Recyclable adsorbent of BiFeO_3/Carbon for purifying industrial dye wastewater via photocatalytic reproducible

It is essential to prepare highly-efficiency reproducible adsorbent for purifying industrial dye wastewater. In this work, biscuit with a layered porous structure as a template is applied to prepare a photocatalytic recyclable adsorbent of BiFeO3/Carbon nanocomposites for purifying simulative industrial dye wastewater. It is found that the structure of the prepared BiFeO3/Carbon nanocomposite is related to the natural structure of the biscuit, annealing temperatures and immersing times, demonstrated by XRD, TEM, UV-Vis and adsorptive activities. Kinetics data shows that the adsorption rate of the adsorbent to the dye is rapid and fitted well with the pseudo-second-order model, that more than 80% of dyes can be removed in the beginning 30 min. The adsorption isotherm can be perfectly described by the Langmuir model as well. It can be seen from the adsorption data that the adsorption performance can reach over 90% at pH ? 2–12, which can imply its universal utilization. The prepared BiFeO_3/Carbon nanocomposites have also displayed excellent capacities(over 90% within 30 min) for adsorption of seven different dyes and their mixed one. According to the five times photocatalytic reproducible experiments, it is proved that BiFeO_3/Carbon nanocomposites show the excellent stability and reproduction for purifying simulative industrial dyes, even the sample have been placed for one year. These research results indicate that the adsorbent BiFeO_3/Carbon can be a suitable material used in treating industrial dye wastewater potentially.

Two arms-three instruments robot-assisted laparoscopic hysterectomy:A reproducible technique

Although laparoscopic hysterectomy has been used for more than 3 decades,it is not universally adopted due to steep learning curve.The robotic platform can bridge this gap and reduce the need for open hysterectomy with enhanced dexterity and accurate depth perception by 3D vision and wristed intuitive movements.This technical note introduces a two arms-three instruments“Sinha-Apollo technique”for da Vinci Si system for performing robotic-assisted laparoscopic hysterectomy in simplified and reproducible steps.

A Reproducible Approach of Preparing High-Quality Overdoped Bi_2Sr_2CaCu_2O_(8+δ) Single Crystals by Oxygen Annealing and Quenching Method

We report a reproducible approach in preparing high-quality overdoped Bi2 Sr2 CaCu2 08＋δ （Bi2212） single crystals by annealing Bi2212 crystals in high oxygen pressure followed by a fast quenching. In this way, high-quality overdoped and heavily overdoped Bi2212 single crystals are obtained by controlling the annealing oxygen pressure. We find that, beyond a limit of oxygen pressure that can achieve most heavily overdoped Bi2212 with a Tc N63 K, the annealed Bi2212 begins to decompose. This accounts for the existence of the hole-doping limit and thus the Tc limit in the heavily overdoped region of Bi2212 by the oxygen annealing process. These results provide a reliable way in preparing high-quality overdoped and heavily overdoped Bi2212 crystals that are important for studies of the physical properties, electronic structure and superconductivity mechanism of the cuprate superconductors.

A 3D-printable device allowing fast and reproducible longitudinal preparation of mouse intestines

Accurate and reproducible analysis of murine small and large intestinal tissue is key for preclinical models involving intestinal pathology.Currently,there is no easily ac-cessible,standardized method that allows researchers of different skill levels to con-sistently dissect intestines in a time-efficient manner.Here,we describe the design and use of the 3D-printed“Mouse Intestinal Slicing Tool”(MIST),which can be used to longitudinally dissect murine intestines for further analysis.We benchmarked the MIST against a commonly used procedure involving scissors to make a longitudinal cut along the intestines.Use of the MIST halved the time per mouse to prepare the intestines and outperformed alternative methods in smoothness of the cutting edge and overall reproducibility.By sharing the plans for printing the MIST,we hope to contribute a uniformly applicable method for saving time and increasing consistency in studies of the mouse gastrointestinal tract.

