Modelling of cement concrete has been on the rise over the past few decades mainly attributed to the rising need to understand its constituents, improve its properties and predictability, and reduce its failure. Due to the increasing housing and infrastructure demands, cement concrete modelling techniques have evolved. These include experimental tests, mathematical and statistical models, and computerized modelling. This review paper presents modelling based studies on cement concrete to identify research gaps and foster improvement by employing scoping literature review technique and social network analysis using VOS Viewer version 1.6.20. The publications were mainly accessed through Google Scholar and Dimensions data bases. The search focused on publications from 2000 to 2024 while the analysis looked at the last ten years from 2015 to 2024. The results show that use of modelling in cement concrete has been on a rise. However, few studies take on individual concrete constituent characteristics as inputs in modelling. Material characteristics like aggregates sizes, distribution patterns and water pH should be included as models' inputs to foster improvements of cement concrete prediction.

The integration of large language models-cognitive assistants (LLM-CAs) in manufacturing within quality management systems (QMS – ISO 9001:2015) presents both transformative opportunities and significant challenges. This study systematically analysed 53 studies (2022–2024) to derive literature-based requirements for LLM-CAs in manufacturing within QMS according to the Humans, Technology, and Organization concept (HTO). Findings highlight persistent challenges, such as transparency, compliance risks, and workforce adaptation, with strong alignment between LLM-CAs and QMS principles. Requirements were categorized into HTO subsystems and their overlaps, ensuring a structured approach for seamless integration. As a result, the potential of LLM-CAs to enhance decision-making, operational efficiency, and human-centred governance within QMS is underscored. On that basis, an essential synergy between traditional practices in manufacturing such as QMS and novel technology is highlighted. This synergy represents a fundamental step to guarantee the deployment of LLM-CAs in manufacturing. Future research should explore stakeholder-driven validation, digital feedback loops, and LLM-CAs-driven continual improvement in manufacturing.

The application of deep learning methods in the research on intelligent recognition of fault images in Electric Multiple Units (EMUs) represents a crucial approach to alleviating the heavy workload of Trouble of moving EMU Detection System (TEDS) inspections and safeguarding train safety. In light of the current TEDS image data’s problems such as irregular formats, substantial quality disparities, and uneven sample distribution, which pose challenges for deep learning, we proposes a method for constructing an intelligent recognition dataset of TEDS EMU fault images. By integrating the characteristics of TEDS images with the prior knowledge of EMU operations, a unified data acquisition protocol and standardized dataset construction techniques are devised. Consequently, an intelligent recognition dataset of TEDS EMU fault images is established. This dataset accomplishes effective data integration and standardization and has been successfully implemented in the research on automatic Recognition Algorithms for TEDS EMU faults. It accelerates the rapid progress of intelligent recognition technology for TEDS fault images and lays a robust technical foundation for ensuring the safety of EMU operations.

This study presents the design and implementation of an innovative educational assistive system tailored for individuals with Autism Spectrum Disorder (ASD), leveraging eye-tracking technology and machine learning algorithms. The objective is to enhance communication and learning experiences through ocular gaze and fixation-based keyboard selection. Data were collected from 15 children with ASD, generating 320 scanpath images that captured fixation duration and gaze direction during left-right and up-down navigation tasks. Using fixation values and Area of Interest (AOI) analysis, a novel keyboard selection method was developed, distinct from traditional digital keyboards. The proposed system was evaluated using an optimized ensemble model, achieving an accuracy of 83.44%, significantly surpassing baseline models. The results demonstrate improved communication efficiency and usability, validating the system’s ability to facilitate hands-free keyboard control. This assistive system fosters better interaction with parents, teachers, and caregivers, promoting social engagement and enhancing the quality of life for individuals with ASD. Overall, this research underscores the transformative potential of integrating eye-tracking and machine learning in educational tools, paving the way for innovative approaches to support neurodiverse learners.

This research analyzes the interfacial bond strength of metakaolin-based geopolymer mortar across two key connections: flexural mortar-mortar and steel-mortar. It investigates the influence of varying interface roughness conditions and curing age on the bond behavior of metakaolin-based geopolymer mortar (GPM) and cement-based mortar (CM). For the mortar-to-mortar bond investigation, the flexural three-point bending test was performed on smooth and grooved interfaces after 28+7 and 28+28 days of curing, complementing previous slant shear and split tensile test results. For the steel-mortar investigation, push-in tests were conducted on 10 mm diameter ribbed and 8 mm diameter smooth steel reinforcing bars, with 7 and 28 days of curing for each. Additional tests included compressive strength, ultrasonic pulse velocity, slant shear, drying shrinkage, and split tensile tests. Results demonstrate that geopolymer mortar achieves superior bond strength compared to cement mortar, with bond strengths more than double that of cement mortar. Geopolymer mortar also attained faster bond strength on grooved surfaces. Similarly, the steel-mortar bond strength of metakaolin-based geopolymer mortar was approximately four times greater than that of cement mortar with ribbed rebars, although results were closer with smooth rebars. These findings highlight the potential of metakaolin-based geopolymer mortar to enhance bond performance while reducing environmental impact, presenting a promising avenue for sustainable construction practices.

