Orthogonal operators can successfully be used to calculate eigenvalues and eigenvector compositions in complex spectra. Orthogonality ensures least correlation between the operators and thereby more stability in the fit, even for small interactions. The resulting eigenvectors are used to transform the pure transition matrix into realistic intermediate coupling transition probabilities. Calculated transition probabilities for close lying levels illustrate the power of the complete orthogonal operator approach.

The nuclear lamina consists of a dense fibrous meshwork made of nuclear lamins, Type V intermediate filaments, and is ~14 nm thick according to recent cryo-electron tomography studies. Recent advances in light microscopy have extended the resolution to a scale allowing for the fine structure of the lamina to be imaged in the context of the whole nucleus. We review quantitative approaches to analyze the imaging data of the nuclear lamina as acquired by structured illumination microscopy (SIM) and single molecule localization microscopy (SMLM), as well as the requisite cell preparation techniques. In particular, we discuss the application of steerable filters and graph based methods to segment the structure of the four mammalian lamin isoforms (A, C, B1, and B2) and extract quantitative information.

Maximizing the indications potential and revenue from drugs that are already marketed offers a new take on the famous mantra of the Nobel Prize-winning pharmacologist, Sir James Black, “The most fruitful basis for the discovery of a new drug is to start with an old drug”. However, rational design of drug mixtures poses formidable challenges because of the lack of or limited information about in vivo cell regulation, mechanisms of genetic pathway activation, and in vivo pathway interactions. Most of the repositioned drugs therefore are the result of “serendipity” - based on late phase clinical studies of unexpected findings. One of the reasons that the connection between drug candidates and their potential adverse drug reactions or new applications could not be identified earlier is that the underlying mechanism associating them is either very intricate and unknown or dispersed and buried in a sea of information. Discovery of such multi-domain pharmacomodules - pharmacologically relevant sub-networks of biomolecules and/or pathways - from collection of databases by independent/simultaneous mining of multiple datasets is an active area of research. Here, while presenting some of the promising bioinformatics approaches and pipelines, we summarize and discuss the current and evolving landscape of computational drug repositioning.

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.

The Leave-One-Out (LOO) Green Fluorescent Protein (GFP) approach to biosensor design com-bines computational protein design with split protein reconstitution. LOO-GFPs reversibly fold and gain fluorescence upon encountering its target peptide, which can be redefined by computa-tional design of the LOO site. Such an approach can be used to create reusable biosensors for the early detection of emerging biological threats. Enlightening biophysical inferences for nine LOO-GFP biosensor libraries are presented, with target sequences from dengue, influenza, or HIV, replacing beta strands 7, 8 or 11. An initially low hit rate was traced to components of the energy function, manifesting in the over-rewarding of over-tight side chain packing. Also, screening by colony picking required a low library complexity, but designing a biosensor against a peptide of at least 12 residues requires a high complexity library. This double-bind was solved using a "piecemeal" iterative design strategy. Also, designed LOO-GFPs fluoresced in the unbound state due to unwanted dimerization, but this was solved by fusing a fully functional prototype LOO-GFP to a fiber-forming protein, Drosophila ultrabithorax, creating a biosensor fiber. One in-fluenza hemagglutinin biosensor is characterized here in detail, showing a shifted excita-tion/emission spectrum, a micromolar affinity for the target peptide, and an unexpected pho-to-switching ability.

This study focuses on enhancing the prediction of flow characteristics in gas pipelines under hydrate-forming conditions using a validated CFD model. Four-Quadrants approach was introduced to analyse the relationship between gas and water flowrates, and to provide further insights into hydrate formation, agglomeration, deposition, and pipeline plugging. The analysis considered subcooling temperatures, water volume fractions, and gas velocities, to assess the risk of hydrate plugging in subsea gas pipelines. Results indicate that higher subcooling temperatures significantly increase the risk of hydrate formation and plugging, especially at lower gas velocities. At subcooling temperatures above 7.1 K, hydrate agglomeration and deposition become more pronounced, leading to a greater risk of plugging. Similarly, water volume fractions above 0.06 were found to increase the likelihood of hydrate blockages. However, higher gas velocities were shown to reduce this risk by promoting the sloughing of hydrates and preventing deposition. The findings align with existing literature and offer practical insights for flow assurance in gas pipeline operations.

The advancement of computational drug discovery and design necessitates continuous innovation to enhance the accuracy and scope of predictive models for early-stage drug research and development. This article introduces a novel workflow for in silico generation of structural and intermolecular binding affinity data with reasonable accuracy, combining two computational tools: Modeller and Prodigy. By leveraging synthetic structural and biophysical data, this approach addresses the limitations of existing experimental datasets, generating extensive, high-quantity binding affinity data with reasonable accuracy for biomolecular binding pairs, which broadens the horizon of computational biomolecule design and discovery by enabling extensive exploration of the sequence space of biomolecular binding pairs, and narrows the gap between experimental binding affinity data and its unexplored territories. Overall, this article presents a methodological advance to enhance the accuracy and scope of computational biomolecule discovery and design, paves the way for the development of preclinical candidates with improved efficacy and specificity, and holds transformative potential for further advancements in artificial intelligence-enabled biomolecule discovery and design in the future.

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

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

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.

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

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.

Traveling salesman problems (TSPs) are well-known combinatorial optimization problems, and most existing algorithms are challenging for solving TSPs when its scale is large. To improve the efficiency of solving large-scale TSPs, this work presents a novel adaptive layered clustering framework with improved genetic algorithm (ALC\_IGA). The primary idea behind ALC\_IGA is to break down a large-scale problem into a series of small-scale problems. First, the $k$-means and improved genetic algorithm are used to segment the large-scale TSPs layer by layer and generate the initial solution. Then, the developed two phases simplified $2$-opt algorithm is applied to further improve the quality of the initial solution. The analysis reveals that the computational complexity of the ALC\_IGA is between $O(n\log n)$ and $O(n^2)$. The results of numerical experiments on various TSP instances indicate that, in most situations, the ALC\_IGA surpasses the state-of-the-art algorithms in convergence speed, stability, and solution quality. Specifically, the ALC\_IGA can solve instances with $2 \times 10^5$ nodes within 0.15h, $1.4 \times 10^6$ nodes within 1h, and $2 \times 10^6$ nodes in three dimensions within 1.5h.

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.

