they visualize through the lens and promise of AI. Conduent’s longstanding presence and pioneering work in AI across various sectors, has led to a simple, underlying philosophy: we leverage Gen AI technologies to assist and enable humans to improve business processes. Instead of developing Gen AI technologies ourselves, we focus our efforts on making advanced technologies accessible to our clients and enabling them to achieve their outcomes. This session focuses on accessibility and enablement for public health agencies looking to jump start their journey to AI and evolve their public health programs with the use of Gen AI, covering topics such as: • The evolution of GenAI in the private sector; • Visualizing objectives in public health outcomes and improved business processes; • What to do about data; • Using human intelligence to drive machine learning; • Managing security, ethics, and equity; • Roadmap your Journey to Gen AI. For Conduent, Gen AI is not intended to replace humans but to harness its potential to support and empower humans in creating innovative, additive opportunities that enhance business process outcomes. In the public health space, its impact is immense and far reaching. Let’s shift from “How do I start” to tackling the questions that improve programs and their outcomes. So how will you optimize your program with Gen AI? Let’s get started. Key messages • Private sector Gen AI capability is expanding at a rapid pace yet many public health agencies are struggling just to start. Conduent offers an informed perspective to jump start your Gen AI journey. • Accessibility and enablement for public health agencies looking to jump start their journey to AI and evolve their public health programs with the use of GenAI. Speakers/Panelists Mark Marostica Conduent Public Health Solutions, Florham Park, USA Dianna Lydiard Conduent, Florham Park, USA Tarun Khatri Conduent, Burlington, Canada

Fuzzy Multi-Agent Simulation for Collective Energy Management of Autonomous Industrial Vehicle Fleets

This paper presents a multi-agent simulation implemented in Python, using fuzzy logic to explore collective battery recharge management for autonomous industrial vehicles (AIVs) in an airport environment. This approach offers adaptability and resilience through a distributed system, taking into account variations in AIV battery capacity. Simulation scenarios were based on a proposed charging/discharging model for an AIV battery. The results highlight the effectiveness of adaptive fuzzy multi-agent models in optimizing charging strategies, improving operational efficiency, and reducing energy consumption. Dynamic factors such as workload variations and AIV-infrastructure communication are taken into account in the form of heuristics, underlining the importance of flexible and collaborative approaches in autonomous systems. In particular, an infrastructure capable of optimizing charging according to energy tariffs can significantly reduce consumption during peak hours, highlighting the importance of such strategies in dynamic environments. An optimal control model is established to improve the energy consumption of each AIV during its mission. The energy consumption depends on the speed, as demonstrated via numerical simulations using realistic data. The speed profile of each AIV is adjusted according to the various constraints within an airport. Overall, the study highlights the potential of incorporating adaptive fuzzy multi-agent models for AIV energy management to boost efficiency and sustainability in industrial operations.

Effect of technological culture and knowledge sharing on organizational performance: The mediating role of digital innovation and self-efficacy as moderation

In the era of IR4.0, environmental dynamism and satisfying customer needs through digital innovations have evolved across IT industries. This article attempts to examine the effect of technological culture (TC) and knowledge sharing (KS) on digital innovation (DI), organizational performance (OP), and the moderating effect of self-efficacy (SE) on the link between TC, KS, and DI. This study evaluates a novel conceptual framework utilizing survey data from 270 samples of IT firms’ employees in Bangladesh and analyzing it employing the PLS-SEM approach. The findings indicate that knowledge sharing and technological culture have a significant impact on DI and DI also significantly mediates the relationship between operational, financial, and employee performance. The findings suggest businesses recognize the chance of developing digital technologies and the digitalization trend in IT sectors by being devoted to embracing new technological cultures and upgrading their knowledge exchange to become innovation leaders and increase OP. This study describes how new digital technologies and knowledge sharing may be exploited to produce innovative digital creative digital solutions’ innovative products and services which ultimately increase their OP, where the managers of the IT organizations can apply this knowledge in respected fields.

