Computational Work Research Articles

Computational demonstrations of density wave of Cooper pairs and paired-electron liquid in the quarter-filled band-A brief review.

There has been strong interest recently in the so-called Cooper pair density wave, subsequent to the proposition that such a state occurs in the hole-doped cuprate superconductors. As of now, there is no convincing demonstration of such a state in the cuprate theoretical literature. We present here a brief but complete review of our theoretical and computational work on the paired-electron crystal (PEC), which has also been experimentally seen in the insulating phase proximate to superconductivity (SC) in organic charge-transfer solid (CTS) superconductors. Within our theory, SC in the CTS does indeed evolve from the PEC. A crucial requirement for the finding of the PEC is that the proper carrier density of one charge carrier per two sites is taken into consideration at the outset. Following the discussion of CTS superconductors, we briefly discuss how the theory can be extended to understand the phase diagram of the cuprate superconductors that has remained mysterious after nearly four decades of the discovery of SC in this family.

How computation explains

Cognitive science gives computational explanations of the brain. Philosophers have treated these explanations as if they simply claim that the brain computes. We have therefore assumed that to understand how and why computational explanation works, we must understand what it is to compute. In contrast, I argue that we can understand computational explanation by describing the resources it brings to bear on the study of the brain. Specifically, I argue that it introduces concepts and formalisms that complement cognitive science's modeling goals. This allows us to understand computational explanation without having to debate what it is to compute.

Computer Self-Efficacy, Work Stress, and Burnout in Gen Z in Mentoring Communities

Computer Self-Efficacy, Work Stress, and Burnout in Gen Z in Mentoring Communities

Position Falsification Detection Approach Using Travel Distance-Based Feature

Abstract This paper addresses the vulnerability of vehicular ad hoc networks (VANETs) to malicious attacks, specifically focusing on position falsification attacks. Detecting misbehaving vehicles in VANETs is challenging due to the dynamic nature of the network topology and vehicle mobility. The paper considers five types (constant attack, constant offset attack, random attack, random offset attack, and eventually stop attack) of position falsification attacks with varying traffic and attack densities, considered the most severe attacks in VANETs. To improve the detection of these attacks, a novel travel distance feature and an enhanced two-stage detection approach are proposed for classifying position falsification attacks in VANETs. The approach involves deploying the misbehavior detection system within roadside units (RSUs) by offloading computational work from vehicles (onboard units, or OBUs) to RSUs. The performance of the proposed approach was evaluated against different classifiers, including a wide range of paradigms (KNN, Decision Tree, and Random Forest), using the VeReMi dataset. Experimental results indicate that the proposed method based on Random Forest achieved an accuracy of 99.9% and an F1-Score of 99.9%, which are better not only than those achieved by KNN and Decision Tree but also than the most recent approaches in the literature survey.

Formation of left-handed helices by C2'-fluorinated nucleic acids under physiological salt conditions.

Recent findings in cell biology have rekindled interest in Z-DNA, the left-handed helical form of DNA. We report here that two minimally modified nucleosides, 2'F-araC and 2'F-riboG, induce the formation of the Z-form under low ionic strength. We show that oligomers entirely made of these two nucleosides exclusively produce left-handed duplexes that bind to the Zα domain of ADAR1. The effect of the two nucleotides is so dramatic that Z-form duplexes are the only species observed in 10 mM sodium phosphate buffer and neutral pH, and no B-form is observed at any temperature. Hence, in contrast to other studies reporting formation of Z/B-form equilibria by a preference for purine glycosidic angles in syn, our NMR and computational work revealed that sequential 2'F…H2N and intramolecular 3'H…N3' interactions stabilize the left-handed helix. The equilibriumbetween B- and Z- forms is slow in the 19F NMR time scale (≥ms), and each conformation exhibited unprecedented chemical shift differences in the 19F signals. This observation led to a reliable estimation of the relative population of B and Z species and enabled us to monitor B-Z transitions under different conditions. The unique features of 2'F-modified DNA should thus be a valuable addition to existing techniques for specific detection of new Z-binding proteins and ligands.

