Adaptive Applications Research Articles

Machine learning algorithm partially reconfigured on FPGA for an image edge detection system

Unmanned aerial vehicles (UAVs) have been widely used in military, medical, wireless communications, aerial surveillance, etc. One key topic involving UAVs is pose estimation in autonomous navigation. A standard procedure for this process is to combine inertial navigation system sensor information with the global navigation satellite system (GNSS) signal. However, some factors can interfere with the GNSS signal, such as ionospheric scintillation, jamming, or spoofing. One alternative method to avoid using the GNSS signal is to apply an image processing approach by matching UAV images with georeferenced images. But a high effort is required for image edge extraction. In this paper, a support vector regression (SVR) model is proposed to reduce this computational load and processing time. The dynamic partial reconfiguration (DPR) of part of the SVR datapath is implemented to accelerate the process, reduce the area, and analyze its granularity by increasing the grain size of the reconfigurable region. Results show that the implementation in hardware is 68 times faster than that in software. This architecture with DPR also facilitates the low power consumption of 4 ​mW, leading to a reduction of 57% than that without DPR. This is also the lowest power consumption in current machine learning hardware implementations. Besides, the circuitry area is 41 times smaller. SVR with Gaussian kernel shows a success rate of 99.18% and minimum square error of 0.0146 for testing with the planning trajectory. This system is useful for adaptive applications where the user/designer can modify/reconfigure the hardware layout during its application, thus contributing to lower power consumption, smaller hardware area, and shorter execution time.

Development and analysis of medical instruction-tuning for Japanese large language models

In the ongoing wave of impact driven by large language models (LLMs) like ChatGPT, the adaptation of LLMs to the medical domain has emerged as a crucial research frontier. Since mainstream LLMs tend to be designed for general-purpose applications, constructing a medical LLM through domain adaptation is a huge challenge. While instruction-tuning, particularly based on low-rank adaptation (LoRA), has become a frequently employed strategy to fine-tune LLMs recently, its precise roles in domain adaptation remain unknown. Here, we investigated how LoRA-based instruction-tuning improves the performance of Japanese medical question-answering tasks by employing a multifaceted evaluation of multiple-choice questions, including scoring based on “Exact match” and “Gestalt distance” in addition to the conventional accuracy. Our findings suggest that LoRA-based instruction-tuning can partially incorporate domain-specific knowledge into LLMs, with larger models demonstrating more pronounced effects. Furthermore, our results underscore the potential of adapting English-centric models for Japanese applications in domain adaptation, while also highlighting the persisting limitations of Japanese-centric models. This initiative represents a pioneering effort in enabling medical institutions to fine-tune and operate models without relying on external services.

Pencil drawn interdigitated capacitive sensors on wood substrate

This research presents the fabrication and characterization of an interdigitated capacitive (IDC) sensor on a wooden substrate using pencil traces. The resistance of the pencil traces decreased from 100 kΩ to 5 kΩ as the pencil grade shifted from HB to 8B. Concurrently, capacitance measurements revealed an increase from approximately 5 pF for a 5-finger IDC made with HB pencil to around 32 pF for an 8B pencil counterpart. Increasing the number of pencil traces from 10 to 50 resulted in a significant decrease in resistance and a proportional increase in capacitance. Application of the IDC sensor demonstrated notable changes in capacitance upon proximity and touch, with a significant decrease upon removal. The interdigitated capacitance sensor exhibits good proximity effects and contact sensitivity in touch, with capacitance increasing exponentially from 0.3 pF (7 cm) to 1.2 pF (direct contact), highlighting its ability to detect objects with high precision. Additionally, environmental factors such as temperature and humidity influence capacitance values. These findings underscore the potential of pencil-drawn IDC sensors for responsive and adaptable applications in various fields.