Reproducible and low-power multistate bio-memristor from interpenetrating network electrolyte design

Bio-memristor can address the inherent limitations of conventional memory components in artificial perceptual systems due to their biocompatibility with biological tissue.The actual deployment of bio-memristor is restricted by the lack of reproducibility,high power consumption,and insufficient storage capacity.Here,a reproducible and low-power multistate biomemristor is developed by designing the chitosan(CS)-reduced graphene oxide(rGO)interpenetrating network electrolyte.The interpenetrating network structure of the CS-rGO electrolyte reinforces structural stability and improves ionic conductivity.The bio-memristor equipped with CS-rGO active layer shows stable bipolar resistive switching up to 100 consecutive cycles,reproducible multistate storage with six different memory states,and low programming power of 9.4μW.The fabricated biocompatible CS-rGO device also exhibits deformation stability of memory operation over 103 bending cycles,high biocompatibility with HEK293 cells,and skin adhesion.This work provides an enlightening design strategy to develop highperformance bio-memristors for applications in artificial perceptual systems.

Reasons,challenges,and some tools for doing reproducible transportation research

This paper introduces reproducible research(RR),and explains its importance,benefits,and challenges.Some important tools for conducting RR in Transportation Research are also introduced.Moreover,the source code for generating this paper has been designed in a way so that it can be used as a template for researchers to write their future journal papers as dynamic and reproducible documents.

Development of a chronic compression spinal cord injury model in neonatal and adult rats

Background:Spinal cord injury presents a significant burden globally,with traumatic spinal cord injury being the predominant cause historically.However,nontraumatic spinal cord injury(NTSCI)is emerging as a significant contributor,particularly in devel-oped nations,yet it remains poorly understood due to underreporting and misclassifi-cation.NTSCI,spanning various etiologies such as bony growths,vascular conditions,infections,neoplastic conditions,and immune disorders,poses unique challenges in diagnosis and treatment,often resulting in lifelong morbidity.This study addresses the lack of suitable animal models for NTSCI research,especially in neonatal animals.Methods:Utilizing a solid spacer approach,we developed a compression NTSCI model applicable to both neonatal and adult Sprague-Dawley rats.Results:Through anatomical measurements and in vivo experiments,we confirmed the feasibility and safety of the spacer insertion procedure and observed no acute off-target effects.Conclusion:The versatility of this model lies in its adaptability to different ages of rats,offering a cost-effective and reproducible means to induce graded injuries.Moreover,behavioral assessments demonstrated observable hindlimb function,validating the model's utility for studying functional outcomes.Although challenges persist,par-ticularly in accounting for spinal column growth in neonatal animals,this model fills a crucial gap in pediatric NTSCI research.By providing a platform to investigate patho-physiological mechanisms and test potential treatments,it offers promising avenues for advancing our understanding and management of pediatric NTSCI.

Toward Training and Assessing Reproducible Data Analysis in Data Science Education

Reproducibility is a cornerstone of scientific research.Data science is not an exception.In recent years scientists were concerned about a large number of irreproducible studies.Such reproducibility crisis in science could severely undermine public trust in science and science-based public policy.Recent efforts to promote reproducible research mainly focused on matured scientists and much less on student training.In this study,we conducted action research on students in data science to evaluate to what extent students are ready for communicating reproducible data analysis.The results show that although two-thirds of the students claimed they were able to reproduce results in peer reports,only one-third of reports provided all necessary information for replication.The actual replication results also include conflicting claims;some lacked comparisons of original and replication results,indicating that some students did not share a consistent understanding of what reproducibility means and how to report replication results.The findings suggest that more training is needed to help data science students communicating reproducible data analysis.

Differentially private SGD with random features

In the realm of large-scale machine learning,it is crucial to explore methods for reducing computational complexity and memory demands while maintaining generalization performance.Additionally,since the collected data may contain some sensitive information,it is also of great significance to study privacy-preserving machine learning algorithms.This paper focuses on the performance of the differentially private stochastic gradient descent(SGD)algorithm based on random features.To begin,the algorithm maps the original data into a lowdimensional space,thereby avoiding the traditional kernel method for large-scale data storage requirement.Subsequently,the algorithm iteratively optimizes parameters using the stochastic gradient descent approach.Lastly,the output perturbation mechanism is employed to introduce random noise,ensuring algorithmic privacy.We prove that the proposed algorithm satisfies the differential privacy while achieving fast convergence rates under some mild conditions.

Convergence analysis for complementary-label learning with kernel ridge regression

Complementary-label learning(CLL)aims at finding a classifier via samples with complementary labels.Such data is considered to contain less information than ordinary-label samples.The transition matrix between the true label and the complementary label,and some loss functions have been developed to handle this problem.In this paper,we show that CLL can be transformed into ordinary classification under some mild conditions,which indicates that the complementary labels can supply enough information in most cases.As an example,an extensive misclassification error analysis was performed for the Kernel Ridge Regression(KRR)method applied to multiple complementary-label learning(MCLL),which demonstrates its superior performance compared to existing approaches.

