Multisite collaborations are becoming increasingly common in psychological science, offering a powerful opportunity for testing effects across diverse samples. However, leading such projects can appear overwhelmingly demanding and inaccessible, particularly for early-career researchers or teams with limited resources and the pressure of strict deadlines. This article offers a practical, experience-based guide for coordinating small-to-medium-scale multisite studies, drawing on lessons learned during my PhD while leading a multisite Registered Report. The structure follows the typical stages of a multisite initiative, providing templates, examples, and recommendations at each step: Preregistering the protocol, with a justified sample size based on power analyses for multilevel data structures and a thoroughly piloted task, recruiting labs by making the project attractive, with clear criteria for co-authorship and credit recognition, collecting multisite data and, finally, preparing and submitting the manuscript to journals and repositories. Throughout this process, we offer tools to translate materials, track laboratories, register participant details, and to navigate unexpected problems. Although several published guides to large-scale collaborations already exist, this piece adds a personal perspective on what can realistically be expected from smaller collaborations, which often require different strategies, and how to adapt best practices to more modest settings. All resources are openly shared, and the aim is to encourage other early-career researchers to consider leading such projects themselves. Multisite studies do not always need to be carried out at a big scale to be valid, powerful, and impactful. With a well-defined protocol and a small but motivated network of collaborators, your team can contribute to high-quality, collaborative science.

Topology-aware stress analysis in shell structures

In this study, we develop a sparse regression framework to predict the frequency response of a complex zigzag structure using frequency domain data from experimental setups. Such structures are widely used in MEMS (Micro-Electro-Mechanical Systems) and vibration energy harvesters where small structures with low-natural frequencies are desired. Our approach employs sparsity-promoting techniques, including LASSO (Least Absolute Shrinkage and Selection Operator) regression, to selectively identify the most relevant nonlinear terms based on the LASSO regression technique, thereby avoiding exhaustive searches across all potential models. The machine learning framework proposed in this research consists of standard scaling for data normalization, trigonometric and polynomial feature transformations, and regularization through LASSO regression, followed by optimization using a genetic algorithm to fine-tune LASSO parameters like regularization strength, maximum iterations, and tolerance. This method ensures a generalized and interpretable model capable of addressing the complexities of dynamic systems with external excitations. The present model achieves an R 2 score of 0.9998, a root mean squared error (RMSE) of 0.021947, and a mean absolute error (MAE) of 0.002580, demonstrating exceptional predictive accuracy. The accuracy and robustness of our model are verified by comparing its predictions with those of the finite element simulations using COMSOL (Computational Software for Multiphysics Simulation). The integration of machine learning with symbolic regression in this framework allows for precise characterizations of system performance and provides a methodological bridge between data-driven models and conventional physics-based analyses. This research demonstrates significant potential for advancing dynamic system modeling and analysis in complex structures, where governing equations are unknown or difficult to determine.

A generative deep learning and explainable machine learning framework for heat transfer prediction and analysis in porous structures with oscillatory flows

ABSTRACT This paper presents a time spectral generalized finite difference method (TS‐GFDM) for three‐dimensional (3D) transient heat conduction in functionally graded materials (FGMs) with space–time dependent coefficients. The time derivative of temperature in the governing equation is approximated as a linear combination of temperatures at Gaussian points within each time step, achieved via the inverse transform of spectral integration. Space derivatives of temperature are evaluated as linear combinations of nodal temperatures, constructed using Taylor series expansion in conjunction with the moving least squares (MLS) approximation. The proposed method allows for large time steps in the temporal direction while ensuring stability over long‐time simulations. In the spatial domain, it eliminates the need for mesh generation, making it particularly well suited for heat conduction analysis in complex structures. The numerical results obtained using the TS‐GFDM are compared with those from existing methods and the analytical solution, demonstrating the higher computational efficiency of the proposed approach.