DIFFICULTIES OF EDUCATION IN SMALL RURAL SCHOOLS CONSIDERING ECONOMIC CONDITIONS

This article deals with modern teaching tactics within the mixed-age learning at Kazakhstan small rural schools (SRS). The article examines the experience of developed countries and includes a thorough review of various teaching philosophies and methodologies in order to look into the efficacy of mixed-age learning in educational settings with tight resources in different economic conditions. Different approaches of mixed-age learning, such as task-based learning, differentiation and individualization, group composition, peer-supported learning and assessment were also highlighted in given article. The authors stress the value of accommodating mixed-age student groups, providing supporting supervision, and establishing conducive learning environments. The adaptability of cross-age teaching techniques in SRS to other types of heterogeneity, such diversity or inclusion classrooms, is also covered in the article. Cross-age didactics provides a framework for discussing how teachers modify and personalize instruction. Drawing from a range of age-appropriate teaching styles and methodologies, the study offers an updated theory of adaptive learning and teacher development in SRS. It places a strong emphasis on the adaptable application of general concepts in a range of instructional settings and presents viable strategies for teacher preparation and successful pedagogy in SRS and related socio-economic contexts.

Fostering Environmental Literacy through the Curriculum at School Level: NEP 2020's Approach and its Implications

Environmental literacy is essential for individuals to comprehend, appreciate, and appropriately solve the urgent environmental issues affecting our globe. In India, the National Education Policy (NEP) 2020 acknowledges the value of instilling environmental consciousness and sustainability in students at a young age. This study examines how NEP 2020 anticipates the inclusion of environmental literacy in the overall school curriculum and considers any potential effects on students, teachers, and the community at large. This article intends to identify the policy's goals, tactics, and expected effects on students' environmental consciousness by scrutinising the clauses and suggestions in NEP 2020 that pertain to environmental education at the school level. The article further explores the application of curricular adaptation, instructional strategies, and evaluation tools intended to advance environmental literacy. By closely examining the provisions and recommendations in NEP 2020 that relate to environmental education at the school level, this paper seeks to establish the policy's objectives, strategies, and anticipated consequences on students' environmental consciousness. The article investigates the use of curriculum modification, teaching techniques, and assessment instruments designed to enhance environmental literacy. The preliminary findings suggest that NEP 2020's approach to fostering environmental literacy through the curriculum has the potential to create environmentally conscious citizens who recognize the interdependence between humans and the environment. Further observations suggest that the alignment of this approach with global trends in education emphasizes sustainability, citizenship, and the need for a comprehensive understanding of environmental issues.

Simulation of fluid‐structure interaction using the boundary data immersion method with adaptive mesh refinement

SummaryThe fluid‐structure interaction is simulated using the boundary data immersion method. As the fluid‐structure interface is smeared in the smoothing region, deviations are incurred in fluid simulations. For compressible flow, high order difference schemes with more mesh cells for the stencils are usually employed to achieve high overall accuracy, but near interfaces it requires wider smoothing region of several mesh cells for computational stability and hence lowers its accuracy significantly. To address this issue, the proposed algorithm switches to lower order difference schemes near the interfaces and applies adaptive mesh refining there to compensate the accuracy loss. Implemented with Structured Adaptive Mesh Refinement Application Infrastructure (SAMRAI), the algorithm shows notable improvement in the overall accuracy and efficiency in cases such as channel flow and flow past a cylinder. The algorithm is used to simulate the shock wave past a fixed or free cylinder with Ma and Re , which reveals the relaxation process and the temporal evolution of the drag coefficient, it goes through a valley and maintains at relatively high value for the fixed cylinder, while that of the free cylinder tends to decrease in fluctuation which is found to be caused by the interaction between the forward moving cylinder and vortexes in the unsteady wake.