An ab-initio deep neural network potential for accurate large-scale simulations of the muscovite mica-water interface

Mineral-water interfaces play a critical role in several geochemical processes relevant to the habitability of our planet. These processes are often underpinned by the hydration of the ions covering the mineral surface and the resultant ion exchange with the aqueous environment. Muscovite mica, whose surface is nominally covered by K + ions, has long been considered an ideal system for probing some of these processes. However, despite several decades of both experimental and computational work, there still remains a lack of consensus on ion hydration and exchange at the mica-water interface. To obtain a detailed microscopic picture of these processes, we have developed an ab-initio based deep neural network potential (DP) describing the potential energy surface (PES) of the mica(001)-water interface. Our training dataset consisted of bulk mica, bulk liquid water, the mica(001) surface in vacuum and the explicit mica(001)-water interface, both with different surface K + arrangements. The trained model is able to reproduce the energies and forces derived from Density functional theory (DFT) based on the SCAN exchange correlation functional with good accuracy. For the surface in vacuum, K + ions located in the ditrigonal cavities composed of 2 Al atoms are predicted to be energetically more favourable, while the specific surface arrangement of K + ions does not affect the water density profile at the mica-water interface. The developed model sets the stage for estimating the energetics of the ion-exchange process at affordable computational cost without compromising the accuracy of first-principles methods.

DETERMINATION OF THE OPTIMUM DESIGN OF THE MEANS TO ENSURE FUEL CONTINUITY USING THE METHOD OF NUMERICAL SIMULATION

Residues of liquid propellant components in the power system of the propulsion system at the end of the operation of the launch vehicle stage significantly affect its energy characteristics. The fuel continuity features found in modern launch vehicle tanks ensure a continuous supply of propellant components from the tank to the propulsion system without disrupting flow and mini- mizing residues. In the fuel tank of launch vehicles, the presence of a tunnel pipeline complicates, and in certain cases excludes the possibility of taking fuel from the pole of the tank. Solving the problem of fuel intake, in this case, allows the use of means to ensure the continuity of fuel of the side type. However, the use of such a device, due to the displacement relative to the pole of the bottom of the tank by a certain angle of the sampling point, can lead to an uneven level of breakthrough and immersion of the boost gas at the inlet to the flow line and, as a result, a significant increase in the remainder of the fuel components. The authors searched for and substantiated the optimal design of the means for ensuring fuel continuity on the example of the fuel tank of the first stage of the Cyclone-4 launch vehicle, which is equipped with a side-type fuel continuity means in the form of a profiled plate (plate). The designs of siphon and annular fuel continuity means are considered. An analytical calculation was carried out using empirical dependences, a physical experiment, and a numerical simulation of their main parameters. Based on the work performed, a comparative analysis of the change in the energy characteristics of the launch vehicle depending on the chosen design was carried out. The efficiency of the operation of the considered means of ensuring the continuity of the fuel is determined in terms of the mass parameter of the remainder of the propellant components under static conditions. The result of the experimental and computational-analytical work was to obtain the most optimal variant of the fuel continuity means, which turned out to be a siphon according to several parameters. The introduction of a siphon means of ensuring continuity, as more optimal, into the design of the fuel tank of the Cyclone-4 launch vehicle will improve its energy characteristics by increasing the mass of the output payload by 5.4 kg. Also, using the numerical modeling methods (CFD methods) for design work on devel- opment, optimization, and improvement, instead of empirical and semi-empirical dependencies, allows you to: obtain more accurate results; reduce the number of experimental designs; reduce the required number of tests; reduce the duration of exami- nations; reduce the total duration of computational and design and experimental work; reduce the overall material and technical costs and, as a result, reduce the final cost of the development product, which will increase its competitiveness.

