In-house Experiments Research Articles

Individual variation in the functional lateralization of human ventral temporal cortex: Local competition and long-range coupling

Abstract The ventral temporal cortex (VTC) of the human cerebrum is critically engaged in high-level vision. One intriguing aspect of this region is its functional lateralization, with neural responses to words being stronger in the left hemisphere, and neural responses to faces being stronger in the right hemisphere; such patterns can be summarized with a signed laterality index (LI), positive for leftward laterality. Converging evidence has suggested that word laterality emerges to couple efficiently with left-lateralized frontotemporal language regions, but evidence is more mixed regarding the sources of the right-lateralization for face perception. Here, we use individual differences as a tool to test three theories of VTC organization arising from: 1) local competition between words and faces driven by long-range coupling between words and language processes, 2) local competition between faces and other categories, 3) long-range coupling with VTC and temporal areas exhibiting local competition between language and social processing. First, in an in-house functional MRI experiment, we did not obtain a negative correlation in the LIs of word and face selectivity relative to object responses, but did find a positive correlation when using selectivity relative to a fixation baseline, challenging ideas of local competition between words and faces driving rightward face lateralization. We next examined broader local LI interactions with faces using the large-scale Human Connectome Project (HCP) dataset. Face and tool LIs were significantly anti-correlated, while face and body LIs were positively correlated, consistent with the idea that generic local representational competition and cooperation may shape face lateralization. Last, we assessed the role of long-range coupling in the development of VTC lateralization. Within our in-house experiment, substantial positive correlation was evident between VTC text LI and that of several other nodes of a distributed text-processing circuit. In the HCP data, VTC face LI was both negatively correlated with language LI and positively correlated with social processing in different subregions of the posterior temporal lobe (PSL and STSp, respectively). In summary, we find no evidence of local face-word competition in VTC; instead, more generic local interactions shape multiple lateralities within VTC, including face laterality. Moreover, face laterality is also influenced by long-range coupling with social processing in the posterior temporal lobe, where social processing may become right-lateralized due to local competition with language.

A porohyperelastic scheme targeted at High-Performance Computing frameworks for the simulation of the intervertebral disc

Background and Objective:The finite element method is widely used for studying the intervertebral disc at the organ level due to its ability to model complex geometries. An indispensable requirement for proper modelling of the intervertebral disc is a reliable porohyperelastic framework that captures the elaborate underlying mechanics. The increased complexity of such models requires significant computational power that is available within high-performance computing systems. The objective of this study is to present such a framework, validated both against literature and experiments, aiming to enable intervertebral disc research to benefit from state-of-the-art computational resources. Methods:In the context of this work, we implement a biphasic model that captures the mechanical response of the intricate, tissue-dependent models of the solid phase along with the hydrostatic pressure effects of the fluid phase. The tissue-dependent models involve the hyperelastic ground substance, fibrillar reinforcement, and osmotic swelling. The derived porohyperelastic, staggered scheme is implemented in Alya, a finite element code targeted at high-performance computing applications. The formulation is subsequently verified and validated by comparing the results of consolidation simulations with literature data for simulations and experiments using either generic or patient-specific geometries. Additionally, in-house experiments are replicated, evaluating the model’s ability to simulate alternating loading. Finally, the implementation’s circadian response is compared to previous implementation of similar material models in commercial software. Results:Results align well with experimental and literature findings in terms of disc height reduction (4% error), intradiscal pressure (14% error) and disc bulging. Validating the patient-specific geometry results in 4% and 7% deviation in measuring height loss. Simulations show excellent agreement with in-house experimental results, with less than 1% error regarding height reduction. Finally, the comparison to similar, published, earlier implementation in commercial software unveils excellent agreement of less than 1% error for the water content during circadian simulations. Simulation times are reported at 4 min per circadian cycle in the supercomputer Marenostrum V. Conclusions:This work presents a clear and validated formulation for simulating porohyperelastic materials based on assumptions that comply with the non-linear elasticity theory. The implementation in Alya enables intervertebral disc research to benefit from high-performance computing systems.

MMD-Net: Image domain multi-material decomposition network for dual-energy CT imaging.

