Adaptive System Research Articles

Analysis of Fractional Order-Adaptive Systems Represented by Error Model 1 Using a Fractional-Order Gradient Approach

In adaptive control, error models use system output error and adaptive laws to update controller parameters for control or identification tasks. Fractional-order calculus, involving non-integer-order derivatives and integrals, is increasingly important for modeling, estimation, and control due to its ability to generalize classical methods and offer improved robustness to disturbances. This paper addresses the gap in the literature where fractional-order gradient methods have not yet been extensively applied in identification and adaptive control schemes. We introduce a fractional-order error model with fractional-order gradient (FOEM1-FG), which integrates fractional gradient operators based on the Caputo fractional derivative. By using theoretical analysis and simulations, we confirm that FOEM1-FG maintains stability and ensures bounded output errors across a variety of input signals. Notably, the fractional gradient’s performance improves as the order, β, increases with β>1, leading to faster convergence. Compared to existing integer-order methods, the proposed approach provides a more flexible and efficient solution in adaptive identification and control schemes. Our results show that FOEM1-FG offers superior stability and convergence characteristics, contributing new insights to the field of fractional calculus in adaptive systems.

PREDICTION SYSTEM FOR THE AMOUNT OF SUGAR PRODUCTION USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM

Sugar is one of the staple foods most Indonesians use, so sugar production needs to be done optimally to meet people's needs. This research will design a prediction system for the amount of sugar production in PTPN XI PG Prajekan using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. ANFIS is a combined method of two systems, namely a fuzzy logic system and an artificial neural network system. This research consists of data collection, ANFIS system design, ANFIS training, ANFIS testing, accuracy calculation, and result analysis. The prediction system for the amount of sugar production is designed to predict the variable which is the amount of sugar production in the year using the input variables (sugarcane harvested area in year ), (amount of sugarcane in year ), (average of yield in year ), and (number of milling days in year ). The experiments in this research used variations of the type of membership function and the number of membership functions. The best model obtained in this research is a model with a difference between two sigmoidal membership functions and a product of two sigmoidal membership functions with a total of 2 membership functions for each input variable. Both models have the same Mean Absolute Percentage Error (MAPE) value, which is 1.79% in the training process and 4.82% in the testing process.

Deep Learning-Driven Analysis of a Six-Bar Mechanism for Personalized Gait Rehabilitation

Abstract Recent advances in robotics and artificial intelligence have highlighted the potential for the integration of computational intelligence in enhancing the functionality and adaptability of robotic systems, particularly in rehabilitation. Designing robotic exoskeletons for the lower limb rehabilitation of post-stroke patients requires frequent adjustments to accommodate individual differences in leg anatomy. This complex engineering challenge necessitates a deep understanding of human physiology, robotics, and optimization to develop adaptive robotic systems and also to swiftly quantify the required adjustments and implement them for each patient. The conventional approaches, which mostly rely on heuristics and manual tuning, often struggle to achieve optimal results. This paper presents a novel method that integrates a genetic algorithm (GA) with a deep learning approach to generate a gait trajectory of the ankle joint from a six-bar linkage mechanism of fixed dimensions. Later, using the same approach, the inverse kinematics solution for this mechanism is also devised whereby, the set of the link dimensions of the six-bar linkage mechanism is obtained for the given gait trajectory of an individual to achieve customization. We simulated the kinematic behavior of the six-bar linkage mechanism within defined mechanical constraints and utilized the generated data for training a feedforward neural network and long short-term memory models. The proposed model, when trained, can produce accurate lengths for the desired gait trajectories in the sagittal plane and vice-versa which further validates our proposed approach for inverse kinematics solution.

