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.

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.

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 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.

The past two decades have witnessed the introduction of and then a steady increase in the use of computational techniques in the study and development of molecularly imprinted polymers (MIPs). Molecular dynamics (MD) based studies have had a significant role in this development as they can provide insights concerning the mechanisms governing the molecular level events underlying MIP synthesis and MIP-ligand interactions and can be used for the identification of preferred monomer compositions and for the prediction of MIP properties. We here review the role that MD has played in the development of molecular imprinting and examine the different types of MD strategies that have been used, including their advantages and challenges. Recent trends in the application of MD to the study of MIPs are presented, along with a perspective on the future importance of MD-based studies for the development of molecular imprinting science and technology.

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.

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

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%.

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.

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.

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.

Unsmooth reversing is one of the most common faults in the four-way reversing valve of air conditioning system, and the airflow in the valve chamber and the shape of the slider of the reversing valve are the main factors in unsmooth reversing. In order to study the influence of the airflow in the chamber and the slider shape on the reversing process, the fluid flow in the valve chamber of a four-way reversing valve is obtained using computational fluid dynamics (CFD) simulations, and the influence of slider structure on slider thrust and resistance of the four-way reversing valve is analyzed. Four-way reversing valves with no-cutting, straight-cutting and arc-cutting sliders are employed to evaluate the reversing characteristics. A pressure measuring device for a four-way reversing valve chamber is designed, which is performed to test the chamber pressure of three different types of sliders of four-way reversing valves. The maximum error of experimental tests and simulations is within 5% of engineering tolerance, so as to verify the reliability of numerical simulation. The results indicate that the chamber pressure and resistance-thrust ratio with the cutting slider are reduced, which is beneficial to the reversing of the four-way reversing valve. Compared with the straight cutting structure, the arc cutting slider four-way reversing valve has a more stable reversing process and better comprehensive reversing performance. The results can provide a theoretical basis for engineers to improve the performance of four-way reversing valves.

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

Scattering has been always considered a problem in most of the imaging and holography systems. In this project, a 3D imaging system has been developed based on scattering against common beliefs. The 3D imaging system consists of only two components namely a scattering lens, fabricated by grinding the surface of a convex lens using sandpaper and a web camera. The point spread function (PSF) in the form of speckle distribution was recorded using a laser source in the first step. A synthetic object was selected which was convolved with the PSF in a computer to generate the object intensity distribution. The image of the object was reconstructed by processing the PSF and object intensity distribution using a computational reconstruction method called non-linear reconstruction. The recorded PSF was scaled and the process was repeated for a different synthetic object. The concept was extended to 3D by summing the object intensity distributions generated using PSFs with different scaling factors. The image at different planes can be reconstructed using the PSFs corresponding to that plane.

Experimental test facilities are generally characterised using linear transfer functions to relate the wavemaker forcing amplitude to wave elevation at a probe located in the wavetank. Second and third order correction methods are becoming available but are limited to certain ranges of waves in their applicability. Artificial intelligence has been shown to be a suitable tool to find even highly nonlinear functional relationships. This paper reports on a numerical wavetank implemented using the OpenFOAM software package which is characterised using artificial intelligence. The aim of the research is to train neural networks to represent non-linear transfer functions mapping a desired surface-elevation time-trace at a probe to the wavemaker input required to create it. These first results already demonstrate the viability of the approach and the suitability of a single setup to find solutions over a wide range of sea states and wave characteristics.

Heart attacks and strokes are one of the leading causes of death in the world today, and heart attacks caused by clogged arteries that carry blood to the heart muscle are a significant part of these strokes. These are caused by the accumulation of fat particles in the walls of the arteries and the reduction of blood flow through it over a long process. The process of fat penetration in the underlying layers of the Artery wall has been the focus of many researchers, and various researches and Simulations have been done on it, in each of them, the effect of specific parameters has been considered. In the present study, the effect of blood flow rate on the flow pattern in a bifurcate artery with two ducts has been investigated using FLUENT software with Computation fluid dynamic Method. The effect of the angle between the two ducts of the Artery on the flow pattern has been investigated.

The current need to develop sustainable power sources has led to the development of ocean-based conversion systems. Wells turbine is a widely used converter in such systems which suffers from a lack of operational range and power production capacity under operational conditions. The profile named IFS which is concave in the post-mid-chord region, can produce significantly larger lift forces and show better separation behavior than the NACA profiles. In the present study, we tested this profile for the first time in a Wells turbine. The performance of six different blade designs with IFS and NACA profiles were evaluated and compared using a validated computational fluid dynamic model. Although the substitution of the NACA profile with the IFS profile in all cases increased the torque generated, the most efficient power generation and the largest efficient range were achieved in the design with varying thickness from the hub with a 0.15 thickness ratio reaching to the ratio of 0.2 at the tip. The operational span of this design with the IFS profile was 24.1% greater and the maximum torque generation was 71% higher than the case with the NACA profile. Therefore, the use of the IFS profile is suggested for further study and practical trials.

This paper presents a numerical two-scale framework for the simulation of fiber reinforced concrete under impact loading. The numerical homogenization framework considers the full balance of linear momentum at the microscale. This allows for the study of microscopic inertia effects affecting the macroscale. After describing the ideas of the dynamic framework and the material models applied at the microscale, the experimental behavior of the fiber and the fiber-matrix bond under varying loading rates are discussed. To capture the most important features, a simplified matrix cracking and a strain rate sensitive fiber pullout model are utilized at the microscale. A split Hopkinson bar tension test is used as an example to present the capabilities of the framework to analyze different sources of dynamic behavior measured at the macroscale. The induced loading wave is studied and the influence of structural inertia on the measured signals within the simulation are verified. Further parameter studies allow the analysis of the macroscopic response resulting from the rate dependent fiber pullout as well as the direct study of the microscale inertia. Even though the material models and the microscale discretization used within this study are still simplified, the value of the numerical two-scale framework to study material behavior under impact loading is shown.

In the pre era of synthetic antibodies, pharmaceutical companies depend on finding novel drugs from medicinal plants and other traditional resources; while in present, technological advances in biology, computer and robotics give the researchers the ability to rewrite and edit DNA in order to synthesize very large sets of drug candidates; these novel and improved candidates serves the basis for creating another library of drug candidates and so on until we find the right biomolecule for the disease of interest. all these technologies combined together to synthesize therapeutic antibodies for many types of cancer, autoimmune diseases, and infectious diseases, that can address diseases much more readily to very rapidly get therapeutics into patients so that we can potentially have an impact on disease. The antibodies mechanism is recognize and bind to disease cells and pinpoint the immune system to attack those cells effectively. Now a days, they dependent on computational approach to guide and accelerate the process of antibodies engineering by combination of selection system and use of high-throughput data acquisition and analysis to build and construct populations of next generation antibodies that are thermo-stable, non-immunogenic as possible, and to be administered to many humans as possible. In this review, I will discuss the latest in silico methods for antibodies engineering.

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

This paper highlights the problems associated with daylight use in industrial facilities. In a case study of a multi-story textile factory, we report how to evaluate daylight (as part of integral light) in the production halls marked F and G. This study follows the article in the Buildings journal, where Hall E was evaluated (unilateral daylight). These two additional halls have large areas that are 54 × 54 meters and are more than 5 meters high. The daylight is only on the side through the attached windows in envelope structures in the vertical position. In this paper, we want to present two case studies of these two production halls in a textile factory in the eastern part of Slovakia. These are halls that are illuminated by daylight from two sides through exterior peripheral walls that are against or next to each other. The results of the case studies can be applied in similar production halls illuminated by a ‘double-sided’ (bilateral) daylight system. This means that they are illuminated by natural illumination through windows on two sides in a vertical position. Such a situation is typical for multi-storied industrial buildings. The proposed approximate calculation method for the daylight factor can be used to predict the daylight in similar spaces in other similar buildings.