Corrosion in circulating cooling water systems of power plants significantly compromises equipment reliability and energy efficiency. This study investigates the anticorrosive performance of 1-hydroxyethylidene-1,1-diphosphonic acid (HEDP) on carbon steel (St-20) commonly used in such systems. Bench tests simulating the operating conditions of a power plant were conducted using circulating cooling water and carbon steel coupons. The corrosion rate was assessed at HEDP concentrations of 2.0, 5.0, and 10.0 mg/dm 3 using weight loss methods over 1000 hours of exposure. Results demonstrated a concentration-dependent reduction in corrosion rate, with a maximum inhibition efficiency of 58% observed at 10.0 mg/dm 3. Statistical analysis confirmed a strong positive correlation between HEDP concentration and inhibition efficiency. Although HEDP effectively reduced corrosion, the occurrence of moderate biological contamination was observed. This unexpected microbial growth indicates that HEDP alone does not prevent biofouling. Furthermore, environmental implications related to phosphonate discharge and the potential for eutrophication must be carefully considered when applying HEDP in power plant cooling systems.

This research proposes and evaluates the effectiveness of the Alpha-Logarithmic Power Transformed Exponential (ALPTE) distribution as a more general purpose, versatile lifetime distribution in comparison to conventional lifetime distributions. Bayesian and non-Bayesian estimation were studied along with some properties of the ALPTE model which were discussed. Mortality, Mobility, and Radiation datasets were analyzed and compared in a detailed manner. Data was evaluated using maximum likelihood estimation, descriptive statistics, and the goodness-of-fit tests such as AIC, BIC, CAIC, HQIC, KolmogorovâĂŞSmirnov, Anderson-Darling, CramÃľrâĂŞvon Mises and analyzed visually through model diagnostic plots such as boxplots, TTT plots, and PâĂŞP plots. The ALPTE model consistently demonstrated superior performance over the Gamma, Lognormal, Gumbel, Weibull, and Exponential models across all datasets. It is remarkable how the ALPTE model not only enhanced representation of light- and heavy-tailed phenomena but also exhibited robustness to outliers alongside multiplicity in failure patterns, including increases, decreases, or constant rates. Estimates of ALPTE remained robust and interpretable even in the presence of data heterogeneity, particularly in large sample sizes. Such claims of flexibility and versatility further highlights the adaptability of the ALPTE distribution.

This paper proposes a sub-optimum Kuhn-Munkres-based resource allocation algorithm to maximize the number of connected links and total throughput served by ultra-dense networks consisting of densely distributed co-channel access points and user equipment. In the proposed seven-step algorithm, users are first assigned to access points supporting higher data rates considering the interference of all access points. Then, only the interference of the selected access points is considered and users connected to these access points that meet the minimum throughput threshold level are found. Afterward, considering the interference of the access points assigned in the first run and the remaining access points selected in the next runs, new users are connected to the remaining selected access points. Simulations in MATLAB for a service area of 250 meters by 250 meters including randomly distributed 250 access points and different numbers of 25 to 250 users, show more connected users and total throughput, and much less processing time of the proposed algorithm than those for Genetic, particle swarm optimization, cuckoo search, and grey wolf optimization algorithms. By changing the number of users from 10% to 100% of the number of access points, the proposed algorithm increases the number of connected users by 10% to 48%, 47% to 96%, 57% to 109%, and 22% to 58%, and the total throughput by 20% to 52%, 44% to 86%, 50% to 105%, and 22% to 69% compared to the four mentioned algorithms. Due to the lower complexity order, it experiences at least 99% less processing time.

Abstract To enhance the economic efficiency and flexibility of Virtual Power Plants (VPPs) and address the limitations of traditional battery storage in terms of lifespan, cost, and environmental impact, this paper proposes a capacity configuration and optimized dispatch method for VPPs that incorporates independent energy storage. Initially, a hydrogen energy system centered around hydrogen production electrolyzers, hydrogen fuel cells, and hydrogen storage tanks is constructed to replace traditional batteries. An alkaline electrolyzer wide power adaptation model is introduced, expanding its operational power range to 5%~130%, significantly enhancing the electrolyzer’s ability to handle fluctuations in renewable energy output. Subsequently, the waste heat generated by the operation of the hydrogen energy system is utilized for combined heat and power supply to the load, and the by-product oxygen from the electrolyzer is sold as an industrial product, further reducing the system’s operational costs. On this basis, combined with the Model Predictive Control (MPC) method, a two-stage optimized dispatch strategy for day-ahead declaration and intraday rolling correction is designed. Through dynamic aggregation of distributed energy storage resources and real-time feedback correction, full renewable energy consumption and economic optimization are achieved. Simulation results show that the proposed method significantly improves the economic efficiency and renewable energy consumption capability of the VPP, with a reduction in lifecycle operational costs by approximately 35.15%, an increase in renewable energy consumption rate to 88.41%, and a 28.45% increase in revenue under a 2-hour rolling cycle for the optimized dispatch strategy, with a notable improvement in computational efficiency. KEYWORDS Virtual Power Plant ; Independent Energy Storage; Wide Power Adaptation; Combined Heat and Power; Model Predictive Control