Multi-material decomposition is an interesting topic in dual-energy CT (DECT) imaging; however, the accuracy and performance may be limited using the conventional algorithms. In this work, a novel multi-material decomposition network (MMD-Net) is proposed to improve the multi-material decomposition performance of DECTimaging. To achieve dual-energy multi-material decomposition, a deep neural network, named as MMD-Net, is proposed in this work. In MMD-Net, two specific convolutional neural network modules, Net-I and Net-II, are developed. Specifically, Net-I is used to distinguish the material triangles, while Net-II predicts the effective attenuation coefficients corresponding to the vertices of the material triangles. Subsequently, the material-specific density maps are calculated analytically through matrix inversion. The new method is validated using in-house benchtop DECT imaging experiments with a solution phantom and a pig leg specimen, as well as commercial medical DECT imaging experiments with a human patient. The decomposition accuracy, edge spreading function, and noise power spectrum are quantitativelyevaluated. Compared to the conventional multiple material decomposition (MMD) algorithm, the proposed MMD-Net method is more effective at suppressing image noise. Additionally, MMD-Net outperforms the iterative MMD approach in maintaining decomposition accuracy, image sharpness, and high-frequency content. Consequently, MMD-Net is capable of generating high-quality material decompositionimages. A high performance multi-material decomposition network is developed for dual-energy CTimaging.

Thermal performance analysis of PCM-integrated structures using the resistance-capacitance model: Experiments and numerics

While holding significant potential to reduce cooling energy requirements in buildings, the incorporation of phase-change materials in building envelopes requires information regarding their diurnal and seasonal behaviour through high-fidelity simulations. However, utilizing short-term simulations and experimentation using state-of-the-art models for such a complex configuration does not accurately represent the true heat transfer dynamics of the building. To address this lacuna, we present a physics-based, low computational cost, experimentally and numerically validated resistance–capacitance (RC) model specifically tailored for PCM-encapsulated structures, designed for long-term simulations. The validation of this model is conducted through in-house experiments. Additionally, we support the credibility of our RC model by subjecting it to validation through 3D numerical simulations, emphasizing its precision and reliability. Then, we use the validated model to optimize the thermal properties of concrete roofs in hot and dry climates, taking a specific instance of a city in Western India for various geometric configurations as an illustrative example. We find that a PCM with a phase change temperature between 37 and 42 °C can reduce the peak ceiling temperature by up to 10 °C and the peak energy ingress by a factor of 2 or more, in a typical roof element subjected to the prevailing climatic conditions during peak summer. This shows the time constant of the modified roof is effective in delaying and damping the imposed solar insolation. We make specific recommendations on the selection and geometry optimization of PCM-incorporated roof elements.

Evaporating capillary bridges of pure and binary liquids

We present a numerical and experimental study on the evaporation of microliter capillary bridges of both pure and binary liquids. Specifically, we focused on capillary bridges of a binary liquid composed of water and isopropanol confined between poly-dimethylsiloxane coated surfaces. We developed a finite-element method-based numerical model to solve Laplace equations for vapor diffusion of the two species present in the capillary bridge, considering quasi-steady and diffusion-limited evaporation. We applied a modified version of Raoult's law, incorporating activity coefficients for binary liquids. The Galerkin finite element method was employed in axisymmetric cylindrical coordinates. The numerical model was validated against in-house experiments of side visualization on an evaporating capillary bridge. We quantified the effect of confinement from the plates on slowing down the diffusion of liquid vapor. The volume evolution of the binary liquid capillary bridge was found to be nonlinear, strongly influenced by the initial concentration of isopropanol in the capillary bridge. This nonlinearity is attributed to the faster diffusion of isopropanol vapor compared to water vapor. We examined the effects of height, substrate radius, contact angle, and composition on the evaporation characteristics. We proposed a computationally efficient reduced-order model for determining evaporation kinetics, which yields predictions very close to those of the numerical model.

