Methodology for Surface Defect Assessment in 3D Concrete Printing using Computer-vision and Ultrasonic Testing Considering Structural Build-up

This study quantifies how interlayer time gaps and mixture rheology jointly influence interfacial integrity and mechanical performance in 3D concrete printing (3DCP). Two mixtures incorporating identical binders, but differing limestone fineness (4 and 50 μm) were printed with interlayer delays of 0, 2, and 5 min, corresponding to stacking rates of approximately 9, 0.5, and 0.2 m/h. The finer limestone mixture exhibited higher plastic viscosity (13.2 vs. 11.5 Pa s) and faster structural buildup (58 vs. 38 Pa/min), resulting in accelerated early stiffening. Deposition yield stress derived from buildup measurements remained low for 0–2 min (1400–1500 Pa for the higher-thixotropy mixture; 1100–1500 Pa for the lower one) but increased markedly at 5 min to 4100 and 2900 Pa, respectively. Six single-wall prints were evaluated via surface defect imaging, layer-resolved ultrasonic S-wave velocity mapping, and 28-d compressive testing of specimens extracted from lower (layers 1–5) and upper (6–10) regions. Defect density rose with both delay and height, reaching 14.5% in the upper layers of the higher-thixotropy mixture at 5 min, while the 0-min condition remained ≤ 2%. S-wave velocity declined from 2100 to 2150 (0 min) to 1,800–1,870 m/s (5 min), accompanied by a strength reduction from 50 to 25–30 MPa. The findings define a practical deposition window, yield stress of 1100–1500 Pa, that minimizes interlayer defects under realistic delays and demonstrate that layer-wise S-wave monitoring sensitively captures vertical stiffness gradients, providing a viable process-control metric for large-scale additive construction

Managing and Accelerating the Circular Economy Transitions within the Construction Value Chain using Network Governance and Game Theory Systems Perspectives

The fragmented nature of the construction industry has hindered its transition toward circular economy (CE) practices. While game theory (GT) has been successfully applied to support the strategic decisions of CE transitions in other sectors, CE-GT models in the construction domain remain extremely limited. This paper addresses two research questions: (1) how are CE network governance activities currently modeled using GT across domains? (2) How can such models be transferred and adapted to solve strategic interactions within the construction value chain in a manner that enables CE practices to emerge as equilibrium outcomes? These questions are driven by the need to reorganize the construction value chain\u27s decision-making environment to better align with CE principles. To answer these questions, the paper adopts a mixed-methods approach combining deductive content analysis with network analysis. Findings show that market creation games, primarily based on Stackelberg models, are most common and are used for CE pricing mechanisms and profit determination. Evolutionary games follow in frequency, typically applied to evaluate macroscale policies and sociocultural changes. Despite the diversity of models identified, most have been developed for manufacturing-oriented value chains, revealing a need for adaptations to reflect the unique characteristics of the construction sector, such as project-based operations, immobility, uncertain demand, fragmentation, and assembly processes. Several gaps were identified, including the need for modeling workforce education, skill development, and improved integration of policy formulation and implementation in CE transitions. Alongside the proposed adaptations to GT models for the construction sector, future research recommendations include incorporating discount factors, consumption decisions, and learning algorithms to enhance model accuracy and decision-making. This study contributes to the body of knowledge by providing a conceptual framework for integrating GT models into network governance processes, supporting more effective decision-making and policy development for CE implementation within the construction value chain

The Kaczmarz Algorithm in Hilbert C∗-modules

The Kaczmarz algorithm in Hilbert spaces is a classical iterative method for stably recovering vectors from inner product data. In this paper, we extend the algorithm to the setting of Hilbert C∗-modules and establish analogues of its effectiveness in both finite-dimensional and stationary cases. Consequently, we demonstrate that continuous families of elements in a Hilbert space can be uniformly recovered using the Kaczmarz algorithm. Additionally, we develop a normalized Cauchy transform for continuous families of measures and use it to provide sufficient conditions under which standard frames in Hilbert C(X)-modules can be generated by the Kaczmarz algorithm and realized as orbits of bounded operators

