Abstract:

To meet the demands of high-speed, high-precision execution of six-axis industrial robotic arms in complex manufacturing environments, this paper presents a real-time motion planning method incorporating multi-source error compensation based on production data and dynamic models. A self-developed control platform (EtherCAT bus, 0.25 ms cycle, <20 μs jitter) enables rapid command issuance and execution. The method first generates an initial trajectory using a calibrated model, then applies online corrections via a multi-source error estimation model to mitigate deviations from flexible structures, load changes, and installation offsets. A lightweight computation module ensures accuracy without increasing computational overhead. In 600 load variation experiments, trajectory error decreased from 0.41 mm to 0.24 mm (41.5% improvement), and path smoothness improved by 28.2%. Under typical assembly tasks, the success rate increased from 89.3% to 95.7%. Results confirm the method's effectiveness in real-time trajectory optimization and its strong engineering applicability across varied scenarios.

Abstract: This study presents a systematic investigation of silica extraction from rice husk ash (RHA) using Taguchi L27 orthogonal array optimization methodology. With global rice production generating 31-39 million tonnes of RHA annually, valorization of this agricultural waste addresses both environmental disposal challenges and sustainable silica production needs. The extraction process involved controlled calcination, acid leaching with hydrochloric acid, alkali solubilization using sodium hydroxide, and acid precipitation to produce high-purity amorphous silica. Three critical process parameters—heating temperature (600-800°C), heating time (2-6 hours), and chemical concentration (1-3 M)—were systematically optimized across 27 experimental runs. Statistical analysis identified optimal conditions of 700°C calcination temperature, 4-hour processing time, and 3 M chemical concentration, achieving maximum silica yield of 7.02 g from 10 g RHA (70.2% extraction efficiency). Main effects analysis revealed chemical concentration as the most influential parameter, followed by temperature exhibiting volcano-shaped behavior with peak efficiency at 700°C, and heating time showing positive linear correlation with yield. Characterization confirmed successful extraction of high-purity silica with white appearance, near-neutral pH, bulk density of 180-200 kg/m³, and 3.1% moisture content. The NaOH/CuSO₄ confirmatory test validated silica presence, while absence of HCl reaction confirmed purity. Results demonstrated superior performance compared to conventional methods, with yields exceeding reported alkali hydrothermal extraction (52.8%) and approaching optimized acid leaching ranges (70-90%). The Taguchi optimization approach reduced experimental requirements by 66% compared to full factorial design while maintaining statistical rigor. This research establishes an efficient, scalable methodology for converting agricultural waste into value-added industrial material suitable for construction, ceramics, and environmental remediation applications, contributing to circular economy principles and sustainable materials development.

Abstract: A central goal of neuroeconomics is to understand how humans make decisions and how their neural processes interact during strategic situations. Game theory provides mathematical tools for modeling such interactions, with equilibrium concepts, most notably the Nash equilibrium, predicting stable patterns of behavior. Classical equilibrium analysis, however, treats cognition as a black box and assumes fully rational agents, whereas human decision making is shaped by bounded rationality, heuristics, and neural constraints. To bridge this gap, we investigate equilibrium behavior directly in the space of neurocognitive activity. Electroencephalogram (EEG) signals provide a high-resolution measurement of neural dynamics underlying attention, conflict monitoring, and evidence accumulation. In this work, we introduce a neuronic Nash equilibrium, an equilibrium concept defined not in the action space but in the EEG-derived neural representation space. We develop a framework for analyzing two-player turn-based games in EEG space by constructing DMD-based neural embeddings and associated directed network representations. Dynamic Mode Decomposition (DMD) reveals statistically significant differences between the neural dynamics associated with distinct strategic actions, demonstrating that EEG-derived features preserve behaviorally meaningful cognitive structure. The resulting neuronic network representation enables equilibrium analysis directly at the neural level and provides a principled method for linking strategic behavior with stable patterns of neural activity. Our findings suggest that neural-state equilibrium concepts can capture the cognitive foundations of strategic interaction and offer a pathway toward characterizing cognitive equilibrium outcomes in multi-agent settings.

Abstract: In urban environments, the accuracy of traditional Global Navigation Satellite System (GNSS) could be compromised due to signal occlusion and multipath interference, particularly during critical operational phases such as drone take-off and landing. This study seeks to enhance drone positioning accuracy by integrating 4G and 5G communication antennas and applying multilateration (MLAT) techniques based on Time-Difference-of-Arrival (TDOA) and Angle of Arrival (AOA) measurements. The research focuses on a real-world case study in the metropolitan area of Valencia, Spain, where extensive mobile network data were analysed using the Cramér-Rao Lower Bound (CRLB) to identify zones with minimal positioning errors and, separately, optimal coverage for connectivity. The results suggest that integrating terrestrial antennas could enhance drone navigation; however, its current applicability remains limited to urban areas.