Towards the prediction of drug solubility in binary solvent mixtures at various temperatures using machine learning

Drug solubility is an important parameter in the drug development process, yet it is often tedious and challenging to measure, especially for expensive drugs or those available in small quantities. To alleviate these challenges, machine learning (ML) has been applied to predict drug solubility as an alternative approach. However, the majority of existing ML research has focused on the predictions of aqueous solubility and/or solubility at specific temperatures, which restricts the model applicability in pharmaceutical development. To bridge this gap, we compiled a dataset of 27,000 solubility datapoints, including solubility of small molecules measured in a range of binary solvent mixtures under various temperatures. Next, a panel of ML models were trained on this dataset with their hyperparameters tuned using Bayesian optimization. The resulting top-performing models, both gradient boosted decision trees (light gradient boosting machine and extreme gradient boosting), achieved mean absolute errors (MAE) of 0.33 for LogS (S in g/100 g) on the holdout set. These models were further validated through a prospective study, wherein the solubility of four drug molecules were predicted by the models and then validated with in-house solubility experiments. This prospective study demonstrated that the models accurately predicted the solubility of solutes in specific binary solvent mixtures under different temperatures, especially for drugs whose features closely align within the solutes in the dataset (MAE < 0.5 for LogS). To support future research and facilitate advancements in the field, we have made the dataset and code openly available.Scientific contributionOur research advances the state-of-the-art in predicting solubility for small molecules by leveraging ML and a uniquely comprehensive dataset. Unlike existing ML studies that predominantly focus on solubility in aqueous solvents at fixed temperatures, our work enables prediction of drug solubility in a variety of binary solvent mixtures over a broad temperature range, providing practical insights on the modeling of solubility for realistic pharmaceutical applications. These advancements along with the open access dataset and code support significant steps in the drug development process including new molecule discovery, drug analysis and formulation.Graphical

Individual variation in the functional lateralization of human ventral temporal cortex: Local competition and long-range coupling.

The ventral temporal cortex (VTC) of the human cerebrum is critically engaged in computations related to high-level vision. One intriguing aspect of this region is its asymmetric organization and functional lateralization. Notably, in the VTC, neural responses to words are stronger in the left hemisphere, whereas neural responses to faces are stronger in the right hemisphere. Converging evidence has suggested that left-lateralized word responses emerge to couple efficiently with left-lateralized frontotemporal language regions, but evidence is more mixed regarding the sources of the right-lateralization for face perception. Here, we use individual differences as a tool to adjudicate between three theories of VTC organization arising from: 1) local competition between words and faces, 2) local competition between faces and other categories, 3) long-range coupling with VTC and frontotemporal areas subject to their own local competition. First, in an in-house functional MRI experiment, we demonstrated that individual differences in laterality are both substantial and reliable within a right-handed population of young adults. We found no (anti-)correlation in the laterality of word and face selectivity relative to object responses, and a positive correlation when using selectivity relative to a fixation baseline, challenging ideas of local competition between words and faces. We next examined broader local competition with faces using the large-scale Human Connectome Project (HCP) dataset. Face and tool laterality were significantly anti-correlated, while face and body laterality were positively correlated, consistent with the idea that generic local representational competition and cooperation may shape face lateralization. Last, we assessed the role of long-range coupling in the development of VTC laterality. Within our in-house experiment, substantial correlation was evident between VTC text laterality and several other nodes of a distributed text-processing circuit. In the HCP data, VTC face laterality was both negatively correlated with frontotemporal language laterality, and positively correlated with social perception laterality in the same areas, consistent with a long-range coupling effect between face and social processing representations, driven by local competition between language and social processing. We conclude that both local and long-range interactions shape the heterogeneous hemispheric specializations in high-level visual cortex.

Machine learning-based prediction of DNA G-quadruplex folding topology with G4ShapePredictor