This paper numerically investigates a 3D mesh independency study of a straight blade horizontal axis tidal turbine modelled using Computational Fluid Dynamics (CFD). The solution was produced by employing two turbulence models, the standard k-ε model and Shear Stress Transport (SST) in ANSYS CFX. Three parameters were investigated: mesh resolution, turbulence model, and power coefficient in the initial CFD, analysis. It was found that the mesh resolution and the turbulence model affect the power coefficient results. The power coefficients obtained from the standard k-ε model are 15% to 20% lower than the accuracy of the SST model. It can also be demonstrated that the torque coefficient increases with the increasing Tip Speed Ratio (TSR), but drops drastically after TSR = 5 and k-ε model failing to capture the non-linearity in the torque coefficient with the increasing TSR.

Drought is one of the most serious abiotic stressors in the environment, restricting agricultural production by reducing plant growth, development, and productivity. To investigate such a complex and multifaceted stressor and its effects on the plants, a systems biology-based approach is necessitated, entailing the generation of co-expression networks, identification of highly-priority transcription factors (TFs) dynamic mathematical modeling, and computational simulations. Here, we studied a high-resolution drought transcriptome of Arabidopsis. We revealed distinct temporal transcriptional signatures and demonstrated the involvement of specific biological pathways. Generation of a large-scale co-expression network followed by network centrality analyses identified 117 TFs that possess critical properties of hubs, bottlenecks, and high clustering coefficients nodes. Dynamic transcriptional regulatory modeling on integrated TF-target and transcriptome datasets uncovered major transcriptional events during the course of drought stress. Mathematical transcriptional simulations allowed us to ascertain the activation status of major TFs as well as the transcriptional intensity and amplitude of their target genes. Finally, we validated our predictions by providing experimental evidence of gene expression under drought stress for a set of four TFs and their major target genes using qRT-PCR. Taken together, we provided a systems-level perspective on the dynamic transcriptional regulation during drought stress in Arabidopsis and uncovered numerous novel TFs that can potentially be used in future genetic crop engineering programs.

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.

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

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

Highly nonlinear characteristics of lithium-ion batteries (LIBs) are significantly influenced by the external and internal temperature of the LIB cell. Moreover, cell temperature beyond the manufacturer’s specified safe operating limit could lead to thermal runaway and even fire hazards and safety concerns to operating personnel. Therefore, accurate information of cell internal and surface temperature of LIB is highly crucial for effective thermal management and proper operation of a battery management system (BMS). Accurate temperature information is also essential to BMS for the accurate estimation of various important states of LIB such as state of charge, state of health and so on. High capacity LIB pack, used in electric vehicles and grid-tied stationary energy storage system essentially consists of thousands of individual LIB cells. Therefore, installing a physical sensor at each cell especially at the cell core is not practically feasible from the solution cost, space and weight point of view. A solution is to develop a suitable estimation strategy which led scholars to propose different temperature estimation schemes aiming to establish a balance among accuracy, adaptability, modelling complexity and computational cost. This article presented an exhaustive review of these estimation strategies covering recent developments, current issues, major challenges, and future research recommendations. The prime intention is to provide a detailed guideline to the researchers and industries towards developing a highly accurate, intelligent, adaptive, easy to implement and compute efficient online temperature estimation strategy applicable to health-conscious fast charging and smart onboard BMS.

This paper focuses on quantifying the economic and financial viability of NB-IoT and LoRaWAN technologies, two low-power wide-area network (LPWAN) technologies with unique characteristics that make them suitable for IoT applications. The purpose of the study is to propose an artefact for performing life cycle cost analysis and demonstrate its application to these technologies. The methodology uses pragmatic computational tools to facilitate the analysis and considers all relevant economic and financial factors, such as operating costs, equipment costs, and revenue potential. The main finding of the study is that NB-IoT and LoRaWAN technologies have different cost structures and revenue potentials, which may affect their economic and financial viability for different IoT applications. Ultimately, the study concludes that a comprehensive life cycle cost analysis is critical to making informed decisions about technology adoption, and that the proposed methodology can be applied to other IoT technologies to gain insight into their economic and financial viability.

In the history of civil aircraft transportation, ice formation has been identifies as a key factor in the safety of flight. Anti-icing and deicing system have emerged through the years with the aim to prevent or to eliminate ice formation on wing airfoil, control surface and probes. Modern flying machine demand more efficiency in order to reduce the carbon footprint and increase the sustainability of flight transport. In order to achieve this goal the need to have efficient aircraft with efficient and low power consuming system is fundamental. This paper proposes a new model for ice accretion using computational fluid dynamics (CFD). This model permits to simulate the shape of the ice formed over a profile varying boundary conditions (i.e.- speed, liquid water content and so on). The proposed model takes into account also the amount of heat transferred between water and the surrounding environment and includes the effects of air turbulence on the ice formation process. The CFD simulations have been validated with NASA experimental outcome and show good agreement. The proposed model can be also used to investigate the effects of various parameters such as air speed, Liquid Water Content and air temperature on the ice formation process. The results evidences that the proposed model can accurately predict ice formation process and is suitable to optimize the design of anti-icing or deicing system for aircraft and helicopters. This approach is not limited to aerospace but can also be exported to other applications such as transportation, wind turbine, energy management and infrastructure.

Deformation behavior of MXene based polymer composites with bioinspired brick and mortar structures is analyzed. MXene/Polymer nanocomposites are modeled at microscale using bioinspired configurations of nacre-mimetic brick-and-mortar assembly structure. MXenes (brick) with polymer matrix (mortar) are modeled using classical analytical methods and numerical methods based on Finite Elements (FE). The analytical methods provide less accurate estimation of elastic properties compared to numerical one. MXene nanocomposite models analyzed with FE method provide estimates of elastic constants in the same order of magnitude as literature reported experimental results with good consistency. Bioinspired design of MXene nanocomposites results in the effective Young’s modulus of the nanocomposite increase by 25.1 % and the strength (maximum stress capacity within elastic limits) increase by 42.3 %. The brick and mortar structure of the nanocomposites leads to interlocking mechanism between MXene fillers in polymer matrix, resulting in effective load transfer, good strength, and damage resistance. This is demonstrated in this paper by numerical analysis of MXene nanocomposites subjected to quasi-static loads.

Solid tumors display complex biology and most therapies including chemotherapy cannot prevent therapy resistance and relapse. Most therapeutics target cancer cells, but recent data suggest the presence of cancer stem cells as cells with self-renewal and tumorigenic abilities. Cancer stem cell markers have been suggested to have prognostic value and can be targeted during cancer treatment and in resistant disease. CSCs have been postulated to play significant contextual roles in tumor initiation, progression, therapy resistance and metastasis. CSCs have thus been targeted by new generation cancer drugs. The transcriptional expression of several CSC markers in different cancers was evaluated by searching publicly available The Cancer Genome Atlas (TCGA) and Gene Expression Profiling Interactive Analysis (GEPIA) databases. We report here new findings on expression and prognostic significance of CSC markers in several cancers by examining the expression of CSCs markers in tumor tissues versus the adjacent normal tissues. We found that CSC markers were mostly highly expressed various tumors such as colon, lung, pancreatic and esophageal cancers. No CSC marker is expressed in the same pattern in all cancers and individual CSC marker expression was not linked to patient survival. This analysis calls for continued research on CSCs and clinical evaluation of the CSC markers in relation to prognosis of cancers in large population samples. Novel cancer drugs ought to target CSCs, cancer cells and tumor microenvironment variations.

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.

