Robust Approach Research Articles

A Hybrid Soft Computing Approach for Prediction of Cloud-Based Learning Management Systems Determinants

A robust, accurate and reliable approach is essential for not only examining people’s acceptance of cloud-based learning management systems in developing nations but also for accurate prediction of factors affecting its implementation and progress. Therefore, in this study, three different soft computing models; Adaptive neuro-fuzzy inference system (ANFIS), Support vector regression (SVR), and Emotional artificial neural network (EANN), were employed for predictions of factors affecting cloud-based learning management systems take-up and progress using data gotten from six Nigerian colleges. The performance of the models was assessed using five arithmetic metrics; MAPE, NSE, RMSE, rRMSE, and RM. All the proposed models forecast the effects of the study inputs on LMS with higher accuracy (NSE > 0.98). However, the SVR model outshone the other models as it increased the performance of the study-reported model by 2% and 4% respectively. Based on the study results, instructors’ quality, motivation, and resource availability were found to be the key factors that affect cloud-based learning technologies take-up and progress in the study area. Interestingly, unlike prior studies, this study found system ease of use and usefulness to have insignificant effects on LMS take-up. Finally, the practical implications and limitations of the study were discussed based on the study findings.

Simultaneous determination of total ergot alkaloids in wheat flour by Orbitrap mass spectrometry

The optimization screening methods for total ergot alkaloids in wheat extracts involve transforming them into a single compound, which is then analyzed via high-resolution Orbitrap mass spectrometry (Orbitrap MS). Orbitrap MS provides highly sensitive and accurate mass measurements, enhancing the selectivity and sensitivity of the analysis. Various hydrolysis and reduction methods have been investigated, and the use of superhydrides has emerged as the most effective method for transforming ergopeptine alkaloids. This study also focused on the epimerization of ergot alkaloids, particularly the differences between R- and S-epimers and their impact on the mass spectra. We validated our method by assessing the linearity, sensitivity, recovery, matrix effects, repeatability, and stability. The limits of detection and quantitation were set at 0.43 and 1.30 μg LSA/kg wheat, respectively. The proposed method offers a robust analytical approach for screening and quantifying total ergot alkaloids in wheat samples, addressing important concerns about their presence in food and feed.

Chlorella sp. EPS loaded alginate microsphere: A novel bionanomaterial for adsorptive removal of sulfamethoxazole from the aqueous solutions

Microalgae exopolysaccharide (EPS) emerge as a sustainable and ecologically robust approach for wastewater treatment, leveraging their inherent capabilities for the adsorption and efficient removal of pollutants. This study proposes novel microalgae EPS polymerized with alginate to form an adsorbent microsphere that can efficiently remove sulfamethoxazole (SMX) from water. The EPS extracted from Chlorella sp. contains different functional groups like amides and polysaccharides that play a significant role in SMX adsorption. FT-IR and 3D-EEM analysis showed that electrostatic attraction is the primary binding force in the adsorption process. The decrease in both the TOC (total organic carbon) value of the SMX solution and its toxicity confirmed the effective adsorption of SMX. When the pH was 7.0, the composite weight was 30 g, and the SMX concentration was 10 mg/L, over 90% of SMX was removed in 90 min. The batch study showed that the pseudo-first-order kinetics and Langmuir adsorption isotherm model fit best. Column studies also showed the potential for semi-industrial scale-up, achieving 98% removal in 800 min, and fitting well to the Adams-Bohart column kinetic model. These conditional changes increase surface area, porosity, and specific binding affinities, optimizing antibiotic remediation by improving adsorption kinetics and responsiveness to environmental conditions. The EPS-alginate-mediated adsorption process is safe-by-design and economical, making the proposed composite adsorbent material highly efficient.

