Adaptive System Research Articles

Role of CRISPR-Cas systems in periodontal disease pathogenesis and potential for periodontal therapy: A review.

Clustered regularly interspaced short palindromic repeats (CRISPRs) are DNA sequences capable of editing a host genome sequence. CRISPR and its specific CRISPR-associated (Cas) protein complexes have been adapted for various applications. These include activating or inhibiting specific genetic sequences or acting as molecular scissors to cut and modify the host DNA precisely. CRISPR-Cas systems are also naturally present in many oral bacteria, where they aid in nutrition, biofilm formation, inter- and intraspecies communication (quorum sensing), horizontal gene transfer, virulence, inflammation modulation, coinfection, and immune response evasion. It even functions as an adaptive immune system, defending microbes against invading viruses and foreign genetic elements from other bacteria by targeting and degrading their DNA. Recently, CRISPR-Cas systems have been tested as molecular editing tools to manipulate specific genes linked with periodontal disease (such as periodontitis) and as novel methods of delivering antimicrobial agents to overcome antimicrobial resistance. With the rapidly increasing role of CRISPR in treating inflammatory diseases, its application in periodontal disease is also becoming popular. Therefore, this review aims to discuss the different types of CRISPR-Cas in oral microbes and their role in periodontal disease pathogenesis and precision periodontal therapy.

The Janus (dual) model of immunoglobulin isotype evolution: Conservation and plasticity are the defining paradigms.

The study of antibodies in jawed vertebrates (gnathostomes) provides every immunologist with a bird's eye view of how human immunoglobulins (Igs) came into existence and subsequently evolved into their present forms. It is a fascinating Darwinian history of conservation on the one hand and flexibility on the other, exemplified by the Ig heavy chain (H) isotypes IgM and IgD/W, respectively. The cartilaginous fish (e.g., sharks) Igs provide a glimpse of "how everything got off the ground," while the amphibians (e.g., the model Xenopus) reveal how the adaptive immune system made an about face with the emergence of Ig isotype switching and IgG-like structure/function. The evolution of mucosal Igs is a captivating account of malleability, convergence, and conservation, and a call to arms for future study! In between there are spellbinding chronicles of antibody evolution in each class of vertebrates and rather incredible stories of how antibodies can adapt to occupy niches, for example, single-domain variable regions, cold-adapted Igs, convergent mechanisms to dampen antibody function, provision of mucosal defense, and many more. The purpose here is not to provide an encyclopedic examination of antibody evolution, but rather to hit the high points and entice readers to appreciate how things "came to be."

Beyond Traditional Metrics: Exploring the Potential of Hybrid Algorithms for Drought Characterization and Prediction in the Tromso Region, Norway

Meteorological drought, defined as a decrease in the average amount of precipitation, is among the most insidious natural disasters. Not knowing when a drought will occur (its onset) makes it difficult to predict and monitor it. Scientists face significant challenges in accurately predicting and monitoring global droughts, despite using various machine learning techniques and drought indices developed in recent years. Optimization methods and hybrid models are being developed to overcome these challenges and create effective drought policies. In this study, drought analysis was conducted using The Standard Precipitation Index (SPI) with monthly precipitation data from 1920 to 2022 in the Tromsø region. Models with different input structures were created using the obtained SPI values. These models were then analyzed with The Adaptive Neuro-Fuzzy Inference System (ANFIS) by means of different optimization methods: The Particle Swarm Optimization (PSO), The Genetic Algorithm (GA), The Grey Wolf Optimization (GWO), and The Artificial Bee Colony (ABC), and PSO optimization of Support Vector Machine (SVM-PSO). Correlation coefficient (r), Root Mean Square Error (RMSE), Nash–Sutcliffe efficiency (NSE), and RMSE-Standard Deviation Ratio (RSR) served as performance evaluation criteria. The results of this study demonstrated that, while successful results were obtained in all commonly used algorithms except for ANFIS-GWO, the best performance values obtained using SPI12 input data were achieved with ANFIS-ABC-M04, exhibiting r: 0.9516, NSE: 0.9054, and RMSE: 0.3108.

Recent Advancements in Gel Polymer Electrolytes for Flexible Energy Storage Applications.

