Modular Features Research Articles

Digital supply chain: An industrial perspective

AbstractThis perspectives article outlines and discusses the future of supply chains. The existing challenges and flaws in supply chains design and management are described, motivating the need for the next‐generation supply chains. We present a holistic paradigm for viewing and analyzing supply chains as networks of networks with various dimensions, hierarchies, and players with their various forms of decision‐making. Cyber‐Physical Systems is offered as a key enabler to obtain a digital supply chain exhibiting the features of heterogeneity, interoperability, modularity, customizability, decentralization, autonomy, and cooperativity. The role of virtual assistants is described, along with research directions to create the technology for establishing true digital supply chains. The role of Process Systems Engineering (PSE) is outlined with the hope that the ideas discussed will motivate the PSE community to become active participants in attaining the next‐generation supply chains.

Artificial Intelligence Signal Control in Electronic Optocoupler Circuits Addressed on Industry 5.0 Digital Twin

The paper is focused on the modeling of a digital twin (DT) through a circuit simulation and artificial intelligence (AI) analysis to determine the effects of disturbances and noise in optocoupler devices integrated into programmable logic controller (PLC) systems. Specifically, the DT analyzes the parametric and the predicted simulations about the sensitivity of the optocouplers versus noise and interference to provide possible corrective actions, compensating for the distortion of the output signal. The model is structured into two main data processing steps: the first is based on the circuit simulation of the optocoupler noise coupling by highlighting the time-domain sensitivity aspects and the frequency behavior of the coupled signals; the second one estimates the predicted disturbed signal by means of supervised random forest (RF) and unsupervised K-Means algorithms to provide further elements to prevent corrective solutions by means of risk maps. This work is suitable for Industry 5.0 scenarios involving machine control supported by AI-based DT platforms. The innovative elements of the proposed model are the DT features of scalability and modularity; the spatial multidimensionality, able to couple the effects of different undesired signals; and the possibility to simulate the whole PLC system, including its control circuits.

Inverse dynamics analysis of 4-DOF stacking robot with closed chain structure

A four-degree-of-freedom PR1300 stacking robot with two parallel quadrilateral structures connected in series at the shoulder was designed. The hybrid robot with local closed chains has the characteristics of compact structure, high stiffness, and high modularity. To solve the dynamic problem of the hybrid robot. Firstly, by analyzing the induced relationship between joints, the stacking robot with a local closed chain structure is transformed into two branch chain mechanisms, and the kinematic model of the robot is established using the D-H parameter method. Secondly, the Kane method is applied to model the dynamics of the two branch chains separately, and the rigid body dynamics Kane equation of the entire 4-DOF stacking robot with a closed chain structure is obtained through the augmented matrix. Finally, the SolidWorks Motion mechanical simulation platform and MATLAB software were used to program dynamic simulations and numerical calculations, and the results were compared to verify the correctness of the dynamic theory derivation. This theory provides a necessary theoretical basis for the subsequent synthesis of robot dynamic scales.

Improved modularity and new features in ipie: Toward even larger AFQMC calculations on CPUs and GPUs at zero and finite temperatures.

ipie is a Python-based auxiliary-field quantum Monte Carlo (AFQMC) package that has undergone substantial improvements since its initial release [Malone et al., J. Chem. Theory Comput. 19(1), 109-121 (2023)]. This paper outlines the improved modularity and new capabilities implemented in ipie. We highlight the ease of incorporating different trial and walker types and the seamless integration of ipie with external libraries. We enable distributed Hamiltonian simulations of large systems that otherwise would not fit on a single central processing unit node or graphics processing unit (GPU) card. This development enabled us to compute the interaction energy of a benzene dimer with 84 electrons and 1512 orbitals with multi-GPUs. Using CUDA and cupy for NVIDIA GPUs, ipie supports GPU-accelerated multi-slater determinant trial wavefunctions [Huang et al. arXiv:2406.08314 (2024)] to enable efficient and highly accurate simulations of large-scale systems. This allows for near-exact ground state energies of multi-reference clusters, [Cu2O2]2+ and [Fe2S2(SCH3)4]2-. We also describe implementations of free projection AFQMC, finite temperature AFQMC, AFQMC for electron-phonon systems, and automatic differentiation in AFQMC for calculating physical properties. These advancements position ipie as a leading platform for AFQMC research in quantum chemistry, facilitating more complex and ambitious computational method development and their applications.