Exploring a general multi-pronged activation strategy for natural product discovery in Actinomycetes

Natural products possess significant therapeutic potential but remain underutilized despite advances in genomics and bioinformatics. While there are approaches to activate and upregulate natural product biosynthesis in both native and heterologous microbial strains, a comprehensive strategy to elicit production of natural products as well as a generalizable and efficient method to interrogate diverse native strains collection, remains lacking. Here, we explore a flexible and robust integrase-mediated multi-pronged activation approach to reliably perturb and globally trigger antibiotics production in actinobacteria. Across 54 actinobacterial strains, our approach yielded 124 distinct activator-strain combinations which consistently outperform wild type. Our approach expands accessible metabolite space by nearly two-fold and increases selected metabolite yields by up to >200-fold, enabling discovery of Gram-negative bioactivity in tetramic acid analogs. We envision these findings as a gateway towards a more streamlined, accelerated, and scalable strategy to unlock the full potential of Nature’s chemical repertoire.

A new C0 continuous refined zigzag {1,2} finite element formulation for flexural and free vibration analyses of laminated composite beams

This study presents a novel C0 continuous refined zigzag finite element formulation {1,2}, namely RZE{1,2}, for the bending and free vibration analyses of laminated composite beams. The Refined Zigzag Theory (RZT) effectively combines accuracy and computational efficiency, making it a robust approach for thin and thick laminated composite structures. The RZT eliminates the need for shear correction factors, thereby enhancing the overall streamline of the analysis process. The present RZE{1,2} formulation takes into account the transverse stretching by introducing quadratic through-thickness variations of deflection components. The governing equations of the RZT are derived by means of Hamilton’s principle. The through-the-thickness variations of the transverse shear and normal stresses are calculated by integrating the stress equilibrium equations in a post-processing step. Therefore, the Peridynamic Least Squares Minimization (PDLSM) approach is utilized to obtain precise derivatives of axial stresses in the stress equilibrium equations. A new finite element formulation is built up with 3-nodes with a total of 15 DOF. In order to study the influence of transverse stretching, different types of boundary conditions and material variations are applied for laminated composite beams for the bending and free vibration analyses. The distribution of displacements as well as the axial and transverse stresses of the thick beams are extensively examined. The outcomes of RZT are in good agreement with the reference solutions in the literature. The findings of the present study reveal that the presence of the transverse stretching produces more realistic predictions for thick beams where the shear deformations are influential.

Geotechnical characterisation of coal spoil piles using high-resolution optical and multispectral data: A machine learning approach

Geotechnical characterisation of spoil piles has traditionally relied on the expertise of field specialists, which can be both hazardous and time-consuming. Although unmanned aerial vehicles (UAV) show promise as a remote sensing tool in various applications; accurately segmenting and classifying very high-resolution remote sensing images of heterogeneous terrains, such as mining spoil piles with irregular morphologies, presents significant challenges. The proposed method adopts a robust approach that combines morphology-based segmentation, as well as spectral, textural, structural, and statistical feature extraction techniques to overcome the difficulties associated with spoil pile characterisation. Additionally, it incorporates minimum redundancy maximum relevance (mRMR) based feature selection and machine learning-based classification. This automated characterisation will serve as a proactive tool for dump stability assessment, providing crucial data for improved stability models and contributing to a greener and more responsible mining industry .

A Robust approach for mitigating load voltage imbalances using glowworm swarm optimizer for power quality enrichment

This paper addresses the paramount issue of maintaining optimal power quality in power system networks to ensure reliable operations. The paper introduces an innovative solution that effectively mitigates power quality challenges stemming from disturbances like voltage sag/swell events and harmonics. The approach combines a Dynamic Voltage Restorer (DVR) unit with a Solar PhotoVoltaic (PV) system, incorporating advanced control strategies. Leveraging the Glowworm Swarm Optimizer (GSO) Tuned Proportional-Integral-Derivative (PID) Controller and the Versatile Control on Synchronous Reference Frame (VCSRF) theory, our system achieves exceptional results. Extensive MATLAB/Simulink simulations demonstrate the superiority of the proposed DVR system over traditional PID, GA, PSO, and SSA controller-based DVR systems. Notably, our approach achieves significant reductions in Total Harmonic Distortion (THD), with reductions of 5.27 % (PID), 3.14 % (GA), 1.29 % (PSO), 0.841 % (SSA), and 0.03 % (GSO-PID), underscoring its substantial effectiveness. Practical implementation further validates the improved THD performance. The paper's findings highlight the efficiency and potential of the integrated DVR unit with a solar PV system in effectively mitigating power quality issues. These results offer valuable insights for researchers and practitioners seeking advanced solutions to power quality challenges. Future research endeavors aim to optimize and expand this approach for diverse power quality improvement applications.

