Eng

This study develops empirical equations relating viscous damping ratios (ξ) and damper coefficients (c) in steel structures for seismic design applications. The objective is to establish predictive formulas that enable conversion between equivalent viscous damping ratios and physical damper characteristics through dynamic analysis. This research employs a two-phase analytical methodology on steel building frameworks. Initially, inherent viscous damping ratios are incrementally varied from 3% to 40% to establish baseline response characteristics. Subsequently, supplemental damping devices are integrated with damper coefficients (c) adjusted according to manufacturer specifications. Linear time-history analyses are conducted for both configurations to determine equivalent damping relationships, with a particular focus on Interstory Drift Ratios (IDR) and Peak Floor Accelerations (PFA) as key seismic demand parameters. By comparing response quantities between inherent and supplemental damping scenarios, empirical relationships linking physical damper coefficients with equivalent viscous damping ratios are formulated. The resulting equations provide practicing engineers with a practical tool for estimating damper specifications based on target damping levels in steel structures. The formulations are derived from linear time-history analysis of steel frame configurations and are applicable within the scope of linear elastic response and viscous damper behavior consistent with typical design conditions. Full article

Composite steel–concrete beams have gained significant attention in modern construction due to their superior structural efficiency, economic viability, and adaptability to diverse applications. This paper presents a comprehensive review of research developments related to both conventional and post-tensioned composite beam systems. Emphasis is placed on the structural behavior, design considerations, and performance improvements achieved through external post-tensioning using high-strength tendons. Such systems enhance ultimate load capacity, extend the elastic range before yielding, and reduce the required amount of structural steel, thereby improving material efficiency and reducing construction costs. The review also examines the influence of tendon application timing, connection type, and load conditions in both positive and negative bending regions. By synthesizing experimental and analytical findings, this study identifies key advantages, limitations, and research needs in optimizing the design and performance of steel–concrete composite beams. The insights presented herein aim to guide engineers, researchers, and practitioners in advancing the application of composite beam strengthening techniques in modern infrastructure. Full article

When drilling inclined and horizontal sections, a significant part of the drill string is in a compressed state which leads to a loss of stability and longitudinal bending. Modeling of the stress–strain state (SSS) of the drill string (DS), including prediction of its stability loss, is carried out using modern software packages; the basis of the software’s mathematical apparatus and algorithms is represented by deterministic statically defined formulae and equations. At the same time, a number of factors such as the friction of the drill string against the borehole wall, the presence of tool joints, drill string dynamic operating conditions, and the uncertainty of the position of the borehole in space cast doubt on the accuracy of the calculations and the reliability of the predictive models. This paper attempts to refine the actual behavior of the drill string in critical and post-critical conditions. To study the influence of dynamic conditions in the well on changes in the SSS of the DS due to its buckling, the following initial data were used: a drill pipe with an outer diameter of 88.9 mm and tool joints causing pipe deflection under gravitational acceleration of 9.81 m/s² placed in a horizontal wellbore with a diameter of 152.4 mm; axial vibrations with an amplitude of variable force of 15–80 kN and a frequency of 1–35 Hz; lateral vibrations with an amplitude of variable impact of 0.5–1.5 g and a frequency of 1–35 Hz; and an increasing axial load of up to 500 kN. A series of experiments are conducted with or without friction of the drill string against the wellbore walls. The results of computational experiments indicate a stabilizing effect of friction forces. It should be noted that the distance between tool joints and their diametrical ratio to the borehole, taking into account gravitational acceleration, has a stabilizing effect due to the formation of additional contact force and bending stresses. It was established that drill string vibrations may either provide a stabilizing effect or lead to a loss of stability, depending on the combination of their frequency and vibration type, as well as the amplitude of variable loading. In the experiments without friction, the range of critical loads under vibration varied from 85 to >500 kN, compared to 268 kN as obtained in the reference experiment without vibrations. In the presence of friction, the range was 150 to >500 kN, while in the reference experiment without vibrations, no buckling was observed. Based on the results of this study, it is proposed to monitor the deformation rate of the string during loading as a criterion for identifying buckling in the DS stress–strain state monitoring system. Full article

