Accurate computational prediction of B-cell epitopes can greatly enhance biomedical research and rapidly advance efforts to develop therapeutics, monoclonal antibodies, vaccines, and immunodiagnostic reagents. Previous research efforts have primarily focused on the development of computational methods to predict linear epitopes rather than conformational epitopes, although the latter is much more biologically predominant. Several conformational B-cell epitope prediction methods have recently been published, but their predictive performances are weak. Here, we present a review of the latest computational methods and assess their performances on a diverse test set of 29 non-redundant unbound antigen structures. Our results demonstrate that ISPIPab outperformed all other methods. Finally, we suggest new strategies and opportu-nities to improve computational predictions of conformational B-cell epitopes.

Viral co-infections, where a host is infected by multiple viruses simultaneously, are common in the human population. Human viral co-infections can lead to complex interactions between viruses and the host immune system, affecting the clinical outcome and posing challenges for treatment. Understanding the types, mechanisms, impacts, and identification methods of human viral co-infections is crucial for the prevention and control of viral diseases. In this review, we first introduce the significance of studying human viral co-infections and summarize the current research progress and gaps in this field. We then classify human viral co-infections into four types based on the pathogenic properties and species of the viruses involved. Next, we discuss the molecular mechanisms of viral co-infections, focusing on virus-virus interactions, host immune responses, and clinical manifestations. We also summarize the experimental and computational methods for identifying viral co-infections, emphasizing the latest advances in high-throughput sequencing and bioinformatics approaches. Finally, we highlight the challenges and future directions in human viral co-infection research, aiming to provide new insights and strategies for the prevention, control, diagnosis, and treatment of viral diseases. This review provides a comprehensive overview of the current knowledge and future perspectives on human viral co-infections, and underscores the need for interdisciplinary collaborations to address this complex and important topic.

Background: Targeted methods that dominated toxicological research until recently did not allow for screening of all molecular changes involved in toxic response. Therefore, it is difficult to infer if all major mechanisms of toxicity have already been discovered, or if some of them are still overlooked. Objectives: To identify molecular mechanisms sensitive to chemical exposures in an unbiased manner. Methods: We used data on 641,516 unique chemical-gene interactions from the Comparative Toxicogenomic Database. Only data from high-throughput gene expression experiments with human, rat or mouse cells/tissues were extracted. The total number of chemical-gene interactions was calculated for every gene, and used as a measure of gene sensitivity to chemical exposures. These values were further used in enrichment analyses to identify molecular mechanisms sensitive to chemical exposures. Results: Remarkably, use of different input subsets with non-overlapping lists of chemical compounds identified largely the same genes and molecular pathways as most sensitive to chemical exposures, indicative of an unbiased nature of our analysis. One of the most important findings of this study is that almost every known molecular mechanism may be affected by chemical exposures. Predictably, xenobiotic metabolism pathways and mechanisms of cellular response to stress and damage were among the most sensitive. Additionally, our analysis identified a range of highly sensitive molecular pathways, which are not widely recognized by modern toxicology as major targets of toxicants, including lipid metabolism pathways, longevity regulation cascade and cytokine mediated signaling. Discussion: Molecular mechanisms identified as the most sensitive to chemical exposures are relevant for significant public health problems, such as aging, cancer, metabolic and autoimmune disease. Thus, public health system will likely benefit from future research focus on these sensitive molecular mechanisms. Additionally, approach used in this study may guide identification of priority adverse outcome pathways (AOP) for in-vitro and in-silico toxicity testing methods.

Electromyographic (EMG) signals provide information about a person's muscle activity. For hand movements, in particular, the execution of each gesture involves the activation of different combinations of the forearm muscles, which generate distinct electrical patterns. Conversely, the analysis of these muscle activation patterns, represented by EMG signals, allows recognizing which gesture is being performed. In this study, we aimed to implement an automatic identification system of hand or wrist gestures based on supervised Machine Learning (ML) techniques. We trained different computational models and determined which of these showed the best capacity to identify six hand or wrist gestures and generalize between different subjects. We used an open access database containing recordings of EMG signals from 36 subjects. Among the results obtained, we highlight the performance of the Random Forest model, with an accuracy of 95.39%, and the performance of a convolutional neural network with an accuracy of 94.77%.

Laser-induced breakdown spectroscopy (LIBS) is an important analysis technique with applications in many industrial branches and fields of scientific research. Nowadays, the advantages of LIBS are impaired by the main drawback in the analysis of collected data. This procedure is essentially based on the comparison of lines present in the spectrum with a literature database. This paper proposes the use of various computational intelligence methods to develop a reliable and fast classification of non-destructively acquired LIBS spectra into a set of predefined classes. We focus on a specific problem of classification of paper-ink samples into 30 separate, predefined classes. For each of 30 classes (10 pens of each of 5 ink types combined with 10 sheets of 5 paper types plus empty pages) 100 LIBS spectra are collected. Four variants of preprocessing, seven classifiers (Decision trees, Random forest, k-Nearest Neighbour, Support Vector Machine, Probabilistic Neural Network, Multi-Layer Perceptron, and Generalized Regression Neural Network), 5-fold stratified cross-validation and test on an independent set (for methods evaluation) scenarios are employed. Our developed system yielded an accuracy of 99.08% with average classification time of about 0.12 s is obtained using the random forest classifier. Our results clearly demonstrates that machine learning methods can be used to identify the paper-ink samples based on LIBS reliably at a faster rate.

Driven by the growing interest of the scientific community and the proliferation of research in this field, cranial implants have seen significant advancements in recent years regarding design techniques, structural optimisation, appropriate material selection and fixation system method. Custom implants not only enhance aesthetics and functionality but are also crucial for achieving proper biological integration and optimal blood irrigation, critical aspects in bone regeneration and tissue health. This research aims to optimize the properties of implants designed from triply periodic minimal surface structures. The gyroid architecture is employed for its balance between mechanical and biological properties. Experimental samples were designed varying three parameters of the surface model: cell size, isovalue and shape factor. Computational simulation tools were used for determining the relationship between those parameters and the response variables: the surface area, permeability, porosity and Young modulus. These tools include computer aided design, finite element method and computational fluid dynamics. With the simulated values, the corresponding regression models were fitted. Using the NSGA-II, a multi-objective optimisation was carried out, finding the Pareto set which includes surface area and permeability as targets, and fulfil the constraints related with the porosity and Young modulus. From these non-dominated solutions, the most convenient for a given application was chosen, and an optimal implant was designed, from a patient computed tomography scan. An implant prototype was additively manufactured for validating the proposed approach.

This work focuses on identifying the most effective machine learning techniques and supervised learning models to precisely estimate power output from Photovoltaic (PV) plants. The performance of various regression models is analyzed by harnessing experimental data, including Random Forest, Support Vector Regression (SVR), Multi-layer Perceptron (MLP), Linear Regressor (LR), Gradient Boosting, and k-Nearest Neighbors (KNN). The methodology applied starts with meticulous data preprocessing steps aimed at ensuring dataset integrity. Following the preprocessing phase, which entails eliminating missing values and outliers using Isolation Feature selection based on a correlation threshold, is performed to identify relevant parameters for accurate prediction in PV systems. Subsequently, Isolation Forest is employed for outlier detection, followed by model training and evaluation using key performance metrics such as Root Mean Squared Error (RMSE), Normalized Root Mean Squared Error (NRMSE), Mean Absolute Error (MAE), and R-squared (R2). Among the array of models evaluated, Random Forest emerges as the top performer, highlighting promising results with an RMSE of 19.413, NRMSE of 0.048% and an R2 score of 0.968. Furthermore, the best-performing model is integrated into a MATLAB application for real-time predictions, thereby enhancing usability and accessibility for a wide range of applications in renewable energy.

Cotton leaf curl Kokhran virus (CLCuKoV) (genus, Begomovirus; family, Geminiviridae) is highly infectious, widespread and the most dangerous pathogen of cotton (Gossypium hirsutum L.) that is responsible to a serious disorder, cotton leaf curl disease (CLCuD). Begomoviruses are spread very efficiently by the whitefly Bemisia tabaci cryptic species, causing economic losses to cotton crop, all over the world. The ‘Lucknow’ strain of CLCuKoV has emerged as a divergent isolate that could cause CLCuD. The monopartite ssDNA genome of CLCuKoV-Lu (2.7 Kb) contains six open reading frames (ORFs) that was shown to encode four major proteins. RNA interference (RNAi)-based antiviral innate immunity is a sequence-specific biological phenomenon and a powerful tool to control plant viruses. The present study aims to determine cotton locus-derived microRNAs (ghr-miRNAs) that are identified for targeting the CLCuKoV-Lu ss-DNA-encoded mRNAs using a predictive approach that involves four computational algorithms, miRanda, RNA22, psRNATarget and RNAhybrid. Mature ghr-miRNA sequences (n=80) from allotetraploid upland cotton (2n = 4x = 52) were selected from the miRBase and were tested for alignment with the CLCuKoV-Lu genome. Among the 80 cotton locus-derived ghr-miRNAs evaluated, only one consensus cotton locus-derived ghr-miRNA (ghr-miR2950) was concluded to have effective ghr-miRNA target site at common nucleotide position 82 in the CLCuKoV-Lu genome respectively, using a stringent criterion, identified by all the algorithms used. The miRNA targeting is reliant on base pairing of miRNA-mRNA target pairings. Conservation of the hybridization binding site of the predicted ghr-miR2950 was validated using multiple sequence alignment within all the strains of CLCuKoV. We constructed a regulatory interaction network of miRNA–mRNA to identify novel targets. The efficacy of the predicted miRNAs against CLCuKoV-Lu was evaluated by RNAi-mediated targeted mRNA cleavage. The current investigated miRNA targets provide evidence for the development of CLCuD-resistant cotton plants.

The objective of this work is to analyze the improvement of heat transfer in trapezoidal fin heat exchangers using numerical simulation. Heat exchangers are essential components frequently used in contemporary electronic gadgets to effectively disperse heat produced by high-power components. Nevertheless, as electronic devices continue to advance and become more powerful, the heat transfer capacity of heat exchangers is facing more requirements, therefore requiring enhanced heat dissipation efficiency. Afterwards, various fin angles were adjusted, and the heat transmission efficiency was compared. Modifying the angles can significantly improve the effi-ciency of convective heat dissipation. Moreover, it has been found that employing an optimized trapezoidal fin structure can further enhance the efficiency of heat transfer. The results indicate that trapezoidal fin heat exchangers provide superior heat dissipation efficiency compared to conventional heat exchangers. This is important for enhancing the thermal dissipation capabili-ties of electrical gadgets, hence guaranteeing steady functionality and extending their longevity. Subsequent study might explore supplementary methods to boost heat transmission in different heat exchanger configurations in order to meet the growing requirements for heat dissipation.

This study explores an optimal strategy for muscle force estimation by integrating simplified cost functions with detailed muscle models, demonstrating significant advantages over traditional complex methods. Using elbow flexion as a case study, we characterize the interplay between muscle models and cost functions, showing that accurate muscle force estimations can be achieved effectively without electromyography (EMG) data while also substantially reducing computational demands. Validated against established bio-mechanical data, our models confirm their accuracy in predicting muscle behavior. This approach has profound implications for enhancing rehabilitation protocols and athletic training, providing a more accessible and adaptable tool for muscle force analysis. It broadens the applicability of muscle force estimation in clinical and sports settings and paves the way for future innovations in bio-mechanical research.

Hemodynamic relevance of differently located stenoses of the internal jugular veins remains undetermined. It particularly concerns nozzle-like strictures in the upper parts of these veins and stenotic jugular valves located at the end of this vein. This study was aimed at understanding flow disturbances caused by such stenoses. Computational fluid dynamics software, Flowsquare+, was used. We constructed 3-dimensional models of venous outflow, comprising two alternative routes: tube representing the internal jugular vein and irregular network representing the vertebral veins. At the beginning of the tube representing the internal jugular vein, differently-shaped and sized short strictures, representing nozzle-like strictures were built in. At the end of this tube, differently-shaped membranes, representing the jugular valve, were built in. With the use of computational fluid dynamics modeling, we studied how these two obstacles influenced the outflow. We found that the most relevant outflow disturbances were evoked by nozzle-like strictures in the upper part of the internal jugular vein that were either small, long or asymmetrically positioned. Very tight stenotic valves and septum-like malformed valve were equally hemodynamically relevant. These findings suggest that both upper and lower strictures of the internal jugular vein can be of clinical significance.

Short-term energy consumption forecasting crucial for the operation of new electrical en-ergy grids, not only due to their own characteristics, but also due to the new elements pre-sent in the energy matrix. Many existing models are limited by their methodologies or by considering only a narrow set of factors influencing energy consumption. This study in-troduces a pipeline for developing an energy forecasting model and evaluates the signifi-cance of factors affecting energy consumption and, consequently, forecast accuracy. The study utilizes a dataset from ISO NE (Independent System Operator New England), span-ning the total electric load of various cities in New England from January 2017 to December 2019. This dataset comprises 23 independent variables, including weather data, economic indicators, and market information. The results outline the steps involved in constructing energy forecast models using time series analysis. By carefully selecting variables and rep-resenting external factors, the study demonstrates the feasibility of generating more accu-rate predictions with reduced computational resources.