Consolidating building greening: Integrating mobile modular vertical greening systems into prefabricated building

Vertical greening systems increase green plant coverage within built environments, enhancing urban air quality while reducing building energy consumption. But they encounter challenges related to intensive maintenance and significant costs. As a mainstream future building form, the characteristics of modularization and factory customization inherent in prefabricated buildings can effectively address the demands of vertical greening system. This study employs structural equation model and cloud model to examine the feasibility of integrating mobile modular vertical greening system into prefabricated structures, based on a survey of 403 respondents in Shandong Province, China. The study highlights the supportive roles and mediating effects of two maintenance management measures. It confirms the strong correlations between cost control strategies and the successful integration of mobile modular vertical greening system into prefabricated building. The results indicate that integrating intelligent technology and plant-microalgae combined systems are essential exogenous and endogenous maintenance management strategies. They exert a positive influence on prefabricated modular vertical greening systems. A positive interconnection exists between integrating intelligent technology and the plant-microalgae combined system. Carbon credit trading incentive proves more advantageous in reducing mobile modular vertical greening system costs. Plant-microalgae combined system playing a partial mediating role. Although crowdfunding-community participation does not significantly reduce the costs of mobile modular vertical greening system, integrated intelligent technology mediates this relationship. This highlights the indirect role of crowdfunding-community participation in facilitating cost control for these systems.

Dynamics of cMOOC learner interactions in different social media: a longitudinal network analysis

ABSTRACT Social media may have an impact on people’s interaction patterns and social relationships. Investigating the interaction patterns of learners across various social media platforms can provide profound insights into the characteristics and mechanisms of connectivist learning. This study explores interaction network characteristics and the dynamic evolution of interaction patterns across multiple social media platforms within a cMOOC by using SNA and SIENA. The results show that cMOOC learners' interaction patterns have evolved with different characteristics on WeChat, blogs, and forums, eventually forming distinct interaction networks on each platform. The interaction networks on WeChat demonstrate superior performance in terms of size, cohesion, and connectivity, followed by the networks on blogs, and lastly, those on forums. WeChat and blog interaction networks exhibit a multi-center structure and modular characteristics, while forum interaction networks display a single-center structure. The evolution of interaction patterns on WeChat reveals more significant effects, such as reciprocity, transitivity, homophily, and preferential attachment, in contrast to blogs and forums, where transitivity and homophily are not prominent. The paper concludes with a discussion on the interaction patterns and network characteristics supported by three types of social media. Additionally, it highlights the significance and implications of these findings for educational practice.

Hexagonal electrohydraulic modules for rapidly reconfigurable high-speed robots.

Robots made from reconfigurable modular units feature versatility, cost efficiency, and improved sustainability compared with fixed designs. Reconfigurable modules driven by soft actuators provide adaptable actuation, safe interaction, and wide design freedom, but existing soft modules would benefit from high-speed and high-strain actuation, as well as driving methods well-suited to untethered operation. Here, we introduce a class of electrically actuated robotic modules that provide high-speed (a peak contractile strain rate of 4618% per second, 15.8-hertz bandwidth, and a peak specific power of 122 watts per kilogram), high-strain (49% contraction) actuation and that use magnets for reversible mechanical and electrical connections between neighboring modules, thereby serving as building blocks for rapidly reconfigurable and highly agile robotic systems. The actuation performance of each hexagonal electrohydraulic (HEXEL) module is enabled by a synergistic combination of soft and rigid components; a hexagonal exoskeleton of rigid plates amplifies the motion produced by soft electrohydraulic actuators and provides a mechanical structure and connection platform for reconfigurable robots composed of many modules. We characterize the actuation performance of individual HEXEL modules, present a model that captures their quasi-static force-stroke behavior, and demonstrate both a high-jumping and a fast pipe-crawling robot. Using embedded magnetic connections, we arranged multiple modules into reconfigurable robots with diverse functionality, including a high-stroke muscle, a multimodal active array, a table-top active platform, and a fast-rolling robot. We further leveraged the magnetic connections for hosting untethered, snap-on driving electronics, together highlighting the promise of HEXEL modules for creating rapidly reconfigurable high-speed robots.

