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.

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.

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.

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.

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.

Precise monitoring of individual crop growth and health status is crucial for precision agriculture practices. However, traditional manual inspection methods are time-consuming, labor-intensive, and often lack the spatial resolution required for detailed analysis. This research addresses the need for efficient and high-resolution crop monitoring by leveraging Unmanned Aerial Vehicle (UAV) imagery and advanced computational techniques. The primary objective was to develop a methodology for precise identification, extraction, and monitoring of individual corn crops throughout their growth cycle. This was achieved by integrating UAV-derived data with image processing, computational geometry, and machine learning techniques. UAV imagery was ac-quired bi-weekly at altitudes of 40m and 70m, capturing the entire growth cycle of a corn crop from planting to harvest. A time-series Canopy Height Model (CHM) was generated by analyzing the differences between the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) derived from the UAV data. Local spatial analysis and image processing techniques were em-ployed to determine the local maximum height of each crop. Subsequently, a Voronoi data model was developed to delineate individual crop canopies, successfully identifying 13,000 out of 13,050 corn crops in the study area. For enhanced accuracy in canopy size delineation, vegetation indices were incorporated into the Voronoi model segmentation, refining the initial canopy area esti-mates by eliminating interference from soil and shadows. The proposed methodology enables precise estimation and monitoring of crop canopy size, height, biomass reduction, lodging, and stunted growth over time, providing valuable insights for precision agriculture practices. This work contributes to the scientific community by demonstrating the potential of integrating UAV technology, computational geometry, and machine learning for accurate and efficient crop mon-itoring at the individual plant level.

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.

Ecuador is an important center of biodiversity of the plant family Amaryllidaceae, specially the subfamily Amaryllidoideae, known as a source of important bioactive molecules. The aim of this study was to assess the chemical and biological potential of four different species of Amaryllidoideae collected in Ecuador: Urceolina formosa, Urceolina ruthiana, Clinanthus incarnatus, and Stenomesson aurantiacum. Sixteen alkaloids were identified in the bulb extract of these species by GC-MS, which the extract of S. aurantiacum exhibiting the greatest structural diversity. The potential of these three genera against Alzheimer’s disease was evaluated by measuring their AChE and BuChE inhibitory activity, revealing that C. incarnatus and U. formosa (from Sucumbíos province) showed the best results with IC50 values of 1.73 ± 0.25 and 30.56 ± 1.56 g·ml-1, respectively. Molecular dynamic assays were conducted to elucidate the possible interactions that occurs among 2-hydroxyanhydrolycorine and AChE enzyme, suggesting some similarity with those observed for galanthamine. This study enhances our understanding of the biodiversity of Amaryllidoideae species from Ecuador, highlighting their potential as a source of chemical compounds with pharmaceutical potential.

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.

Soccer is the leading cause of all sports-related ocular injuries in Europe and Israel and leads to blindness at a disproportionately high rate, especially among children (Thompson et al., 2019). With soccer being the most popular sport in the world, it is surprising the only solution to ocular injuries are sports glasses which can cause direct damage to the eye and impair peripheral vision by 15%, calling for an alternative (Smith & Jones, 2020). This work, based on an imported facial model, focuses on both the qualitative and quantitative aspects of building a personalized, low-cost (through optimizing material needed), superior apparatus to conventional sports glasses. The quantitative side examines a modified and model-validated (through a SRS of thirty unique extrapolated points) multivariate linear regression equation, based on horizontal and vertical rotation angles (n=60) trained with residual and computer simulation data, that can accurately extrapolate and quantify ocular impact from all horizontal and vertical rotation angles and a modified Hertzian Contact Stress Equation to determine the thickness of the apparatus using the elasticity modulus, impact force, and geometrical facial and soccer-ball features. The qualitative side examines where firstly, ocular impact is most prevalent, and secondly, where most impact is seen from rotating the ball horizontally and vertically through analysis of Pearson’s Correlation Coefficient at the supraorbital, medial canthus, infraorbital, and lateral canthus regions from both regression equations as well as a cost-benefit analysis for comparison with conventional sports glasses. The findings discovered in this work lay the groundwork for future research to build a customized low-cost ocular injury prevention device which can simultaneously ensure complete ocular safety while enhancing athletic performance for over 250,000,000 players (Johnson & Garcia, 2022).

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.

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.

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.

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.

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.

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.

This article presents a new methodology for modeling and analyzing discrete dynamic systems through the construction of inverse algebraic models, giving rise to the Theory of Inverse Discrete Dynamical Systems. Key concepts such as inverse modeling, structural analysis in inverse algebraic trees, and the establishment of topological equivalences for the analytical transfer of properties between the canonical system and its inverted counterpart are introduced. Central theorems on homeomorphic invariance and topological transport are demonstrated, consolidating the validity of transferring cardinal attributes between both equivalent dynamic representations. As a concrete application, an alternative proof of the historic Collatz Conjecture is presented through the rigorous construction of the associated inverse model and the analytical transfer of properties exhibited in the inverted tree structure. Furthermore, evidence has been presented on Conway's Conjecture in the Game of Life through meticulous inverse modeling in bounded cases, overcoming initial limitations of the theory in combinatorial explosions. Although a complete proof of the conjecture is not achieved, this positively expands the modeling and analysis capacity of the methodology. The impact of properly applying this novel theory to expand understanding and solve open problems in discrete dynamical systems seems vast and profound.

The emergence of electric vehicles has brought new issues such as the problem of rolling element bearings (REBs) operating at high speeds. Losses due to these components in mechanical transmissions are a key issue and must therefore be taken into account right from the design stage of these systems. Among these losses, the one induced by the motion of rolling elements , known as drag loss, becomes predominant in high-speed REBs. Although an experimental approach is still possible, it is difficult to isolate this loss in order to study it properly. A numerical approach based on CFD is therefore a possible way forward, even if other issues arise. The aim of this article is to study the ability of such an approach to correctly estimate the drag coefficient associated with the motion of rolling elements. The influence of the numerical domain extension, the mesh refinement, the simplification of the ring shape and the presence of the cage, on the values of the drag coefficient is presented. While it seems possible to compromise on the calculation domain and mesh size, it appears that the other parameters must be taken into account as much as possible to obtain realistic results.

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.

The exploration of three-dimensional (3D) DNA structures marks a pivotal shift in genomics, offering unprecedented insights into the intricacies of cellular regulation, disease genesis, and the potential for novel diagnostic and therapeutic strategies. This study delves into the dynamic nature of 3D genome organization, investigating how these structures influence cellular functions in response to environmental changes and over time. Through a combination of real-time imaging and advanced computational models, we aim to unravel the complex interplay between 3D DNA structures, non-coding RNAs, and chromatin organization, and their collective impact on gene expression and cellular differentiation. A significant focus is placed on understanding the role of 3D DNA structures in the development and progression of genetic disorders. By employing comprehensive genomic and epigenomic profiling, this research seeks to identify structural aberrations linked to diseases, paving the way for the development of targeted diagnostics and therapies. Additionally, the study explores the potential of genome editing technologies, such as CRISPR-Cas systems, in correcting these structural abnormalities, while also addressing the ethical, legal, and social implications (ELSI) of such interventions. Integrating data from various genomic analyses, we propose the development of advanced computational models to predict the effects of 3D DNA structures on gene expression and cellular function. This integrative approach not only promises to enhance our understanding of genomic regulation but also sets the stage for groundbreaking advances in genomics-based diagnostics and therapeutics. The findings of this study have the potential to revolutionize our approach to treating genetic disorders, offering a new frontier in personalized medicine.

To establish simulated micro-gravity conditions, biological organisms are placed on a simulated microgravity system, which primarily rotate in two or three dimensions and rely on slow sedimentation with latency to detect gravity. Fluid dynamics arise as a consequence of rotation of the chambers with in which the cells are filled. To minimize the effects of centrifugal forces, it is necessary to position the samples as close to the center of rotation as possible. Strong shear forces and velocities within the vessel can have an adverse impact on cells, making this a critical topic in cell biology. During experimental investigation, this specific phenomenon is not apparent, but numerical modelling readily illustrates and is not widely analyzed in literature's. Furthermore, bulk flask are frequently employed on random positioning machines (RPM), not merely because they demand a substantial quantity of reagents. The flask presented in the study can be easily developed and fitted in conventional cell culture plates containing medium and reagents similar to those used in ground experiments, thus allowing it to remain close to the center of rotation. In the present study, a series of fluid dynamics simulations are performed to examine the impact of shear forces within the microvessel presented under varying conditions using a foreign medium condition, and these results are compared with existing vessels by observing the effects of shear forces and maximum velocity zones.

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.

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.

Liver is structurally organized into zonation where LSECs endothelial cells play a crucial role during chronic liver injury and early stages of fibrosis. Fibrosis can be reversed if diagnosed early at molecular level in zonation before progressing to advanced stages like bridging fibrosis. This study identified zonation marker genes using scRNA-seq and spatial transcriptomics molecular profiling technologies in normal and diseased fibrotic human liver. DGE analysis is performed over LSECs and identified the top 20 expressed genes in periportal, perivenous, and intermediate acinar zones. Multi-omics and scRNA-seq analysis over Visium images and ECs liver cells figured out OIT3, DNASE1L3, CLEC4G, LYVE1, FCN2, CRHBP as commonly expressed mid-lobular zonation-specific genes. Also, this study detected STAB2, F8, AQP1, TEK, TIMP3, TIE1, CTSL genes as expressed in DILI and NASH ECs populations. The connection between LSECs marker genes in zone 2 and liver fibrosis holds significant promise for advancing our understanding in developing new therapeutic strategies for fibrosis reversal and designing computational molecular biomarkers in NASH and DILI fibrotic liver diseases.