Enhanced electrical activity in detrusor smooth muscle (DSM) cells is a key factor in detrusor overactivity which causes overactive bladder pathological disorders. Transient receptor potential melastatin-4 (TRPM4) channels, calcium-activated cation channels, play a role in regulating DSM electrical activities. These channels likely contribute to depolarizing the DSM cell membrane, leading to bladder overactivity. Our research focuses on understanding TRPM4 channel function in mouse DSM cells using computational modeling. We aimed to create a detailed computational model of the TRPM4 channel based on existing electrophysiological data. We employed a modified Hodgkin-Huxley model with an incorporated TRP-like current to simulate action potential firing in response to current and synaptic stimulus inputs. Validation against experimental data showed close agreement with our simulations. Our model is the first to analyze the TRPM4 channel's role in DSM electrical activity, potentially revealing insights into bladder overactivity. In conclusion, TRPM4 channels are pivotal in regulating human DSM function and TRPM4 channel inhibitors could be promising targets for treating overactive bladder.

Topological interlocking assembly (TIA) and computational architecture treat form as an emergent property of a material system, where the final shape results from the interplay of geometries and geometric interdependencies influenced by material, structural, and fabrication constraints. This paper posits that TIA is an ideal pedagogical tool for introducing students to computational architecture, its theoretical foundations and design principles. Specifically, defining TIA as a material system provides a robust educational approach for engaging students with computation, fostering design processes through bottom-up, hands-on investigations and procedural logic, understanding generative geometric rules, and exploring the flexibility of parametric variations. The methodology is illustrated through a Design Workshop and study unit from the Bachelor’s and Master’s programs at the Faculty for the Built Environment, University of Malta. The paper presents four investigations of defining a material system based on TIA - of tetrahedra, cones, octahedra, and osteomorphic blocks—as case studies to demonstrate how these exercises introduce students to computational thinking, parametric design, and fabrication techniques. Finally, the paper discusses the advantages and limitations of this pedagogical methodology.

Brain is the most complex organ in the human body and it is divided into two hemispheres - left and right hemispheres. Left hemisphere is responsible for control of right side of our body whereas right hemisphere is responsible for control of left side of our body. Brain image segmentation from different neuroimaging modalities is one of the important parts in clinical diagnostic tools. Neuroimaging based digital imagery generally contain noise, inhomogeneity, aliasing artifacts, and orientational deviations. Therefore, accurate segmentation of brain images is a very difficult task. However, the development of accurate segmentation of brain images is very important and crucial for a correct diagnosis of any brain related diseases. One of the fundamental segmentation tasks is to identify and segment inter-hemispheric fissure/mid-sagittal plane, which separate the two hemispheres of the brain. Moreover, the symmetric/asymmetric analyses of left and right hemispheres of brain structures are important for radiologists to analyze diseases such as Alzheimer's, Autism, Schizophrenia, Lesions and Epilepsy. Therefore, in this paper we have analyzed the existing computational techniques used to find brain symmetric/asymmetric analysis in various neuroimaging techniques (MRI/CT/PET/SPECT), which are utilized for detecting various brain related disorders.

This study presents the complexity and sensitivity of chaotic system dynamics in the case of the double pendulum. It applied detailed numerical analyses of the double pendulum in multiple computing platforms in order to demonstrate the complexity in behavior of the system of double pendulums. The equations of motion were derived from the Euler-Lagrange formalism, in order to capture the system's dynamics, which is coupled nonlinearly. These were solved numerically using the efficient Runge-Kutta-Fehlberg method, implemented in Python, R, GNU Octave, and Julia, while runtimes and memory usage were extensively benchmarked across these environments. Time series analyses, including the calculation of Shannon entropy and the Kolmogorov-Smirnov test, quantified the system's unpredictability and sensitivity to infinitesimal perturbations of the initial conditions. Phase space diagrams illustrated the intricate trajectories and strange attractors, as further confirmation of the chaotic nature of the double pendulum. All the findings have a clear indication of the importance of accurate measurements of the initial condition in a chaotic system, contributing to an increased understanding of nonlinear dynamics. Future research directions are faster simulations using Numba and GPU computing, stochastic effects, chaotic synchronization, and applications in climate modeling. This work will be useful for understanding chaos theory and efficient computational approaches in complex systems of dynamical nature.

Predictive numerical models in the study of ground-borne vibrations generated by railway sys-tems have traditionally relied on the segmented approach of subsystems. In this approach, loads are individually applied, and the cumulative effect of rolling stock is obtained through superposi-tion. While this method serves to mitigate computational costs, it may not fully capture the com-plex interactions involved in ground-borne vibrations. Recent advancements in computational and software tools have enabled the development of more sophisticated vibrational pollution models. These advanced models encompass the entire dynamic system, from the rolling stock to the terrain, allowing for continuous simulations with a defined time step. Furthermore, the incorporation of computational contact tools between various ele-ments not only ensures temporal accuracy but also extends the analysis to the frequency domain. This paper explores the evolution of predictive numerical models in the context of ground-borne vibrations generated by trains on ballasted tracks. It highlights the shift from segmented models to continuous simulations, demonstrating the progressive advancement towards more comprehen-sive and accurate representations of railway-induced and generated vibrations in the track sur-roundings as an additional way of evaluate sustainability of infrastructures. In particular, this development of a numerical model from its inception aimed to expand upon ex-isting emission - prediction models by enriching the data with frequency-domain information. This approach has been driven by the aspiration to bridge the gap between numerical predictions and measured signals, striving for a higher level of accuracy in our obtained results. This model can predict the impact of a high-speed rail (HSR) vehicle passing on its own track. It possesses sufficient information in both the time and frequency domains to precisely characterize vibrations. This initiative marks a pivotal advancement, enabling us to more effectively capture the intricacies of ground-borne vibrations and their impact on the surrounding environment due to a deeper comprehension of the occurrences in the frequency domain, there's a heightened ca-pability to accurately characterize vibrations.

Fractional flow reserve (FFR) is well recognized as a gold standard measure for the estimation of functional coronary stenosis. Technological progressions in image processing have empowered the reconstruction of three-dimensional models of the coronary arteries via both non-invasive imaging modalities, such as Coronary Computed Tomography Angiography (CCTA), and invasive coronary angiography or intravascular imaging. The application of computational fluid dynamics (CFD) techniques to coronary 3D anatomical models allows the virtual evaluation of the hemodynamic significance of a coronary lesion with high diagnostic accuracy in comparison to the invasive FFR using pressure wires. These opportunities providing hemodynamic information based on anatomy have given rise to a new era of functional angiography and coronary imaging. However, further validations are needed to overcome several scientific and computational challenges before these methods are applied in everyday clinical practice. This review aims to present all the data on the studies and methodologies that have been presented for the virtual estimation of FFR, and the diagnostic execution of the virtual tools for the hemodynamic assessment of coronary lesions compared to the invasive FFR.

In the power plant industry, the turbine inlet temperature (TIT) plays a key role in the efficiency of the gas turbine and, therefore, the overall—in most cases combined—thermal power cycle efficiency. Gas turbine efficiency increases by increasing TIT. However, an increase of TIT would increase the turbine component temperature which can be critical (e.g., hot gas attack). Thermal barrier coatings (TBCs)—porous media coatings—can avoid this case and protect the surface of the turbine blade. This combination of TBC and film cooling produces a better cooling performance than conventional cooling processes. The effective thermal conductivity of this composite is highly important in design and other thermal/structural assessments. In this article, the effective thermal conductivity of a simplified model of TBC is evaluated. This work details a numerical study on the steady-state thermal response of two-phase porous media in two dimensions using personal finite element analysis (FEA) code. Specifically, the system response quantity (SRQ) under investigation is the dimensionless effective thermal conductivity of the domain. A thermally conductive matrix domain is modeled with a thermally conductive circular pore arranged in a uniform packing configuration. Both the pore size and the pore thermal conductivity are varied over a range of values to investigate the relative effects on the SRQ. In this investigation, an emphasis is placed on using code and solution verification techniques to evaluate the obtained results. The method of manufactured solutions (MMS) was used to perform code verification for the study, showing the FEA code to be second-order accurate. Solution verification was performed using the grid convergence index (GCI) approach with the global deviation uncertainty estimator on a series of five systematically refined meshes for each porosity and thermal conductivity model configuration. A comparison of the SRQs across all domain configurations is made, including uncertainty derived through the GCI analysis.

After UV excitation, gas phase thymine returns to ground state in 5 to 7 ps, showing multiple time constants. There is no consensus on the assignment of these processes, with a dispute between models claiming that thymine is trapped either in the first (S1) or in the second (S2) excited states. In the present study, nonadiabatic dynamics simulation of thymine is performed on the basis of ADC(2) surfaces, to understand the role of dynamic electron correlation on the deactivation pathways. The results show that trapping in S2 is strongly reduced in comparison to previous simulations considering only non-dynamic electron correlation on CASSCF surfaces. The reason for the difference is traced back to the energetic cost for formation of a CO p bond in S2.

We report on the state of the art of proteins recognized as potential targets for the development of leishmania treatments through the search of biologically active chemical species, either from experimental in vitro, in vivo, or in silico sources. We classify the gathered information, in several ways: vector taxonomy and geographical distribution, leishmania parasite taxonomic and geographical distribution and enzymatic function (oxidoreductases, transferases, hydrolases, lyases, isomerases, ligases and cytokines). Our aim is to provide a much needed reference layout for research efforts aimed to understand the background of ligand-protein activation/inactivation processes, in this specific case, related with enzymes known to be part of biochemical cascade reactions initiated following a leishmania infectious episode.

Conditional Independence (CI) testing is fundamental in statistical analysis. For example, CI testing helps validate causal graphs or longitudinal data analysis with repeated measures in causal inference. CI testing is difficult, especially when testing involves categorical variables conditioned on a mixture of continuous and categorical variables. Current parametric and non-parametric testing methods designed for continuous variables and can quickly fall short in the categorical case. This paper presents a computational approach for CI testing for categorical data types, which we call computational conditional independence (CCI) testing. The test procedure is based on permutation and combines machine learning prediction algorithms and Monte Carlo Cross-Validation. We evaluated the approach through simulation studies and assessed the performance against alternative methods; the generalized covariance measure (GCM) test, the kernel conditional independence (KCI) test, and testing with multinomial regression. We find that the computational approach to testing has utility over the alternative methods, and we can achieve better control over type-I-error rates. We hope this work can expand the toolkit for CI testing for practitioners and researchers.

This paper explores the transformative impact of computational pharmaceutics, integrating AI, multi-scale modeling, quantum computing, AI-driven drug discovery, and blockchain frameworks. Molecular dynamics simulations provide insights into atomic behaviors crucial for drug design. Quantum computing reduces computational costs for simulating protein-drug interactions. AI and Graph Neural Networks accelerate drug discovery and personalized medicine. Blockchain frameworks facilitate secure pharmacogenetic data sharing. These advancements revolutionize drug development, offering unprecedented efficiency, innovation, and personalized treatments.

Despite the growing capabilities of large language models, concerns exist about the biases they develop. In this paper, we propose a novel, automated mechanism for debiasing through specified dataset augmentation in the lens of bias producers that can be useful in a variety of industries, especially ones that are “restricted” and have limited data. We consider that bias can occur due to intrinsic model architecture and dataset quality. The two aspects are evaluated using two different metrics we created. We show that our dataset augmentation algorithm reduces bias as measured by our metrics.

In this paper, a two-phase model of air shock wave induced by rock-fall was described. The model was made up of the uniform motion phase (velocity was close to 0 m·s^-1) and the acceleration movement phase. The uniform motion phase was determined by experience, meanwhile the acceleration movement phase was derived by the theoretical analysis. A series of experiments were performed to verify the two-phase model and obtained the law of the uniform motion phase. The acceleration movement phase was taking a larger portion when height of rock-fall was higher with the observations. Experimental results of different falling heights showed good agreements with theoretical analysis values. Computational fluid dynamics (CFD) numerical simulation had been carried out to study the variation velocity with different falling height. As a result of this, the two-phase model could accurately and convenient estimating the velocity of air shock wave induced by rock-fall. The two-phase model could provide a reference and basis for estimating the air shock waves' velocity and designing the protective measures.

Elevated blood glucose levels, known as glycemia, play a significant role in sudden cardiac arrest, often resulting in sudden cardiac death, particularly among those with diabetes. Understanding the internal mechanisms has been a challenge for healthcare professionals, leading many research groups to investigate the relationship between blood glucose levels and cardiac electrical activity. Our hypothesis suggests that glucose-sensing biophysics mechanisms in cardiac tissue could clarify this connection. To explore this, we adapted a single-compartment, computational model of the human atrial node's action potential. We incorporated glucose-sensing mechanisms with voltage-gated sodium ion channels using ordinary differential equations. Parameters for the model were based on existing experimental studies to mimic the impact of glucose levels on atrial node action potential firing. Simulations using voltage clamp and current clamp techniques showed that elevated glucose levels decreased sodium ion channel currents, leading to a reduction in the sinoatrial node action potential frequency. In summary, our mathematical model provides a cellular-level understanding of how high glucose levels can lead to bradycardia and sudden cardiac death.