Mechanically Stabilized Earth Wall Reliability Analysis Using Response Surface Methodology, ANN, ANFIS and Multi-objective Genetic Algorithm

Considering uncertainty in the analysis of geotechnical structures is a necessary condition for optimal and robust design. An alternative method for studying the reliability of a mechanically reinforced earth wall in granular soil is used to account for these uncertainties more rigorously. This allows for the inclusion of various uncertainties in a mathematical risk formulation based on random variables. The deterministic model is a benchmark taken from the literature used in a numerical simulation to determine the maximum horizontal displacement of the wall. In this case, the serviceability limit state is considered, allowing the wall's actual behavior to be described. ANOVA was used to identify the most influential parameters on the system's response. As uncorrelated random variables, only the parameters (E, φ and γ) were considered. The mathematical model serving as the limit state function was numerically predictedby three methods, response surface methodology (RSM), artificial neural network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS), and their predictive capacities were then compared. The results showed that the ANN model outperformed the RSM and ANFIS models regarding prediction. ANN models and multi-objective genetic algorithm (MOGA) are used to optimize the Hasofer-Lind reliability index. The analysis is then carried out by taking into account the various types of functions of parameter distributions, which allowed us to better appreciate the effects of the uncertainties and identify the set of parameters with a high incidence.

Integration of Neuro-Fuzzy Systems in Medical Diagnostics and Data Security - A Review

Adaptive Neuro-Fuzzy Systems (ANFS) have become increasingly prevalent in a variety of fields due to their ability to process complex and uncertain data with high accuracy. This research article reviews three major contributions of ANFS: their application in deep neuro-fuzzy systems (DNFS) for healthcare and industrial systems, neuro-fuzzy logic controllers for paralysis estimation, and ANFIS-based solutions for secure cloud storage in medical IoT (MIoT). The findings emphasize the importance of ANFS in improving decision-making, diagnosis, and data security. This paper concludes with a discussion on challenges, future research directions, and the need for optimization in real-time applications.

Intragenomic conflicts with plasmids and chromosomal mobile genetic elements drive the evolution of natural transformation within species.

Natural transformation is the only mechanism of genetic exchange controlled by the recipient bacteria. We quantified its rates in 786 clinical strains of the human pathogens Legionella pneumophila (Lp) and 496 clinical and environmental strains of Acinetobacter baumannii (Ab). The analysis of transformation rates in the light of phylogeny revealed they evolve by a mixture of frequent small changes and a few large quick jumps across 6 orders of magnitude. In standard conditions close to half of the strains of Lp and a more than a third in Ab are below the detection limit and thus presumably non-transformable. Ab environmental strains tend to have higher transformation rates than the clinical ones. Transitions to non-transformability were frequent and usually recent, suggesting that they are deleterious and subsequently purged by natural selection. Accordingly, we find that transformation decreases genetic linkage in both species, which might accelerate adaptation. Intragenomic conflicts with chromosomal mobile genetic elements (MGEs) and plasmids could explain these transitions and a GWAS confirmed systematic negative associations between transformation and MGEs: plasmids and other conjugative elements in Lp, prophages in Ab, and transposable elements in both. In accordance with the hypothesis of modulation of transformation rates by genetic conflicts, transformable strains have fewer MGEs in both species and some MGEs inactivate genes implicated in the transformation with heterologous DNA (in Ab). Innate defense systems against MGEs are associated with lower transformation rates, especially restriction-modification systems. In contrast, CRISPR-Cas systems are associated with higher transformation rates suggesting that adaptive defense systems may facilitate cell protection from MGEs while preserving genetic exchanges by natural transformation. Ab and Lp have different lifestyles, gene repertoires, and population structure. Nevertheless, they exhibit similar trends in terms of variation of transformation rates and its determinants, suggesting that genetic conflicts could drive the evolution of natural transformation in many bacteria.

Co-evolution, organizational capacity building, logistics capabilities and operational agility during health emergencies

PurposeGlobally, health care facilities often fail to respond quickly to health emergencies, resulting in significant deaths. Thus, the study examines the mediating effect of logistics capabilities in the relationship between co-evolution, organization capacity building and operational agility.Design/methodology/approachSmartPLS 4.0.8.3, variance-based structural equation models (VB-SEM) were used to test direct and indirect hypotheses across 45 health facilities. The respondents were 140 nurses and doctors. Disproportionate stratified simple random sampling was used based on government and private health facilities in Kampala City, Uganda.FindingsA significant and positive relationship exists between logistics capability and operational agility. However, co-evolution and organizational capacity are intimately linked to operational agility via logistics capability.Research limitations/implicationsA quantitative cross-sectional survey was conducted using self-administered questionnaires. The results did not show any variation in the operational agility of health facilities during emergency management; thus, qualitative approaches are needed.Practical implicationsDuring emergency management, health facilities need to cooperate by sharing information, building capacity to increase their responsiveness and flexibility. This should be done by sharing equipment, tools, drugs and medical supplies, and we hope to encourage joint medical research.Originality/valueA complex adaptive systems (CAS) framework is used in this study to examine how co-evolution, organizational capacity building and logistical capability relate to the operational agility of healthcare during times of emergency.