Drug repurposing, the practice of utilizing existing drugs for novel clinical indications, has tremendous potential for improving human health outcomes and increasing therapeutic development efficiency. The goal of multidisease multitarget drug repurposing, also known as shotgun drug repurposing, is to develop platforms that assess the therapeutic potential of each existing drug for every clinical indication. Our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget repurposing implements several pipelines via large scale modelling and simulation of interactions between comprehensive libraries of drugs/compounds and protein structures. In these pipelines, each drug is described by an interaction signature that is then compared to all other signatures that are then sorted and ranked based on similarity. Pipelines within the platform are benchmarked based on their ability to recover known drugs for all indications in our library, and predictions are generated based on the hypothesis that (novel) drugs with similar signatures may be repurposed for the same indication(s). The drug-protein interactions in the platform used to create the drug-proteome signatures may be determined by any screening or docking method but the primary approach used thus far has been an in house similarity docking protocol. In this study, we calculated drug-proteome interaction signatures using the publicly available molecular docking method Autodock Vina and created hybrid decision tree pipelines that combined our original bio- and cheminformatic approach with the goal of assessing and benchmarking their drug repurposing capabilities and performance. The hybrid decision tree pipeline outperformed the corresponding two docking-based pipelines it was synthesized from, yielding an average indication accuracy of 13.3% at the top10 cutoff (the most stringent), relative to 10.9% and 7.1% for its constituent pipelines, and a random control accuracy of 2.2%. We demonstrate that docking based virtual screening pipelines have unique performance characteristics and that the CANDO shotgun repurposing paradigm is not dependent on a specific docking method. Our results also provide further evidence that multiple CANDO pipelines can be synthesized to enhance drug repurposing predictive capability relative to their constituent pipelines. Overall, this study indicates that pipelines consisting of varied docking based signature generation methods can capture unique and useful signal for accurate comparison of drug-proteome interaction signatures, leading to improvements in the benchmarking and predictive performance of the CANDO shotgun drug repurposing platform.

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.

Distributed Systems architectures are becoming the standard computational model for processing and transportation of information, especially for Cloud Computing environments. The increase in demand for application processing and data management from enterprise and end-user workloads continues to move from a single-node client-server architecture to a distributed multitier design where data processing and transmission are segregated. Software development must considerer the orchestration required to provision its core components in order to deploy the services efficiently in many independent, loosely coupled - physically and virtually interconnected - data centers spread geographically, across the globe. This network routing challenge can be modeled as a variation of the Travelling Salesman Problem (TSP). This paper proposes a new optimization algorithm for optimum route selection using Algorithmic Information Theory. The Kelly criterion for a Shannon-Bernoulli process is used to generate a reliable quantitative algorithm to find a near optimal solution tour. The algorithm is then verified by comparing the results with heuristic solutions in 3 test cases. A statistical analysis is designed to measure the significance of the results between the algorithms and the entropy function can be derived from the distribution. The tested results shown an improvement in the solution quality by producing routes with smaller length and time requirements. The quality of the results proves the flexibility of the proposed algorithm for problems with different complexities without relying in nature-inspired models such as Genetic Algorithms and Simulated Annealing. This algorithm can be used by orchestration applications to deploy services across large cluster of nodes by making better decision in the route design.

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.

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

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

Earthquakes present a significant risk to marine and underwater structures such as seawalls, piers, dolphins, breakwaters, buried pipelines, and sheet-piled constructions. These structures are particularly susceptible to the destabilizing effects of seismic events, which can result in severe damage. Furthermore, earthquakes can indirectly threaten these installations by destabilizing the underlying soil. Soil liquefaction, induced by seismic shaking, can undermine the stability and integrity of structures. This review discusses advanced simulation programs that have been systematically developed and validated using comparisons with commercial software and existing research . It extensively examines various factors that influence earthquake-induced dynamics in marine environments, including soil conditions, quality of structural components, tunnel length, mooring intervals, seismic wave propagation, and seaquake impacts . Over the past four decades, significant insights have been gained into the seismic design of marine structures, leading to the development of comprehensive guidelines. Recently, the simulation of marine infrastructure—such as breakwaters, platform jackets, and tunnels—has progressed considerably, driven by rapid economic growth and technological advancements in engineering. This review aims to evaluate and categorize recent studies on the numerical simulation of marine structures under seismic forces, with a focus on mitigating earthquake-related damage.

This study examines the dynamic fluid behavior of a PWM-controlled Solenoid-Operated Gas Admission Valve (SOGAV) for large-bore CNG engines using 3D CFD simulations with dynam-ic mesh techniques. The research focuses on the influence of orifice geometry variations in the multi-hole restrictor and pressure differentials between the inlet and outlet on flow stability, turbulence, and valve performance. Results demonstrate that multi-hole restrictors with differ-ent-sized orifices improve flow uniformity and reduce turbulence, thereby mitigating flow re-sistance. Transient simulations further reveal standing wave formation and pressure wave in-terference, emphasizing that steady-state models cannot capture critical transient phenomena, such as accelerated and decelerated jet-like flows and flow separation. These findings provide key insights into SOGAV optimization, contributing to enhanced fuel efficiency and engine re-sponsiveness, meeting the performance requirements of modern gas engines.

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

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

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

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

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

An adaptive deep neural network is used in an inverse system identification setting to approximate the inverse of a nonlinear plant with the aim of constituting the plant controller by copying to the latter the weights and architecture of the converging deep neural network. This deep learning (DL) approach to the adaptive inverse control (AIC) problem is shown to outperform adaptive filtering techniques and algorithms normally used in adaptive control, especially when the plant is nonlinear. The deeper the controller the better the inverse function approximation, provided that the nonlinear plant have an inverse and that this inverse can be approximated. Simulation results prove the feasibility of this DL-based adaptive inverse control scheme. The DL-based AIC system is robust to parameter change of the nonlinear plant in that, under such change, the plant output reassumes the value of the reference signal considerably faster than with the adaptive filter counterpart of the deep neural network. Settling times and rise times of the step response are shown to improve in the DL-based AIC system.

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.

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.

In this study, an optimization of the first blade in new test rig presented. Blade tuning is conducted using 3D geometrical parameters. Sweep and dihedral play an essential role in this study. Compressor characteristics and blades vibrational behavior are the main objective of the evaluation. Here, the attachments are designed to isolate blade dynamics from Disk. So, the Vibrational behaviors of the one's blade are tuned based on the self-excited and forced vibration phenomenon. Using a semi analytical MATLAB code instability conditions are satisfied. The code takes advantages of whitehead and force response theory to predict classically and stall flutter speeds. Beside, Forced vibrations instability is controlled using a theory presented by Campbell. Aerodynamics of new blade geometry determined using multistage simulations Computational fluid dynamics (CFD) software. Numerical results show increasing performance near the surge line and working interval along with increasing mass flow.

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.

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.