Since the last decade, the need for deformable electronics exponentially increased, requiring adaptive energy storage systems, especially batteries and supercapacitors. Thus, the conception and elaboration of new deformable electrolytes becomes more crucial than ever. Among diverse materials, gel polymer electrolytes (hydrogels, organogels, and ionogels) remain the most studied thanks to the ability to tune the physicochemical and mechanical properties by changing the nature of the precursors, the type of interactions, and the formulation. Nevertheless, the exploitation of this category of electrolyte as a possible commercial product is still restrained, due to different issues related to the nature of the gels (ionic conductivity, evaporation of filling solvent, toxicity, etc.). Therefore, this review aims to resume different strategies to tailor the properties of the gel polymer electrolytes as well as to provide recent advancements in the field toward the elaboration of deformable batteries and supercapacitors.

Discovery of an unconventional lamprey lymphocyte lineage highlights divergent features in vertebrate adaptive immune system evolution

Lymphocyte receptors independently evolved in both jawed and jawless vertebrates with similar adaptive immune responses. However, the diversity of functional subtypes and molecular architecture in jawless vertebrate lymphocytes, comparable to jawed species, is not well defined. Here, we profile the gills, intestines, and blood of the lamprey, Lampetra morii, with single-cell RNA sequencing, using a full-length transcriptome as a reference. Our findings reveal higher tissue-specific heterogeneity among T-like cells in contrast to B-like cells. Notably, we identify a unique T-like cell subtype expressing a homolog of the nonlymphoid hematopoietic growth factor receptor, MPL-like (MPL-L). These MPL-L+ T-like cells exhibit features distinct from T cells of jawed vertebrates, particularly in their elevated expression of hematopoietic genes. We further discovered that MPL-L+ VLRA+ T-like cells are widely present in the typhlosole, gill, liver, kidney, and skin of lamprey and they proliferate in response to both a T cell mitogen and recombinant human thrombopoietin. These findings provide new insights into the adaptive immune response in jawless vertebrates, shedding new light on the evolution of adaptive immunity.

Retraction Note: A novel Neyman–Pearson criterion-based adaptive neuro-fuzzy inference system (NPC-ANFIS) model for security threats detection in cognitive radio networks

Retraction Note: A novel Neyman–Pearson criterion-based adaptive neuro-fuzzy inference system (NPC-ANFIS) model for security threats detection in cognitive radio networks

Generational persona and entrepreneurial behavior among millennial entrepreneurs: the mediating role of adaptability tendencies

PurposeThe purpose of this study is to examine the relationship between generational persona, adaptability tendencies and entrepreneurial behavior. The paper also aims at testing the mediating role of adaptability tendencies in the relationship between generational persona and entrepreneurial behavior among millennial entrepreneurs in an African setting.Design/methodology/approachThe study adopts a quantitative methodological approach with a cross-sectional, questionnaire survey and correlational design where hypotheses were statistically tested using Structural Equation Modelling based on survey data (n = 382) from millennial entrepreneurs in Kampala Uganda.FindingsDrawing on the sample of 382 millennial entrepreneurs in Kampala, findings show that both generational persona and adaptability tendencies are positively and significantly associated with entrepreneurial behavior. Results further indicate that adaptability tendencies partially mediates the relationship between generational persona and entrepreneurial behavior among millennial entrepreneurs.Research limitations/implicationsThis study focused only on millennial entrepreneurs in Kampala Uganda ignoring other equally important groups of entrepreneurs like the baby boomers, generation Xers, generation Y and others. As such, the findings of this research do not entirely apply to all entrepreneurs in the country and this may have affected the generalizability of the results. Therefore, future studies can be done on the entrepreneurial behavior focusing on all entrepreneurs from all generations. Also, the study used a quantitative approach, future studies should consider a mixed methodology, which may give a more holistic understanding of entrepreneurial behavior.Practical implicationsIn practice, millennial entrepreneurs may use the results of the study to see how they can improve their performance for their businesses to benefit. Specifically, they ought to focus on adaptability, and generational persona to exhibit those entrepreneurial behaviors which will generally lead to the improvement of their businesses.Originality/valueTo the authors’ knowledge, this study provides a shred of initial empirical evidence on the relationship between generational persona, adaptability tendencies and entrepreneurial behavior using evidence from a low developed African country Uganda. Mostly, this study provides initial evidence of the mediating role of adaptability tendencies in the relationship between generational persona and entrepreneurial behavior. This study incorporates the Generational Cohort Theory and the Complex Adaptive Systems Theory into an applied theoretical framework that explains entrepreneurial behavior. More still, this study answers the call for more empirical studies on entrepreneurial behavior.