Three-dimensional characterization of particle size, shape, and internal porosity for Apollo 11 and Apollo 14 lunar regolith and JSC-1A lunar regolith soil simulant

Samples of soils collected by the Apollo 11 mission (10084,2036) and the Apollo 14 mission (14163,940) were obtained from the NASA Curation and Analysis Planning Team for Extraterrestrial Materials (CAPTEM) program. The particle size, shape, and internal porosity were characterized in three dimensions (3D) using a combination of X-ray computed tomography (XCT) and mathematical analysis, with various size and shape parameters measured and calculated for each particle. High-resolution scanning electron microscopy (SEM) was used to image the particles that were too small for the two XCT instruments used. Similar characterization was carried out on samples of JSC-1A lunar soil simulant, updating a previous analysis. Approximately 14,000 lunar regolith particles and 128,000 JSC1-A particles, covering a wide range of size and shape, were characterized for this paper and the results stored in a publicly accessible database. This large number of particles enabled, for the first time, statistically valid particle shape distributions to be generated. The 3D shape distributions of the two regoliths and JSC-1A were quantitatively compared and it was found that the way particle shape and porosity depends on particle size was different between regolith and simulant. The measured size distribution of particles in the lunar soils was applied to estimate the relative contributions of different sizes to the ensemble average particle single scattering albedo and phase function. By linking our particle counts to published sieve weight fractions for the lunar samples, we find that ∼80% of the total cross-section area is contributed by particles <20 μm diameter and ∼ 50% by particles <8 μm diameter. The orientation-averaged two-dimensional projected areas of the actual regolith particles were computed so that this estimate was also based on real particle shapes. Such small sizes dominating the total cross-section area suggest that calculations of the elements of the scattering matrix for individual particles may be possible with modest computing capabilities leading to the development of improved models for the quantitative interpretation of remote sensing spectrophotometry and polarimetry. This 3D characterization and database will enable other computational work to be done with real lunar regolith particle shapes, including discrete element method mechanical modeling, packing simulations, further light scattering calculations, dust contamination modeling, and modeling of lunar rover interactions with collected and packed regolith particles.

Local fractional Laplace transform iterative method for solving Korteweg–de Vries equation with the local fractional derivative

The local fractional Korteweg–de Vries equations were considered in this paper. Series solutions for the linear and nonlinear case were examined using the local fractional Laplace transform iterative method. This proposed method is the coupling of the local fractional Laplace transform with the new iterative method. The existence and uniqueness of the solutions is considered using the principle of mathematical induction. Furthermore, illustrative examples were considered and the graphs are shown. The results obtained in this study reveal the benefit of this method and provide valuable insight into the behavior of complex phenomena in a precise and efficient manner with less computational work and implementation ease.

MechanoProDB: a web-based database for exploring the mechanical properties of proteins.

The mechanical stability of proteins is crucial for biological processes. To understand the mechanical functions of proteins, it is important to know the protein structure and mechanical properties. Protein mechanics is usually investigated through force spectroscopy experiments and simulations that probe the forces required to unfold the protein of interest. While there is a wealth of data in the literature on force spectroscopy experiments and steered molecular dynamics simulations of forced protein unfolding, this information is spread and difficult to access by non-experts. Here, we introduce MechanoProDB, a novel web-based database resource for collecting and mining data obtained from experimental and computational works. MechanoProDB provides a curated repository for a wide range of proteins, including muscle proteins, adhesion molecules and membrane proteins. The database incorporates relevant parameters that provide insights into the mechanical stability of proteins and their conformational stability such as the unfolding forces, energy landscape parameters and contour lengths of unfolding steps. Additionally, it provides intuitive annotations of the unfolding pathways of each protein, allowing users to explore the individual steps during mechanical unfolding. The user-friendly interface of MechanoProDB allows researchers to efficiently navigate, search and download data pertaining to specific protein folds or experimental conditions. Users can visualize protein structures using interactive tools integrated within the database, such as Mol*, and plot available data through integrated plotting tools. To ensure data quality and reliability, we have carefully manually verified and curated the data currently available on MechanoProDB. Furthermore, the database also features an interface that enables users to contribute new data and annotations, promoting community-driven comprehensiveness. The freely available MechanoProDB aims to streamline and accelerate research in the field of mechanobiology and biophysics by offering a unique platform for data sharing and analysis. MechanoProDB is freely available at https://mechanoprodb.ibdm.univ-amu.fr.