In this article, one-dimensional viscous quantum hydrodynamical model of semiconductor devices is numerically investigated. The model treats the propagation of electrons in a semiconductor device as the flow of a charged compressible fluid. It plays an important role in predicting the behavior of electron flow in semiconductor devices. The nonlinear viscous quantum hydrodynamic models contain Euler-type equations for density and current, viscous and quantum correction terms, and a Poisson equation for electrostatic potential. Due to high nonlinearity of model equations, numerical solution techniques are applied to obtain their solutions.. The proposed numerical scheme is a splitting scheme based on the kinetic flux-vector splitting (KFVS) method for the hyperbolic step, and a semi-implicit Runge-Kutta method for the relaxation step. The KFVS method is based on the direct splitting of macroscopic flux functions of the system on the cell interfaces. The second order accuracy of the scheme is achieved by using MUSCL-type initial reconstruction and Runge-Kutta time stepping method. Several case studies are considered. For validation, the results of current scheme are compared with those obtained from the splitting scheme based on the NT central scheme. The effects of various parameters such as device length, viscosities, different doping and voltage are analyzed. The accuracy, efficiency and simplicity of the proposed KFVS scheme validates its generic applicability to the given model equations.

Partial Differential Equations (PDEs) constitute a fundamental framework for modeling various physical phenomena across diverse fields such as physics, engineering, and finance. Solving PDEs accurately and efficiently remains a significant challenge, particularly when dealing with complex systems or high-dimensional data. In this paper, we present a novel approach based on deep learning techniques to investigate the effective factors in solving PDEs. Our methodology leverages the expressive power of deep neural networks to learn and represent the underlying relationships between input parameters and the solutions of PDEs. By incorporating domain knowledge into the network architecture and training process, our approach aims to provide insights into the key factors influencing the solution of PDEs. We demonstrate the effectiveness of our method through numerical experiments on various PDEs, showcasing its capability to identify important features and improve predictive accuracy. Our findings suggest that deep learning techniques offer a promising avenue for understanding and analyzing the complex dynamics inherent in PDEs, paving the way for enhanced computational methods in scientific and engineering applications.

Wall Shear Stress (WSS) abnormalities in carotid artery blood flow are key hemodynamic factors leading to arterial endothelial damage, atherosclerotic plaque formation, and carotid artery stenosis. Precise simulation of carotid artery blood flow WSS in vitro play an important role in understanding the underlying hemodynamic mechanism of carotid artery stenosis. This study presents a design for a microfluidic cell culture chamber with a variable cross-section and a step structure, leveraging microfluidic technology and principles of fluid dynamics. We propose an optimization strategy for microchannel dimensions based on Computational Fluid Dynamics (CFD) simulations. This strategy aims to replicate the distinct WSS waveform features found at various locations within the carotid bifurcation as observed in vivo. Our simulations indicate that with a step height of 0.09 mm, a width of 4 mm for the initial segment, and 5 mm for the subsequent wider channel, the device can effectively model the characteristics of low oscillatory WSS seen at sites of carotid sinus stenosis. Furthermore, it accurately represents the high-magnitude pulsatile WSS waveforms in the more uniform arterial sections downstream of the carotid sinus. The study also reveals that vortex formation and variations in low oscillatory WSS within the stepped section of the microchannel correlate with the step's height and width, as well as with the dimensions of the wider channel section. An increase in step width leads to a decrease in both the vortex area and the magnitude of negative WSS oscillations. When the step height is less than 0.03 mm, vortex formation is inhibited, challenging the simulation of low oscillatory WSS patterns. The variable cross-section microfluidic channel developed in this study provides a platform for simulating carotid artery WSS waveforms and facilitates research into the interplay between low oscillatory WSS at sites of carotid sinus stenosis and arterial endothelial dysfunction.

This research study focuses on addressing practical optimization challenges in audio-visual content using advanced computational intelligence methods, with a specific emphasis on privacy-preserving issues. The research will be presented as a proposal and can be conducted based on the provided ideas. Computational intelligence encompasses various approaches, including evolutionary algorithms, learning methods, fuzzy systems, and neural networks, which have become essential tools for tackling complex engineering optimization problems due to computational expenses, dimensional complexities, and interdisciplinary integration. The research objectives include a thorough investigation of optimization challenges in audio-visual content, the delineation of problem characteristics, and the development of relevant methodologies based on these specifications. The main goals of the study, titled "Advanced Computational Intelligence Assisted Less-Expensive Privacy-Preserving Optimization," encompass the development of a metamodel-based learning algorithm integrated with evolutionary computation for privacy-preserving optimization, less-expensive signal watermarking optimization using hybrid metamodeling and evolutionary algorithms, and a closed-loop robust intelligent PID control system applied in real-time dynamic signal watermarking. Additional objectives may be defined as the research progresses and further background review is conducted.

Drug-target binding is a crucial parameter in drug discovery and design, ensuring drug efficacy and specificity. Semaglutide, a potent GLP-1 receptor agonist, is widely used to treat type 2 diabetes mellitus by regulating blood glucose levels and promoting weight loss. This study introduces a novel approach utilizing the concept of a general intermolecular binding affinity calculator (GIBAC) for designing semaglutide analogues with enhanced binding affinity to GLP-1R. For the first time, a Val27-Arg28 exchange was manually introduced to strengthen the semaglutide-GLP-1R binding affinity. A comprehensive structural and biophysical analysis was conducted to explore the semaglutide-GLP-1R sequence space, leading to the identification of promising analogues. Among these, one semaglutide analogue demonstrated a binding affinity to GLP-1R that is more than two orders of magnitude (113.3 times) higher than native semaglutide, achieving a Kd of 3.0 x 10^-8 M compared to the Kd of 3.4 x 10^-6 M for native semaglutide. This article proposes a promising structural biophysical approach for developing GLP-1 receptor agonists with improved efficacy. The prototype GIBAC, termed semaGIBAC, represents a paradigm shift in precise drug discovery and design, advocating for the construction of a full-scale GIBAC to be prioritized within the drug discovery and design community.

Background and Objectives: To assess femoral shaft bowing (FSB) in the coronal and sagittal planes and introduce the clinical implications of total knee arthroplasty (TKA) by analyzing a three-dimensional (3D) model with virtual implantation of the femoral component. Materials and Methods: Sixty-eight patients (average age: 69.1 years) underwent 3D model reconstruction, incorporating medullary canals, by importing computed tomography (CT) data into Mimics® software. A mechanical axis (MA) line was drawn from the midportion of the femoral head to the center of the intercondylar notch; proximal/distal straight centerlines (length, 60 mm; diameter, 1 mm) were placed in the center of the medulla canal. Acute angles between the two centerlines were measured as lateral and anterior bowing. The acute angle by distal centerline and MA line was measured as distal coronal and sagittal alignment in both anteroposterior (AP) and lateral views. The diameter of curve (DOC) of the medulla along the posterior border was measured. Results: The mean lateral bowing on the AP view was 3.71°, and the mean anterior bowing on the lateral view was 11.82°. The average DOC of the medullary canal was 1501.68 mm. The distal coronal alignment of all femurs was an average of 6.40°, and the distal sagittal alignment was 2.66°. Overall, 22 femurs had coronal bowing, 42 had sagittal bowing, and 15 had both. Conclusion: In Asians, FSB is present in coronal, sagittal, or both directions. Increased anterolateral FSB may lead to cortical abutment in the sagittal plane despite limited space in the coronal plane. During TKA, distal coronal alignment can guide distal femoral valgus cut angle, whereas distal sagittal alignment helps anticipate femoral component positioning, avoiding anterior notching. Osteotomy along the anterior cortical bone to prevent notching may result in outliers due to differences between distal sagittal alignment and distal anterior cortical axis.

Although Eulerian approaches are standard in computational acoustics, they are less effective for certain classes of problems like bubble acoustics and combustion noise. A different approach for solving acoustic problems is to compute with individual particles following particle motion. In this paper, a Lagrangian approach to model sound propagation in moving fluid is presented and implemented numerically, using three meshfree methods to solve the Lagrangian acoustic perturbation equations (LAPE) in the time domain. The LAPE split the fluid dynamic equations into a set of hydrodynamic equations for the motion of fluid particles and perturbation equations for the acoustic quantities corresponding to each fluid particle. Then, three meshfree methods, the smoothed particle hydrodynamics (SPH) method, the corrective smoothed particle (CSP) method, and the generalized finite difference (GFD) method, are introduced to solve the LAPE and the linearized LAPE (LLAPE). The SPH and CSP methods are widely used meshfree methods, while the GFD method based on the Taylor series expansion can be easily extended to higher orders. Applications to modeling sound propagation in steady or unsteady fluids in motion are outlined, treating a number of different cases in one and two space dimensions. A comparison of the LAPE and the LLAPE using the three meshfree methods is also presented. The Lagrangian approach shows good agreement with exact solutions. The comparison indicates that the CSP and GFD method exhibit convergence in cases with different background flow. The GFD method is more accurate, while the CSP method can handle higher Courant numbers.

This study presents an extensive collection of data on the aerodynamic behavior at a low Reynolds number and geometric coefficients for 2900 airfoils obtained through the class shape transformation (CST) method. By employing a verified OpenFOAM-based CFD simulation framework, lift and drag coefficients were determined at a Reynolds number of 10^5. Considering the limited availability of data on low Reynolds number airfoils, this dataset is invaluable for a wide range of applications, including unmanned aerial vehicles (UAVs) and wind turbines. Additionally, the study offers a method for automating CFD simulations that could be applied to obtain aerodynamic coefficients at higher Reynolds numbers. The breadth of this dataset also supports the enhancement and creation of machine learning (ML) models, further advancing research into the aerodynamics of airfoils and lifting surfaces.

COVID-19 has been extremely difficult to control. The lack of understanding of key aspects of pandemics has affected virus transmission. On the other hand, there is a demand to incorporate Computational Thinking (CT) in the curricula with applications in STEM. However, there are still no exemplars in the curriculum that apply CT to real-world problems such as controlling a pandemic or other similar global crises. In this paper, we fill this gap by proposing exemplars of CT for modeling the pandemic. We designed exemplars following the three pillars of the APEC InMside framework for CT: algorithmic thinking, computational modeling, and machine learning. For each pillar, we designed a progressive sequence of activities that covers from elementary to high school. In an experimental study with elementary and middle school students from 2 schools of high vulnerability, we found that the computational modeling exemplar can be implemented by teachers and correctly understood by students. We conclude that it is feasible to introduce the exemplars at all grade levels, and that this is a powerful example of STEM integration that helps reflect and tackle real-world and challenging public health problems of great impact for students and their families.

Gastric cancer is the fifth most common and fourth deadliest cancer worldwide, with a bleak 5-year survival rate of about 20%. Despite significant research into its pathobiology, prognostic predictability remains insufficient due to pathologists’ heavy workloads and the potential for diagnostic errors. Consequently, there is a pressing need for automated and precise histopathological diagnostic tools. This study leverages Machine Learning and Deep Learning techniques to classify histopathological images into healthy and cancerous categories. By utilizing both handcrafted and deep features and shallow learning classifiers on the GasHisSDB dataset, we conduct a comparative analysis to identify the most effective combinations of features and classifiers for differentiating normal from abnormal histopathological images without employing fine-tuning strategies. Our methodology achieves an accuracy of 95% with the SVM classifier, underscoring the effectiveness of feature fusion strategies. Additionally, cross-magnification experiments produced promising results with accuracies close to 80% and 90% when testing the models on unseen testing images with different resolutions.

The vertebrate basal ganglia are thought to play an important role in action selection—the resolution of conflicts between alternative motor programs. The effective operation of basal ganglia circuitry is also known to rely on appropriate levels of tonic dopamine transmission. We show that when the tonic level of simulated dopamine in a robotic model of the basal ganglia is significantly reduced or increased, relative to an effective operating baseline, a variety of behavioral outcomes are observed that provide interesting comparisons with the results of human and animal studies. The main findings were that progressive reductions in the levels of simulated dopamine caused a slowing of the robot’s movements and eventually an inability to initiate movement. These states were partially relieved at increased salience levels (stronger sensory/motivational input). Conversely, increased levels of simulated dopamine could cause distortion of the robot’s motor acts through partially-expressed motor activity relating to losing actions; this could also lead to increased frequency of switching between behaviors. Levels of simulated dopamine that were either significantly lower or higher than baseline could cause changes to the timing of behavior switching that could cause a loss of behavioral integration, sometimes leaving the robot in a ‘behavioral trap’. That some analogous traits are observed in animals and humans affected by dopamine dysregulation suggests that embodied (robotic) models could prove useful in understanding the role of dopamine neurotransmission in basal ganglia function and dysfunction. That the effects of simulated dopamine on robot action selection were partially, but not fully, predictable from the selection properties of the non-embodied basal ganglia model, also points to the added value of using robotic models to explore the relationship between brain activity and behavior.

The development of computational acoustics allows simulation of sound generation and propagation in complex environment. In particular, meshfree methods are widely used to solve acoustics problems through arbitrarily distributed field points and approximation smoothness flexibility. As a Lagrangian meshfree method, smoothed particle hydrodynamics (SPH) method reduce the difficulty in solving problems with deformable boundaries, complex topologies, or multiphase medium. The traditional SPH method has been applied in acoustic simulation. This study presents the corrective smoothed particle method (CSPM), which is a combination of SPH kernel estimate and Taylor series expansion. The CSPM is introduced as a Lagrangian approach to improve accuracy in solving acoustic wave equations in the time domain. Moreover, a boundary treatment technique based on the hybrid meshfree and finite difference time domain (FDTD) method is proposed to represent different acoustic boundaries with particles. To model sound propagation in pipes with different boundaries, soft, rigid, and absorbing boundary conditions are built with this technique. Numerical results show that the CSPM algorithm is consistent and demonstrates convergence with exact solutions. Main computational parameters are discussed, and different boundary conditions are validated to be effective for benchmark problems in computational acoustics.