Deoxyribonucleic acid (DNA) is able to form non-canonical four-stranded helical structures with diverse folding patterns known as G-quadruplexes (G4s). G4 topologies are classified based on their relative strand orientation following the 5’ to 3’ phosphate backbone polarity. Broadly, G4 topologies are either parallel (4+0), antiparallel (2+2), or hybrid (3+1). G4s play crucial roles in biological processes such as DNA repair, DNA replication, transcription and have thus emerged as biological targets in drug design. While computational models have been developed to predict G4 formation, there is currently no existing model capable of predicting G4 folding topology based on its nucleic acid sequence. Therefore, we introduce G4ShapePredictor (G4SP), an application featuring a collection of multi-classification machine learning models that are trained on a custom G4 dataset combining entries from existing literature and in-house circular dichroism experiments. G4ShapePredictor is designed to accurately predict G4 folding topologies in potassium (K+\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {K}^+$$\\end{document}) buffer based on its primary sequence and is able to incorporate a threshold optimization strategy allowing users to maximise precision. Furthermore, we have identified three topological sequence motifs that suggest specific G4 folding topologies of (4+0), (2+2) or (3+1) when utilising the decision-making mechanisms of G4ShapePredictor.

Synthesis of Polyacrylamide Nanomicrospheres Modified with a Reactive Carbamate Surfactant for Efficient Profile Control and Blocking

Urethane surfactants (REQ) were synthesized with octadecanol ethoxylate (AEO) and isocyanate methacrylate (IEM). Subsequently, reactive-carbamate-surfactant-modified nanomicrospheres (PER) were prepared via two-phase aqueous dispersion polymerization using acrylamide (AM), 2-acrylamido-2-methylpropanesulfonic acid (AMPS) and ethylene glycol dimethacrylate (EGDMA). The microstructures and properties of the nanomicrospheres were characterized and examined via infrared spectroscopy, nano-laser particle size analysis, scanning electron microscopy, and in-house simulated exfoliation experiments. The results showed that the synthesized PER nanomicrospheres had a uniform particle size distribution, with an average size of 336 nm. The thermal decomposition temperature of the nanomicrospheres was 278 °C, and the nanomicrospheres had good thermal stability. At the same time, the nanomicrospheres maintained good swelling properties at mineralization &lt; 10,000 mg/L and temperature &lt; 90 °C. Under the condition of certain permeability, the blocking rate and drag coefficient gradually increased with increasing polymer microsphere concentration. Furthermore, at certain polymer microsphere concentrations, the blocking rate and drag coefficient gradually decreased with increasing core permeability. The experimental results indicate that nanomicrospheres used in the artificial core simulation drive have a better ability to drive oil recovery. Compared with AM microspheres (without REQ modification), nanomicrospheres exert a more considerable effect on recovery improvement. Compared with the water drive stage, the final recovery rate after the drive increases by 23.53%. This improvement is attributed to the unique structural design of the nanorods, which can form a thin film at the oil–water–rock interface and promote oil emulsification and stripping. In conclusion, PER nanomicrospheres can effectively control the fluid dynamics within the reservoir, reduce the loss of oil and gas resources, and improve the economic benefits of oil and gas fields, giving them a good application prospect.

Determination of the average crystallite size and the crystallite size distribution: the envelope function approach EnvACS.

A procedure is presented to exactly obtain the apparent average crystallite size (ACS) of powder samples using standard in-house powder diffraction experiments without any restriction originating from the Scherrer equation. Additionally, the crystallite size distribution within the sample can be evaluated. To achieve this, powder diffractograms are background corrected and long-range radial distribution functions G(r) up to 300 nm are calculated from the diffraction data. The envelope function f env of G(r) is approximated by a procedure determining the absolute maxima of G(r) in a certain interval (r range). Fitting of an ACS distribution envelope function to this approximation gives the ACS and its distribution. The method is tested on diffractograms of LaB6 standard reference materials measured with different wavelengths to demonstrate the validity of the approach and to clarify the influence of the wavelength used. The latter results in a general description of the maximum observable average crystallite size, which depends on the instrument and wavelength used. The crystallite site distribution is compared with particle size distributions based on transmission electron microscopy investigations, providing an approximation of the average number of crystallites per particle.