Fault Tolerant Control in Underwater Vehicles

The article discusses the problem of adaptive system synthesis to provide the detection, estimation, and elimination of the negative consequences of faults in the thrusters of underwater robots and deviations of their parameters. For this purpose, an approach consisting of three stages is proposed. At the first stage, based on a bank of diagnostic observers, deviations of the thruster parameters from their nominal values or the occurrence of errors in the readings of its sensors are detected. At the second stage, the magnitudes of the detected deviations and errors are estimated using sliding mode observers. To estimate not only single but also multiple faults, the additional sliding mode observers are used. At the third stage, a control signal is generated to retain the basic dynamic properties of the thruster based on the estimated magnitudes of deviations and errors. Thus, the main contribution of this paper is designing the systems compensating for the consequences of faults occurring in the thrusters of the UR and errors in the readings of the angular velocity sensors. This avoids reducing the accuracy of movement along specified trajectories, as well as preventing accidents that could lead to the loss of the vehicle. The operability and efficiency of the proposed systems were tested using computer simulations and experimental studies on an electro-mechanical stand.

Targeting the IL-5 pathway in eosinophilic asthma: A comparison of anti-IL-5 versus anti-IL-5 receptor agents.

Eosinophilic asthma is characterized by frequent exacerbations, poor symptom control and accelerated lung function decline. It is now recognized that the immune response underlying eosinophilic asthma involves a complex network of interconnected pathways from both the adaptive and innate immune systems. Within this response, interleukin-5 (IL-5) plays a central role in eosinophil differentiation, activation and survival and has emerged as a key target for therapies treating severe asthma. The monoclonal antibodies mepolizumab and reslizumab target the ligand IL-5, preventing its interaction with eosinophils; in contrast, benralizumab binds to the IL-5 receptor (IL-5R), preventing IL-5 from binding and leading to substantially greater eosinophil reduction by enhanced antibody-dependent cell-mediated cytotoxicity. Although no direct head-to-head clinical trials of asthma have been published to formally evaluate the clinical significance of these different therapeutic approaches, the potential benefits of partial versus complete eosinophil depletion continue to remain an important area of study and debate. Here, we review the existing real-world and clinical study data of anti-IL-5/anti-IL-5R therapies in severe eosinophilic asthma.

CRISPR/Cas system in human genetic diseases

The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) system is an adaptive immune system derived from bacteria which is designed to recognize and cleave invading DNA, such as viruses and plasmids. The advent of this innovative gene-editing technology has emerged as influential force in the category of medicine, delivering unprecedented precision in targeting and modifying genetic material. This review explores the potential and challenges of CRISPR in the context of human diseases. Specific applications in diseases affecting hemoglobin, muscle, the eye, and the liver are examined, demonstrating the versatility of CRISPR in addressing both monogenic and complex diseases. The use of CRISPR for gene therapy in conditions like sickle cell disease, Duchenne muscular dystrophy, retinal degeneration, and liver disorders is discussed, showcasing early clinical trials and preclinical studies that underscore the promise of CRISPR in medicine. Finally, it is expected that CRISPR will be able to play a greater role in human health and bring more precise and personalized treatment plans to patients.