Constant inner potential DFT for modelling electrochemical systems under constant potential and bias

Electrochemical systems play a decisive role in, e.g. clean energy conversion but understanding their complex chemistry remains an outstanding challenge. Constant potential and grand canonical ensemble (GCE) simulations are indispensable for unraveling the properties of electrochemical processes as a function of the electrode potential. Currently, GCE calculations performed at the density functional theory (DFT) level require fixing the Fermi level within the simulation cell. Here, we illustrate that this method is inadequate when modeling outer sphere reactions and a biased two-electrode cell. For these systems, the Fermi level obtained from DFT calculations does not accurately present the experimentally controlled electrode potential or describe the thermodynamic independent variable in GCE-DFT. To address this limitation, we developed and implemented a constant inner potential (CIP) method offering a more robust and general approach to conducting GCE-DFT simulations of electrochemical systems under constant potential or bias conditions. The primary advantage of CIP is that it uses the local electrode inner potential as the thermodynamic parameter for the electrode potential, as opposed to the global Fermi level. Through numerical and analytical studies, we demonstrate that the CIP and Fermi level GCE-DFT approaches are equivalent for metallic electrodes and inner-sphere reactions. However, CIP proves to be more versatile, as it can be applied to outer-sphere and two-electrode systems, addressing the limitations of the constant Fermi-level approach in these scenarios. Altogether, the CIP approach stands out as a general and efficient GCE-DFT method simulating electrochemical interfaces from first principles.

Linear quadratic mean-field game with volatility uncertainty

In this paper, we consider a linear quadratic (LQ) mean field game (MFG) with uncertain volatility. Applying a robust optimization approach and based on mean-field approximation, we firstly obtain the worst uncertainty by dealing with an LQ MFG with so-called soft-constraint. Then with the help of convex analysis theory, we derive the auxiliary optimal control problems for the agents and construct the decentralized strategies. Finally, by virtue of estimates of (forward) backward stochastic differential equations, the asymptotical optimality of the decentralized strategy is verified. A numerical example is given to illustrate the above theoretical results.

Fire modelling: The success, the challenges, and the dilemma from a modeller's perspective

The presentation will review the achievements of my team and the wider IAFSS community in advancing computational fluid dynamics (CFD) techniques for modelling a wide range of fire scenarios, including physics-based sub-models for combustion, soot, and radiative heat transfer as well as robust modelling approaches for flame spread over solids, liquid fuels, and fire suppression. These will be followed by revisiting some successful applications in which CFD simulations influenced design, development of fire protection strategies as well as fire safety in emerging energy technologies like hydrogen and batteries. Some challenges for which robust modelling techniques/approaches are still lacking will be flagged up. Examples in which codes and standards for fire protection should consider explosion safety and the role of consequence analysis using CFD to inform such consideration will be elucidated. Finally, the presentation will reflect on some dilemma faced by fire modelers.

Multimodal deep learning model for Covid-19 detection

This study introduces a novel multimodal deep learning model for detecting Covid-19, uniquely combining chest X-ray images and cough sound features. We incorporated an attention layer to effectively influence the inputs from both features to enhance the detection process. The integration of time and frequency domain cough features with chest X-ray images represents a significant advancement in Covid-19 detection methodologies, offering promising results and potential applications in the medical field. In the proposed model, we modified vgg16 and a faster region-based convolutional neural network (FRCNN). The modified vgg16 is used to extract the features from both datasets, whereas the modified FRCNN is used for detection. Utilizing the Vgg16 model for feature extraction and faster-rcnn for Covid-19 detection allows for a robust and efficient approach. We analyzed the performance of the proposed methodology using a dataset of 2059 Covid-19 positive chest X-ray images and a diverse collection of 25,000 cough recordings, including 716 positive, 7693 negative, and 1591 symptomatic cases. The datasets were divided into 80% for training and 20% for testing. The model achieves 99.80% accuracy, an f1-score of 99.70%, and 0.0132 validation loss. We further performed an extensive ablation experiment on the features. We compared the results of the proposed model to already existing networks using chest X-rays and coughs as datasets, and the proposed model outperformed them. Experiments also show that the trained model resists training data quantity and quality and can effectively detect Covid-19 in chest X-rays and coughs.