Fog computing is a potential solution to overcome the shortcomings of the cloud computing processing of IoT tasks. These drawbacks can be high latency, location awareness and security, and it is attributed to the distance between IoT devices and servers, network congestion and other variables. Although fog computing has evolved as a solution to these challenges, it is known for having limited resources that need to be consciously utilised, or any of its ad-vantages would be lost. Computational offloading and resource management are critical concerns to be considered to get maximum benefit of the available resource at fog computing systems and benefit from its advantages. Computational offloading and resource management are important issues to be considered to get maximum benefit of the available resource at fog computing systems and benefit from its advantages. In this article, in vehicular traffic applications, we introduce a dynamic online offloading scheme that involves the execution of delay-sensitive ac-tivities. This paper proposes an architecture of a fog node that enables a fog node to adjust its offloading threshold dynamically (i.e., the criteria by which a fog node decides whether tasks should be offloaded rather than executed locally) using two algorithms: dynamic task scheduling (DTS) and dynamic energy control (DEC). These algorithms seek to solve an optimisation problem aimed at minimising overall delay, improving throughput, and minimising energy consumption at the fog layer, while maximising the use of resource-constrained fog nodes. Compared with other benchmarks, our approach can reduce the delay by up to 95.38% and reduce energy consumption by up to 67.71% in fog nodes. Additionally, this approach enhances throughput by 71.08%.

The accurate prediction of molecular properties such as lipophilicity and aqueous solubility are of great importance and pose challenges in several stages of the drug discovery pipeline. Machine learning methods like graph-based neural networks (GNNs) have shown exceptionally good performance in predicting these properties. In this work, we introduce a novel GNN architecture, called directed edge graph isomorphism network (D-GIN). It is composed of two distinct sub-architectures (D-MPNN, GIN) and achieves an improvement in accuracy over its sub-architectures employing various learning, and featurization strategies. We argue that combining models with different key aspects help make graph neural networks deeper and simultaneously increase their predictive power. Furthermore, we address current limitations in assessment of deep-learning models, namely, comparison of single training run performance metrics, and offer a more robust solution.

The details of antigen-antibody interactions and the identification of epitopes are critical for the development of monoclonal antibody drugs. Ab42 is a native human-derived anti-CFH monoclonal antibody. In this study, the interaction between antigen pCFH and antibody (Ab42) was theoretically demonstrated by molecular docking and MD simulation, combined with free energy calculation and computational alanine scanning (CAS), and key amino acids and epitopes were identified. Experimental alanine scanning (EAS) was then carried out to verify the results of the calculation, and our results indicated that Ab42 antibody forms hydrogen bonds and interacts hydrophobically with pCFH through the Tyr315, Ser100, Gly33, and Tyr53 residues on its CDR, while the main pCFH epitopes are located at the six sites of Pro441, Ile442, Asp443, Asn444, Ile447, and Thr448. In conclusion, this study has explored the mechanism of antigen-antibody interaction from both theoretical and experimental aspects, and our results have important theoretical significance for the design and development of relevant antibody drugs.

One of the most used techniques for reducing drag on blunt bodies when they move at speeds above the speed of sound are spikes. An important number of numerical works based on CFD have highlighted the fluid dynamics behind its use, as well as its advantages. However, most of docu-mentation focuses mainly on modified spike physical properties, while keeping constant supersonic or hypersonic flow conditions. In this sense, it is necessary to resort to current numerical techniques as machine learning, which can be obtain the most appropriate technical solution in compared to the costly iterative simulations that can delay the rapid development and testing of new aerody-namic configurations. This investigation postulates a specific hypothesis: that the K-Nearest Neighbours algorithm, renowned for its simplicity and effectiveness in classification problems, can be extrapolated as a robust tool for predicting drag reduction on spike blunt bodies. A wide range of Mach number and aspect ratios between blunt body diameter, spike length, and spike diameter are used for the algorithm dataset. The algorithm presents good predictions of the coefficient with errors of less than 5% percent, applying a multivariable regression method. Even, in the vicinity of the hypersonic speed zone, with a minimum of 3 neighbour nodes. The above validates the flexibility of the method and shows a new area of opportunity for the calculation of aerodynamic properties in a body moving near or above the speed of sound.

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

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

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.

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.

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.

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.

The four-way reversing valve is a key component determining whether a heat pump air conditioning system can work. Studying the four-way reversing valve is of great significance for improving the performance of the heat pump air conditioning system. In this paper, the control equation of the sliding block movement process is constructed to simulate the reversing process of the four-way reversing valve in fluid mechanics, and a detailed study is carried out from the sliding block speed, sliding block acceleration, sliding block force, refrigerant leakage, and other aspects. Research has shown that optimizing the structure of the slider can greatly improve the working performance of the four-way reversing valve. Increasing the height of the slider not only improves the pressure relief ability of the four-way reversing valve and prevents the slider from being damaged by high-pressure gas impact, but also reduces the leakage of refrigerant during the directional process; When reducing the length of the slider, although it will improve the pressure relief capacity of the four-way reversing valve, it also increases the amount of refrigerant leakage. The research results provide a theoretical basis and improvement direction for designers to improve the performance of four-way reversing valves.

The mRNA vaccines for SARS-CoV-2 have demonstrated efficacy and immunogenicity in the re-al-world setting. However, most of the research on vaccine immunogenicity has been centered on characterizing the antibody response, with limited exploration into the persistence of spike-specific memory B cell response. Here we monitored the durability of the memory B cell response and characterize the trajectory of spike-specific B cell phenotypes in healthy individuals who have received two doses of the BNT162b2 vaccine up to 9 months post-vaccination. To profile the spike-specific B cell response we applied the tSNE and Cytotree automated approaches. Our data showed the induction of spike-specific IgA+ and IgG+ plasmablasts and IgA+ activated cells 7 days after the second dose which disappeared 3 months later, while subsets of spike-specific IgG+ resting memory B cells became predominant 9 months after vaccination, and they were capable to differentiate into spike-specific IgG secreting cells when in vitro restimulated. Other subsets of spike-specific B cells, such as IgM+ or unswitched IgM+IgD+ or IgG+ double negative/atypical cells, were also elicited by the BNT162b2 vaccine and persisted up to month 9. The analysis of circulating spike-specific IgG, IgA and IgM was in line with the plasmablasts observed. The longitudinal analysis of the antigen-specific B cell response elicited by mRNA-based vaccines provides valuable insights into our understanding of the immunogenicity of this novel vaccine platform destined to a future widespread use, and it is crucial in guiding future decisions and vaccination schedules.

The number and size of computational models in biology have drastically increased over the past years and continue to grow. Modeled networks are becoming more complex, and reconstructing them from the beginning in an exchangeable and reproducible manner is challenging. Using precisely defined ontologies enables the encoding of field-specific knowledge and the association of disparate data types. In computational modeling, the medium for representing domain knowledge is the set of orthogonal structured controlled vocabularies named Systems Biology Ontology ( SBO). The SBO terms enable modelers to explicitly define and unambiguously describe model entities, including their roles and characteristics. Here, we present the first standalone tool that automatically assigns SBO terms to multiple entities of a given SBML model, named the SBOannotator. The main focus lies on the reactions, as the correct assignment of precise SBO annotations requires their extensive classification. Our implementation does not consider only top-level terms but examines the functionality of the underlying enzymes to allocate precise and highly specific ontology terms to biochemical reactions. Transport reactions are examined separately and are classified based on the mechanism of molecule transport. Pseudo-reactions that serve modeling purposes are given reasonable terms to distinguish between biomass production and the import or export of metabolites. Finally, other model entities, such as metabolites and genes, are annotated with appropriate terms. Including SBO annotations in the models will enhance the reproducibility, usability, and analysis of biochemical networks. Availability: The open-source project SBOannotator is freely available under the terms of LGPL version 3.0 from https://github.com/draeger-lab/SBOannotator/.

