Dynamic Adaptation Research Articles

TYPING SPEED TEST: INNOVATION IN TYPING SPEED MEASUREMENT FOR OFFICE MANAGEMENT STUDENTS IN THE INDEPENDENT CURRICULUM

This study aims to develop and evaluate an artificial intelligence (AI)-based typing speed test platform for Office Management program students in the Merdeka Curriculum. In the ever-evolving digital era, fast and accurate typing skills are becoming increasingly important both in the workplace and in education. This study follows the Research & Development method with five main steps: initial research, product development, expert validation, small-scale and large-scale trials. The developed platform, called the Typing Speed ​​Test, is designed to provide real-time feedback, performance analysis, and personalized exercises based on user abilities. The trial involved 150 students from various schools in East Java, showing positive results with 90% of students reporting significant improvements in their typing skills. The platform is considered effective in improving typing speed and accuracy and supports the principles of the Merdeka Curriculum which emphasizes independent learning and the development of 21st-century competencies. The integration of AI into the platform allows for dynamic adaptation of learning, making it a modern solution that can be easily integrated into the educational curriculum. The findings of this study emphasize the importance of digital literacy and the potential of AI in providing innovative and effective learning experiences to prepare students for the demands of an increasingly technology-dependent workforce

Revolutionizing Urban Pest Management with Sensor Fusion and Precision Fumigation Robotics

Effective pest management in urban areas is critically challenged by the rapid proliferation of mosquito breeding sites. Traditional fumigation methods expose human operators to harmful chemicals, posing significant health risks ranging from respiratory problems to long-term chronic conditions. To address these issues, a novel fumigation robot equipped with sensor fusion technology for optimal pest control in urban landscapes is proposed. The proposed robot utilizes light detection and ranging data, depth camera inputs processed through the You Only Look Once version 8 (YOLOv8) algorithm for precise object recognition, and inertial measurement unit data. These technologies allow the robot to accurately identify and localize mosquito breeding hotspots using YOLOv8, achieving a precision of 0.81 and a mean average precision of 0.74. The integration of these advanced sensor technologies allows for detailed and reliable mapping, enhancing the robot’s navigation through complex urban terrains and ensuring precise targeting of fumigation efforts. In a test case, the robot demonstrated a 62.5% increase in efficiency by significantly reducing chemical usage through targeted hotspot fumigation. By automating the detection and treatment of breeding sites, the proposed method boosts the efficiency and effectiveness of pest management operations and significantly diminishes the health risks associated with chemical exposure for human workers. This approach, featuring real-time object recognition and dynamic adaptation to environmental changes, represents a substantial advancement in urban pest management, offering a safer and more effective solution to a persistent public health issue.

Advancements in Cooperative Mobile Robots Control Strategies for Large-Scale Material Transport

This paper explores groundbreaking advancements in control strategies for cooperative mobile robots used in large-scale material transport, a critical aspect of modern industrial, manufacturing, logistics, and construction sectors. It delves into the development of sophisticated systems that enable seamless coordination among multiple mobile robot systems. The research presents a novel hierarchical finite state automaton for dynamic mission adaptation and a null space-based control scheme for precise task execution and enhanced system resilience. The introduction of Mecanum wheels facilitates flexible movement and manipulation of materials, thereby increasing operational efficiency and safety. Cutting-edge sensory technology, including LiDAR, and the implementation of the Robot Operating System are highlighted for their roles in enhancing autonomous navigation and intelligent operation. Additionally, the paper discusses the impact of centralized and decentralized control methods in ensuring safe, cooperative object transport. The findings contribute to the vision of Industry 4.0 by promoting the integration of automation and robotic cooperation in complex environments and present a foundational blueprint for further research. Challenges for future work such as scalability, communication efficiency, collision avoidance, and energy efficiency are also considered, underscoring the need for ongoing development of robust and scalable robotic systems to address modern transport challenges.