Abstract: This article presents a conceptual framework for using artificial intelligence for collision triage in a BIM (Building Information Modeling) environment. Modern collision detection tools generate huge numbers of reports, which directly burdens BIM coordinators and makes it difficult for them to effectively manage the interdisciplinary coordination process. Previous approaches have focused mainly on collision detection itself and simple, rule-based prioritization, rarely exploiting the potential of AI (Artificial Intelligence) methods in the area of post-processing of results. The proposed framework describes a modular system in which collision detection results and data from BIM models, schedules (4D), and cost estimates (5D) are processed by a set of AI components. These include: a classifier that filters out irrelevant collisions (noise), algorithms that group recurring collisions into single design problems, a model that assesses the significance of collisions by determining a composite 'AI Triage Score' indicator, and a module that assigns responsibility to the appropriate trades and process participants. The article also discusses a potential way to integrate the framework into the existing BIM workflow and possible scenarios for its validation based on case studies and expert evaluation. The proposed conceptual framework represents a step towards moving from manual, intuitive collision triage to a data- and AI-based approach, which can contribute to increased coordination efficiency, reduced risk of errors, and better use of design resources.

Abstract: This paper proposes a comprehensive framework for integrating Digital Twins (DT) with real-time traffic optimization systems to enhance urban mobility management in Smart Cities. Using the Pobitno Roundabout in Rzeszów as a case study, we established a cali-brated microsimulation model (validated via the GEH statistic) that serves as the core of the proposed Digital Twin. The study goes beyond static scenario analysis by introducing an Adaptive Inflow Metering (AIM) logic designed to interact with IoT sensor data. While traditional geometrical upgrades (e.g., turbo-roundabouts) were analyzed, simulation re-sults revealed that geometrical changes alone—without dynamic control—may fail under peak load conditions (resulting in LOS F). Consequently, the research demonstrates how the DT framework allows for the testing of "Software-in-the-Loop" (SiL) solutions where Python-based algorithms dynamically adjust inflow parameters to prevent gridlock. The findings confirm that combining physical infrastructure changes with digital, real-time optimization algorithms is essential for achieving sustainable "green transport" goals and reducing emissions in congested urban nodes.

Abstract: Electricity trading is a critical revenue source for South African municipalities, enabling cross-subsidisation of basic services, debt repayment, and infrastructure financing, which is essential for sustainable municipal operations. Nevertheless, municipal electricity utilities consistently experience variances between budgeted and actual financial performances due to increasing Eskom bulk tariffs, technical and non-technical losses, a degrading distribution system, billing malpractices, and governance breakdown, thereby threatening service delivery sustainability. This paper examines the financial performance of four major distributors—City of Ekurhuleni, City of Cape Town, City Power (Johannesburg), and City of Tshwane—using a mixed documentary research approach, ensuring comprehensive sectoral analysis. Data sources include audited municipal financial statements, National Energy Regulator of South Africa (NERSA) tariff materials, Auditor-General findings, and national electricity statistics, supplemented by secondary literature on distribution losses and utility governance as well as international best practices. Key performance measures point out that an increase in bulk electricity cost and restricted variations in tariffs have enhanced trading limits, electricity theft, meter tampering, and billing inaccuracies, leading to massive revenue leakages, significantly undermining financial stability. The lack of investment in the maintenance of assets also increases such long-term costs. The paper recommends a set of five strategic interventions to be implemented, such as better revenue management, non-technical loss reduction, recovery of the infrastructure that is ring-fenced, transparent cost-reflective tariffs, and governance and institutional controls. A sequenced implementation framework entails the short-term protection of revenue, the medium-term deployment of smart meters, and the long-term reforms of assets. The findings emphasise that sustainable improvement requires integrating operational reform, technological advancement, financial discipline, and governance enhancement.

Abstract: This research work presents a novel integrated approach combining experimental mechanical characterization of a manufactured AA6061 aluminum alloy with quality control procedures to ensure product reliability. The quality control framework includes microstructural examination, tensile testing, and measurement uncertainty assessment in accordance with relevant international standards. The combined standard uncertainty was calculated using the law of propagation of uncertainty, and the expanded uncertainty was obtained with a coverage factor of k=2, corresponding to a confidence level of approximately 95%. The measured tensile strength was found to be 309.4±4.0 N/mm², demonstrating good repeatability and a low relative uncertainty of about 1.3%. The results are in accordance to the international standard mechanical property requirements for AA6061 alloy, confirming both the reliability of the experimental procedure and the suitability of the material for structural and engineering applications.

