Model Complex Research Articles

Mechanistic Studies of Second Coordination Sphere Interactions in the Dioxygen Activation of a Copper(I) Complex with a N‐(2‐Ethoxyethanol)‐bis(2‐picolyl)amine Ligand

While first coordination sphere interactions have been investigated in great detail for the reactivity of copper(I) model complexes with dioxygen, the interactions of the secondary coordination sphere with the bound dioxygen so far remain underexplored. A series of copper(I) complexes with ligands based on N‐(2‐ethoxyethanol)‐bis(2‐picolyl)amine (L(O)OH) were synthesized, and the reactivity towards dioxygen was monitored by low‐temperature stopped‐flow UV/vis spectroscopy. Various "oxygen adduct complexes" were observed, and the influence of the second coordination sphere interactions was analyzed. If the ether moiety or the hydroxy group in L(O)OH is removed/replaced, the formation of a trans‐µ‐1,2‐peroxido dicopper(II) complex or a bis(µ‐oxido) dicopper(III) complex is observed. Establishing stronger hydrogen bonding while maintaining the ether moiety leads to hydrogen bond stabilized trans‐µ‐1,2‐peroxido dicopper(II) complexes. Based on these results, we deduced that the copper(I) complex with L(O)OH as ligand reacts with dioxygen first to a hydrogen bond stabilized trans‐µ‐1,2‐peroxido dicopper(II) complex, which rapidly converts to a bis(µ‐oxido) dicopper(III) complex.

Optimising torque for three-disc afpmsm in electric vehicles using bp_ann and anfis algorithms

Designing an optimal torque distribution controller for the three-disc axial flux permanent magnet synchronous (three-disc AFPMSM) optimises performance while ensuring robustness, stability, and adaptability in a real-world condition. This is crucial for maximising the potential of AFPMSM, particularly in a modern application like an electric vehicle and a renewable energy. Thus, a controller compatible with more complex systems in the future is essential. This paper presents a system that combines torque control algorithms based on a back-propagation neural network (BP-ANN) and an Adaptive Neuro-Fuzzy Inference System (ANFIS). The BP-ANN uses a multi-layer structure where the input layer processes factors such as load torque, rotational speed, and stator current, hidden layers model complex nonlinear interactions, and the output layer predicts optimal torque for AFPMSM operation. Training involves minimising the error between predicted and actual torque through gradient descent and iterative adjustments of weights and biases. The ANFIS-based control enhances performance by integrating neural network learning with fuzzy logic to optimise torque output. By leveraging the strengths of both BP-ANN and ANFIS, the system offers a stable, efficient, and adaptable solution for three-disc AFPMSMs. The Matlab/Simulink simulations confirm its effectiveness, showing balanced torque distribution, reduced energy losses, improved drivetrain efficiency, and adaptability to sudden load or road changes, ensuring stability and enhanced dynamic response

Stock Price Forecasting Using Long Short-Term Memory Networks: A Comprehensive Analysis of Financial Time-Series Data

In this study, we employ Long Short-Term Memory (LSTM) networks to predict stock prices using historical data from Tesla Inc. spanning 10 years. The research emphasizes the importance of LSTM's capability to model complex temporal dependencies in financial time-series data, outperforming traditional statistical methods. Various feature combinations and time steps are tested, identifying a 30-day window as the optimal setup. Key evaluation metrics, including Mean Squared Error (MSE), training loss, and validation loss, are utilized to assess model performance. The results show that LSTM networks are particularly effective for short-term stock price predictions, while longer-term forecasts experience decreasing accuracy. This work contributes to the growing body of knowledge on deep learning applications in financial markets, offering practical insights for investors and financial institutions

Drivers and dampers of distance desire in crisis periods: A German market perspective

Modelling complex consumptive decision-making during crisis periods represents a burgeoning research avenue, particularly in the context of intercontinental travel. Examining data generated from a representative online sample of n = 2,021 Germans as potential long-haul travellers, this study investigated which socio-demographic, psychographic and behavioural factors positively (drivers) or negatively (dampers) influenced distance desire during the contemporary era characterised by crisis events. After applying multiple linear regressions with distance desire as the dependent variable, it was found that in crisis periods, hedonistic travel motives, a high affinity for risk and previous long-distance travel experience are the drivers of distance desire, whilst socio-demographic factors (e.g., income or age) and the desire to maintain a low climate footprint do not exert a significant dampening influence. Results can support attempts to influence long-distance travel behaviour in crisis periods since they clarify which travel motives and other factors drive the desire for long-distance travel in Germany.