Transformation of The Meaning of Njalang Tradition: From A Personal Ritual To A Medium of Preservation And Cultural Tourism Among The Nasal Clan In Bengkulu

The changing meaning of the Njalang tradition within the Nasal clan community in Kaur Regency, Bengkulu, demonstrates significant social and cultural dynamics. Originally, the Njalang tradition was a personal pilgrimage activity limited to specific times such as Eid al-Fitr. However, it has now transformed into a structured and organized mass pilgrimage event. This study aims to understand the process of this change and the meanings now associated with the Njalang tradition. Using an ethnographic communication approach, the research focuses on the interactions and collaborations that occur among individuals in the context of the Njalang tradition. The researcher was directly involved with the study subjects to observe and interpret the meaning of this traditional transformation. The research question raised is how communication processes and actions occur during the implementation of the Njalang mass pilgrimage in the Nasal community, Kaur Regency. The findings of this study reveal that the Njalang mass pilgrimage tradition now holds various meanings that have evolved over time, including as a spiritual medium, a means of fostering social ties, and in the current context, as a tool for cultural preservation and cultural tourism. The implementation process of the Njalang pilgrimage involves a series of activities starting from pre-implementation meetings, announcements, social services, and the main event on the second day of Eid al-Fitr in the month of Shawwal. This transformation reflects the dynamic cultural adaptation within the Nasal community and highlights how local traditions can serve as complex and multifaceted cultural communication tools.

Comprehensive analysis of hyperspectral features for monitoring canopy maize leaf spot disease

Accurate quantification of hyperspectral features altered by plant disease infection is pivotal for effective disease management. However, the sensitivity of hyperspectral features to plant disease progression remains elusive, primarily because these features are often influenced by plant growth and environmental factors in addition to the specific disease. This study explores the sensitivity of biophysical and spectral features as indicators for maize adaptation to leaf spot disease. Using high-resolution UAV hyperspectral imaging, we captured maize adaptation dynamics over 30 days post-infection. We evaluated the sensitivity and importance of hyperspectral features for disease monitoring, including biophysical parameters retrieved by the PROSAIL model, and spectral features, including spectral reflectance, vegetation indices (VIs), and wavelet features (WFs). Our findings reveal that WFs first indicate disease response as early as 6 days after infection (DAI), followed by VIs at DAI 8, and variations in chlorophyll content (Cab) become apparent by DAI 10. The Cab, plant senescence reflectance index (PSRI), and normalized photosynthetic reflectance index (NPRI) are determined to be important features at the early stage of the disease. Our experimental results show that the different feature sets are complementary at the early and severe stages of the disease. Our classification models integrating Cab, VIs, and WFs showed higher overall accuracy than models using only spectral features or VIs, with a maximum improvement of 9.36 %. However, these feature sets are redundant in the mild and initial severe disease stages, where models using only spectral features achieve the highest overall accuracy of 86.21 %. This study underscores the novel insights by offering an understanding of plant responses to disease infection and enhancing early detection strategies.

Cultural adaptation and integration of Indonesian peacekeepers in Lebanon

This research aims to explore the cultural adaptation and integration of Indonesian peacekeeping personnel in Lebanon in the framework of the United Nations Interim Force in Lebanon (UNIFIL) mission. This study uses a qualitative approach with in-depth interviews and participatory observation as the main method of data collection. The results show that despite the challenges in cultural adaptation, Indonesian peacekeepers are able to adapt through effective cultural adaptation strategies. In addition, interpersonal relationships with local communities as well as cooperation with multinational forces play an important role in their integration process. This research provides in-depth insights into the dynamics of cultural adaptation in the context of international peacekeeping operations and provides recommendations to improve the effectiveness of future peacekeeping missions.