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.

This paper introduces an innovative approach to numerically model Structure-Soil-Structure Interaction (SSSI) by integrating the Boundary Element Method (BEM) and the Finite Element Method (FEM) in a coupled manner. To assess the accuracy of the proposed method, a comparative study is undertaken, comparing its outcomes with those generated by the conventional FEM technique. Alongside accuracy, the computational efficiency aspect is crucial for the analysis of large-scale SSSI problems. Hence, the computational performance of the coupled BEM-FEM method undergoes a thorough examination and is compared with that of the standalone FEM method. The results from these comparisons illustrate the superior capabilities of the proposed method in comparison to the FEM method. The novel approach provides more reliable results compared to traditional FEM methods, serving as a valuable tool for engineers and researchers involved in structural analysis and design.

The aim of this study is to investigate transient dynamic flow characteristics of the fluid flow through 5-way electric coolant valve (PCCV : Penta-Control Coolant Valve). To achieve this goal, the present paper attempts the three-dimensional dynamic simulation of the fluid flow through the valve using a commercial CFD solver with moving mesh technique to consider flow inertia and dynamic flow in the opening and closing stages of the ball valve rotating motion. Firstly, dynamic flow characteristics and the thermal mixing inside the PCCV ball valve during the opening and closing stage was analyzed. It is found that the discrepancies between dynamic and steady state simulation is remarkable when fluxes with different levels of enthalpy and momentum flow into the PCCV leads to strong flow interference and flow inertia, while the discrepancies are relatively small at low rotation speed and weak flow interference. Then, the effect of dynamic flow characteristics of the valve on dynamic thermal mixing characteristics at two different ball valve rotational speeds and rotational directions were investigated. It was found that the dynamic flow and thermal mixing characteristics inside the PCCV are greatly affected by the rotation speed, rotation direction, and degree of flow interference between fluxes. It is helpful for the design of the better coolant control strategies and the improvement of the FCEV thermal management system.

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.

Accurate and robust deep learning models for facial expression recognition are challenging to achieve, given the diversity of human faces and variations in images, including different facial poses and lighting conditions. In this work, we proposed a clock-driven convolutional Spiking Neural Network (SNN) and STDP fine-tune architecture, meticulously calibrated its hyperpa-rameters, and experimented with various optimization methods. The best model resulted was trained and evaluated on the Fer2013 and FER+ database, obtaining an accuracy of 61.87% and 79.97% without requiring auxiliary training data or face registration. To our best knowledge, the proposed SNN achieved comparable accuracy to CNNs of similar depth and possessed advantages of low energy consumption and high computational efficiency. The computational efficiency of the proposed SNN is approximately three times that of CNNs. Along with this, we introduced the very recent cumulative spike guided encoder visualization technique and revealed the strong encoding capability of the proposed SNN.

This is the second in a series of two articles introducing the Gravitas computational general relativity framework, in which we now focus upon the design and capabilities of Gravitas's numerical subsystem, including its ability to perform general 3+1 decompositions of spacetime via the ADM formalism, its support for the definition and construction of arbitrary Cauchy surfaces as initial data, its support for the definition and enforcement of arbitrary gauge and coordinate conditions, its various algorithms for ensuring the satisfaction of the ADM Hamiltonian and momentum constraints, and its unique (totally-unstructured) adaptive refinement algorithms based on hypergraph rewriting via Wolfram model evolution. Particular attention will be paid to the seamless integration between Gravitas's symbolic and numerical subsystems, its ability to configure, run, analyze and visualize complex numerical relativity simulations and their outputs within a single notebook environment, and its capabilities for handling generic curvilinear coordinate systems and spacetimes with general (and often highly non-trivial) topologies using its specialized and highly efficient hypergraph-based numerical algorithms. We also provide illustrations of Gravitas's functionality for the visualization of hypergraph geometries and spacetime embedding diagrams in two and three dimensions, the ability for Gravitas's symbolic and numerical subsystems to be used in concert for the extraction of gravitational wave signals and other crucial simulation data, and Gravitas's in-built library of standard initial data, matter distributions and gauge conditions. We conclude by demonstrating how the numerical subsystem can be used to set up, run, visualize and analyze a standard yet nevertheless reasonably challenging numerical relativity test case: a binary black hole collision and merger within a vacuum spacetime (including the extraction of its outgoing gravitational wave profile).

Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by swelling in at least one joint. Owing to an overactive immune response, extra-articular manifestations are observed in certain cases, with interstitial lung disease (ILD) being the most common. Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is characterized by chronic inflammation of the interstitial space, which causes fibrosis and the scarring of lung tissue. Controlling inflammation and pulmonary fibrosis in RA-ILD is important because they are associated with high morbidity and mortality. Pirfenidone and nintedanib are specific drugs against idiopathic pulmonary fibrosis and have shown efficacy against RA-ILD in several clinical trials. Immunosuppressants and disease-modifying antirheumatic drugs (DMARDs) with antifibrotic effects have also been used to treat RA-ILD. Immunosuppressants moderate the overexpression of cytokines and immune cells to reduce pulmonary damage and slow the progression of fibrosis. DMARDs with mild antifibrotic effects target specific fibrotic pathways to regulate fibrogenic cellular activity, extracellular matrix homeostasis, and oxidative stress levels. Therefore, specific medications are required to effectively treat RA-ILD. In this review, the commonly used RA-ILD treatments are discussed based on their molecular mechanisms and clinical trial results. In addition, a computational approach is proposed to develop specific drugs for RA-ILD.

Small peptides are an important component of the vertebrate immune system. They are important molecules for distinguishing proteins that originate in the host from proteins derived of a pathogenic organism, such as a virus or bacterium. Consequently, these peptides are central for the vertebrate host response to intracellular and extracellular pathogens. Computational models for prediction of these peptides have been based on a narrow sample of data with an emphasis on the position and chemical properties of the amino acids. In past literature, this approach has resulted in higher predictability than models that rely on the geometrical arrangement of atoms. However, protein structure data from experiment and theory are a source for building models at scale, and, therefore, knowledge on the role of small peptides and their immunogenicity in the vertebrate immune system. The following sections introduce procedures that contribute to theoretical prediction of peptides and role in immunogenicity. Lastly, deep learning is discussed as it applies to immunogenetics and the acceleration of knowledge by a capability for modeling the complexity of natural phenomena.

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.

Abdominal aortic aneurysm (AAA) is a chronic disease in which the abdominal aorta expands until it ruptures. AAA incidence: 10 million/year (India), deaths: 40%-90% (13th leading cause of death in the USA). Current treatment methods make diagnosis difficult due to the lack of noticeable symptoms and inaccurately planned operations. Rupture Scope is a mathematical model to dramatically reduce AAA deaths and improve treatment efficiency. Using a case-specific replica/simulation of real-time abdominal aortic blood flow for monitoring without frequent imaging scans and a novel derived mathematical linear regression model to predict rupture date and expansion rate with an overall accuracy of 95.64%.

This research paper presents a comprehensive study on the gasification of wheat straw pellets in a 10-kW fixed bed gasifier through a combination of Computational Fluid Dynamics (CFD) simulation and experimental validation. The developed 2D CFD model in ANSYS meshing simulates the gasification process in ANSYS Fluent software. The investigation evaluates key parameters such as equivalence ratio, higher heating value, and temperature distribution within the gasifier to enhance syngas production efficiency. The simulated results exhibit a noteworthy agreement with experimental data obtained from gasification. The impact of the equivalence ratio on gas production and lower heating value (LHV) is systematically explored, revealing that an equivalence ratio of 0.35 is optimal for syngas production. The resulting producer gas composition at this optimum condition includes CO (~27.67%), CH4 (~3.29%), CO2 (~11.09%), H2 (~11.09%), and N2 (~51%). The study further investigates the positive influence of reactor temperature on increasing syngas quantity while considering the uneven shape of the reactor and various thermal reactions affecting temperature, static pressure, velocity, and density. The findings contribute valuable insights to improve the efficiency of fixed-bed gasifiers, offering essential information on performance parameters for sustainable and optimized syngas production.

In view of the inability to directly and accurately obtain the athlete's aerodynamic force during take-off phase through wind tunnel test, the athlete's aerodynamic force and surrounding flow field form under different take-off postures are obtained through numerical simulation research, and the effects of different take-off modes on the aerodynamic characteristics during take-off in ski jumping are discussed. The multi-body system composed of the athlete and skis was selected as the research objects. By using partially averaged Navier-Stokes (PANS) turbulence model and 3D numerical simulation of computational fluid dynamics (CFD), the aerodynamic characteristics of the athlete under different take-off postures were predicted. Take-off modes include knee-push-hip (KPH) mode and hip-drive-knee (HDK) mode, and the hip joint angle of HDK mode is significantly greater than KPH mode. First, the aerodynamic force ratio of the athlete’s torso and legs is obviously large. Although the aerodynamic force of arms themselves is not obvious, they have a great impact on the overall aerodynamic characteristics of the athlete, so the posture of arms cannot be ignored. Then, the total drag and moment of HDK mode are significantly higher than that of KPH mode, and the lift-to-drag ratio of HDK mode is significantly lower than that of KPH mode. At first the total lift of HDK mode is higher than that of KPH mode, but in the last attitude the total lift of HDK mode does not rise but fall, and finally the total lift of HDK mode is lower than KPH mode. The aerodynamic characteristics change dramatically during take-off phase, and the aerodynamic characteristics of the two take-off modes are quite different, and these changes and differences are difficult to be observed in real training and competition site. KPH mode has obvious aerodynamic advantage over HDK mode. During the take-off process, the athlete should increasethe force generated by the knee joint extension and appropriately reduce the speed of hip joint extension, and control the using force order of the lower limb joints, and push hip joint extension by knee joint extension, in order to avoid issues such as the hip joint angle is too large, the hip joint extension angle is too fast, the center of gravity is to back and other problems. Studying the aerodynamic characteristics during the take-off phase provides valuable insights for athletes to achieve favorable flight postures after take-off, offering scientific guidance to improve training strategies and enhance competitive performance.