Adaptive Neuro-Fuzzy Inference System Application of Flashover Voltage of High-Voltage Polluted Insulator

Adaptive Neuro-Fuzzy Inference System Application of Flashover Voltage of High-Voltage Polluted Insulator

Enhancing critical learning through an adaptive web application: A case study of lower secondary school students studying artificial intelligence technology

This research aims to develop and study the effects of an adaptive web application on critical learning of artificial intelligence technology in students under the Suphan Buri and Nakhon Pathom Secondary Educational Service Area Office. The sample was divided into two groups: 4,264 lower secondary school teachers that were surveyed to create a needs analysis on media use, teaching management and readiness to use technology and a second group of 709 lower secondary school students who volunteered to use the adaptive web application. The research instruments consisted of 1) an opinion and guidelines survey for developing the adaptive web application, 2) an adaptive web application to enhance learning of artificial intelligence, 3) a learning style assessment, 4) a critical learning test on artificial intelligence technology, and 5) a student opinion questionnaire. Data were analysed using content analysis, frequency, percentage, mean, standard deviation, need assessment, one-way ANOVA, and hypothesis tests by paired sample t-test. The results showed that 1) the conditions for media use, teaching management and readiness to use technology were at a high level; 2) there was no significant difference between pre-test and post-test scores at 0.05 significance; 3) there was no significant difference between pre-test and post-test scores among the student learning styles at 0.05 significance; 4) there was no significant difference in the frequency of media use classified by learning style at 0.05 significance; and 5) the students’ opinions toward learning activities via an adaptive web application was at the highest level (M = 4.59, SD = 0.49).

Enhancing Business Competitiveness: Strategic Adaptation and Business Model Canvas Application in PT Maritim Maju

In this detailed analysis, we explore the strategic repositioning of PT Maritim Maju within the context of the global maritime industry’s ongoing transformation, influenced by technological advancements and economic shifts. This study meticulously applies the Business Model Canvas framework, incorporating Resource-Based View, VRIO, and SWOT analyses, to diagnose and propose necessary strategic enhancements for PT Maritim Maju. It underscores the imperative for the company to diversify its services beyond traditional maritime activities, suggesting an expansion into maritime consulting and logistics technology. Additionally, the research advocates for the integration of advanced technologies such as AI in fleet management, aiming to optimize route planning and implement predictive maintenance, thereby achieving cost efficiencies, enhanced service reliability, and superior customer satisfaction. Strategic partnerships are highlighted as crucial for leveraging technological innovation and expanding service capabilities. The study also recommends a reevaluation of marketing strategies, emphasizing the adoption of digital marketing techniques to better position PT Maritim Maju on the global stage, highlighting its commitment to safety, reliability, and customer service excellence. Furthermore, it stresses the importance of continuous staff development in leadership and maritime technology to foster a culture of innovation and strategic agility. Compliance with international regulations and a proactive risk management approach are advised to mitigate operational risks. This comprehensive strategic plan aims to guide PT Maritim Maju through the intricacies of the modern maritime landscape, capitalizing on new opportunities for growth and establishing a foundation for enduring competitiveness and resilience.

Peptide Supramolecular Self-Assembly: Regulatory Mechanism, Functional Properties, and Its Application in Foods.

Peptide self-assembly, due to its diverse supramolecular nanostructures, excellent biocompatibility, and bright application prospects, has received wide interest from researchers in the fields of biomedicine and green life technology and the food industry. Driven by thermodynamics and regulated by dynamics, peptides spontaneously assemble into supramolecular structures with different functional properties. According to the functional properties derived from peptide self-assembly, applications and development directions in foods can be found and explored. Therefore, in this review, the regulatory mechanism is elucidated from the perspective of self-assembly thermodynamics and dynamics, and the functional properties and application progress of peptide self-assembly in foods are summarized, with a view to more adaptive application scenarios of peptide self-assembly in the food industry.

Sunlight-driven photocatalytic and anticancer properties of biogenic synthesized gold nanoparticles (AuNPs) employing Polygala elongata

