Abstract: The growing importance of integrating renewable energy sources (RES) into mainline railway traction networks stems from the sector's substantial electricity demands, traditionally met by carbon-intensive thermal generation. This paper addresses the potential of wind and solar power to enhance energy efficiency and reduce emissions in rail transport. It details the devel-opment of digital models for simulating DC traction power systems (TPS) coupled with RES, specifically wind turbines. Given the complexity of TPS, effective integration requires digital modeling that accounts for their unique properties. The proposed methodology, based on phase coordinates algorithms, offers a universal and comprehensive framework. It enables the identi-fication of various operational modes (normal, emergency, special) for diverse network com-ponents, including traction networks, transmission lines, and transformers. These models were used to simulate real-world train operations, generating data on electrical parameter dynamics and transformer thermal conditions. The results confirm that wind integration can improve energy efficiency, validating the methodology's practical applicability for RES projects in DC traction networks, including advanced high-voltage systems.

Abstract: Buildings located in highly urbanized areas have not been considered for photovoltaic (PV) deployment on building walls due to limitation of ground and rooftops space. As the need for increasing energy demand due to population growth in cities, and the advancements in the efficiency of semi-transparent (ST-PV) solar cell technology, the integration of ST-PV modules into building windows, become feasible. The present article proposes a novel methodology for calculating the incident solar energy on PV vertical modules deployed on building walls and windows facing the southern direction and obscured by a nearby building in front. The present work analyses analytically, for the first time, the incident energy and its distribution on PV vertical modules along a wall height. Monthly and annually direct beam, diffuse and global energies are calculated for different wall height, building separation and orientation. The results shows, for example, that both the front and rear building walls receive the same amount of annual direct beam energy 913 kWh/m² for a distance 25 m between the buildings. Decreasing the distance from 25 m to 10 m, decreases the annual incident global energy on the rear-building wall by 15 %.

Abstract: In order to respond to the national " double carbon " strategic goal, promote the green and low-carbon transformation of the building materials industry, and develop low-carbon and environmentally friendly grouting materials, an AACGMs was prepared in this study. The effects of CG content, alkali activator modulus and alkali activator content on material fluidity, setting time, compressive strength and impermeability were systematically studied by orthogonal test. The optimal mix ratio was determined, and its internal mechanism was revealed by microscopic analysis. The results show that the comprehensive performance is the best when the content of CG is 50%, the modulus of alkali activator is 1.6 and the content of alkali activator is 14%. The primary and secondary order of the influence of various factors on the performance is : CG content > alkali activator content > alkali activator modulus. Microscopic analysis reveals that the hydrolysis polymerization products of the material are mainly C-S-H, C- (N) -A-S-H gel and zeolite-like phase, forming a dense three-dimensional network structure, which is the internal mechanism of its good mechanical and impermeability properties. This study provides a new idea for the utilization of CG, and the prepared materials are of great significance in the field of grouting reinforcement in underground engineering.

Abstract: This review examines implementation dimensions of integrated lemon biorefinery systems, including cascade valorisation design, circular-economy integration, life-cycle assessment, techno-economic feasibility, and regulatory frameworks. Bibliometric analysis of Web of Science data (2015–2025) reveals exponential growth in citrus-biorefinery research, with lemon representing a burgeoning subset. Techno-economic assessments indicate that cascade biorefineries recovering essential oils, pectin, polyphenols, nanocellulose, and bioenergy can achieve cumulative revenues of USD 400–650 per tonne of dry peel. Whilst small-scale units (<500 tonnes/year) struggle to achieve viability, industrial simulations demonstrate Internal Rates of Return exceeding 18% at processing scales above 100,000 tonnes annually (2025 basis). Life-cycle assessments confirm environmental benefits, with greenhouse gas reductions of 60–85% relative to conventional disposal. Critical success factors include adopting green extraction technologies to preserve bioactive integrity and mitigating D-limonene inhibition in downstream anaerobic digestion. These findings establish lemon biorefineries as technically mature, economically viable pathways for circular bioeconomy transitions, provided regulatory hurdles—Novel Foods authorisation (EU) and GRAS determination (US)—are effectively navigated.