In this paper we propose a computational framework that merges mechanistic modeling with deep neural networks obeying the Systems Biology Markup Language (SBML) standard. Over the last 20 years, the systems biology community has developed a large number of mechanistic models in SBML that are currently stored in public databases. With the proposed framework, existing SBML mechanistic models may be upgraded to hybrid systems through the incorporation of deep neural networks into the model core, using a freely available python tool. The so-formed hybrid mechanistic/neural network models are trained with a deep learning algorithm based on the adaptive moment estimation method (ADAM), stochastic regularization and semidirect sensitivity equations. The trained hybrid models are encoded in SBML and stored back in model databases, where they can be further analyzed as regular SBML models. The application of this approach is illustrated with three well-known case studies: the threonine synthesis model in Escherichia coli, the P58IPK signal transduction model, and the Yeast glycolytic oscillations model. The proposed framework is expected to greatly facilitate the widespread use of hybrid modeling techniques for systems biology applications.

In this paper, we introduce a model for the automatic generation of literary sentences in French. It is based on algorithms that we have previously used to generate sentences in Spanish and Portuguese, and on a new corpus consisting of literary texts in French that we have constructed, called [FR]. Our automatic text generation algorithm combines language models, shallow parsing and deep learning, artificial neural networks. We have also proposed and implemented a manual evaluation protocol to assess the quality of the artificial sentences generated by our algorithm, by testing if they fulfill four simple criteria. We have obtained encouraging results from the evaluators for most of the desired features of our artificially generated sentences.

Turbulent flow is the first and fundamental physical phenomena to evaluate when optimising cost and reducing emissions from an Anode Baking Furnace (ABF). Gas flow patterns, velocity field, pressure drop, shear stress, and turbulent dissipation rate variables are the main operational parameters to be optimised, considering a specific geometry. Computational Fluid Dynamics (CFD) allows simulating physical phenomena using numerical methods with computer resources. In particular, the finite element method is one of the most used methods to solve the flow equations. This method requires a discretisation of the geometry of the ABF, called mesh. Hence, mesh is the main input to the finite element method. A suitable mesh for applying a discretisation method determines whether the problem can be simulated or not. Generating an appropriate mesh remains a challenge to perform accurate simulations. In this work, a comparison between meshes generated using two mesh generation tools is presented. Results of different study cases are included.

In this study, the external magnetic field emitted by a wireless power transfer (WPT) system and the internal electric field induced into human body models during recharging operations of a compact electric vehicle (EV) are evaluated. To this aim an ad-hoc formulation for the source modeling is coupled with a commercial software that performs numerical dosimetry. Specifically, two realistic anatomical models both in a driving position and in a standing posture are considered, and the chassis of the EV is modeled either as a currently employed aluminum alloys and as a futuristic carbon fiber composite panel. Aligned and misaligned coil configurations of the WPT system are considered as well. The analysis of the obtained results shows that the ICNIRP reference levels are exceeded in the driving position, especially for the carbon fiber chassis, whereas no exceedance is observed in terms of basic restrictions, at least for the considered scenarios.

Consumer products contain the chemical substances that threaten human health. The modeling methods and experimental methods have been used to estimate the inhalation exposure concentration by the consumer products. The model and measurement methods have the spatial property problem and time/cost consuming problem, respectively. For solving the problems due to the conventional methodology, this study performed the feasibility of applying CFD for evaluation of inhalation exposure by comparing the experiment results and the zero-dimensional results with CFD results. To calculate the aerosol concentration, the CFD was performed by combined the 3D Reynolds averaged Navier Stoke’s equation and discrete phased model using ANSYS FLUENT. As a result of comparing the three methodologies performed under the same simulation/experimental conditions, we found the zero-dimensional spray model shows approximately 5 times underestimated inhalation exposure concentration when compared with the CFD results and measurement results in near field. Also, the results of the measured concentration of aerosols at five locations and the CFD results at the same location were compared to show the possibility of evaluating inhalation exposure at various locations using CFD instead of experimental method. The CFD results according to measurement positions can predict rationally the measurement results with low error. In conclusion, in the field of exposure science, a guideline for exposure evaluation using CFD was found that complements the shortcomings of the conventional methodology, the zero-dimensional spray model and measurement method.

This paper proposes the use of non-uniform extended surfaces installed externally to the tubes of the radiation section of fired heaters, in order to obtain a better heat flux distribution to the coils. To this end, the heat transfer mechanisms present in such equipment were studied through computational fluid dynamics (CFD), using simplified geometries that represent typical sizes of fired heaters. Also, a simplified model for the combustion was considered. Although this model oversimplifies the physics of the problem, it was able to give satisfactory results for the parameters being optimized, considering the main objective of this paper, that is to minimize the non-uniformity of heat flux in the tubes of the radiant section of fired heaters. It was possible to obtain optimized geometric parameters for different types of extended surfaces evaluated, coupling the results of these models with the Particle Swarm optimization method through the use of a response surface technique,. The results indicate a significant improvement in the uniformity of the heat flux distribution to the tubes through the use of the proposed extended surfaces. Thus, this solution reveals to be an interesting alternative to reduce the risks of fluid degradation and coking formation. Future studies must investigate the non-uniformity of the heat flux due to the presence of the flame and consider the interaction between the reactive flow and the participating medium. Nevertheless, this paper presents some results that justify the optimization of such extended surfaces taking into consideration thermal radiation.

In order to perceive the behavior presented by the multiphase chemical reactors, the ant colony optimization algorithm was combined with computational fluid dynamics (CFD) data. This intelligent algorithm creates a probabilistic technique for computing flow and it can predict various levels of three-dimensional bubble column reactor (BCR). This artificial ant algorithm is mimicking real ant behavior. This method can anticipate the flow characteristics in the reactor using almost 30 % of the whole data in the domain. Following discovering the suitable parameters, the method is used for predicting the points not being simulated with CFD, which represent mesh refinement of Ant colony method. In addition, it is possible to anticipate the bubble-column reactors in the absence of numerical results or training of exact values of evaluated data. The major benefits include reduced computational costs and time savings. The results show a great agreement between ant colony prediction and CFD outputs in different sections of the BCR. The combination of ant colony system and neural network framework can provide the smart structure to estimate biological and nature physics base phenomena. The ant colony optimization algorithm (ACO) framework based on ant behavior can solve all local mathematical answers throughout 3D bubble column reactor. The integration of all local answers can provide the overall solution in the reactor for different characteristics. This new overview of modelling can illustrate new sight into biological behavior in nature.

In this research, the computational fluid dynamic (CFD) approach that has been used in wind power generation field was applied for the solution of the problems of local strong wind areas in railway fields, and the mechanism of wind generation was discussed. At the same time, the affectivity of the application of computational fluid dynamic approach to railway field was discussed. The problem of local wind that occurs on the railway line in winter was taken up in this research. A computational simulation for the prediction of wind conditions by LES was implemented and it was clarified that the local strong wind area is mainly caused by separated flows originating from the small‐scale terrain positioned at its upstream (at approximately 180.0 m above sea level). Meanwhile, the effects of the size of calculation area and spatial grid resolution on the result of calculation and the effect of atmospheric stability were also discussed. It was clarified that when the air flow characteristic of the separated flow originating from the small‐scale terrain (at altitude of approximately 180.0 m) targeted in this research is reproduced at high accuracy by computational simulation of wind conditions, approximately 10.0 m of spatial resolution of computational grid in horizontal direction is required. As a result of the computational simulation of wind conditions of stably stratified flow (Fr = 1.0), lee waves were excited at the downstream of the terrain over time. As a result, the reverse‐flow region lying behind the terrain that had been observed at a neutral time was inhibited. Consequently, local strong wind area was generated at the downstream of the terrain and the strong wind area passing through the observation mast was observed. By investigating the speed increasing rate of local strong wind area induced at the time of stable stratification, it was found that the wind was approximately 1.2 times stronger than what was generated at a neutral time.