Quantifying Magnetic Anisotropy of Series of Five-Coordinate CoII Ions: Experimental and Theoretical Insights.

Stabilizing large easy-axis type magnetic anisotropy in molecular complexes is a challenging task, yet it is crucial for the development of information storage devices and applications in molecular spintronics. Achieving this requires a deep understanding of electronic structure and the relationships between structure and properties to develop magneto-structural correlations that are currently unexplored in the literature. Herein, a series of five-coordinate distorted square pyramidal CoII complexes [Co(L)(X2)].CHCl3 (where X = Cl (1), Br (2), or I (3)) is reported, all exhibiting easy-axis magnetic anicotropy. The size of the zero field splitting axial parameter (D) is quantitatively determined (1 = -72; 2 = -67 and 3 = -25 cm-1) using a cantilever torque magnetometry which is further firmly supported by magnetic susceptibility, and EPR measurements. The study of the magnetization relaxation dynamics reveals field-induced slow relaxation of magnetization due to the predominant Raman relaxation process. Theoretical calculations on 1-3 and optimized model complexes of 1 reveal insights into the electronic structure and highlight the impact of steric and electronic effects on modulating the D values. Overall, the studies reported pave the way for designing a new generation of CoII complexes with enhanced axiality and a lower rhombicity.

A Weighted Bayesian Kernel Machine Regression Approach for Predicting the Growth of Indoor-Cultured Abalone

The cultivation of abalone, a species with high economic value, faces significant challenges due to its slow growth rate and sensitivity to environmental conditions, resulting in prolonged cultivation periods and increased mortality risks. To address these challenges, we propose a novel probabilistic machine learning approach based on a Bayesian framework to predict abalone growth by modeling key environmental factors, including water temperature, pH, salinity, nutrient supply, and dissolved oxygen levels. The proposed method employs a weighted Bayesian kernel machine regression model, integrating Gaussian processes with a spike-and-slab prior to identify influential variables. This approach accommodates heteroscedasticity, capturing varying levels of variance across observations, and models complex, non-linear relationships between environmental factors and abalone growth. Our analysis reveals that time, dissolved oxygen, salinity, and nutrient supply are the most critical factors influencing growth, while water temperature and pH play relatively minor roles under controlled indoor farming conditions. Interaction analysis highlights the non-linear dependencies among factors, such as the combined effects of salinity and nutrient supply. The proposed model not only improves prediction accuracy compared to baseline methods, but also provides actionable insights into the environmental dynamics that optimize abalone growth. These findings underscore the potential of advanced machine learning techniques in enhancing aquaculture practices and offer a robust framework for managing complex, multi-variable systems in sustainable farming.

MLP Enhanced CO2 Emission Prediction Model with LWSSA Nature Inspired Optimization

Environmental degradation due to the rapid increase in CO₂ emissions is a pressing global challenge, necessitating innovative solutions for accurate prediction and policy development. Machine learning (ML) techniques offer a robust approach to modeling complex relationships between various factors influencing emissions. Furthermore, ML models can learn and interpret the significance of each factor’s contribution to the rise of CO2. This study proposes a novel hybrid framework combining a Multi-Layer Perceptron (MLP) with an enhanced Locally Weighted Salp Swarm Algorithm (LWSSA) to address the limitations of traditional optimization techniques, such as premature convergence and stagnation in locally optimal solutions. The LWSSA improves the standard Salp Swarm Algorithm (SSA) by incorporating a Locally Weighted Mechanism (LWM) and a Mutation Mechanism (MM) for greater exploration and exploitation. The LWSSA-MLP framework achieved a prediction accuracy of 97% and outperformed traditional optimizer-based MLP models across several evaluation metrics. A permutation feature significance analysis identified global trade, coal energy, export levels, urbanization, and natural resources as the most influential factors in CO₂ emissions, offering valuable insights for targeted interventions. The study provides a reliable and scalable framework for CO₂ emission prediction, contributing to actionable strategies for sustainable development and environmental resilience.

