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Detecting community structure is fundamental for uncovering the links between structure and function in complex networks and for practical applications in many disciplines such as biology and sociology. A popular method now widely used relies on the optimization of a quantity called modularity, which is a quality index for a partition of a network into communities. We find that modularity optimization may fail to identify modules smaller than a scale which depends on the total size of the network and on the degree of interconnectedness of the modules, even in cases where modules are unambiguously defined. This finding is confirmed through several examples, both in artificial and in real social, biological, and technological networks, where we show that modularity optimization indeed does not resolve a large number of modules. A check of the modules obtained through modularity optimization is thus necessary, and we provide here key elements for the assessment of the reliability of this community detection method.

Sentiment analysis is an important natural language processing (NLP) task due to a wide range of applications. Most existing sentiment analysis techniques are limited to the analysis carried out at the aggregate level, merely providing negative, neutral and positive sentiments. The latest deep learning-based methods have been leveraged to provide more than three sentiment classes. However, such learning-based methods are still black-box-based methods rather than explainable language processing methods. To address this gap, this paper proposes a new explainable fine-grained multi-class sentiment analysis method, namely MiMuSA, which mimics the human language understanding processes. The proposed method involves a multi-level modular structure designed to mimic human’s language understanding processes, e.g., ambivalence handling process, sentiment strength handling process, etc. Specifically, multiple knowledge bases including Basic Knowledge Base, Negation and Special Knowledge Base, Sarcasm Rule and Adversative Knowledge Base, and Sentiment Strength Knowledge Base are built to support the sentiment understanding process. Compared with other multi-class sentiment analysis methods, this method not only identifies positive or negative sentiments, but can also understand fine-grained multi-class sentiments, such as the degree of positivity (e.g., strongly positive or slightly positive) and the degree of negativity (e.g., slightly negative or strongly negative) of the sentiments involved. The experimental results demonstrate that the proposed MiMuSA outperforms other existing multi-class sentiment analysis methods in terms of accuracy and F1-Score.

In order to study the influence of modular filled and composite material forms on the axial crushing and energy absorption properties of structures, modular filled composite structures were constructed, and innovatively applied to the inner side of double-hat beam (DHB) structures in automobiles. The modular filled structures comprise hexagonal, quadrilateral, and triangular sections. By analyzing the collision performance of modular filled DHB structures, significant enhancements were observed in both the sectional characteristics and the specific Mean Crushing Force of modular filled DHBs compared to the conventional double-hat beam structure. These advancements notably improved the plastic deformation characteristics of the structures. Additionally, dynamic weightlessness experiments were conducted to validate the accuracy of the simulation model. Among the proposed schemes, namely QU-5, HE-5, and TR-5, notable improvements in crashworthiness were identified. Specifically, crashworthiness indicators increased by 32.54%, 78.9%, and 116.53%. Compared with other thin-walled structures, modular filled composite DHBs have advantages in axial crushing and energy absorption. By optimizing layout characteristics, the modular filled structures will achieve significant lightweight and energy absorption performance improvements. This work has clear reference value for automotive engineers and scholars to further explore the axial crash safety, platform modularization, and lightweight design of vehicles.

The parametric resonance, found in a single floating body, discloses that the kinetic energy could be transferred from heave mode to roll mode and causes motion instability if there is an integer multiple relationship between the two mode natural frequencies. For multi-module floating structures, the event of parametric resonance has not been investigated, but important for the stability and safety design of the floating platforms. In this paper, an experimental test is carried out using five box-type floating modules in a wave flume and observes the existence of the parametric resonance between the heave mode and roll mode. A mathematical model, validated by the experiment data, is built up for the theoretical analysis of the influential factors of the parametric resonance. The effects on the motion instability of wave condition, connector stiffness and number of modules are analyzed. It reveals that an appropriate stiffness setting of the connectors could eliminate the parametric resonance of multi-module floating structures. This theoretical finding is confirmed in a further experiment test on a five-module floating structure in the wave flume.

This article presents the design process and structural analysis for Halls 3, 4, and 6 commissioned by Fira de la Gran Vía in Barcelona. Its objective is to document the complete development of a real structure—from the initial briefing to final execution—highlighting key decisions related to cost, quality, construction speed, and standardization. Rather than simply describing the finished building, the article compares alternative solutions considered at each stage and explains the rationale behind the choices made. Close collaboration between the architectural and structural teams has resulted in a cost-effective solution that has remained relevant twenty-five years after completion. Each structural component is examined in detail, considering its behavior, preliminary sizing, fabrication, transportation, and rapid on-site assembly, all essential under the client’s demanding schedule. It also describes how specific structural details were resolved under project constraints, including instances that required unconventional approaches. Finally, it discusses the role of prestressed longitudinal frames as a strategy for reducing steel consumption. This article underscores the value of integrated architectural and structural thinking in shaping the building from the ground up.

