Real-time Adjustment Research Articles

Strategies for Real-time Adjustment of Stimulation Patterns in Closed-loop Deep Brain Stimulation for Parkinsons Disease: A Comprehensive Review

Parkinsons disease (PD) is a prevalent neurodegenerative disorder that poses significant healthcare challenges worldwide. Deep brain stimulation (DBS) has proven effective for managing PD, particularly for patients with inadequate medication response. However, traditional high-frequency DBS systems lack the capability to adjust stimulation patterns in real time based on patient responses. In contrast, closed-loop DBS systems, which adjust stimulation based on recorded biomarkers, present a promising alternative that allows more flexible, on-demand electrical pulses delivery, offering possibility of achieving better therapeutic outcomes with less invasive stimulus. This review article explored various strategies for real-time adjustment of stimulation parameters in closed-loop DBS primarily based on electrophysiological biomarkers, particularly local field potentials (LFPs). We categorize these strategies into four categories: simple on-demand methods, control methods in systems theory, optimization or machine learning algorithms, and desynchronization approaches. By synthesizing existing literature, we provide a comprehensive overview of these strategies, highlighting contributions from multiple disciplines and the prospect of optimizing treatment for Parkinsons disease using closed-loop approach.

A Fuzzy Control Strategy for Multi-Goal Autonomous Robot Navigation

This paper addresses the complex problem of multi-goal robot navigation, framed as an NP-hard traveling salesman problem (TSP), in environments with both static and dynamic obstacles. The proposed approach integrates a novel path planning algorithm based on the Bump-Surface concept to optimize the shortest collision-free path among static obstacles, while a Genetic Algorithm (GA) is employed to determine the optimal sequence of goal points. To manage static or dynamic obstacles, two fuzzy controllers are developed: one for real-time path tracking and another for dynamic obstacle avoidance. This dual-controller system enables the robot to adaptively adjust its trajectory while ensuring collision-free navigation in unpredictable environments. The integration of fuzzy logic with TSP-based path planning and real-time dynamic obstacle handling represents a significant advancement in autonomous robot navigation. Simulations conducted in CoppeliaSim validate the effectiveness of the proposed method, demonstrating robust navigation and obstacle avoidance in realistic environments. This work provides a comprehensive framework for solving multi-goal navigation tasks by incorporating TSP optimization with dynamic, real-time path adjustments.

Shipyard Manpower Digital Recruitment: A Data-Driven Approach for Norwegian Stakeholders

This study examines the effectiveness of digital recruitment platforms in addressing labor shortages within Norway’s shipbuilding and ship-repair industry. Through a data analysis of 446 job applications over a 12-month period, this study evaluates key recruitment metrics, including application response times (ARTs), candidate acceptance rates (CARs), and the impact of machine learning on hiring outcomes. The findings reveal that specialized digital platforms can significantly improve recruitment efficiency, with 70% of applications being received within 24 h, highlighting the platforms’s great potential for time-sensitive sectors. Additionally, pre-vetting candidates enhances hiring precision, achieving a CAR of 90% and reducing mismatches. The application of machine learning algorithms provides predictive insights that support real-time adjustments to job postings, optimizing recruitment strategies. This study contributes uniquely to the literature on cross-border digital recruitment, aligning with the European Union goals of sustainable labor mobility and economic resilience.