Synergistic V2CTₓ MXene–PANI Hybrid with Expanded Interlayers for Ultrastable and High-rate Pseudocapacitive Energy Storage

Recently, MXene-conducting polymer hybrids have emerged as promising electrode materials for sustainable energy storage applications, owing to their impressive electrochemical properties. Herein, we report the synthesis of vanadium carbide MXene nanoparticles (V2CTx-MXene) using innovative Spark Plasma Sintering (SPS) technology followed by exfoliation steps. The V2CTx nanoparticles were incorporated with PANI (MXene-PANI) by electrochemical polymerization of aniline monomers in the presence of V2CTx nanolayers, to be used as a highly efficient material for charge storage application. PANI nanofibers form a conductive and porous architecture, which intercalates the V2CTx nanoflakes. The resulting structure increases the interlayer spacing of V2CTx sheets, which provides a larger accessible surface area, facilitates ion transport capability, and enhances the diffusion coefficient within the composite electrode. Benefiting from the strong interaction between V2CTx and PANI, high electrical conductivity, and improved surface hydrophilicity, the MXene-PANI nanocomposite presented an excellent specific capacitance of 677.21 F/g, surpassing pristine PANI with 397.71 F/g. Furthermore, the MXene-PANI exhibited remarkable capacitance retention of 91.4 % after 10,000 GCD cycles. The impressive electrochemical performance of the composite electrode can also be attributed to the pseudocapacitive performance (redox behavior) of V2CTx nanoparticles. The resulting synergy in the V2CTₓ MXene-PANI heterojunction significantly enhances the physicochemical properties of the hybrid, which, combined with its outstanding electrochemical performance, makes it a promising material for charge storage in supercapacitors and beyond

Long Short-term Memory (LSTM) -Based Neural Network Model for Optimizing Composite Manufacturing Process using Autoclave

Producing high-quality fiber-reinforced composites requires precise temperature control during autoclave curing, as even small variations can lead to defects that compromise strength and reliability. At the same time, manufacturers aim to reduce energy use and shorten curing cycles without sacrificing material performance. To address these challenges, this study develops a data-driven Long Short-Term Memory (LSTM) neural network model capable of forecasting temperature evolution inside the autoclave throughout the curing cycle. The model is trained on time-series temperature data collected from multiple sensing locations, enabling it to learn the spatial and temporal trends that govern heat flow during curing. Data augmentation techniques such as time shifting, scaling, and jittering were applied, helping the model better handle noise and inconsistencies in the dataset. The resulting predictions closely match expected temperature patterns, showing that learning-based models can effectively capture the complex and dynamic thermal behavior within the autoclave. By offering early insight into temperature behavior during curing, the LSTM approach can support better heating control, improve curing consistency, and help reduce overall cycle time. This capability leads to more uniform temperature distribution, fewer unnecessary dwell periods, and higher-quality composite parts. These results show that predictive deep learning can be successfully integrated into autoclave operations, providing a strong foundation for future real-time, adaptive process control in smart composite manufacturing

Reflections on Linear B (part 13): Sign 21 may derive from the Ancient Egyptian \u27wedjat eye’ (‘Eye of Horus’ but also associated with Ra)

This article suggests that Linear B sign 21 (of unknown origin) may derive ultimately from the Ancient Egyptian ‘wedjat eye’ (Eye of Horus). A point of special interest here is the possibility that the pronunciation of Linear B sign 21 (designated as ‘qi’) derives in abbreviated form from the Proto-Indo-European dual of ‘eye’ and would therefore be the oldest Greek attestation of that reconstructed PIE form

Early Detection of Infectious Diseases: A Review of Recent Advances in Pathogen Identification, Molecular Tools, and Metabolomics-Driven Biomarker Discovery