Objective: The stability of the flight phase in ski jumping is crucial for athletes' performance and safety. This study aims to investigate the influence of unfavorable conditions on aerodynamic characteristics and flight stability through computational fluid dynamics (CFD) numerical simulations. Methods: The ski jumper and the skis are considered as a multi-body system. A detailed three-dimensional (3D) model of this multi-body system under a commonly observed posture during flight is established. The partially averaged Navier-Stokes (PANS) turbulence model is employed, and CFD simulations are conducted to predict the aerodynamic characteristics of the multi-body system under lateral environmental wind and asymmetric postures during flight phase. The conditions of asymmetric postures include yaw rotation and roll rotation. Results: (1) Lateral environmental wind generated yaw force, yaw moment, and roll moment, which influenced the lift, drag, and pitch moment of the athlete. These forces and moments were relatively small at lower wind speeds (less than 3 m/s) and became more significant at higher wind speeds (greater than 4.5 m/s). (2) Under the influence of yaw rotation or roll rotation, the multi-body system exhibited noticeable yaw force, yaw moment, and roll moment, all showing a monotonic increasing trend. Moreover, they had a significant impact on the lift, drag, and pitch moment of the multi-body system. Conclusion: (1) The influence of unfavorable conditions is complex, resulting in significant yaw force, yaw moment, and roll moment on the multi-body system. The adverse effects of roll rotation were generally greater than those of yaw rotation. (2) The multi-body system exhibited self-stabilizing tendencies in yaw and roll. This phenomenon can provide a solution to maintain flight stability by employing appropriate yaw or (and) roll rotation angles, effectively compensating for or even eliminating the adverse effects of lateral environmental wind. (3) Understanding the mechanisms of how unfavorable conditions affect the aerodynamic characteristics and stability during flight in ski jumping can provide valuable assistance for real-time prediction and decision-making during competitions, as well as scientific guidance for training athletes stable flight control and techniques and improving their sport performance.

Covid-19 is an airborne disease, with the vast majority of infections occurring indoors. By comparison, little transmission occurs outdoors. Here, we investigate the airborne transmission pathways that differentiate the indoors from outdoors, and conclude that profound differences exist, which help to explain why SARS-CoV-2 transmission is much more prevalent indoors. Near and far-field transmission pathways are discussed along with factors that affect infection risk, with aerosol concentration, air entrainment, thermal plumes, and occupancy duration all identified as being influential. In particular, we present the fundamental equations that underpin the Wells-Riley model, and show the mathematical relationship between inhaled virus particles and quanta of infection. A simple model is also presented for assessing infection risk in spaces with incomplete air mixing. Transmission risk is assessed in terms of aerosol concentration using simple 1D equations, followed by a description of thermal plume-ceiling interactions. With respect to this, we present new experimental results using Schlieren-visualisation and Computational Fluid Dynamics (CFD) based on the Eulerian-Lagrangian approach. Pathways of airborne infection are discussed, with the key differences identified between indoors and outdoors. In particular, the contribution of thermal and exhalation plumes is evaluated, and the presence of near-field/far-field feedback loop postulated, which is absent outdoors.

In Active Flow Control (AFC) applications, to activate the boundary layer the use of pulsating flow has notorious energy advantages versus constant blowing/suction jet injections. For a given AFC application, five parameters, jet location and width, inclination angle, frequency of injection and momentum coefficient need to be tuned. Two main devices are nowadays capable of injecting pulsating flow with the sufficient momentum coefficient to delay the boundary layer separation, these are the Zero Net Mass Flow actuators (ZNMFA) and the Fluidic Oscillators (FO). In the present research, a novel FO configuration is analyzed for the first time at relatively high Reynolds numbers, fluid is considered as incompressible. After obtaining the typical linear correlation between the incoming Reynolds number and the outlet flow oscillating frequency, the effect of outlet width and mixing chamber wedge inclination angle dimensional modifications is addressed, modifications of the outlet width are observed to create large variations on the FO performance. The origin of the self-sustained oscillations is also analyzed in the present manuscript and greatly helps in clarifying the forces acting on the jet inside the mixing chamber. In fact we can conclude saying that the current FO configuration is pressure driven although the mass flow forces appear to be a much more relevant than in previous FO configurations studied.

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.

Efficient implementation of clean energy technologies, including pioneering solutions like mobile solar PV systems mounted on trailers (MSPT), is pivotal for addressing energy challenges, especially in regions with constrained power grid access. This endeavour is vital for meeting escalating elec-tricity demands and aligning with the UN Sustainable Development Goal (SDG) aimed at ensuring dependable and sustainable energy provision in developing countries. This paper investigates the aerodynamic behaviour of a designed MSPT using numerical and experimental approaches. Spe-cifically, the study focuses on the effects of wind velocity and tilt angels on the drag and lift forces, as well as drag and lift coefficients on the panel used in the MSPT system. Additionally, the overall wind force acting on the entire MSPT (including 9 large solar PV panels) was examined, considering the combined effects of wind flow and the system's geometry. The simulations were conducted using ANSYS-Fluent software and experimental testing was performed utilizing scaled-down models to validate the accuracy of the simulation. The insights and the knowledge from this study can be utilized to optimize the design, installation, and operation of such systems, ultimately enhancing their performance, reliability, and paving ways for further improvements in mobile solar PV technology.

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.

Tissue engineered heart valves can grow, repair, and remodel after implantation, presenting a more favorable long-term solution compared to mechanical and porcine valves. Achieving functional engineered valve tissue requires the maturation of human cells seeded onto valve scaffolds under favorable growth conditions in bioreactors. The mechanical stress and strain on developing valve tissue caused by different pressure and flow conditions in bioreactors are currently unknown. The aim of this study is to quantify the wall shear stress (WSS) magnitude in heart valve prostheses under different valve geometries and bioreactor flow rates. To achieve this, the study used fluid-structure interaction simulations to obtain the valve's opening geometries during the systolic phase. These geometries were then used in computational fluid dynamics simulations with refined near-wall mesh elements and ranges of prescribed inlet flow rates. The data obtained included histograms and regression curves that characterized the distribution, peak, and median WSS for various flow rates and opening configurations. The study also found that the upper region of the valve near the commissures experienced higher WSS magnitudes than the rest of the valve.

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.

Liver is structurally organized into zonation where LSECs endothelial cells play a crucial role during chronic liver injury and early stages of fibrosis. Fibrosis can be reversed if diagnosed early at molecular level in zonation before progressing to advanced stages like bridging fibrosis. This study identified zonation marker genes using scRNA-seq and spatial transcriptomics molecular profiling technologies in normal and diseased fibrotic human liver. DEG analysis is performed over LSECs and identified the top 20 expressed genes in periportal, perivenous, and intermediate acinar zones. Multi-omics and scRNA-seq analysis over Visium images and ECs liver cells figured out OIT3, DNASE1L3, CLEC4G, LYVE1, FCN2, CRHBP as commonly expressed mid-lobular zonation-specific genes. Also, this study detected STAB2, F8, AQP1, TEK, TIMP3, TIE1, CTSL genes as expressed in DILI and NASH ECs populations. The connection between LSECs marker genes in zone 2 and liver fibrosis holds significant promise for advancing our understanding in developing new therapeutic strategies for fibrosis reversal and designing computational molecular biomarkers in NASH and DILI fibrotic liver diseases.

AliAmbra is a project developed to explore and promote high-quality catches of the Amvrakikos Gulf GP to Artas’ wider regions. In addition, this project aimed to implement an integrated plan of action, to form a business identity with high-added value and achieve integrated business services adapted to the special characteristics of the area. The action plan for this project was to actively search for new markets, create a collective identity for the products, promote their quality and added value, engage in gastronomes and tasting exhibitions, dissemination and publicity actions, as well as enhance the quality of the products and markets based on the customer needs. The primary focus of this publication is to observe and analyze the data retrieved from various tasting exhibitions of the AliAmbra project, with a target goal of improving customer experience and product quality.

Interdisciplinary conferences play a vital role in advancing explainable Artificial Intelligence (AI) within increasingly intersecting healthcare and computational biology arenas. Challenges result-ing from algorithmic bias and the necessity for domain-specific Explainable AI (XAI) techniques underscore the increasing significance of transparent and interpretable AI systems used in patient care. Collaborative efforts between clinicians, data scientists, and various stakeholders are nec-essary to innovate solutions tailored to healthcare needs. Specialized conferences may foster these interdisciplinary interactions, providing platforms for knowledge exchange, networking, and collaborative partnerships. The legal, ethical, and societal dimensions of medical AI advocate for an integrative approach that aligns technological advancements with patient-centered care val-ues. The role of specialized conferences is therefore essential for shaping future directions in ex-plainable AI and computational biology that contribute to improved patient outcomes and healthcare innovations.