An Improved Adaptive Neuro-fuzzy Inference Framework for Lung Cancer Detection and Prediction on Internet of Medical Things Platform

It has become increasingly difficult for medical practitioners to recognize illness in recent years due to the emergence of new diseases from their myriad causes on a daily basis. Due in large part to inadequate diagnostic and monitoring infrastructure, a substantial amount of illness and death are associated with lung cancer (LC). The aim of the paper is to find lung cancer early and help patients receive curative treatment. Quitting smoking or never starting is the best way to mitigate the potential for disease-related death. As a result, cutting-edge detection and monitoring technologies must be developed to enable rapid, accurate, and timely diagnosis. Fuzzy logic (FL) is one of the best approaches to modeling complex and uncertain systems; therefore, it helps us deal with these challenges. Fuzzy expert system for lung cancer [FES-LC] detection and prediction on Internet of medical things (IoMT) is employed to overcome the challenges. Hence, an enhanced adaptive neuro-fuzzy inference framework [ANF-IF] is proposed in the current research. The cloud-based application of an adaptive neuro-fuzzy inference system yields four risk categories: not at risk, slightly at risk, moderately at risk, and severely at risk. New methods and theoretical frameworks have made it possible to diagnose LC in its earliest stages with the help of magnetic nanoparticles (MNPs), which allow researchers to overcome the limitations of conventionally slow diagnostic efficiency. The proposed system exhibits a precision of 93.4%, accuracy of 95.1%, specificity of 90.6%, sensitivity of 92.8%, false positive rate of 0.22%, false negative ratio of 0.18%, and classification accuracy of 98.2%. The proposed method outperforms all methods and provides better lung cancer detection accuracy than others.

Towards the development of believable agents: Adopting neural architectures and adaptive neuro-fuzzy inference system via playback of human traces

Artificial intelligence (AI) research on video games primarily focused on the imitation of human-like behavior during the past few years. Moreover, to increase the perceived worth of amusement and gratification, there is an enormous rise in the demand for intelligent agents that can imitate human players and video game characters. However, the agents developed using the majority of current approaches are perceived as rather more mechanical, which leads to frustration, and more importantly, failure in engagement. On that account, this study proposes an imitation learning framework to generate human-like behavior for more precise and accurate reproduction. To build a computational model, two learning paradigms are explored, artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS). This study utilized several variations of ANN, including feed-forward, recurrent, extreme learning machines, and regressions, to simulate human player behavior. Furthermore, to find the ideal ANFIS, grid partitioning, subtractive clustering, and fuzzy c-means clustering are used for training. The results demonstrate that ANFIS hybrid intelligence systems trained with subtractive clustering are overall best with an average accuracy of 95%, followed by fuzzy c-means with an average accuracy of 87%. Also, the believability of the obtained AI agents is tested using two statistical methods, i.e., the Mann–Whitney U test and the cosine similarity analysis. Both methods validate that the observed behavior has been reproduced with high accuracy.

Research on adaptive optics technology based on phase contrast Gerchberg Saxton algorithm

Abstract In wireless optical communication, the optical signal passes through the atmospheric turbulence, resulting in the simultaneous distortion of the amplitude and phase of the optical signal at the receiving end. This article applies the idea of phase contrast method to simultaneously compensate the amplitude and phase of the optical signal at the receiving end, derives the mapping relationship between the compensated phase and light intensity, and obtains the required compensated phase for full-field compensation. Numerical calculations show that when the difference between zero-frequency phase compensation and nonzero-frequency phase compensation is ±π, the maximum light intensity compensation is obtained. Based on the Gerchberg Saxton algorithm, a new method is proposed to invert the compensated phase with the maximum light intensity after full-field compensation and perform full-field correction through the adaptive optical system. The numerical calculation and experiment show that the phase contrast Gerchberg Saxton algorithm can effectively compensate the distorted optical signals under different turbulence, and thus the received signals of optical wireless communication can be corrected in full field.