Lateral stiffness of modular steel joint with semi-rigid bolted intra-module connection

In contrast to the beam-column joints found in traditional assembled steel structures, modular steel structures feature inter-module and intra-module connections within their joints. While existing research has largely focused on the mechanical properties of inter-module connections, the impact of intra-module connection stiffness on the performance of modular steel joints remains unclear. This study introduces a novel corner-fitting-reinforced fully bolted joint specifically designed for modular steel structures. A series of experiments were conducted, including a flexural test on bolted intra-module connection to determine its initial rotational stiffness, and four groups of lateral static tests on full-scale modular steel joints with varying intra-module connection stiffnesses. These tests aimed to characterize mechanical properties such as load-carrying capacity, lateral stiffness, strain development, and ductility. The study elucidates the influence of intra-module connection stiffness on the lateral stiffness of modular steel joints. Furthermore, a refined finite-element (FE) model of the corner-fitting-reinforced fully bolted joint was developed. Simulation results showed good agreement with experimental findings., with an average error of less than 10 % for ultimate load-carrying capacity prediction. The FE model also, analyzed the stress-strain development of the corner fitting throughout the process. The study establishes a theoretical analysis model for the corner-fitting-reinforced fully bolted joint and derives a theoretical formula for the initial lateral stiffness of the modular steel joint, considering semi-rigid intra-module connections. This formula aligns well with both experimental and FE results, with a maximum error of 15 %. Finally, the study delves into the force-transfer mechanism of the corner-fitting-reinforced fully bolted joint, providing valuable insights for its design.

A feature extension and reconstruction method with incremental learning capabilities under limited samples for intelligent diagnosis

Existing data-driven Intelligent Fault Diagnosis (IFD) methods often struggle with continuous learning and integrating new diagnostic knowledge. These methods necessitate a complete retraining process when new fault data emerges. Furthermore, collecting sufficient data for model training is impractical in real-world applications. This study proposes a Feature Extension and Reconstruction (FER) method for model incremental updating with limited samples. FER employs scalable and modular feature extractors for continuous representation learning of new fault modes. In addition, a domain mapping algorithm based on optimal transport is devised to leverage old classification nodes in aiding new nodes’ training, enhancing models’ generalization performance under limited samples. A feature reconstruction technique is also developed to compress the expanded multi-backbone model, eliminating redundant parameters and dimensions for adaptability in subsequent incremental tasks. Validated on simulated transmission systems and real planetary gearboxes, FER demonstrates a better stability-plasticity trade-off in incremental tasks with limited training samples, offering a novel solution for adaptive IFD model updates in sample-constrained scenarios.

Dynamic functional connectivity and gene expression correlates in temporal lobe epilepsy: insights from hidden markov models

BackgroudTemporal lobe epilepsy (TLE) is associated with abnormal dynamic functional connectivity patterns, but the dynamic changes in brain activity at each time point remain unclear, as does the potential molecular mechanisms associated with the dynamic temporal characteristics of TLE.MethodsResting-state functional magnetic resonance imaging (rs-fMRI) was acquired for 84 TLE patients and 35 healthy controls (HCs). The data was then used to conduct HMM analysis on rs-fMRI data from TLE patients and an HC group in order to explore the intricate temporal dynamics of brain activity in TLE patients with cognitive impairment (TLE-CI). Additionally, we aim to examine the gene expression profiles associated with the dynamic modular characteristics in TLE patients using the Allen Human Brain Atlas (AHBA) database.ResultsFive HMM states were identified in this study. Compared with HCs, TLE and TLE-CI patients exhibited distinct changes in dynamics, including fractional occupancy, lifetimes, mean dwell time and switch rate. Furthermore, transition probability across HMM states were significantly different between TLE and TLE-CI patients (p < 0.05). The temporal reconfiguration of states in TLE and TLE-CI patients was associated with several brain networks (including the high-order default mode network (DMN), subcortical network (SCN), and cerebellum network (CN). Furthermore, a total of 1580 genes were revealed to be significantly associated with dynamic brain states of TLE, mainly enriched in neuronal signaling and synaptic function.ConclusionsThis study provides new insights into characterizing dynamic neural activity in TLE. The brain network dynamics defined by HMM analysis may deepen our understanding of the neurobiological underpinnings of TLE and TLE-CI, indicating a linkage between neural configuration and gene expression in TLE.