In this study we discuss critical issues in modelling the structure and function of lymph nodes (LNs), with emphasis on how LN physiology is related to its multi-scale structural organization. In addition to macroscopic domains such as B-cell follicles and the T cell zone, there are vascular networks which play a key role in the delivery of information to the inner parts of the LN, i.e., the conduit and blood microvascular networks. We propose object-oriented computational algorithms to model the 3D geometry of the fibroblastic reticular cell (FRC) network and the microvasculature. Assuming that a conduit cylinder is densely packed with collagen fibers, the computational flow study predicted that the diffusion should be a dominating process in mass transport than convective flow. The geometry models are used to analyze the lymph flow properties through the conduit network in unperturbed- and damaged states of the LN. The analysis predicts that elimination of up to 60–90 % of edges is required to stop the lymph flux. This result suggests a high degree of functional robustness of the network.

We extend a previously developed model for the Stark resonances of the water molecule. The method employs a partial-wave expansion of the single-particle orbitals using spherical harmonics. To find the resonance positions and decay rates, we use the exterior complex scaling approach which involves the analytic continuation of the radial variable into the complex plane and yields a non-hermitian Hamiltonian matrix. The real part of the eigenvalues provides the resonance positions (and thus the Stark shifts), while the imaginary parts $-\Gamma/2$ are related to the decay rates $\Gamma$, i.e., the full-widths at half-maximum of the Breit-Wigner resonances. We focus on the three outermost (valence) orbitals, as they are dominating the ionization process. We find that for forces directed along the three Cartesian co-ordinates, the fastest ionizing orbital always displays a non-monotonic Stark shift. For the case of fields along the molecular axis we show results as a function of the number of spherical harmonics included ($\ell_{\max}=3,4$). Comparison is made with total molecule resonance parameters from the literature obtained with Hartree-Fock and coupled cluster methods.

Embryonic development proceeds through a series of differentiation events. The mosaic version of this process (binary cell divisions) can be analyzed by comparing early development of Ciona intestinalis and Caenorhabditis elegans. To do this, we reorganize lineage trees into differentiation trees using the graph theory ordering of relative cell volume. Lineage and differentiation trees provide us with means to classify each cell using binary codes. Extracting data characterizing lineage tree position, cell volume, and nucleus position for each cell during early embryogenesis, we conduct several statistical analyses, both within and between taxa. We compare both cell volume distributions and cell volume across developmental time within and between single species and assess differences between lineage tree and differentiation tree orderings. This enhances our understanding of the differentiation events in a model of pure mosaic embryogenesis and its relationship to evolutionary conservation. We also contribute several new techniques for assessing both differences between lineage trees and differentiation trees, and differences between differentiation trees of different species. The results suggest that at the level of differentiation trees, there are broad similarities between distantly related mosaic embryos that might be essential to understanding evolutionary change and phylogeny reconstruction. Differentiation trees may therefore provide a basis for an Evo-Devo Postmodern Synthesis.

3D bioprinting, like 3D printing, is a process that builds structures by depositing material layer by layer. In the specific case of bioprinting, these layers are composed of a biocompatible material together with living cells, allowing the creation of three-dimensional structures that recreate functional tissues and organs. Bioprinting cell-laden structures is complicated by the high rate of cell damage and stress that occurs during the process, caused by the high pressures and stresses to which they are subjected. To minimise damage during the process, it is important to study and optimise certain bioprinting conditions beforehand and to analyse how they affect the cells by means of computational simulations. In this review, both quantitative and qualitative data are collected to improve the nozzle geometry through computational simulation studies. Optimal ranges for nozzle diameter (0.2-1mm) and length (8-10mm; 300-900μm) have been defined, and recommendations for improving nozzle performance during bioprinting, such as the provision of an inner angle of 20-30°, an inner EDTA coating and a shear stress of less than 10kPa, have been gathered. Finally, based on the data collected, a design of experiments is proposed to obtain an optimal bioprinting configuration for a particular bioink.

Even though inhalation dosimetry is determined by three factors (i.e., breathing, aerosols, and the respiratory tract), the first two categories have been more widely studied than the last. Both breathing and aerosols are quantitative variables that can be easily changed, while respiratory airway morphologies are difficult to reconstruct, modify, and quantify. Despite several methods are available for model reconstruction and modification, developing an anatomically accurate airway model and morphing it to various physiological conditions remains labor-intensive and technically challenging. The objective of this study is to explore the feasibility of using an adjoint-CFD model to understand airway shape effects on vapor deposition and control vapor flux into the lung. A mouth-throat model was used, with the shape of the mouth and tongue being automatically varied via adjoint morphing, and the vapor transport being simulated using ANSYS Fluent coupled with a wall absorption model. Two chemicals with varying adsorption rates, Acetaldehyde and Benzene, were considered, which exhibited large differences in dosimetry sensitivity to airway shapes. For both chemicals, large shape deformations were explored to find the maximal possible morphing; both the maximal and intermediate deformations were simulated for morphology parametric studies. Results show that changing the mouth-tongue shape can alter the oral filtration by 3.2% for Acetaldehyde and 0.27% for Benzene under a given inhalation condition. This study demonstrates that the hybrid adjoint-CFD approach can be a practical and efficient method to investigate morphology-associated variability in inhalation dosimetry of vapors and nanomedicines.

The massive amount of human biological, imaging and clinical data produced by multiple and diverse sources, necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels as well as their orchestration and links are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof of concept study cases. Personalized simulations over the actual anatomy of a patient have been carried out. The hypermodel has also applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin based clinical decision support system and the core of future in silico trial platforms, although additional retrospective adaptation and validation is necessary.

This article encapsulates an exploration of seizure dynamics through the lens of computational modeling in epilepsy. Starting with a foundational understanding of the FitzHugh-Nagumo model, asimplified representation of neuronal activity, we try to understand the intricacies of epileptic seizures. We methodically demonstrated the transition from normal neuronal activity to a seizure-like state by altering key parameters in the model, such as external current and coupling strength. This was followed by an extension of the model to a network of neurons, simulating the complex interactions and synchronization patterns indicative of seizure propagation. Numerical simulations were conducted to visualize the impact of varying coupling strengths on network dynamics, offering insights into the mechanisms of seizure initiation and spread. The study was complemented by a discussion on the implications of these findings for understanding epilepsy, highlighting the bridging of theoretical models with clinical understanding. Our approach not only illuminates the potential of computational models in epilepsy research but also underscores the significance of interdisciplinary collaboration in advancing our comprehension of neurological disorders. Through this article, we aim to provide a nuanced perspective on the modeling of epileptic seizures, offering a valuable resource for researchers and clinicians in the field.

Blood vessels are essential for maintaining tumor growth, progression, and metastasis, yet the tumor vasculature is under a constant state of remodeling. Since the tumor vasculature is an attractive therapeutic target, there is a need to predict the dynamic changes in intratumoral fluid pressure and velocity which occur across the tumor microenvironment (TME). The goal of this study is to get insight into perfusion anisotropy within lung tumors. To achieve this goal, we used the perfusion marker Hoechst 33342 and vascular endothelial marker CD31 to stain tumor sections from C57BL/6 mice harboring Lewis Lung Carcinoma tumors on their flank. Vasculature, capillary diameter, and permeability distribution were extracted at different time points along the tumor growth curve. A computational model was generated applying a unique modeling approach based on the smeared physical fields (Kojic Transport Model, KTM). KTM predicts spatial and temporal changes in intratumoral pressure and fluid velocity within the growing tumor. Anisotropic perfusion occurs within two domains - capillary and extracellular space. Anisotropy in tumor structure causes nonuniform distribution of pressure and fluid velocity. These results provide insights regarding local vascular distribution for optimal drug dosing and delivery to better predict distribution and duration of retention within the TME.

Ever since Varela and Maturana proposed the concept of autopoiesis as the minimal requirement for life, there has been a focus on cellular systems that erect topological boundaries to separate themselves from their surrounding environment. Here we reconsider whether the existence of such a spatial boundary is strictly necessary for self-producing entities. This work presents a novel computational model of a minimal autopoietic system inspired by dendrites and molecular dynamics simulations in three-dimensional space. A series of simulation experiments where the metabolic pathways of a particular autocatalytic set are successively inhibited until autocatalytic entities that could be considered autopoietic are produced. These entities maintain their distinctness in an environment containing multiple identical instances of the entities without the existence of a topological boundary. This gives rise to the concept of a metabolic boundary which manifests as emergent self-selection criteria for the processes of self-production without any need for unique identifiers. However, the adoption of such a boundary comes at a cost, as these autopoietic entities are less suited to their simulated environment than their autocatalytic counterparts. Finally, this work showcases a generalized metabolism-centered approach to the study of autopoiesis that can be applied to both physical and abstract systems alike.

Algorithmic systems used in healthcare contexts are primarily developed for the benefit of the public. It is therefore essential that these systems are trusted by the individuals for whose benefit they are deployed. Drawing inspiration from the principles embedded in the testing of the safety, efficacy and effectiveness of new medicinal products, concurrent design engineering and pro-fessional certification requirements, the authors propose, for the first time, a preliminary com-petency-based ‘Algorithmic Ethics’ Effectiveness Impact Assessment’ framework for developers of AI systems used in healthcare contexts. They concluded that this set of principles should en-compass the algorithmic systems ‘production lifecycle’, to guarantee the optimized use of the AI technologies, avoiding biases and discrimination while ensuring the best possible outcomes, simultaneously increasing decision-making capacity and the accuracy of the results. As AI is as good as those who program it and the system in which it operates, the robustness and trustworthiness of their ‘creators’ and ‘deployers’, should be fostered by a certification system guaranteeing the latter’s knowledge and understanding of ethical aspects as well as their com-petencies in integrating these aspects in trustworthy AI systems when used in healthcare con-texts.

With the increasing use of data-driven modeling methods new approaches to complex problems in the field of wind energy can be addressed. Topics reviewed through literature include wake modeling, performance monitoring and controls applications, condition monitoring and fault detection, and other data-driven research. Literature shows the advantages of data-driven methods reduce computational expense or complexity, particularly in the cases of wake modeling and controls, as well as various data-driven methodologies’ aptitude for predictive modeling and classification, as in the cases of fault detection and diagnosis. Significant work exists for fault detection while less work is found for controls applications. A methodology for creating data-driven wind turbine models for arbitrary performance parameters is proposed. Results are presented utilizing the methodology to create wind turbine models relating active adaptive twist to steady-state rotor thrust as a performance parameter of interest. Resulting models are evaluated by comparing root-mean-square-error (RMSE) on both the training and validation data sets, with Gaussian Process Regression (GPR), deemed an accurate model for this application. The resulting model undergoes particle swarm optimization to determine the optimal aerostructure twist shape at a given wind speed with respect to the modeled performance parameter, aerodynamic thrust load. The optimization process shows an improvement of 3.15% in thrust loading for the 10 MW reference turbine, and 2.66% for the 15 MW reference turbine.

Elevated blood glucose levels, known as glycemia, play a significant role in sudden cardiac arrest, often resulting in sudden cardiac death, particularly among those with diabetes. Understanding this link has been a challenge for healthcare professionals, leading many research groups to investigate the relationship between blood glucose levels and cardiac electrical activity. Our hypothesis suggests that glucose-sensing mechanisms in cardiac tissue could clarify this connection. To explore this, we adapted a single-compartment, in-silico model of the human atrial node's action potential. We incorporated glucose-sensing mechanisms with voltage-gated sodium ion channels using ordinary differential equations. Parameters for the model were based on existing experimental studies to mimic the impact of glucose levels on atrial node action potential firing. Simulations using voltage clamp and current clamp techniques showed that elevated glucose levels decreased sodium ion channel currents, leading to a reduction in the sinoatrial node action potential frequency. In summary, our model provides a cellular-level understanding of how high glucose levels can lead to bradycardia and sudden cardiac death.

This paper examines the correlation between numbers of computer cores in parallel genetic algorithms. The objective to determine the linear polynomial complementary equation in order represent the relation between number of parallel processing and optimum solutions. Model this relation as optimization function (f(x)) which able to produce many simulation results. F(x) performance is outperform genetic algorithms. Compression results between genetic algorithm and optimization function is done. Also the optimization function give model to speed up genetic algorithm. Optimization function is a complementary transformation which maps a TSP given to linear without changing the roots of the polynomials.

The question does P = NP has confounded mathematicians and computer scientists alike for over 50 years and although there is an almost unanimous agreement that it in fact does not, there still is no absolute proof. In this paper, I attempt to prove to that P does not equal NP.

A therapy’s outcome is determined by a tumor’s response to treatment which, in turn, depends on multiple factors such as the severity of the disease and the strength of patient’s immune response. Gold standard cancer therapies are in most cases fragile when sought to break the ties to either tumor kill ratio or patient toxicity. Lately, research has shown that cancer therapy can be at most robust when handling adaptive drug resistance and immune escape patterns developed by evolving tumors. This is due to the stochastic and volatile nature of the interactions, at the tumor environment level, tissue vasculature, and immune landscape, induced by drugs. Herein, we explore the path towards antifragile therapy control, that generates treatment schemes that are not fragile but go beyond robustness. More precisely, we describe a first instantiation of a control-theoretic method to make therapy schemes cope with the systemic variability in the tumor–immune–drug interactions and gain more tumor kill with less patient toxicity. Considering the anti-symmetric interactions within a model of the tumor–immune–drug network, we introduce the antifragile control framework that demonstrates promising results in simulation. We evaluate our control strategy against state-of-the-art therapy schemes on various experiments and discuss the insights we gained on the potential that antifragile control could have in treatment design in clinical settings.