THE SPARSE REPRESENTATION RELATED WITH FRACTIONAL HEAT EQUATIONS

This study introduces a pre-orthogonal adaptive Fourier decomposition(POAFD)to obtain approximations and numerical solutions to the fractional Laplacian initial value problem and the extension problem of Caffarelli and Silvestre(generalized Poisson equation).As a first step,the method expands the initial data function into a sparse series of the fundamental solutions with fast convergence,and,as a second step,makes use of the semigroup or the reproducing kernel property of each of the expanding entries.Experiments show the effectiveness and efficiency of the proposed series solutions.

AHermitian C^(2) Differential Reproducing Kernel Interpolation Meshless Method for the 3D Microstructure-Dependent Static Flexural Analysis of Simply Supported and Functionally Graded Microplates

This work develops a Hermitian C^(2) differential reproducing kernel interpolation meshless(DRKIM)method within the consistent couple stress theory(CCST)framework to study the three-dimensional(3D)microstructuredependent static flexural behavior of a functionally graded(FG)microplate subjected to mechanical loads and placed under full simple supports.In the formulation,we select the transverse stress and displacement components and their first-and second-order derivatives as primary variables.Then,we set up the differential reproducing conditions(DRCs)to obtain the shape functions of the Hermitian C^(2) differential reproducing kernel(DRK)interpolant’s derivatives without using direct differentiation.The interpolant’s shape function is combined with a primitive function that possesses Kronecker delta properties and an enrichment function that constituents DRCs.As a result,the primary variables and their first-and second-order derivatives satisfy the nodal interpolation properties.Subsequently,incorporating ourHermitianC^(2)DRKinterpolant intothe strong formof the3DCCST,we develop a DRKIM method to analyze the FG microplate’s 3D microstructure-dependent static flexural behavior.The Hermitian C^(2) DRKIM method is confirmed to be accurate and fast in its convergence rate by comparing the solutions it produces with the relevant 3D solutions available in the literature.Finally,the impact of essential factors on the transverse stresses,in-plane stresses,displacements,and couple stresses that are induced in the loaded microplate is examined.These factors include the length-to-thickness ratio,the material length-scale parameter,and the inhomogeneity index,which appear to be significant.

Reproducible Research Publication Workflow:A Canonical Workflow Framework and FAIR Digital Object Approach to Quality Research Output

In this paper we present the Reproducible Research Publication Workflow(RRPW)as an example of how generic canonical workflows can be applied to a specific context.The RRPW includes essential steps between submission and final publication of the manuscript and the research artefacts(i.e.,data,code,etc.)that underlie the scholarly claims in the manuscript.A key aspect of the RRPW is the inclusion of artefact review and metadata creation as part of the publication workflow.The paper discusses a formalized technical structure around a set of canonical steps which helps codify and standardize the process for researchers,curators,and publishers.The proposed application of canonical workflows can help achieve the goals of improved transparency and reproducibility,increase FAIR compliance of all research artefacts at all steps,and facilitate better exchange of annotated and machine-readable metadata.

Solving Neumann Boundary Problem with Kernel-Regularized Learning Approach

We provide a kernel-regularized method to give theory solutions for Neumann boundary value problem on the unit ball. We define the reproducing kernel Hilbert space with the spherical harmonics associated with an inner product defined on both the unit ball and the unit sphere, construct the kernel-regularized learning algorithm from the view of semi-supervised learning and bound the upper bounds for the learning rates. The theory analysis shows that the learning algorithm has better uniform convergence according to the number of samples. The research can be regarded as an application of kernel-regularized semi-supervised learning.

Highly effective and reproducible surface-enhanced Raman scattering substrates based on Ag pyramidal arrays

Close-packed Ag pyramidal arrays have been fabricated by using inverted pyramidal pits on Si as a template and used to generate plentiful and homogeneous surface-enhanced Raman scattering （SERS） hot sites. The sharp nanotip and the four edges of the Ag pyramid result in strong electromagnetic field enhancement with an average enhancement factor （EF） of 2.84 × 10^7. Moreover, the features of the close-packed Ag pyramidal array can be well controlled, which allows SERS substrates with good reproducibility to be obtained. The relative standard deviation （RSD） was lower than 8.78% both across a single substrate and different batches of substrates.