Abstract: A digital pre-distortion (DPD) scheme for concurrent multi-beam transmission in fully digital multiple-input, multiple-output (MIMO) systems, using fast Fourier transform (FFT) beamforming and so-called virtual array processing, is proposed. In a MIMO array with nonlinear power amplifiers (PAs), transmitting multiple beams concurrently yields intermodulation products that end up in both user and non-user directions. In the setting with few users in a large array, the array dimension will typically be much larger than the number of generated intermodulation products. At the same time, linearization per-PA is excessively costly for large arrays. This work shows that is instead possible to linearize the system by producing predistorted user beams, and non-user intermodulation products, through DPD processing in a virtual array, of a much smaller dimension than the physical array. Theoretical derivations and simulation examples show how this approach can lead to manyfold reductions in DPD complexity.

Abstract: This study presents a comprehensive framework combining finite element analysis, machine learning, and generative AI for aluminum cold spray deposition analysis. Abaqus explicit dynamic simulations modeled high-velocity particle impact at 700 m/s, capturing stress tensor components and von Mises equivalent stress distributions. The maximum von Mises stress of 537.73 MPa exceeded aluminum yield strength by 3.6 times, confirming successful deposition through severe plastic deformation. Three machine learning algorithms were trained on stress tensor components (S11, S22, S33, S12, S13, S23) to predict von Mises stress. Random Forest, Gradient Boosting, and Neural Network models achieved exceptional accuracy with R² values of 0.9975, 0.9955, and 0.9922 respectively. Hyperparameter optimization further improved performance to R² = 0.9977, 0.9887, and 0.9985. Feature importance analysis identified S22 transverse stress as the dominant predictor with 80% importance. Google Gemini generative AI provided engineering insights confirming bonding mechanisms through adiabatic shear instability and oxide disruption. Process optimization recommendations addressed velocity control, particle distribution, and substrate preparation. This integrated approach enables rapid stress prediction and intelligent process optimization for industrial cold spray applications.

Abstract: Class II mesio-occluso-distal (MOD) restorations are routinely used to rehabilitate molars with extensive structural loss, where cavitary bases are commonly applied to protect dentine and modulate stress transfer. Nevertheless, polymer-based restorative materials undergo polymerisation shrinkage during curing, generating residual stresses that subsequently superimpose on functional masticatory loads—a coupled effect that is rarely addressed explicitly in finite element analyses of Class II MOD restorations and may increase stress concentrations and mechanical failure risk. This study investigates the biomechanical influence of cavitary base type and the absence of a cavitary base in Class II MOD restorations through three-dimensional finite element analysis. A patient-based three-dimensional human molar model was reconstructed from cone-beam computed tomography (CBCT) data. A transient occlusal loading cycle from 0 to 0.25 s was simulated, reaching a peak load of 600 N. Restorations incorporating cavitary bases were modelled using ceramic and glass–ceramic restorative materials combined with different base materials, while restorations without bases employed composite materials with varying silica content. Mechanical behaviour was evaluated using von Mises stress and maximum principal stress distributions. The results indicate that restorations without cavitary bases exhibit a more uniform stress distribution and reduced stress concentrations compared with restorations incorporating cavitary bases under transient occlusal loading. These findings suggest that, when polymerisation shrinkage and functional loading are considered simultaneously, the absence of a cavitary base may offer biomechanical advantages in stress transfer and structural integrity for Class II MOD restorations.

Abstract: A new, efficient algorithm for generating monitoring spreadsheets for the optical coating production with monochromatic layer thickness monitoring is presented. Due to its high computational efficiency, it has no limit on the number of different wavelengths used to monitor different groups of deposited coating layers. The algorithm allows for the use of various criteria to select the optimal sequence of monitoring wavelengths. Several examples are provided demonstrating the application of the algorithm to various types of multilayer filters.

Abstract: This study presents a retrospective computational analysis of heart rate variability (HRV) derived from long-term (24-hour) Holter electrocardiographic recordings obtained from publicly available PhysioNet databases. HRV provides a noninvasive measure of auto-nomic nervous system regulation and cardiovascular complexity, whose alterations are associated with arrhythmic conditions. Although 24-hour Holter monitoring is considered the clinical reference standard for HRV analysis, its large data volume poses significant computational challenges. The objective of this work was to develop and apply a fully reproducible MATLAB-based pipeline for automated HRV analysis and arrhythmic burden quantification. Recordings from approximately 85 subjects with documented rhythm disorders were analyzed and compared with reference recordings from healthy individuals. Signal preprocessing included digital filtering, QRS detection using the Pan–Tompkins algorithm, artifact correction, and NN interval interpolation. HRV metrics were computed in time and frequency domains, as well as through nonlinear methods capturing signal complexity. The results demonstrated a pronounced reduction in HRV indices, decreased spectral power, and increased arrhythmic events in pathological subjects, reflecting impaired autonomic regulation and elevated inter-subject variability. The proposed framework enables standardized, automated, and reproducible HRV analysis, supporting entropy-based characterization of cardiovascular dynamics and future risk stratification studies.