Capability Indices for Digitized Industries: A Review and Outlook of Machine Learning Applications for Predictive Process Control

Leveraging machine learning applications for predictive process control signifies a decisive advancement in manufacturing quality management, transitioning from traditional descriptive to predictive capability indices. This review highlights the growing importance of predictive process control, essential for quality assurance and the dynamic adaptability of production lines, which is paramount in satisfying stringent quality standards and evolving consumer demands. The investigation into the integration of comprehensive sensor networks and sophisticated algorithmic analytics enriches continuous improvement strategies, markedly enhancing the accuracy and efficiency of production quality monitoring and control mechanisms. By moving beyond the limits of statistical process control to predictive methods enabled by machine learning algorithms, the study presents a transformative leap in manufacturing processes. The presented findings illustrate the critical role of predictive algorithms in navigating the complexities of process variability, thereby ensuring consistent adherence to established quality specifications. This approach not only facilitates immediate and accurate product quality categorization, increasing overall operational efficiency, but also equips manufacturers to swiftly respond to the variable nature of manufacturing requirements. Furthermore, this research delves into the multifaceted impacts of predictive process control on the manufacturing ecosystem. The ability to predict process quality decrease before it occurs, the optimization of resource allocation, and the anticipation of production bottlenecks before they impact output are among the notable benefits of this technological evolution. These developments to predictive process control is instrumental in propelling the manufacturing industry toward a more agile, sustainable, and customer-centric future. This shift not only complements the industry’s drive toward comprehensive digitization but also promises significant strides in achieving superior process improvements and maintaining a competitive edge on the global market.

Dynamic Injection and Permutation Coding for Enhanced Data Transmission.

In this paper, we propose a novel approach to enhance spectral efficiency in communication systems by dynamically adjusting the mapping between cyclic permutation coding (CPC) and its injected form. By monitoring channel conditions such as interference levels and impulsive noise strength, the system optimises the coding scheme to maximise data transmission reliability and efficiency. The CPC method employed in this work maps information bits onto non-binary symbols in a cyclic manner, aiming to improve the Hamming distance between mapped symbols. To address challenges such as low data rates inherent in permutation coding, injection techniques are introduced by removing δ column(s) from the CPC codebook. Comparative analyses demonstrate that the proposed dynamic adaptation scheme outperforms conventional permutation coding and injection schemes. Additionally, we present a generalised mathematical expression to describe the relationship between the spectral efficiencies of both coding schemes. This dynamic approach ensures efficient and reliable communication in environments with varying levels of interference and impulsive noise, highlighting its potential applicability to systems like power line communications.

Photosystem I: A Paradigm for Understanding Biological Environmental Adaptation Mechanisms in Cyanobacteria and Algae.

The process of oxygenic photosynthesis is primarily driven by two multiprotein complexes known as photosystem II (PSII) and photosystem I (PSI). PSII facilitates the light-induced reactions of water-splitting and plastoquinone reduction, while PSI functions as the light-driven plastocyanin-ferredoxin oxidoreductase. In contrast to the highly conserved structure of PSII among all oxygen-evolving photosynthetic organisms, the structures of PSI exhibit remarkable variations, especially for photosynthetic organisms that grow in special environments. In this review, we make a concise overview of the recent investigations of PSI from photosynthetic microorganisms including prokaryotic cyanobacteria and eukaryotic algae from the perspective of structural biology. All known PSI complexes contain a highly conserved heterodimeric core; however, their pigment compositions and peripheral light-harvesting proteins are substantially flexible. This structural plasticity of PSI reveals the dynamic adaptation to environmental changes for photosynthetic organisms.

A novel type-2 decision mechanism for dynamic parameter adaptation: theory and application in mathematical and structural problems

Metaheuristic algorithms are stochastic-based search techniques widely used for solving different types of optimization problems. These methods mostly adjust their search behavior using pre-defined search pattern(s) regardless of the current problem specifications. Therefore, integrating them with logical auxiliary modules can significantly enhance their search efficiency by enabling them to dynamically adapt their search behavior. The present study introduces a novel decision-making approach that employs interval type-2 fuzzy logic to balance the search behavior during optimization process. The proposed approach, designed as a stand-alone module with the flexibility to be integrated into various algorithms, is incorporated into the Interactive Search Algorithm. The developed reinforced technique is named Type-2 Fuzzy Interactive Search Algorithm. Performance of the proposed method is tested on different unconstrained mathematical functions and constrained structural and mechanical optimization problems. The attained results are compared with standard ISA method and seven other metaheuristic techniques through a suite of numerical and statistical evaluations. Drawing from the obtained results, the integration of the type-2 fuzzy decision module significantly enhances the algorithm's search capability. This improvement is evident in terms of stability, accuracy, and computational cost. It is worth noting that the employed comparative performance index for the proposed method indicates improvements of 3.38, 13.09, 16.61, and 27.23 percent compared to the best solution found by the selected methods for engineering problems.