The recent COVID-19 pandemic has heightened public interest in noninvasive methods for early diagnosis of infectious diseases. In addition, various government agencies have implemented infectious disease preparedness to mitigate future outbreaks. This review highlights conventional and advanced methods for infectious disease diagnosis with an emphasis on emerging mass spectrometry methods. Conventional methods for pathogen identification, such as culture-based techniques and molecular methods, have limitations with respect to sensitivity, specificity, and turnaround time. Recent advances in high-resolution mass spectrometry have revolutionized the field of infectious disease biomarker discovery. These techniques enable the comprehensive profiling of metabolites in various biological samples, identification of disease-specific biomarkers, and elucidation of complex host–pathogen interactions. While liquid chromatography–mass spectrometry has been extensively used to identify metabolic alterations in diseases, such as COVID-19, tuberculosis, pneumonia, and influenza, this often requires the use of body fluids. On the other hand, advances in gas chromatography-high resolution mass spectrometry are enabling noninvasive detection of infectious diseases by means of breath-based volatile organic compounds. These methods offer high sensitivity and specificity, enabling the detection of low-abundance biomolecules and the elucidation of complex biological pathways. This review further examines the limitations of each approach while emphasizing the essential applications of metabolomics in infectious disease diagnosis

Multi-Parameter Optimization and Adaptive Temperature Compensation for FBG Strain Sensors in Wide-Temperature-Range Aerospace Applications

Accurate strain measurement in cryogenic fuel pipelines is crucial for ensuring the structural integrity and reliability of rocket engine systems operating under extreme thermal conditions. Fiber Bragg grating (FBG) sensors show significant potential for such applications; however, their inherent temperature-strain cross-sensitivity limits performance over wide temperature ranges. This research presents an enhanced compensation strategy combining multi-parameter optimization with temperature-zone-specific adaptation to improve the accuracy and stability of FBG-based strain sensing in harsh aerospace environments. Four special steel substrate materials including S03, S06, S07, and 1Cr18Ni9Ti, were evaluated using strain transfer theory and thermo-mechanical coupling simulations. Genetic algorithms optimized key design parameters, which were validated through experimental testing from 77 K to 433 K. S03, S06, and S07 exhibited favorable cryogenic performance, achieving an average strain transfer efficiency of approximately 87% at extreme temperatures. Conversely, 1Cr18Ni9Ti demonstrated the highest strain transfer efficiency at high temperatures, exceeding 95% at 433 K, with a temperature sensitivity of 30.44 pm/K and a compensation error below 2.63 με. The implementation of segmented temperature self-compensation further reduced residual strain fluctuations to within 0.5 με. This method significantly enhances the reliability of FBG sensors for high-precision strain monitoring under extreme temperature conditions, offering a practical solution for critical aerospace instrumentation

Multi-population Sufficient Dimension Reduction

A novel dimension-reduction method is introduced for multi-population data. The approach conducts a joint analysis that exploits information shared across populations while accommodating population-specific effects. Unlike partial dimension reduction methods, which identify related directions across all populations, or conditional analyses conducted independently within each population, the proposed two-step procedure leverages cross-population information to enhance estimation accuracy. The methodology is demonstrated through simulations and two real-data applications

Emergent Spin Fluctuation and Structural Metastability in Self-Intercalated Cr1+xTe2 Compounds

Intercalated van der Waals (vdW) magnetic materials host unique magnetic properties due to the interplay of competing interlayer and intralayer exchange couplings, which depend on the intercalant concentration within the van der Waals gaps. Magnetic vdW compound chromium telluride, (Formula presented.), has demonstrated rich magnetic phases at various Cr concentrations, such as the coexistence of ferromagnetic and antiferromagnetic phases in (Formula presented.) (equivalently, (Formula presented.)). The compound is created by intercalating 0.25 Cr atom per unit cell within the van der Waals gaps of (Formula presented.). In this work, we report a notably increased Curie Temperature and an emergent in-plane spin fluctuation by slightly reducing the concentration of Cr intercalants in (Formula presented.). Moreover, the intercalated Cr atoms form a metastable 2 (Formula presented.) 2 supercell structure that can be manipulated by electron beam irradiation. This work offers a promising approach to tuning magnetic and structural properties by adjusting the concentration of intercalated magnetic atoms