Search Results (378)

Bipartite network community detection is of significant importance for understanding the underlying structure and functional organization of real-world complex systems. Although many mature community detection algorithms exist for unipartite networks, they cannot be directly applied to bipartite networks due to their unique topological structure, characterized by heterogeneous node types and cross-layer connections. Furthermore, some existing bipartite network community detection methods still rely heavily on manual experience to set key parameters, which limits their applicability and scalability in practical scenarios. To address these issues, this paper proposes an enhanced framework—the Adaptive Network Reconstruction Framework (ANRF)—by introducing an adaptive parameter optimization mechanism based on the existing Network Reconstruction Framework (NRF). This framework can be effectively integrated with traditional unipartite network community detection algorithms to achieve automatic community detection with reduced dependence on manual parameter tuning. The core procedure of the method consists of four main steps. First, we calculate the interaction forces between node pairs. Second, through comprehensive analysis of the network topological features, we adaptively determine the threshold parameter $\theta$ and related parameters for the interaction forces. Third, based on these thresholds and parameters, we perform edge filtering on the bipartite network to construct a reconstructed network. Finally, we apply unipartite community detection algorithms directly to the reconstructed network to obtain the community structure. To validate the effectiveness of ANRF, we combined it with the Louvain method and the Greedy modularity method, and conducted experimental evaluations on multiple synthetic and real-world network datasets. A systematic comparison with current state-of-the-art algorithms was made. The experimental results on multiple synthetic and real-world datasets within our evaluated scope demonstrate that ANRF achieves competitive performance in terms of community modularity and community density compared to state-of-the-art algorithms, while significantly reducing reliance on manual parameter tuning and enhancing robustness under the tested conditions. Full article

This study presents an integrated workflow for the multiobjective optimization of directional well trajectories in underground gas storage (UGS) reservoirs. A modular well-path construction model is developed, enabling flexible assembly of linear and curved segments in a local reference frame and their transformation into the reservoir. The optimization problem is formulated to simultaneously maximize working-gas capacity and minimize total drilling length for ten new directional wells. A calibrated UGS reservoir with more than 30 years of production history is used as the simulation environment, and solution quality is explored using the NSGA-II (non-dominated sorting genetic algorithm) evolutionary algorithm. The results reveal a diverse Pareto front of feasible designs. The best configurations achieve either an 8.6% reduction in total drilling length while still delivering a 2.12% capacity increase, or a 3.18% capacity enhancement at a modest drilling-length increase of 4%. These outcomes demonstrate that strategic redesign of well trajectories alone can deliver measurable improvements in UGS performance without modifying well controls or facility constraints. The proposed methodology provides a generalizable and computationally efficient framework for large-scale multiwell planning in UGS systems. Its modularity supports future extensions, including collision avoidance, perforation optimization, and adaptive well-control strategies. Full article