In high-density urban areas where ecological protection constraints are increasingly stringent, transportation infrastructure layout must balance service efficiency and environmental preservation. From an ecological-prioritization perspective, this study proposes a three-stage multi-objective optimization strategy for siting underground smart parking facilities using the NSGA-III algorithm, with Haidian District, Beijing, as a case study. First, spatial identification and screening are conducted using GIS, integrating urban fringe-space extraction with POI, AOI, population, and transportation network data to determine candidate locations. Second, a multi-objective model is constructed to minimize green space occupation, walking distance, and construction cost while maximizing service coverage, and is solved with NSGA-III. Third, under the ecological-prioritization strategy, the solution with the lowest land occupation is selected, and marginal benefit analysis is applied to identify the optimal trade-off between ecological and economic objectives, forming a flexible decision-making framework. The findings show that several feasible schemes can achieve zero green-space occupation while maintaining high service coverage, and marginal benefit analysis identifies a cost-effective solution serving about 20,000 residents with an investment of 7 billion CNY. These results confirm that ecological protection and urban service efficiency can be reconciled through quantitative optimization, offering practical guidance for sustainable infrastructure planning. The proposed methodology integrates spatial analysis, multi-objective optimization, and post-Pareto analysis into a unified framework, addressing diverse infrastructure planning problems with conflicting objectives and ecological constraints. It offers both theoretical significance and practical applicability, supporting sustainable urban development under multiple scenarios. Full article

Spiking neural networks (SNNs) provide a biologically inspired, event-driven alternative to artificial neural networks (ANNs), potentially delivering competitive accuracy at substantially lower energy. This tutorial-study offers a unified, practice-oriented assessment that combines critical review and standardized experiments. We benchmark a shallow fully connected network (FCN) on MNIST and a deeper VGG7 architecture on CIFAR-10 across multiple neuron models (leaky integrate-and-fire (LIF), sigma–delta, etc.) and input encodings (direct, rate, temporal, etc.), using supervised surrogate-gradient training implemented in Intel Lava, SLAYER, SpikingJelly, Norse, and PyTorch. Empirically, we observe a consistent but tunable trade-off between accuracy and energy. On MNIST, sigma–delta neurons with rate or sigma–delta encodings achieve 98.1% accuracy (ANN baseline: 98.23%). On CIFAR-10, sigma–delta neurons with direct input reach 83.0% accuracy at just two time steps (ANN baseline: 83.6%). A GPU-based operation-count energy proxy indicates that many SNN configurations operate below the ANN energy baseline; some frugal codes minimize energy at the cost of accuracy, whereas accuracy-oriented settings (e.g., sigma–delta with direct or rate coding) narrow the performance gap while remaining energy-conscious—yielding up to threefold efficiency compared with matched ANNs in our setup. Thresholds and the number of time steps are decisive factors: intermediate thresholds and the minimal time window that still meets accuracy targets typically maximize efficiency per joule. We distill actionable design rules—choose the neuron–encoding pair according to the application goal (accuracy-critical vs. energy-constrained) and co-tune thresholds and time steps. Finally, we outline how event-driven neuromorphic hardware can amplify these savings through sparse, local, asynchronous computation, providing a practical playbook for embedded, real-time, and sustainable AI deployments. Full article

Cement bond evaluation helps check wellbore integrity and zonal isolation in carbon capture, utilisation, and storage (CCUS) projects. This overview describes various cement bond evaluation methods, focusing on acoustic logging and ultrasonic imaging tools supplemented by emerging data-driven interpretation techniques. Their advantages, limitations, and recent advancements are described with illustrative example on ultrasonic-image-based machine learning classifier that detect microannulus. Key research gaps remain in field-scale validation of long-term cement behaviour and in establishing comprehensive 3-D bond-strength benchmarks. To address these gaps, this review recommends (i) creating an open, standardised ML dataset for CCUS well logs, (ii) adopting best-practice pressure-monitoring protocols during and after injection, and (iii) integrating ML analytics with advanced modelling while exploring alternative binder systems. The next step is to test these ML models on real CO₂-storage well data, paving the way toward more reliable cement-bond integrity assessments in future CCUS projects. Full article