Differential conformational dynamics in two type-A RNA-binding domains drive the double-stranded RNA recognition and binding.

Trans-activation response (TAR) RNA-binding protein (TRBP) has emerged as a key player in the RNA interference pathway, wherein it binds to different pre-microRNAs (miRNAs) and small interfering RNAs (siRNAs), each varying in sequence and/or structure. We hypothesize that TRBP displays dynamic adaptability to accommodate heterogeneity in target RNA structures. Thus, it is crucial to ascertain the role of intrinsic and RNA-induced protein dynamics in RNA recognition and binding. We have previously elucidated the role of intrinsic and RNA-induced conformational exchange in the double-stranded RNA-binding domain 1 (dsRBD1) of TRBP in shape-dependent RNA recognition. The current study delves into the intrinsic and RNA-induced conformational dynamics of the TRBP-dsRBD2 and then compares it with the dsRBD1 study carried out previously. Remarkably, the two domains exhibit differential binding affinity to a 12-bp dsRNA owing to the presence of critical residues and structural plasticity. Furthermore, we report that dsRBD2 depicts constrained conformational plasticity when compared to dsRBD1. Although, in the presence of RNA, dsRBD2 undergoes induced conformational exchange within the designated RNA-binding regions and other residues, the amplitude of the motions remains modest when compared to those observed in dsRBD1. We propose a dynamics-driven model of the two tandem domains of TRBP, substantiating their contributions to the versatility of dsRNA recognition and binding.

A Study on the English Translation of “Taipei People” from the Perspective of Adaptation Theory

The collection of short stories, Taipei People, is one of the representative works of the famous writer Pai Hsien-yung. In this book, Pai portrays characters from different regions and fields vividly, displaying a contrast between the past and the present. The stories in this book are rich in Chinese culture and customs, which is concerned in this paper. This paper compares and analyzes the English translation of Taipei People from four aspects from the perspective of the adaptation theory, namely, contextual correlates of adaptability, structural objects of adaptability, dynamics of adaptability, salience of the adaptation processes. It is found that the translators consider the differences in language, culture and social environment between China and western countries, and make translation choices of different degrees of salience of adaptation. They adopt different translation strategies to dynamically adapt to the original book and target readers, make flexible choices according to different contexts, which show the salience and purpose of translators in the translation process. In view of the literary value of Taipei people, this paper hopes to provide some reference for more translators to translate and disseminate Chinese literature.

Empowering quadrature mirror filter bank architectures: dynamic-grey wolf optimization approach for elevated filter orders

The present study introduces a revised version of the Grey Wolf Optimization (GWO) algorithm called Dynamic-GWO, which integrates a dynamic parameter adaptation mechanism for population diversity enhancement. This mechanism implements the real-time adjustment of control parameters throughout the optimization procedure. The algorithm's effectiveness is demonstrated by its consistent performance across a variety of intricate benchmark functions. Further, the suggested approach is employed to determine the eminent filter tap coefficients of the Quadrature-Mirror-Filter (QMF) bank that enables the configuration with filter orders of 120 and 150, which have yet to be achieved using existing methods. The uniqueness of the proposed approach lies in its capability to overcome the inherent difficulties associated with higher-order filter design, including increased filter length and computational complexity. The primary objective function is to minimize the algebraic sum of three types of distortion: pass-band error, excess power of stop-band, and squared error of the overall transfer function at the quadrature frequency ω q = π / 2 , which generally occur in the procedure of Near-Perfect-Reconstruction (NPR) QMF bank design. In addition, the measured maximum reconstruction errors are 1.303 × 10 − 15 , 2.93 × 10 − 16 and 2.18 × 10 − 06 dB, and aliasing distortions are A L = 85.80 , 128.49 and 153.52 dB for filter orders N = 100, 120 and 150, correspondingly. The proposed algorithm yields significant improvements with an 86.68% reduction in the ripple factor and a 10.45% increase in attenuation compared to the baseline GWO for filter order 120, underscoring its effectiveness in enhancing filter performance. The observed outcomes indicate that the Dynamic-GWO algorithm consistently surpasses the baseline GWO and other Nature-Inspired Algorithms (NIAs) regarding computational ease and convergence speed.