Impacts of multiple environmental factors on soil bacterial community assembly in heavy metal polluted paddy fields

Soil microorganisms play pivotal roles in driving essential biogeochemical processes in terrestrial ecosystems, and they are sensitive to heavy metal pollution. However, our understanding of multiple environmental factors interaction in heavy metal polluted paddy fields to shape microbial community assembly remain limited. In the current study, we used 16S rRNA amplicon sequencing to characterize the microbial community composition in paddy soils collected from a typical industry town in Taihu region, eastern China. The results revealed that Cd and Pb were the major pollutant, and Proteobacteria, Acidobacteria and Chloroflexi were the dominate indigenous bacterial phyla. Linear regression and random forest analysis demonstrated that soil pH was the most important predictor of bacterial diversity. Mantel analysis showed that bacterial community structure was mainly driven by pH, CEC, silt, sand, AK, total Cd and DTPA-Cd. The constructed bacterial co-occurrence network, utilizing a random matrix theory-based approach, exhibited non-random with scale-free and modularity features. The major modules within the networks also showed significant correlations with soil pH. Overall, our study indicated that soil physiochemical properties made predominant contribution to bacterial community diversity, structure and their association in Cd/Pb polluted paddy fields. These findings expand our knowledge of the key environmental drivers and co-occurrence patterns of bacterial community in polluted paddy fields.

Design and analysis of a new deployable articulated mast mechanism with two-dimensional deploying-folding motion

Abstract A traditional deployable articulated mast mechanism can only provide one-dimensional (1D) deploying-folding motion. This paper aims to investigate the design and analysis of a novel deployable articulated mast mechanism with two-dimensional (2D) deploying-folding motion. Firstly, the topological motion of deployable mast mechanisms can provide 2D deploying-folding motion is analyzed, and a potential rectangular prism linkage unit is presented to construct single degree-of-freedom (DOF) deployable mast mechanisms with 2D deploying-folding motion. Secondly, the kinematics of the new deployable mast mechanism is established based on its structural symmetry and modular features. Thirdly, the deploying dynamics of the new mechanism are built based on the Lagrange equation. Fourthly, the deployed/folded ratio, interference and singularity of the new mechanism are analyzed. Finally, a numerical example is used to illustrate the effectiveness of the theoretical analysis, and a physical prototype is developed to show the fabrication feasibility of the new deployable mechanism. Compared with the traditional counterparts with 1D deploying-folding motion, the new deployable mast mechanism has a larger deployed/folded ratio, which has a good application prospect in space missions to support solar arrays, magnetometers, cameras and antennas.

Voltage balancing for active power filter using dual-frequency multicarrier modulation with balanced H-bridge control

In this paper, a switching control method integrated with voltage balancing feature is presented to manage operation of single-phase 5-level cascaded H-bridge (CHB) inverter as parallel-connected active power filter (PAPF), and at the same time maintain its voltage balance. CHB inverters are always preferred for PAPF due to their dominating features of modularity, flexibility and least components count. However, inherent voltage unbalance issues of CHB inverter which is due particularly to unequal switching operation across its individual H-bridge module have always been the most troublesome when handling CHB inverter. For consistent voltage balance across each H-bridge module, proportional-integral (PI) controller for voltage balancing control and multicarrier sinusoidal pulse-width modulation (MCSPWM) for switching control have commonly been integrated together. However, these conventional approaches do not directly address the issue of unequal switching. Hence, in this work, effort is performed to allow each individual H-bridge module to operate with balance switching pattern, by evenly distributing the PWM switching signal to them. Design concept of the proposed method is modeled in MATLAB-Simulink platform. To critically assess its effectiveness, comparative analysis involving two types of highly nonlinear rectifier loads and distorted grid is performed. The presented simulation findings confirmed effectiveness of the proposed method in maintaining voltage balance of the 5-level CHB inverter when mitigating harmonics under adverse grid.