THE ROLE OF AI IN ENHANCING IDENTITY AND ACCESS MANAGEMENT SYSTEMS

In the modern digital world, safeguarding sensitive data and managing access to critical systems are essential for organizations. Identity and Access Management (IAM) is a key framework for controlling access to systems and data, ensuring only authorized users can gain access. Traditionally relying on static methods like passwords, IAM systems are now facing challenges due to the complexity of cyber threats and the increasing number of users and devices. To address these issues, AI is transforming IAM by improving user authentication, detecting security anomalies, and refining permission management. AI contributes to user authentication through biometric technologies like facial recognition, fingerprint scanning, and voice recognition, reducing vulnerabilities from traditional methods. Machine learning also enhances authentication by continuously analyzing user behavior, adapting systems to recognize legitimate users more accurately. Additionally, AI plays a vital role in anomaly detection by analyzing user activity data across various platforms and identifying unusual patterns that indicate potential threats. AI's impact extends to dynamic and context-aware permission management, offering real-time adjustments based on factors such as user role and location. Furthermore, AI supports continuous risk assessment and regulatory compliance by monitoring user activities and ensuring proper access controls. The integration of AI in IAM also strengthens cloud security, as seen in the research of Muppa (2022), Mandru (2022), Oduri (2019), Mohammed (2021), Ramakrishnan (2021), and Subburaman (2022), who explore how AI helps mitigate emerging threats, optimize authentication, and improve access control in cloud environments. Ultimately, AI in IAM offers a more adaptive, resilient, and precise solution to evolving security challenges. Organizations adopting AI-powered IAM systems will be better equipped to face future cyber threats, ensuring both data protection and operational continuity.

Leveraging AI To Enhance Green Marketing Strategies

Abstract: The increasing focus on sustainability and environmental conservation has reshaped the marketing landscape, prompting businesses to adopt green marketing strategies. Simultaneously, advancements in Artificial Intelligence (AI) have transformed traditional marketing approaches, offering data-driven insights and innovative tools. This study explores the integration of AI technologies into green marketing to enhance its effectiveness and sustainability impact. It examines how AI-powered tools, such as predictive analytics, machine learning, and automation, can optimize green marketing strategies by enabling precise targeting, real-time campaign adjustments, and sustainability performance measurements. Through an analysis of existing literature, case studies, and real-world applications, this research highlights AI's potential to improve consumer engagement, build trust in eco-friendly brands, and overcome implementation challenges in diverse markets. The findings provide actionable insights for businesses, policymakers, and marketers, emphasizing the role of AI in advancing green marketing initiatives globally. This study bridges the gap between AI innovation and sustainable marketing, offering a comprehensive framework for leveraging technology to achieve environmental and business goals.

Research on real-time adaptive adjustment method for HSER stable manufacture and consistent quality

Research on real-time adaptive adjustment method for HSER stable manufacture and consistent quality

Enhancing ride comfort of semi-active suspension through collaboration control using dung beetle optimizer optimized Fuzzy PID controller

The enhancement of automobile quality is a significant area of modern research and development, with a focus on improving ride comfort and driving experience. This is achieved not only through structural improvements but also through the optimization of suspension control strategies. To enhance the ability of online real-time adjustment and overcome the limitations of fuzzy control, this study combines dung beetle optimizer (DBO), Fuzzy control, and Proportional-Integral-Derivative (PID) control to propose a reduced optimization complexity control method to improve MR damper control. The control parameters generated by fuzzy control are utilized as the initial values for DBO iteration to obtain the optimal solution, which is subsequently fed into the PID controller to regulate changes in the actuator’s magnetic field. Use MATLAB/Simulink to build a quarter semi-active suspension model with magnetorheological (MR) dampers for simulation analysis. Under sinusoidal and random disturbances, compared with the passive suspension, the vehicle acceleration of the Fuzzy-DBO-PID controller is reduced by 51%, 50.82%, 47.56% and 11.42%. Compared with Fuzzy-PID, the vehicle acceleration of Fuzzy-DBO-PID is reduced by 25.98%, 32.86%, 29.41%, and 7.19% on sinusoidal and random disturbances, which improves the ride comfort and verifies the effectiveness of the proposed control strategy.