The current study uses an economical and eco friendly method to produce gold (Au) nanoparticles (NPs) using Polygala elongata leaf (PEL) extract as a reducing and stabilizing agent. The synthesized materials were characterized using a range of methods, including as BET analysis, TEM-SAED, FT-IR, UV–Vis, and XRD. Biogenic AuNPs' spherical form was shown by SEM and TEM investigations, which also showed that the particles' size range was 10–20 nm, their distribution was homogeneous, and there observed minimal aggregation. The production of biogenic AuNPs was confirmed by the observation of a surface plasmon resonance peak in the absorption spectra, which was detected at 540 nm. Additionally, studies were conducted on the catalytic, cytotoxic, antioxidant, and anticancer characteristics of biogenic AuNPs. The biogenic AuNPs exhibited adaptable applications as a heterogeneous catalyst, achieving percentage degradations of 73 % and 88 % under UV light irradiation, particularly in catalyzing the reduction of Methylene Blue (MeB). Furthermore, the antioxidant properties were evaluated using DPPH free radical scavenging assay in regarding rutin, a widely used antioxidant. With a cytotoxicity value of 500 µg/ml, the produced AuNPs had a strong dose-dependent inhibitory effect on the development of the lung cancer cell line (A549). Therefore, biogenic AuNPs hold potential applications across various research domains, spanning biological and industrial sectors, as well as in the removal of harmful contaminants in water purification.

Grey Wolf Optimization Algorithm for Embedded Adaptive Filtering Applications

Nowadays, meta-heuristic algorithms have been emerged as a potential solution in adaptive filtering applications since they offer good convergence properties. Nonetheless, most of them fall into local minimum since their optimization is based on single-solution technique. As a consequence, these algorithms present a high misadjustment level and require a large population to find the optimal solution. Recently, the grey wolf optimization (GWO) algorithm have emerged as a potential solution since it requires a smaller population and possesses a stronger global optimization ability with lesser control parameters. From an engineering perspective, its compactness is an attractive feature. Therefore, this opens new horizons in the implementation of this algorithm in resource-constrained devices. In this paper, we present for the first time the use of the GWO algorithm for system identification and acoustic echo canceller (AEC) and its implementation in a field programmable gate array (FPGA) device to validate its effectiveness. Our results show that the use of the GWO algorithm achieves lower steady-state mean square error (MSE) and requires less computational resources when compared with one of the most used meta-heuristic algorithm.

On particular integrability in classical mechanics

In this study the notion of particular integrability in Classical Mechanics, introduced in Turbiner (2013 J. Phys. A: Math. Theor. 46 025203), is revisited within the formalism of symplectic geometry. A particular integral I is a function not necessarily conserved in the whole phase space T∗Q but when restricted to a certain invariant subspace W⊆T∗Q it becomes a Liouville first integral. For natural Hamiltonian systems, it is demonstrated that such a function I allows us to construct a lower dimensional Hamiltonian in W . This symmetry reduction is intimately related with a phenomenon beyond separation of variables and it is based on an adaptive application of the classical results due to Lie and Liouville on integrability. Three physically relevant systems are used to illustrate the underlying key aspects of the symplectic theory approach to particular integrability: (I) the integrable central-force problem, (II) the chaotic two-body Coulomb system in a constant magnetic field as well as (III) the N-body system.

Thermally Induced Lattice-Defective Oxygen Breathing in Perovskite-Structure Stannates with High-Contrast Reversible Thermochromism.

Inorganic thermochromic materials exhibit a tunable color gamut and a wide chromatic temperature range, indicating their potential for intelligent adaptive applications in thermal warning, temperature indication, thermal regulation, and interactive light-to-thermal energy conversion. However, most metal-oxide-based thermochromic materials show weak chromaticity adaption with the change of temperature, which needs further understanding of the microscopic principle to clarify the potential route to improve the contrast and identifiability for fabricating better thermochromic materials. Using perovskite-structure (AMO3) alkaline earth metal stannate (Ba1-xSrxSnO3, 0.0 ≤ x ≤ 1.0) as a model system, this paper reports for the first time the mechanism of the properties of thermally induced defect-enhanced charge transfer-type (CTT) thermochromic materials and the strategy for regulating their thermochromic properties by A-site cations. BaSnO3 exhibits continuously reversible thermochromic properties with high contrast from weak light yellow (b* = 11) to strong bright yellow (b* = 58) between room temperature and 550 °C. In-situ high-temperature X-ray diffraction (in-situ XRD), in-situ UV-vis absorption spectroscopy (in-situ UV-vis), thermogravimetric (TG), and electron paramagnetic resonance (EPR) spectra indicate that this excellent thermochromic phenomenon is attributed to the weakening of Sn-O bond hybridization at high temperatures, as well as the formation of a large number of oxygen vacancies at the top of the valence band, and the enhanced charge transfer resulting from the generation of impurity levels in the Sn2+ 5s2 intermediate. Replacing Ba2+ by Sr2+ in Ba1-xSrxSnO3 successfully tuned the thermochromic properties, which is attributed to the Sr2+ doping level-directed oxygen defect concentration and deoxygenation rate. The demonstrated defect-enhanced charge transfer behavior promotes a feasible route for lattice oxygen-mediated thermochromic materials and provides a fundamental relationship between thermally induced defects and colorimetry.