In this paper, we generalize the problems of finding simple polygons with the minimum area, maximum perimeter and maximum number of vertices so that they contain a given set of points and their angles are bounded by $\alpha+\pi$ where $\alpha$ ($0\leq\alpha\leq \pi$) is a parameter. We also consider the maximum angle of each possible simple polygon crossing a given set of points, and derive an upper bound for the minimum of these angles. The correspondence between the problems of finding simple polygons with the minimum area and maximum number of vertices is investigated from a theoretical perspective. We formulate the three generalized problems as nonlinear programming models, and then present a Genetic Algorithm to solve them. Finally, the computed solutions are evaluated on several datasets and the results are compared with those from the optimal approach.

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

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

Meshfree particle method, which is always regarded as a pure Lagrangian approach, is easily represented complicated domain topologies, moving boundaries, and multiphase media. Solving acoustic problems with the mesfree particle method forms a branch of the acoustic wave modeling field, namely, particle-based computational acoustics (PCA). The aim of this paper is to improve the accuracy of using the PCA method to solve two-dimensional acoustic problems, and realize the particle representation with a hybrid meshfree and finite-difference time-domain (FDTD) method for acoustic boundary conditions at both the plane and curved surface. As a widely used Lagrangian meshfree method, the smoothed particle hydrodynamics (SPH) based on the support domain and the kernel function has developed rapidly in recent years. The traditional SPH method is easily implements parallel processing and has been applied in sound wave simulation. As a corrective method with higher accuracy than SPH, the acoustic propagation and scattering in the time domain is simulated with the corrective smoothed particle method (CSPM). Moreover, a hybrid meshfree-FDTD boundary treatment technique is utilized to represent different acoustic boundaries in the Lagrangian approach. In this boundary treatment technique, the parameter value of virtual particles is obtained with the FDTD method, which concerns truncation errors based on the Tayler series expansion. Soft, rigid, and Mur’s absorbing boundary conditions are developed to simulate sound waves in finite and infinite domain. Results of modeling acoustic propagation and scattering show that CSPM is accurate and convergence with exact solutions, and different acoustic boundaries are validated to be effective in the computation.

With the increasing availability of large digitized fine art collections, automated analysis and classification of paintings is becoming an interesting area of research. However, due to domain specificity, implicit subjectivity, and pervasive nuances that vaguely separate art movements, analyzing art using machine learning techniques poses significant challenges. Residual networks, or variants thereof, are one the most popular tools for image classification tasks, which can extract relevant features for well-defined classes. In this case study, we focus on the classification of a selected painting 'Portrait of the Painter Charles Bruni' by Johann Kupetzky and the analysis of the performance of the proposed classifier. We show that the features extracted during residual network training can be useful for image retrieval within search systems in online art collections.

Tapping panel dryness (TPD) syndrome is a complex disease of Rubber tree (Hevea brasiliensis L.) which causes cessation of latex drainage upon tapping of rubber tree. Rubber tree virus (RTV1) was identified as a novel pathogen associated with rubber tree and a potential causal agent of TPD. RTV1 is a monopartite RNA virus that is linear, non-enveloped and has a single-stranded (ss) positive RNA genome of approximately 6081 nucleotides and is composed of two major open reading frames (ORFs), ORF1 (polyprotein), and ORF2 (movement protein. This study aimed to investigate the possibility of rubber genome encoded tree microRNAs (miRNAs) as novel therapeutic targets against RTV1 using in silico algorithms. Mature rubber tree miRNAs are retrieved from the miRBase database and are used for hybridization of RTV1 using five different five different computational algorithms including miRanda, RNA22, RNAhybrid and psRNATarget. A total of eleven common rubber tree miRNAs were identified based on consensus genomic positions. The consensus of four algorithms predicted the hybridization sites of hbr-miR396a and hbr-miR398 at common locus positions 6676, 1840 respectively. To validate the prediction, secondary structures of the consensual rubber tree miRNAs and free energy of duplex binding were calculated using the RNAfold and RNAcofold algorithms respectively. We created a plot between rubber tree miRNAs and RTV1 ORFs by using Circos algorithm. In this study, we predicted eleven consensual rubber tree miRNAs. Among these miRNAs, hbr-miR398 was identified as the most effectual miRNA that may target the ORF1 gene of the RTV1 genome. The predicted data will be important in the development of rubber trees resistant to RTV1.

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.

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.

Current therapeutic strategies targeting cancer cells within solid tumors have displayed limited success owing to the presence of non-cancer components referred to as the tumor stroma within the tumor microenvironment (TM). These stromal cells, extracellular matrix and blood vessels influence cancer cell response to therapy and play key roles in tumor relapse and resistance. Of the stromal cells present in the TM, a lot of attention has been given to cancer-associated fibroblasts (CAFs) as they are the most abundant and are important in cancer initiation, progression and therapy resistance. In this updated review I emphasize the role of CAFs in the regulation of tumor cell behaviour and reveal how CAF-derived factors and signaling influence tumor cell heterogeneity and development of novel strategies to combat cancer. To investigate the expression of CAF markers in tumor tissues versus normal tissues, transcriptomic data from The Cancer Genome Atlas (TCGA) and the Gene Expression Profiling Interactive Analysis (GEPIA) databases was used. Bioinformatic analysis reveals differential expression of CAF markers in several cancer types, underscoring the need for further multiomics and biochemical studies on CAFs, CAF subsets and markers. Differences in CAF markers’ expression could be due to different cellular origins as well as the effect of cancer-specific tumor microenvironmental effect on CAFs. Lastly, I present recent advances in therapeutic targeting of CAFs and the success of such endeavours or its lack thereof. It is recommended that for patients’ outcomes to improve, cancer treatment be combinatorial in nature, targeting both cancer cells and stromal cells and interactions.

Intelligent algorithms are recently used in the optimization process in chemical engineering and application of multiphase flows such as bubbling flow. This overview of modeling can be a great replacement with complex numerical methods or very time-consuming and disruptive measurement experimental process. In this study, we develop the adaptive network-based fuzzy inference system (ANFIS) method for mapping inputs and outputs together and understand the behavior of the fluid flow from other output parameters of the bubble column reactor. Neural cells can fully learn the process in their memory and after the training stage, the fuzzy structure predicts the multiphase flow data. Four inputs such as x coordinate, y coordinate, z coordinate, and air superficial velocity and one output such as pressure gradient are considered in the learning process of the ANFIS method. During the learning process, the different number of the membership function, type of membership functions and the number of inputs are examined to achieve the intelligent algorithm with high accuracy. The results show that as the number of inputs increases the accuracy of the ANFIS method rises up to almost for all cases, while the increment in the number of rules has a effect on the intelligence of artificial algorithm. This finding shows that the density of neural objects or higher input parameters enables the moded for better understanding. We also proposed a new evaluation of data in the bubble column reactor by mapping inputs and outputs and shuffle all parameters together to understand the behaviour of the multiphase flow as a function of either inputs or outputs. This new process of mapping inputs and outputs data provides a framework to fully understand the flow in the fluid domain in a short time of fuzzy structure calculation.

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

This paper presents a discrete compartmental Susceptible-Exposed-Infected-Recovered/Dead (SEIR/D) model to address the expansion of Covid-19 pandemic. When time passes, the status of the cells is determined by binary rules that update following both a neighbourhood and a delay pattern. The model assumes the environmental parameters have a crucial impact on the expansion of the disease so a grid is assigned to each parameter to model the single effect caused by this parameter. The expansion is then the weighted sum of all the grids. This proposal shows how the grid architecture, along with an update rule and a neighbourhood pattern is a valuable tool to model the pandemic expansion. This model has already been analyzed in previous works and compared with the corresponding continuous models solved by ordinary differential equations (ODE), coming to find the homologous parameters between both approaches. Thus, it has been possible to prove that the combination neighborhood-update rule is responsible for the rate of expansion and recovering/death of the illness. The delays (between Susceptible and Asymptomatic, Asymptomatic and Infected, Infected and Recovered/Dead) may have a crucial impact on both the peak of Infected and the Recovery/Death rate. This theoretical model has been successfully tested in the case of the dissemination of information through mobile social networks and in the case of plant pests.