Deflection and stress characteristics of single lap joint (adhesively bonded) theoretical analysis and experimental verification

The adhesively bonded single-lap joint strength is computed numerically and verified with the experiment. An ABAQUS model is prepared to analyze by adding the primary information, i.e., geometry, material properties, element, and solution type, including the boundary conditions. The model accuracy has been verified through two-step comparisons with published numerical deflection data and in-house experiments. The validated model is used to compute the energy-absorbing capacity better to understand lap joint strength. Further, the statistical analysis (variance-based sensitivity analysis) is conducted to measure the model output variability with the model input parameter. Additionally, the influences of geometry and property-dependent design parameters (layup schemes, loading position, adherend thickness ratio (L/t), and adhesive thickness ratio: (a/h), including the overlapping length (25, 30, 35, and 40 mm) are examined through several examples. The conclusions on the overlap length in bonded joints are that an increase in intact/lap length improves the joint stiffness and decreases the deflection. Similarly, a few insights on layer sequence (angle-ply) and shear stress thickness ratio are discussed in detail.

Integrative Analysis Identified rs907091 at the miRNA Binding Region of the IKZF3 Gene as a Potential Functional Variant for Rheumatoid Arthritis

Objective: Previous studies have identified a large number of associations between genetic variants and rheumatoid arthritis (RA), but the functional mechanisms underlying the associations are largely unknown. Methods: Based on publicly available datasets and results, we explored the functional mechanisms underlying the associations between regulatory polymorphisms in miRNA target sites (poly-miRTSs) and RA by combining integrative analyses (expression quantitative trait locus analysis, eQTL, and gene enrichment analysis) with in-house molecular experiments (allelic expression imbalance experiment, AEI, and dual-luciferase reporter gene assay). Results: By integrating the results from the MirSNP and eQTL databases, a total of 114 pairs between poly-miRTSs and target genes were identified, which correspond to 70 unique poly-miRTSs and 28 genes. Most of the 28 genes were located in the HLA region, and they tended to be enriched in multiple immune-related GO terms or pathways, e.g., “antigen processing and presentation” and “immune response”. Among these poly-miRTSs, rs907091 in the 3’UTR of IKZF3 (non-HLA region) was highlighted. Further AEI experiments showed that the C allele had a higher expression of ~18% than the T allele, suggesting an obvious allelic expression imbalance. Dual-luciferase reporter gene assays showed that miR-326 and miR-330-5p could regulate IKZF3 expression (P&lt;0.05) mediated by rs907091 in the 3’UTR binding site. Conclusion: Our study explored some functional mechanisms underlying the associations between poly-miRTSs and target genes and highlighted rs907091 at the miRNA binding region of the IKZF3 gene as a potential functional variant for rheumatoid arthritis.

Drive-by bridge damage detection by vehicle-bridge interaction neural operator

Drive-by bridge damage detection using vibrations of vehicles for damage detection of bridges has been studied since it needs no instrumentation on bridges. However, it is still a challenge to be able to extract features that are sensitive to bridge damage and relevant to the responses of the bridge. To address the challenges of drive-by bridge damage detection, machine learning-based approaches have also been actively investigated in recent research. With supervised learning methods, it is almost impossible to obtain training data on the damage state of a bridge. On the other hand, unsupervised learning approaches have been considered, but they only provide changes in relative features rather than physical properties that are useful for estimating the structural performance. To address the above issues, this study aims to propose and investigate the feasibility of drive-by bridge damage detection using a vehicle-bridge interaction neural operator (VINO). In the proposed method, numerical simulations of vehicle-bridge interactions considering the damage field of the bridge are first carried out to generate physically informed training data. The VINO is then constructed using the physically informed training data. However, VINOs are still insufficient to model real bridges. Therefore, in order to improve the practicality of the physically-informed VINO, data-informed fine-tuning is carried out using available data from real bridges. It is noted that no damage data is required in the fine-tuning stage. The feasibility of the drive-by bridge damage detection using VINO is discussed using an in-house experiment on a model bridge under a moving vehicle.