Background and Aims: The rising global incidence of cholangiocarcinoma (CCA) coincides with epidemics of type 2 diabetes (T2D) and chronic hepatitis B virus (HBV) infection. Although both are established independent risk factors, the shared molecular mechanisms by which they contribute to cholangiocarcinogenesis remain poorly understood. We hypothesized that T2D and HBV converge on a state of chronic metabolic inflammation (“metaflammation”) that drives CCA progression through a conserved transcriptomic network. Methods: We performed an integrative bioinformatics analysis of transcriptomic data from public repositories, including samples of CCA (TCGA-CHOL, n = 45; GSE107943, n = 163), T2D-affected liver (GSE23343, n = 20), and HBV-infected liver (GSE58208, n = 102). Acknowledging that the T2D and HBV datasets were derived from whole-liver tissue, whereas CCA originates in the biliary epithelium, we identified differentially expressed genes (DEGs) across conditions and defined a core gene set shared among them. Subsequent analyses included functional enrichment, construction of protein–protein interaction (PPI) networks, survival analysis, and protein validation. Results: We identified a core metaflammation signature comprising 156 genes that were consistently dysregulated across T2D, HBV, and CCA. Pathway analysis revealed significant enrichment in PPAR signaling, cytokine–cytokine receptor interaction, PI3K-Akt, and TNF signaling pathways. Protein–protein interaction (PPI) network analysis identified IL6, TNF, AKT1, STAT3, and PPARG as the top hub genes. These hubs were functionally modularized into clusters associated with inflammatory signaling, metabolic regulation, and cell growth and survival. In the TCGA CCA cohort, high expression of IL6, TNF, AKT1, and STAT3 and low expression of PPARG correlated with advanced tumor stage and poorer overall survival (e.g., IL6: ρ = 0.42, p = 0.01). A metaflammation score derived from these hubs (weighted combination of the five genes) emerged as an independent prognostic factor (HR = 2.8, p < 0.001). Protein-level dysregulation of these hubs was confirmed via immunohistochemistry. Conclusions: This study defines a conserved metaflammation network that links T2D and HBV to CCA, identifying key hub genes and pathways. This signature provides a mechanistic explanation for epidemiological risks, serves as a novel prognostic tool, and offers a rationale for targeting metaflammation in prevention and therapy for high-risk populations. Full article

In the modular construction of liquefied natural gas (LNG) plants and receiving terminals, transport beams are critical components that enable modular mobility. However, these beams are susceptible to large deformations due to complex loads during land and sea transportation. Traditional monitoring methods (i.e., strain gauge and deflection meters) often suffer from low efficiency and poor accuracy and may disrupt operational continuity in real-time monitoring systems. This paper presents a non-contact, real-time deformation detection system for LNG modular transport beams based on digital image technology, which integrates a high-resolution camera with a real-time software framework to remotely monitor structural integrity. An experiment was conducted on a full-scale support column-transport beam frame with specialized connection joints designed for rapid assembly. Five digital image correlation (DIC) detection regions (5 cm × 5 cm) were established on box-shaped beam sleeves, column sleeves, and the end plates of the beam–column joints. In addition, displacement gauges were installed at the same DIC locations. The experimental results demonstrate that the DIC measurements show good agreement with traditional measurement methods, verifying the applicability of the proposed system for large-scale LNG engineering structures. Full article

To address the unobservability of distribution networks caused by insufficient coverage of measurement terminals as well as communication failures and missing data, and to cope with operating-state fluctuations induced by distributed generation integration and external environmental disturbances, this paper proposes an integrated state-estimation and voltage-regulation strategy that combines distribution-network-partitioning-based optimal PMU placement with pseudo-measurement construction using power transfer distribution factors (PTDFs). First, nodal reactive-power sensitivity information is derived from the power-flow Jacobian matrix, and an improved modularity function is employed to obtain the optimal partitioning of the distribution network, based on which PMUs are deployed at partition boundary buses. Second, PTDF-based power pseudo-measurements are constructed for unobservable buses and incorporated into the measurement model via a measurement transformation; a weighted least-squares method is then adopted to achieve system-wide state estimation. Finally, the estimated voltage states are fed into flexible voltage-regulation devices to enable fast and continuous voltage adjustment across buses. Case studies on the IEEE 33-bus system demonstrate that the proposed method effectively improves voltage quality. Full article

Over the last decade, prefabrication has emerged as a strategic alternative to address the global construction industry’s challenges concerning sustainability, productivity, and the housing deficit. This study analyzes the advances, benefits, limitations, and research gaps associated with its application in real estate projects between 2015 and 2025. A systematic literature review was conducted under the PRISMA protocol, which allowed for the selection of 58 high-quality articles sourced from Scopus, Web of Science, SciELO, and Redalyc. The findings highlight Asia as the leader in innovation and industrialization, while Latin America is identified as an emerging region with applications in social housing, education, and modular infrastructure. Reported benefits include reduced time and costs, improved environmental performance, and the integration of digital technologies such as BIM, 3D printing, and digital twins. Nevertheless, regulatory gaps, cultural resistance, and limited coordination among industry, government, and academia persist. The study concludes that prefabrication constitutes a transformative engine for the real estate sector, but its consolidation requires stronger regulatory frameworks, broader empirical research in Latin America, and the adoption of circular economy and digitalization strategies to ensure a sustainable and socially accepted impact. Full article