LQG/LTR based robust youla parameterized adaptive vibration control for the supporting platform of rotating liquid mirror

In mechatronic systems, the vibrations which commonly consist of band-limited random signals mixed with large multiple narrow-band deterministic signals may negatively impact the system performance. For example, due to the incomplete matching of the mechanism and the rotation of the motor, random and deterministic vibration disturbance may occur in the supporting platform of the rotating liquid mirror. In this paper, a robust Youla ( Q) parameterized adaptive regulation approach has been proposed to minimize such kind of vibration signals for the rotating platform system with model uncertainties. Firstly, the inner-loop robust controller with linear quadratic Gaussian with loop transfer recovery ( LQG/LTR) is optimally designed by choosing the suitable weighting functions to achieve the trade-off between robust stability and control performance to deal with random vibration signals. Then the Youla parameter is augmented to construct a set of Q-parameterized stabilizing controllers, and the robust stability of the system is analyzed through dual-Youla parameterization of the uncertain model. The recursive least squares (RLS) adaptive algorithm is developed to tune the Q parameter online to construct a desired adaptive controller for further residual vibration elimination. An experimental evaluation of the controller in reducing the vibration of the supporting platform of a rotating liquid mirror has been carried out, and the results illustrate that the proposed adaptive robust vibration regulation approach can effectively suppress the band-limited random and narrow-band deterministic vibration signals.

Application of a Closed-Form Model in Analyzing the Fracture of Quasi-Brittle Materials.

Fracture failure in quasi-brittle materials poses a persistent challenge in materials science and engineering. This study presents a thorough investigation of the Boundary Effect Model (BEM), offering a nuanced understanding of the size effect on fracture properties. The conceptual framework, evolutionary process, and applicability scope of BEM are elucidated, highlighting its accuracy and reliability in calculating fracture properties across various quasi-brittle materials. Through the integration of BEM with diverse fracture tests-such as three-point bending, four-point bending, and wedge-splitting-a linear correlation between maximum failure loads and material fracture properties is established. Notably, the study demonstrates that fracture properties, determined by BEM, can be regarded as consistent material constants across specimens of varying sizes, initial notch lengths, geometries, and microstructures. Validation of the BEM's reliability encompasses the analysis of 140 fracture test results involving concrete, hard rocks, and bamboo scrimber. The synergy of non-linear and linear BEM analyses emerges as a robust approach for accurately predicting the fracture behavior of quasi-brittle materials. This comprehensive exploration sheds light on the potential of the Boundary Effect Model as a valuable tool for predicting and understanding fracture mechanics in diverse materials and scenarios. This research serves as an effective approach to accurately evaluating the fracture properties of quasi-brittle materials, which is of great practical significance for material design, engineering construction, and various industrial applications.

A Python-based Empirical Study of Markowitz's Portfolio Theory in the Chinese Stock Market

Markowitz's portfolio theory serves as a pivotal framework in investment science, offering investors a scientifically robust approach to asset allocation. This paper conducts an empirical analysis of the theory within the context of the Chinese stock market, utilising Python for data manipulation and computation. A selection of five stocks was made based on pre-established criteria, and Python was employed to examine their correlation for portfolio construction viability. Subsequently, Python facilitated the identification of the portfolio with the maximum Sharpe ratio and the minimum variance, as well as the delineation of the efficient frontier. Results from the empirical study confirm the applicability and utility of Markowitz's portfolio theory for Chinese investors, thereby substantiating its significant relevance in China's financial market. Based on the findings, investors are advised to diversify their portfolios following the guidelines of the efficient frontier to optimize returns while managing risks. Moreover, considering the complexities of the Chinese stock market, harnessing computational tools like Python can grant an analytical edge, enabling more informed decision-making. It's also pivotal for investors to routinely review and adjust portfolio compositions in line with market dynamics, ensuring they stay aligned with the principles of the efficient frontier.