Shape-dependent aerosol dynamics in indoor environments: Penetration, deposition, and dispersion

Particle shape exerts a significant influence on their dynamic behavior, and it is imperative to elucidate these effects given the potential for severe environmental toxicity associated with shaped particles. Despite extensive research on the dynamical processes of spherical particles, the behaviors of non-spherical particles have been insufficiently investigated. In this study, we have developed a suite of computation-based models that account for particle shape and have reported on the typical dynamical behaviors of non-spherical particles within indoor environments. We have explored three typical scenarios, i.e., particle penetration into indoor spaces through building cracks, indoor particle deposition, and indoor particle dispersion. The shape-induced deviations are associated with dynamical processes, showing a decrease trend among penetration, deposition, and dispersion of the non-spherical particles. The maximum discrepancy due to particle shape during the penetration process exceeds 1000 %, observed with particles of approximately 0.02 μm in diameter interacting with straight cracks 4.5 cm in length and 0.25 mm in height. Moreover, there is a discrepancy of more than 70 % in the deposition of particles with a diameter of approximately 10 μm on side walls when using side air supply ventilation. Similarly, a discrepancy of nearly 11 % is noted for particles around 0.02 μm in diameter during dispersion under displacement ventilation within indoor settings. The interaction between shape-related particle dynamics, particularly their diffusion characteristics, and the properties of the flow field leads to these shape-dependent dynamical discrepancies. These findings offer a comprehensive understanding of how the shape of particles affects their indoor dynamic behavior, thereby supporting the control of hazardous particles in indoor environments.

A Wideband Circularly Polarized Four Port Microstrip MIMO Antenna with High Gain and Enhanced Isolation for 5G Millimeter Frequency Applications

Abstract In this article, a MIMO antenna system featuring wide bandwidth, high gain, and circular polarization for 5G communication networks is presented. The proposed antenna enables high data rates, enhanced signal reliability, and improved performance in dynamic and challenging 5G environments. Wide bandwidth ensures compatibility with various 5G frequency bands, while circular polarization allows for better signal propagation and reception, particularly in non-line-of-sight scenarios. In that regard, the present endeavor exhibits an enhanced bandwidth 4-port circularly polarized MIMO antenna to encounter polarization mismatch along with high data rates. The presented MIMO antenna comprises four identical patches strategically positioned in orthogonal orientations in such a way as to provide the circular polarization direction. Meanwhile, the bandwidth is extended by inserting straightforward defected ground structure (DGS) and L slots. The MIMO antenna anticipated in this work offers an impedance bandwidth covering from 24 GHz to 28.5 GHz and an axial ratio bandwidth of 62% within the overall band. It demonstrates excellent performance with a high peak gain of approximately 14.84 dB. Additionally, it exhibits strong isolation of more than 20dB among the closely packed antennas, ensuring reliable communication and minimizing interference. The combination of these features in a MIMO antenna system is crucial for meeting the demanding requirements of 5G communication, including increased data capacity, low latency, and reliable connectivity. The simulated and measured outcomes exhibit robust concurrence, signifying a high level of accuracy. Consequently, the suggested MIMO antenna appears well-suited for encompassing the 5G bands designated for deployment in the USA (27.50–28.35 GHz) and Europe (24.25 - 27.5 GHz).

R3T*-MOSafeRL($$\lambda $$): path planning of mobile robots in unknown dynamic environments

R3T*-MOSafeRL($$\lambda $$): path planning of mobile robots in unknown dynamic environments

An Exploratory Study of Contextual Control Modes in Teamwork.

This study investigated the relationship between human team member contextual control and team performance under time constraints. Contextual control modes, which describe different strategies for action selection in dynamic environments, characterize how humans maintain performance under variable demands. Control modes have not yet been studied in teamwork settings. Modeling of the cross-level interaction between team members' control modes and emerging team behaviors can improve understanding of effective teamwork in dynamic environments. A human-subjects study explored the relationship between individual contextual control and team performance. Questionnaires about contextual control were used to elicit individual control modes. Analysis compared team members' control modes and investigated how control modes changed under varying time pressures. Participant's control modes differed in their look ahead horizon, the extensiveness of prior action evaluation, and their prior experience. Many team members shifted control modes during trials, resulting in both convergence and divergence of paired control modes. No effects on communication rate were found due to changes in team members' control modes, but partially significant findings may suggest that the control mode divergence affects performance. Teams can operate in multiple control mode configurations that change dynamically according to context. Further research with an increased sample size is warranted to analyze how time constraints influence team members' control modes and overall teaming processes and whether divergence of team control mode is favorable under time pressures. Further study of contextual control in teams may help improve team design to better support teams in coping with time constraints in dynamic environments.

