This study investigates vibration propagation from a single fracturing pump in a large-scale integrated fracturing vessel. Vibration testing is performed on an SCF5000 electric-driven fracturing skid to measure vibration acceleration levels (VALs) at various motor speeds. A finite element model of the fracturing cabin system is developed to analyze motion and stress fields under both harmonic and measured excitations. Results indicate that VALs across the skid base remain uniform (Coefficient of Variation (CoV) < 10%), except at two locations influenced by the motor’s large mass. VALs increase by 51% as the speed increases from 600 to 2100 rpm, with vibration energy shifting toward higher frequencies. Motion responses, such as vertical displacement (VD) and vertical acceleration (VA), attenuate in an approximately half-sinusoidal pattern, with VA amplification near the active pump reaching about 126%. An optimal startup sequence (edge pumps first, central pump last) is proposed. Under measured excitations, peak VD, VA, and deck stress increase on average by 18% with rising speed. The layout scheme with a smaller deck span and a 2–3 pump distribution is identified as superior. This study provides a validated framework for vibration control and equipment arrangement in fracturing vessels.
This study investigates free-convective cooling of a pouch-cell lithium-ion battery using air, dielectric oil, and a dielectric–TiO₂ nanofluid, providing previously unreported surface-temperature and vertical-temperature-gradient measurements under realistic C-rates. Dielectric cooling significantly enhanced thermal performance compared with air cooling. At 0.625 C, the maximum battery temperature decreased from approximately 33.5 °C under air cooling to 30.5 °C with dielectric oil and further to 28.5–29.0 °C with a 2.5 vol.% TiO₂ nanofluid. These reductions correspond to approximately 9% and 15% lower peak temperatures than air cooling, respectively. The nanofluid also reduced the vertical temperature gradient from about 1.5 °C to less than 0.7 °C, improving temperature uniformity by more than 50%. The enhanced cooling performance is attributed to the higher density, specific heat capacity, and thermal conductivity of the dielectric fluid, together with the heat-transfer enhancement provided by TiO₂ nanoparticles. Although increasing the C-rate increased internal heat generation and accelerated temperature rise, dielectric–TiO₂ nanofluid cooling maintained battery temperatures closer to ambient conditions, demonstrating its potential as an effective passive thermal management strategy for safer and more efficient lithium-ion battery operation.
Wear-induced material loss is a major concern in industrial applications, driving the development of wear-resistant surface coatings. High-velocity oxy-fuel (HVOF) spraying is widely used to deposit coatings with high bond strength, low porosity, and improved wear resistance. Among these, WC–Co cermet coatings exhibit excellent hardness and tribological performance; however, wear behaviour can change significantly at elevated temperatures. The present study investigates the dry sliding wear behaviour of HVOF-deposited WC–17Co coatings from RT to 600 °C under constant load, sliding velocity, test duration, and sliding distance. The coatings were characterized before and after wear testing using SEM, EDS, XRD, and 3D surface profilometry to evaluate changes in morphology, elemental distribution, phase composition, and wear-track geometry. The coating showed a thickness of ~268.99 µm, 1.53% porosity, and an average surface roughness (Sa) of 10.2 µm. The coating showed favourable wear resistance at 200–400 °C, with minimum wear and friction at 400 °C likely due to a protective tribo-oxide layer and compacted debris. Above 400 °C, particularly at 500 and 600 °C, the wear rate increased markedly because of Co binder-phase degradation, intensified oxidation, and reduced adherence of the oxide layer, and the optimal operating range is 200–400 °C.