Flexible and Advanced DBS: A distributed control strategy for a DC[sbnd]Nanogrid

Nowadays, it is well known that the energy system is going towards a clear and necessary energy transition. New enabling technologies are needed to accelerate this transition, like the dc nanogrids. The dc nanogrids are designed for a bottom-up approach and play an important role due to their scalability and modularity features. To take advantage of these features, in this paper, the authors propose a flexible and advanced DBS distributed control strategy so to be independent from the dc nanogrid configuration and operating both in stand-alone and in the grid-connected mode. At this scope the dc nanogrid is modelled as the connection of multiple generic nodes and a hierarchical control architecture is applied as a single control structure. It facilitates the seamless transition between the operating modes, independently from the dc nanogrid configuration. The effectiveness of the proposed flexible and advanced DBS is validated by some experiments performed on a testbed integrating a dc nanogrid laboratory prototype.

Intra-community link formation and modularity in ultracold growing hyperbolic networks

Hyperbolic network models offer a straightforward yet powerful method for understanding the small-world, scale-free, highly clustered, and modular characteristics typical of complex systems that are often called as real-world networks. These models involve randomly positioning nodes in a hyperbolic space and connecting them based on a probability that diminishes with distance. In this study, we examine the community structure within networks created by the Popularity Similarity Optimization model, a fundamental hyperbolic model, when the temperature parameter (responsible for tuning the clustering coefficient) is set to the limiting value of zero. We focus on link formation within communities and show their close relation with non-linear preferential attachment processes. Based on this, we derive analytical expressions that significantly improve previous estimates of the expected modularity for partitions formed by equally sized angular sectors in the 2d hyperbolic space. Our formulas can now predict average modularity, confirmed by numerical simulations, with high accuracy over a broader range of model parameters and community sizes relative to the whole network. These results advance our understanding of module formation in hyperbolic networks, highlighting the surprising emergence of communities despite the lack of explicit community formation steps in the model definition.

System-level time computation and representation in the suprachiasmatic nucleus revealed by large-scale calcium imaging and machine learning

The suprachiasmatic nucleus (SCN) is the mammalian central circadian pacemaker with heterogeneous neurons acting in concert while each neuron harbors a self-sustained molecular clockwork. Nevertheless, how system-level SCN signals encode time of the day remains enigmatic. Here we show that population-level Ca2+ signals predict hourly time, via a group decision-making mechanism coupled with a spatially modular time feature representation in the SCN. Specifically, we developed a high-speed dual-view two-photon microscope for volumetric Ca2+ imaging of up to 9000 GABAergic neurons in adult SCN slices, and leveraged machine learning methods to capture emergent properties from multiscale Ca2+ signals as a whole. We achieved hourly time prediction by polling random cohorts of SCN neurons, reaching 99.0% accuracy at a cohort size of 900. Further, we revealed that functional neuron subtypes identified by contrastive learning tend to aggregate separately in the SCN space, giving rise to bilaterally symmetrical ripple-like modular patterns. Individual modules represent distinctive time features, such that a module-specifically learned time predictor can also accurately decode hourly time from random polling of the same module. These findings open a new paradigm in deciphering the design principle of the biological clock at the system level.