Rapid screening of botanical extracts for the discovery of bioactive natural products was performed using a fractionation approach in conjunction with flow-injection high-resolution mass spectrometry for obtaining chemical fingerprints of each fraction, enabling the correlation of the relative abundance of molecular features (representing individual phytochemicals) with the read-outs of bioassays. We applied this strategy for discovering and identifying constituents of Centella asiatica (C. asiatica) that protect against Aβ cytotoxicity in vitro. C. asiatica has been associated with improving mental health and cognitive function, with potential use in Alzheimer’s disease. Human neuroblastoma MC65 cells were exposed to subfractions of an aqueous extract of C. asiatica to evaluate the protective benefit derived from these subfractions against amyloid β-cytotoxicity. The % viability score of the cells exposed to each subfraction was used in conjunction with the intensity of the molecular features in two computational models, namely Elastic Net and selectivity ratio, to determine the relationship of the peak intensity of molecular features and % viability. Finally, the correlation of mass spectral features with MC65 protection and their abundance in different sub-fractions were visualized using GNPS molecular networking. Both computational methods unequivocally identified dicaffeoylquinic acids as providing strong protection against Aβ-toxicity in MC65 cells, in agreement with the protective effects observed for these compounds in previous preclinical model studies.

This research presents a comprehensive bibliometric and sentiment analysis of nanocomposite literature from 1990 to 2024. Employing cutting-edge computational methods, the study delves deep into the progression of microstructural characterization, electrical properties, and mechanical behaviors of nanocomposites. The analysis utilizes advanced Boolean search strategies from the Scopus database, ensuring a thorough extraction of thematic content. The results explore various themes and insights, shedding light on trends evident in the Scopus database, particularly the prominence of research on nanocomposite microstructure, electrical attributes, and mechanical performance. The paper also offers textual analytics and bibliometric data, showcasing critical collaborative efforts and influential studies. Significant discoveries encompass the evolution of research language, shifts in thematic focus, and global contributions, providing a comprehensive perspective on the current landscape of nanocomposite research and its dynamic evolution. Moving forward, the "State-of-the-Art and Gaps Extracted from Results and Discussions" section of the paper delves into the most recent advancements in nanocomposite research. It delves into various types of nanocomposites, with a particular emphasis on their microstructural characteristics, electrical properties, mechanical dynamics, and the application of nanocomposite films. This section identifies key research themes, traces historical progress, and highlights emerging trends while underscoring the significance of collaboration and the influence of pivotal studies that have shaped the field. Lastly, in the "Literature Review Guided by Artificial Intelligence" section, the paper introduces a revised approach for researching nanocomposites through AI-guided techniques. This approach prioritizes articles published in 2023 based on citation frequency. Here, the focus is on exploring the relationship between nanocomposites and their electrical properties, emphasizing their fundamental interactions and impact on electrical characteristics. Various nanocomposite systems are covered, highlighting their composition, structure, and functionality. Findings from recent studies are integrated to provide a comprehensive overview of the current state of knowledge in this area. Notably, the sentiment analysis, anchored by an average sentiment score of 0.638771, underscores a positive trajectory in academic discourse, emphasizing the growing recognition of the potential of nanocomposites. The bibliometric exploration maps the intellectual domain, emphasizing pivotal research themes and the influence of crosslinking time on nanocomposite attributes. While this study is thorough, it acknowledges its limitations and advocates for broader database inclusion in future research endeavors. This work elucidates the prevailing trends in nanocomposite research, emphasizing the indispensable role of computational tools in comprehending this vast wealth of information.

We review the literature on medical discourse and attempt to build a computational model of it. Medical discourse sheds a light on communication structure of patient-doctor and other communication scenarios in healthcare and should be leveraged to facilitate and automate this communication when it is possible and practical. We propose a unified framework to represent communication discourse at the meta-level, where the subject of the communication is expressed in a language object. So far, the broad range of work on medical discourse is detached from computational discourse analysis, and we explore the possibilities of filling this gap and computationally treat the peculiarities of how information is passed between the agents in a hospital setting. We select the domain of question answering (QA) against a corpus of medical documents of diverse nature to evaluate our computational model of medical discourse. It turns out that applying specific structures obtained in medical discourse studies improves the relevance and efficiency of question answering.

Barley straw was used to reinforce PHB in a discontinuous semi-industrial process. Four different fibres were used as reinforcing material : sawdust (SW), defibred (DFBF), delignified (DBF) and bleached (BBF) fibres. Looking at the mechanical properties, onlythe elastic moduli (Et) revealed an improvement. Oppositely, the strengths at break, under both tensile and bending assays, tended to decrease, evidencing a poor affinity between both components. Thanks to the mechani-cal treatment applied on the fibre, DFBF appeared as the most promising filler with the mechani-cal properties of the composites closest to those of neat PHB. DFBF-based composites were then produced by means of a process intensification using a twin-screw extruder, and then moulded into flowerpots. Mechanical results showed almost the same properties between the discontinuous and intensification processes. The suitability of the material to be used on agriculture flowerpots was demonstrated by simulation. . PHB-DFBF composite material can be considered an alterna-tive to replace petrol-based products for applications in agriculture and horticulture.

Pathological Brain Detection (PBD) is a crucial field aimed at identifying and diagnosing structural or functional abnormalities in the brain related to neurological, neurodegenerative, or psychiatric disorders. This detection process typically employs medical imaging technologies such as MRI, CT, or PET scans, along with neurological evaluations, blood tests, and other diagnostic tools. Early detection is essential for effective treatment, improving prognosis, and enhancing the quality of life for affected individuals. Each common computational method provides unique insights into brain pathologies. Despite significant advancements in technology and methodology, PBD faces challenges such as variability in brain anatomy, the complexity of disorders, data quality, standardization issues, interpretability of models, and ethical concerns. Addressing these challenges necessitates collaboration among researchers, clinicians, and policymakers to develop robust and ethical methods for improving the detection and diagnosis of brain disorders.

The Bessel function of the first kind J_N(kx) is expanded in a Fourier-Legendre series, as is the modified Bessel functions of the first kind I_N(kx) . Known polynomial approximations for the range −3≤x≤3 (having five-digit accuracy) are compared with those arising from this Legendre series and substitute versions are given with twice the number of terms and range (with five-digit accuracy or having fifteen-digit accuracy over −3≤x≤3). It is shown that infinite series of like-powered contributors (involving _2F_3 hypergeometric functions) extracted from the Fourier-Legendre series may be summed, having values that are inverse powers of the first five primes 1/(2^i 3^j 5^k 7^l 11^mm) for powers of order x24.

The automotive industry has witnessed a significant push towards increased fuel efficiency and reduced carbon emissions. Among many factors influencing overall vehicle efficiency, aerodynamic drag is a critical consideration, accounting for a substantial portion of fuel consumption. Our investigation focused on the drag coefficients of three prominent 2023 passenger car models – the Toyota Corolla, Hyundai Elantra, and Kia Soul. Using ANSYS Fluent to perform computational fluid dynamics, we demonstrated an inverse correlation between drag force and gas mileage, underlining the significance of aerodynamic design in fuel efficiency. Notably, the Hyundai Elantra emerged as the most aerodynamically efficient of the three vehicles, followed closely by the Toyota Corolla. In contrast, the boxier-designed Kia Soul exhibited significantly higher drag coefficients and drag forces, rendering it the least efficient of the trio. These findings emphasize the importance of environmental factors in car design. Shaping aerodynamic profiles directly impacts fuel efficiency, stability, and overall performance.

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.

This research provides a computationally efficient numerical tool for predicting 3-D turbulent flows over hydrofoils. It addresses a gap in existing numerical methods by extending the capabilities of the laminar VIScous Vorticity Equation (VISVE) solver to handle 3-D turbulent flow scenarios. The implementation integrates the k − ω SST model into the 3-D VISVE solver using the Finite Volume Method (FVM), extending its capability to handle turbulent flows. The turbulence model is also parallelized to improve computational efficiency. The application of the newly developed model to 3-D hydrofoil cases expands its scope and serves as preparatory work for future rotational propeller applications. Rigorous testing includes a convergence analysis concerning grid numbers and time step sizes, and validation against a Reynolds-Averaged Navier-Stokes (RANS) solver ensures the reliability and accuracy of the enhanced VISVE solver for turbulent flow simulations. The turbulent VISVE solver offers advantages such as reduced computational domain and costs through a vorticity-based approach. Turbulence concentration within boundary layers and free shear flows does not compromise the advantages of the small computational domain. The simplified meshing process, automatically generated by denoting the number of panels on the hydrofoil, makes the solver accessible for researchers and engineers.

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.

Recently, more wind turbine systems are being installed in deep waters far from the coast. Several concepts of floating wind turbine systems (FWTSs) are developed, among which the semisubmersible platform, due to its applicability in different water depths, good hydrodynamic performance, and facility in the installation process, constitutes the most explored technology compared to the others. However, a significant obstacle to the industrialization of this technology is a design of a cost-effective FWTS, which can be achieved by optimizing the geometry, size and weight of the floating platform, along with the mooring system. This is only possible by selecting a method capable of accurately analyzing the FWTS coupled hydro-aero-structural dynamics at each design stage. Accordingly, this paper aims at providing a detailed overview of the most common coupled numerical and physical methods, including their basic assumptions, formulations, limitations, and costs, used for analyzing the dynamics of FWTSs, mainly those supported by a semisubmersible, to assist the choice of the most suitable method at each design phase of FWTSs. Finally, the article discusses possible future research to address challenges in modeling FWTSs dynamics that persist to date.