The protection of production data and the cell-chemistry formula is very important in battery cell production. The potential of blockchain technology in this context is significant. This article focuses on the integration of blockchain technology into the continuous mixing process of battery cell production to securely and transparently store process data. A private blockchain architecture was implemented, using SHA-256 as a hash function and a Proof-of-Authority (PoA) consensus mechanism. The system supports both Linux and Windows environments and was connected directly to an extruder at the process level. Validation tests showed complete consistency between real process values and blockchain-stored records, confirming data integrity. A Failure Mode and Effects Analysis (FMEA) was conducted, and it is shown that none of the five highest-risk scenarios are related to blockchain technology. The first blockchain-specific risk (potential software failure) is ranked in seventh place. The results demonstrate that a private PoA-based blockchain is a suitable and effective solution for ensuring transparency, traceability, and security in industrial production environments. However, ensuring data authenticity prior to blockchain entry remains essential, highlighting the need for robust upstream data security.
This study examines the magnetohydrodynamic (MHD) stagnation-point flow of a micropolar fluid over a stretching/shrinking sheet, incorporating temperature-dependent viscosity and thermal conductivity under first-order slip conditions. The governing equations are transformed using similarity variables and solved analytically using the homotopy analysis method (HAM). The analysis shows that the slip parameter significantly enhances both velocity and microrotation near the wall (≈10–20%) by reducing surface resistance. In contrast, the magnetic parameter suppresses fluid motion, leading to a reduction in velocity and microrotation (≈2–3%) due to the opposing Lorentz force. Increasing the micropolar parameter decreases velocity while modifying microrotation, highlighting the strong coupling between linear and angular momentum transfer. Temperature-dependent viscosity reduces velocity (by 15–18%) due to increased internal friction, whereas variable thermal conductivity alters the temperature field and affects heat transfer. Additionally, higher Prandtl numbers decrease the thermal boundary layer thickness, thereby enhancing heat transfer rates. The results are in good agreement with existing studies, confirming the validity of the approach. This work provides useful insights for applications in microfluidics, polymer processing, and thermal management systems.
To address the challenges of fragmented fault information and low diagnostic efficiency in traditional maintenance of complex cigarette-making and filter-attaching machines, this study proposes a fault analysis method based on knowledge graphs and semantic modeling, using the ZJ17 machine as the research object. A fault knowledge graph comprising 563 nodes was constructed by extracting entities and relationships from semi-structured maintenance records via Python and Neo4j. The graph structures fault data into standardized triplets: and < root cause, solution, measure>, transforming unstructured text into a semantic network. Using Cypher query language, the system enables fuzzy keyword-based retrieval of fault chains (e.g., phenomenon -> reason -> solution) within milliseconds. For dynamic knowledge base updates, a dual-similarity criterion integrating character-level cosine similarity (>0.5) and Text2vec-based semantic similarity (>0.91) was implemented. This mechanism effectively identifies synonymous fault descriptions (e.g., 'cigarette empty heads' vs. 'insufficient tobacco fill') while filtering redundancies, ensuring graph consistency and scalability. Experimental results demonstrate that the approach significantly enhances fault diagnosis efficiency and knowledge maintenance accuracy, outperforming BERT in inference speed while matching its precision for Chinese short-text matching. The framework provides a structured, visualized decision-support tool for intelligent maintenance of tobacco machinery.
The adoption of electric vehicles (EVs) has become crucial in reducing greenhouse gas emissions and dependency on fossil fuels as the world accelerates its transition towards sustainable transportation. This research provides a comprehensive analysis of EV adoption, particularly in the Indian context. The research prioritizes gap-focused analysis, identifying various hurdles, infrastructural shortcomings, and policy limitations to suggest actionable solutions, in contrast to conventional studies. The study’s distinctive feature is the integrated assessment of technological progress, including battery efficiency and charging standards, alongside governmental initiatives such as subsidies, incentives, and regulations; therefore, it emphasizes the interplay between technology and governance. The results indicate that the widespread deployment of rapid and inductive charging is essential for user convenience, while regulated charging can reduce system costs and peak demand. Advancements in battery technology, strategic placement of charging stations, and fiscal incentives are essential for expediting adoption.