The eighty years old empirical Colebrook function widely used as an informal standard for hydraulic resistance relates implicitly the unknown flow friction factor , with the known Reynolds number and the known relative roughness of a pipe inner surface ; . It is based on logarithmic law in the form that captures the unknown flow friction factor in a way from which it cannot be extracted analytically. As an alternative to the explicit approximations or to the iterative procedures that require at least a few evaluations of computationally expensive logarithmic function or non-integer powers, this paper offers an accurate and computationally cheap iterative algorithm based on Padé polynomials with only one -call in total for the whole procedure (expensive -calls are substituted with Padé polynomials in each iteration with the exception of the first). The proposed modification is computationally less demanding compared with the standard approaches of engineering practice, but does not influence the accuracy or the number of iterations required to reach the final balanced solution.

We present observations from evaluation of internal environmental quality of industrial halls with priority on daylighting in combination with the integral lighting. The physical parameters related to indoor lighting in large industrial halls in winter and summer periods were analyzed using in situ measurements and computational methods. These are part of a comprehensive research on indoor environmental quality of industrial halls with the aims of saving energy and providing a comfortable environment for the workers while improving the productivity. The results showed that the procedures used for evaluation of residential or office buildings may not be used for industrial buildings. We also observed that the criteria of occupants’ comforts for indoor industrial buildings may differ from those of other kinds of buildings. Based on these results, an adequate attention is required for designing the industrial buildings. For this reason, appropriate evaluation methods and criteria should be created. We found the measured values of daylight factor very close to the skylight component of the total illumination. The skylight component was observed on average 30% that of the measured daylight factor values. Although the daylight is not emphasized when designing the industrial buildings and its contribution is small, but it is very important for the workers psychology and physiology. The workers must feel a connection with the exterior environment; otherwise, their productivity decreases.

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.

This study investigates the application of fractal geometry to the analysis of vertebrate respiratory systems, focusing on the branching patterns of the bronchial tree across diverse species. Using a combination of mathematical modeling and computational visualization, we explore how fractal dimensions and branching characteristics reflect evolutionary adaptations to various habitats and physiological demands. The research encompasses a range of vertebrates, including humans, horses, dolphins, chickens, iguanas, and bullfrogs, representing diverse taxonomic groups and ecological niches. Our analysis reveals a clear gradient of complexity in respiratory structures, correlating strongly with metabolic rates and environmental adaptations. Mammals, particularly those adapted for high-performance activities like horses, demonstrate the most complex fractal patterns, indicating highly efficient gas exchange systems. In contrast, ectothermic species such as iguanas and bullfrogs exhibit simpler structures, reflecting their lower metabolic demands and alternative respiratory strategies. The study employs fractal dimension calculations, L-system modeling, and lacunarity analysis to quantify and visualize these differences. Our findings suggest that fractal analysis provides valuable insights into the relationship between form and function in vertebrate respiratory systems, offering a novel perspective on comparative physiology and evolutionary biology. This research not only enhances our understanding of respiratory system evolution but also has potential applications in biomimetic design, medical diagnostics, and ecological physiology. It demonstrates the power of interdisciplinary approaches, combining advanced mathematics with biological principles to uncover fundamental patterns in nature.

This study examines the relationship between chondroitin sulphate, proteinoids, and computational neuron models, with a specific emphasis on the Izhikevich neuron model. We investigate the effect of chondroitin sulphate-proteinoid complexes on the behaviour and dynamics of simulated neurons. Through the use of computational simulations, we provide evidence that these biomolecular components have the power to regulate the responsiveness of neurons, the patterns of their firing, and the ability of their synapses to change within the Izhikevich architecture. The findings suggest that the interactions between chondroitin sulphate and proteinoid cause notable alterations in the dynamics of membrane potential and the timing of spikes. We detect adjustments in the features of neuronal responses, such as shifts in the thresholds for firing, alterations in spike frequency adaptation, and changes to bursting patterns. The findings indicate that chondroitin sulphate and proteinoids may have a role in precisely adjusting neuronal information processing and network behaviour. This study offers new and valuable information about the complex connection between the many components of the extracellular matrix, protein-based structures, and the functioning of neurons. In addition, our analysis of the proteinoid-chondroitine system using game theory uncovers a significant Prisoner’s Dilemma scenario. The system’s inclination towards defection, due to the appeal of cheating and the significant penalty for cooperation, with a mean voltage of −9.19 mV, indicates that defective behaviors may prevail in the long-term dynamics of these neuronal interactions.

The shaft hydropower plant (SHPP) is a novel hydraulic concept for low-head hydropower sites with several environmental and operational advantages over conventional layouts. However, the first two projects implementing this concept have shown comparatively high construction costs and project risks. Therefore, further optimization is required to the economic attractiveness and enabling broader market adoption. Initial model tests recommend a square-shaped shaft inlet with a three-sided approach flow for low-loss and fish-friendly inflow conditions. Yet, this design requires significant space for structural implementation and may be unsuitable for use with multiple shafts or as an extension of non-powered dams and weirs. This research paper presents the application of a computational fluid dynamics simulation setup to evaluate the hydraulic per-formance of various design configurations, especially, alternative design layouts with one-sided approach flow without further physical model tests,. The simulation setup is calibrated against observations including head loss and velocity measurements from the physical model tests, and its satisfactory performance enables the analysis of alternative design layouts. This aims to derive the most significant design parameters for achieving the desired hydraulic conditions at the intake. Increasing the flow depth before the intake and enlarging the inlet area have the most significant impact, while increasing the overflow of the front gate has the least significant effect.. The chosen CFD application is deemed suitable for hydraulic design optimization and provides guidance on the key parameters to focus on for tailored site-specific design development.

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.

Advanced state-of-the-art technologies, mainly computational intelligence including Machine/Deep Learning and Fuzzy Computing, through applied research, can provide added value to modern science and, in general, to entrepreneurship and the economy. Artificial intelligence is the field of computer science that attempts to model concepts such as learning, adaptability and perception to synthesize intelligent behavior in solving complex problems, utilizing elements of adaptation to the environment and philosophical reasoning. About the science of civil engineering and, in general, the construction industry, which is one of the most important in economic entrepreneurship, both in terms of the size of the workforce employed and the amount of capital invested, the penetration of artificial intelligence is possible to change industry business models, eliminate costly mistakes, reduce Jobsite injuries and generally make large-scale engineering projects more efficient. The purpose of the paper is to present the recent research on artificial intelligence methods (machine-deep learning, computer vision, natural language processing, fuzzy systems, robotics, etc.) and the corresponding related technologies related to it (extensive data analysis, blockchain, cloud computing, internet of things, augmented reality), in the fields of application of civil engineering science, including structural engineering, geotechnical engineering, hydraulics and water resources management, marine and coastal technology, transport and transportation infrastructure, planning and technical project management, critical infrastructure security and disaster mitigation..

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.