A Study the optimisation of smart grid power flow

Optimal Power Flow (OPF) is a fundamental concept in the management and operation of modern electrical power systems, particularly within the context of smart grids. As the demand for electricity continues to grow and the energy landscape evolves towards greater sustainability, the need for efficient, reliable, and adaptive power systems has become more critical than ever. The primary objective of OPF is to determine the most efficient way to operate the power system while satisfying various technical and operational constraints. This involves the optimization of several variables, including generator outputs, voltage levels, and power flows, to minimize costs, reduce losses, and enhance system reliability. Smart grids can enhance their resilience to disturbances, support the efficient use of renewable energy, and provide reliable electricity to consumers.

Improved Sliding Mode Model Reference Adaptive System Observer

In order to improve the accuracy and stability of sensorless control system of permanent magnet synchronous motor, considering the influence of external load torque on system performance, the symbol function of sliding mode control system of vehicle-mounted permanent magnet synchronous motor is prone to chattering and the robustness of traditional PI control is insufficient. In this paper, a method of combining Non-singular Fast Terminal Sliding Mode (NFTSM) and Model Reference Adaptive System (MRAS) is proposed. A Non-singular Fast Terminal Sliding Mode Controller (NFTSMC) is designed. A control strategy combining NFTSMC and Model Reference Adaptive System Observer (MRASO) (NFTSMC-MRASO) converts the observed torque value into torque current feedforward compensation to the current loop input. The large sliding mode gain is avoided, and the chattering problem of the system is overcome when the external disturbance is suddenly added. At the same time, the robustness of the system is improved under the premise of suppressing the chattering.

Integer PI, fractional PI and fractional PI data trained ANFIS speed controllers for indirect field oriented control of induction motor

Induction motor drives with variable speed applications that employ vector control are quite popular nowadays because they provide strong dynamic performance and flexible speed control. By decoupling the torque-producing current components of stator current from the rotor flux, Indirect Field Oriented Control is recognized for generating excellent performance in induction motor drives. This investigation is being done to show the effectiveness of the novel FPI input-output data-trained ANFIS controller and compare the three controllers' performance in terms of load variation capabilities, motor parameter variation, and speed tracking. Consequently, a comparison of the three controllers is important to select which controller performs high in induction motor drive. Indirect Field Oriented Control of induction motor with Fractional Proportional Integral (FPI), Integer Proportional Integral (IPI), and Adaptive Neuro-Fuzzy Inference System (ANFIS) controllers are all discussed in this work along with their designs and comparative analysis. The square of error was used as a fitness function to genetically optimize the FPI and IPI controller parameters. The suggested Adaptive Neuro-Fuzzy Inference System (ANFIS) controller uses a hybrid learning approach. It is trained by the FPI controller's input-output data. Using the results of MATLAB simulations under various operating situations, the performance of the ANFIS controller was compared with FPI and IPI controllers. Because of FPI controller includes an extra parameter for adjustment, namely integration order, it performed better than IPI controller for speed control of the induction motor. According to the simulation findings, the percentage peak overshoots while employing ANFIS, FPI, and IPI controllers were 0.495 %, 12.062 %, and 14.699 % respectively. As a result, ANFIS exhibits a drastic reduction in overshoot. Additionally, with the ANFIS controlled induction motor drive, the speed achieves the required set value at 0.14 s. For no load, constant, and changing loads, the induction motor drive's performance has been examined.

DualFaceNet: augmentation consistency for optimal facial landmark detection and face mask classification

<p>In an era where face masks are commonplace, facial recognition faces new challenges and opportunities. This study introduces DualFaceNet (DFN), a cutting-edge neural network that efficiently combines facial landmark detection with mask classification. Benefiting from multi-task learning (MTL) and enhanced with a unique consistency loss, DFN outperforms traditional single-task models. Tests using the reputable 300W dataset and a face mask dataset showcase DFN’s strengths: a significant reduction in landmark error to 5.42 and an increase in mask classification accuracy to 92.59%. These results highlight the potential of integrating MTL and custom loss functions in facial recognition. As face masks continue to be globally essential, DFN’s integrated approach offers a fresh perspective in facial recognition studies. Furthermore, DFN paves the way for adaptive facial recognition systems, emphasizing the adaptability needed in our current era.</p>

Strategies to Overcome Antimicrobial Resistance in Nosocomial Infections, A Review and Update.