Design of a Novel Fractional Whale Optimization-Enhanced Support Vector Regression (FWOA-SVR) Model for Accurate Solar Energy Forecasting

This study presents a novel Fractional Whale Optimization Algorithm-Enhanced Support Vector Regression (FWOA-SVR) framework for solar energy forecasting, addressing the limitations of traditional SVR in modeling complex relationships within data. The proposed framework incorporates fractional calculus in the Whale Optimization Algorithm (WOA) to improve the balance between exploration and exploitation during hyperparameter tuning. The FWOA-SVR model is comprehensively evaluated against traditional SVR, Long Short-Term Memory (LSTM), and Backpropagation Neural Network (BPNN) models using training, validation, and testing datasets. Experimental results show that FWOA-SVR achieves superior performance with the lowest MSE values (0.036311, 0.03942, and 0.03825), RMSE values (0.19213, 0.19856, and 0.19577), and the highest R2 values (0.96392, 0.96104, and 0.96192) for training, validation, and testing, respectively. These results highlight the significant improvements of FWOA-SVR in prediction accuracy and efficiency, surpassing benchmark models in capturing complex patterns within the data. The findings highlight the effectiveness of integrating fractional optimization techniques into machine learning frameworks for advancing solar energy forecasting solutions.

Hybrid Models of Atmospheric Block Columns of Primary Oil Refining Unit Under Conditions of Initial Information Deficiency

Hybrid Models of Atmospheric Block Columns of Primary Oil Refining Unit Under Conditions of Initial Information Deficiency

Binuclear ruthenium complex linker length tunes DNA threading intercalation kinetics.

Binuclear ruthenium complexes have been investigated for potential DNA-targeted therapeutic and diagnostic applications. Studies of DNA threading intercalation, in which DNA base pairs must be broken for intercalation, have revealed means of optimizing a model binuclear ruthenium complex to obtain reversible DNA-ligand assemblies with the desired properties of high affinity and slow kinetics. Here, we used single-molecule force spectroscopy to study a binuclear ruthenium complex with a longer semi-rigid linker relative to the model complex. Equilibrium results suggest a DNA affinity that is an order of magnitude higher than the parent binuclear ruthenium complex, likely due to a sterically-relieved DNA threading intercalation mechanism. Notably, kinetics analysis shows that less DNA elongation is required for threading intercalation compared to the parent complex, and the association rate is two orders of magnitude faster. The ruthenium complex elongates the DNA duplex by ∼0.3 nm per bound ligand to reach the equilibrium intercalated state, with a significantly different energy landscape relative to the parent complex. Mechanical properties of the ligand-saturated DNA duplex show a higher persistence length, indicating that the longer semi-rigid linker provides enough molecular spacing to allow a single monomer to fully stack with base pairs, comparable to the monomeric parent ruthenium complex. The DNA base pairs in the equilibrium threading intercalated state are likely intact and the ruthenium complex is shielded from the polar solution, providing measurable single-molecule confocal fluorescence signals. The obtained confocal fluorescence imaging of the bound dye confirms mostly uniform intercalation along the tethered DNA, consistent with other intercalators. The results of this study, along with previously examined ruthenium complex variants, illustrate tunable intercalation mechanisms guided by rational design of therapeutic and diagnostic small molecules to target and modify the DNA duplex.

On multiple infections by parasites with complex life cycles

Host manipulation is a common strategy of parasites with complex life cycles. It directly affects predator–prey dynamics in trophically transmitted parasites. Theoretical studies suggest that predation‐enhancing manipulation often decimates the prey population, making parasites prone to extinction. Host manipulation, however, can also reduce predation due to conflicting interests when multiple parasites infect a host, which is often neglected in theoretical studies. Misaligned interests of coinfecting parasites can occur due to limited carrying capacity or parasitoid developmental stage. Including this realistic complexity in a mathematical model, the results depart from previous studies substantially. We show that coinfecting multi‐trophic parasites can preserve the predator–prey system and themselves through manipulation and reproduction parameters. Our study highlights the necessity of and provides the means for incorporating the reality of multiple parasites and their multi‐trophic life cycles into the theory of parasite ecology.