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.

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.

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.

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.

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).

Gold-centered carbene-metal-amides (CMAs) containing cyclic (alkyl)(amino)carbenes (CAACs) are promising emitters for thermally activated delayed fluorescence (TADF). Aiming at design and optimization of new TADF emitters, we report a density functional theory study of over 60 CMAs with various CAAC ligands, systematically evaluating computed parameters in relation to photoluminescence properties. We demonstrate that the efficiency of TADF of CMAs, arising from a compromise of exchange energy and oscillator strength, is governed by the overlap of HOMO and LUMO orbitals, where HOMO is localized on amide and LUMO on Au-carbene. The S0 ground states and excited T1 states of the CMAs adopt approximately coplanar geometries of carbenes and amides, but rotate perpendicular in the excited S1 states, resulting in degeneracy or near-degeneracy of S1 and T1, accompanied with lowering of the S1-S0 oscillator strength from its maximum at coplanar geometries to near zero at rotated geometries. Based on computations, promising new TADF emitters are proposed and synthesized. Bright CMA complex (Et2CAAC)Au(carbazolide) is obtained and fully characterized to demonstrate that high radiative rates up to 106 s-1 can be obtained for the gold-CMA complexes with small CAAC-carbene ligands.

Magnetorheological fluids (MRFs) are smart materials consisting of micro-scale magnetizable particles suspended in a carrier fluid. The rheological properties of a MRF can be changed from a fluid-state to a solid-state upon the application of an external magnetic field. This study reports the development of a particle-level simulation code for magnetic solid spheres moving through an incompressible Newtonian carrier fluid. The numerical algorithm is implemented within an open-source finite-volume solver coupled with an immersed boundary method (FVM-IBM) to perform fully-resolved simulations. The particulate phase of the MRF is modeled using the discrete element method (DEM). The resultant force acting on the particles due to the external magnetic field (i.e., magnetostatic polarization force) is computed based on the Clausius-Mossotti relationship. The fixed and mutual dipole magnetic models are then used to account for the magnetic (MAG) interactions between particles. Several benchmark flows were simulated using the newly-developed FVM-IBM-DEM-MAG algorithm to assess the accuracy and robustness of the calculations. First, the sedimentation of two spheres in a rectangular duct containing a Newtonian fluid is computed without the presence of an external magnetic field, mimicking the so-called drafting-kissing-tumbling (DKT) phenomenon. The numerical results obtained for the DKT case study are verified against published data from the scientific literature. Second, we activate both the magnetostatic polarization and the dipole-dipole forces and resultant torques between the spheres for the DKT case study. Next, we study the robustness of the FVM-IBM-DEM-MAG solver by computing multi-particle chaining (i.e., particle assembly) in a two-dimensional (2D) domain for area volume fractions of 20% (260 particles) and 30% (390 particles) under vertical and horizontal magnetic fields. Finally, the fourth computational experiment describes the multi-particle chaining in a three-dimensional (3D) domain allowing to study fully-resolved MRF simulations of 580 magnetic particles under vertical and horizontal magnetic fields.

Large language model (LLM) is a representation of a major advancement in AI, and has been used in multiple natural language processing tasks. Nevertheless, in different business scenarios, LLM requires fine-tuning by engineers to achieve satisfactory performance, and the cost of achieving target performance and fine-tuning may not match. Based on the Baidu STI dataset, we study the upper bound of the performance that classical information retrieval methods can achieve under a specific business, and compare it with the cost and performance of the participating team based on LLM. This paper gives an insight into the potential of classical computational linguistics algorithms, and which can help decision-makers make reasonable choices for LLM and low-cost methods in business R&D.

This paper analyzes the collection of George Viau (1855-1939) from a computational perspective. Indeed, this dental surgeon, close to the Impressionists, collected several hundred works of art in the first half of the 20th century: the auctions of his collection, in 1907, 1930 and after his death, in 1942, 1943 and 1948, accounted for 642 artworks and, during the Occupation, the 1942 sale produced a total of more than 46 million francs. Thanks to statistics, econometrics, network analysis and cartography, it is possible to understand the salient features of the collection and its place in the world of Parisian auctions.

This paper presents a novel theoretical and computational methodology for the generation of an onset turbulent field with prescribed properties in the numerical simulation of an arbitrary viscous flow. The methodology is based on the definition of a suitable distribution of volume force terms in the right–hand side of the Navier–Stokes equations. The distribution is represented by harmonic functions that are randomly variable in time and space. The intensity of the distribution is controlled by a simple PID strategy in order to obtain that the generated turbulent flow matches a prescribed turbulence intensity. A further condition is that a homogeneous isotropic flow is es- tablished downstream of the region where volume force terms are imposed. Although it is general, the proposed methodology is primarily intended for the computational modelling of hydrokinetic turbines in turbulent flows representative of tidal or riverine installations. A first numerical applica- tion is presented by considering the injection of homogeneous and isotropic turbulence with 16% intensity into a uniform unbounded flow. The analysis of statistical properties as auto-correlation, power spectral density, probability density functions, demonstrates that the generated flow tends to achieve satisfactory levels of stationarity and isotropy, whereas the simple control strategy used determines overestimated turbulent intensity levels.

The Colebrook equation is a popular model for estimating friction loss coefficients in water and gas pipes. The model is implicit in the unknown flow friction factor . To date, the captured flow friction factor can be extracted from the logarithmic form analytically only in the term of the Lambert -function. The purpose of this study is to find an accurate and computationally efficient solution based on the shifted Lambert -function also known as the Wright -function. The Wright -function is more suitable because it overcomes the problem with the overflow error by switching the fast growing term of the Lambert -function to the series expansions that further can be easily evaluated in computers without causing overflow run-time errors. Although the Colebrook equation transformed through the Lambert -function is identical to the original expression in term of accuracy, a further evaluation of the Lambert -function can be only approximate. Very accurate explicit approximations of the Colebrook equation that contains only one or two logarithms are shown. The final result is an accurate explicit approximation of the Colebrook equation with the relative error of no more than 0.0096%. The presented approximations are in the form suitable for everyday engineering use, they are both accurate and computationally efficient.

Every biological image contains quantitative data that can be used to test hypotheses about how patterns were formed, what entities are associated with one another, and whether standard mathematical methods inform our understanding of biological phenomena. In particular, spatial point distributions and polygonal tessellations are particularly amendable to analysis with a variety of graph theoretic, computational geometric, and spatial statistical tools such as: Voronoi Polygons; Delaunay Triangulations; Perpendicular Bisectors; Circumcenters; Convex Hulls; Minimal Spanning Trees; Ulam Trees; Pitteway Violations; Circularity; Clark-Evans spatial statistics; Variance to Mean Ratios; Gabriel Graphs; and, Minimal Spanning Trees. Furthermore, biologists have developed a number of empirically related correlations for polygonal tessellations such as: Lewis’s Law (the number of edges of convex polygons are positively correlated with the areas of these polygons): Desch’s Law (the number of edges of convex polygons are positively correlated with the perimeters of these polygons); and Errara’s Law (daughter cell areas should be roughly half that of their parent cells’ areas). We introduce a new Pitteway Law that the number of sides of the convex polygons in a Voronoi tessellation of biological epithelia is proportional to the minimal interior angle of the convex polygons as angles less than 90 degrees result in Pitteway violations of the Delaunay dual of the Voronoi tessellation.