During the promotion of the modular steel structure in the architecture, engineering, and construction (AEC) industry, building information modeling (BIM) is leveraged to integrate the design process into the whole construction sequence. The absence of standards and interactive, tech-friendly tools for project participants limits the general implementation of the BIM-based design process. The present study proposes an automatic design optimization method based on the BIM platform for modular steel structures. The method consists of digital modeling sequences that contain data exchange between different software applications and the program of structural design optimization. A prototype workflow of the method is explained and assessed in a case study to indicate its reliability and practicability. The proposed design coheres with common design rules and enhances the utilization rate of column structure by 40–50% with minimal redundancy compared to initial designs. The proposed method is also discussed through interviews with and surveys of engineers working in the AEC industry in terms of its potential adoption in actual projects. The discussion shows that this method can reduce the time consumption of the model creation and optimization of modular steel structures effectively. Special knowledge of the relevant software is no longer a hindrance for engineers.

Inferring relations from correlational data allows researchers across the sciences to uncover complex connections between variables for insights into the underlying mechanisms. The researchers often represent inferred relations using Gaussian graphical models, requiring regularization to sparsify the models. Acknowledging that the modular structure of these inferred networks is often studied, we suggest module-based regularization to balance under- and overfitting. Compared with the graphical lasso, a standard approach using the Gaussian log-likelihood for estimating the regularization strength, this approach better recovers and infers modular structure in noisy synthetic and real data. The module-based regularization technique improves the usefulness of Gaussian graphical models in the many applications where they are employed.

The article considers the universal traditional and innovative methods of the architect's work as: hereditary and theoretical-experimental methods of “pre-project analysis” (the method of “conservation” of the object by providing it with an educational function; the method of visual communication object with a person using temporary modular structures); traditional theoretical and practical methods (“stylistic” and “artistic”); innovative theoretical methods – “hermeneutic-semiotic” (“semantic-pragmatic” and “syntactic”), innovative practical method – “hermeneutic-semiotic” (“semionic”). The practical implementation of this method is shown in the examples of experimental conceptual projects for the reconstruction of destroyed and abandoned objects of various scales in Ukraine (city, building fragment, building). Stages of action for the first time reveal the mechanisms of architectural activity of architects based on the implementation of a unique innovative “hermeneutic-semiotic” (“semionic”) method of creativity and the theory of “informative architecture”.

To investigate the initial rotational stiffness and ultimate moment of fully bolted connections in panelized steel modular structures, a finite element analysis was carried out on 20 joint models. High-fidelity models were developed using ABAQUS, and their accuracy was confirmed through comparison with experimental tests. A parametric study was performed to systematically evaluate the effects of the column wall thickness in the core zone, internal diaphragm configurations, angle steel thickness, and stiffener layouts on the joint stiffness and ultimate strength, leading to practical optimization suggestions. Additionally, a mechanical model and a corresponding formula for predicting the initial rotational stiffness of the joints were proposed based on the component method in Eurocode EC3. The model was validated against the finite element results, showing good reliability. Three failure modes were identified as follows: buckling deformation of the beam flange, buckling deformation of the column flange, and deformation of the joint panel zone. In joints with a weak core zone, both the use of internal diaphragms and increased column wall thickness effectively improved the initial rotational stiffness and ultimate bearing capacity. For joints with weak angle steel connections, adding stiffeners or increasing the limb thickness significantly enhanced both the stiffness and capacity. The diameter of bolts in the endplate-to-column flange connection was found to have a considerable effect on the initial rotational stiffness, but minimal impact on the ultimate strength. This study offers a theoretical foundation for the engineering application of panelized steel modular structural joints.

This article presents the technology of automated placement of an injection molding gate based on a parametric optimization algorithm with technological constraints consideration. The algorithm is based on the modification of the genetic algorithm using the criterion of maximum equivalent stresses on the weld line as an optimization criterion. The proposed software’s modular structure combines the authors’ modules that implement a new optimization algorithm with the ANSYS 2022R1 and Moldflow calculation kernels called via API interfaces. This structure provides an opportunity to implement developed technology to solve industrial problems using standard mesh generation tools and complex geometric models due to the flexibility of modules and computing kernel scalability. The consideration of the technological constraints allows us to reduce the population size and optimization problem solution computational time to 1.9 times. The developed algorithms are used to solve the gate location optimization problem using the example of an aerospace bracket made of short-reinforced composite material with a nonzero genus surface and a weld line. The use of the proposed technology made it possible to increase the strength of the studied structure by two times.