Abstract: We presented a high-precision endoscopic shape sensing method using only two calibrated outer cores of a multicore fiber Bragg grating (MC-FBG) array. By leveraging the geometric relationship between two non-collinear outer cores and the central core, the approach determines curvature and bending angle without multiple outer-core measurements, reducing computational complexity and error propagation. Experimental results demonstrate that the proposed method achieves maximum relative reconstruction errors of 1.62% and 2.81% for 2D circular and 3D helical shapes, respectively. Furthermore, arbitrary endoscopic configurations such as α-loops and N-loops are accurately reconstructed, validating the robustness of the method under realistic clinical conditions. This work provides a resource-efficient and high-fidelity solution for endoscopic shape sensing, with strong potential for integration into next-generation image-guided and robot-assisted surgical systems.

Abstract: This paper proposes a new hypothesis that breaks through the framework of traditional archaeological chronology and engineering to explain the mystery of megalithic structures that exist widely around the world and whose technical features exceed current understanding.The core of this hypothesis is: 1) The Earth civilization has periodicity with the period of the Sun's movement around the Milky Way as the period, and at least two periods have already existed; 2) The continuous expansion of the universe leads to the continuous expansion of atoms and the continuous reduction of material strength. Based on this, a coupling model of "atomic characteristic scale - material strength - individual human physique and physiological function - engineering technology limit" was established, and the quantitative relationship between the maximum weight of a single stone in a megaliite building and its construction year, as well as the qualitative relationship between the splicing accuracy and the construction year, were derived. Analysis using this model indicates that the construction dates of ultra-precise megalithic structures such as Pumapengu, the Pyramids of Giza, and Balbek may be much older than traditional dating results, or they might be the products of the previous or even earlier civilization cycle.

Abstract: This study presents a robust constitutive model for clays capable of capturing mechanical behavior over a wide range of plasticity and overconsolidation ratios (OCR). The model is formulated within a bounding-surface plasticity framework and employs a teardrop-shaped yield surface controlled by two shape parameters, Ψ and Ω, which regulate yield-surface skewness and shear strength, respectively. An explicit plastic potential is introduced to eliminate the stress–dilatancy paradox and to obtain a linear, physically interpretable stress–dilatancy relation. Model parameters are calibrated using conventional laboratory data and are linked to standard oedometer indices, preserving practical applicability. Validation against triaxial test results for clays with contrasting plasticity demonstrates that the model consistently reproduces both curved and nearly linear stress paths at low stress ratios, as well as a smooth transition from normally consolidated to overconsolidated behavior. The proposed formulation provides a unified and robust framework suitable for numerical implementation and geotechnical engineering applications.

Abstract: Visual Impression in Architectural Space (VIAS) plays a central role in how users intuitively respond to surrounding environment, where visual stimuli such as signage, layout, and spatial density immediately shape attention, movement, and engagement. While designers intentionally deploy these visual attractors, the resulting perceptual and behavioural responses remain uncertain and vary across cultural and methodological contexts. To address this challenge, this study reframes urban public space, taking event-space as a case study, by integrating architecture and data-science into a framework that combines VIAS theory, behaviour-perception analysis, and sentiment-aware linguistic modelling. Firstly, we introduce a visual behavioural layer that identifies how spatial attractors such as advertising banners, product displays and event layouts. Secondly, we construct an expanded dataset from previous research comprising eight native participants interviewed in their native language, enabling linguistically accurate and culturally grounded comparison with the previous English-based mixed cohort. Thirdly, we develop a multi-modal sentiment-weighted keyword extraction algorithm that captures participant-initiated perceptual themes while suppressing interviewer influence and modality-specific bias, enabling alignment between verbal impressions and visual-behavioural evidence. Finally, we compare three interview modalities (onsite, video-based and virtual-environment) against behavioural observation data collected at a small-scale event in Matsue City, Japan. Results demonstrate that onsite participants exhibit systematic positive bias driven by the festive atmosphere, while remote modalities elicit more balanced assessments of visual clarity, signage effectiveness, stall arrangement, and missing spatial amenities. Furthermore, cross-linguistic analysis reveals cultural differences: native participants emphasise holistic spatial atmosphere, whereas international participants identify discrete visual focal points. By integrating visual attractors, behavioural metrics, and sentiment-aware linguistic patterns, the proposed framework provides a replicable method for explaining how designed visual elements trigger, reinforce, or contradict actual user behaviour. The findings offer evidence-based guidance for designing inclusive temporary event spaces, highlighting how architectural visual elements can be validated and refined through multi-modal computational analysis.