Many cellular functions important for tumor initiation and progression are mediated by members of the integrin family, a diverse family of cell adhesion receptors. With recent studies emphasising on the role of the tumor microenvironment (TME) in tumor initiation and progression, it is not surprising that a lot of attention is being given to integrins. Several integrins are under trial with many demonstrating appealing activity in patients with different cancers. A deeper knowledge of the functions of integrins within the tumor microenvironment is still required, and might lead to better inhibitors being discovered. Integrin expression is commonly dysregulated in many tumors with integrins playing key roles in signaling as well as promotion of tumor cell invasion and migration. This review report new data on the differential expression of integrins within solid tumors using two publicly available resources: The Cancer Genomic Atlas (TCGA) and Gene Expression Profiling Interactive Analysis (GEPIA). In this analysis, I investigated the expression of integrin alpha 2 (ITGα2), ITGα3, ITGβ4 and ITGβ6 in tumor tissues versus adjacent normal tissues. This analysis showed that integrins were differentially expressed in cervical squamous cell carcinoma (CESC), head and neck squamous cell carcinoma (HNSC), esophageal carcinoma (ESCA) and lung adenocarcinoma (LUAD). This analysis showed that ITGα2 and ITGβ6 expression are upregulated in CESC and ESCA, ITGα3 is upregulated in HNSC and ESCA whilst ITGβ4 is highly expressed in CESC, HNSC, ESCA and LUAD tumor tissues compared to adjacent normal tissues. Integrins also play a major role in adhesion of circulating tumor cells to new sites and the resulting formation of secondary tumors. Furthermore, integrins have demonstrated the ability to promoting stem cell-like properties in tumor cells as well as drug resistance. Anti-integrin therapies rely heavily on the doses or concentrations used as these determine whether the drugs act as antagonists or as integrin agonists. This review offers the latest synthesis in terms of current knowledge of integrins functions within the tumor microenvironment and potential targets for different cancers.

This paper looks at key elements of global culture that are driving a new paradigm shift in museums causing them to question their raison d'être, their design and physical space, recognizing the need to accommodate visitor interaction and participation, and to reprioritize institutional outcomes and goals reexamining their priorities. As heritage sharing in online spaces reaches across national, political, and social boundaries on platforms and networks, this has been driven by museum engagement with Internet life during the pandemic. Museum relationships and interactions with communities both local and global, continue to challenge core values and precepts, leading to radical changes in how museums define their roles and responsibilities. In this new cultural landscape, museums are responding to human digital identity in a tidal wave of human interactions on the Internet, from social media to online sharing of images and videos. This is revealing shared perspectives on cultural conflict as being tied to freedom of expression of one’s heritage embedded in digital identity.

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.

In today's technologically advanced landscape, sensors feed critical data for accurate decision-making and actions. Ensuring the integrity and reliability of sensor data is paramount to system performance and security. This paper introduces an innovative approach utilizing discrete wavelet transforms (DWT) embedded within microcontrollers to scrutinize sensor data meticulously. Our methodology aims to detect and isolate malfunctions, misuse, or any anomalies before they permeate the system, potentially causing widespread disruption. By leveraging the power of wavelet-based analysis, we embed computational intelligence directly into the microcontrollers, enabling them to monitor and validate their outputs in real-time. This proactive anomaly detection framework is designed to distinguish between normal and aberrant sensor behaviors, thereby safeguarding the system from erroneous data propagation. Our approach significantly enhances the reliability of embedded systems, providing a robust defense against false data injection attacks and contributing to overall cybersecurity.

Understanding the structural intricacies of RNA molecules is essential for deciphering numerous biological processes. Traditionally, scientists have relied on experimental methods to gain insights and draw conclusions. However, the recent advent of advanced computational techniques has significantly accelerated and refined the accuracy of research results in several areas. A particularly challenging aspect of RNA analysis is the prediction of its secondary structure, which is crucial for elucidating its functional role in biological systems. This paper deals with the prediction of pseudoknots in RNA, focusing on two types of pseudoknots: K-type and M-type pseudoknots. Pseudoknots are complex RNA formations in which nucleotides in a loop form base pairs with nucleotides outside the loop and thus contribute to essential biological functions. Accurate prediction of these structures is crucial for understanding RNA dynamics and interactions. Building on our previous work in which we developed a framework for the recognition of H- and L-type pseudoknots, an extended grammar-based framework tailored to the prediction of K- and M-type pseudoknots is proposed. This approach uses syntactic pattern recognition techniques and provides a systematic method to identify and characterize these complex RNA structures. Our framework uses context-free grammars (CFGs) to model RNA sequences and predict the occurrence of pseudoknots. By formulating specific grammatical rules for type K and type M pseudoknots, we enable efficient parsing of RNA sequences to recognize potential pseudoknot configurations. This method ensures an exhaustive exploration of possible pseudoknot structures within a reasonable time frame. In addition, the proposed method incorporates essential concepts of biology, such as base pairing optimization and free energy reduction, to improve the accuracy of pseudoknot prediction. These principles are crucial to ensure that the predicted structures are biologically plausible. By embedding these principles into our grammar-based framework, we aim to predict RNA conformations that are both theoretically sound and biologically relevant.

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.

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.

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 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.

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.

We investigated experimentally, analytically and numerically the formation process of double emulsion formations under dripping regime in a tri-axial co-flow capillary device. The results show that mismatches of core and shell droplets under a given flow condition can be captured both experimentally and numerically. We propose a semi-analytical model using the match ratio between the pinch-off length of the shell droplet and the product of the core growth rate and its pinch-off time. The mismatch issue can be avoided if the match ratio is lower than unity. We considered a model with the wall effect to predict the size of the matched double emulsion. The model shows slight deviations with experimental data if the Reynolds number of continuous phase is lower than 0.06, but asymptotically approaches to good agreement if the Reynolds number increases from 0.06 to 0.14. The numerical simulation generally agrees with the experiments under various flow conditions.

Detailed flow distributions in vascular systems are the key to identifying hemodynamic risk factors for the development and progression of vascular diseases. Although computational fluid dynamics (CFD) has been widely used in bioengineering research on hemodynamics predictions, not only are high-fidelity CFD simulations time-consuming and computing-expensive, but also not friendly to clinical applications due to the difficulty of comprehensive numerical calculations. Machine learning (ML) algorithums to estimate the flow field in vascular systems based on the angiographic images of the blood flow using existed diagnostic tools are emerging as a new pathway to facilitate the mapping of hemodynamics. In present work, the dye injection in a water flow was simulated as an analogy of the contrast perfusion in blood flow using CFD. In the simulation, the light passes through the flow field and generates projective images, as an analogy of X-ray imaging. The simulations provide both the ground truth velocity field and the projective images of the flow with dye patterns. A rough velocity field was estimated using the optical flow method (OFM) based on projective images. ML algorithums are then trained using the ground truth CFD data and the OFM velocity estimation as the input. Finally, the interpretable (logistic regression) and deep (neural networks, convolutional neural networks, long short term memory) machine learning models are validated by using parallel in vitro experiments on the same flow setup. The validation results showed that the employed ML model significantly reduced the error rate from 53.5% to 2.5% in average for the v-velocity estimation.

This paper focuses on the combination of the method of moments and the wire-grid approximation as an effective computational technique for modeling symmetrical antennas with low computational cost and quite accurate results. The criteria and conditions for the use of wire-grid surface approximation from various sources are presented together with new recommendations for modeling symmetrical antenna structures using the wire-grid approximation. These recommendations are used to calculate the characteristics of biconical and horn antennas at different frequencies. The results obtained using different grid and mesh settings are compared to those obtained analytically. Moreover, the results are compared to those obtained using the finite difference time domain numerical method, as well as the measured ones. All results are shown to be in a good agreement. The used recommendations for building a symmetrical wire-grid of those symmetrical antenna elements provided the most advantageous parameters of the grid and mesh settings and the wire radius, which are able to give a quite accurate results with low computational cost. Additionally, the known equal area rule was modified for a rectangular grid form. The obtained radiation patterns of a conductive plate using both the original rule and the modified one are compared with the electrodynamic analysis results. It is shown that the use of the modified rule is more accurate when using a rectangle grid form.