Abstract: Sample-wise detection of P-, R-, and T-peaks in electrocardiograms (ECGs) is challenging because each peak type is sparsely represented (≈1:500 samples in a typical 10-s, 500-Hz ECG at 60 bpm), such that even a small number of false-positives (FPs) can markedly degrade positive predictive value (PPV) and limit the practicality of classifier-only approaches. This study proposes a lightweight ECG peak detection framework that combines binary classifiers with a physiological temporal constraints (PTC) algorithm to address extreme sample-level class imbalance. Local morphological features are first evaluated using lightweight machine-learning models, among which XGBoost (XGB) exhibited the most stable score-ranking performance. Rather than directly thresholding classifier outputs, prediction scores are interpreted through PTCs that encode physiological timing relationships. R-peaks are detected using score ranking combined with a refractory-period constraint, and the detected R-peaks serve as temporal landmarks for subsequent P- and T-peak detection within physiologically plausible time windows reflecting the P–QRS–T sequence. Quantitative evaluation was conducted using the Lobachevsky University Electrocardiography Database, hereafter referred to as LUDB. With a temporal tolerance of ±20 ms, the XGB-based system achieved an F1-score of 0.87 for R-peak detection (sensitivity 0.96, PPV 0.79), corresponding to approximately 9–10 true R-peaks with only 2–3 FP samples per 10-s segment. For P- and T-peaks, F1-scores of 0.70 and 0.69 were obtained, respectively. Additional evaluation on arrhythmic LUDB records and qualitative application to ECG recordings from the PTB-XL database demonstrated physiologically consistent behavior. These results indicate that reliable and interpretable ECG peak detection under extreme class imbalance can be achieved by integrating lightweight classifiers with explicit PTC algorithms, without reliance on complex deep learning architectures.

Abstract: The extremely high pressure and low temperature inherent to deep-sea environments pose significant challenges for the lubrication performance of gear transmission systems. The synergistic effects of high pressure and low temperature not only cause an exponential increase in lubricant viscosity, leading to reduced fluidity, startup difficulties, and lubrication starvation, but also allow seawater intrusion, which may induce lubricant emulsification, additive failure, and tooth surface corrosion, further exacerbating the risk of lubrication failure. This article reviews recent research progress in gear lubrication under deep-sea high-pressure and low-temperature conditions, with a specific focus on Elasto-Hydrodynamic Lubrication (EHL) theory and gear interface texturing. By thoroughly analyzing deep-sea environmental characteristics and their influence on lubricant properties, this article explores the applicability of Thermal Elasto-Hydrodynamic Lubrication (TEHL) theory in extreme environments. The discussion covers advancements in numerical simulations as well as key challenges. Additionally, the paper elaborates on the anti-friction and wear-resistance mechanisms of interface texturing, emphasizing its ability to improve gear lubrication states and boost tribological performance. Consequently, this study summarizes the limitations of existing research. It proposes future development directions, including multiphysics coupling modeling, synergistic texture and coating design, experimental validation, and engineering applications. Ultimately, this review aims to provide theoretical support and technical references for the reliable design and long-term stable operation of gear transmission systems in deep-sea equipment.

Abstract: The smart agriculture system requires high efficiency to automatically maximize crop yields and minimize losses. Wireless sensor networks (WSNs) are essential for maintaining system sustainability through sensing and connectivity. However, they encounter challenges related to cost, interoperability, and reliability. Efforts have been made to expand sensing capabilities while managing costs and addressing variability in sensor communication and power consumption. Despite these efforts, a comprehensive solution—especially for orchard fields—remains undeveloped. This study introduces a coordinated WSN design to optimize sensing and connectivity in agricultural fields. We employ an integrated sensing and connectivity (ISAC) strategy to create a complete solution. Our hybrid approach combines graphical computation with distance-vector algorithms for reliable, cost-effective deployment. Additionally, resilient connectivity is achieved through effective channel modeling and adaptive beamforming. The proposed method, combined with quantitative heterogeneous network selection using MLR-AHP, addresses interoperability issues and improves network resilience. Results indicate improved sensor placement and wireless ranking, even with only 5 nodes. The solution extends sensor battery life, maintains 99% coverage, and empirical tests validate its effectiveness for designing and deploying WSNs in orchard fields.