Wood furniture manufacturing SMEs in emerging economies face persistent operational inefficiencies, material waste, long lead times, high setup times, and limited monitoring capabilities, which restrict their ability to improve productivity while advancing sustainability-oriented manufacturing practices. Although Lean tools, multicriteria decision-making methods, and digital monitoring mechanisms have been widely studied, they are often applied separately and rarely integrated into a practical, low-investment framework suitable for resource-constrained SMEs. To address this gap, this study proposes and validates a hybrid Lean–Green–Digital framework for sustainability-oriented operational improvement in wood furniture manufacturing SMEs. The framework integrates current-state diagnosis through Value Stream Mapping, Lean improvement tools including 5S, SMED, and Work Standardization, sustainability-oriented prioritization using the Analytic Hierarchy Process, and a basic digital monitoring layer to reinforce process standardization. A real industrial case study was conducted in a Peruvian wood furniture SME operating under a make-to-order system. Improvement scenarios were evaluated through discrete-event simulation using Arena software and statistically assessed through paired-sample analysis. Results showed reductions in lead time, setup time, and defect levels, with the largest lead-time reductions observed in cutting, assembly, and sanding operations. Work Standardization and SMED were identified as the highest-priority interventions. The study contributes a structured, replicable, and low-investment decision-support framework that integrates operational diagnosis, multicriteria prioritization, digital reinforcement, and simulation-based validation for SMEs seeking sustainability-oriented operational improvement.
This study addresses the critical business challenge of strategically allocating resources between research and development (R&D) and marketing across sequential product lifecycles in the mobile game industry. Using a system dynamics methodology, we develop a simulation model that captures the dynamic interplay among these investments and their impact on user acquisition, retention, and financial performance. The simulation analysis illustrates how strategically reallocating resources between two products at different lifecycle stages can significantly improve user acquisition rates, reduce player dissatisfaction, and enhance overall profitability. Our findings reveal that a phased investment approach, prioritizing R&D for initial quality development, followed by targeted marketing deployment, yields more stable growth patterns and superior economic outcomes than parallel investment strategies. This research provides managers and entrepreneurs in digital industries with a practical decision-support framework for improving sequential product launches, offering actionable insights for improving portfolio management and sustaining long-term revenue streams. The study contributes to the fields of innovation management and strategic marketing by formalizing lifecycle dynamics within a dynamic simulation environment.
The physical properties of jute fibres, including root content, defect, bundle strength, and fineness, have a significant influence on yarn properties. In the present study, nine swarm intelligence-based optimization approaches (Cuckoo search algorithm (CSA), Firefly Optimization Algorithm (FFA), Sparrow Search Algorithm (SSA), Harris Hawks Optimizer (HHO), Bat Algorithm (BA), Artificial Bee Colony (ABC), Particle swarm optimization (PSO), Grey wolf optimization (GWO) and Whale optimization (WO)) integrated with Support Vector Regression were employed to estimate jute yarn properties using fibre quality parameters. For model development, a dataset consisting of 414 experimental observations was used, where 70% of the samples were allocated for training and the remaining 30% for testing. The input variables included bundle strength (g/tex), defect (%), root content (%), and fineness (tex), while the output responses targeted for prediction were yarn tenacity (cN/tex) and elongation (%). Among the developed models, WO-SVR model stands out best performing model (For tenacity = R 2 TR = 0.950 & R 2 TS = 0.89 and for elongation = R 2 TR = 0.912 & R 2 TS = 0.869) both in training and testing phase. Furthermore, WO-SVR had the lowest COM score of 1.228 and demonstrated outstanding predictive accuracy and generalization. An Android App was developed using the WO-SVR model to enable practical implementation and real-time prediction.