To investigate the optimum layouts of small vertical axis wind turbines, a two-dimensional analysis of dynamic fluid body interaction is performed via computational fluid dynamics for a rotor pair in various configurations. The rotational speed of each turbine rotor (diameter: D = 50 mm) varies based on the equation of motion. First, the dependence of rotor performance on the gap distance (gap) between two rotors is investigated. For parallel layouts, counter-down (CD) layouts with blades moving downwind in the gap region yield a higher mean power than counter-up (CU) layouts with blades moving upwind in the gap region. CD layouts with gap/D = 0.5–1.0 yield a maximum average power that is 23% higher than that of an isolated single rotor. Assuming isotropic bidirectional wind speed, co-rotating (CO) layouts with the same rotational direction are superior to the combination of CD and CU layouts regardless of the gap distance. For tandem layouts, the inverse-rotating configuration (IR) shows an earlier wake recovery than the CO configuration. For 16-wind-direction layouts, both the IR and CO configurations indicate similar power distribution at gap/D = 2.0. For the first time, this study demonstrates the phase synchronization of two rotors via numerical simulation.

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.

The Kwee van Woerden (KvW) method for the determination of eclipse minimum times has been a staple in eclipsing binary research for decades, due its simplicity and independence of external input parameters. However, its estimates of the timing error have been known to be of low reliability. During the analysis of very precise photometry of CM Draconis eclipses from TESS space mission data, KvW’s original equation for the timing error estimate produced numerical errors, which evidenced a fundamental problem in this equation. This contribution introduces an improved way to calculate the timing error with the KvW method. A code that implements this improved method, together with several further updates over the original method is presented as well. An example application on the CM Draconis light curves from TESS is given, where we show that its timing error estimates of about 1 second are in excellent agreement with error estimates obtained by other means.

This review paper provides an overview of recent advances in computational fluid dynamics (CFD) simulations of ejector pumps for vacuum generation. It examines various turbulence models, multiphase flow approaches, and numerical techniques employed to capture complex flow phenomena like shock waves, mixing, phase transitions, and heat/mass transfer. Emphasis is placed on comprehensive assessment of flow characteristics within ejectors, including condensation effects such as nucleation, droplet growth, and non-equilibrium conditions. The review highlights efforts in optimizing ejector geometries and operating parameters to enhance the entrainment ratio, a crucial performance metric for ejectors. The studies reviewed encompass diverse working fluids, flow regimes, and geometric configurations, underscoring the significance of ejector technology across various industries. While substantial progress has been made in developing advanced simulation techniques, several challenges persist, including accurate modeling of real gas behavior, phase change kinetics, and coupled heat/mass transfer phenomena. Future research efforts should focus on developing robust multiphase models, implementing advanced turbulence modeling techniques, integrating machine learning based optimization methods, and exploring novel ejector configurations for emerging applications.

The recent demand for reducing pollutant and CO2 emissions from internal combustion engines has created an urgent need for the development of ultra-lean burn engines that ensure combus-tion stability and reducing knocking tendency. One of the most promising methods is the Pre-chamber Spark Ignition (PCSI) system, where a jet of high-energy reactive gases, produced by pilot combustion in a pre-chamber, ignites the main combustion event in the cylinder. Given the intricate phenomena inherent to PCSI systems, conducting 3D CFD studies is imperative for a comprehensive analysis and optimal design. Furthermore, the detailed CFD model, combined with the calibrated 0-D/1-D model, is anticipated to yield a significant amount of new data that would be difficult to obtain through experimental approaches, making it essential for advancing our understanding and optimization of these systems. However, recently published papers re-port that some state-of-the-art models developed for conventional SI operation may not be pre-dictive under the challenging conditions of TJI systems, particularly under lean conditions. This review elucidates the reasons behind the widespread adoption of CFD in the optimal design of PCSI engines. It presents significant examples and delves into the potential and challenges of employing CFD not just as a predictive tool but also as a design instrument for enhancing PCSI engine performance.

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

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

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.

This paper investigates urban microclimate in relation to the typical major streets and mobility layout in Turin, i.e. boulevards with lateral minor roads, called “controviali”, an almost unique urban mobility layout. The aim is to examine the possibility of intervention in relation to the type and scope of mobility for microclimate mitigation and climate adaptation purposes. To this, this paper evaluates to which extent urban morphology and mobility features contribute to determining microclimatic conditions, to finally identify site-specific prioritised strategies to cope with its mitigation. This research is structured in two phases. The first phase is dedicated to urban-scale analysis of the streets with “controviali”, resulting in the creation of a database containing their properties and typology, to finally derive six clusters of boulevards. The second phase involves the selection of one representative case study per cluster and simulation of their microclimate conditions, performed by the software ENVI-met. The results are discussed in relation to the parameters proven in the prior literature review to have a major impact on the microclimate. The assessment of the results indicated which elements affect the climatic conditions the most, allowing for the proposal of different prioritised mitigation strategies in the conclusion.

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.

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.

We construct a family of two-step optimal fourth order iterative methods for finding single root of non-linear equations. We generalize these methods to simultaneous iterative methods for determining all the distinct as well as multiple roots of single variable non-linear equations. Convergence analysis is present for both cases to show that the order of convergence is four in case of single root finding method and is twelve for simultaneous determination of all roots of non-linear equation. The computational cost, Basin of attraction, efficiency, log of residual and numerical test examples shows, the newly constructed methods are more efficient as compared to the existing methods in literature.

Multiphase systems are important in minerals processing, and usually include solid-solid and solid-fluid systems. Examples of operations in multiphase systems include flotation, dewatering, and magnetic separation, among several other unit operations. In this paper, the current trends in the process system engineering tasks of modeling, design, and optimization, in multiphase systems, are analyzed. Different scales of size and time are included, and therefore the analysis includes modeling at the molecular level and unit operation level, and the application of optimization for the design of a plant. New strategies for the modeling and optimization of multiphase systems are also included, with a strong focus on the application of artificial intelligence (AI) and the combination of experimentation and modeling with response surface methodology (RSM). The paper finishes with tools to study the uncertainty, both epistemic and stochastic, which is present in all mineral processing operations. It is shown that all these areas are very active and can help to understand, operate, design, and optimize mineral processing that involves multiphase systems.

Machine learning (ML) methods has recently contributed very well in the advancement of the prediction models used for energy consumption. Such models highly improve the accuracy, robustness, and precision and the generalization ability of the conventional time series forecasting tools. This article reviews the state of the art of machine learning models used in the general application of energy consumption. Through a novel search and taxonomy the most relevant literature in the field are classified according to the ML modeling technique, energy type, perdition type, and the application area. A comprehensive review of the literature identifies the major ML methods, their application and a discussion on the evaluation of their effectiveness in energy consumption prediction. This paper further makes a conclusion on the trend and the effectiveness of the ML models. As the result, this research reports an outstanding rise in the accuracy and an ever increasing performance of the prediction technologies using the novel hybrid and ensemble prediction models.