Microfluidic devices or microdevices refer to systems with a characteristic length in the micrometer range. Systems in this size allow handling small quantities of reagents and samples, with reduced residence time, better control of chemical species concentration, high heat and mass transfers, and high surface/volume ratio. These characteristics led to the application of these microdevices in several areas, such as biological systems, energy, liquid-liquid extraction, food, agricultural sectors, pharmaceuticals, flow chemistry, microreactors, and biodiesel synthesis. Microreactors are devices that have interconnected microchannels, in which small amounts of reagents are manipulated and react for a certain period of time. The traditional characteristics of microreactors are less quantities of reagents and samples, high surface area in relation to volume (10000 m² m^-3), reduction of resistance to heat and mass transfer, reduced reaction times, and narrower residence time distributions. In recent years, several studies have been carried out on biodiesel production in microreactors that explore the influence of operating conditions, mixing and reaction yield, numbering, and especially the microdevices design. Despite all the advantages of microreactors, the literature shows that there are only a few applications on an industrial scale. Two main reasons that hinder the adoption of this technology are the scale-up to a large enough volume to deliver the necessary production capacity and the costs related to industrial manufacturing microreactors. It is often stated that large-scale production of microreactors can be easily achieved by numbering-up. However, researches show that an incredibly high number of microdevices would be needed, which results in a technical unfeasibility and a strong impact on the construction costs of the industrial system. The present review aims to show whether microreactors can replace conventional biodiesel production processes and how this replacement technology could be carried out. The current chapter was divided into the following sections: Introduction, Synthesis and Purification of Biodiesel in Microreactors, Fundamentals of CFD, and Fundamentals of Scale-up. Finally, conclusions and future perspectives are exposed.

A computational model for analysing the tooth deflection of polymer gears is presented in this paper. Because polymer gears have less stiffness compared to metal gears, the proposed approach considers a comprehensive analysis to determine the most suitable numerical model, i.e., the number of teeth that should be modelled. The developed computational model has been evaluated on the spur gear pair, where the pinion made of POM has meshed with a support gear made of steel. Material properties were assigned with linear elastic characteristics for the gear, while the pinion was characterised by hyperelastic properties using POM material. Furthermore, a nonlubricated frictional contact between the gear and pinion was considered in the numerical computations. The computational results that were obtained were compared to the empirical results according to VDI 2736 guidelines. Here, the computational approach showed more accurate results due to the hyperelastic material characteristics of POM. However, VDI 2736 calculation showed the comparability with computational results with a slightly larger deviation at lower loads.

Abrasive Water Jet Machining (AWJM) is a non-conventional machining process that, unlike other methods, is deemed superior because of its applicability, versatility, and no heat-affected zones in the target material. The main objective of this paper was to develop a cheap, practical model for a low-pressure water jet cutting machine and provide some empirical test data for cutting delicate materials. Proper design calculations were performed and are included for both the water jet machine and the two-axis numerical control. Finite Element Analysis results validate the structural integrity of beams and columns in AWJM. Computational Fluid Dynamic analysis of the nozzle shows good agreement with the theoretical calculations regarding the pressure and velocity of the water-abrasive mixture exiting the nozzle. Modal analysis in ANSYS was done to mitigate potential failure due to resonance induced by vibrations. Limited testing on various thicknesses of polystyrene foam ranging from 1-5 mm thickness and rubber sheet of 1 mm thickness showed that even low pressure of 25-30 bar was sufficient to produce a kerf without any burr. However, extensive testing to report optimized parameters like nozzle transverse speed, standoff distance, abrasive flow rate, etc for working at low-pressure ranges is still a consideration for future investigation.

The term Concept has been a prominent part of investigations in psychology and neurobiology where, mostly, it is mathematically or theoretically represented. The Concepts are also studied computationally through their symbolic, distributed and hybrid representations. The majority of these approaches focused on addressing concrete concepts notion, but the view of the abstract concept is rarely explored. Moreover, most computational approaches have a predefined structure or configurations. The proposed method, Regulated Activation Network (RAN), has an evolving topology and learns representations of Abstract Concepts by exploiting the geometrical view of Concepts, without supervision. In the article, the IRIS data was used to demonstrate: the RAN's modeling; flexibility in concept identifier choice; and deep hierarchy generation. Data from IoT's Human Activity Recognition problem is used to show automatic identification of alike classes as abstract concepts. The evaluation of RAN with 8 UCI benchmarks and the comparisons with 5 Machine Learning models establishes the RANs credibility as a classifier. The classification operation also proved the RAN's hypothesis of abstract concept representation. The experiments demonstrate the RANs ability to simulate psychological processes (like concept creation and learning) and carry out effective classification irrespective of training data size.

Glioblastoma is a highly aggressive brain tumor characterized by rapid growth, extensive infiltration, and poor prognosis. Understanding the complex interplay of biological processes driving glioblastoma progression is crucial for developing effective therapeutic strategies. In this study, we present a comprehensive mathematical model that incorporates key aspects of glioblastoma growth and dissemination, including tumor cell diffusion, angiogenesis, nutrient availability, molecular signaling pathways, and blood-brain barrier (BBB) interactions. The model is based on a system of coupled partial differential equations (PDEs) and ordinary differential equations (ODEs) that capture the spatiotemporal dynamics of tumor cell density, nutrient concentration, signaling molecule concentrations, and BBB integrity. We employ numerical simulation techniques, such as the finite difference method and the Runge-Kutta method, to solve the model equations and visualize the evolution of tumor growth and its associated biological processes. The model parameters are estimated using experimental data from the literature, and the model is validated against clinical observations. Furthermore, we perform a comprehensive sensitivity analysis to identify the key parameters that significantly influence tumor growth, diffusion, and dissemination. The sensitivity analysis reveals the relative importance of each parameter in driving glioblastoma progression and highlights potential targets for therapeutic interventions. Our computational framework provides a powerful tool for understanding the complex dynamics of glioblastoma growth and offers valuable insights into the underlying biological mechanisms. The model can be used to test hypotheses, predict tumor response to various treatment strategies, and guide the design of targeted therapies. The findings from this study contribute to the ongoing efforts in developing personalized treatment approaches for glioblastoma patients and improving their clinical outcomes.

An inducer is one of the most important components of centrifugal pumps, whose presence will result in a significant increase in hydraulic performance and pump efficiency. The primary function of the inducer, however, is to delay the destructive phenomenon of cavitation, which has presented a considerable design challenge. Nevertheless, the amount of improvement and increase in inducer performance in both cavitation and non-cavitation modes depends critically on the radial laxity of the blade tip. As part of this study, the performance of the inducer in the cavitation state has been simulated and compared with the experimental data, which are in good agreement. This paper examines the effect of blade tip lagging on cavitation, and the results indicate that this will improve cavitation ­and delay this destructive phenomenon ­but will negatively affect the non-cavitation performance. With the increase in clearance, the range of the return current will increase at the tip of the inducer blade as well.

We present a general process for the halting problem, valid regardless of the time and space computational complexity of the decision problem. It can be interpreted as the maximization of entropy for the utility function of a given Shannon-Kolmogorov-Bernoulli process. Applications to non-polynomials problems are given. The new interpretation of information rate proposed in this work is a method that models the solution space boundaries of any decision problem (and non polynomial problems in general) as a communication channel by means of Information Theory. We described a sort method that order objects using the intrinsic information content distribution for the elements of a constrained solution space - modeled as messages transmitted through any communication systems. The limits of the search space are defined by the Kolmogorov-Chaitin complexity of the sequences encoded as Shannon-Bernoulli strings. We conclude with a discussion about the implications for general decision problems in Turing machines.

Introduction: EEGLAB is one of the most famous software for processing, analyzing, and researching experiments that have Electroencephalography (EEG) datasets. Due to the numerous and famous add-ins along with global, widespread communications and online free training, its popularity increased every year. Method: To address this phenomenon from a bibliographic perspective, we found 20,464 citations in Google Scholar for the main EEGLAB reference since 8/27/2023. Then, only the Web of Science (WOS) articles were 12,700 that they were extracted. The results were analyzed with Bibliometrix package from CRAN R software. Results: The time span of these articles is from 2004 to 2023 with 12,700 documents in 1,125 sources (journals, books, etc.), 29,125 authors, 19,062 author’s keywords, 13,707 keywords PLUS, 279,617 references. The annual growth rate is 28.12 %, international Co-authorship is 37.27 % and Co-authors per document is s 4.89 and the average citations per document is 22.51. The most relevant sources are Neuroimage, Frontiers in Human Neurosciences, Scientific Reports, Psychophysiology, and PLOS One with 780, 526, 446,425, and 371 articles, respectively. The most cited countries are the USA, Germany, and the United Kingdom with 93,093, 32,621, and 20,748 total citations, respectively. The ERPLAB, ADJUST, and ICLabel add-ins have the local to global citation ratios equal to 85.4%, 65.1%, and 78.2% respectively. Other bibliometric analyses such as co-occurrence networks and thematic maps of abstracts, titles, and keywords are estimated and presented. Conclusions: EEGLAB is among the most cited MATLAB toolboxes in computational neuroscience. Many developed and developing countries use it in their research publications.

Intraprocedural rupture (IPR) is a severe complication of coil embolization (CE) for intracranial aneurysms, yet the clinical and hemodynamic features of rupture risk areas remain unclear. From January 2013 to December 2023, 435 saccular cerebral aneurysms (316 unruptured, 119 ruptured) underwent CE at our institution. Included were cases where extravasation or coil protrusion occurred during CE. The rupture point was determined from postoperative data, and computational fluid dynamics (CFD) analysis was conducted to assess hemodynamic features. IPR occurred in six aneurysms (1.3%; three ruptured, three unruptured; dome size: 4.7±1.8 mm, D/N: 1.5±0.5, locations: four internal carotid artery [ICA], one anterior cerebral artery, one middle cerebral artery). ICA aneurysms were treated using adjunctive techniques (three balloon-assisted, one stent-assisted). Two aneurysms (M1M2 and A1) were treated simply, yet had relatively small and misaligned domes. CFD analysis identified the rupture point as a flow impingement zone with maximum pressure (Pmax) in five aneurysms (83.3%). Time-averaged wall shear stress (WSS) was locally reduced around this area (1.3±0.7 [Pa]). Hemodynamically unstable areas indicated fragile, thin walls with rupture risk. Microcatheter insertion along the inflow zone, directed towards the caution area, was practiced. The presence of a flow impingement zone with Pmax and low WSS suggests potential aneurysm rupture in CE, particularly with adjunctive techniques. Small aneurysms, especially those with axial misalignment, warrant careful consideration due to the risk of excessive flow impact stress on the aneurysm wall.

Decades ago, when computational was expensive and limited, the structural design was mostly performed by hand calculations using simple mathematical models. For example, it was a common practice to design a structure as complex as the wing of an aircraft by simple beam analysis. However, ever since the classic paper by Turner et al., due to a rapid increase in computational, more complex mathematical models are being used to simulate the physical behavior of complex structural components. To solve intractable problems unsolvable by hand calculations, numerical techniques like Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), Finite Difference Method etc. are being employed. In fact, the availability of these methods has led to the development of an entirely new area of research known as Multidisciplinary Design Optimization (MDO) where various disciplines are considered in an optimization problem. The most important question while using a mathematical model to represent practical industrial problems is to what extent these models represent the real-life situation. Computational models are always built on upon assumptions. Simply at looking at the simulation outcomes i.e. the graphical and numerical results, it is often very difficult to ensure if the underlying assumption holds and that the results are reliable. This has led to the development of another field of research known as Verification and Validation (called V&V in short).

We present a novel approach for adapting text written in standard Finnish to different dialects. We experiment with character level NMT models both by using a multi-dialectal and transfer learning approaches. The models are tested with over 20 different dialects. The results seem to favor transfer learning, although not strongly over the multi-dialectal approach. We study the influence dialectal adaptation has on perceived creativity of computer generated poetry. Our results suggest that the more the dialect deviates from the standard Finnish, the lower scores people tend to give on an existing evaluation metric. However, on a word association test, people associate creativity and originality more with dialect and fluency more with standard Finnish.

Molecular motors play a vital role in the transport of material within the cell. A family of motors of growing interest are burnt bridge ratchets (BBRs). BBRs rectify spatial fluctuations into directed motion by creating and destroying motor-substrate bonds. It has been shown that the motility of a BBR can be optimized as a function of the system parameters. However, the amount of energy input required to generate such motion and the resulting efficiency has been less well characterized. Here, using a deterministic model, we calculate the efficiency of a particular type of BBR, namely a polyvalent hub interacting with a surface of substrate. We find that there is an optimal burn rate and substrate concentration that leads to optimal efficiency. Additionally, the substrate turnover rate has important implications on motor efficiency. We also consider the effects of force-dependent unbinding on the efficiency and find that under certain conditions the motor works more efficiently when bond breaking is included. Our results provide guidance for how to optimize the efficiency of BBRs.