Small submersible drainage pumps discharge leaking water and rainwater in buildings. In an emergency (e.g., heavy rain or accident), monitoring the flow rate in advance is necessary to enable optimal operation, considering the point where the pump operates abnormally when the water level increases rapidly. In another, pump performance optimization is crucial for energy saving policy. Therefore, it is necessary to meet the challenges of submersible pump systems, including sustainability and pump efficiency. The final goal of this study is to develop an energy-saving, high-efficiency submersible drainage pump capable of responding to emergencies. In particular, this paper targeted the hydraulic performance improvement of a submersible drainage pump model. Before the development of driving mode-related technology capable of emergency response, we first tried to find a way to improve the performance characteristics of the existing submersible drainage pump. Rather than designing a new pump, we disassembled the current pump and reverse engineering. Then, numerical simulation was performed to analyze the flow characteristics and efficiency of the pump. Then, the pump test was carried out to obtain the performance and validated with numerical results. Results revealed that changing the cross-sectional shape of the impeller reduced the flow separation and enhanced velocity and pressure distributions. Also, it reduced the power and increased efficiency. Results also showed that the pump's efficiency increased to 5.56% at a discharge rate of 0.17 m3/min, and overall average efficiency increased to 6.53%. In addition, the submersible pump design method could be suitable for the optimized pump's impeller and casing numerically. This paper can provide insight and information on the design optimization of pumps.

In recent years, Neural networks are increasingly deployed in various fields to learn complex patterns and make accurate predictions. However, designing an effective neural network model is a challenging task that requires careful consideration of various factors, including architecture, optimization method, and regularization technique. This paper aims to comprehensively overview the state-of-the-art artificial neural network (ANN) generation and highlight key challenges and opportunities in machine learning applications. It provides a critical analysis of current neural network model design methodologies, focusing on the strengths and weaknesses of different approaches. Also, it explores the use of different learning approaches, including convolutional neural networks (CNN), deep neural networks (DNN), and recurrent neural networks (RNN) in image recognition, natural language processing, and time series analysis. Besides, it discusses the benefits of choosing the ideal values for the different components of ANN, such as the number of Input/output layers, hidden layers number, activation function type, epochs number, and model type selection, which help improve the model performance and generalization. Furthermore, it identifies some common pitfalls and limitations of existing design methodologies, such as overfitting, lack of interpretability, and computational complexity. Finally, it proposes some directions for future research, such as developing more efficient and interpretable neural network architectures, improving the scalability of training algorithms, and exploring the potential of new paradigms, such as Spiking Neural Networks, quantum neural networks, and neuromorphic computing.

The present paper is aimed at presenting an updated review of prediction methods for the aerodynamic noise of ducted rotor-stator stages. Indeed, ducted rotating-blade technologies are in continuous evolution and increasingly used, for both aeronautical propulsion units, power generation and air conditioning systems. Different needs are faced, from the early design stage to the final definition of a machine. Fast-running, approximate analytical approaches and high-fidelity numerical simulations are considered as the best suited tools for each, respectively. Recent advances are discussed, with emphasis on their pros and cons.

Electrowetting – induced detachment of droplets from solid surfaces is important for numerous applications in the fields of heat transfer and fluid mechanics. The forced oscillations of droplets on solid surfaces and their ability to detach are studied. In this study the process is efficiently simulated by implementing a powerful methodology developed by our team. Our results agree with experiments, showing that optimal detachment, in terms of actuation energy, is achieved when the application of voltage is synchronized with the spreading time of the droplet. Under these conditions, the droplet oscillates with a period close to that of a mirrored Rayleigh droplet. The relation between the droplet’s oscillation period and its physical properties is examined. During voltage-droplet synchronization, the droplet’s ability to detach depends mostly on its contact angle, its viscosity, and the applied voltage. An energy analysis is also conducted, revealing how energy is supplied to the droplet by electrowetting-induced detachment.

New Schiff base ofN'-((E)-3,3-diphenyl-2,3-dihydro-1H-inden-1-ylidene)-2-((Z)-2-oxoindolin-3-ylidene)hydrazine-1-carbothiohydrazide (HL) and its Co(II),Ni(II), Fe(III), Cu(II), Cd(II), Zn(II) and Mn(II) complexes were created, isolated, and characterized by analytical analysis in good yields of 60 to 90%, UV–Vis, magnetic measurements, molar conductivity, and FT-IR. Additionally, mass spectrometry was used to examine the new ligand complexes' structural characteristics.1H NMR spectroscopy and elemental analysis. Depending on the electronic spectra and magnetic susceptibility measurements, these complexes are predicted to have octahedral molecular geometry. As an example, the quantum chemical properties, bond lengths, and bond angles were discussed. The structural formula for the examined ligand was improved using the Gaussian09 tool. The DFT/B3LYP method was used to calculate the energy gaps and other crucial theoretical features. The prepared hydrazine ligand complexes Co (II), Ni (II), Fe (III), Cu (II), Cd (II), Zn (II) and Mn (II) complexes were evaluated for its antimicrobial properties. As antimicrobial agents, it was found that the organic ligand was less biologically active than the targeted complexes. Gram positive bacteria (3ty7-Staphylococcus), Gram negative bacteria (3t88-Escherichia coli), and fungi (5k04-Candida albicans) all have crystal structures that interact well with the chemicals in the title, according to molecular docking.

The basis for computation in the brain is the quantum threshold of the ‘soliton’ accompanying the ion changes of the action potential and the refractory membrane at convergences. We provide a logical explanation from the action potential to a neuronal model of the coding and computation of the retina and have explained how the visual cortex operates by quantum phase processing. In the small world network parallel frequencies collide into definable patterns of distinct objects. Elsewhere we have shown how many sensory cells are mean sampled to a single neuron and that convergences of neurons are common. We have also demonstrated, using the threshold and refractory period of a quantum phase pulse, that action potentials diffract across a neural network due to the annulment of parallel collisions in phase ternary computation (PTC). Thus, PTC applied to neuron convergences results in collective mean sampled frequency and is the only mathematical solution within the constraints of brain neural networks (BNN). In the retina and other sensory areas, we discuss how this information is coded and then understood in terms of network abstracts within the lateral geniculate nucleus (LGN) and visual cortex. First by defined neural patterning within a neural network, and then in terms of contextual networks, we demonstrate that the output of frequencies from the visual cortex contain information amounting to abstract representations of objects in increasing detail. We show that nerve tracts from the LGN provide time synchronisation to the neocortex (defined as the location of the combination of connections of the visual cortex, motor cortex, auditory cortex, etc). The full image is therefore combined in the neocortex with other sensory modalities so that it receives information about the object from the eye, and all abstracts that make up the object. Spatial patterns in the visual cortex are formed from individual patterns illuminating the retina and memory is encoded by reverberatory loops of computational actions potentials (CAPs). We demonstrate that a similar process of PTC may take place in the cochlea and associated ganglia, as well as ascending information from the spinal cord, and that this function should be considered universal where convergences of neurons occur.

Haemodynamic simulations are increasingly used to study vascular diseases like Intracranial Aneurysms (IA) and to further develope treatment options. However, due to limited data, some aspects must rely on heuristics, especially at the simulation’s distal ends. In literature, Murray’s Law is often used to model the outflow split based on vessel cross-section area, but this poses challenges for the communicating arteries in the Circle of Willis (CoW). In this study, we contribute by assessing the impact of Murray’s Law in patient-specific geometries featuring IA at the posterior communication. We simulate different domain extensions, representing common modelling choices. We establish Full CoW simulations as a baseline to evaluate the effect of these modelling assumptions on haemodynamic indicators, focusing on IA growth and rupture-related factors like Wall Shear Stress (WSS) and Oscillatory Shear Index (OSI). Our findings reveal qualitative alterations in haemodynamics when not modeling posterior communication. Comparisons between computing the anterior circulation and computing the whole Circle of Willis reveal quantitative changes in WSS may reach up to 80%, highlighting the significance of modelling choices in assessing IA risks and treatment strategies.

Glioblastoma (GBM) is a representative malignant brain tumor characterized by a dismal prognosis, with survival rates of less than 2 years and high recurrence rates. Despite surgical resection and several alternative treatments, GBM remains a refractory disease due to its aggressive invasiveness and resistance to anticancer therapy. In this report, we explore the role of fibronectin type III domain containing 3B (FNDC3B) and its potential as a prognostic and therapeutic biomarker in GBM. GBM exhibited a significantly higher cancer-to-normal ratio compared to other organs, and patients with high FNDC3B expression had a poor prognosis (p < 0.01). In vitro studies revealed that silencing FNDC3B significantly reduced the expression of Survivin, an apoptosis inhibitor, and also reduced cell migration, invasion, extracellular matrix adhesion ability, and stem cell properties in GBM cells. Furthermore, we identified that FNDC3B regulates PTEN/PI3K/Akt signaling in GBM cells using MetaCore integrated pathway bioinformatics analysis and a proteome profiler phospho-kinase array with sequential western blot analysis. Collectively, our findings suggest FNDC3B as a potential biomarker for predicting GBM patient survival and for the development of treatment strategies for GBM.