We consider the maximum entropy Markov chain inference approach to characterize the collective statistics of neuronal spike trains, focusing on the statistical properties of the inferred model. We review large deviations techniques useful in this context to describe properties of accuracy and convergence in terms of sampling size. We use these results to study the statistical fluctuation of correlations, distinguishability and irreversibility of maximum entropy Markov chains. We illustrate these applications using simple examples where the large deviation rate function is explicitly obtained for maximum entropy models of relevance in this field.

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

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

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.

This paper examines the fundamental distinctions and complementary relationships between generative and descriptive methods in research and analysis. Through a systematic review of their applications across various fields, we explore how descriptive methods excel in capturing and characterizing existing phenomena, while generative methods enable the creation of new instances based on learned patterns. The analysis reveals that while these approaches serve different primary purposes, their integration often leads to more robust and comprehensive research outcomes. Our findings suggest that understanding the strengths and limitations of both methodologies is crucial for researchers and practitioners in choosing appropriate approaches for their specific contexts.

In recent years, the abundance of streaming data has posed significant challenges for traditional machine learning algorithms due to the dynamic nature and high volume of data. To address these challenges, this paper presents an adaptive supervised learning framework designed for data streams, leveraging the power of reproducing kernel Hilbert spaces (RKHS) while incorporating a data sparsity constraint. The proposed framework aims to adaptively update the model to efficiently handle the continuous arrival of streaming data while ensuring accurate and reliable predictions. The primary objective is to exploit the RKHS framework, which provides a rich mathematical structure for learning in high-dimensional feature spaces. By utilizing RKHS, the model can capture complex patterns and nonlinear relationships in the streaming data. Furthermore, the framework incorporates a data sparsity constraint to address the issue of limited resources and computational efficiency. The constraint promotes the selection of a subset of relevant features, reducing the dimensionality of the problem and enhancing the scalability of the learning algorithm. This constraint not only improves computational efficiency but also mitigates the effects of noisy or irrelevant features, leading to more robust and accurate predictions. To achieve adaptability, the proposed framework employs an online learning approach that incrementally updates the model parameters as new data arrives. This allows the model to adapt to concept drift and changing data distributions, ensuring its relevance and effectiveness over time. The adaptation process is guided by a mechanism that balances the exploitation of current knowledge with the exploration of new information, enabling the model to gradually evolve and refine its predictions. Experimental evaluations on various benchmark datasets demonstrate the efficacy of the proposed framework in handling streaming data. The results indicate that the adaptive supervised learning approach in RKHS with a data sparsity constraint outperforms traditional batch learning methods and exhibits superior accuracy, scalability, and adaptability in dynamic data stream scenarios. Overall, this research contributes to the development of adaptive supervised learning methods for data streams, highlighting the effectiveness of RKHS and the significance of incorporating a data sparsity constraint. The proposed framework offers a promising solution to handle the challenges posed by streaming data, paving the way for real-time, efficient, and accurate learning in diverse application domains.

In the realm of metal steel and iron manufacturing, there is a constant drive to optimize production efficiency and enhance the quality of the final products. This abstract explores a paradigm shift in this industry through the utilization of Computational Fluid Dynamics (CFD) as a powerful tool for achieving these goals .The study focuses on various critical aspects of the manufacturing process. Firstly, it delves into three-phase processes such as slag and gas entrainment in liquid steel, which have a significant impact on the overall product quality. Understanding and optimizing these processes is crucial to ensure the desired chemical composition and physical properties of the final metal.The abstract investigates the role of CFD in vacuum degassing, a process that plays a pivotal role in removing impurities, such as hydrogen and oxygen, from the liquid steel. By leveraging CFD to simulate and analyze the fluid flow patterns, researchers and engineers can design more efficient vacuum degassing systems, leading to improved product purity and reduced waste.Another crucial aspect explored in this abstract is the alloy melt and mixing process. By employing CFD simulations, researchers can gain insights into the movement of different alloy components and optimize their distribution within the melt. This aids in achieving the desired alloy composition, ensuring uniformity, and enhancing the mechanical properties of the final product.In addition, the study delves into the movement and flotation of inclusions, which are unwanted impurities present in the metal. CFD can be utilized to model and analyze the behavior of these inclusions, enabling engineers to design efficient strategies for their removal, thereby enhancing the purity and integrity of the final product.Moreover, the abstract considers the impact of melt temperature losses, which can occur during different stages of the manufacturing process. By employing CFD, it becomes possible to understand and mitigate these temperature losses, leading to improved energy efficiency and better control over the manufacturing parameters.Part 1 of this abstract critically evaluates the use of CFD in iron making, analyzing its benefits and limitations. It highlights how CFD can aid in process optimization, improving the understanding of complex phenomena involved in iron making, and ultimately leading to enhanced production efficiency.Part 2 focuses on the application of CFD in steel making and steel operation. It explores the fluid flow dynamics during different stages of steel production, including refining, casting, and rolling. By leveraging CFD, engineers can optimize these processes, leading to improved product quality, reduced defects, and increased productivity.This abstract demonstrates the transformative potential of Computational Fluid Dynamics in metal, steel, and iron manufacturing. By utilizing CFD as a tool to model and analyze complex fluid flow phenomena, researchers and engineers can revolutionize the production process, maximizing efficiency, and ensuring high-quality end products.

The epithelial-mesenchymal (E/M) hybrid state has emerged as an important mediator of elements of cancer progression, facilitated by epithelial mesenchymal plasticity (EMP). We review here evidence for the presence, prognostic significance, and therapeutic potential of the E/M hybrid state in carcinoma. We further assess modelling predictions and validation studies to demonstrate stabilised E/M hybrid states along the spectrum of EMP, as well as computational approaches for characterising and quantifying EMP phenotypes, with particular attention to the emerging realm of single-cell approaches through RNA sequencing and protein-based techniques.

Tissue engineering has the potential for repairing large bone defects, which impose a heavy financial burden on the public health. However, difficulties with O₂ delivery to the cells residing in the interior of tissue engineering scaffolds make it challenging to grow artificial tissues of clinically-relevant sizes. This study uses image-based simulation in order to provide insight into how to better optimize the scaffold manufacturing parameters, and the culturing conditions, in order to resolve the O₂ bottleneck. To do this, high resolution 3D X-ray images of two common scaffold types (salt leached foam and non-woven fiber mesh) are fed into a Lattice Boltzmann Method fluid dynamics and reactive Lagrangian Scalar Tracking mass transfer solvers. The obtained findings indicate that the scaffolds should have maximal surface area-to-solid volume ratios, for higher chances of the molecular collisions with the cells. Furthermore, the cell culture media should be flown through the scaffold pores as fast as practically possible (without detaching or killing the cells). Finally, we have provided a parametric sweep that maps how the molecular transport within the scaffolds is affected by variations in rates of O₂ consumption by the cells. Ultimately, the results of this study are expected to benefit the computer-assisted design of tissue engineering scaffolds and culturing experiments.