This article examines the governance of Global Value Chains (GVCs) through the lens of economic competence based on a systematic literature review of 32 selected studies. The findings show that economic competence functions as a governance-contingent construct whose effects vary across hierarchical, captive, relational, and modular governance structures. Rather than directly determining upgrading outcomes, competence dimensions operate through governance repositioning and shifts in dependence asymmetries within value chains. The review identifies recurring mechanisms—such as substitutability reduction, coordination cost mitigation, and institutional alignment—that explain how competence and governance interact. The analysis further demonstrates that economic competence is multidimensional, encompassing innovation-oriented, market-oriented, decision-making, relational, and systemic components. These dimensions operate differently depending on coordination complexity and power distribution within the chain. By advancing a contingency-based framework, the study refines GVC governance theory through a micro-foundational explanation of upgrading dynamics. From a managerial perspective, the framework offers a structured tool for aligning competence development strategies with specific governance configurations, supporting informed capability investments and improved strategic positioning. Overall, the study contributes by systematically integrating competence theory with governance typologies and power asymmetries, providing a coherent analytical model for future empirical research. Full article

While traditional 2D in vitro models have been widely used for drug screening, 3D tissue culture systems are gaining traction due to their superior ability to replicate in vivo tumor microenvironments. In this study, we utilize Microtissues™, a validated, scaffold-free, high-throughput 3D tissue culture platform, as the basis for a microscale tissue-engineered model to study drug absorption and transport dynamics. Despite their physiological relevance, such 3D constructs pose analytical challenges, particularly in quantifying trace drug levels within the microenvironment. We developed and validated an integrated experimental workflow combining optimized liquid–liquid extraction and protein precipitation with LC-MS/MS analysis to accurately quantify paclitaxel absorption in Microtissues™ molds using small sample volumes. The assay achieved a validated lower limit of quantification of 0.03 μM, with robust linearity across analytical runs (R² ≥ 0.90; best-run performance > 0.99) and precision (CV ≤ 10%) across both MRMs. This microengineered in vitro system allows for precise characterization of drug–tissue interactions in MCF7 breast cancer Microtissues™, enabling in vitro-to-in vivo extrapolation (IVIVE) relevant to therapeutic optimization. The platform’s scalability and modularity support its application in precision medicine, where patient-derived microtissues can guide individualized treatment decisions. Full article

To continuously improve the efficiency of the construction project delivery process, various innovative methods and technologies have been developed and adopted in the past decades. Among these methods, modular construction has become a popular option due to its short on-site installation time generated by off-site prefabrication. However, the process of modular construction requires a highly integrated system to accurately connect multiple phases, including material packaging, transportation logistics, locating and tracking, and on-site installation. Accordingly, this process typically poses a significant challenge for contractors to efficiently manage the materials needed for daily tasks. This paper introduces a construction material management system that integrates every phase from off-site packaging to on-site installation. The integrated system was developed based on Logistic Chain and Building Information Modeling (BIM) using a three-layer framework, namely material packaging, inventory management, and material locating and tracking. The new system utilizes recent innovative technologies for transparent consolidation and highly efficient operation of off-site inventory management and on-site visualization. The developed system was further examined in a real-world case study project. The material handling time was then analyzed and compared with benchmark data without using the integrated system. The results indicated that the newly developed system was able to effectively reduce the time of locating materials and the rate of missing materials during on-site installation. In addition, this case study project added value to the verification of the broader system’s capabilities for inventorying, tracking, and visualizing construction materials. The findings of this project provide valuable knowledge and insight into improving construction efficiency through an integrated material management system. Future research is needed to expand the applicability of multiple framework designs and assess the cost–benefit analysis for production-scale and commercial use. Full article