Two filters using Defected Ground Structures have been proposed. First, a multiple frequency band stop filter utilizing a semi-H defect in the ground plane is presented. This structure is then prototyped on a Rogers 4350B substrate of overall size 45 mm $\times$ 15 mm, and external SMD capacitors have been employed to control the resonance of the circuit, for the stopband frequencies of 433 MHz, 700 MHz and 915 MHz. An equivalent circuit is also proposed for this multi-band design. The second filter is a combination of a band-stop and band-pass filter in one structure. The filter, operating with a controllable passband and stopband frequency is fabricated, on Rogers 4350B lossy substrate, to validate the EM and circuit simulation results. Two SMD capacitors have been loaded in the filter to control the pass band and stop band frequencies of the filter with a structure size of 20 mm x 20 mm. Furthermore, a novel equivalent circuit model encompassing the band-pass and band-stop frequency response of the DGS based filter is proposed.

Under proper loading conditions, micro-to-nanoscale heterogeneities (i.e., the bond system) that are commonly found within the materials of a system can coalesce until causing macroscopic alterations of the system properties. The bond system is responsible for atypical and invariant-scale non-linear elastic processes in granular media, from laboratory-tested materials (mm) to the Earth’s crust (km). The unusual observed behavior involves slow recovery, or relaxation, of the elastic properties after dynamic loading. Several models have been designed to explain non-linear elasticity, although their physics is still partially unknown. Here, we show that recovery processes are also observed at intermediary scales (m) in civil engineering structures, and that they might be related to structural health due to the healing of cracks. For Japanese buildings subjected to earthquakes, we observe rapid co-seismic reductions of their resonance frequency, followed by fascinating recoveries over different time-scales: over short times (i.e. seconds) for a single earthquake; over intermediate times (i.e. months) for a sequence of aftershocks; and over long times (i.e. years) for a series of earthquakes. By comparing two buildings with different damage levels after the 2011 Tohoku earthquake, we show how relaxation models can characterize the level of cracking caused by damaging events. Our results bridge the gap between the laboratory and seismological observation scales, verifying in this way the universality of recovery processes, and demonstrating their value for the detection and characterization of damage.

In the biopharmaceutical industry, Raman spectroscopy is now a proven PAT tool that enables in-line simultaneous monitoring of several CPPs and CQAs in real-time. However, as Raman monitoring requires multivariate modeling, variabilities unknown by models can impact the monitoring prediction accuracy. With the widespread use of Raman PAT tools, it is necessary to fix instrumental variability impacts, encountered for instance during a device replacement. In this work, we investigated the impact of instrumental variability between probes inside a multi-channel analyzer and between two analyzers, and explored solutions to correct them on model prediction errors in cell cultures. We found that the Kennard Stone Piecewise Direct Standardization (KS PDS) method enables to lower model prediction errors and that only one batch with the unknown device in the calibration dataset was sufficient to correct the prediction gap induced by instrumental variability. As a matter of fact, during device replacement a first cell culture monitoring can be performed with the KS PDS method. Then, the new data obtained can be inserted in the calibration dataset to integrate instrumental variability in the chemometric model. This methodology provides good multivariate calibration model prediction errors throughout the instrumental changes.

Simulated microgravity (s-µg) devices provide unique conditions for elucidating the effects of gravitational unloading on biological processes. However, s-µg devices are being increasingly applied for mechanobiology studies without proper characterization of the mechanical environment generated by these systems, which confounds results and limits their interpretation. Furthermore, the cell culture methodology central to s-µg approaches introduces new conditions that can fundamentally affect results, but these are currently not addressed. It is essential to understand the complete culture environment and how constituent conditions can individually and synergistically affect cellular responses in order to interpret results correctly, otherwise outcomes may be misattributed to the effects of microgravity alone. For the benefit of the growing space biology community, this article critically reviews a typical s-µg cell culture environment in terms of three key conditions: fluid-mediated mechanical stimuli, oxygen tension and biochemical (cell signalling). Their implications for biological analysis are categorically discussed. A new set of controls is proposed to properly evaluate the respective effects of s-µg culture conditions, along with a reporting matrix and potential strategies for addressing the current limitations of simulated microgravity devices as a platform for mechanobiology.