Epidemiological surveillance and phylogenetic studies rely nowadays on processing and analysing huge volumes of data. Processing tasks consist on running and refining a series of intertwined computational tasks. And, despite of existing several web applications for data processing and interactive visualization for phylogenetic studies, integrating many different tools and algorithms, their execution is total or partially on the client side, making them unsuitable for dealing with huge volumes of data. Studies are often also not easy to reproduce. On the other hand, in recent years, data-centric workflow systems have been proposed, allowing to deal better with increasingly larger datasets. The integration of these systems within phylogenetic tools will allow to scale them as required, and will contribute also to promote studies reproducibility. We propose then the FLOWViZ middleware for facilitating the integration of a state of the art data-centric workflow system, Apache Airflow, within web applications for phylogenetic analyses. This framework abstracts contracts and a core API for defining tools and workflows, where tools are assumed to be containerized. FLOWViZ has been tested and evaluated within the PHYLOViZ web application, a tool supporting phylogenetic inference and data visualization.

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.

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.

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.

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.

Potentilla nepalensis belongs to the Rosaceae family, and has numerous therapeutic applications as potent plant-based medicine. Forty phytoconstituents (PCs) from the root and stem through n-hexane (NR and NS) and methanolic (MR and MS) extracts were identified in our earlier studies. However, the PCs affecting human genes and their roles in the body are not disclosed till now. In this study, we employed network pharmacology, molecular docking, molecular dynamics simulations (MDS), and MMGBSA methodologies. SMILES format of PCs from the PubChem used as input to DIGEP-Pred, 764 identified as the inducing genes. Their enrichment studies have shown inducing genes gene ontology descriptions, involved pathways, associated diseases, and drugs. PPI networks constructed in String DB and network topological analysing parameters done in Cytoscape v3.10 revealed three biomarkers, TP53 from MS, NR, and NS induced genes; HSPCB and Nf-kB1 from MR induced genes. From 40 PCs, two PCs 1b (MR) and 2a (MS), showed better binding scores (kcal/mol) with p53 protein of -8.6, and -8.0; three PCs 3a, (NR) 4a and 4c (NS) with HSP protein of -9.6, -8.7, and -8.2. MDS and MMGBSA revealed these complexes are stable without higher deviations with better free energy values. Biomarkers identified in this study, have a prominent role in numerous cancers. Thus, further investigations such as in-vivo and in-vitro should be done to find the molecular functions and interlaying mechanism of the identified biomarkers on numerous cancer cell lines in considering the PCs of P.nepalensis.

This research, which focuses on wave energy generating technology, suggests a brand-new oscillating float-type wave energy conversion system that is appropriate for the offshore wave environment. To accurately model the device, a three-dimensional viscous numerical pool is created based on STAR-CCM+. The array-type power generation device is numerically simulated using theoretical analysis and numerical simulation, and the wave energy power generation device is arranged as double floats in the front and rear rows, representing triangular array type and rectangular array type, respectively. The triangular array type is a superior layout and can make greater use of marine resources, according to this paper's analysis of the hydrodynamic and power generation impacts of the three array types. The findings are meant to serve as a guide for engineering practice.

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.

In recent years, applying deep learning to solve physics problems has attracted much attention. Data-driven deep learning methods produce operators that can learn solutions to the whole system of partial differential equations. However, the existing methods are only evaluated on simple flow equations (e.g., Burger's equation), and only consider the generalization ability on different initial conditions. In this paper, we construct CFDBench, a benchmark with four classic problems in computational fluid dynamics (CFD): lid-driven cavity flow, laminar boundary layer flow in circular tubes, dam flows through the steps, and periodic Karman vortex street. Each flow problem includes data with various boundary conditions, fluid physical properties, and domain geometries. Compared to existing datasets, the advantages of CFDBench are (1) comprehensive. It contains common physical parameters such as velocity, pressure, and cavity fraction. (2) realistic. It is very suitable for deep learning solutions of fluid mechanics equations. (3) challenging. It has a certain learning difficulty, prompting to find models with strong learning ability. (4) standardized. CFDBench facilitates a comprehensive and fair comparison of different deep learning methods for CFD. We make appropriate modifications to popular deep neural networks to apply them to CFDBench and enable the accommodation of more changing inputs. The evaluation on CFDBench reveals some new shortcomings of existing works and we propose possible directions for solving such problems.

In this work, we incorporate a thixotropic-viscoelastic model into the widely used Computational Fluid Dynamics (CFD) software OpenFOAM, along with the rheoTool library. The model we implement is known as the Modified-Bautista-Manero (MBM), and effectively describes the rheological behavior of worm-like micellar solutions in extensional flows. We provide a detailed explanation of the numerical implementation of the model, specifically using the log-conformation tensor approach. Unlike previous works focused on this kind of fluids, we simulate inertial flows while considering convective terms in the governing equations, thus obtaining a more realistic behavior on the calculated results. The MBM model implementation is validated through numerical simulations on two different industrial-relevant geometries: the planar 4:1 contraction and the 4:1:4 contraction-expansion configurations. Furthermore, we investigate the influence of inertial, viscoelastic, and thixotropic effects on various flow field variables. These variables include velocity, viscosity, normal stresses, and corner vortex size. Our analysis encompasses both transient and steady solutions of corner vortexes across a range of Deborah and Reynolds numbers. Our results are also directly compared with simulations obtained using the non-thixotropic rubber network-based exponential Phan-Thien-Tanner (EPTT) model. From our planar 4:1 contraction results, we found that vortex-enhancement is seen when high elasticity is coupled with quick structural reformation and very low inertial effects. From our planar 4:1:4 contraction-expansion simulations, we show that an increase in inertia leads both to vortex-inhibition in the upstream channel and slight vortex-enhancement in the downstream channel. Lastly, we show the strong effect of the convection of fluidity into the fluidity profiles and into the upstream/downstream corner vortex sizes.

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.

The current study presents a computational fluid dynamics model designed to simulate the isolation of 2D graphene components from their dispersion using a microfiltration hollow fiber membrane unit. The model utilizes Navier-Stokes and mass conservation equations to predict the flow and concentration behaviors in the system, and it employs the resistance-in-series approach to couple the fouling of the non-exfoliated graphites in the dispersion to the model. Specifically, the model accounts for the effects of the concentration polarization and cake formation of the fouling component, resulting in the increment of the transmembrane pressure (TMP) of the system. Additionally, we assumed the wettability of polymeric membrane’s inner wall enhances upon fouling by the flat layered structure material, leading to the reduction of the transmemrabe pressure. This approach demosntrates accurate reproduction of the exceptional experimental TMP behavior corresponding to the fouling of the graphite.

Computational Fluid Dynamics (CFD)-based simulation has been the traditional way to model complex industrial systems and processes. One very large and complex industrial system that has benefited from CFD-based simulations is the steel blast furnace. The problem with the CFD-based simulation approach is that it tends to be very slow to generate data. The CFD-only approach may not be fast enough for use in real-time decision-making. To address this issue, in this work, the authors propose the use of machine learning techniques to train and test models based on data generated via CFD simulation. Regression models based on neural networks are compared to tree boosting models. In particular, several areas (tuyere, raceway, and shaft) of the blast furnace are modeled using these approaches. The results of the model training and testing are presented and discussed. The obtained R2 metrics are, in general, very high. The results look promising and may help to improve the efficiency of operator and process engineer decision-making when running a blast furnace.

Abstract: In this study, a computational fluid dynamics (CFD) simulation was conducted to validate the CFD model with experimental data from Zimparov et.al (2012), which investigates the heat transfer performance in a spirally corrugated tube that has a twisted tape inserted. The heat transfer was then compared to a simple corrugated tube without the twisted tape and to a smooth tube with no corrugations and no twisted tape. This simulation is compared with a previous experimental study conducted by (Zimparov et al. 2012), which focuses on tubes with a height-to-diameter ratio e/Di > 0.04 and small relative pitches of the twisted tape, H/Di. The largest improvement was noticed in the tube with e/Di= 0.057 and ridge pitch-to-height ratio p/e=6.77 with a twisted tape of H/Di= 4.7. This tube is labeled 5035. Tube 5035 was found to have the most significant enhancement, hence the focus of the CFD simulation will be on this tube, and the simulation will range for Reynolds numbers, 3.5 x 103 < Re < 5.0 x 104. The focus of this simulation is the evaluation of heat transfer and friction factors; a metric was used to obtain an empirical representation of the tube’s performance. Keywords: Spirally corrugated tube; Twisted tape; Heat transfer enhancement; Friction factor; Nusselt number; Computational fluid dynamics (CFD); Star CCM+

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.

With an ever increasing amount of research data available, it becomes constantly more important to possess data literacy skills to benefit from this valuable resource. An integrative course was developed to teach students the fundamentals of data literacy through an engaging genome sequencing project. Each cohort of students performed planning of the experiment, DNA extraction, nanopore sequencing, genome sequence assembly, prediction of genes in the assembled sequence, and assignment of functional annotation terms to predicted genes. Students learned how to communicate science through writing a protocol in the form of a scientific paper, providing comments during a peer-review process, and presenting their findings as part of an international symposium. Many students enjoyed the opportunity to own a project and to work towards a meaningful objective.