Closed-form optimal design of the tuned inerter damper (TID) connecting adjacent flexible buildings

The tuned inerter damper (TID) is a viable solution for addressing the vibration issue of the adjacent-building system. However, the conventional optimal calibration of the TID is conducted based on the classic 3-degree-of-freedom (3-DOF) model with one resonant DOF for each structure, which ignored the background flexibility contribution that comes from non-resonant modes of each one, and thus leads to non-optimal control performance. This paper developed a novel analogue 3-DOF model, in which the background flexibility contribution of each flexible building at the damper attachment location is represented by an equivalent stiffness element. An enhanced formula for the TID optimal frequency is developed accordingly (targeting the fundamental mode of the taller building) using the ‘root-locus’ method, where the background flexibility as well as the resonant mode contributions that come from both buildings are accounted for. For completeness, the optimal frequency formula that ignores the background flexibility contribution is derived based on the classic 3-DOF model. A robust approach is adopted to determine the optimal damping ratio based on the classic model. Numerical analysis is conducted based on a 25-15 floors adjacent-building system. Results demonstrate that when the enhanced frequency-tuning formula is used, a much more balanced frequency response curve of the taller building is achieved. Further, time-domain seismic analysis indicates that the TID performance is consistently improved when background flexibility is accounted for in the design formulas.

Recent Advances in the Construction of Coumarin or Quinolin-2-ones Compounds via Radical Cascade Reactions

Abstract: Free radical reactions occupy an important position in synthetic organic chemistry as well as medicinal chemistry, significantly complementing and extending the synthesis of coumarin or quinolin-2-one compounds. Moreover, the use of constructing coumarin or quinolin-2-one compounds functionalized at the C3 position has certain advantages over traditional noble metalcatalyzed or high-temperature cyclization, which is more in line with the advocates of green chemistry. The design of alkenes or alkynes as radical acceptors for cascade reactions presents a novel and robust approach to obtaining coumarin or quinolin-2-one molecules. These radical cascade cyclization reactions have been well explored and studied over the past decade. As a result, we have compiled a review of the rapidly developing cutting-edge research in this field, featuring typical examples and in-depth exploration of the underlying mechanisms.

A Novel Approach to Determine Multi-Tiered Nearly Zero-Energy Performance Benchmarks Using Probabilistic Reference Buildings and Risk Analysis Approaches

The Energy Performance of Buildings Directive (EPBD) mandates European Union Member States (MS) to conduct cost-optimal studies using the national calculation methodology (NCM), typically through non-calibrated asset-rating software. Nearly zero-energy building (NZEB) levels must be derived for each chosen Reference Building (RB), which are generally defined using deterministic parameters. Previous research proposed an innovative cost-optimal method that replaces ‘non-calibrated deterministic RBs’ with ‘probabilistically Bayesian calibrated reference building (RB)’ to better handle building stock uncertainties and diversities when deriving benchmarks. This paper aims to develop a framework to address two research gaps necessary for the successful application of the innovative cost optimal method: (1) providing objective criteria for defining NZEB benchmarks and (2) propagating uncertainties and financial risk for each defined benchmark. A robust approach for defining NZEB benchmarks according to four different ambition levels (low, medium, high, and highest) was developed by objectively considering distinct points from multiple cost-optimal plots employing different financial perspectives. Risk analysis is then performed for each defined benchmark by propagating risk from the posterior calibration parameter distributions to visualize and statistically quantify the financial risk, including robust risk, that the private investor could face for reaching each derived benchmark ambition level. The innovative cost-optimal methodology that incorporates the developed framework was applied to a hotel RB case study. The results showed that the developed framework is capable of deriving distinct benchmarks and quantitatively uncovering the full financial risk levels for the four different renovation ambition levels. The current cost-optimal method was also performed for the hotel case study with the RB defined determinitically and using the non-calibrated NCM software, SBEM-mt v4.2c. It was found that the financial feasibility and energy-saving results per benchmark are significantly more realistic and transparent for the proposed innovative cost-optimal method including a better match between the simulated and metered energy consumption with a difference of less than 1% in annual performance. Thus, the performance gap between calculated and actual energy performance that is synonymous with the EPBD methodology, as reported in the literature, is bridged. The case study also showed the importance of the risk analysis. Performing the cost-optimal analysis for a Bayesian calibrated RB using the mean value of the posterior calibrated parameter distributions without propagating uncertainty produced highly optimistic results that obscured the real financial risk for achieving the higher ambition levels of the NZEB benchmarks. Consequently, the developed framework demonstrated a time-bound tightening approach to achieve higher energy performance ambitions, improve risk transparency to private investors, and facilitate more targeted policies towards a net zero-carbon status. Thus, the proposed method considering parameter uncertainty and calibrated RBs is instrumental for devising robust policy measures for the EPBD, to achieve a realistic and long-lasting sustainable energy goal for European buildings.