Hybrid Working and Employee Job Performance in Felcra Berhad: An Examination of The Moderating Role of Job Satisfaction

In today's dynamic work environment, understanding the impact of hybrid working on job performance is crucial. Many organizations, including FELCRA Berhad, adopted hybrid working during the COVID-19 pandemic to enhance employee efficiency and flexibility. This study explores FELCRA Berhad's job performance and satisfaction during the pandemic and evaluates the potential success of hybrid working post-pandemic. Using a quantitative research design and purposive sampling, data were collected from FELCRA Berhad employees through online and physical questionnaires. The data were analyzed with IBM SPSS Version 28 to examine the relationship between hybrid working (working conditions, work-life balance, and recognition) and job performance, with job satisfaction as a moderating factor. The results indicated a significant positive relationship between working conditions and job performance. While work-life balance and recognition showed moderate positive relationships with job performance, they were not statistically significant. Job satisfaction did not significantly moderate these relationships. However, the group effect for hybrid working remained significant across all dimensions. In conclusion, the study confirms a significant positive relationship between hybrid work conditions and job performance, providing a foundation for FELCRA Berhad to consider implementing hybrid work practices based on their direct effects on job performance.

An efficient Protocol for Enhanced Flynet Communication in Presence of Partitioning

This paper presents an adaptive fault-tolerant resource allocation protocol tailored for FlyNet, a dynamic aerial network characterized by its mobility and three-dimensional operational space. Addressing the challenges of network partitioning and resource allocation in aerial networks, the proposed algorithm dynamically adjusts to the network's changing topology, ensuring consistent and efficient resource management. Through robust fault tolerance mechanisms, the protocol enhances FlyNet's reliability, maintaining seamless communication and optimal resource utilization even amid node mobility and disruptions. Simulation results demonstrate the algorithm's effectiveness in adapting to dynamic environments, maximizing resource utilization, and minimizing communication delays.

Toward a Generic Framework for Mission Planning and Execution with a Heterogeneous Multi-Robot System

This paper presents a comprehensive framework for mission planning and execution with a heterogeneous multi-robot system, specifically designed to coordinate unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) in dynamic and unstructured environments. The proposed architecture evaluates the mission requirements, allocates tasks, and optimizes resource usage based on the capabilities of the available robots. It then executes the mission utilizing a decentralized control strategy that enables the robots to adapt to environmental changes and maintain formation stability in both 2D and 3D spaces. The framework’s architecture supports loose coupling between its components, enhancing system scalability and maintainability. Key features include a robust task allocation algorithm, and a dynamic formation control mechanism, using a ROS 2 communication protocol that ensures reliable information exchange among robots. The effectiveness of this framework is demonstrated through a case study involving coordinated exploration and data collection tasks, showcasing its ability to manage missions while optimizing robot collaboration. This work advances the field of heterogeneous robotic systems by providing a scalable and adaptable solution for multi-robot coordination in challenging environments.

The effects of sport-specific training on individuals action strategies while avoiding a virtual player approaching on a 45° angle while completing a secondary task.

Sports provide varying scenarios where athletes must interact with and avoid opposing players in dynamic environments. As such, sport-specific training can improve one's ability to integrate visual information which may result in improved collision avoidance behaviours. However, improved visuomotor capabilities are highly task dependent (i.e., athletes must be tested in sport-specific settings). The current study examined whether sport-specific training influenced individuals' collision avoidance behaviours during a sport-specific task in virtual reality. Untrained young adults (N = 21, 22.9±1.9 yrs, 11 males) and specifically trained athletes (N = 18, 20±1.5 yrs, 7 males) were immersed in a virtual environment and were instructed to walk along a 7.5m path towards a goal located along the midline. Two virtual players positioned 2.83m to the left and right of the midline approached participants on a 45° angle at one of three speeds: 0.8x, 1.0x, or 1.2x each participant's average walking speed. Participants were instructed to walk to a goal without colliding with the virtual players while performing a secondary task; reporting whether a shape changed above either of the virtual players' heads. Results revealed that athletes had a higher percentage of correct responses on the secondary task compared to untrained young adults. However, there was no group differences in the average time to first avoidance or average minimum clearance, but athletes were more variable in their avoidance behaviours. Findings from this study demonstrate that athletes may be more adaptive in their behaviours and may perform better on attentionally demanding tasks in dynamic environments.