Abstract: To solve the problem of constructing the frequency responses (FR) of filters on switched capacitors, which belong to the class of electronic circuits with a periodically changing structure, a method for modeling them in Micro-Cap and Delta Design environments is proposed. It allows you to evaluate the nature of changes in the FR of such filters in the time domain. As an example, a comparative analysis of the frequency response of a second-order analog bandpass filter, as well as two bandpass filter circuits with switching resistors and capacitors, is given. An assessment of the current state of EDA and trends in their development is given.

Abstract: This study presents an integrated experimental and modeling framework to investigate human–robot collision dynamics involving a collaborative manipulator (KUKA LBR iiwa 14 R820). A dedicated impact test prototype was developed to reproduce con- trolled contact scenarios between the robot and human body analogues under various dynamic conditions. The experimental setup enables the acquisition of synchronized force, velocities, and displacement signals during contact events. This data are used to calibrate and validate a set of contact models, ranging from classical formulations such as Hertz and Hunt–Crossley to more recent supervised machine learning models. The proposed methodology allows a quantitative assessment of model accuracy and physical consistency in replicating real collision phenomena. Furthermore, the effective mass of the robot along its kinematic chain is estimated to compute impact energy and predict the interaction severity according to ISO 10218-1/2:2025 safety limits. The results highlight the trade-off between model complexity and predictive capability, offering alternative guidelines for collision severity evaluation in collaborative robotics applications.

Abstract: This work investigated the structural, morphological, electrical and photovoltaic properties of n-ZnNiO/p-Si heterostructures. ZnNiO nanocomposite thin films were fabricated on p-Si (100) substrates using pulsed laser deposition, enabling the formation of n-type oxide/p-type silicon heterojunctions. The crystalline structure and surface morphology of the deposited thin films were examined using X-ray diffraction and scanning electron microscopy, revealing well-defined crystalline features and uniform surface morphology. The electrical characteristics were analyzed through current–voltage measurements, allowing the extraction of key diode parameters. In addition, the optoelectronic response under ultraviolet illumination was investigated, demonstrating pronounced photosensitivity in the UV spectral range. Several important electrical and optoelectronic parameters relevant to ultraviolet photodetection were determined and discussed. The obtained results indicate that ZnNiO-based heterostructures combined with silicon substrates constitute a promising material platform for advanced optoelectronic and ultraviolet applications.

Abstract: On-chip integration of two-dimensional (2D) materials provides a promising route for implementing nonlinear integrated photonic devices that break existing barriers and unlock new capabilities. Although 2D materials with ultrahigh optical nonlinearity have driven this technological progress, their high optical absorption also constitutes an Achilles’ heel. Whether 2D materials can overcome their intrinsic absorption and generate net gain (NG) via optical parametric amplification (OPA) processes is a critical and intriguing question, which is central to many nonlinear optical applications. Recently [1], we experimentally demonstrated enhanced OPA and achieved NG in silicon nitride waveguides integrated with 2D graphene oxide (GO) under pulsed pumping. Based on material parameters from this work, this perspective systematically analyzes the feasibility of achieving NG in more widely used, yet more challenging, scenarios involving silicon waveguides incorporating GO and continuous-wave pumping. The results show that a gap still exists toward achieving this goal, but it can be bridged through combined efforts in optimizing waveguide structure, reducing loss of GO, and improving GO’s thermal stability. We also investigate different waveguide structures as well as other 2D materials, and analyze the gap in each case. This work provides a critical roadmap and useful guidance for future developments towards achieving NG via OPA in integrated photonic devices incorporating 2D materials.