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

Left ventricular (LV) longitudinal function is mechanically coupled to the elasticity of the ascending aorta (AA). The pathophysiologic link between stiff AA and reduced longitudinal strain and subsequent deterioration in longitudinal LV systolic function is likely relevant in heart failure with preserved ejection fraction (HFpEF). A proposed therapeutic effect of freeing the LV apex and allowing for LV inverse longitudinal shortening was studied in-silico utilizing Living Left Heart Human Model (Dassault Systémes Simulia Corporation). LV function was evaluated in a model with A) Elastic AA; B) Stiff AA; and C) Stiff AA with free LV apex. The cardiac model simulation demonstrated that freeing the apex caused inverse LV longitudinal shortening that can abolish the deleterious mechanical effect of a stiff AA on LV function. A stiff AA and impairment of LV longitudinal strain are common in patients HFpEF. The hypothesis-generating model strongly suggests that freeing the apex and inverse longitudinal shortening may improve LV function in HFpEF patients with a stiff AA.

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

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

The knowledge of what separates us genetically from our less-evolved relatives is crucial for gaining new biomedical insights about the human-chimpanzee relatedness for the use of appropriate stand-in towards the development of new treatments and diagnostic aids for various ailments. Although the genomes of humans and chimpanzees share 99% similarity, significant differences exist between the two species in their non-coding intronic regions. However, no work has been carried out in the aspects of target prediction concerning the ‘predicted homology’ in their microRNA sequences. Non-coding miRNAs which are post-transcriptional regulators of development, differentiation, growth, and metabolism, harboring the intronic regions may be crucial for expanding the horizons of our understanding. In this study, we proposed to perform the target prediction for the human-chimp miRNA homologs in the PHEX gene of the human X chromosome using various computational tools and databases. We identified a total of 1296 human miRNAs, 46, 957 gene targets, and 30, 563 targets of human and homologous chimp miRNAs respectively. Furthermore, we analysed gene interacting networks to identify the top interacting targets in both the species. Finally, we interpreted the biological importance of top-interacting miRNAs and their targets. The results demonstrated varying levels of multiplicity and cooperativity between the predicted miRNAs and target genes in the two genera. Such miRNAs may be responsible for the dysregulation of gene expression in several signaling pathways.

Liquid manipulation by photoisomerization attracts recent attentions as a new active droplet control method for micro-chemical analysis. Such a non-inverse manipulation can be realized by a use of solution liquid of surfactant that exhibits the \emph{cis}-\emph{trans} isomerization triggered by light irradiation with a specific wavelength such as ultraviolet light. Since the isomerization is accompanied by changes in fluid properties, a light irradiation on one of liquid-air interfaces of a liquid column in a tube would generate differences in the wettability accompanied between the both sides of the finite liquid column. Although this technique has been demonstrated experimentally by Muto et al. (\emph{Euro.~Phys.~J.~Special Topics}, {\bf 226}, 2016, 1199--1205), its dynamics and developments of each isomer distribution are not understood. In order to reveal the liquid-column migration phenomenon, we have performed numerical simulations of air-liquid two-phase flows and its scalar transport of the isomer, using the Volume-of-Fluid method in conjunction with the Continuum-Surface-Force model and Continuous-Species-Transfer method. We validated present results by comparison with experimental result in terms of the migration distance of the liquid column. We confirmed a termination of the liquid-column migration occurs when the \emph{cis} isomer distribution reaches the non-irradiated region. The migration speed was less dependent on the liquid-column length and was proportional to the tube diameter.

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

The rising popularity of UAVs and other autonomous control systems, coupled with real-time operating systems, has increased the complexity of developing systems with the proper robustness, performance, and reactivity. Concurrently, the growing demand for more sophisticated computational tasks, proportionally bigger payloads, battery limitations, and smaller take-off mass necessitates higher energy efficiency for all avionics and mission computers. The purpose of this paper is to develop the technology for experimental studies of indicators of reactivity and energy consumption of the computing platform for unmanned aerial vehicles (UAVs). The paper provides an experimental assessment of the ’Boryviter’ 0.1 computing platform, which is implemented on the ATSAMV71 microprocessor and operates under the open-source FreeRTOS operating system. The results obtained are the basis for developing algorithms and energy-efficient design strategies for the mission computer to solve the optimization problem. The paper provides experimental results of measurements of the energy consumed by the microcontroller and estimates of the reduction in system energy consumption due to additional time costs for suspending and resuming the computer’s operation. The results show that the ’Boryviter’ 0.1 computing platform can be used as a UAV mission computer for typical flight control tasks that require real-time computing under the influence of external factors. As the further work direction, authors plan to carry out the investigation of the proposed energy saving algorithms in scope of the planned NASA’s F’Prime software flight framework. Such an investigation that should be done with the real flight computation load for the mission computer, will help to qualify the obtained energy saving methods and their implementation results.

Computationally modelling a nuclear reactor startup core for a benchmark against the existing models is highly desirable for an independent assessment informing nuclear engineers and energy policymakers. This work presents a startup core model of the UK’s first Evolutionary Pressurised Water Reactor (EPR) based on Monte Carlo simulations of particle collisions using Serpent 2, a continuous-energy Monte Carlo reactor physics burnup code. Coupling between neutronics and thermal-hydraulic conditions with the fuel depletion is incorporated into the multi-dimensional branches, obtaining the thermal flux and fission rate (power) distributions radially and axially from the three dimensional (3D) single assembly level to a 3D full core. Shannon entropy is employed to characterise the convergence of the fission source distribution, with 3 billion neutron histories tracked by parallel computing. Source biasing is applied for the variance reduction. Benchmarking the proposed Monte Carlo 3D full-core model against the traditional deterministic transport computation suite used by the UK Office for Nuclear Regulation (ONR), a reasonably good agreement within statistics is demonstrated for the safety-related reactivity coefficients, which creates trust in the EPR safety report.

Several problems of radiative transfer are yet unsolved because of the difficulties of the calculations involved in them, especially if the intervening shapes are geometrically complex. The main goal of our investigation in this domain is to convert the formulas that were previously derived, into a graphical interface based on the projected solid-angle principle. Such procedure is now feasible by virtue of several widely diffused programs for Algorithms Aided Design (AAD). Accuracy and reliability of the process is controlled by means of the analytical software DianaX developed at an earlier stage by the authors. With this new approach the often cumbersome procedure of lighting and thermal exchange calculations can be simplified and made available for the neophyte, with the undeniable advantage of reduced computer time.