AnFiS-MoH: Systematic exploration of hybrid ANFIS frameworks via metaheuristic optimization hybridization with evolutionary and swarm-based algorithms

The adaptive neuro-fuzzy inference system (ANFIS) has shown promising performance in modeling nonlinear problems, leveraging the strengths of both neural networks and fuzzy inference systems. However, as the problem scale increases, the growing number of tunable parameters in ANFIS can make it challenging to optimize via traditional gradient-based methods alone. This study introduces AnFiS-MoH, a novel framework that synergistically integrates ANFIS with metaheuristic optimization algorithms to address these challenges. By leveraging the global search capabilities of metaheuristics such as ant colony optimization (ACO), particle swarm optimization (PSO), genetic algorithm (GA), and simulated annealing (SA), ANFIS-MOH enhances the parameter tuning process of ANFIS models. We evaluate ANFIS-MOH on benchmark datasets including Boston Housing and Wine Quality, demonstrating significant improvements in prediction accuracy and generalization compared to traditional ANFIS and neural network approaches. The proposed framework achieves up to 20% reduction in Mean Squared Error and 15% increase in R2 scores, particularly excelling in handling high-dimensional, noisy data. This work contributes to the field of hybrid intelligent systems by introducing effective ways to combine the strengths of ANFIS with powerful metaheuristic optimization algorithms. The findings suggest that such hybrid approaches can be effective in tackling challenging nonlinear modeling problems. Our code is available at https://github.com/AmbitYuki/Metaheuristic-Adaptive-ANFIS.

Evolution of water technology from a structural perspective

Rivers are cradles of human civilisations and continual innovations in water technologies are the key to sustainable human development. Yet, limited research has explored the interactive dynamics among different technologies, hindering our ability to effectively manage technological transition. This paper presents a framework that conceptualizes water technology as a complex adaptive system containing three interrelated sub-systems: water demand, water supply, and water management. The interactions among these sub-systems were measured using three network-based metrics: intensity, brokerage, and efficiency. Patents registered in the World Intellectual Property Organization from 1863 to 2020 were used as the data source. It was found that 40,304 patents from 44 countries were registered, with 40% of them belonging to the water management sub-system, followed by water supply (35%), and water demand (25%). Technological development in the three sub-systems presented linear growth over the past 160 years, focusing on water treatment, hydroelectric power, hydraulic engineering and water monitoring. The water-demand sub-system was identified as the structural “bottleneck” with the highest brokerage value, which was considered crucial for knowledge transfer between the other two sub-systems. Overall, the water technology system is characterized by slow development, skewed spatial coverage, categorical homogeneity, and structural imbalances, limiting our ability to address the escalating global water threats.

Analysis on the Evolution Characteristics of Rural Tourism Public Service System from the Perspective of Digitalization—Empirical Evidence from the Silk Road Economic Belt

The integration of digitalization with public tourism services has emerged as a new model for the development of the rural tourism public services. The Silk Road Economic Belt is a crucial area for the future development of the tourism industry. Exploring the coupling of the digitalization and the development trends of the rural tourism public services is significant for advancing rural tourism in this region. This study applies Complex Adaptive Systems theory and Synergy theory to the development of the digitization of rural tourism public services, clarifying the structure of the digitization of the rural tourism public service system. Based on this, a collaborative development evaluation system for the digitization of rural tourism public services is constructed, focusing on the Silk Road Economic Belt. By using the SMI-P evaluation model, ArcGIS spatial visualization, Gini coefficient, and kernel density methods, the collaborative development of the digitization of the rural tourism public service system was measured and analyzed in this paper. The findings reveal the following: (1) The collaborative development of the digitization of the rural tourism public service system in the Silk Road Economic Belt shows a gradual upward trend, yet the overall level of the collaboration is transitioning from a state of imbalance. (2) Compared to the Southwest, the Northwest exhibits a lower overall level of collaboration, with growing disparities in collaborative development indices among provinces, although there is a trend toward higher levels of aggregation. (3) The development of the rural tourism public services is trending toward a favorable state of multi-dimensional collaboration, characterized by significant regional features, including multi-polarization and unbalanced development. Finally, optimization paths for the digital development of the rural tourism public services were proposed in this study, which can provide guidance for improving the construction of the digitization of the rural tourism public service system and exploring clear development pathways.