The detection of metallic surface defects is an essential task to control the quality of industrial products. During the production of metal materials, several defect types may appear on the surface, accompanied by a large amount of background texture information, leading to false or missing detections during small-defect detection. Computer vision is a crucial method for the automatic detection of defects. Yet, this remains a challenging problem, requiring the continuous development of new approaches and algorithms. Furthermore, many industries require fast and real-time detection. In this paper, a lightweight deep learning model is presented for implementation on embedded devices to perform in real time. The YOLOX-Tiny model is used for detecting and classifying metallic surface defect types. The YOLOX-Tiny has 5.06M parameters and only 6.45 GFLOPs, yet performs well, even with a smaller model size than its counterparts. Extensive experiments on the dataset demonstrate that the proposed model is robust and can meet the accuracy requirements for metallic defect detection. Full article

Traffic safety continues to be a pressing worldwide issue, with young drivers especially exposed to accidents because of limited experience, reckless behaviors, and risky practices such as driving under the influence of alcohol or other substances. In this scenario, reliable methods to evaluate psychomotor and sensory abilities essential for safe driving are highly needed. This study presents the development of a Virtual Reality (VR) prototype aimed at enhancing psychometric testing. The platform incorporates immersive environments to assess peripheral vision, reaction time, and motor accuracy, implemented with Oculus Quest 2, Blender, and Unity. The VR-based system was validated through black-box testing and user satisfaction surveys with a sample of 80 licensed drivers in single-session evaluations. The findings demonstrate that VR increases both precision and realism in psychomotor evaluations: 81.25% of participants perceived the scenarios as realistic, and 85% agreed that the system effectively measured critical driving skills. While a few users experienced minor discomfort, 97.5% recommended its application in practical assessments. This study highlights VR as a robust alternative to conventional psychometric/psychotechnical tests, capable of improving measurement reliability and user engagement and paving the way for more efficient and inclusive driver training initiatives. Full article

In this study, supercritical carbon dioxide (scCO₂) was investigated as a sustainable medium for cotton yarn sizing and desizing, eliminating the need for water and conventional organic solvents. Cellulose acetate was employed as the sizing agent with acetone as a co-solvent, achieving a 10% add-on comparable to conventional starch-sized yarns. Since starch sizing is typically reported in the range of 3–10% add-on, a 3% starch level was selected as the industrially relevant benchmark for 20/1 cotton yarn. Trials conducted at 15–20 MPa and 40–60 °C demonstrated uniform size deposition and efficient removal during desizing, as confirmed by weight gain distribution and friction testing. Mechanical characterization further revealed that scCO₂-sized yarns exhibited tensile strength and break elongation within the range of industry benchmarks. Overall, these findings establish scCO₂-based sizing as a viable and eco-friendly alternative, with encouraging preliminary performance that suggests potential alignment with textile industry standards. The process also shows promise for solvent recovery and effluent reduction; however, full quantification of recovery yields, energy requirements, and wastewater impacts remains an important direction for future investigation. Full article

Natural fibers have been widely used for reinforcing polymers, attributed to their sustainable nature, light weight, biodegradability, and low cost compared with synthetic fibers, for example, carbon or glass fibers. The objective of this research was to promote the use of natural resource-blended polypropylene (PP) to reduce greenhouse gas emissions and to explore the potential of using grain by-products, such as coconut shell (CS), as fillers for thermoplastic materials. CS (30 wt%) is embedded in the PP matrix of the composite. Thereafter, CS/PP composites were produced utilizing a hot press compounding machine to produce the specimens and a high-speed mixer set at 3000 rpm for five minutes. The impact of coconut shell content on the mechanical and thermal properties of CS/PP composites was examined. The results show the CS/PP composite’s tensile strength and tensile modulus improved by 36% and 30%, respectively. In the meantime, the CS/PP composite’s flexural strength and flexural modulus increased by 16% and 13%, respectively. At a maximum temperature of 260 °C, the CS/PP composite demonstrated thermal stability. Due to the unprocessed particles, the coconut fiber appeared on the surface as homogenous particles. Researchers and industry professionals can use these results to help create new products. Full article