Damage evolution analysis in bioinspired composite structures by using a machine learning-based multiscale modeling approach

An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures. The proposed approach integrates spectral time discretization with neural network approximation, forming a spectral-integrated neural network (SINN) scheme tailored for problems characterized by long-time evolution. Temporal derivatives are treated through a spectral integration strategy based on orthogonal polynomial expansions, which significantly alleviates stability constraints associated with conventional time-marching schemes. A fully connected neural network is employed to approximate the temperature-related variables, while governing equations and boundary conditions are enforced through a physics-informed loss formulation. Numerical investigations demonstrate that the proposed method maintains high accuracy even when large time steps are adopted, where standard numerical solvers often suffer from instability or excessive computational cost. Moreover, the framework exhibits strong robustness for ultrathin configurations with extreme aspect ratios, achieving relative errors on the order of 10−5 or lower. These results indicate that the SINN framework provides a reliable and efficient alternative for transient thermal analysis of thin-walled structures under challenging computational conditions.

Investigating the outline of a reconfigurable leaf jig using static structural analysis

A comparative study of structural properties of oleic acid coated metal nanoparticles (Co, Ni, Fe) by co-precipitation method

This study examines the optimal additional pathways to reduce the maximum evacuation time using Dijkstra’s algorithm, targeting a deep underground station structure in Seoul. The reduction rates in the maximum evacuation time were evaluated across seven cases, including baseline scenarios without additional pathways. Pedestrian speed was adjusted by applying a crowd density-based reduction factor. The maximum evacuation time was 590 s without the additional pathways and 369 s when additional pathways were implemented on floors B2 and B1, representing a 37.5% reduction. The most significant reduction occurred when pathways were simultaneously added on B6 and B1, as well as B2 and B1, resulting in a maximum evacuation time of 340 s, a 42.4% decrease compared with the baseline scenario. These findings underscore the importance of constructing additional pathways to ensure occupants can evacuate within the 6-min golden time specified by the Korean Ministry of Land, Infrastructure, and Transport. • This study identifies optimal evacuation routes in a deep subway station under disaster scenarios. • Additional pathways reduced the maximum evacuation time by 42.4%, meeting the 6-minute standard. • Congestion alleviation rate was proportional to the reduction in maximum evacuation time. • Mitigating congestion in evacuation-vulnerable zones, which have the highest crowd density, can effectively reduce the maximum evacuation time. • Utilizing this zone to alleviate congestion in future studies or designs can enhance subway safety.

<br><b>Introduction:</b> Lipids are crucial biomolecules that confer structural integrity to cell membranes, facilitate signalling and regulate energy dynamics. Dysregulation of lipids is associated with various diseases, including diabetes, chronic inflammation, and neurological and cardiovascular disorders.</br> <br><b>Objective:</b> This review seeks to critically evaluate recent advancements in lipidomics, particularly concerning membranous nanoparticles such as extracellular vesicles (EVs), and to investigate the analytical potential of time-of-flight secondary ion mass spectrometry (ToF-SIMS) for nanoscale lipid mapping.</br> <br><b>Methods:</b> A comprehensive literature review was conducted, focusing on mass spectrometry (MS)-based lipidomic methodologies. Particular emphasis was placed on studies utilising ToF-SIMS to image lipid distribution and composition in cells and membrane-bound nanoparticles. While traditional MS techniques are proficient in identifying and quantifying lipids, they lack spatial resolution. ToF-SIMS addresses this limitation by enabling in situ molecular imaging at micrometre scales, revealing lipid heterogeneity within biological structures and providing unique insights into membrane architecture and lipid sorting. A comparative evaluation highlights both the strengths (e.g., spatial accuracy) and limitations (e.g., challenges in quantification) of ToF-SIMS in relation to alternative methods.</br> <br><b>Conclusions:</b> ToF-SIMS introduces a critical spatial dimension to lipidomics, bridging conventional bulk analysis with nano-lipidomic imaging. Its integration with complementary techniques holds promise for novel insights into lipid biology, biomarker discovery, and translational applications in diagnostics and drug delivery.</br>