Neutrosophic Approaches to Soliton Solutions for Nonlinear Time-Fractional Coupled Jaulent–Miodek System Using a Modified Laplace Adomian Dec omposition Method

This paper presents a modified Laplace Adomian decomposition method (MLADM) to solve the nonlinear time-fractional coupled Jaulent–Miodek system. The proposed approach provides convergent series solutions with easily computable components, demonstrating both accuracy and simplicity in its application. By employing the Caputo fractional derivative, this study establishes a robust framework for analyzing nonlinear behavior in fractional differential equations. The effectiveness of the method is validated through comparisons with previous studies, with results illustrated using graphical representations. The solutions proposed herein are significant for modeling complex and dynamic real-world phenomena across various scientific disciplines. All computations and graphical results were carried out using Mathematica, emphasizing the method’s reliability, precision, and ease of application to nonlinear fractional systems. The study of fractional nonlinear systems is crucial for modeling complex, dynamic, and uncertain processes, which are core aspects of neutrosophic science. By addressing the intricate behavior of the nonlinear time-fractional coupled Jaulent–Miodek system, this work advances mathematical models that encapsulate uncertainty, indeterminacy, and complex interactions. Such an alignment with the principles of neutrosophic science underscores the relevance of our approach to the objectives of the International Journal of Neutrosophic Science, highlighting its potential to enhance the understanding and practical applications of complex systems.

Development of polarizable and hydration-focused water models for the Martini 3 force field

Coarse-grained simulations have been increasingly employed in many fields of molecular chemistry. These models enable the evaluation of complex molecular structures by simplifying their description compared to all-atoms models. Specifically, the Martini 3 force field has been developed to describe biomolecular structures such as proteins, lipids, and carbohydrates. Their “building block” approach has been successfully used in modeling complex systems ranging from carbohydrate solutions, biological membranes, and even deep eutectic solvents. However, one main issue arises when considering interfacial systems: water is represented as a single particle with no effect of electrostatic interactions. In this work, we developed a polarizable water model for the Martini 3 force field, introducing charged particles to model the electrostatic interactions present in water systems accurately. Gaussian Processes were trained with simulation data for both bulk water and cross-interaction parameterization to consistently explore the parameter space and fine-tune our model to the target properties. The developed model showed good performance in reproducing the density and dielectric constant of bulk water. Regarding cross-interaction with other beads, two different sets of parameters were developed. The first, called the polarizable model, is parameterized to reproduce the hydration free energies of the original Martini 3 organic beads. This allows for a good description of free energies of transfer between water and organic phases, the main target property for the Martini 3 force field. The second, called the polarizable-hydration model, is parameterized to match the experimental hydration free energy of selected molecules. This allows for more accurate evaluation of hydration properties, which can be useful for water solutions. In particular, we show that the partitioning and interfacial behavior of nonionic surfactants is improved by the polarizable-hydration model when compared to the standard Martini 3 water model.

PDE-regularised spatial quantile regression

We consider the problem of estimating the conditional quantiles of an unknown distribution from data gathered on a spatial domain. We propose a spatial quantile regression model with differential regularisation. The penalisation involves a partial differential equation defined over the considered spatial domain, that can display a complex geometry. Such regularisation permits, on one hand, to model complex anisotropy and non-stationarity patterns, possibly on the basis of problem-specific knowledge, and, on the other hand, to comply with the complex conformation of the spatial domain. We define an innovative functional Expectation-Maximisation algorithm, to estimate the unknown quantile surface. We moreover describe a suitable discretisation of the estimation problem, and investigate the theoretical properties of the resulting estimator. The performance of the proposed method is assessed by simulation studies, comparing with state-of-the-art techniques for spatial quantile regression. Finally, the considered model is applied to two real data analyses, the first concerning rainfall measurements in Switzerland and the second concerning sea surface conductivity data in the Gulf of Mexico.

Clustering time-evolving networks using the spatiotemporal graph Laplacian.

Time-evolving graphs arise frequently when modeling complex dynamical systems such as social networks, traffic flow, and biological processes. Developing techniques to identify and analyze communities in these time-varying graph structures is an important challenge. In this work, we generalize existing spectral clustering algorithms from static to dynamic graphs using canonical correlation analysis to capture the temporal evolution of clusters. Based on this extended canonical correlation framework, we define the spatiotemporal graph Laplacian and investigate its spectral properties. We connect these concepts to dynamical systems theory via transfer operators and illustrate the advantages of our method on benchmark graphs by comparison with existing methods. We show that the spatiotemporal graph Laplacian allows for a clear interpretation of cluster structure evolution over time for directed and undirected graphs.

Random walks with long-range memory on networks.