We report a scalable route to hybrid aluminum matrix composites (AMCs) based on Al6060 (as-fabricated condition) reinforced with 2 wt.% TiB₂ and 1 wt.% microsilica, fabricated by ultrasonically assisted stir casting (UASC) followed by radial-shear rolling (RSR). Premixing and preheating of powders combined with acoustic cavitation/streaming during UASC ensured uniform, non-sedimentary particle dispersion and low-defect cast billets. X-ray diffraction of the as-cast composite shows fcc-Al with weak TiB₂ reflections and no reaction products; microsilica remains amorphous. Electron microscopy and EBSD after RSR reveal full erasure of cast dendrites, fine equiaxed grains, weakened texture, and a high fraction of high-angle boundaries due to the concurrent action of particle-stimulated nucleation (micron-scale TiB₂) and Zener pinning/Orowan strengthening (50–350 nm microsilica). Mechanical testing shows that, in the cast state—comparing cast monolithic Al6060 to the cast hybrid-reinforced composite—yield strength (YS) increases from 61.7 to 77.2 MPa and ultimate tensile strength (UTS) from 103.4 to 130.7 MPa, without loss of ductility. After RSR to Ø16 mm (cumulated true strain ≈ 0.893), the hybrid attains YS 101.2 MPa, UTS 150.6 MPa, and elongation ≈ 22.0%, i.e., comparable strength to rolled Al6060 (UTS 145.1 MPa) while restoring/raising ductility by ~9.7 percentage points. Microhardness follows the same trend, increasing from 50.2 HV0.2 to 73.1 HV0.2 when comparing the base cast condition with the rolled hybrid. The route from UASC to RSR thus achieves a favorable mechanical strength–ductility balance using an economical, eco-friendly oxide/boride hybrid reinforcement, making it attractive for formable AMC bar and rod products. Full article

Distributed measurement networks, from semiconductor testing arrays to environmental sensor grids, medical diagnostic systems, and agricultural monitoring stations, face a fundamental challenge: undetected site-to-site variations that silently corrupt data integrity. These variations create systematic biases between supposedly identical measurement units, which undermine scientific reproducibility and yield. The current site-to-site variation detection methods require extensive sampling or make rigid distributional assumptions, making them impractical for many applications. We introduce a novel framework that transforms measurement data into density-based feature vectors using Kernel Density Estimation, followed by anomaly detection with Isolation Forest. To automate the final classification, we then apply a novel probabilistic thresholding method using Gaussian Mixture Models, which removes the need for user-defined anomaly proportions. This approach identifies problematic measurement sites without predefined anomaly proportions or distributional constraints. Unlike traditional methods, our method works efficiently with limited samples and adapts to diverse measurement contexts. We demonstrate its effectiveness using semiconductor multisite testing as a case study, where our approach consistently outperforms state-of-the-art methods in detection accuracy and sample efficiency when validated against industrial testing environments. Full article

The urgent global challenge of environmental sustainability has intensified interest in integrating Lean Management practices with environmental objectives, particularly within the automotive industry, a sector known for both innovation and high environmental impact. This study investigates the systemic relationships between 16 lean practices and three environmental performance metrics: energy consumption, CO₂ emissions, and waste generation. Using the Fuzzy Decision-Making Trial And Evaluation Laboratory (DEMATEL) methodology, data were collected from seven lean experts in the Italian automotive industry to model the cause–effect dynamics among the selected practices. The analysis revealed that certain practices, such as Total Productive Maintenance (TPM), just-in-time (JIT), and one-piece-flow, consistently act as influential drivers across all environmental objectives. Conversely, practices like Statistical Process Control (SPC) and Total Quality Management (TQM) were identified as highly dependent, delivering full benefits only when preceded by foundational practices. The results suggest a strategic three-step implementation roadmap tailored to each environmental goal, providing decision-makers with actionable guidance for sustainable transformation. This study contributes to the literature by offering a structured perspective on lean and environmental sustainability in the context of the automotive sector in Italy. The research is supported by a data-driven method to prioritize practices based on their systemic influence and contextual effectiveness. Full article