Predictive Energy Demand and Optimization in Metro Systems Using AI and IoT Technologies

Introduction: With the rapid urbanization of modern cities, metro systems have become indispensable for efficient mobility. However, the increasing demand for public transportation has led to rising energy consumption, posing significant challenges for operational sustainability. Current energy management strategies in metro networks rely on static models and centralized systems, which often fail to adapt to real-time fluctuations in energy demand, leading to inefficiencies and wasted resources. Methods: This paper proposes an innovative approach to optimizing energy demand in metro systems by integrating Artificial Intelligence (AI) and the Internet of Things (IoT). By leveraging real-time data collected from IoT sensors deployed throughout the metro network, we apply machine learning algorithms such as Random Forests and Neural Networks to dynamically predict energy demand. These predictions enable metro operators to adjust energy consumption in real-time, thus improving overall system efficiency and reducing operational waste. Our approach was validated using data from the Parisian metro system through extensive simulations. Results: The results of simulations demonstrate significant improvements in energy efficiency. Optimized energy demand management led to a reduction in wasted energy during metro operations, particularly through the utilization of regenerative braking systems. Conclusions: Our findings suggest that integrating AI and IoT technologies into metro systems significantly improves energy efficiency by enabling dynamic energy demand prediction and real-time adjustment of energy consumption. The proposed system is scalable and adaptable, making it suitable for application in metro networks globally, thereby enhancing energy efficiency and supporting sustainable transport initiatives.

Optimizing Email Campaigns based on Pricing Variability

Email marketing is now a mainstay in most e-commerce customer engagement platforms especially considering dynamic pricing and personalized campaign tactics. This paper explores how pricing-based cohort segmentation can best optimize email campaigns by identifying the appropriate consumer groups sensitive to price changes so marketers can adjust the timing and content of their campaigns for maximum engagement and conversion. Three effective strategies identified in this paper for cohorts and their related targeted email dispatch are leveraging user behavior data, real-time pricing adjustments, and automation. Beyond that, the paper includes a review of challenges like synchronization issues, as well as data privacy issues, while incorporating real-world case studies demonstrating the impact of cohort-based strategies on campaign performance. The future directions discussed look into the role of AI and machine learning in generating higher accuracy levels for cohort segmentation and personalization, which shall serve as the road to refined datadriven email marketing strategies for e-commerce.

Effective neurolearning of english in university students through NLP

The article examines the impact of neurolearning on English language learning in the educational context of the Autonomous University of Nayarit. The research is carried out with 154 undergraduate students in International Business, distributed in morning and afternoon shifts. The main objective is to analyze how neurolearning techniques influence the development of language skills and academic performance of the participants. The methodology uses a survey administered through Google Forms, which includes eight items aimed at evaluating the students' perception of the effectiveness of neurolearning in their language learning process. The results indicate that students highlight the improvement in information retention, personalization of learning and the development of critical problem-solving skills. They also emphasize the importance of continuous feedback, which allows for real-time adjustment of study strategies, thus favoring a deeper understanding of the content. Thus, neurolearning emerges as a valuable approach to optimize English language learning in International Business students. Its integration into academic programs is suggested, with a particular focus on teacher training and the development of methodologies that ensure its effectiveness. This not only contributes to improving language skills but also enriches their preparation to face the challenges of a globalized professional context.

Real-time wheel-rail adhesion anti-skid control model for high-speed articulated trains on long downhill slopes during braking

To improve the braking efficiency of articulated trains on long-steep downhill gradients under different wheel-rail adhesion levels, this paper develops a real-time train braking control system equipped with an optimal adhesion anti-slip controller. Simulation validation is conducted under both dry and wet wheel-rail contact conditions with different braking torques applied on a steep downhill slope. The anti-slip adhesion control system comprises two modules: an adhesion optimization module using a forgetting factor recursive least squares (FFRLS) method to determine creep force function slopes for enhancing wheel-rail adhesion utilization, and a controller employing a neural network with a Levenberg–Marquardt (L-M) algorithm. Through the co-simulation of the train-track coupling dynamic model and the anti-skid control system, the real-time adjustment of the braking torque of the train can be quickly corrected. The results indicate that the anti-skid control system demonstrates excellent self-adjustment capabilities under various operating conditions. It effectively controls wheel slip, achieving optimal wheel-rail adhesion. This ensures the shortest possible braking distance while maintaining train stability and improving train transmission efficiency.