Dynamics of Peripheral Lymphocyte Subsets from Birth until Old Age

The immune system is inexperienced before birth and tends to be tolerogenic, rather than immunogenic. After birth, the adaptive immune system develops while facing microbial challenges, but it can become impaired as old age progresses and persistent inflammation can lead to chronic morbidity, disability and frailty. To investigate the potential contributions of lymphocyte subsets to immunity from birth until old age, we enumerated circulating innate and conventional lymphocytes and measured serum cytokine levels in 10 cord blood samples and in peripheral blood from 10 healthy term neonates, 23 healthy school-age children, 25 young adults and 11 older subjects. Flow cytometric analysis revealed that B cell frequencies increase during childhood and gradually decrease into adulthood, whereas natural killer cell frequencies increase throughout life. T cell frequencies remained relatively constant throughout life, as did their expression of CD4 and CD8. However, all four innate T cell populations studied—invariant natural killer T cells, mucosa-associated invariant T cells and the Vδ1 and the Vδ2 subsets of γδ T cells—were extremely rare in cord blood and in peripheral blood of neonates, but they expanded after birth reaching highest levels in adulthood. Analysis of serum cytokine levels revealed that proinflammatory and T helper type 1 (Th1) cytokine levels increase in adulthood, whereas Th2 and Th17 cytokine levels remain relatively constant. These changes in lymphocyte numbers and cytokine levels across the lifetime are likely to affect immunocompetence, leaving newborn and elderly people susceptible to infection, cancer and immune-mediated disease.

PLAbDab-nano: a database of camelid and shark nanobodies from patents and literature.

Nanobodies are essential proteins of the adaptive immune systems of camelid and shark species, complementing conventional antibodies. Properties such as their relatively small size, solubility and high thermostability makeVHH (variable heavy domain of theheavy chain) and VNAR (variable new antigen receptor) modalities a promising therapeutic format and a valuable resource for a wide range of biological applications. The volume of academic literature and patents related to nanobodies has risen significantly over the past decade. Here, we present PLAbDab-nano, a nanobody complement to the Patent and Literature Antibody Database (PLAbDab). PLAbDab-nano is a self-updating, searchable repository containing ∼5000 annotated VHH and VNAR sequences. We describe the methods used to curate the entries in PLAbDab-nano, and highlight how PLAbDab-nano could be used to design diverse libraries, as well as find sequences similar to known patented or therapeutic entries. PLAbDab-nano is freely available as a searchable web server (https://opig.stats.ox.ac.uk/webapps/plabdab-nano/).

Metamaterial-inspired infrared electrochromic devices with wideband microwave absorption for multispectral camouflage

Infrared (IR) electrochromic devices, capable of dynamically controlling thermal radiation, hold promising applications in adaptive camouflage. However, the strong microwave reflective properties inherent in the device’s electrodes present a significant challenge, rendering them susceptible to radar detection and weakening their camouflage effect. Inspired by the remarkable electromagnetic control capabilities of metamaterials, the integration of frequency selective surfaces into IR electrochromic devices is proposed to address this multispectral compatibility challenge. The designed integrated metadevices simultaneously exhibit large and reversible IR emissivity tunability (Δε≥0.55 at 3–5 μm, Δε≥0.5 at 7.5–13 μm) and wideband microwave absorption (reflection loss ≤−10 dB at 8.5–18 GHz). Furthermore, the monolithic integrated design of the shared barium fluoride substrate offers a simple device architecture, while careful design considerations mitigate coupling between IR electrochromism and microwave wideband absorption. This work introduces opportunities for the development of multispectral adaptive camouflage systems, offering potential advancements in concealment technology.

Enhancing Real Estate Valuation in Kazakhstan: Integrating Machine Learning and Adaptive Neuro-Fuzzy Inference System for Improved Precision

Enhancing Real Estate Valuation in Kazakhstan: Integrating Machine Learning and Adaptive Neuro-Fuzzy Inference System for Improved Precision

A competent CCHFMO with AMDH for QoS improvisation and efficient route protection in MANET