Interferometric Synthetic Aperture Radar Phase Linking with Level 2 Coregistered Single Look Complexes: Enhancing Infrastructure Monitoring Accuracy at Algeciras Port

This paper presents an advanced workflow for processing radar imagery stacks using Persistent Scatterer and Distributed Scatterer Interferometry (PSDS) to enhance spatial coherence and improve displacement detection accuracy. The workflow leverages Level 2 Coregistered Single Look Complex (L2-CSLC) images generated by the open-source COMPASS (Coregistered Multi-temporal Sar SLC) framework in combination with the Combined eigenvalue maximum likelihood Phase Linking (CPL) approach implemented in MiaplPy. Starting the analysis directly from Level 2 products offers a significant advantage to end-users, as they simplify processing by being pre-geocoded and ready for immediate analysis. Additionally, the open-source nature of the workflow and the use of L2-CSLC products simplify the processing pipeline, making it easier to distribute directly to users for practical applications in monitoring infrastructure stability in dynamic environments. The ISCE3-MiaplPy workflow is compared against ISCE2-MiaplPy and the European Ground Motion Service (EGMS) to assess its performance in detecting infrastructure deformations in dynamic environments, such as the Algeciras port. The results indicate that ISCE3-MiaplPy delivers denser measurements, albeit with increased noise, compared to its counterparts. This higher resolution enables a more detailed understanding of infrastructure stability and surface dynamics, which is critical for environments with ongoing human activity or natural forces.

Impact of inbound marketing on Consoagro junior company of the State University of Londrina

This work addressed the transition of Consoagro Empresa Júnior to Inbound Marketing in 2021, 2022, and 2023, highlighting the growing relevance of agricultural innovation and communication strategies. The adoption of Inbound Marketing was motivated by the need to acquire a strategy that would strengthen Consoagro's brand presence in the digital environment and assist in the passive acquisition of clients. With the general objective of analyzing the impacts of this transition, a quantitative approach was employed, using Consoagro as a case study. The methodology included document analysis and impact assessment over the years using Excel data spreadsheets and statistical analysis using the ANOVA and Tukey test in R software. After analyzing the results, it was possible to identify that the strategy not only strengthened Consoagro's online presence but also contributed to the promotion of digital agriculture. Facing challenges such as increasing market competition and the lack of prospecting, the company turned these difficulties into strategic opportunities, using the production of interesting and relevant content to attract and engage its audience effectively. In summary, this study highlighted the importance of innovation as an essential catalyst for facing contemporary challenges in the agricultural context. By integrating Inbound Marketing as part of a broader strategy of growth and adaptation, Consoagro not only improved its visibility and operational efficiency but also established a successful model for other junior companies in the agricultural sector seeking to differentiate and thrive in a competitive and dynamic environment.

Research Dynamics and Frontiers of Environmental, Social, and Governance Disclosure: Bibliometric Visual Analysis Based on CiteSpace

Abstract: With the increasing global attention on sustainable development and corporate social responsibility, studying the dynamics and frontiers of Environmental, Social, and Governance (ESG) disclosure is of great significance to academia, corporate practice, and policy-making. This study employs CiteSpace for bibliometric visual analysis, utilizing methods such as keyword co-occurrence analysis, cluster analysis, and research theme evolution analysis to comparatively analyze the differences in ESG research between domestic and international contexts. The study reveals the hotspots, trends, and development directions of ESG disclosure research. The findings indicate a steady growth in the number of publications in the ESG disclosure research field, with research themes shifting from singular environmental information disclosure to a more comprehensive focus on social responsibility and governance structures. This study not only provides researchers with new perspectives, expanding and deepening the themes of ESG research, but also offers references for companies to enhance ESG disclosure and for policy-making, thus holding significant theoretical and practical implications.