Non-local second-order boundary value problems with exponential nonlinearity

In this article, the Numerical treatment (Impilict-Explicite) was applied, for solving problems- related to the nonlocal second-order boundary value problem with exponential nonlinearity. The finite difference method of treatment of the BVP is used as implicit technique within the smallest available subdomain, . A reduced system of nonlinear algebraic equations corresponding to standard BVP is generated within the smallest domain, . A reduction of the computational work is introduced through reducing the size of the nonlinear algebraic system. A marching explicit approach is reemployed to generate the solution in , the complement of the interval with respect to the overall domain. Simple numerical experiments confirming the applicability of the treatment are introduced.

Complete Solutions for the Approximate Synthesis of Spherical Four-Bar Function Generators

Abstract Kinematic synthesis to meet an approximate motion specification naturally forms a constrained optimization problem. Instead of using local methods, we conduct global design searches by direct computation of all critical points. The idea is not new, but performed at scale is only possible through modern polynomial homotopy continuation solvers. Such a complete computation finds all minima, including the global, which allows for a full exploration of the design space, whereas local solvers can only find the minimum nearest to an initial guess. We form equality-constrained objective functions that pertain to the synthesis of spherical four-bar linkages for approximate function generation. We consider the general case where all mechanism dimensions may vary and a more specific case that enables the placement of ground pivots. The former optimization problem is shown to permit 268 sets of critical points, and the latter permits 61 sets. Critical points are classified as saddles or minima through a post-process eigenanalysis of the projected Hessian. The discretization points are contained within the coefficients of the stationarity polynomials, so the algebraic structure of the synthesis equations remains invariant to the number of points. The results of our computational work were used to design a mechanism that coordinates the folding wings. We also use this method to parameterize mechanism dimensions for a family of hyperbolic curves.

Traditional versus dynamic sitting: Lumbar spine kinematics and pain during computer work and activity guided tasks

Dynamic sitting may mitigate low back pain during prolonged seated work. The current study compared pelvis and lumbar spine kinematics, pain, and work productivity, in traditional and dynamic sitting. Sixteen participants completed three 20-min blocks of computer work and activity guided tasks in a traditional office chair or backless and multiaxial rotating seat pan while kinematics were measured from accelerometers on the low back. Pain ratings were recorded on a visual analogue scale every 10 min. Similar pelvis and lumbar kinematics emerged when performing computer work in traditional and dynamic sitting. Pelvis and lumbar sagittal and frontal plane shifts and fidgets were largest for dynamic sitting in the activity guided tasks. Buttocks pain was higher in dynamic sitting, but low back pain and work productivity were unaffected. Dynamic sitting increased spine movement during activity guided tasks, without negatively impacting lumbar kinematics, low back pain, or productivity during seated computer work.

Whale Optimization for Cloud-Edge-Offloading Decision-Making for Smart Grid Services.

As IoT metering devices become increasingly prevalent, the smart energy grid encounters challenges associated with the transmission of large volumes of data affecting the latency of control services and the secure delivery of energy. Offloading computational work towards the edge is a viable option; however, effectively coordinating service execution on edge nodes presents significant challenges due to the vast search space making it difficult to identify optimal decisions within a limited timeframe. In this research paper, we utilize the whale optimization algorithm to decide and select the optimal edge nodes for executing services' computational tasks. We employ a directed acyclic graph to model dependencies among computational nodes, data network links, smart grid energy assets, and energy network organization, thereby facilitating more efficient navigation within the decision space to identify the optimal solution. The offloading decision variables are represented as a binary vector, which is evaluated using a fitness function considering round-trip time and the correlation between edge-task computational resources. To effectively explore offloading strategies and prevent convergence to suboptimal solutions, we adapt the feedback mechanisms, an inertia weight coefficient, and a nonlinear convergence factor. The evaluation results are promising, demonstrating that the proposed solution can effectively consider both energy and data network constraints while enduring faster decision-making for optimization, with notable improvements in response time and a low average execution time of approximately 0.03 s per iteration. Additionally, on complex computational infrastructures modeled, our solution shows strong features in terms of diversity, fitness evolution, and execution time.