Advanced virtual modelling aided stochastic nonlinear dynamic stability analysis of the GPLR-FGP plate in thermal environments

This paper explores the crucial yet challenging task of stochastic nonlinear dynamic buckling investigations of the GPLR-FGP plate under biaxial impacts in thermal environments with multi-dimensional uncertainties for the first time. An advanced virtual modelling technique, namely the Extended Support Vector Regression (XSVR) with a fresh S-spline polynomial kernel, is newly developed to alternatively depict the underpinned and sophisticated relationship between the inevitable system uncertainties and concerned nonlinear buckling response in an efficient fashion. Adequate statistical information of stability performance is furnished for the safety assessment and reliability-based design of the GPLR-FGP composite. The superior competence, advisable exactness, and appealing efficiency of the proposed uncertainty quantification framework are vividly demonstrated through benchmark tests and comprehensive numerical experiments against established machine learning algorithms. Moreover, the user-friendly modularity feature, and unique capabilities of expeditious information update and responsiveness highlight the versatile applicability of the proposed technique in realistic structural implementation under swiftly changing environments.

Fault diagnosis of accelerometer servo circuit output saturation based on feature electrical parameters extraction

The servo circuit of quartz flexible accelerometer is an extremely vital microelectronic device in inertial system. The paper introduces the traditional analysis process about output saturation fault of accelerometer servo circuit. For the shortcomings of traditional analysis methods, a fault diagnosis method based on feature electrical parameters extraction is proposed. According to the fault phenomenon, combined with the modular characteristics of hybrid integrated circuits, the feature electrical parameters of fault modes are extracted, and the fault modes' judgment basis is set. By the judgment results of the feature electrical parameters of the fault circuit, the circuit with output saturation fault caused by the electrical damage to the differential capacitance detector and other faults can be located quickly and accurately, and it is verified by the analysis of faulty circuit samples. Targeted for these faults, the improvement measures are put forward. This analysis method improves the fault diagnosis efficiency and reliability about the accelerometer servo circuit, and has guiding significance for enhancing the success rate of the failure analysis in the hybrid integrated circuits.

Isothermal Reciprocal Catalytic DNA Circuit for Sensitive Analysis of Kanamycin.

Inappropriate use of veterinary drugs can result in the presence of antibiotic residues in animal-derived foods, which is a threat to human health. A simple yet efficient antibiotic-sensing method is highly desirable. Programmable DNA amplification circuits have supplemented robust toolkits for food contaminants monitoring. However, they currently face limitations in terms of their intricate design and low signal gain. Herein, we have engineered a robust reciprocal catalytic DNA (RCD) circuit for highly efficient bioanalysis. The trigger initiates the cascade hybridization reaction (CHR) to yield plenty of repeated initiators for activating the rolling circle amplification (RCA) circuit. Then the RCA-generated numerous reconstituted triggers can reversely stimulate the CHR circuit. This results in a self-sufficient supply of numerous initiators and triggers for the successive cross-invasion of CHR and RCA amplifiers, thus leading to exponential signal amplification for the highly efficient detection of analytes. With its flexible programmability and modular features, the RCD amplifier can serve as a universal toolbox for the high-performance and accurate sensing of kanamycin in buffer and food samples including milk, honey, and fish, highlighting its enormous promise for low-abundance contaminant analysis in foodstuffs.

Multi-level mechanism of biomimetic TPMS hybridizations with tailorable global homogeneity and heterogeneity

The materials with tailorable plasticity plateau have outstanding advantages in addressing the scenario requirements of impact overload and energy absorption. However, this regulation normally adopts structures with heterogeneities or graded-density isomorphisms, which undermine structural continuity and affect structural performance under dynamic loads. This work constructs a bioinspired hybrid TPMS structure that exhibits macroscopic modular heterogeneous features and module internal homogeneous qualities. The hybridizations are constructed along [0,0,1], [0,1,1], [1,1,1] directions, where the implicit smooth surface characteristic facilitates the transitional hybrid and provides effective load-bearing in globally spatial directions. An RVE method validated by experiments is used to efficiently calculate their plastic behaviors. And the tailorable design of hybrid TPMS properties can be achieved based on macro-micro interaction analysis of hybridizations. The hybrid TPMS structures retain the characteristics of the original structure locally and exhibits macroscopic tailorable multi-level behaviors.