Despite its recognized advantages in sustainability and efficiency, the widespread adoption of Modular Integrated Construction (MiC) is impeded by transaction costs (TCs). While previous studies have cataloged numerous barriers, a systematic, quantitative synthesis of their relative impact on TCs is lacking. This study bridges this gap by conducting a hybrid systematic review and meta-analysis of 37 empirical studies (2005–2025) to identify and rank the key drivers of TCs in MiC. Grounded in Transaction Cost Economics, 32 factors were categorized into transaction attributes, the transaction environment, and stakeholder-related aspects. The meta-analysis quantified the pooled effect sizes, revealing that operational and procedural hurdles—specifically “Poor Logistics,” “Design Change,” and “Insufficient Quality Inspection Standards and Regulations”—are the most critical determinants. A key finding is the divergence between the most frequently cited barriers and those with the highest impact, underscoring the value of meta-analytic synthesis over simple frequency counts. Subgroup analyses further indicated that the severity of institutional and regulatory drivers is more pronounced in developing economies. This research provides a novel, evidence-based framework for stakeholders to develop and prioritize mitigation strategies, advocating for investments in integrated digital tools, rigorous front-end planning, and context-sensitive policy development to reduce transactional inefficiencies and promote MiC adoption. Full article

The integration of Generative AI into civil engineering is currently constrained by the risk of non-compliant outputs and an inherent lack of physics-based knowledge. To address these limitations, this paper presents a conceptual framework for the integration of Agentic Artificial Intelligence (AI) into the complete lifecycle of seismic-resistant structural engineering. The proposal employs a modular software architecture built on the Model Context Protocol (MCP), enabling distributed collaboration among specialised AI agents. We operationalise this architecture across six critical stages, where specific agents govern distinct phases: (1) Seismic Hazard and (2) Structural Modelling agents quantify demands through deterministic tool execution; the (3) Design agent optimises element sizing under the strict governance of Retrieval-Augmented Generation (RAG) for code compliance; (4) Construction Quality Control and (5) Structural Health Monitoring (SHM) agents validate as-built geometry and service-life performance; and an overarching (6) Ethical Audit agent supervises the ecosystem to ensure safety and algorithmic transparency. By decoupling probabilistic design iteration from immutable numerical execution, this framework ensures that generative outputs are traceable, transparent, and professionally accountable, offering a verified pathway for the deployment of AI systems in structural engineering. Full article

This work introduces Hybrid Membrane–Neural P systems (HMN P systems), a computational model that integrates principles from membrane computing and spiking neural P systems. The resulting framework offers a versatile foundation for the development of bio-inspired arithmetic architectures. Within this setting, we propose a compact family of arithmetic kernels capable of executing signed addition, subtraction, multiplication, and division in both modular and non-modular arithmetic domains. By leveraging intrinsic spike aggregation, spike–anti-spike annihilation, and exhaustive rule application, the proposed designs achieve efficient and reliable arithmetic computation in a constant number of simulation steps under exhaustive semantics and assuming synchronized input, independent of operand values. Addition and subtraction are executed intrinsically upon spike arrival, requiring no internal computation steps, while multiplication and division are completed in a single simulation step by one neuron. Furthermore, we introduce a modular-reduction kernel that operates in two simulation steps with a single neuron, and leverage its modular structure to construct modular multiplication and division through composition with non-modular arithmetic modules. Comparative evaluations against representative SNP and SNQ arithmetic designs demonstrate that HMN kernels achieve operand-independent execution time while requiring fewer neurons. Distinct from most existing approaches, the HMN framework natively supports signed operands through a dual-spike representation, thereby eliminating the need for auxiliary sign-handling mechanisms. Asynchronous spike arrivals can be managed by an optional synchronization membrane; since this mechanism is decoupled from the arithmetic kernels, its overhead is excluded from kernel performance and reported separately. Collectively, these results establish HMN systems as an efficient and modular platform for constant-time arithmetic computation, offering reusable arithmetic kernels that serve as a foundation for higher-level constructions, including those arising in elliptic-curve and modular arithmetic. Full article