Design and advancement of the durable urban train infrastructures are of utmost importance for reliable mobility in the smart cities of the future. Given the importance of urban train lines, tunnels, and subway stations, these structures should be meticulously analyzed. In this research, two-dimensional modeling and analysis of the soil-structure mass of the Alan Dasht station of Mashhad Urban Train are studied. The two-dimensional modeling was conducted using Hashash’s method and displacement interaction. After calculating the free-field resonance and side distortion of the soil mass, this resonance was entered into PLAXIS finite element program, and finally, stress and displacement contours together with the bending moment, shear force and axial force curves of the structure were obtained.

A quantum hybrid (QH) intelligent approach that blends the adaptive search capability of the quantum-behaved particle swarm optimisation (QPSO) method with the intuitionistic rationality of traditional fuzzy k-nearest neighbours (Fuzzy k-NN) algorithm (known simply as the Q-Fuzzy approach) is proposed for efficient feature selection and classification of cells in cervical smeared (CS) images. From an initial multitude of seventeen (17) features describing the geometry, colour, and texture of the CS images, the QPSO stage of our proposed technique is used to select the best subset features (i.e. global best particles) that represent a pruned down collection of seven (7) features. Using a dataset of almost 1000 images, performance evaluation of our proposed Q-Fuzzy approach assesses the impact of our feature selection on classification accuracy by way of three experimental scenarios that are compared alongside two other approaches: The All-features (i.e. classification without prior feature selection) and another hybrid technique combining the standard PSO algorithm with the Fuzzy k-NN technique (P-Fuzzy approach). In the first and second scenarios, we further divided the assessment criteria in terms of classification accuracy based on the choice of best features and those in terms of the different categories of the cervical cells. In the third scenario, we introduced new QH hybrid techniques, i.e. QPSO combined with other supervised learning methods, and compared the classification accuracy alongside our proposed Q-Fuzzy approach. Furthermore, we employed statistical approaches to establish qualitative agreement with regards to the feature selection in scenarios 1 and 3. The synergy between the QPSO and Fuzzy k-NN in the proposed Q-Fuzzy approach marginally improves classification accuracy as manifest in the reduction in number cell features, which is crucial for effective cervical cancer detection and diagnosis.

This study investigates the application of deep residual networks for predicting the dynamics of interacting three-dimensional rigid bodies. We present a framework combining a 3D physics simulator implemented in C++ with a deep learning model constructed using PyTorch. The simulator generates training data encompassing linear and angular motion, elastic collisions, fluid friction, gravitational effects, and damping. Our deep residual network, consisting of an input layer, multiple residual blocks, and an output layer, is designed to handle the complexities of 3D dynamics. We evaluate the network's performance using a dataset of 10,000 simulated scenarios, each involving 3-5 interacting rigid bodies. The model achieves a mean squared error of 0.015 for position predictions and 0.022 for orientation predictions, representing a 25% improvement over baseline methods. Our results demonstrate the network's ability to capture intricate physical interactions, with particular success in predicting elastic collisions and rotational dynamics. This work significantly contributes to physics-informed machine learning by showcasing the immense potential of deep residual networks in modeling complex 3D physical systems. We discuss our approach's limitations and propose future directions for improving generalization to more diverse object shapes and materials.

Biomolecular structure drives function, and computational capabilities have progressed such that the prediction and computational design of biomolecular structures is increasingly feasible. Because computational biophysics attracts students from many different backgrounds and with different levels of resources, teaching the subject can be challenging. One strategy to teach diverse learners is with interactive multimedia material that promotes self-paced, active learning. We have created a hands-on education strategy with a set of fifteen modules that teach topics in biomolecular structure and design, from fundamentals of conformational sampling and energy evaluation to applications like protein docking, antibody design, and RNA structure prediction. Our modules are based on PyRosetta, a Python library that encapsulates all computational modules and methods in the Rosetta software package. The workshop-style modules are implemented as Jupyter Notebooks that can be executed in the Google Colaboratory, allowing learners access with just a web browser. The digital format of Jupyter Notebooks allows us to embed images, molecular visualization movies, and interactive coding exercises. This multimodal approach may better reach students from different disciplines and experience levels as well as attract more researchers from smaller labs and cognate backgrounds to leverage PyRosetta in their science and engineering research. All materials are freely available at https://github.com/RosettaCommons/PyRosetta.notebooks.

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

This paper attempts to develop a Dynamic Mode Decomposition (DMD) based Reduced Order Model (ROMs) which can quickly but accurately predict the forces and moments experienced by a road vehicle such that they be used by an on-board controller to determine the vehicle's trajectory. DMD can linearize a large dataset of high-dimensional measurements by decomposing them into low-dimensional coherent structures and associated time-dynamics. This ROM then also can be applied to predict the future state of the fluid flow. Existing literature on DMD is limited to low Reynolds number applications. This paper presents DMD analyses of the flow around an idealized road vehicle, called the Ahmed body, at a Reynolds number of 2.7E6. The high dimensional dataset used in this paper was collected from a computational fluid dynamics (CFD) simulation performed using Menter's shear stress transport (SST) turbulence model within the context of Improved Delayed Detached Eddy Simulations (IDDES). The DMD algorithm, as available in literature, was found to suffer nonphysical dampening of the medium-to-high frequency modes. Enhancements to the existing algorithm were explored, and a modified DMD approach is presented in this paper which includes: (a) a requirement of higher sampling rate to obtain higher resolution of data, and (b) a custom filtration process to remove spurious modes. The modified DMD algorithm thus developed was applied to the high Reynolds number, separation dominated flow past the idealized ground vehicle. The effectiveness of the modified algorithm was tested by comparing future predictions of force and moment coefficients as predicted by the DMD-based ROM to the reference CFD simulation data, and found to offer significant improvement.

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.

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

In this paper, dual-rotor counter-rotating (CR) configurations of vertical axis wind turbines (VAWT) are briefly inspected and divided into three types. This investigation was focused on one of these types – the CR-VAWT with co-axial rotors, in which two equal rotors are placed on the same shaft, displaced from each other along it and rotate in opposite directions. For this CR-VAWT with three-blade H-Darrieus rotors, the properties of the design in terms of aerodynamics, mechanical transmission and electric generator, as well as control system are analyzed. A new direct-driven dual-rotor (DR) permanent magnet synchronous generator (PMSG) was proposed, in which two built-in low-power PM electric machines have been added. They perform two functions – start-up and overclocking of the rotors to the angular velocity at which the lifting force of the blades is generated and stabilizing the CR-VAWT work as wind gusts act on the two rotors. Detailed in this paper is the evaluation of aerodynamic performance of the CR-VAWT via 3D computational fluid dynamics (CFD) simulations. The evaluation was conducted using the CONVERGE CFD software with the inclusion of the actuator line model for the rotor aerodynamics, which significantly reduces the computational effort. Obtained results show that both rotors, while they rotate in opposite directions, had a positive impact on each other: the power coefficients of the upper and lower rotors in the CR-VAWT increased by 5.5% and 13.3% respectively compared to the single-rotor VAWT at the optimal distance between the rotors of 0.3 from a rotor height.