A generalized prediction model for complete performance investigation of pump operated as turbine in microhydro applications using novel approach for improving sustainability

Centrifugal pumps operated in reverse mode (popularly called pump as turbines (PATs)) are well-established as green energy converters, mainly used in microhydro applications due to their lower capital cost. However, the appropriate selection of the pump for reverse-mode application is a critical issue due to the unavailability of its characteristic curve in turbine mode. Therefore, various researchers have proposed models to predict PAT parameters from pump characteristics based on diverse approaches. Still, researchers are striving to develop a prediction model, which can predict the head and flow rates for the PAT with less than ±20 % deviation for both. This work proposed a novel approach to predict the characteristics of PAT with the integration of ensemble regression modelling for Best Efficiency Point (BEP) and from it, by polynomial equations complete characteristics curve. The data from in-house experiments and open literature are used to develop the proposed model excluding the data of four pumps, which are typically used for validation of it. The predicted parameters of PAT from pump characteristics show good agreement for four selected PATs with <10 % maximum deviation. Compared to the literature prediction models, it shows a lower deviation for the same four PATs. A case study based on the field data is carried out to justify the applicability of the proposed model. By using this model selected pump when operated in PAT will produce higher power and generate more revenue compared to literature models, resulting in the economical utilization of resources and improving sustainability. Though the savings are lower, it will be significant for a large number of PAT applications.

Discovering two general characteristic times of transient responses in solid oxide cells

A comprehensive understanding of the transient characteristics in solid oxide cells (SOCs) is crucial for advancing SOC technology in renewable energy storage and conversion. However, general formulas describing the relationship between SOC transients and multiple parameters remain elusive. Through comprehensive numerical analysis, we find that the thermal and gaseous response times of SOCs upon rapid electrical variations are on the order of two characteristic times (τh and τm), respectively. The gaseous response time is approximately 1τm, and the thermal response time aligns with roughly 2τh. These characteristic times represent the overall heat and mass transfer rates within the cell, and their mathematical relationships with various SOC design and operating parameters are revealed. Validation of τh and τm is achieved through comparison with an in-house experiment and existing literature data, achieving the same order of magnitude for a wide range of electrochemical cells, showcasing their potential use for characterizing transient behaviors in a wide range of electrochemical cells. Moreover, two examples are presented to demonstrate how these characteristic times can streamline SOC design and control without the need for complex numerical simulations, thus offering valuable insights and tools for enhancing the efficiency and durability of electrochemical cells.

On the investigation of the aerodynamics performance and associated flow physics of the optimized tubercle airfoil

In the present study, the evolutionary multi-objective optimization is employed to design the optimum amplitude and wavelength of sinusoidal leading-edge tubercles for the NACA0012 (National Advisory Committee for Aeronautics) airfoil to improve its aerodynamic performance at Reynolds number of 5.0 × 104 than that of the NACA0012 smooth leading-edge or baseline airfoil. Here, the optimum tubercle is found to have an amplitude of 11.71% and a wavelength of 25% of the baseline airfoil chord, respectively. Through a combination of in-house water tunnel experiments and numerical simulations, it is additionally established that the optimized tubercle airfoil exhibits superior lift and reduced drag characteristics compared to the baseline airfoil, particularly in the post-stall high angle of attack regime. Furthermore, it is noticed that the optimized tubercle design enhances the gap between large separation regions or stall cells along the tubercle airfoil span during the post-stall regime. Consequently, a more pronounced attached flow regime is developed between the consecutive stall cells, contributing to the tubercle airfoil's improved aerodynamic characteristics compared to the baseline airfoil. Our investigations also revealed that the formation and arrangement of the stall cells on the tubercle airfoil span are associated with a biased wake mechanism similar to the one observed in the wake of side-by-side arranged circular cylinders.

Discovery of potential anti- Staphylococcus aureus natural products and their mechanistic studies using machine learning and molecular dynamic simulations

The structure-activity analysis (SAR) and machine learning were used to investigate potential anti-S. aureus agents in a faster method. In this study, 24 oxygenated benzene ring components with S. aureus inhibition capacity were confirmed by literature exploring and in-house experiments, and the SAR analysis suggested that the hydroxyl group position may affect the anti-S. aureus activity. The 2D-MLR-QSAR model with 9 descriptors was further evaluated as the best model among the 21 models. After that, hesperetic acid and 2-HTPA were further explored and evaluated as the potential anti-S. aureus agents screening in the natural product clustering library through the best QSAR model calculation. The antibacterial capacities of hesperetic acid and 2-HTPA had been investigated and proved the similar predictive pMIC value resulting from the QSAR model. Besides, the two novel components were able to inhibit the growth of S. aureus by disrupting the cell membrane through the molecular dynamics simulation (MD), which further evidenced by scanning electron microscopy (SEM) test and PI dye results. Overall, these results are highly suggested that QSAR can be used to predict the antibacterial agents targeting S. aureus, which provides a new paradigm to research the molecular structure-antibacterial capacity relationship.