Optimized Dynamic Deployment of UAVs in Maritime Networks with Route Prediction

The limited coverage of terrestrial base stations and the limited transmission distance and onboard resources of satellite communications make it difficult to ensure the quality of communication services for marine users by relying only on satellites and terrestrial base stations. In contrast, UAVs, as flexible mobile communication nodes, have the capacity for dynamic deployment and real-time adjustment. They can effectively make up for the communication blind spots of traditional satellites and ground base stations in the marine environment, especially in the vast and unpredictable marine environment. Considering the mobility of maritime users, one can effectively reduce the communication delay and optimize the deployment scheme of UAVs by predicting their sailing trajectories in advance, thus enhancing the communication service quality. Therefore, this paper proposes a communication coverage model based on mobile user route prediction and a UAV dynamic deployment algorithm (RUDD). It aims to optimize the coverage efficiency of the maritime communication network, minimize the communication delay, and effectively reduce the energy consumption of UAVs. In this algorithm, the RUDD algorithm employs a modified Long Short-Term Memory (LSTM) network to predict the maritime user’s trajectory, utilizing its strengths in processing time-series data to provide accurate predictions. The prediction results are then used to guide the Proximal Policy Optimization (PPO) algorithm for the dynamic deployment of UAVs. The PPO algorithm can optimize the deployment strategy in dynamic environments, improve communication coverage, and reduce energy consumption. Simulation results show that the proposed algorithm can complement the existing satellite and terrestrial networks well in terms of coverage, with a communication coverage rate of more than 95%, which significantly improves the communication quality of marine users in areas far from land and beyond the reach of traditional networks, and enhances network reliability and user experience.

Morphing Quadrotors: Enhancing Versatility and Adaptability in Drone Applications—A Review

The advancement of drone technology has underscored the critical need for adaptability and enhanced functionality in unmanned aerial vehicles (UAVs). Morphing quadrotors, capable of dynamically altering their structure during flight, offer a promising solution to extend and optimize the operational capabilities of conventional drones. This paper presents a comprehensive review of current advancements in morphing quadrotor research, focusing on morphing concept, actuation mechanisms and flight control strategies. We examine various active morphing approaches, including the integration of smart materials and advanced actuators that facilitate real-time structural adjustments to meet diverse mission requirements. Key design considerations—such as structural integrity, weight distribution, and control algorithms—are meticulously analyzed to assess their impact on the performance and reliability of morphing quadrotors. Despite their significant potential, morphing quadrotors face challenges related to increased design complexity, higher energy consumption, and the integration of sophisticated control systems. The discussion on challenges and opportunities highlights the necessity for ongoing advancements in morphing quadrotor technologies, particularly in addressing adaptive control problems associated with highly nonlinear and dynamic morphing aircraft systems, and in the potential integration with smart materials. By synthesizing the latest research and outlining prospective directions, this paper aims to serve as a valuable reference for researchers and practitioners dedicated to advancing the field of morphing quadrotor technologies.

Smart Automatic Anesthesia Regulation Dosage

The system uses a MAX30100 sensor to monitor heart rate and oxygen saturation, enabling real-time adjustments to anesthesia delivery via a servo motor connected to a syringe. The motor's movements are calibrated to heart rate data, ensuring precise dosage control. A manual override button allows health care providers to intervene during emergencies. A DHT11 sensor monitors temperature and humidity for environmental awareness, while an LCD displays real-time patient and environmental data. An Arduino Uno processes sensor inputs, controlling servo angles to administer accurate anesthesia volumes based on pre-set thresholds, ensuring patient safety and stability. Index Terms - Syringe pump, Servomotor, Anesthesia regulation, over dose

Stable sequential dynamics in prefrontal cortex represents subjective estimation of time.