Nosocomial infections, also known as healthcare-associated infections, are a significant global concern due to their strong association with high mortality and morbidity in both developed and developing countries. These infections are caused by a variety of pathogens, particularly the ESKAPE group of bacteria, which includes the six pathogens Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp. These bacteria have demonstrated noteworthy resistance to different antibiotics. Antimicrobial resistance mechanisms can manifest in various forms, including restricting drug uptake, modifying drug targets, inactivating drugs, active drug efflux, and biofilm formation. Accordingly, various strategies have been developed to combat antibiotic-resistant bacteria. These strategies encompass the development of new antibiotics, the utilization of bacteriophages that specifically target these bacteria, antimicrobial combination therapy and the use of peptides or enzymes that target the genomes or essential proteins of resistant bacteria. Among promising approaches to overcome antibiotic resistance, the CRISPR/Cas system stands out and offers many advantages. This system enables precise and efficient editing of genetic material at specific locations in the genome. Functioning as a bacterial "adaptive immune system," the CRISPR/Cas system recognizes, degrades, and remembers foreign DNA sequences through the use of spacer DNA segments that are transcribed into CRISPR RNAs (crRNA). This paper has focused on nosocomial infections, specifically the pathogens involved in hospital infections, the mechanisms underlying bacterial resistance, and the strategies currently employed to address this issue. Special emphasis has been placed on the application of CRISPR/Cas technology for overcoming antimicrobial resistance.

Detection Limits of Thermal-infrared Observations with Adaptive Optics. I. Observational Data

Ground-based thermal infrared observations face substantial challenges in correcting the predominant background emitted as thermal radiation from the atmosphere and the telescope itself. With the upcoming 40 m class ELTs, unprecedented sensitivities from ground will be reached, underlining the need of even more sophisticated background correction strategies. This study aims to investigate the impact of thermal backgrounds on ground-based observations and identify possible limiting factors in dedicated correction strategies. We evaluate temporal and spatial characteristics of the thermal background in direct imaging data obtained with different telescopes and observation modes. In particular, three distinct data sets, acquired using VLT/NACO and KECK/NIRC2, are analyzed. Our analysis reveals that the observations are not fully photon shot noise limited, but exhibit additional sensitivity losses caused by imperfect background compensation in the different data sets. We identify correlations between background fluctuations and the activity of the adaptive optics system. We hypothesize that the pupil modulation of the adaptive optics mirrors introduces high frequency spatial and temporal fluctuations to the background, which could ultimately constrain the detection limit if they are not compensated adequately.

Optimization and prediction of biogas yield from pretreated Ulva Intestinalis Linnaeus applying statistical-based regression approach and machine learning algorithms

A statistical-based regression approach and machine learning (ML) algorithms (response surface methodology (RSM), feed-forward backpropagation artificial neural network (ANN) and multi-layer adaptive neuro-fuzzy inference system (ANFIS)) were explored for the optimization and prediction of biogas resulting from the anaerobic digestion (AD) of pretreated Ulva Intestinalis Linnaeus (UIL). ANFIS model was found to better predict and model the process of biogas production from the AD of pretreated UIL owing to low mean square error (MSE) and RMSE values (0.8841 and 0.9402-US, 0.9628 and 0.9812-O3, 0.1387 and 0.3724-MW and 0.3018 and 1.1410-Fe3O4) and highest values of R2 (0.9998-US, 0.9996-O3, 0.9996-MW and 0.9995-Fe3O4). Optimum conditions for biogas yield as a result of the various pretreatment processes based on the ANFIS model were US-15 power/time, time-40 min and the biogas yield-181.0 mL.gVS−1, O3-15.0 mg/min, time-40.0 min and biogas yield of 164.0 mL.gVS−1, MW-2.3 power/time, time-40.0 min and biogas yield-81.7 mL.gVS−1 and Fe3O4-20.0 mg L−1, time-40.0 min and biogas yield-154 mL.gVS−1. The results obtained show that the ML and statistical-based models were effective in approximating the biogas yield with high precision and low error and could be beneficial for the biogas production scale-up process.