Safety is a central concern in aviation, where aircraft operations involve complex processes and interactions exposed to multiple hazards. Addressing these hazards requires systematic risk management and the selection of effective safety measures. This study introduces a novel hybrid multi-criteria decision-making (MCDM) framework that integrates the grey Delphi method, the grey Analytic Hierarchy Process (AHP), and the grey Axial-Distance-Based Aggregated Measurement (ADAM) method. The framework provides a rigorous engineering-based approach for evaluating and ranking safety measures under uncertainty and diverse stakeholder perspectives. Application of the model to aircraft operations demonstrates its ability to identify the most effective measures, including the development of critical infrastructure protection plans, rerouting of flight paths from high-risk areas, and strengthening of regulatory oversight. The proposed methodology advances decision-support tools in aviation safety engineering, offering structured guidance for optimizing resource allocation and improving system resilience. Full article

Urban transportation is a major source of air pollution. On urban highways, driver behavior significantly influences vehicle emissions, as tailpipe pollutants depend on driving patterns. Therefore, estimating the emission factors of key pollutants namely carbon monoxide (CO), carbon dioxide (CO₂), nitrogen oxides (NO_X), and hydrocarbons (HC) is essential. This study investigates the impact of driver behavior on environmental pollutants and derives field-based emission factors on urban highways in Mashhad, Iran, during June 2022. A total of 150 drivers were classified using the K-means algorithm based on their aggressiveness scores from the Driver Behavior Questionnaire (DBQ), maximum acceleration, frequency of maximum acceleration events, and the number of traffic accidents recorded over the past five years. The clustering quality was evaluated using the Silhouette score, leading to two categories: aggressive and non-aggressive drivers. Cochran’s formula was applied to select 10 drivers from each group, and emissions were measured using an onboard monitoring device. Results indicate that aggressive drivers exhibit higher speeds, more pronounced acceleration and deceleration (A/D) patterns, and elevated engine RPM compared with non-aggressive drivers. Spearman’s rank correlation analysis revealed a strong and significant relationship between engine RPM and tailpipe emissions in both driver groups, indicating increased emissions at higher RPMs. In contrast, A/D behavior showed no significant association with emissions, suggesting a minimal direct effect. Overall, emission factors for NO_X, CO₂, CO, and HC were 37.50%, 23.60%, 41.90%, and 53.13% higher, respectively, in aggressive drivers compared with non-aggressive drivers. Furthermore, the Mann–Whitney U test confirmed statistically significant differences in tailpipe emissions between the two groups. These findings demonstrate that distinct driving behaviors are closely linked to variations in vehicular emissions. Full article

Combustion at the meso-scale is constrained by large surface-to-volume ratios that shorten residence time and intensify wall heat loss. We perform steady, three-dimensional CFD of two asymmetric vortex combustors: Model A (compact) and Model B (larger-volume) over inlet-air mass flow rates $\dot{m}$ (40–170 mg s⁻¹) and equivalence ratios ϕ (0.7–1.5), using an Eddy-Dissipation closure for turbulence–chemistry interactions. A six-mesh independence study (the best mesh is 113,133 nodes) yields ≤ 1.5% variation in core fields and ~2.6% absolute temperature error at a benchmark station. Results show that swirl-induced CRZ governs mixing and flame anchoring: Model A develops higher swirl envelopes (S up to ~6.5) and strong near-inlet heat-flux density but becomes breakdown-prone at the highest loading; Model B maintains a centered, coherent Central Recirculation Zone (CRZ) with lower $u_{\theta }$ (~3.2 m s⁻¹) and S ≈ 1.2–1.6, distributing heat more uniformly downstream. Peak flame temperatures (~2100–2140 K) occur at ϕ ≈ 1.0–1.3, remaining sub-adiabatic due to wall heat loss and dilution. Within this regime and $\dot{m}$ ≈ 85–130 mg s⁻¹, the system balances intensity against flow coherence, defining a stable, thermally efficient operating window for portable micro-power and thermoelectric applications. Full article