Permanent Magnet Electrodynamic Suspension System Integrated With a Car: Design, Implementation, and Test

In this paper, a permanent magnet electrodynamic suspension (PMEDS) system integrated with a car has been developed by our group, along with a 100-m-long test track made of copper. The entire vehicle weighs 2.8 ton and is freely suspended in air over 35 mm without a lateral constraint on guideway. The car uses its own wheels and engine to provide a take-off speed for the PMEDS system, eliminating the excessive costs of the motor, with a maximum speed of 70 km/h. Firstly, a new topology structure equipped with the damping magnets is presented. Afterward, the onboard magnets and guideway are designed via the simulation method verified by the high-speed rotary experiment platform with a diameter of 2500 mm, clarifying the mapping between the electromagnetic forces and structure parameters. The suspension frame and gap adjustment mechanism are developed, and a complete test system is built to monitor the levitation gap, accelerations and deflection angle. Lastly, the integrated PMEDS system is implemented and tested. In-house experiment and line test results not only reveal that the PMEDS has a significant weak guidance capability, but also confirm the effectiveness of the proposed damping magnets. Through the damping magnets, the maximum lateral deflection is limited to less than 11°. The tested levitation-drag-ratio is in general agreement with the simulation results. The great suspension capacity of the PMEDS system can be visually demonstrated. This paper conducts the free suspension test of the PMEDS system for the first time in China, which can open up prospects for further engineering applications.

Development of single point prediction model using artificial neural network and experimental validation for pump as turbine applications

This paper proposed a new approach based on artificial neural networks (ANNs) to select the most appropriate centrifugal pumps for reverse operation i.e. pump as turbines (PAT), by predicting parameters at Best Efficiency Point from pump mode data. Both, exhaustive experimental data of 49 pumps (specific speeds 9−104) collected from open literature and by in-house experimentation data, considered input data-set as pump and target data-set as PAT are used for training the ANN models with two different training functions: Levenberg–Marquardt and Bayesian regularisation. The proposed ANN prediction model shows a deviation lower than 10% compared to the respective experimental value. Additionally, the trained ANN model tested with four pumps (not included in training data sets of proposed models) shows maximum absolute deviation in head number, flow number, and efficiency within the 10%, 7%, and 6% range, respectively, compared to the experimental values. Furthermore, the efficiency of reverse mode evaluated for variation in rotational speed shows a maximum of 3.3% (within 5%) absolute deviation compared to respective experimental results. Overall, the ANNs based prediction model of PAT parameters is recommended compared to conventional models available in the literature as it gives superior results (less than 10% deviation) for practical application.

Prediction of DNA i-motifs via machine learning.

i-Motifs (iMs), are secondary structures formed in cytosine-rich DNA sequences and are involved in multiple functions in the genome. Although putative iM forming sequences are widely distributed in the human genome, the folding status and strength of putative iMs vary dramatically. Much previous research on iM has focused on assessing the iM folding properties using biophysical experiments. However, there are no dedicated computational tools for predicting the folding status and strength of iM structures. Here, we introduce a machine learning pipeline, iM-Seeker, to predict both folding status and structural stability of DNA iMs. The programme iM-Seeker incorporates a Balanced Random Forest classifier trained on genome-wide iMab antibody-based CUT&Tag sequencing data to predict the folding status and an Extreme Gradient Boosting regressor to estimate the folding strength according to both literature biophysical data and our in-house biophysical experiments. iM-Seeker predicts DNA iM folding status with a classification accuracy of 81% and estimates the folding strength with coefficient of determination (R2) of 0.642 on the test set. Model interpretation confirms that the nucleotide composition of the C-rich sequence significantly affects iM stability, with a positive correlation with sequences containing cytosine and thymine and a negative correlation with guanine and adenine.