An axial wind turbine's performance is analyzed numerically using geometrical and operational parameters. The performance of an axial wind turbine is simulated using three-dimensional computational fluid dynamics (CFD) and compared with experimental data, which confirms the validity of the assumptions and method used. A study of the effects of air in clean and sand particlesy conditions is then conducted. A study was also conducted on mechanical power and the impact of the blade tip on the extension of the vortex. It has been found that different sand particle sizes have a significant impact on wind performance numerically from 0.1 to 0.9, and the results indicate that mechanical power and thrust increase as wind velocity increases, but larger sand particle sizes reduce power, thrust, and torque as wind velocity increases. The turbulence intensity causes the pressure distribution on the walls to be irregular, thereby increasing mechanical power and propulsion force. Moreover, as the wind velocity increases, the tip speed ratio increases and the vortexes are subsequently extended.

The hepatic sinusoids are lined by thin endothelium containing transcellular pores dubbed fenestra. These fenestrations are completely open channels connecting the sinusoidal lumen to the underlying Space of Disse (SoD) and the hepatocytes of the liver parenchyma. The fenestrations are in the size range 0.05-0.35µm in diameter and cover between 5-15% of the sinusoidal endothelial surface area depending on their location along the sinusoids. Motivation/Research Question: So far, the narrowness of the sinusoids prevented the direct measurement of hemodynamic features such as sinusoidal pressure and flow velocity. A better understanding of these parameters might help in better understanding the physiology of the hepatic niche and possible implications in liver diseases. Few simulations of liver blood flow focus on the level of the individual sinusoid, fewer still include the transcellular pores (fenestrations) of the sinusoidal endothelium, and none have included i) a porosity gradient along the sinusoid wall modelled with through-all pores rather than a porus medium, ii) the presence of the Space of Disse, iii) lymphatic drainage. Model: Computed fluid dynamic (CFD) simulations performed using a numerical model with relevant anatomical characteristics (length, diameter, porous size and porosity). Boundary conditions included physiological/pathological values of inlet/outlet pressure and lymphatic outflow from the portal region of the SoD. Results: The pressure and flow velocity of the sinusoidal lumen was entirely dependent on the shape, i.e. constant versus divergent radius and not dependent on porosity in the sinusoidal wall. The velocity through the space of Disse (SoD) was affected by the addition of lymphatic drainage and increases in porosity in the pericentral area of the model. Variations in porosity also affected flow velocity through the fenestrations. Conclusions: The flow velocity in the SoD was modified by differences in porosity, while the flow velocity in the lumen of the sinusoids was unaffected, even by the absence of fenestrations. The overall shape of the vessel is the singular most important factor in the pressure flow behavior of the sinusoidal lumen. The flow rate over the hepatocytes is the flow rate through the SoD, and is modestly affected by the distribution of porosity along the sinusoid, and by the addition of a lymphatic drainage, this parameter would be of interest for modelling blood exchange with the hepatic parenchyma, especially if accounting for zonation.

Stellatolides are natural compounds that have shown promising biological activities, including antitumor, antimicrobial, and anti-inflammatory properties, making them potential candidates for drug development. Chemical Reactivity Theory (CRT) is a branch of chemistry that explains and predicts the behavior of chemical reactions based on the electronic structure of molecules. Conceptual Density Functional Theory (CDFT) and Computational Peptidology (CP) are computational approaches used to study the behavior of atoms, molecules, and peptides. In this study, we present the results of our investigation of the chemical reactivity and ADMET properties of Stellatolides A-H using a novel computational approach called Conceptual DFT-based Computational Peptidology (CDFT-CP). Our study uses CDFT and CP to predict the reactivity and stability of molecules and to understand the behavior of peptides at the molecular level. We also predict the ADMET properties of the Stellatolides A-H to provide insight into their effectiveness, potential side effects, and optimal dosage and route of administration. This study sheds light on the potential of Stellatolides A-H as promising candidates for drug development and highlights the potential of CDFT-CP for the study of other natural compounds and peptides.

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.

Polycyclic aromatic hydrocarbons (PAHs) have emerged as promising materials for organic electronics, including organic photovoltaics (OPVs), organic field-effect transistors (OFETs), and organic light-emitting diodes (OLEDs). Particularly, non-hexagonal ring-fused PAHs are highly desirable due to their unique optoelectronic properties. Herein, a new redox-active azulene-perylene diimide triad 1 and its ring-fused counterpart, diazulenocoronene diimide 2, were synthesized and fully characterized by a combination of NMR, cyclic voltammetry, spectroelectrochemistry, and UV-visible absorption spectroscopy. Density functional theory (DFT) and time-dependent DFT (TD-DFT) calculations were performed to gain further insight into various electronic transitions. Interestingly, we found that the adaptive response to acids and bases manifests itself in a reversible two-color-change that can be attributed to changes in the chemical structures.

Marine vessels operating on the Arctic Sea route are constantly prone to collisions and friction with ice. This study discusses the wear of the hull plate caused by the collision of ice against vessels operating in Arctic Sea routes. The abrasive wear of the hull due to ice impact was numerically assessed based on both the incident behavior of ice particles interacting with the flow around the hull and the wear loss of the hull surface caused by the contact force of ice particles. A multi-phase approach was adopted to account for the behavior of ice particles continuously affected by the fluid force around the hull. The fluid force acting on the ice floe was evaluated using computational fluid dynamics (CFD) and the dynamic motion of the drift ice was evaluated using the discrete element method (DEM). The motion of the floating ice particles was updated in real time by iteratively coupling the fluid force and the motion of the ice floe at each time step of the numerical simulation. The results of the wear simulation models were presented in terms of the shape change of the hull surface due to wear. At first, the wear was evaluated for cases in which only the surface paint of the hull was damaged. Thereafter, a computation model considering the shape change of the hull surface experiencing long-term friction of ice particles was introduced. Finally, the numerical procedures to predict the abrasive wear of the hull surface by ice impact were discussed.

Polymerase chain reaction (PCR) technique is one of the molecular methods in amplifying DNA for the detection of malaria. However, the limitations of PCR especially when used for routine clinical practice can hamper its sensitivity and specificity. With that, this study focuses on designing a microfluidic device that will mimic the function of a conventional genus-specific PCR based on 18S rRNA gene to detect malaria parasites (Plasmodium falciparum) at low grade parasitemia. The design was drawn and simulated using ANSYS 14.5 Computational Fluid Dynamics (CFD). The simulation shows that adding loops to the design increases its relative deviation but to a minimal extent as compared to having only a straight path design, which indicates that looping is acceptable in designing a microfluidic device to minimize chip length. Also, increasing the cross-sectional area of the fluid path decreases the efficiency of the design, thus, the design with a relatively smaller cross-sectional area is favored. And lastly, among the three materials utilized, the chip made of polypropylene is the most efficient with a relative deviation of 0.94 as compared to polycarbonate and polydimethylsiloxane which have relative deviations of 2.78 and 1.92, respectively.

Accurate evaluation of thermo-fluid dynamic characteristics in tank are critically important for designing liquid hydrogen tank of small-scale hydrogen liquefier to minimize heat in-leak into the liquid and ullage. Due to the huge cost, most future liquid hydrogen storage tank designs will have to rely on predictive computational models for storage tank pressurization and heat-leak minimization. Thus, reliable on predictive numerical models to aid design process of tank design have been required. Therefore, in this study, in order to improve the storage efficiency of the small-scale hydrogen liquefier, a three-dimensional CFD model that can predict the boil-off rate and the thermo-fluid characteristics due to heat penetration has been developed. The prediction performance and accuracy of the CFD model is validated on the basis of the comparisons between its results and previous experimental data and a good agreement is obtained. To evaluate insulation performance of polyurethane foam according to thickness, the pressure change and thermo-fluid characteristics in a partially liquid hydrogen tank, subject to fixed ambient temperature and wind velocity, has been investigated numerically. Three different insulation thickness were considered to investigate not only interfacial characteristics between liquid and ullage but thermo-fluid dynamics in hydrogen storage tank for various heat-leak situation. The results indicate that as the insulation thickness becomes thinner and more heat penetration exists, natural convection by buoyancy develops stronger, so strong vortices are created near the interfacial zone by upward flow driven by buoyancy. In particular, as the amount of heat in-leak increased, wider and stronger heat accumulation was found in the top dished head area of the ullage area. In addition, it was confirmed that the CFD model developed in this study could well describe the phenomenon in which the temperature of the tank wall and the insulation mat increased at a faster rate due to faster heat transfer from the vapor and interface. In the future, the numerical model developed in this study will be used for optimizing insulation system of storage tank for small-scale hydrogen which is cost-effective and highly efficient.

Backgroumd: The feeling of nasal obstruction is one of the most complaints of the Ear Nose and Throats (ENT) patients. The reason of this complaint is variable, such as Nasal Septal Deviation (NSD). NSD is a significant obstacle to normal respiration and the volume of air entering the lungs. Understanding how the deviation causes the obstruction is so important, because it leads to better management of this disease. Rhinomanometry and acoustic rhinometry are methods that are used for objective evaluation of upper airway, but they are semi invasive and the accuracy are not guaranteed. With development of Computed Tomography (CT scan) and Computational Fluid Dynamics (CFD), numerical studies on airflow in different parts of the nasal cavity have been performed. Objectives: The purpose of this study was to compare nasal airflow patterns between patient with NSD and healthy nasal cavity using a computational fluid dynamics (CFD) Methods: A patient with NSD and a healthy human were selected. Using CFD, the maximal airflow velocity, nasal resistance, maximal Wall Shear Stress (WSS), and pressure drop in bilateral nostrils of both cases were assessed. Results: Pressure and velocity profiles are evaluated at a mass flow rate of 17.4 Liters Per Minute (LPM). The velocity profile shows an abnormal flow profile in the septal deviated nasal cavity and the peak velocity is observed at the middle nasal region, whereas this point is at the nasal valve level in a healthy nasal cavity. Wall shear stress (WSS) caused by NSD is lower than in normal nasal cavities. The pressure drop in a nasal cavity with deviated septum is higher than in a healthy nasal cavity. Based on the results of this investigation nasal resistance in NSD was higher than in normal, healthy nasal cavity. Conclusions: NSD plays an important role in nasal airflow regime and it results in the feeling of obstruction comparing to normal nasal cavity. Also there is a promising future for CFD to become a useful tool in understanding airflow physiology and pathology.