Kinetic façades represent a climate-responsive design solution that improves building adaptability by responding to seasonal needs such as daylighting and shading. They offer an attractive retrofit strategy that improves both the esthetics and environmental performance of buildings. This study investigated the integration of an origami-inspired kinetic façade into a student dormitory building located in Dhahran, Saudi Arabia. Using numerical simulations, 35 façade configurations were analyzed under varying conditions of façade orientations, closure ratios (from 5% to 95%), and cavity depths (from 20 cm to 100 cm). The findings highlight the critical impact of kinetic façade design characteristics on daylight availability and solar exposure and the required trade-off between these two variables. In this context, this study observed that at higher façade closure ratios, increasing cavity depth could effectively mitigate daylight reduction by promoting reflected daylight penetration inside the cavity. As for heat gains and cooling load reduction, mid-range façade closure, 50 cm in this study, achieved balanced performance across the three examined orientations. However, the southern façade showed slightly higher efficiency compared to the eastern and western façades, which achieved lower cooling reductions and showed a similar UDI compromise. Thus, a dynamic façade operation is recommended, where higher closure ratios could be applied during peak solar hours on the east in the morning and the west in the afternoon to maximize cooling savings, while moderate closure ratios can be maintained on the south to preserve daylight. Future work should incorporate real-time climatic data and smart control technologies to further optimize kinetic façade performance. Full article

The rapid development of Generative Adversarial Networks (GANs) has transformed medical image processing, enabling realistic image synthesis, augmentation, and restoration. This study presents a comparative evaluation of three representative GAN architectures, Pix2Pix, SPADE GAN, and Wasserstein GAN (WGAN), across multiple medical imaging tasks, including segmentation, image synthesis, and enhancement. Experiments were conducted on three benchmark datasets: ACDC (cardiac MRI), Brain Tumor MRI, and CHAOS (abdominal MRI). Model performance was assessed using Fréchet Inception Distance (FID), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Dice coefficient, and segmentation accuracy. Results show that SPADE-inpainting achieved the best image fidelity (PSNR ≈ 36 dB, SSIM > 0.97, Dice ≈ 0.94, FID < 0.01), while Pix2Pix delivered the highest segmentation accuracy (Dice ≈ 0.90 on ACDC). WGAN provided stable enhancement and strong visual sharpness on smaller datasets such as Brain Tumor MRI. The findings confirm that no single GAN architecture universally excels across all tasks; performance depends on data complexity and task objectives. Overall, GANs demonstrate strong potential for medical image augmentation and synthesis, though their clinical utility remains dependent on anatomical fidelity and dataset diversity. Full article

The design philosophy of a new smart gas meter is presented, based on an ultrasonic sensor employing LoRa and/or NB-IoT protocols and blockchain technologies to overcome the data integrity and security issues with a completely modular design. The architecture is organized into two separate blocks, the former for measurement and the latter for communication, and it presents original characteristics with respect to the state of the art. The accuracy of measured data is studied, paying attention to the fluid dynamic effects of the geometrical layout on the flow rate ultrasonic sensor and the environmental temperature and pressure for variable gas flow rate values. As for data security issues, the proposed solution is critically analyzed with reference to the data string organization and the procedure by which the data are stored and prepared for transmission into the blockchain. Finally, a local network of counters is designed and simulated in order to check the compliance of the provided hardware and software solutions with the predicted computational load. Full article