SummaryThe ability of mobile ad hoc networks (MANET) to be used as communication tools in a variety of industries, including healthcare, the military, smart traffic, and smart cities, has drawn special consideration. Traditional Manet's multicast routing methods seem to be inappropriate to massive with Adaptive systems because the problem is NP‐complete, resulting in an enchanting QoS restrictions. In order to conquer that the paper proficiently introduces the Conglomerate Crumb Horde Formicary Meta‐Heuristic (CCHFMO) with Asymmetrical Meander Diffie‐Hellman (AMDH) to tackle the major obstacles are multicast routing problems and lack of data protection. Initially, the fusion of crumb horde optimization (CHO) and formicary optimization (FO) is exploited to strengthen QoS limitations and reduce QoS data loss. However, the massive and dynamic nature of the network with the combination of more QoS restrictions, deficient security has become extremely difficult. Therefore, the research work establishes the asymmetrical meander Diffie‐Hellman (AMDH) to significantly improve performance and concealment while ensuring channel security during data transfer. Finally, the results demonstrated that by employing the novel optimization approaches, the MANET can increase data protection while still achieving high transmission rates and sophistication of communication. As a consequence, it adequately explicates the article to improve QoS performances.

Dynamic Programming-Based ANFIS Energy Management System for Fuel Cell Hybrid Electric Vehicles

Reducing reliance on fossil fuels has driven the development of innovative technologies in recent years due to the increasing levels of greenhouse gases in the atmosphere. Since the automotive industry is one of the main contributors of high CO2 emissions, the introduction of more sustainable solutions in this sector is fundamental. This paper presents a novel energy management system for fuel cell hybrid electric vehicles based on dynamic programming and adaptive neuro fuzzy inference system methodologies to optimize energy distribution between battery and fuel cell, therefore enhancing powertrain efficiency and reducing hydrogen consumption. Three different approaches have been considered for performance assessment through a simulation platform developed in MATLAB/Simulink 2023a. Further validation has been conducted via a rapid control prototyping device, showcasing significant improvements in hydrogen usage and operational efficiency across different drive cycles. Results manifest that the developed controllers successfully replicate the optimal control trajectory, providing a robust and computationally feasible solution for real-world applications. This research highlights the potential of combining advanced control strategies to meet performance and environmental demands of modern heavy-duty vehicles.

Antioxidant and immunomodulatory activities of ethanol extracts from Syzygium cumini L. Skeels and Pogostemon cablin Benth

Syzygium cumini and Pogostemon cablin are mostly cultivated in tropical climates for culinary and perfumery purposes, yet their potential medicinal properties remain underreported. The aim of this study was to examine the antioxidant and immunomodulatory activities of ethanol extracts from S. cumini (EESC) and P. cablin (EEPC). Reflux extraction was carried out using 96% ethanol on the collected plant specimens to produce EESC and EEPC. Secondary metabolites of each extract were identified using gas chromatography-mass spectrometry (GC-MS). The extracts were measured for total phenol and flavonoid levels and 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging activity. The immunomodulatory activity test was carried out in vivo by assessing several parameters, including the phagocytic index via the carbon clearance method, organ indices, antibody titers, and delayed-type hypersensitivity (DTH) response, using sheep red blood cells (SRBC) as antigens. The extracts were also examined for their anti-inflammatory activity in acute and chronic inflammation models. In the DPPH antioxidant test, EESC and EEPC had IC50 values of 12.33 µg/mL and 182.17 µg/mL, respectively. Both extracts showed immunosuppressant activity, marked by a phagocytic index of <1. EESC yielded lower organ indices for the liver (p=0.025 at 200 mg/kg BW), spleen (p=0.028 at 100 mg/kg BW), and thymus (p=0.032 at 200 mg/kg BW) compared to the control group. For EEPC, lower organ indices were observed in the liver at 100 mg/kg BW (p=0.005) and 200 mg/kg BW (p=0.031). In the primary antibody titer and DTH tests, both EESC and EEPC showed immunosuppressant activity at 200 and 400 mg/kg BW (p<0.05). The extracts suppressed both the innate and adaptive immune systems. Both EEPC (p=0.004) and EESC at 100 mg/kg BW (p=0.03) significantly reduced serum TNF-α levels. In conclusion, EESC and EEPC have the potential as immunosuppressive and anti-inflammatory agents.