Advancements in Learning-Based Navigation Systems for Robotic Applications in MRO Hangar: Review.

The field of learning-based navigation for mobile robots is experiencing a surge of interest from research and industry sectors. The application of this technology for visual aircraft inspection tasks within a maintenance, repair, and overhaul (MRO) hangar necessitates efficient perception and obstacle avoidance capabilities to ensure a reliable navigation experience. The present reliance on manual labour, static processes, and outdated technologies limits operation efficiency in the inherently dynamic and increasingly complex nature of the real-world hangar environment. The challenging environment limits the practical application of conventional methods and real-time adaptability to changes. In response to these challenges, recent years research efforts have witnessed advancement with machine learning integration aimed at enhancing navigational capability in both static and dynamic scenarios. However, most of these studies have not been specific to the MRO hangar environment, but related challenges have been addressed, and applicable solutions have been developed. This paper provides a comprehensive review of learning-based strategies with an emphasis on advancements in deep learning, object detection, and the integration of multiple approaches to create hybrid systems. The review delineates the application of learning-based methodologies to real-time navigational tasks, encompassing environment perception, obstacle detection, avoidance, and path planning through the use of vision-based sensors. The concluding section addresses the prevailing challenges and prospective development directions in this domain.

Phenotypic Patterning through Copy Number Adaptation to Environmental Gradients.

We recently described a paradigm for engineering bacterial adaptation using plasmids coupled to the same origin of replication. In this study, we use plasmid coupling to generate spatially separated and phenotypically distinct populations in response to heterogeneous environments. Using a custom microfluidic device, we continuously tracked engineered populations along induced gradients, enabling an in-depth analysis of the spatiotemporal dynamics of plasmid coupling. Our observations reveal a pronounced phenotypic separation within 4 h exposure to an opposing gradient of AHL and arabinose. Additionally, by modulating the burden strength balance between coupled plasmids, we demonstrate the inherent limitations and tunability of this system. Intriguingly, phenotypic separation persists for an extended time, hinting at a biophysical spatial retention mechanism reminiscent of natural speciation processes. Complementing our experimental data, mathematical models provide invaluable insights into the underlying mechanisms and guide optimization of plasmid coupling for prospective applications of environmental copy number adaptation engineering across separated domains.

Effect of No-tillage on Soil Aggregates in Farmland：A Meta Analysis

In order to clarify the impact of no-tillage on the quality of farmland soil aggregates in China and promote the adaptive application of no-tillage practices， a Meta-analysis was conducted by collecting data from 116 published studies. The effects of no-tillage on aggregate size distribution， mean weight diameter （MWD）， and aggregate-associated C were studied. The results showed that compared with that under tillage， no-tillage significantly increased the proportion of macroaggregates （10.9%） and MWD （12.8%） and decreased the proportion of clay and silt （-15.5%） but had no significant effect on soil microaggregate and aggregate-associated C. The subgroup and Meta regression analysis showed that no-tillage significantly increased the proportion of macroaggregates in Northwest China （17.6%） and MWD in North China （15.4%）. In upland and clay loam， no-tillage increased MWD by 12.6% and 18.4%， respectively. The effect of no-tillage on increasing the proportion of macroaggregates increased with the soil pH. When straw returned， no-tillage significantly increased the proportion of macroaggregates （9.6%） and MWD （11.6%）， but no significant effect of no-tillage on aggregates was found after straw removal. Regarding test duration， short-term （ < 5 a） no-tillage could significantly increase the proportion of macroaggregates， whereas long-term （ > 10 a） no-tillage could improve the MWD. In different soil layers， no-tillage could only significantly improve the aggregate size distribution and MWD in topsoil （0-20 cm） but had no effect in subsoil （ > 20 cm）. In summary， no-tillage could improve aggregate size distribution and stability but had no effect on aggregate-associated C. Production region， soil properties， field management methods， and other factors should be fully considered in production practice to effectively improve the quality of soil aggregates.