Time estimation is an essential prerequisite underlying various cognitive functions. Previous studies identified 'sequential firing' and 'activity ramps' as the primary neuron activity patterns in the medial frontal cortex (mPFC) that could convey information regarding time. However, the relationship between these patterns and the timing behavior has not been fully understood. In this study, we utilized in vivo calcium imaging of mPFC in rats performing a timing task. We observed cells that showed selective activation at trial start, end, or during the timing interval. By aligning long-term time-lapse datasets, we discovered that sequential patterns of time coding were stable over weeks, while cells coding for trial start or end showed constant dynamism. Furthermore, with a novel behavior design that allowed the animal to determine individual trial interval, we were able to demonstrate that real-time adjustment in the sequence procession speed closely tracked the trial-to-trial interval variations. And errors in the rats' timing behavior can be primarily attributed to the premature ending of the time sequence. Together, our data suggest that sequential activity maybe a stable neural substrate that represents time under physiological conditions. Furthermore, our results imply the existence of a unique cell type in the mPFC that participates in the time-related sequences. Future characterization of this cell type could provide important insights in the neural mechanism of timing and related cognitive functions.

Stable sequential dynamics in prefrontal cortex represents subjective estimation of time

Time estimation is an essential prerequisite underlying various cognitive functions. Previous studies identified ‘sequential firing’ and ‘activity ramps’ as the primary neuron activity patterns in the medial frontal cortex (mPFC) that could convey information regarding time. However, the relationship between these patterns and the timing behavior has not been fully understood. In this study, we utilized in vivo calcium imaging of mPFC in rats performing a timing task. We observed cells that showed selective activation at trial start, end, or during the timing interval. By aligning long-term time-lapse datasets, we discovered that sequential patterns of time coding were stable over weeks, while cells coding for trial start or end showed constant dynamism. Furthermore, with a novel behavior design that allowed the animal to determine individual trial interval, we were able to demonstrate that real-time adjustment in the sequence procession speed closely tracked the trial-to-trial interval variations. And errors in the rats’ timing behavior can be primarily attributed to the premature ending of the time sequence. Together, our data suggest that sequential activity maybe a stable neural substrate that represents time under physiological conditions. Furthermore, our results imply the existence of a unique cell type in the mPFC that participates in the time-related sequences. Future characterization of this cell type could provide important insights in the neural mechanism of timing and related cognitive functions.

Integration of Visible Light Communication, Artificial Intelligence, and Rerouting Strategies for Enhanced Urban Traffic Management

This study combines Visible Light Communication (VLC) and Artificial Intelligence (AI) to enhance traffic signal control, reduce congestion, and improve safety, through real-time monitoring and dynamic traffic management. Leveraging VLC technology, the system uses existing road infrastructure to transmit live data on vehicle and pedestrian positions, speeds, and queues. AI agents, employing Deep Reinforcement Learning (DRL), process this data to manage traffic flows dynamically, applying anti-bottleneck and rerouting techniques to balance pedestrian and vehicle waiting times. A centralized global agent coordinates the local agents controlling each intersection, enabling indirect communication and data sharing to train a unified DRL model. This model makes real-time adjustments to traffic light phases, utilizing a queue/request/response system for adaptive intersection management. Tested using simulations and real-world trials involving standard and rerouting scenarios, the approach demonstrates significantly better performance in regard to the rerouting configuration, reducing congestion and enhancing traffic flow and pedestrian safety. Scalable and adaptable to various intersection types, including four-way, T-intersections, and roundabouts, the system’s efficacy is validated using the SUMO urban mobility simulator, resulting in notable reductions to travel and waiting times for both vehicles and pedestrians.