Development of machine learning-based standalone GUI application for predicting hydraulic conductivity and compaction parameters of lateritic soils

Hydraulic conductivity and compaction parameters are the key considerations in selecting lateritic soils for engineering construction. Nevertheless, the complexity and high cost of the required tests have driven many contractors and field engineers to skip them, resulting in a succession of engineering structure failures. To overcome this limitation, this study developed machine learning-based standalone GUI application to predict lateritic soils’ hydraulic conductivity (K), maximum dry density (MDD) and optimum moisture content (OMC) from indices including specific gravity, liquid limit, plasticity index, linear shrinkage and fine content. To achieve this goal, the geotechnical properties of three hundred samples, collected using grid sampling method from thirty different lateritic deposits in southwestern Nigeria, were evaluated through laboratory tests. The test results were used to train predictive models using artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and Gaussian process regression (GPR). The models’ performance was compared using coefficient of determination (R2), root mean squared error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE). Based on these performance metrics, ANN demonstrated the best performance (R2 = 0.9835, 0.9797, 0.9999; RMSE = 7.938, 0.252, 2.09E-08; MAPE = 0.288, 1.114, 1.587; MAE = 5.432, 0.169, 1.1E-08) for MDD, OMC and K, respectively, followed by GPR and then ANFIS. Thus, the ANN models were selected and embedded in a standalone GUI application to enhance easy and quick prediction of lateritic soils’ MDD, OMC and K. The validity of the ANN-based standalone GUI application was demonstrated by comparing it favorably to notable regression-based models in the literature.

Enhancing flood risk mitigation by advanced data-driven approach

Flood events in the Sefidrud River basin have historically caused significant damage to infrastructure, agriculture, and human settlements, highlighting the urgent need for improved flood prediction capabilities. Traditional hydrological models have shown limitations in capturing the complex, non-linear relationships inherent in flood dynamics. This study addresses these challenges by leveraging advanced machine learning techniques to develop more accurate and reliable flood estimation models for the region. The study applied Random Forest (RF), Bagging, SMOreg, Multilayer Perceptron (MLP), and Adaptive Neuro-Fuzzy Inference System (ANFIS) models using historical hydrological data spanning 50 years. The methods involved splitting the data into training (50–70 %) and validation sets, processed using WEKA 3.9 software. The evaluation revealed that the nonlinear ensemble RF model achieved the highest accuracy with a correlation of 0.868 and an root mean squared error (RMSE) of 0.104. Both RF and MLP significantly outperformed the linear SMOreg approach, demonstrating the suitability of modern machine learning techniques. Additionally, the ANFIS model achieved an exceptional R-squared accuracy of 0.99. The findings underscore the potential of data-driven models for accurate flood estimating, providing a valuable benchmark for algorithm selection in flood risk management.

GWO-ANFIS-RD3PG: A reinforcement learning approach with dynamic adjustment and dual replay mechanism for building energy forecasting

Reliable prediction of building energy consumption is essential for effective and sustainable energy management. Traditional forecasting methods, however, face challenges when dealing with sudden changes in energy consumption. To address the above-mentioned issue, we introduce the GWO-ANFIS-RD3PG prediction model in this paper. This model aims to ensure global forecasting accuracy while minimizing prediction errors at sudden change points. A Grey Wolf Optimization (GWO) algorithm with the Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed to predict the energy consumption type and its fluctuation magnitude for the next time step. Within the Recurrent Dual Experience Replay Buffer DDPG (RD3PG) prediction module, we employ Long Short-Term Memory (LSTM), as the actor network in the Deep Deterministic Policy Gradient (DDPG), to account for long-term dependencies in time series data. Notably, we introduce an adaptive action modification mechanism that allows the agent to optimize actions based on the output of the aforementioned ANFIS, enabling real-time adjustments during sudden change. To further enhance the model’s performance, we adopt a dual experience replay mechanism to store data samples from sudden change points, capturing insight information during these anomalies. Experimental results on two real-world datasets demonstrate that this method reduces MAE by 5.81% and 13.47%, and RMSE by 5.69% and 15.21% compared to the state-of-art models, showing the significance of the proposed method for energy efficiency improvement in real-world building operations.