Evaluating the impacts of a large-scale voluntary REDD+ project in Sierra Leone

Carbon offsets from the REDD+ (reducing emissions from deforestation and degradation) framework to protect forests are expected to see a 100-fold increase in market value by 2050. However, independent causal impact evaluations are scarce and only a few studies assess benefits to communities themselves, a core objective of REDD+. Following a pre-analysis plan, we use a before-after-control-intervention (BACI) framework to evaluate the impact of a large-scale voluntary REDD+ project in Sierra Leone—the Gola project. We use a panel of both satellite images and household surveys to provide causal evidence of the impact of the project on local deforestation rates and socioeconomic indicators over the first 5 yr of its implementation. We find that REDD+ slowed deforestation by 30% relative to control communities while not changing economic wellbeing and conservation attitudes. We find suggestive evidence that the programme increased the value of alternative income sources, by shifting labour away from forest-dependent farming activities. A cost-to-carbon calculation shows that REDD+ led to 340,000 tCO2 in avoided emissions per year, with an estimated cost of US$1.12 per averted tCO2. Our study contributes to developing an evidence base for voluntary REDD+ projects and offers a robust approach to carry out BACI assessments.

ELECTRON BEAM LITHOGRAPHY PROCESS QUALITY IMPROVEMENT BY ROBUST ENGINEERING APPROACH

The model-based robust approach is successfully applied to different industrial processes for improving their quality performance characteristics. The Robust engineering approach is implemented in cases of heteroscedasticity of the performance characteristics in presence of errors in the process parameters or noise factors, which is typical for the production processes. Higher quality and reproducibility of the quality characteristics can be achieved both for existing production processes or for technological process design at the production planning and development stages. In this paper the model-based robust engineering approach is implemented for the quality improvement of polymethyl methacrylate (PMMA) resist profiles, obtained by electron beam lithography process. Specific technological requirements concerning the developed profile structures, their variances (reproducibility) in production conditions and robustness toward the errors in the process parameters, are considered. Different multicriteria optimization approaches were applying and compared, aiming to simultaneously fulfil the requirements for the geometric characteristics of the positive resist PMMA.

Strengthening Digital Security: Dynamic Attack Detection with LSTM, KNN, and Random Forest

Digital security is an ever-escalating concern in today's interconnected world, necessitating advanced intrusion detection systems. This research focuses on fortifying digital security through the integration of Long Short-Term Memory (LSTM), K-Nearest Neighbors (KNN), and Random Forest for dynamic attack detection. Leveraging a robust dataset, the models were subjected to rigorous evaluation, considering metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. The LSTM model exhibited exceptional proficiency in capturing intricate sequential dependencies within network traffic, attaining a commendable accuracy of 99.11%. KNN, with its non-parametric adaptability, demonstrated resilience with a high accuracy of 99.23%. However, the Random Forest model emerged as the standout performer, boasting an accuracy of 99.63% and showcasing exceptional precision, recall, and F1-score metrics. Comparative analyses unveiled nuanced differences, guiding the selection of models based on specific security requirements. The AUC-ROC comparison reinforced the discriminative power of the models, with Random Forest consistently excelling. While all models excelled in true positive predictions, detailed scrutiny of confusion matrices offered insights into areas for refinement. In conclusion, the integration of LSTM, KNN, and Random Forest presents a robust and adaptive approach to dynamic attack detection. This research contributes valuable insights to the evolving landscape of digital security, emphasizing the significance of leveraging advanced machine learning techniques in constructing resilient defenses against cyber adversaries. The findings underscore the need for adaptive security solutions as the cyber threat landscape continues to evolve, with implications for practitioners, researchers, and policymakers in the field of cybersecurity.