Efficient p-i-n structured perovskite solar cells employing low-cost and highly reproducible oligomers as hole transporting materials

The development of p-i-n structured perovskite solar cells(PSCs) requires more extensive explorations on seeking efficient, low cost and stable hole transporting materials(HTMs). Small molecular HTMs are superior to polymeric ones in terms of synthetic reproducibility as well as purity. However, thin films composed of small molecules are usually labile during the solution-based perovskite deposition. Herein, we propose a molecular engineering strategy of incorporating oligothiophene as conjugation bridge to develop robust oligomer HTMs for p-i-n type PSCs. Upon increasing the oligothiophene chain length from α-bithiophene to α-quaterthiophene and α-hexathiophene, their HOMO energy levels remain unchanged, but their solubility in common organic solvents decreased remarkably, thus greatly enhancing their tolerance to the perovskite deposition. The rational design of oligothiophene chain length can effectively tune their optoelectronic properties as well as thin film stability under polar solvent soaking. The best performance is achieved by an α-quaterthiophene based HTM(QT), showing a high efficiency of 17.69% with fill factor of 0.81, which are comparable to those of a commercially available benchmark polymer HTM(poly[bis(4-phenyl)(2,4-dimethylphenyl) amine], PTAA) based devices fabricated under the same conditions. Our developed oligomer system not only provides the definite molecular structures like small molecule-type HTMs, but also exhibits the excellent filmforming like polymer-type HTMs, thus achieving the well-balanced parameters among solvent tolerance, thin film conductivity,and interfacial charge transfer efficiency, especially building up a platform to develop low cost and reproducible efficient HTMs in p-i-n structured perovskite solar cells.

SWIRRL.Managing Provenance-aware and Reproducible Workspaces

Modern interactive tools for data analysis and visualisation are designed to expose their functionalities as a service through the Web.We present in this paper a Web API(SWIRRL)that allows Virtual Research Environments(VREs)to easily integrate such tools in their websites and re-purpose them to their users.The APl deals,on behalf of the clients,with the underlying complexity of allocating and managing resources within a target cloud platform.By combining storage and containerised services,offering analysis notebooks and other visualisation software,the APl creates dedicated working sessions on-demand,which can be accessed collaboratively.Thanks to the API's support for workflow execution,SWIRRL workspaces can be automatically populated with data of interest collected from external data providers.The system keeps track of updates and changes affecting the data and the tools by adopting versioning and standard provenance technologies.Users are provided with interactive controls enabling traceabilityand recovery actions,including the possibility of creating executable snapshots of their environments.SWIRRL is built in cooperation with two research infrastructures in the field of solid earth science and climate data modeling.We report on the particularadoptions and use cases.

Local Radial Basis Function Methods: Comparison, Improvements, and Implementation

Radial Basis Function methods for scattered data interpolation and for the numerical solution of PDEs were originally implemented in a global manner. Subsequently, it was realized that the methods could be implemented more efficiently in a local manner and that the local approaches could match or even surpass the accuracy of the global implementations. In this work, three localization approaches are compared: a local RBF method, a partition of unity method, and a recently introduced modified partition of unity method. A simple shape parameter selection method is introduced and the application of artificial viscosity to stabilize each of the local methods when approximating time-dependent PDEs is reviewed. Additionally, a new type of quasi-random center is introduced which may be better choices than other quasi-random points that are commonly used with RBF methods. All the results within the manuscript are reproducible as they are included as examples in the freely available Python Radial Basis Function Toolbox.

Adaptive multi-step piecewise interpolation reproducing kernel method for solving the nonlinear time-fractional partial differential equation arising from financial economics

This paper is aimed at solving the nonlinear time-fractional partial differential equation with two small parameters arising from option pricing model in financial economics.The traditional reproducing kernel(RK)method which deals with this problem is very troublesome.This paper proposes a new method by adaptive multi-step piecewise interpolation reproducing kernel(AMPIRK)method for the first time.This method has three obvious advantages which are as follows.Firstly,the piecewise number is reduced.Secondly,the calculation accuracy is improved.Finally,the waste time caused by too many fragments is avoided.Then four numerical examples show that this new method has a higher precision and it is a more timesaving numerical method than the others.The research in this paper provides a powerful mathematical tool for solving time-fractional option pricing model which will play an important role in financial economics.