Optimization of node deployment in underwater internet of things using novel adaptive long short‐term memory‐based egret swarm optimization algorithm

SummaryOptimizing node deployment in the underwater Internet of Things (UIoT) poses significant challenges due to the complex and dynamic nature of underwater environments. This research introduces the adaptive long short‐term memory‐based egret swarm optimization algorithm (ALSTM‐ESOA), a novel approach designed to enhance network coverage and performance efficiently. Unlike traditional methods, ALSTM‐ESOA incorporates cognitive learning capabilities from long short‐term memory (LSTM) and dynamic adaptation strategies inspired by the hunting behaviors of egrets. The algorithm's effectiveness was tested through extensive simulations in MATLAB, demonstrating notable improvements over existing models: network throughput increased by up to 55.56%, deployment time decreased by 88.89%, and energy efficiency improved significantly. These enhancements are critical for robust, real‐time data collection and monitoring in underwater settings, providing substantial benefits for marine research and resource management. The findings suggest that ALSTM‐ESOA significantly outperforms conventional algorithms, offering a promising new tool for the advancement of UIoT applications. After being implemented in MATLAB, the suggested ALSTM‐ESOA model for the node deployment optimization in UIoT is examined. The proposed ALSTM‐ESOA in terms of network throughput is 55.56%, 38.89%, 36.11%, and 11.11% better than CNN, LSTM, ARO‐RTP, and IGOR‐TSA, respectively. Similarly, the proposed ALSTM‐ESOA with respect to deployment time is 88.89%, 81.82%, 75%, and 50% better than CNN, LSTM, ARO‐RTP, and IGOR‐TSA, respectively. For the purpose of exploring marine resources, monitoring underwater environments, and conducting marine scientific investigation, the research's findings are extremely valuable.

Dynamic adaptation to novelty in the brain is related to arousal and intelligence.

How does the human brain respond to novelty? Here, we address this question using fMRI data wherein human participants watch the same movie scene four times. On the first viewing, this movie scene is novel, and on later viewings it is not. We find that brain activity is lower-dimensional in response to novelty. At a finer scale, we find that this reduction in the dimensionality of brain activity is the result of increased coupling in specific brain systems, most specifically within and between the control and dorsal attention systems. Additionally, we found that novelty induced an increase in between-subject synchronization of brain activity in the same brain systems. We also find evidence that adaptation to novelty, herein operationalized as the difference between baseline coupling and novelty-response coupling, is related to fluid intelligence. Finally, using separately collected out-of-sample data, we find that the above results may be linked to psychological arousal.