Energy harvesting from wind turbines has been explored by researchers for more than a century from conventional turbines up to the latest bladeless turbines. Amongst these bladeless turbines, vortex bladeless wind turbine (VBT) harvests energy from oscillation of a turbine body. Due to the novelty of this science and the widespread researches around the world, one of the most important issues is to optimize and predict produced power. To enhance the produced output electrical power of VBT, the fluid-solid interactions (FSI) were analyzed to collect a dataset for predicting procedure. Long short-term memory (LSTM) method has been used to predict the produced power of VBT from the collected data. The reason of choosing LSTM from various artificial neural network methods is that the parameters of VBT study are all time- dependent and the LSTM is one of the most accruable algorithms for predicting time series data. In order to find the relationship between the parameter and the variables used in this research, a correlation matrix was presented. According to the value of 0.3 for the root mean square error (RMSE), a comparative analysis between the simulation results and its prediction shows that the LSTM method is very accurate for these types of research. Furthermore, the LSTM method has significantly reduced the computation time so that the prediction time of desired values has been reduced from an average of 2 and a half hours to two minutes. Also, one of the most important achievements of this study is to suggest a mathematical relation of VBT output power which helps to extend it in a different size of VBT with a high range of parameter variations.

In this article, author performs computational analysis for the generalized zeta functions by using computational software Mathematica. To achieve the purpose recently obtained difference equations are used. These difference equations have a computational power to compute these functions accurately while they can not be computed by using their known integral represenations. Several authors investigated such functions and their analytic properties, but no work has been reported to study the graphical representations and zeors of these functions. Author performs numerical computations to evaluate these functions for different values of the involved parameters. Taylor series expansions are also presented in this research.

Three finger toxins (3FTX) are a group of peptides that affect multiple receptor types. One group of proteins affected by 3FTX are nicotinic acetylcholine receptors (nAChR). Structural information on how neurotoxins interact with nAChR is limited and are confined to a small group of neurotoxins. Therefore, in silico methods are valuable in understanding the interactions between 3FTX and different nAChR subtypes, but there are no established protocols to model 3FTX – nAChR interactions. We developed a homology modeling and protein docking protocol to address this issue and tested its success on three different systems. First, neurotoxin peptides co-crystallized with acetylcholine binding protein (AChBP) were re-docked to assess whether Rosetta protein – protein docking can reproduce the native poses. Second, experimental data on peptide binding to AChBP was used to test whether the docking protocol can qualitatively distinguish AChBP-binders from non-binders. Finally, we docked eight peptides with known α7 and muscle-type nAChR binding properties to test whether the protocol can explain the differential activities of the peptides at the two receptor subtypes. Overall, our docking protocol succeeded in predicting both qualitative and specific aspects of 3FTX binding to nAChR and shed light on some unknown aspects of 3FTX binding to different receptor subtypes.

Using only a limited number of computationally expensive functions, we show a way how to construct accurate and computationally efficient approximations of the Colebrook equation for flow friction. The presented approximations are based on the asymptotic series expansion of the Wright ω-function and symbolic regression. The results are verified with 8 million of Quasi-Monte Carlo points covering the domain of interest for engineers. In comparison with the built-in “wrightOmega” feature of Matlab R2016a, the herein introduced related approximations of the Wright ω-function significantly accelerate the computation. With only two logarithms and several basic arithmetic operations used, the presented approximations are not only computationally efficient but also extremely accurate. The maximal relative error of the most promising approximation which is given in the form suitable for engineers’ use is limited to 0.0012%, while for a little bit more complex variant is limited to 0.000024%.

A study on the selection of hyperelastic constitutive model for polymeric materials is performed using a hybrid experimental-computational approach. Bis-GMA polymer is used as a case study of hyperelastic material to describe the polymer characteristics by determining its Poisson’s ratio and its valid range of the hyperelastic stress-strain curves. These two parameters are then used to determine the hyperelastic constitutive model by using the hybrid approach. Several uniaxial compression tests along with their finite element simulations are implemented in a systematic way, to identify the polymer behavior under the compressive loading conditions. Nano-indentation experiments are conducted to verify the hyperelastic behavior of the polymer. The experimental and computational evidences confirm that the Poisson’s ratio of Bis-GMA is 0.40 and the appropriate hyperelastic constitutive model for this polymer is of a second order polynomial. It is shown that, the results can be used to determine the true stress-strain curve of hyperelastic materials.

Discovery of inhibitors for endothelial-related transcription factors can contribute to the development of angiogenic therapies that treat diseases ranging from cardiovascular to cancer. The role of transcription factor Vezf1 in vascular development and regulation of angiogenesis has been defined by several earlier studies. Through construction of a computational model for Vezf1, work here has identified a novel small molecule drug capable of inhibiting Vezf1 from binding to its cognate DNA binding site. Using structure-based design and virtual screening of the NCI Diversity Compound Library, 12 shortlisted compounds were tested for their ability to interfere with the binding of Vezf1 to DNA using electrophoretic gel mobility shift assays. We identified one compound, T4, which has an IC50 of 20uM. Using murine endothelial cells, MSS31, we tested the effect of T4 on endothelial cell viability and angiogenesis by using tube formation assay. Our data show that addition of T4 in cell culture medium does not affect cell viability at concentrations lower or equal to its IC 50 but strongly inhibits the network formation by MSS31 in the tube formation assays. Given its potential efficacy, this inhibitor has significant therapeutic potential in several human diseases raging from wound healing to cancer.

With the increase in life expectancy and consequent aging of the world’s population, the prevalence of many neurodegenerative diseases is increasing, without concomitant improvement in diagnostics and therapeutics. These diseases share neuropathological hallmarks, including mitochondrial dysfunction. In fact, as mitochondrial alterations appear prior to neuronal cell death at an early phase of the disease onset, the study and modulation of mitochondrial alterations rise as promising strategies to predict and prevent neurotoxicity and neuronal cell death before the onset of cell viability alterations. In this work, differentiated SH-SY5Y cells were treated with the mitochondrial-targeted neurotoxicants 6-hydroxydopamine and rotenone. These compounds were used at different concentrations and for different time points to understand the similarities and differences in their mechanisms of action. To accomplishing this, data on mitochondrial parameters was acquired and analyzed using unsupervised (hierarchical clustering) and supervised (decision tree) machine learning methods. Both biochemical and computational analyses resulted in an evident distinction between the neurotoxic effects of 6-hydroxydopamine and rotenone, specifically for the highest concentrations of both compounds.

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.

Indirect imaging methods involve at least two steps, namely optical recording, and computational reconstruction. The optical recording process uses an optical modulator that transforms the light from the object into a typical intensity distribution. This distribution is numerically processed to reconstruct the object’s image corresponding to different spatial and spectral dimensions. There have been numerous optical modulation functions and reconstruction methods developed in the past years for different applications. In most cases, a compatible pair of optical modulation function and reconstruction method gives optimal performance. A new reconstruction method termed non-linear reconstruction (NLR) was developed in 2017 to reconstruct the object image in the case of optical scattering modulators. During the years, it was revealed that the NLR could reconstruct an object’s image modulated by an axicons, bifocal lenses and even exotic spiral diffractive elements, which generate deterministic optical fields. Apparently, NLR seems to be a universal reconstruction method for indirect imaging. In this review, the performance of NLR has been investigated for many deterministic and stochastic optical fields. Simulation and experimental results for different cases are presented and discussed.

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.