Diabetes mellitus – type 2 (DM2) is a hypercoaguable state with enhanced platelets (PLTs) activation and increased clotting factor production. Simultaneously, the fibrinolytic cell system is inhibited due to formation of clots with high resistance to fibrinolysis. The stages of PLTs "activation" have been well-characterized microscopically, morphometrically and nanomechanically (by light microscope, transmisiion electron microscope - TEM, scanning electron microscope - SEM and atomic force microscope - AFM). Thrombocytes in "acivated" (pro-coagulant) state play central role in two main biological processes: hemostasis and repair of vascular vessels. Pro-coagulant PLTs participate in generation of thrombin and in development of fibrin fibers in the clot matrix. Enhanced PLTs reactivity in dibetic- (diabetes mellitus - DM2) patients is considered as a "pro-thrombotic" state. In the retrospective- and prospective- studies on the topic, the higher are the PLTs' hematometrical indices: PLTs (count), MPV (mean platelet volume), PDW (platelet distribution width), PCR (plateletcrit), as well as PLTs/Ly ratio. The PLТs parameters/indices are usefull biomarkers in early diagnosis and prognosis of DM2. The precise studies of PLTs' state of activation during DM2 might be useful for creation of new diabetes (DM2) treatment strategies and effective medicines. Using the amount of blood glucose, attached to hemoglobin (HbA1c values) as markers of glycemic control in diabetic patients, researchers have observed association between MPV and medications as Insulin, Metformin, sulphonyl ureas. Computational modellng of PLTs' activation in DM2 is also a controlling factor for thrombocyte distribution and margination in bood vessels, associated with micro- and macrovascular disease in DM2. PLTs-derived miRNAs are novel molecular biomarkers for diagnosis and prognosis of DM2, insulin resistance and diabetes complications. We have proposed (2010) oleic acid (OA) as a substance suppressing "PLT hyperactivation" and inhibiting “thrombocyte aggregation”. Antiplatelet agents, natural plant products including, could be effective in the prevention and the secondary treatment of micro- and macrovascular complications in the type 2 Diabetes mellitus. Our proposal on following up of a combination of hematological-, hemorheological- and hemostatic parameters (indices) as a new way in diagnosis, treatment, predicting and management of DM2 and its related vascular complications, probably could be more explored in future studies.

Drug repurposing is a valuable tool for combating the slowing rates of novel therapeutic discovery. The Computational Analysis of Novel Drug Opportunities (CANDO) platform performs shotgun repurposing of 2030 indications/diseases using 3733 drugs/compounds to predict interactions with 46,784 proteins and relating them via proteomic interaction signatures. An accuracy is calculated by comparing interaction similarities of drugs approved for the same indications. We performed a unique subset analysis by breaking down the full protein library into smaller subsets and then recombining the best performing subsets into larger supersets. Up to 14% improvement in accuracy is seen upon benchmarking the supersets, representing a 100–1000 fold reduction in the number of proteins considered relative to the full library. Further analysis revealed that libraries comprised of proteins with more equitably diverse ligand interactions are important for describing compound behavior. Using one of these libraries to generate putative drug candidates against malaria results in more drugs that could be validated in the biomedical literature than the list suggested by the full protein library. Our work elucidates the role of particular protein subsets and corresponding ligand interactions that play a role in drug repurposing, with implications for drug design and machine learning approaches to improve the CANDO platform.

The endeavor to decode the brain's intricate architecture and its multifaceted functions has propelled scientists into the domain of complex differential equations, aiming to construct a computational framework capable of representing its uncountable intricacies. This article delves into the sophisticated and daunting task of modeling the brain, a system of seemingly uncountable coupled differential equations, each symbolizing the vast array of interconnected neural pathways and processes. We discuss the current limitations of computational resources and the significant challenges faced in accurately capturing the non-linear, high-dimensional, and plastic nature of neural dynamics. Despite these challenges, we explore the progressive strides in computational neuroscience that lay the groundwork for partial models, offering insights into the brain's localized circuits and broader network interactions. These models serve as pivotal stepping stones towards a more comprehensive understanding, which is incrementally built upon by interdisciplinary approaches, leveraging advancements in neuroimaging, data analysis, and theoretical biology. Looking ahead, the article speculates on the potential paradigm shifts that could arise from breakthroughs in computational technology, such as quantum computing, that may one day enable the simulation of the brain in its entirety. The article ultimately paints a picture of cautious optimism, where each incremental discovery is a valuable piece of the grand puzzle, bringing us closer to the epochal goal of comprehensively modeling the human brain.

This article explores the intricate journey of neural progenitor cells from the ventricular zones of the embryonic brain, focusing on their pivotal role in the formation of rich club networks, which are integral to the brain's functional architecture. Initially, the ventricular zones, particularly the cerebral ventricular zone, serve as the primary sites for the proliferation and differentiation of neural progenitors, giving rise to various types of neurons and glial cells. We delve into the sequential migration patterns of these cells, emphasizing how early-migrating neurons, destined to form deeper cortical layers, establish extensive and robust connections, potentially evolving into rich club nodes. These nodes are characterized by their high-degree connectivity and serve as crucial hubs for integrating and disseminating information across disparate brain regions.The article further discusses the implications of this developmental pathway in the context of brain network efficiency, resilience, and cognitive function. We highlight how disruptions in early cell migration can lead to anomalies in rich club formation, drawing connections to various neurodevelopmental disorders such as autism, ADHD and schizophrenia. Additionally, the significance of rich club nodes in maintaining efficient information transfer and network resilience is elucidated, underscoring their role in supporting complex cognitive processes and overall brain dynamics.In summary, this article offers a comprehensive overview of the developmental trajectory from the ventricular zones to the establishment of rich club networks, providing insights into the fundamental processes that shape the brain's intricate connectivity and functional capabilities. Ric

Docked ships are a source of contamination for the city while they keep their engine working. Plumes emissions from large boats can carry a number of pollutants to the nearby cities causing a detrimental effect on the life quality and health of local citizens and ecosystems. A computational fluid dynamics model of the harbour area of Tromsø has been built in order to model the deposition of CO2 gas emitted by docked vessels within the city. The ground level distribution of the emitted gas has been obtained and the influence of the wind speed and direction, vessel chimney height, ambient temperature and exhaust gas temperature has been studied. The deposition range is found to be the largest when the wind speed is low. At high wind speeds, the deposition of pollutants along the wind direction is enhanced and spots of high pollutant concentration can be created. The simulation model is intended for the detailed study of the contamination in cities near the coast or an industrial pollutant source of any type of gas pollutants and can easily be extended for the study of particulate matter.

Mutations in epidermal growth factor receptor (EGFR) occur in about 48% of Asian and 19% of Western patients with lung adenocarcinoma (LUAD), leading to its constitutive activation and subsequent uncontrolled cell proliferation. To counteract this, tyrosine kinase inhibitors (TKIs) have been developed, targeting EGFR activity. However, the emergence of metastases and resistance mutations often undermines the durability of the treatment response. Knowledge-based mechanistic models that replicate existing clinical trial outcomes, tailored to specific population characteristics, provide invaluable assistance in designing future clinical trials. These models are particularly effective in identifying optimal responders to specific treatments, thereby enhancing therapeutic efficacy. We have developed physiologically based pharmacokinetic (PBPK) models for gefitinib and osimertinib, representing two distinct generations of EGFR TKIs, to accurately simulate the drugs’ distribution within the primary tumor and metastatic sites following oral administration. These models were integrated with a pathophysiological mechanistic model of EGFR-mutant LUAD, enabling the representation of the effects of both gefitinib and osimertinib on EGFR activation-induced signaling pathways. This comprehensive model outputs detailed evolutions of the primary tumor and each metastatic site, facilitating the evaluation of patient progression in alignment with RECIST guidelines (version 1.1). Notably, the model encapsulates the heterogeneity within the tumor, through the representation of various subclones, each characterized by unique mutation profiles, thereby reflecting differential responses to treatment. Calibration of the model was achieved using publicly available data from the NEJ002 and FLAURA clinical trials. Rigorous visual predictive checks and statistical tests were employed to ensure the proper behavior of the model. The model adeptly replicated the time to progression observed in real clinical trials in EGFR-mutant LUAD patients receiving gefitinib or osimertinib as first-line therapy. In addition, the model was able to reproduce the results from a retrospective study comparing gefitinib and osimertinib, a study that was not used to inform the model. With a consistent virtual population as a reference, the combined model facilitated a comparative analysis of the efficacy of both treatments, thereby underscoring its utility in evaluating therapeutic strategies. The faithful replication of real-world data significantly enhances the credibility of our model, rendering it a vital investigational tool for deriving pertinent insights and informing treatment strategies, particularly in identifying optimal responders to various generations of EGFR TKIs. Following its consecutive prospective validations in the FLAURA2 and MARIPOSA trials, the model is poised to facilitate the generation of synthetic control arms in future clinical trials. This advancement promises a more nuanced analysis of covariate relationships, especially when comparing investigational treatments to established standards of care. Additionally, it potentially reduces the required patient cohort size in such trials, optimizing resource utilization.

In the face of the burgeoning electricity demands and the imperative for sustainable development amidst rapid industrialization, this study introduces a dynamic and adaptable framework suitable for policy-makers and renewable energy experts working on integrating and optimizing renewable energy solutions. While using a case study representative model for Sub-Saharan Africa (SSA) to demonstrate the challenges and opportunities present in introducing optimization methods to bridge power supply deficits and the scalability of the model to other regions, this study presents an agile multi-criteria decision tool that pivots on four key development phases, advancing upon established methodologies and pioneering refined computational techniques, to select optimal configurations from a set of Policy Decision Making Metrics (PDM-DPS). Central to this investigation lies a rigorous comparative analysis of variants of three advanced algorithmic approaches: Swarm-Based Multi-objective Particle Swarm Optimization (MOPSO), Decomposition-Based Multi-objective Evolutionary Algorithm (MOEA/D), and Evolutionary-Based Strength Pareto Evolutionary Algorithm (SPEA2). These are applied to a grid-connected hybrid system, evaluated through a comprehensive 8760-hour simulation over a 20-year planning horizon. The evaluation is further enhanced by a set of refined Algorithm Performance Evaluation Metrics (AL-PEM) tailored to the specific constraints. The findings not only underscore the robustness and consistency of the SPEA2 variant over 15 runs of 200 generations each, which ranks first on the AL-PEM scale but also validate the strategic merit of combining multiple technologies and empowering policymakers with a versatile toolkit for informed decision-making.

The combination of artificial intelligence algorithms and numerical methods has recently become popular in the prediction of macroscopic and microscopic hydrodynamics parameters of bubble column reactors. The multi inputs and outputs machine learning can cover small phase interactions or large fluid behavior in industrial domains. This numerical combination can develop the smart multiphase bubble column reactor with the ability of low-cost computational time. It can also decrease case studies for the optimization process when big data is appropriately used during learning. There are still many model parameters that need to be optimized for a very accurate artificial algorithm, including data processing and initialization, the combination of inputs and outputs, number of inputs and model tuning parameters. For this study, we aim to train four inputs big data during learning process by an adaptive neuro-fuzzy inference system or adaptive-network-based fuzzy inference system (ANFIS) method, and we consider the superficial gas velocity as one of the input variables, while for the first time, one of the computational fluid dynamics (CFD) outputs named gas velocity is used as an output of the artificial algorithm. The results show that the increasing number of input variables improves the intelligence of the ANFIS method up to , and the number of rules during learning process has a significant effect on the accuracy of this type of modeling. The results also show that propper selection of model parameters results in more accuracy in prediction of the flow characteristics in the column structure.

Conventional data visualization software have greatly improved the efficiency of the mining and visualization of biomedical data. However, when one applies a grid computing approach the efficiency and complexity of such visualization allows for a hypothetical increase in research opportunities. This paper will present data visualization examples presented in conventional networks, then go into higher details about more complex techniques related to leveraging parallel processing architecture. Part of these complex techniques include the attempt to build a basic general adversarial network (GAN) in order to increase the statistical pool of biomedical data for analysis as well as an introduction to the project utilizing the decentralized-internet SDK. This paper is meant to show you said conventional examples then go into details about the deeper experimentation and self contained results.

This study investigates the ergodic behavior of brain waves through an innovative multidisciplinary approach, integrating insights from neuroscience, physics, economics, and biology. By examining the statistical properties and dynamic characteristics of alpha (8-13 Hz), beta (13-30 Hz), theta (4-8 Hz), and delta (0.5-4 Hz) brain waves, we argue that the ergodic nature of these neural oscillations can be comprehensively understood and quantified using analytical tools from diverse scientific disciplines. Our research begins with a thorough review of current neurophysiological understanding of brain wave generation and propagation. We then introduce the concept of ergodicity, originating from statistical physics, and explore its applicability to neural systems. The study employs advanced time series analysis techniques and stochastic process models, borrowed from economics and physics, to analyze large-scale electroencephalographic (EEG) datasets. Key findings suggest that brain waves exhibit ergodic properties under specific physiological conditions, with implications for understanding neural information processing and brain state transitions. We propose a novel framework that characterizes the ergodic behavior of brain waves across different frequency bands and cognitive states. This framework provides new insights into the relationship between local neural dynamics and global brain function.Furthermore, we discuss the biological implications of ergodic brain waves, considering evolutionary perspectives and potential adaptive advantages. Our analysis reveals how the ergodic properties of neural oscillations may contribute to the brain's ability to balance stability and flexibility, a crucial aspect of adaptive behavior and cognition. The study's findings have significant implications for multiple fields. In neuroscience, our framework offers new tools for analyzing brain function and may inform the development of innovative therapeutic interventions for neurological disorders. In physics and complex systems theory, it provides a concrete biological example of ergodic behavior in a highly complex, non-linear system. For the field of economics, it demonstrates the successful application of financial modeling techniques to biological time series data. This research contributes to a more holistic understanding of the brain as a complex, adaptive system operating at the intersection of physics, biology, and information processing. It also opens new avenues for future research, including investigations into the relationship between ergodic properties of brain waves and consciousness, cognitive flexibility, and the emergence of complex behavior from neural ensembles.