We study an exactly solvable random walk model with long-range memory on arbitrary networks. The walker performs unbiased random steps to nearest-neighbor nodes and intermittently resets to previously visited nodes in a preferential way such that the most visited nodes have proportionally a higher probability to be chosen for revisit. The occupation probability can be expressed as a sum over the eigenmodes of the standard random walk matrix of the network, where the amplitudes slowly decay as power-laws at large times, instead of exponentially. The stationary state is the same as in the absence of memory, and detailed balance is fulfilled. However, the relaxation of the transient part becomes critically self-organized at late times, as it is dominated by a single power-law whose exponent depends on the second largest eigenvalue and on the resetting probability. We apply our findings to finite networks, such as rings, complete graphs, Watts-Strogatz, and Barabási-Albert networks, and to Barbell and comb-like graphs. Our study could be of interest for modeling complex transport phenomena, such as human mobility, epidemic spreading, or animal foraging.

Reactivity of a heterobinuclear heme-peroxo-Cu complex with para-substituted catechols shows a pK a-dependent change in mechanism.

In biological systems, heme-copper oxidase (HCO) enzymes play a crucial role in the oxygen reduction reaction (ORR), where the pivotal O-O bond cleavage of the (heme)FeIII-peroxo-CuII intermediate is facilitated by active-site (peroxo core) hydrogen bonding followed by proton-coupled electron transfer (PCET) from a nearby (phenolic) tyrosine residue. A useful approach to comprehend the fundamental relationships among H-bonding/proton/H-atom donors and their abilities to induce O-O bond homolysis involves the investigation of synthetic, bioinspired model systems where the exogenous substrate properties (such as pK a and bond dissociation energy (BDE)) can be systematically altered. This report details the reactivity of a heme-peroxo-copper HCO model complex (LS-4DCHIm) toward a series of substituted catechol substrates that span a range of pK a and O-H bond BDE values, exhibiting different reaction mechanisms. Considering their interactions with the bridging peroxo ligand in LS-4DCHIm, the catechol substrates are importantly capable of one or two (i) H-bonds, (ii) proton transfers, and/or (iii) net H-atom transfers, thereby making them attractive, yet complex candidates for studying the redox chemistry of the metal-bound peroxide. A combination of spectroscopic studies and kinetic analysis implies that the suitable modulation of pK a and O-H bond BDE values of catechols result in either double proton transfer with the release of H2O2 or double PCET resulting in reductive O-O bond rupture. The distinguishing role of substrate properties in directing the mechanism and outcome of O2 protonation/reduction reactions is discussed in terms of designing O2-reduction catalysts based on biological inspiration.

Finite Element Analysis of Cracked Beams: A Literature Review

Finite Element Analysis (FEA) is a powerful numerical tool for investigating the dynamic behavior of cracked beams, offering detailed insights into the effects of cracks on structural performance. FEA provides the ability to model complex geometries, varying material properties, and diverse boundary conditions with precision, making it an essential method for analyzing cracked beams. Finite Element Analysis (FEA) is a powerful numerical tool for investigating the dynamic behavior of cracked beams, offering detailed insights into the effects of cracks on structural performance. FEA provides the ability to model complex geometries, varying material properties, and diverse boundary conditions with precision, making it an essential method for analyzing cracked beams, and considering these facts, the present research work is devoted to the contributions of researchers in field of FEA of cracked beams. The review focuses finite element analysis used to analyze the cracked beams, and concludes with the investigated gaps in the research and objectives of a proposed research work.

Neural fractional differential networks for modeling complex dynamical systems

Neural fractional differential networks for modeling complex dynamical systems

Graph Neural Networks in Brain Connectivity Studies: Methods, Challenges, and Future Directions

Brain connectivity analysis plays a crucial role in unraveling the complex network dynamics of the human brain, providing insights into cognitive functions, behaviors, and neurological disorders. Traditional graph-theoretical methods, while foundational, often fall short in capturing the high-dimensional and dynamic nature of brain connectivity. Graph Neural Networks (GNNs) have recently emerged as a powerful approach for this purpose, with the potential to improve diagnostics, prognostics, and personalized interventions. This review examines recent studies leveraging GNNs in brain connectivity analysis, focusing on key methodological advancements in multimodal data integration, dynamic connectivity, and interpretability across various imaging modalities, including fMRI, MRI, DTI, PET, and EEG. Findings reveal that GNNs excel in modeling complex, non-linear connectivity patterns and enable the integration of multiple neuroimaging modalities to provide richer insights into both healthy and pathological brain networks. However, challenges remain, particularly in interpretability, data scarcity, and multimodal integration, limiting the full clinical utility of GNNs. Addressing these limitations through enhanced interpretability, optimized multimodal techniques, and expanded labeled datasets is crucial to fully harness the potential of GNNs for neuroscience research and clinical applications.