Coded aperture 3D imaging techniques have been rapidly evolving in the recent years. The two main directions of evolution are in aperture engineering to generate the optimal optical field and in development of computational reconstruction to reconstruct the object’s image from the intensity distribution with a minimal noise. The goal is to find the ideal aperture-reconstruction method pair and if not, to optimize one to match the other for designing an imaging system with required 3D imaging characteristics. Lucy-Richardson-Rosen algorithm (LR²A), a recently developed computational reconstruction method was found to perform better than its predecessors such as matched filter, Weiner filter, phase-only filter, Lucy-Richardson algorithm and non-linear reconstruction (NLR) for certain apertures when the point spread function (PSF) is a real and symmetric function. For other cases of PSF, NLR performed better than the rest of the methods. In this tutorial, LR²A has been presented as a generalized approach for any optical field along with MATLAB codes for reconstruction of any image when the PSF is known. The common problems and pitfalls in using LR²A has been discussed. Simulation and experimental studies for common optical fields such as spherical, Bessel, vortex beams and exotic optical fields such as Airy, scattered and self-rotating beams have been presented. From this study, it can be seen that it is possible to transfer the 3D imaging characteristics from non-imaging type exotic fields to indirect imaging systems faithfully using LR²A. The application of LR²A to medical images such as colonoscopy images and cone beam computed tomography images with synthetic PSF has been demonstrated. We believe that the tutorial will provide a deeper understanding of computational reconstruction using LR²A.

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.

Aim: Streptococcus pneumoniae and influenza H1N1 virus are common organisms associated with human infections. These infections could play a significant role in immune regulation. The study was performed to analyse the genome sequences of these organisms with human genome and study its functional significance. Methods: The study was performed to analyse the overlapping of genome sequences in Streptococcus pneumoniae and Influenza (H1N1) virus against human genome sequences by BLASTn sequence analysis. The alignments are studied against the corresponding genes for their functional significance with DAVID and NDEx software.Results: Several hits or overlapping nucleotide segments were identified. Between streptococcus and homo sapiens 287 overlaps were identified, and among influenza and homo sapiens 124 hits were identified. A wide range of functional significance of these genes were identified, and the results are presented in this study. The results show insights into functional pathways and biological activities associated with the respective vaccinations or infections by these microorganisms. Conclusion: The common organisms like Streptococcus pneumoniae and Influenza H1N1 virus actively interact with the immune system and result in a wide range of immune regulations.

Healthy brain functioning depends on balancing stable integration between brain areas for effective coordinated functioning, with bursts of desynchronisation to allow subsystems to reconfigure and express functional specialisation. Metastability, a concept originated in statistical physics and dynamical systems theory, has been proposed as a key signature that characterises this balance. Building on this principle, the neuroscience literature has employed markers of metastability to investigate various aspects of brain function including cognitive performance, healthy ageing, meditation, sleep, responses to pharmacological challenges, and to characterise psychiatric conditions or disorders of consciousness. However, this body of work often uses the notion of metastability heuristically, and sometimes inaccurately, making it hard for the uninitiated to navigate the vast literature, interpret findings, and foster further development of theoretical and experimental methodologies. In this paper we provide a comprehensive review of metastability and its applications in neuroscience, covering its scientific and historical foundations and the practical estimators used to estimate it in empirical data. We also provide a critical analysis of recent theoretical developments, clarifying common misconceptions and paving the road for future developments.

As cancer remains resistant to several modes of treatment, novel therapeutics are still under active investigation to overcome treatment inefficacy in cancer. Given the high attrition rate of de novo drug discovery, drug screening, and drug repurposing have offered time- and cost-effective alternative strategies for the identification of potentially effective therapeutics. In contrast to large-scale drug screens, computational approaches for drug repurposing leverage the increasing amounts of biomedical data to predict candidate therapeutic agents prior to testing in biological models. Current studies in drug repurposing for cancer therapy prediction have increasingly focused on the prediction of combination therapies, as combination therapies have numerous advantages over monotherapies. These include increased effect from synergistic interactions, reduced toxicity from lowered drug doses, and a reduced risk of resistance due to multiple non-overlapping mechanisms of action. This review provides a summary of several classes of computational methods used for drug combination therapy prediction in cancer research, including networks, regression-based machine learning, classifier machine learning models, and deep learning approaches, with the goal of presenting current progress in the field, particularly to non-computational cancer biologists. We conclude by discussing the need for further advancements in technologies that incorporate disease mechanisms, drug characteristics, multi-omics data, and clinical considerations to generate effective patient-specific drug combinations, as holistic data integration will inevitably result in optimal targeted therapeutics for cancer.

This study presents the numerical investigation of bioconvective nanofluid (NF) flow compris-ing gyrotactic microorganisms through convective boundary conditions (CBC) enclosing heat and mass transmission analysis above an inclined extending cylinder. Via appropriate transfor-mation, the set of Partial Differential Equations is transformed into a scheme of nonlinear ODEs. A new set of variables are presented in the directive to get the first-order convectional equations and, then solved numerically by using bvp4c. The influence of several factors on velocity, ener-gy, concentration, and the density of motile microorganisms (DMM) is investigated and exam-ined. The analysis describes and addresses all physical measures of concentration such as Skin Friction (SF), Sherwood number (SN), DMM, and Nusselt number (NN). The present conse-quences display a validation with published. For confirmation of bvp4c, the ND-solve approach is also applied. Error analysis is also examined for the confirmation of the mathematical model.

Traditional numerical techniques such as sliding mesh, dynamic grid mesh, and others, have many limitations in dealing with flow simulation with large-scale movement of solid boundaries; which is the case for complex-shaped hydraulic turbomachinery such as propellers, pumps and turbines. The immersed boundary (IB) method provides a new approach to solve the above limitations. Therefore, this study proposes an sharp-interface IB method based on the level-set function that is suitable for simulating the flow through turbomachinery with complex geometries. This method is applied to actual three-dimensional numerical simulations of high-Reynolds number propellers using an in-house computational fluid dynamics solver. The results show that the proposed method can provide comparatively accurate predictions of unsteady load coefficients within the propeller flow passage, and capture the correct propeller wake characteristics as well as the interaction between the propeller wake and free surface. This study aims at providing a theoretical basis and engineering reference for the application of the IB method in engineering numerical simulations.

Efficient natural convection-cooled heat sinks are vital to the future of electronics cooling due to their low energy demand in the absence of an external pumping agency in comparison to other cooling methods. The present study is aimed to identify the most effective fin design for enhancing heat transfer in natural convection applications. Initially, a baseline case with rectangular fins was considered in the present study and it was optimized with respect to fin spacing. This optimized baseline case is then validated against the semi-empirical correlation proposed by Elen-baas (1942) [2]. Upon good agreement, the validated model is used for comparative analysis of different heat sink configurations with rectangular, trapezoidal, curved, and angled fins. The optimised fin spacing obtained for the baseline case is also used for the other heat sink configura-tions and then the fin designs are further optimized for better performance. However, for the an-gled fin case, the optimized configuration proposed by Zhang et al. (2020) [3] is adopted in the present study. This study is carried out with Ansys Fluent for a Rayleigh number of 2.4 × 10^6. The proposed novel curved fin design with a shroud defined as Case C4 showed a 4.1% decrease in the system’s thermal resistance with an increase in the heat transfer coefficient of 4.4% when compared to the optimized baseline fin case. The obtained results are further non-dimensionalized with proposed scaling in terms of the baseline case for the two novel heat sink cases (trapezoidal, curved).

The Oxford English Dictionary includes 17 definitions for the word “model” as a noun and other 11 as a verb. Therefore, context is necessary to understand the meaning of the word model. For in-stance, “model railways” refer to replicas of railways and trains at a smaller scale and a “model student” refers to an exemplar individual. In some cases, a specific context, like cancer research, may not be sufficient to provide one specific meaning of a model. Even if the context is narrowed, specifically to the research related to the tumour microenvironment, a “model” can be understood in a wide variety of ways, from an animal model, to a mathematical expression. This paper presents a review of the different “models” of the tumour microenvironment grouped by the different def-initions of the word into four categories: model organisms, in vitro models, mathematical models, and computational models. Then, the frequency of different meanings of the word “model” related to the tumour microenvironment is measured from the number of entries in the MEDLINE database of the United States National Library of Medicine at the National Institutes of Health. The frequency of the main components of the microenvironment and the organ-related cancers modelled are also assessed quantitatively with specific keywords. Whilst animal models, in particular xenografts and mouse models are the most common used “models”, the number of entries have been slowly de-creasing. Mathematical models, prognostic and risk models follow in frequency and these have been growing in use.