Background: Monkeypox virus (MPXV) is a zoonotic pathogen that affects both humans and animals, posing a significant publichealth concern due to its emergence and circulation. The structural dynamics and features of several MPXV proteins, includingM1R, are not completely studied. Methods: This experiment focuses on the prediction and analysis of the secondary and tertiary constructs for the M1R protein.Briefly, its amino acid sequence was collected from the UniProt database. A wide range of in silico approaches were employed,including ProtParam, SOPMA, PSIPRED, CD Search, GalaxyTMB, Robetta, I-TASSER, and GROMACS, in order to explore thephysicochemical properties, structural features, and functional insights of the M1R protein. The tertiary structure models wereevaluated to detect the most reliable solution, which was then used for Immunoinformatics analyses such as MHC I/II and B-cellepitope prediction using the IEDB and Ellipro tools, respectively. Epitopes from the M1R protein were evaluated based on anti-genicity, affinity of binding, along solubility. Furthermore, active sites were forecast by the CASTp v3.0 tool. Results: Physicochemical calculations indicate that M1R had favorable thermostability and hydrophilic features. Structural anal-yses suggested that M1R is a lipid membrane protein component of DNA viruses, suggesting it as a robust antigenic target. Im-munogenicity analyses indicated it as a potentially suitable target for immunogenic protein design. As well, molecular dynamicssimulations (MDS) were carried out for 100-ns using an all-atom forcefield. Analysis of various molecular dynamics parametersof M1R throughout the MDS trajectory, including RMSD, RMSF, radius of gyration (Rg), and solvent accessible surface area(SASA), indicated good stability of the M1R and unveiled important molecular dynamics characteristics such as the flexibility ofcertain protein regions. Multiple epitopes were detected in our experiment, with 12 B-cell epitopes identified using the Robettamodel and 6 B-cell epitopes predicted by the Galaxy model, alongside 3 MHC-I and 3 MHC-II epitopes, which scored favorably. Conclusion: The results of the present computational analysis provide clues to unleash the potential of M1R as an immunother apy target for the development of antiviral solutions against MPXV in the future

The finite-volume theory is an effective numerical technique for structural analysis in solid mechanics and has emerged as an alternative to the finite element method. This approach is based on equilibrium principles and utilizes surface-averaged tractions and displacements acting on the faces of subvolumes within the discretized analysis domain. The theory enforces equilibrium equations at the subvolume level and imposes continuity conditions across the interfaces of adjacent subvolumes. The finite-volume theory has primarily focused on two-dimensional problems, and the extension to three-dimensional structures has encountered numerical instabilities. These issues can be mitigated using a modified stiffness matrix, which directly relates resultant forces to surface-averaged displacements, and applying Tikhonov regularization to add artificial stiffness to the main diagonal of the global matrix. This contribution presents a new three-dimensional finite-volume formulation for stress analysis in continuum elastic structures. Two benchmark problems are investigated to assess the accuracy and stability of the proposed approach under the assumptions of linear elasticity. The results exhibit excellent agreement with analytical solutions and finite-element results. A third example involving a complex geometry without analytical solution is analyzed, and finite-element results are employed to verify the finite-volume predictions, further demonstrating the effectiveness and applicability of the proposed formulation.

Deep learning-enhanced acoustic emission localization for damage evolution analysis in coal-concrete composite structures under cyclic loading

Ankara and lace fabrics have been in use for some years by many tribes in Nigeria. These two local fabrics are dynamic and unique to Africa in general. Despite the uniqueness of these two fabrics, there is a dearth of in-depth study on them. This study presents a comparative analysis of the physical structures of lace and Ankara fabrics through direct field research using a qualitative method to analyse the data with random sampling. This study was conducted with the aim of giving insight into the growth of the arts so as to preserve the designs and styles for future development through the understanding of the two fabrics. The study reveals that the fabrics are texturally good in the body and therefore widely used by the low, middle and high-class personalities in Nigeria.

In the face of innovations triggered by advances in digital technology, biomedicine, and behavioural/neurological research, the analysis of power relations has moved into substantially new territory. Attempts to chart this new territory in terms of ‘surveillance capitalism’, ‘algorithmic governmentality’, ‘sensory power’, ‘post-liberal government’, or ‘neuroliberalism’ have emphasized different dimension of emerging systems of power. These power relations challenge many established understandings of ‘freedom’ as something distinct from ‘domination’, ‘determination’, or ‘necessity’. However, critical assessments have thus far proceeded on the basis of an assumed and idealized subjective ‘autonomy’, anti-subjectivist genealogies of ‘governmentality’, or technologically oriented theories of the posthuman. The ambition of this paper is to address freedom in relation to embodied and negotiated experience. Ricoeur's meticulous, anti-dualist phenomenology of freedom enables us to anchor our analysis in intelligible structures of lived experience located between subjective autonomy and dissolution and technological enhancement and subjugation. We explore these conceptual concerns through an empirical investigation of the negotiated forms of freedom that emerge in and around people's use of smart/intelligent technology. Reflecting on a SenseMaker ® analysis, we consider emerging configurations of freedom and oppression (or in Ricoeurian terms, the voluntary and involuntary). We draw specific attention to how, in an age of smart/intelligent technology, practices of freedom are not experienced as forms of either digital liberation or oppression but as complex negotiations between the biological and digital involuntary and voluntary acts of negotiated consent.