Energy-based approach to the assessment of traffic flow

This article focuses on modeling vehicle acceleration noise in different road conditions, emphasizing urban, highway, and rural roads in Ukraine. Acceleration noise, which refers to the fluctuations in a vehicle's acceleration, is a critical factor in vehicle safety, fuel efficiency, and driving comfort. The research aims to improve current vehicle dynamics models by integrating multi-body dynamics and machine learning algorithms, allowing for more precise predictions of acceleration variability in real-time. The study is based on the existing literature, showing that road surface quality significantly affects acceleration noise. With frequent stop-and-go traffic, urban roads produce moderate but irregular noise patterns. Highways show stable acceleration noise at moderate speeds, but noise increases sharply as vehicles approach higher speeds due to aerodynamic forces. Rural roads, especially those in poor condition, exhibit the highest variability in acceleration noise, even at low speeds. The proposed model has been validated using real-world data. It demonstrates a strong correlation between the predictions and actual vehicle behavior on various road types. One of the key innovations in this research is the use of machine learning to adjust model parameters in real-time dynamically. This adaptive approach enhances the model’s accuracy and applicability, especially in intelligent transport systems. The model can inform traffic management strategies, allowing for real-time adjustments to speed limits, traffic signals, and routing decisions based on road conditions. This contributes to safer, more efficient, and sustainable transport systems, particularly in regions with inconsistent road infrastructure. The research concludes that integrating acceleration noise modeling into intelligent transport systems can significantly improve traffic flow and vehicle safety. Future research will expand the dataset to include a broader range of vehicle types and road conditions, further refining the model's predictive capabilities.

Robotic Assist and Virtual Surgical Planning in Orthognathic Surgery

Orthognathic surgery, critical for correcting jaw deformities and improving facial function and aesthetics, has undergone transformative changes with the introduction of robotic assistance and Virtual Surgical Planning (VSP). These technologies have revolutionized the field by enhancing precision, reducing operative times, and enabling more predictable surgical outcomes. Robotic systems, including the Da Vinci® and ROSA® platforms, provide sub-millimeter precision in osteotomies, while VSP enables comprehensive preoperative planning by integrating advanced 3D imaging and simulation techniques. Together, these technologies provide an unparalleled level of control and precision in surgical procedures, significantly enhancing patient outcomes. Major advancements in the field include the integration of artificial intelligence and machine learning into surgical planning, which allows for better prediction of postoperative outcomes and real-time adjustments during surgery. Augmented reality is also gaining traction as a tool for intraoperative guidance, further enhancing the precision of robotic-assisted procedures. Emerging technologies such as haptic feedback systems and next-generation robotic arms hold promise for even greater improvements in surgical accuracy and efficiency. The relevance of these technologies to clinical practice is profound. By reducing complications, enhancing accuracy, and improving both functional and aesthetic results, robotic assistance and VSP are redefining standards in orthognathic surgery. However, barriers related to cost, surgeon training, and infrastructure must be addressed to enable the widespread adoption of these technologies. Future research should focus on validating these technologies in large-scale clinical trials and assessing their long-term benefits and cost-effectiveness. Ultimately, the integration of these cutting-edge technologies has the potential to revolutionize orthognathic surgery, making it safer, more efficient, and more personalized for patients.

DEFT: A Dynamic Environmental Filtering and Thresholding Algorithm for Adaptive Headlamp Control Using Ride Height Sensors

DEFT (Dynamic Environmental Filtering and Thresholding) algorithm is proposed to optimize vehicle height control and improve the performance of Adaptive Headlamp Systems. The DEFT algorithm is designed to enhance the reliability and stability of headlamp control systems by dynamically adapting to various driving conditions and environmental changes in real time. To detect and stabilize irregular fluctuations in vehicle height due to load variations, this study integratesreal-time data acquisition based on Hall sensors, dynamic boundary setting using linear interpolation, and signal stabilization by applying Kalman and Median filters with hysteresis. These components work together to suppress control signal instability caused by noise and abnormal signals, thereby reinforcing the reliability and consistency of vehicle height control. In particular, the hysteresis function reduces unnecessary signal fluctuations near threshold values, which not only extends the control system’s lifespan but also ensures stable operation. Experimental results demonstrate that the DEFT algorithm overcomes the limitations of conventional variable-resistance sensors and significantly enhances adaptive headlamp control performance under various driving conditions. This study presents a high-reliability solution for real-time vehicle height adjustment within ADASs (Advanced Driver Assistance Systems) and demonstrates the potential for application in diverse vehicle control systems.