In this paper, we present the extension of the pressure-based solver designed for the simulation of compressible and/or incompressible two-phase flows of viscous fluids. The core of the numerical scheme is based on the hybrid Kurganov— Noele — Petrova/PIMPLE algorithm. The governing equations are discretized in the conservative form and solved for velocity and pressure, with the density evaluated by an equation of state. The acoustic-conservative interface discretization technique helps to prevent the unphysical instabilities on the interface. The solver was validated on various cases in wide range of Mach number, both for single-phase and two-phase flows. The numerical algorithm was implemented on the basis of the well-known open-source Computational Fluid Dynamics library OpenFOAM in the solver called interTwoPhaseCentralFoam. The source code and the pack of test cases are available on GitHub: https://github.com/unicfdlab/hybridCentralSolvers

I review previous attempts, including recent ones, to introduce technical aspects of digital information and computation into the discussion of ethics. I survey some limitations and advantages of these attempts to produce guiding principles at different scales. In particular, I briefly introduce and discuss questions, approaches, challenges, and limitations based on, or related to, simulation, information theory, integrated information, computer simulation, intractability, algorithmic complexity, and measures of computational organisation and sophistication. I discuss and propose a set of features that ethical frameworks must possess in order to be considered well-grounded, both in theoretical and methodological terms. I will show that while global ethical frameworks that are uncomputable are desirable because they provide non-teleological direction and open-ended meaning, constrained versions should be able to provide guidelines at more local and immediate time scales. In connection to the ethics of artificial intelligence, one point that must be underscored about computational approaches is that (General) AI should only embrace an ethical framework that we humans are willing to adopt. I think that such a framework is possible, taking the form of a general and universal (in the sense of computation) framework built from first computational principles.

Autism spectrum disorder (ASD) is characterized by diverse patterns of social interaction, communication, and behavior. This study explores the application of clustering and stochastic migration models to visualize and understand the social and neural connectivity patterns associated with autism. The clustering model, with its strong internal connections and limited external connections, mirrors the tight-knit social groups and communication barriers often observed in individuals with autism. In contrast, the stochastic model, characterized by more dispersed connections and greater adaptability, represents the variability and flexibility seen in social interactions across typical individuals. By comparing these models, we can gain deeper insights into the unique challenges and strengths of individuals with autism, highlighting the importance of personalized interventions. Additionally, these models provide a visual framework for understanding the differences in brain connectivity patterns observed in autism, with implications for both behavioral and neural correlates. This study underscores the value of computational models in advancing our understanding of autism and guiding effective support strategies.

Multiple Sclerosis (MS) is a chronic autoimmune disorder characterized by the demyelination of neural fibers in the central nervous system. Understanding the impact of demyelination on neural network efficiency is crucial for elucidating the pathophysiological mechanisms underlying MS. This study aims to model the effects of demyelination on neural network efficiency, specifically focusing on how increased resistance and path lengths due to demyelination correlate with the clinical symptoms of MS. We employed a graph-theoretical approach to simulate neural networks, introducing varying degrees of demyelination. The demyelination was represented by increasing the resistance (weights) of selected edges within the network, affecting both random and clustered distributions of network connections. The primary metric analyzed was the average shortest path length, indicative of network efficiency. The models demonstrated a significant increase in the average shortest path length in demyelinated networks compared to intact ones. Specifically, networks with clustered demyelination exhibited localized inefficiencies, correlating with the focal nature of MS lesions. The increase in path length was associated with reduced signal transmission efficiency, providing insights into a range of MS symptoms including motor and sensory impairments, cognitive dysfunction, visual disturbances, and fatigue. The graph-theoretical model of neural network demyelination offers a novel perspective on how structural changes in the brain's connectivity due to MS can lead to diverse clinical manifestations. While the model simplifies the complexity of neural networks, it underscores the potential of computational approaches in understanding and predicting the course of neurological disorders like MS.

Today, computer programming is an essential pillar in the education of individuals. Even though technological evolution has eased the massive need for computer programming, a systematic lit-erature review shows some problems remain with teaching computer programing. To address such issues, in this paper we propose an adapted theory, a method, and a model for teaching some knowledge objects associated with the concepts of variables, expressions, comparisons, loops, and functions in programming fundamentals in computer sciences. The main contribution of this study comprises the extension of the concept of didactic transposition to improve teaching in context; in this paper, the idea is called didactic transposition in extensa sensu. The proposal involves the development of a linguistic corpus based on syllabi, textbooks, reference manuals, and a survey applied to experts in teaching programming fundamentals based on seventy-eight universities worldwide. An adapted theory, a method, and a model for teaching programming fundamentals using computational linguistics are produced. In addition, a template is created for designing teaching practices in this regard. The proposal generates an important alternative for the devel-opment of teaching strategies where the conditions of the context are addressed as a priority; ad-ditionally, several aspects are considered for elaborating teaching strategies using computational linguistic techniques from written documents.

This dissertation is a rigorous study of ecology and macrocellular biology as a subfield of abstract algebra. We begin with the creation of an axiomatic paradigm, then move onto constructing a universal genetic code of biology. We use this to define increasingly complex algebraic structures (ecosystem, evolving populations, etc.). We prove a variety of theorems regarding to the members of the previous mathematical constructs, notably the following three: 1. There is one unique phenotypic representation of each organism. For example, if you subdivide any piece of genetic code into its phenotypic components, then two identical organisms have identical decomposed DNA 2. There are a finite number of indivisible phenotypic traits. 3. The three sophioid-definitions are equivalent: (a) dynamical evolutionary enlargement of the medial temporal lobe and frontal lobe, (b) reliance upon intelligence, (c) the existence of an intellectually- or socially-hierarchical society. Much has yet to be done on this work, but as a first draft, it stands as a jumping point for future exploits; I am working on an entirely revised second draft.

Many of engineering’s problems are about to optimise equations, functions and process models equations which appear on common or even complex science’s cases. Most applied optimisation methods nowadays can only be used on particular cases, so the main objective of this article is to define a computational method that can optimise or even find target values for specified objective functions and variables, by just using computer effort. Chemical plants involve many differential equations and they can be optimised with more facility now. On this paper, there are shown as example, an optimisation for a chemical reactor, which is found the optimal temperature and volumetric flow rate of feed, for the given objective function being the composition of the desired product. Named Domain’s Sweep, it is an algorithm that evaluate given(s) mathematical function(s) and/or equation(s) by varying your(s) independent variable(s) through loops with a given step size and solving it after closing the degrees of freedom, and finally, with some condition statements, store all the optimum values of given or created objective functions with its respective independent variables. In another words, the user create an objective function and this method find the function’s maximum, minimum or a certain chosen target value, even if it does not have an inflection point in the given search interval of the independent variables.

This study proposes a customized industrial nozzle for generating impulsive air jets. It is installed on an automatic machine where wine caps are stacked and shot consecutively into a rotating cylinder by an air jet. This process is very noisy; hence, this study aimed to investigate possible geometric variations of the nozzle that can reduce the emitted noise. First, the nozzle was tested in a laboratory to measure the air consumption at different supply pressures. Subsequently, 3D models of the nozzle and its variations were created and used for computational fluid dynamics simulations. Different boundary conditions were set, first to validate the model and compare it with the experimental test results, and then to simulate the real working conditions and determine the geometry that is less noisy while maintaining the velocity peak. Among the various possibilities, shortening the final ducts of the nozzle appears to be the most promising solution. These modified nozzles could be easily added to current machines to provide immediate benefits, and this study represents a promising start for action on other machines where this type of device is present.

Differential equation models to understand interaction between plant and nutrient solution are presented. The root cells selectively emit H+ ions with active transport consuming ATPs to establish electrical gradient along the cell membrane. It establishes electrical field with Nernst potential to make positively charged ions outside the cell membrane flow into the root cell. Anion influx is also modulated by H+ ion concentration because plant root cell absorbs negatively charged particles with symport. If an anion collides with H+ cell to make net charge as neutral, at symport channel, it can flow through. In this paper, mathematical models for cation and anion absorption are introduced. Cation absorption model was induced from Ohm's law combined with Goldman's equation. Anion absorption model is similar to chemical reaction rate model. Both models have physiological terms influenced by gene expression pattern, species or phenotypes. Cation model also includes terms for ion's kinetic and electrical properties, growth of plant and interaction between the root and the surroundings. Simulation for 20 different sets of coefficients showed that the physiology-related coefficient has important role on nutrition absorption tendencies of plants.

The MLL1 gene located on chromosome 11q23, is crucial for histone lysine-specific methylation and is frequently implicated in various leukemia types. Despite prior in silico investigations into non-synonymous single nucleotide polymorphisms (nsSNPs) associated with leukemia in the MLL1 gene, there is a dearth of comprehensive studies evaluating their impact on the protein's structure, function, and post-translational modifications. Addressing this, our study screened 2049 nsSNPs from the dbSNP database, identifying 62 high-risk variants. A thorough domain analysis revealed that these nsSNPs are distributed across seven domains of the MLL1 protein, potentially affecting their functions. Our conservation analysis highlighted 31 nsSNPs as deleterious, suggesting their potential effects on protein stability and function. Furthermore, utilizing the MutPred2 tool, we identified mutations affecting metal-binding, protein loop structures, and post-translational modifications, which could alter the MLL1 protein's native functionality. Interaction network analysis underscored the role of MLL1 in hematopoietic cell development, implying that mutations may influence hematological malignancies. Additionally, a subset of non-coding SNPs was found to possess regulatory capabilities, potentially affecting gene expression and chromatin modification. Most significantly, using CScape and CScape-somatic tools, four nsSNPs were predicted to be oncogenic, with two classified as cancer drivers, underscoring their potential role in leukemia development. The oncogenic nsSNP, D2724G, located between the FY-rich N and FY-rich C domain of the MLL1 protein, has raised significant interest due to its potential impact on proteolytic cleavage. Predictive analyses, such as those by Mutpred2, suggest a loss of cleavage at D2724 caused by this mutation, potentially affecting the formation of essential MLL1 fragments, p320 and p180. These fragments play a crucial role in creating a stable multiprotein complex that localizes to a specific subnuclear region. Therefore, mutations like D2724G can potentially alter gene expression patterns, influencing cellular behavior and possibly fostering a cancer-prone environment. In conclusion, our research provides an in-depth assessment of the effects of nsSNPs in the MLL1 gene on leukemia, offering potential avenues for personalized treatment strategies, early detection, prognosis, and a better understanding of hematological malignancy genesis.

In this paper, we study the black hole solution of self-similar gravitational collapse in the Einstein-axion-dilaton system for the elliptic class in four dimensions. The solution is invariant under space-time dilation, which is combined with internal SL(2,R) transformations. Due to the complex and highly nonlinear pattern of the equations of motion in the physics of black holes, researchers typically have to use various numerical techniques to make the equations tractable to estimate the parameters and the critical solutions. To this end, they have to ignore the numerical measurement errors in estimating the parameters. To our knowledge, for the first time in the literature on axion-dilation systems, we propose to estimate the critical collapse functions in a Bayesian framework. We develop a novel methodology to translate the modelling of the complexity of the elliptic black hole to a sampling problem using Hamiltonian Monte Carlo with stacked neural networks. Unlike methods in the literature, this probabilistic approach enables us not only to recover the available deterministic solution but also to explore possibly all physically distinguishable self-similar solutions that may occur due to numerical measurement errors.

This study aimed to rank cancers by the strength of relationship between comprehensive mRNA expression of the most harmful or protective genes and patient survival. Using TCGA dataset including RNA-SEQ and clinical data, we investigated not only gene specific prognostic availability, but also comprehensive prognostic availability of prognostic genes filtered by cox coefficient, and ranked cancers by specially designed prognostic indicator. Through Kaplan-Meier plots, we checked that cancers vary in the strength of influence of prognostic genes, and they follow as the rank. Developing treatment with method to reduce or increase expression of biomarkers for specific cancer which ranked bottom, it would be not efficient in high probability. The results of this study can be a scientific evidence for that.