Recovering 3D salt dome by gravity data inversion using ResU-Net++

In geophysical research, gravity-based inversion is essential for identifying geologic anomalies, mapping rock structures, and extracting resources such as oil and minerals. However, traditional gravity inversion methods face challenges, such as the volumetric effects of gravity fields and the management of large complex matrices. Unsupervised learning techniques often struggle with overfitting and interpreting gravity data. This study explores the application of various U-Net-based network architectures in gravity inversion, each offering distinct challenges and advantages. Nested U-Net, although effective, requires a high parameter count, leading to extended training periods. The recurrent residual U-Net’s implicit attention mechanism restricts its dynamic adaptability, whereas the attention U-Net’s lack of residual connections raises concerns about gradient issues. This research comprehensively analyzes the training processes, core functionalities, and module distribution of these networks, including the residual U-Net++. Our synthetic studies compare these networks with traditional focused regularized gravity inversion for reconstructing density anomalies. The results demonstrate that the nested U-Net closely approximates the actual model despite some redundancy. The recurrent residual U-Net indicates an improved alignment with minimal redundancies, and the attention U-Net is effective in density prediction but encounters difficulties in areas of low density. Notably, the residual U-Net++ excels in inversion modeling, achieving the lowest misfit percentage and accurately replicating density values. In practical applications, the residual U-Net++ impressively reconstructs the F2 salt diapir in the Nordkapp Basin with well-defined boundaries that closely match seismic data interpretations. These results underscore the capabilities of the residual U-Net++ in geophysical data analysis, structural reconstruction, and inversion, demonstrating its effectiveness in simulated settings and real-world scenarios.

Continuum percolation of two-dimensional adaptive dynamics systems.

The percolation phase transition of a continuum adaptive neuron system with homeostasis is investigated. In order to maintain their average activity at a particular level, each neuron (represented by a disk) varies its connection radius until the sum of overlapping areas with neighboring neurons (representing the overall connection strength in the network) has reached a fixed target area for each neuron. Tuning the two key parameters in the model, i.e., the density defined as the number of neurons (disks) per unit area and the sum of the overlapping area of each disk with its adjacent disks, can drive the system into the critical percolating state. These two parameters are inversely proportional to each other at the critical state, and the critical filling factors are fixed about 0.7157, which is much less than the case of the continuum percolation with uniform disks. It is also confirmed that the critical exponents in this model are the same as the two-dimensional standard lattice percolation. Although the critical state is relatively more sensitive and exhibits long-range spatial correlation, local fluctuations do not propagate in a long-range manner through the system by the adaptive dynamics, which renders the system overall robust against perturbations.

The integration of En route flow optimization, complex network clustering, and rule-based approach to airspace sub-sectorization for enhanced air traffic monitoring

This study proposes the use of a novel integrated framework for 2D en route airspace sub-sectorization. The integrated framework combines the multi-commodity flow optimization approach, complex network clustering approach, and Minimum Bounding Geometry (MBG)-coupled Rule-based Approach for boundary design. A decomposition-based discrete particle swarm optimization (DPSO) is used to solve the clustering problem. The output of the flow optimization is used as a guiding standard for the DPSO. Experimentations were performed using the Indian airspace sector to validate the framework and DPSO was run for different maximum number of generations (maxgen). The findings reveal that the multi-commodity flow approach captures system-wide flow operations. Clustering results corresponding to maxgen=100 and maxgen=150 perform best in terms of equitable and balanced distribution of cluster size and traffic load. The MBG-coupled Rule-based Approach leads to compact and convex sub-sector boundary design. Major implications of this research include dynamic adaptability of the integrated framework, increased sensitivity of sector design to network evolution, and a computationally tractable framework. The higher controllability of the proposed framework also offers an increased acceptance among practitioners.

Stability of ecologically scaffolded traits during evolutionary transitions in individuality

Evolutionary transitions in individuality are events in the history of life leading to the emergence of new levels of individuality. Recent studies have described an ecological scaffolding scenario of such transitions focused on the evolutionary consequences of an externally imposed renewing meta-population structure with limited dispersal. One difficulty for such a scenario has been explaining the stability of collective-level traits when scaffolding conditions no longer apply. Here, we show that the stability of scaffolded traits can rely on evolutionary hysteresis: even if the environment is reverted to an ancestral state, collectives do not return to ancestral phenotypes. We describe this phenomenon using a stochastic meta-population model and adaptive dynamics. Further, we show that ecological scaffolding may be limited to Goldilocks zones of the environment. We conjecture that Goldilocks zones—even if they might be rare—could act as initiators of evolutionary transitions and help to explain the near ubiquity of collective-level individuality.

Dynamic Adaptation in Deep Learning for Enhanced Hand Gesture Recognition

Dynamic Adaptation in Deep Learning for Enhanced Hand Gesture Recognition