Abstract: In this paper, we presented BlockShare, a blockchain-basedsystem developed to facilitate privacy-preserving data sharing across de-centralized networks. The proposed system enables users to retain controlover their sensitive data while enabling secure, verifiable sharing with au-thorized parties. We implemented an authenticated data structure (ADS)to support decentralized verification and utilized zero-knowledge proofmechanisms to validate conditions without exposing the underlying data.Experimental analysis demonstrated that BlockShare performs efficientlyin constructing data structures, generating proofs, and verifying themwith minimal computational overhead. The platform successfully reducedprivacy risks and enhanced trust in cross-organization data exchanges.

Abstract: The determination of the actual series and sequence impedances, including the reduction factor of a certain HV or EHV distribution cable line, as well as the resulting screening factor of its sheaths and surrounding metal installations, including its inductive influence on any of the surrounding metal installations, is not possible by calculations alone. Considering the inductive influence of surrounding metal installations on the values of these quantities is possible only by the method that includes the test measurements during a simulated ground fault in the supplied substation. However, such measurements presuppose putting at least one HV substation and its feeding line out of service. That is why electricity distribution companies rarely allow such measurements, i.e., only immediately before the commissioning of a newly built HV substation or during a periodical overhaul. In this paper, it is demonstrated that these characteristics of cable lines can also be determined based on the results of synchronous measurements performed permanently in the substations at their ends. In this way, the need to perform a simulated ground fault and corresponding test measurements in HV distribution substations is practically disаpear, and the necessary characteristics can be obtained whenever a need for them appears.

Abstract:

In urban corridors, roundabouts often operate in close proximity to signalized intersections, yet the safety implications of their mutual interaction remain insufficiently explored. This study combines field measurements and VISSIM microsimulation with the Surrogate Safety Assessment Model (SSAM) to analyze roundabout–signalized intersection pair under varying outer radii (12–22 m), spacings (40–160 m), signal red times (17–27 s), and traffic distributions. A multiple linear regression model for predicting the total number of conflicts is developed and partially validated using calibrated real-site models for corridors in Osijek and Poreč, Croatia. Small spacings (40 m) increase the total number of conflicts by 40–60% for small roundabouts (R = 12 m) and 20–40% for larger radii compared with isolated operation. Increasing the outer radius from 12 to 17 m reduces conflicts by up to about 90%, while longer red times further lower conflicts, especially for small roundabouts. The final regression model, based on spacing, red time, and outer radius, explains about 80% of the variance in conflicts and shows good agreement with SSAM estimates within its applicability range, providing a practical tool for safety-oriented design of urban roundabout–signalized intersection corridors thereby contributing to the goals of developing a sustainable transport system in complex urban environment.

Abstract: Injection well placement and rate allocation are among the most decisive factors in determining the efficiency and bankability of CCS projects. However, optimizing these parameters is notoriously complex: even a small number of injection wells leads to a vir-tually infinite set of injection scenarios, while traditional optimization techniques typically require thousands of high-fidelity reservoir simulations. For project developers, this computational burden can stall critical Final Investment Decisions (FID). The proposed approach here addresses this bottleneck by using a Design of Experiments (DoE) framework combined with nonlinear surrogate modeling, which efficiently maps the relationship between injection rates and storage performance, to identify near-optimal solutions with a minimal number of simulations. We show that our method achieves up to 97% of the initially targeted CO2 sequestration with as few as 15 simulations, demonstrating a step-change reduction in time and cost. From a business standpoint, CCS operators can de-risk projects earlier, accelerate FID timelines, and evaluate multiple site configurations in parallel while minimizing computational overhead. Rather than waiting weeks or months for exhaustive optimization, decision-makers can gain timely, reliable insights that directly support capacity commitments, regulatory submissions, and ultimately revenue realization.