Chemical stability of anion exchange membranes for alkaline fuel cells and water electrolysers: Experiment, multi-dimensional modeling and hybrid simulations

The unstable nature of anion exchange membranes (AEMs) has hindered the progress of alkaline fuel cells and water electrolyzers. Density functional theory (DFT) can explore the correlation between the structure and properties of model compounds. However, theoretical explorations of AEMs remain immensely insufficient owing to the complexity of modeling the realistic solution system which caused by multi-dimensional parameters such as polymer chain entanglement, steric hindrance, and migration of ions. In this study, molecular dynamic simulation was utilized to obtain the multi-dimensional parameters of polyelectrolytes, which were considered when calculating the degradation energy barrier by DFT, thus leading to more accurate results and optimizing the conventional DFT calculations. Combined with the computational works, pyridinium functionalized polyolefin and polyether sulfone were synthesized to verify the feasibility of the simulation scheme. This optimized scheme fills the gap in the DFT calculations scale and is easy to extend to other types of AEMs, which help design the chemically stable AEMs and promote the development of alkaline fuel cells and water electrolysers.

Automating literature screening and curation with applications to computational neuroscience.

ModelDB (https://modeldb.science) is a discovery platform for computational neuroscience, containing over 1850 published model codes with standardized metadata. These codes were mainly supplied from unsolicited model author submissions, but this approach is inherently limited. For example, we estimate we have captured only around one-third of NEURON models, the most common type of models in ModelDB. To more completely characterize the state of computational neuroscience modeling work, we aim to identify works containing results derived from computational neuroscience approaches and their standardized associated metadata (eg, cell types, research topics). Known computational neuroscience work from ModelDB and identified neuroscience work queried from PubMed were included in our study. After pre-screening with SPECTER2 (a free document embedding method), GPT-3.5, and GPT-4 were used to identify likely computational neuroscience work and relevant metadata. SPECTER2, GPT-4, and GPT-3.5 demonstrated varied but high abilities in identification of computational neuroscience work. GPT-4 achieved 96.9% accuracy and GPT-3.5 improved from 54.2% to 85.5% through instruction-tuning and Chain of Thought. GPT-4 also showed high potential in identifying relevant metadata annotations. Accuracy in identification and extraction might further be improved by dealing with ambiguity of what are computational elements, including more information from papers (eg, Methods section), improving prompts, etc. Natural language processing and large language model techniques can be added to ModelDB to facilitate further model discovery, and will contribute to a more standardized and comprehensive framework for establishing domain-specific resources.

Microbial Biofilm Inhibition Using Magnetic Cross-Linked Polyphenol Oxidase Aggregates.

Microbial biofilm accumulation poses a serious threat to the environment, presents significant challenges to different industries, and exhibits a large impact on public health. Since there has not been a conclusive answer found despite various efforts, the potential green and economical methods are being focused on, particularly the innovative approaches that employ biochemical agents. In the present study, we propose a bio-nanotechnological method using magnetic cross-linked polyphenol oxidase aggregates (PPO m-CLEA) for inhibition of microbial biofilm including multidrug resistant bacteria. Free PPO solution showed only 55-60% biofilm inhibition, whereas m-CLEA showed 70-75% inhibition, as confirmed through microscopic techniques. The carbohydrate and protein contents in biofilm extracellular polymeric substances (EPSs) were reduced significantly. The m-CLEA demonstrated reusability up to 5 cycles with consistent efficiency in biofilm inhibition. Computational work was also done where molecular docking of PPO with microbial proteins associated with biofilm formation was conducted, resulting in favorable binding scores and inter-residual interactions. Overall, both in vitro and in silico results suggest that PPO interferes with microbial cell attachment and EPS formation, thereby preventing biofilm colonization.