Design of adaptive array using least mean square beamformer

<span>This paper introduces an 8-element linear array designed for adaptive array applications, using least mean square (LMS) algorithm to enhance the directivity of the array. Microstrip antenna has been optimized at 2.3 GHz, a pivotal frequency ranges relevant to 4G and 5G applications. This design is thoughtfully extended to encompass 8-elements, achieved through the art of parameterization using computer simulation technology (CST) microwave studio. This geometry of 8-element exhibits considerable promise, significantly elevating the gain from 6.13 dBi for a single element to an impressive 15.5 dBi for all eight-element array. To further empower the array’s adaptability and beam-steering capabilities, the LMS algorithm is simulated. This intelligent algorithm computes complex weights, thoughtfully with various angles, including those for the interested user at 60° and 30°, as well as potential interferers at 10° and 15°, as simulated in MATLAB. These meticulously calculated weights are effectively applied to antenna elements using CST, facilitating beam steering in various directions. During CST simulations, notable peaks in performance emerge at 54° and 28°, strategically aligned with nulls at 10° and 15°. Remarkably, these results exhibit a remarkable degree of concurrence with those obtained through MATLAB simulations, affirming effectiveness of the proposed adaptive array design.</span>

Electrochromic Smart Window Based on Transition-Metal Phthalocyanine Derivatives.

Phthalocyanines have been widely investigated as electrochromic materials because of their large conjugated structure. However, they have shown limited applicability due to their complex electrochromism mechanism and low solubility in common organic solvents. Replacement of central metal ions in phthalocyanines affects their stability and is responsible for various electrochromic phenomena, such as color change. Herein, the relationship between the electron d-orbital arrangement in the outermost layer of transition metals and the electrochromic stability of phthalocyanine derivatives has been investigated. An enhanced solubility of phthalocyanines in organic solvents was obtained through the introduction of quaternary tert-butyl substitution. Electrochromic devices fabricated with transition-metal phthalocyanine derivatives showed high response speeds and good stability. The fast color-switching feature between blue/green and blue/purple makes it a promising candidate for smart windows and adaptive camouflage applications.

What is behind the meta-learning initialization of adaptive filter? — A naive method for accelerating convergence of adaptive multichannel active noise control

Active noise control (ANC) is a typical signal-processing technique that has recently been utilized extensively to combat the urban noise problem. Although numerous advanced adaptive algorithms have been devised to enhance noise reduction performance, few of them have been implemented in actual ANC products due to their high computational complexity and slow convergence. With the rapid development of deep learning technology, Meta-learning-based initialization appears to become an efficient and cost-effective method for accelerating the convergence of adaptive algorithms. However, few dedicated Meta-learning algorithms exist for adaptive signal processing applications, particularly multichannel active noise control (MCANC). Hence, we proposed a modified Model-Agnostic Meta-Learning (MAML) initialization for the MCANC system.11The code of the proposed method is available on https://github.com/ShiDongyuan/Meta_Learning_for_MCANC. Additional theatrical research reveals that the nature of MAML, when applied to signal processing, is the expectation of a weight-sum gradient. Based on this discovery, we devised the Monte-Carlo Gradient Meta-learning (MCGM) algorithm, which employed a more straightforward procedure to accomplish the same performance as the Modified MAML algorithm. Furthermore, the numerical simulation of ANC using raw noise samples on measured paths validates the efficacy of the proposed methods in accelerating the convergence of the multichannel-filtered reference least mean square algorithm (McFxLMS).