The pulsatile flow rate (PFR) in the cerebral artery system and shunt ratios in bifurcated arteries are two patient-specific parameters which may affect the hemodynamic characteristics on the pathobiology of cerebral aneurysms (CAs). Accordingly, a systematic study was employed to investigate the effects of the two parameters on hemodynamic characteristics in two internal carotid artery sidewall aneurysms (i.e., ICASA-1 and ICASA-2) models. Numerical results indicate larger PFRs can cause higher WSS in some local regions of the aneurysmal dome that may increase the probability of small/secondary aneurysms generation than under smaller PFRs. The low WSS and relatively high oscillatory shear index (OSI) could appear under a smaller PFR, which has the potential to cause aneurysmal sac growth and rupture. However, the variances in PFRs and bifurcated shunt ratios have rare impacts on the time-average pressure distributions on the aneurysmal sac, although a higher PFR can contribute more to the pressure increase in ICASA-1 dome due to the relatively stronger impingement by the redirected blood stream than in ICASA-2. Simulation results also present the variances of shunt ratios have rare impacts on the hemodynamic characteristics in sacs, mainly because the bifurcated location is not close enough to the sac in present models. Furthermore, it has been found that the vortex location plays a major role in the temporal and spatial distribution of the WSS on the luminal wall, varying significantly with the cardiac period.

The implementation of morphing wing mechanisms holds promise in enhancing aircraft perfor-mance, as indicated in current literature. The Clean Sky 2 AirGreen 2 European project is currently conducting ground and wind tunnel tests to validate innovative studies on morphing wing structures. The project aims to showcase the effectiveness of these morphing architectures on a potentially flying demonstrator at true scale. This article describes design methodology and struc-tural testing of a scaled morphing flap structure capable of 3 different morphing modes required for low-speed (take-off/landing) and high-speed (cruise) conditions. A significant scale factor of 1:3 was selected to enhance the importance and relevance of the wind tunnel test campaign. Geometrical scaling to the real flap prototype was deemed impractical due to limitations associated with scaling the embedded mechanisms and actuators responsible for shape changing. Static analyses were performed using the finite element method to address the most critical loads determined through three-dimensional CFD analysis. The Finite Element (FE) analysis results were briefly outlined, and comparisons were drawn between the obtained results and the empirical data collected during the structural test. Good correlations were observed between the results and numerical predictions, encompassing static deflections under applied loads and elastic deformations. Consequently, the modeling approaches utilized during the design and testing phases were considered highly suc-cessful. Therefore, based on the results extrapolated from simulations under ultimate load condi-tions relevant to the wind tunnel test campaign, the scaled flap prototype was deemed suitable for testing.

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

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

Appropriate estimation of soil settlement is of significant importance since it directly influences the performance of building and infrastructures that are built on soil. In particular, the settlement of fine-grained soils is critical because of low permeability and continuous settlement with time. Coefficient of consolidation (Cc) is a key parameter to estimate settlement of fine-grained soil layers. However, estimation of this parameter is time consuming, needs skilled technicians, and specific equipment. In this study, Cc was estimated using several soil parameters such as liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Estimating such parameters in laboratory is straight forward and needs substantially less time and cost compared to conventional tests to estimate Cc such as Oedometer test. This study presents a novel prediction model for Cc of fine-grained soils using gene-expression programming (GEP). GEP is a biologically inspired technique capable of offering closed-form solution for the optimal solution. A database consisted of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of developed GEP-based model was evaluated through coefficient of determination (R2), root mean squared error (RMSE), and mean average error (MAE). High R2 and low error values indicated the descent performance of the model. Furthermore, the model was evaluated using the additional performance measures and met all the suggested criteria. Furthermore, the model had a better performance in terms of R2, RMSE, and MAE compared to most of existing models. It is expected that the developed model will decrease the time and cost associate with determining Cc of fine-grained soils.

Appropriate estimation of soil settlement is of significant importance since it directly influences the performance of building and infrastructures that are built on soil. In particular, the settlement of fine-grained soils is critical because of low permeability and continuous settlement with time. Coefficient of consolidation (C_c) is a key parameter to estimate settlement of fine-grained soil layers. However, estimation of this parameter is time consuming, needs skilled technicians, and specific equipment. In this study, C_c was estimated using several soil parameters such as liquid limit (LL), plastic limit (PL), and initial void ratio (e₀). Estimating such parameters in laboratory is straight forward and needs substantially less time and cost compared to conventional tests to estimate C_c such as oedometer test. This study presents a novel prediction model for C_c of fine-grained soils using gene-expression programming (GEP). GEP is a biologically inspired technique capable of offering closed-form solution for the optimal solution. A database consisted of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate C_c based on LL, PL, and e₀. The performance of developed GEP-based model was evaluated through coefficient of determination (R²), root mean squared error (RMSE), and mean average error (MAE). High R² and low error values indicated the descent performance of the model. Furthermore, the model was evaluated using the additional performance measures and met all the suggested criteria. Furthermore, the model had a better performance in terms of R², RMSE, and MAE compared to most of existing models. It is expected that the developed model will decrease the time and cost associate with determining C_c of fine-grained soils.Keywords: evolutionary model, gene-expression programming (GEP), prediction, soil compression index, estimation, soil engineering, soil informatics, civil engineering, machine learning, data science, big data, soft computing, deep learning, forecasting, subject classification codes, construction informatics, computational intelligence (CI), artificial intelligence (AI), estimation

Synthetic Aperture RADAR (SAR) is a radar imaging technique in which the relative motion of the sensor is used to synthesize a very long antenna and obtain high spatial resolution. Standard SAR raw data processing techniques assume uniform motion of the satellite (or aerial vehicle) and a fixed antenna beam pointing sideway orthogonally to the motion path, assumed rectilinear. Despite SAR data processing is a well established imaging technology that has become fundamental in several fields and applications, in this paper a novel approach has been used to exploit coherent illumination, demonstrating the possibility of extracting a large part of the ancillary data information from the raw data itself, to be used in the focusing procedure. In this work an effort has been carried out to try to focus the raw SAR complex data matrix without the knowledge of anyof the parameters needed in standard focusing procedures as Range Doppler (RD) algorithm, ω - K algorithm and Chirp Scaling (CS) algorithm. All the literature references regarding the algorithms needed to obtain a precise image from raw data use such parameters that refer both to the SAR system acquisition geometry and its radiometric specific parameters. In [12], authors introduced a preliminary work dealing with this problem and able to obtain, in the presence of a strong point scatterer in the observed scene, good quality images, if compared to the standard processing techniques. In this work the proposed technique is described and performances parameters are extracted to compare the proposed approach to RD.