Assessing the Preservation of Lumbar Lordotic Curvature in Everyday Sitting Conditions Assessed with an Inertial Measurement System.

Background/Objectives: Lumbar lordotic curvature (LLC), closely associated with low back pain (LBP) when decreased, is infrequently assessed in clinical settings due to the spatiotemporal limitations of radiographic methods. To overcome these constraints, this study used an inertial measurement system to compare the magnitude and maintenance of LLC across various sitting conditions, categorized into three aspects: verbal instructions, chair type, and desk task types. Methods: Twenty-nine healthy participants were instructed to sit for 3 min with two wireless sensors placed on the 12th thoracic vertebra and the 2nd sacral vertebra. The lumbar lordotic angle (LLA) was measured using relative angles for the mediolateral axis and comparisons were made within each sitting category. Results: The maintenance of LLA (LLAdev) was significantly smaller when participants were instructed to sit upright (-3.7 ± 3.9°) compared to that of their habitual sitting posture (-1.2 ± 2.4°) (p = 0.001), while the magnitude of LLA (LLAavg) was significantly larger with an upright sitting posture (p = 0.001). LLAdev was significantly larger when using an office chair (-0.4 ± 1.1°) than when using a stool (-3.2 ± 7.1°) (p = 0.033), and LLAavg was also significantly larger with the office chair (p < 0.001). Among the desk tasks, LLAavg was largest during keyboard tasks (p < 0.001), followed by mouse and writing tasks; LLAdev showed a similar trend without statistical significance (keyboard, -1.2 ± 3.0°; mouse, -1.8 ± 2.2°; writing, -2.9 ± 3.1°) (p = 0.067). Conclusions: Our findings suggest that strategies including the use of an office chair and preference for computer work may help preserve LLC, whereas in the case of cueing, repetition may be necessary.

Simulation of Effect a Variable Height of Porous Absorber on Ventilation Solar Chimney Performance

The improvement in solar chimneys' thermal performance and thermal behavior that can be achieved by adding metal foam has been tested in computational work. The flow and heat transfer governing equations for solar chimney models were solved using computational fluid dynamics (CFD). It was solved using the control volume numerical method in ANSYS FLUENT 14.5. It is used to construct a finite volume modeling technique for solving the governing equations and the radiation heat transfer equations. With standard flat absorber plates, the results showed that heat transmission was increased by the inclusion of metal foam (10 PPI), leading to an increase in air velocity at the solar chimney of around 13.3%. The highest average air velocity with 10 PPI drops by 54.4% as the height of the absorber plate changes from 5 cm to 25 cm respectively.

Mechanical intelligence via fully reconfigurable elastic neuromorphic metasurfaces

The ability of mechanical systems to perform basic computations has gained traction over recent years, providing an unconventional alternative to digital computing in off grid, low power, and severe environments, which render the majority of electronic components inoperable. However, much of the work in mechanical computing has focused on logic operations via quasi-static prescribed displacements in origami, bistable, and soft deformable matter. Here, we present a first attempt to describe the fundamental framework of an elastic neuromorphic metasurface that performs distinct classification tasks, providing a new set of challenges, given the complex nature of elastic waves with respect to scattering and manipulation. Multiple layers of reconfigurable waveguides are phase-trained via constant weights and trainable activation functions in a manner that enables the resultant wave scattering at the readout location to focus on the correct class within the detection plane. We further demonstrate the neuromorphic system’s reconfigurability in performing two distinct tasks, eliminating the need for costly remanufacturing.