Abstract: As a solid-state joining technology, friction stir welding (FSW) exhibits significant advantages for aluminium alloys, including low heat input and minimal intermetallic compounds formation, thereby enhancing joint quality and mitigating deformation. This study investigates the single-sided and double-sided FSW processes of 3-mm-thick 7075-T6 aluminium alloy sheets, focusing on characterizing the microstructure and mechanical properties of the joints. Experimental results show that under 1500 rpm rotation speed and 80 mm/min welding speed, the double-sided co-directional FSW joint achieves a tensile strength of 388 MPa and an elongation of 7.09%, significantly outperforming the other two welding paths. In the weld nugget zone (WNZ), continuous dynamic recrystallization (CDRX) occurs, generating uniformly refined equiaxed grains (average size: 1.10 μm) and facilitating the transformation of low-angle grain boundaries (LAGBs) to high-angle grain boundaries (HAGBs). Meanwhile the strong Rotated Cube texture is remarkably weakened into random recrystallized Brass textures with the lowest kernel average misorientation (KAM) value in the WNZ. In contrast, the thermo-mechanically affected zone (TMAZ) accumulates high-density LAGBs due to welding-induced plastic deformation. Microhardness testing reveals a typical "W"-shaped distribution: WNZ hardness is relatively high but slightly lower than that of the base metal (BM), and the minimum hardness in the advancing side (AS) heat-affected zone (HAZ) is higher than that on the retreating side (RS). This study confirms that double-sided co-directional FSW crucially regulates microstructural evolution and improves mechanical properties of 7075-T6 joints, providing a viable process optimization strategy for high-quality welding of thin-gauge sheets.

Abstract: The present study evaluates occupational health and safety (OHS) risks at the 400/220/110/20 kV Arad Power Substation, a critical infrastructure node in Romania’s energy network, within the context of industrial development and the need to prevent energy crises. As the demand for electricity grows alongside industrial expansion, substations face increasing operational pressures, making risk management essential for ensuring workforce safety and system reliability. The assessment integrates hazard identification, risk analysis, and mitigation strategies specific to high-voltage environments, including electrical, mechanical, ergonomic, and environmental hazards. Particular attention is given to high-voltage exposure, fire hazards, equipment malfunction, and emergency response readiness. Using a combination of qualitative and quantitative approaches, the study identifies high-risk operations and proposes targeted interventions, such as improved protective equipment, training programs, maintenance protocols, and real-time monitoring systems. The findings underscore that proactive OHS measures not only safeguard personnel but also enhance operational continuity, thereby contributing to regional energy security and supporting industrial growth. By aligning health and safety management with strategic energy planning, the study demonstrates how systematic risk assessment at high-voltage substations can mitigate industrial disruptions and prevent cascading energy crises. The results provide a framework for policymakers, engineers, and OHS professionals seeking to balance workforce protection with energy infrastructure resilience.

Abstract:

Additive manufacturing of polymer tools represents a promising alternative to conventional steel tooling for low-force and low-volume sheet metal air bending. However, accurate prediction of sheet springback and the resulting deviation of the bending angle after elastic unloading remains a major challenge. This study presents an integrated experimental–numerical framework for the analysis of air bending with additively manufactured polymer tools, with emphasis on material characterization, springback prediction, and tool angle compensation. The methodology combines uniaxial tensile testing, controlled air-bending experiments, finite element modelling with rigid and deformable tools, and optical 3D scanning for angle measurement. Low-carbon steel DC04 sheets were modeled using an elastoplastic constitutive law, while FDM-printed ABS tools were described by experimentally calibrated material models. Numerical simulations were performed over a range of forming forces to evaluate springback behavior and elastic tool deformation. The results show very good agreement between experiments and simulations. Deviations in bending angle were below 1.5% for metallic tools and below 0.5% for springback compensation, with the smallest discrepancy obtained using a two-dimensional model with deformable tools. Experimental validation with ABS tools confirmed bending accuracy within ±1°. The proposed framework provides a reliable basis for springback prediction and rational design of additively manufactured polymer tools for air-bending applications.

Abstract:

In this study, both linear and nonlinear parametric models (M1–M6) and machine learning (ML)–based approaches were evaluated for the reliable and interpretable prediction of tunnel boring machine (TBM) penetration rate (ROP). The analyses incorporated rock hardness index (BI), uniaxial compressive strength (UCS), joint angle (α), excavation depth (DPW), and BTS as input variables. Parametric model coefficients were optimized using the Differential Evolution (DE) algorithm, and variable effects were examined via Jacobian-based elasticity analysis under both original and standardized data scenarios. Parametric results indicate that the proposed M6 model outperforms existing literature correlations in terms of prediction accuracy and represents variable contributions in a more balanced and physically meaningful manner. While the dominant influence of BI and UCS on ROP is preserved across all models, interaction terms allow the indirect contributions of variables such as DPW and BTS to be captured more clearly. Model performance systematically improves when moving from linear to nonlinear and interaction-inclusive structures, with R² increasing from 0.62 for M1 to 0.69 for M6. Machine learning–based variable importance analyses largely corroborate the parametric findings, highlighting BI and α in tree-based methods, and UCS and α in SVM and GAM models. Notably, the GAM model exhibited the highest predictive performance under both data scenarios. Overall, the combined use of parametric and ML approaches provides a robust hybrid framework for accurate and interpretable prediction of TBM penetration rates.