This paper presents the design, construction, and calibration of a modular low-cost 3D printer based on open-source technologies, developed as part of an academic research project. The printer utilises fused filament fabrication (FFF) and is built using locally available materials and components, including a T-slot aluminium frame, NEMA 23 stepper motors, and an Arduino Mega 2560 with RAMPS 1.4 control board. The system integrates Marlin firmware and CURA slicing software, enabling autonomous operation via an LCD panel and encoder interface. A detailed methodology is provided for mechanical assembly, electronic integration, firmware configuration, and calibration procedures. Special attention is given to the challenges encountered during the initial testing phase, including filament feeding issues, thermal inconsistencies, and mechanical misalignments. Solutions such as replacing inadequate components (e.g., fibreglass bushings with PTFE), adjusting spring tension, and refining firmware parameters are discussed. The results demonstrate successful printing of complex geometries after iterative calibration, validating the printer’s performance and replicability. This work contributes to the democratisation of additive manufacturing by offering a replicable, open-source solution for educational and prototyping purposes. The findings are relevant to machine design, automation, and robotics communities seeking practical insights into low-cost fabrication systems. Full article

Digital transformation in public administration is shaped not only by technology but also by institutional expectations, legitimacy concerns and uneven local capacities. However, existing qualitative instruments rarely support structured reflection on how these conditions influence digital change. This study develops a modular, theory-informed focus group guide designed to help practitioners articulate institutional influences on municipal digital transformation. Using an Action Design Research framework, institutional concepts were embedded into the guide and iteratively refined across six focus groups with municipal actors. Through recursive Alpha and Beta cycles, the artifact evolved via authentic and concurrent evaluation, integrating practitioner feedback, visual scaffolds and accessible translations of theoretical constructs. Results show that the guide enabled participants to identify coercive, mimetic and normative pressures, surface assumptions across administrative roles and externalize institutional relationships. These patterns point to an institutionally dominant mode of artifact development in which interpretive engagement and legitimacy dynamics shape refinement. The study demonstrates that institutional theory can serve as a productive kernel for qualitative instrument design and offers transferable design principles for developing tools that support reflective, inclusive and socially aware digital transformation in public sector contexts. The resulting artifact, referred to as the Modular Institutional Instrument (MII), is made publicly available to support application in similar governance contexts. Full article

Post-disaster reconstruction in resource-constrained contexts is often delayed by limited material supply, skilled labor, and planning capacity. Following the Mw 7.7 earthquake that struck near Mandalay, Myanmar, in March 2025, extensive housing damage and displacement underscored the need for economical and rapidly constructible reconstruction housing that can also support longer-term recovery. This study proposes a progressive and resident-modifiable housing scheme based on light-gauge steel framing, integrating the seismic design principle of strong-column–weak-beam to improve structural reliability during aftershocks and future events. The proposed system combines a standardized light-gauge steel framing (LGSF) structural frame with locally accessible enclosure and infill materials, allowing rapid assembly of an initial modular unit to meet urgent shelter needs while enabling progressive upgrading of façades and interior space over time to enhance habitability and resilience. Validation analyses focusing on construction efficiency and mechanical performance indicate that the strong-column–weak-beam LGSF scheme, when paired with local materials, offers favorable applicability in terms of buildability, cost-effectiveness, and seismic behavior under realistic conditions in Mandalay. The study provides a feasible technical solution and design approach for progressive post-disaster reconstruction housing in the region. Full article