Driven by mounting environmental apprehensions stemming from the over dependence on fossil fuels in energy and transportation sectors, extensive exploration into alternative energy sources, particularly biomass, has stimulated profound research on harnessing the potential of computational and mathematical techniques. This comprehensive survey delves into optimal models and strategies, unveiling their pivotal role in the design of biomass gasification processes.of hydrogen through biomass gasification. In this study, we aim to provide a comprehensive overview of the computational and mathematical techniques employed in optimizing biomass gasification processes, with a specific focus on enhancing hydrogen yield. Through an extensive literature review, various models and strategies will be examined, including thermodynamic analysis, kinetic modeling, reactor design, and process optimization. By uncovering the power of these techniques, we aim to contribute to the advancement of sustainable and efficient biomass utilization for the hydrogen-based future economy.This paper aims to provide an updated and comprehensive coverage of the investigations conducted on the potential of producing hydrogen from biomass through gasification. To achieve this, we incorporate the latest works that have utilized numerical modeling, simulation, optimization techniques, process heat integration, and co-generation in their studies. By encompassing these aspects, we aim to offer a broader and more in-depth re-appraisal of the subject matter. This will ensure that readers gain a holistic understanding of the advances made in the field and the potential for sustainable hydrogen production of biomass gasification.Through a meticulous re-appraisal and analysis of each subject, we can identify their respective strengths and areas that require further research effort. By thoroughly examining numerical modeling, simulation, optimization techniques, process heat integration, and co-generation, we can assess their effectiveness and applicability in the context of biomass gasification for hydrogen production. This analysis shed light on the areas where these techniques excel, as well as pinpoint limitations or gaps in current understanding. By identifying these areas, we can highlight the need for further research and development, enabling us to make significant advancements in biomass gasification processes and pave the way for a sustainable and efficient hydrogen-based future economy..

Regulation at the level of translation plays an essential role in modulating gene expression. Given the importance of knowledge about translationally active mRNAs as an indicator of translation efficiency, it is crucial for researchers to have at their disposal organised sources of functional information on translation - plant translatomic maps similar to transcription maps. The development of a general experimental and computational methodology for constructing translatome maps is essential to address this challenge. In this manuscript, we discuss what researchers can do to design plant translatome maps, namely, we review: (i) experimental methods to obtain translationally active mRNA fractions, considering their advantages and limitations; (ii) additional methodological approaches to obtain complete pools of translationally active mRNAs; (iii) sequencing methods to qualitatively and quantitatively assess different mRNA fractions; and (iv) multivariate analyses of sequencing results by bioinformatics methods, including metrics for assessing mRNA translational activity.

The main objective of this study is to demonstrate proof of principle that computational fluid dynamics (CFD) modeling is a tool for studying the contribution of covert and overt vascular architecture to the risk of cerebrovascular disease in in sickle cell disease (SCD) as well as uncover one or more mechanism of response to therapy such as chronic red blood cell (cRBC) transfusion. We analyzed baseline (screening), pre-randomization and study exit magnetic resonance angiogram (MRA) images from 10 (5 each from the transfusion and observation arms) pediatric sickle SCD participants in the silent cerebral infarct transfusion (SIT) trial, using CFD modeling. We reconstructed the intracranial portion of the internal carotid artery and branches and extracted the geometry using 3D Slicer. We cut specific potions of the large intracranial artery to include segments of the internal carotid, middle, anterior, and posterior cerebral artery such that the vessel segment analyzed extended from the intracranial beginning of the internal carotid artery up to immediately after (~0.25 inches) the middle cerebral artery branching point. Cut models were imported into Ansys 2021R2/2022R1 and laminar and time-dependent flow simulation was performed. Change in time averaged mean velocity, wall shear stress, and vessel tortuosity were compared between the observation and cRBC arm. We did not observe a correlation between time averaged mean velocity (TAMV) and mean transcranial doppler (TCD) velocity at study entry. There was also no difference in change in time average mean velocity, wall shear stress (WSS), and vessel tortuosity between the observation and cRBC transfusion arms. WSS and TAMV were abnormal for 2 (developed TIA) out of the 3 participants (one participant had SCI) that developed neurovascular outcomes. CFD approaches allows for the evaluation of vascular topology and hemodynamics in SCD using MRA images. In this proof of principle study, we show that CFD could be a useful tool and we intend to carry out future studies with a larger sample to enable more robust conclusions.

The aim of this research is to investigate and use a variety of immersive multisensory media techniques in order to create convincing digital models of fossilised tree trunks for use in XR. This is made possible via the use of geospatial data derived from sources such as aerial imaging using UAS, terrestrial material using cameras and also include both the visual and audio element for better immersion, accessible and explorable in 6 Degrees of Freedom (6DoF). Immersiveness is a key factor in order to result in output that is especially engaging to the user. Both conventional and alternative methods are explored and compared, emphasising in the advantages made possible via the help of Machine Learning Computational Photography. Material is collected using both UAS and terrestrial camera devices, including a 3D-360º camera with 6 sensors, using stitched panoramas as sources for photogrammetry processing. Difficulties such as capturing large free standing objects using terrestrial means were overcome using practical solutions involving mounts and remote streaming solutions. Conclusions indicated that superior fidelity can be achieved by the help of Machine Learning Computational Photography processes and higher resolutions and technical specs of equipment not necessarily translating to superior outputs.

: Predicting long-term student learning is a critical task for teachers and for educational data mining. However, most of the models do not consider two typical situations in real-life classrooms. The first is that teachers develop their own questions for formative assessment. Therefore, there are a huge number of possible questions, each of which is answered by only a few students. Second, formative assessment often involved open-ended questions that students answer in writing. These types of questions in formative assessment are highly valuable. However, analyzing the responses automatically can be a complex process. In this paper, we address these two challenges. We analyzed 621,575 answers to closed-ended questions and 16,618 answers to open-ended questions by 464 fourth-graders from 24 low-SES schools. We constructed a classifier to detect incoherent responses to open-ended mathematics questions. We then used it in a model to predict scores on an end-of-year national standardized test. We found that despite answering 36.4 times fewer open-ended questions than closed questions, including features of the students’ open responses in our model improved our prediction of their end-of-year test scores. To the best of our knowledge, this is the first time that a predictor of end-of-year test scores has been improved by using automatically detected features of answers to open-ended questions on formative assessments.

Computational rhinology is a specialized branch of biomechanics leveraging engineering techniques for mathematical modelling and simulation to complement the medical field of rhinology. Computational rhinology has already contributed significantly to advancing our understanding of the nasal function, including airflow patterns, mucosal cooling, particle deposition, and drug delivery, and is foreseen as a crucial element in e.g. the development of virtual surgery as a clinical, patient-specific decision support tool. The current paper delves into the field of computational rhinology from a nasal airflow perspective, highlighting the use of computational fluid dynamics to enhance diagnostics and treatment of breathing disorders. The paper consists of three distinct parts – an introduction to and review of the field of computational rhinology, a review of published literature on in vitro and in silico studies of nasal airflow, and the presentation and analysis of previously unpublished high fidelity CFD simulation data of in silico rhinomanometry. While the two first parts of the paper summarize the current status and challenges in the application of computational tools in rhinology, the last part addresses the gross disagreement commonly observed when comparing in silico and in vivo rhinomanometry results. It is concluded that this discrepancy cannot readily be explained by CFD model deficiencies caused by poor choice of turbulence model, insufficient spatial or temporal resolution, or neglecting transient effects. Hence, alternative explanations such as nasal cavity compliance or drag effects due to nasal hair should be investigated.

The plate heat exchanger (PHE) is a component that provides heat to be transferred from hot water to domestic cold water without mixing them with high efficiency. Over the lifetime of the PHE, cyclic pressures act on the brazing points and the plates, and this may lead to fatigue failure. The fatigue behaviour of the PHE, designed by using copper-brazed 316L stainless steel, was investigated in this study. First, the fatigue tests under the load ratio R = 0.1 were performed on the Vibrophore 100 testing machine to obtain the S-N curve of the analysed brazed joint. Based on the obtained experimental results, an appropriate material model of the analysed brazed joint has been created, which was validated with numerical calculation in the framework of a program code Ansys. A validated material model was then used for the subsequent numerical analysis of PHE. In order to carry out a numerical calculation using the finite element method (FEM), a 3D model of the heat exchanger was created based on the previous scanning of PHE-geometry. Thereafter, the geometry was parameterised, which allowed us to perform parametric simulations (monitoring different responses depending on the input geometry). Numerical simulations were carried out in the framework of the Ansys software, whereby the obtained results were analysed, and the responses were appropriately characterised according to previously determined load cases.

The aim of this research is to investigate and use a variety of immersive multisensory media techniques in order to create convincing digital models of fossilised tree trunks for use in XR. This is made possible via the use of geospatial data derived from sources such as aerial imaging using UAS, terrestrial material using cameras and also include both the visual and audio element for better immersion, accessible and explorable in 6 Degrees of Freedom (6DoF). Immersiveness is a key factor in order to result in output that is especially engaging to the user. Both conventional and alternative methods are explored and compared, emphasising in the advantages made possible via the help of Machine Learning Computational Photography. Material is collected using both UAS and terrestrial camera devices, including a 3D-360º camera with 6 sensors, using stitched panoramas as sources for photogrammetry processing. Difficulties such as capturing large free standing objects using terrestrial means were overcome using practical solutions involving mounts and remote streaming solutions. Conclusions indicated that superior fidelity can be achieved by the help of Machine Learning Computational Photography processes and higher resolutions and technical specs of equipment not necessarily translating to superior outputs.

A lensless camera is an ultra-thin computational-imaging system. Existing lensless cameras are based on the axial arrangement of an image sensor and a coding mask, and therefore, the back side of the image sensor cannot be captured. In this paper, we propose a lensless camera with a novel design that can capture the front and back sides simultaneously. The proposed camera is composed of multiple coded image sensors, which are complementary-metal-oxide-semiconductor~(CMOS) image sensors in which air holes are randomly made at some pixels by drilling processing. When the sensors are placed facing each other, the object-side sensor works as a coding mask and the other works as a sparsified image sensor. The captured image is a sparse coded image, which can be decoded computationally by using compressive-sensing-based image reconstruction. We verified the feasibility of the proposed lensless camera by simulations and experiments. The proposed thin lensless camera realizes super field-of-view imaging without lenses or coding masks, and therefore can be used for rich information sensing in confined spaces. This work also suggests a new direction in the design of CMOS image sensors in the era of computational imaging.

This study presents the computational model to determine the wear behaviour of polymer gears. Using PrePoMax finite element numerical calculation software, a proposed computational model was built to predict wear behaviour based on Archard's wear model. This allows the calculation of the wear depth in each loading cycle with constant mesh updating. The developed computational model has been evaluated on the spur gear pair, where the pinion made of POM has meshed with a support gear made of steel. The computational results that were obtained were compared with the analytical results according to the VDI 2736 guidelines. Based on this comparison, it was concluded that the proposed computational model could be used to simulate the wear behaviour of contacting mechanical elements like gears, bearings, etc. The main advantage of the model, if compared to the standardised procedure according to the VDI 2736 guidelines, is the geometry updating after a chosen number of loading cycles, which enables a more accurate prediction of wear behaviour under rolling/sliding loading conditions.

Conceptual distance refers to the degree of proximity between two concepts within a conceptualization. It is closely linked with semantic similarity and relationship, but its computation relies entirely on the context of the given concepts. The DIS-C algorithm, which requires using search algorithms such as Breadth First Search, represents an advance in computing the semantic similarity/relation regardless of the type of knowledge structure and semantic relationships. The shortest path algorithm facilitates the determination of the semantic closeness between two indirectly connected concepts in an ontology by propagating local distances. This process is implemented for each concept pair to establish the most effective and efficient paths to connect these concepts. The algorithm identifies the shortest path between the concepts, allowing for the inference of the most relevant relationships between them. This approach contributes to developing a comprehensive understanding of the ontology and enhances the accuracy and precision of the semantic representation of the concepts. However, one of the critical issues is associated with the computational complexity due to the nature of the algorithm, which is errorn3. This paper studies alternatives to accelerate the DIS-C based on approximation and optimized algorithms, focusing on Dijkstra, pruned Dijkstra, and Sketched-based algorithms to compute conceptual distance. Based on the experiments, we discovered that the bottleneck can be avoided using the proposed 2-hop coverages, bringing DIS-C almost linearity.

This work is aimed at investigating the capabilities and limits of the mid-fidelity numerical solver DUST for the evaluation of wind turbines aerodynamic performance. In particular, this study was conducted by analysing the benchmarks NREL- 5MW and Phase VI wind turbines, widely investigated in literature by from experimental and numerical activities. The work started by simulating simpler configuration of the NREL- 5MW turbine to progressively integrate complexities such as shaft tilt, cone effects and yawed inflow conditions, offering a detailed portrayal of their collective impact on turbine performance. A particular focus was then given to the evaluation of aerodynamic responses from the tower and nacelle, as well as aerodynamic behavior in yawed inflow condition, crucial for optimizing farm layouts. In the second phase, the work was focused on NREL Phase VI turbine due to the availability of experimental data on this benchmark case. Comparison of DUST simulations results with both experimental data and high-fidelity CFD tools shows the robustness and adaptability of this mid-fidelity solver for this applications, thus opening a new scenario for the use of such mid-fidelity tools for the preliminary design of novel wind turbines configurations or complex environments as wind farms, characterised by robust interactional aerodynamics.

Working memory refers to the capability of the nervous system to selectively retain short-term memories in an active state. The long-standing viewpoint is that neurons play an indispensable role and working memory is encoded by synaptic plasticity. Furthermore, some recent studies have shown that calcium signaling assists the memory processes and the working memory might be affected by the astrocyte density. Over the last few decades, growing evidence has also revealed that astrocytes exhibit diverse coverage of synapses which are considered to participate in neuronal activities. However, very little effort has yet been made to attempt to shed light on the potential correlations between these observations. Hence, in this article we will leverage a computational neuron-astrocyte model to study the short-term memory performance subject to various astrocytic coverage and we will demonstrate that the short-term memory is susceptible to this factor. Our model may also provide plausible hypotheses for the various sizes of calcium events as they are reckoned to be correlated with the astrocytic coverage.