Reduction in the torsional vibration of heavy rotors like turbo-generator rotor is important for the safe and efficient functioning of the power plant. In this paper theoretical study is performed to control the torsional vibration in the turbo-generator rotor using piezoelectric material as sensor and actuator. Polyvinylidene fluoride (PVDF) layer is used as sensor and actuator. Proportional and velocity feedback is used as control law. The variation in the electromagnetic torque of synchronous generator during various electrical faults is evaluated using dq0 model. Finite element method is used to model the rotor elements. The coupled equations are solved in MATLAB using Newmark-beta integration method. The coupling elements of turbine and generator are most susceptible to the shear failure so torsional vibration of coupled rotor on coupling elements are compare for controlled and uncontrolled scenario. Simulation results show that for actively controlled rotor significant reduction in the amplitude of torsional vibrations is observed.

A study is considered to a steady, two-dimensional boundary layer flow of an incompressible MHD fluid for the Blasius and Sakiadis flows about a flat plate in the presence of thermo-diffusion (Dufour) and thermal-diffusion (Soret) effects for variable parameters. The governing partial differential equations are transformed into a system of nonlinear ordinary differential equations using similarity variables. The transformed systems are solved numerically by Runge-Kutta Gills method with shooting techniques. The variations of the flow velocity, temperature and concentration as well as the characteristics of heat and mass transfer are presented graphically with tabulated results. The numerical computations show that thermal boundary layer thickness is found to be increased with increasing values of Eckert number (Ec), Prandtl number (Pr) and local Grashof number (Gr_x) for both Blasius and Sakiadis flow. The Blasius flow elevates the thickness of the thermal boundary layer compared with the Sakiadis flow. The local magnetic field has shown that flow is retarded in the boundary layer but enhances temperature and concentration distributions.

This paper presents a complete design procedure, with an optimized feeding method, of two-dimensional slotted waveguide antenna arrays (2D SWAs). For a desired sidelobe level ratio, the proposed system provides a pencil shape pattern with a narrow halfpower beamwidth, large sidelobe level ratio (SLR), and very low sidelobe levels (SLL), which makes it suitable for high power microwave applications. The radiating slotted waveguide antennas use longitudinal slots, designed for a specified slidelobe level ratio and resonance frequency. The resulting two-dimensional slotted waveguide antenna array is formed by stacking a number of similarly designed radiating SWAs, and fed with an additional SWA. The proposed feeding method uses longitudinal coupling slots rather than the conventional inclined coupling slots, which can provide better values of SLR and easily obtain very low SLLs, in comparison with the conventional systems. The feeder dimensions and slots positions are deduced from the dimensions and total number of the radiating SWAs. For a desired SLR, the slots excitation in the radiating and feeder SWAs are calculated based on a specified distribution. Then, using simplified closed-form equations and for a desired resonance frequency, the slots lengths, widths, and their distribution along the length of the radiating SWAs and feeder SWA can be found. Two examples are illustrated with different number of slots and radiating elements, and one is fabricated and tested. Chebyshev distribution is used to estimate the excitations of the SWA slots in the examples. The obtained measured and simulated results are in accordance with the design objectives.

In this paper, a novel beam steerable 2.4 GHz MIMO antenna array is proposed based on the Yagi-Uda principle. The antenna consists of two co-axially excited patch radiators with modified ground plane. A conducting strip with an integrated PIN diode is optimally placed between the patch radiators to act as a director or a reflector to steer the main beam by an angle of +/- 60◦. For all switching modes, the MIMO antenna demonstrates an average gain and efficiency of 5 dB and 92%, respectively, at the resonance frequency of 2.4 GHz. Reduced envelope correlation coefficient in one switching mode exhibited 17 dB improvement in mutual isolation. The simulated results agreed well with measured data. This simple, low-cost, efficient, and mutually isolated antenna array can be very useful in MIMO WLAN applications.

The main goal of this study is to determine the aerodynamic performance and to characterize unsteady flows in a high-speed high-reaction pre-whirl axial flow fan. The pressure waves’ main diametrical modes where two blades interact with two vanes and their sequences are predicted. There are mainly two mechanisms of IGV-rotor interactions identified; the first is attributed to the potential effect whereas the second is due to the wake-blade interaction and the advection of wake mixing into the blades’ passages. Both effects are dependent on the circumferential positions of blades and the axial inter-distance between IGV and rotor. The time mode analyses of pressure fluctuations recorded from different monitor points are determined and the frequencies of prevailing modes and those related to the vortical flow structure through the components are also identified. The understanding of vanes and blade rows interactions at various axial inter-distances is an important step in determining the beneficial and detrimental effects on the design of high performance axial fan stage.