Abstract: Cultural heritage sites worldwide face escalating threats from climate change, urbanization, and material degradation, necessitating innovative, resilient engineering solutions. This paper introduces a transformative interdisciplinary framework that synergistically integrates civil engineering's nano-enhanced materials such as graphene oxide consolidate and nano-silica infusions with computer science's intelligent digital twin ecosystems. These technologies enable adaptive conservation across structural scales, from molecular-level artifact repairs to comprehensive building-wide retrofitting.Nano-enhanced materials provide superior mechanical reinforcement, self-healing properties, and environmental resistance, restoring structural integrity without altering aesthetic authenticity. Concurrently, digital twins leverage IoT sensors, AI-driven simulations, and BIM models to create real-time virtual replicas, facilitating predictive maintenance, degradation forecasting, and optimized material deployment. The proposed hybrid methodology employs simulation-calibrated workflows to minimize invasive interventions, ensuring data interoperability and scalability.Case studies from Italian frescoes and historic bridges demonstrate lifespan extensions of 30-50% through nano-consolidation monitored by twin analytics. Despite challenges like nanomaterial scalability and data privacy, this approach pioneer’s sustainable heritage preservation. Future directions emphasize blockchain integration for provenance tracking and ethical AI governance, offering policymakers a blueprint for resilient cultural engineering in the digital era.

Abstract: Digital technologies have been widely adopted to improve efficiency, transparency, and decision making in the construction industry. However, regulatory processes such as building license and registration applications remain complex, fragmented, and difficult for applicants to navigate, particularly for early career practitioners and small businesses. This study presents the design and development of a graph-based retrieval-augmented generation (RAG) artificial intelligence (AI) system that assists users in applying for building licenses and registrations in New South Wales, Australia. The proposed approach integrates eight complementary frameworks of regulatory burden and service design to identify ten categories of licensing-related burden and translate them into concrete system requirements. The developed prototype provides context aware responses, step-by-step guidance, and tailored information based on user queries, thereby reducing regulatory burden for individuals, companies, and industry bodies. Prototype evaluation against general-purpose AI tools indicates that the system can improve information accessibility and reduce application-related friction in representative licensing scenarios. This study sheds light on AI-enabled regulatory support systems and demonstrates how RAG can be applied to improve accessibility and usability of construction related licensing processes. The findings have implications for policymakers, regulators, and researchers seeking to leverage AI to support digital transformation in the construction industry.

Abstract: This article presents a dataset generated for a techno-economic assessment (TEA) of the methanol-to-jet (MtJ) fuel production pathway. The dataset was produced using a large-scale Monte Carlo (MC) sampling approach applied to a steady-state process model implemented in Aspen Plus V14. The techno-economic evaluation was conducted using an external cost model, with subsequent data processing performed in Python. In total, three million individual data points were generated by varying key technical and economic input parameters within predefined ranges and are under public access. For each MC sample, the net production cost on a mass basis (NPC_m, EUR _kgjet-fuel^-1) of synthetic jet fuel was calculated as the primary economic performance indicator. The dataset comprises both the sampled input parameters and the corresponding techno-economic output variables and is intended to support transparency, reproducibility, and further uncertainty analysis of MtJ fuel production pathways.

Abstract: Zirconia is known as a strong and bioinert load bearing material for dental implants. It usually exhibits no antibacterial activity. Inflammation is a crucial problem for dental implant surgery. About 3-5% of all dental implants experience inflammation. It is very central finding of the present study that fullerene C₆₀ films as well as a tribomechanical loading of zirconia without the fullerene C₆₀ topping can cause an improvement of antibacterial activity against gram-positive Staphylococcus aureus. The moderate antibacterial activity is especially important, because a strong antibacterial effect could disturb the sensitive oral bacterial flora and should be prevented. In the present study, different conditions of the fullerene C₆₀ film were provided. In addition to fullerene C₆₀ film in the “as deposited” condition, treatment in a nitrogen plasma as well as tribomechanical produced surface pattern without and with plasma post-treatment were testified. 85.8% reduction of gram-positive Staphylococcus aureus bacterial formation was measured on zirconia with fullerene C₆₀ film. Plasma treatment of the C₆₀ film effect an increase of the antibacterial impact of 72.2% only in comparison to zirconia without fullerene C₆₀ film. Also, tribomechanical loaded fullerene C₆₀ films suppress the growth of gram-positive Staphylococcus aureus. The tribomechanical loading seems to compensate the effect of a plasma treatment. ZrO₂ samples with fullerene C₆₀ film and tribomechanical loading achieve an increase of antibacterial impact of 83.36%. Furthermore, surprisingly yttria-stabilized zirconia bioceramic without fullerene C₆₀ film also shows a high antibacterial effectivity after a tribomechanical patterning procedure. Due to surficial patterning the ZrO₂ by scratching microgroove arrangements with a diamond tip, its antibacterial effect against gram-positive Staphylococcus aureus was increased 70.46%.