To mitigate thermal cracking in concrete box girders during construction, this study introduces an inversion method for thermal parameters by integrating machine learning with finite element simulation. The research aims to accurately identify key thermal parameters—thermal conductivity k, total hydration heat Q0, convection coefficient h, and reaction coefficient m—through an efficient and reliable data-driven approach. An orthogonal experimental design was used to construct a representative sample database, and a Bayesian-optimized XGBoost (BO-XGBoost) model was developed to establish a nonlinear mapping between temperature peaks and thermal parameters. Validated against field monitoring data from a prestressed concrete continuous rigid-frame bridge, the method demonstrated high accuracy: the inversiontemperature curves closely matched measured data, with a maximum peak temperature error of only 1.40 °C (relative error 2.5%). Compared to conventional machine learning models (DT, SVR, BP and LSTM), BO-XGBoost showed superior predictive performance and convergence efficiency. The proposed approach provides a scientific basis for real-time temperature control and crack prevention in concrete box girders and is applicable to temperature field analysis in mass concrete structures.

Given the well-established possibility of elemental migration in perovskite devices, often driven by multiple stressors and contributing to device degradation, it is essential to evaluate and quantify their elemental distribution using characterization techniques that minimize measurement-induced inter-mixing of elements. Techniques like time-of-flight secondary ion mass spectrometry and x-ray photoelectron spectroscopy based depth profiling are commonly employed for such analysis. However, despite their feasibility and effectiveness, these methods can inadvertently cause additional damage to the delicate perovskite as well as other organic structures present in the devices, which are vulnerable to preferential elemental sputtering and compositional matrix mixing effects. Such issues can lead to quantification errors and unwanted artifacts, particularly when using keV-range heavy ions such as Ar+, O+, Cs+, or Ar cluster ions for sputtering. In contrast, Rutherford Backscattering Spectrometry (RBS) utilizing MeV range energetic He or H ions is generally regarded as a non-destructive technique that does not require reference standards or extensive sample preparation. It provides a comprehensive analysis of the composition, thickness, and depth profiles of the elements present in the device layers, while also enabling a concurrent evaluation of radiation tolerance with the probing light ion beam (e.g., H+ and He+). This perspective highlights the key benefits of using RBS for elemental depth profiling and migration in perovskite-based devices and explores recent developments, ongoing challenges, and efforts toward achieving accurate quantitative analysis in complex multilayer thin-film structures.

Real-time monitoring of structural deformation in space infrastructure is a critical factor in ensuring mission reliability and structural integrity. FBG sensors represent an effective solution due to their advantages, such as high sensitivity, low weight, resistance to electromagnetic interference, and multiplexing capability. This review article examines the scientific foundations of using FBG sensors in space environments to accurately monitor structural deformation under conditions including high temperature, radiation, vacuum, and micrometeoroid impacts. Additionally, it analyzes modern research focused on methods of integrating FBG sensors into small spacecraft structures, interrogation techniques, sensor network architectures, and their ability to operate in real time.

With the advancement of engineering technology, prestressed concrete has been increasingly applied in various structures. To accurately and efficiently evaluate the long-term performance of prestressed concrete members, this paper proposes trapezoidal and difference methods for long-term deformation calculation based on the principle of creep superposition. Compared with existing creep refinement approaches and experimental data, the methods presented in this study demonstrate higher accuracy. Moreover, they significantly reduce computational complexity, offering a practical theoretical foundation for creep analysis in large-scale structures. These methods are further extended to two-way prestressed concrete members, addressing the engineering need for accurate long-term performance evaluation in such systems. The findings indicate that the creep development in two-way prestressed members is slower than that in one-way members.