This study presents a comparative thermal analysis of a tube-in-tube counterflow heat exchanger under steady-state operating conditions, relevant to engine oil cooling applications, with water and engine oil serving as the working fluids in the inner tube and outer annular region, respectively. An analytical framework was developed to evaluate the logarithmic mean temperature difference (LMTD) and four alternative thermal averages - arithmetic mean temperature difference (AMTD), geometric mean temperature difference (GMTD), harmonic mean temperature difference (HMTD), and root mean square temperature difference (RMSTD) - using outlet temperatures obtained from energy balance relations and regime-appropriate heat transfer correlations. The analytical model accounts for laminar developing flow in the annular region and turbulent flow in the inner tube using appropriate Nusselt correlations, with temperature-dependent viscosity considered for engine oil. In parallel, a computational fluid dynamics (CFD) model was established in ANSYS Fluent to predict outlet temperatures and resolve flow development and near-wall effects. The analytical results demonstrate that GMTD consistently provides the closest approximation to LMTD under moderate operating conditions when analyzed in terms of the dimensionless temperature ratio, while HMTD and RMSTD exhibit larger deviations. The CFD predictions show qualitative agreement with the analytical trends.
In this study, we propose a method for estimating inter-vehicle distances to multiple preceding vehicles in blind-spot environments by combining a 24 GHz FMCW radar with passive metal sheets and triangular corner reflectors. The method uses the radiation directivity of the radar, the boundary reflection characteristics of passive metal sheets, and the high radar cross section gain of corner reflectors. Reflected signal characteristics obtained from the transmission angle and discrete Fourier transform are used to detect vehicles that conventional sensors, such as cameras, LiDAR, and millimeter-wave radars, may fail to detect under non-line-of-sight conditions. Experiments were conducted using three vehicles, including the radar-equipped vehicle, while varying the distances of two preceding vehicles and the radar transmission angle. The results showed that the proposed method successfully estimated the distances to multiple preceding vehicles in blind-spot conditions with an error of approximately 0.36 m. These findings indicate that the method can enhance environmental perception for next-generation mobility robots and may contribute to traffic safety by helping prevent chain-reaction collisions caused by operational errors or delayed judgment.
Metal oxide nanoparticles are essential functional materials for environmental, energy storage, catalytic and sensing applications. Conventional synthesis lacks control over nucleation and growth leading to broad particle size and - poor reproducibility. Microfluidic platforms offers regulation of flow behavior improving control over the material properties. This systematic review summarizes recent studies on microfluidic synthesis of Metal oxide nanoparticles with emphasizing control, quality, and clogging prevention during continuous operating conditions under long term reactor operation. Peer reviewed articles from the last decade were considered,80 studies were retained for qualitative synthesis. The review shows that microreactors enable effective control of mixing time, residence time, temperature, and precursor concentration.Compared to traditional synthesis methods, microfluidic techniques offer superior crystallinity, control over particle morphology and improved reproducibility. However, channel clogging, nanoparticle deposition remain major challenges at high concentrations and long operation. Surface functionalisation, segmented flow , periodic flow reversal, ultrasonic assistance, use of stabilisers, chelating agents as mitigation techniques are discussed. Microfluidic synthesis show link between reactor geometry, operating parameters, product quality. The review evaluates methods for clogging control . This review provides a framework for designing and developing microfluidic platforms for high quality metal oxide nanomaterials synthesis.
Differential settlement at railway bridge-embankment transitions remains a persistent maintenance challenge due to abrupt stiffness contrasts and complex load-transfer mechanisms within the track substructure. This study presents a representative numerical case study to interpret settlement evolution across a bridge-embankment transition and the influence of material stiffness transitions over time. A three-dimensional train-track-soil modelling framework combined with empirical settlement formulations is used to evaluate stress redistribution and cumulative deformation under cyclic train loading. Four transition configurations are examined, including an untreated case, an auxiliary rail solution, and two earthwork-based treatments with graded stiffness. The results show that settlement mechanisms vary spatially within the transition zone. Localised deformation near the bridge interface is associated with stress concentration, whereas settlement toward the open-track side is governed primarily by progressive subgrade compression. Substructure-based treatments modify long-term settlement evolution by promoting broader stress redistribution, while superstructure measures mainly influence early-stage response. The analysis also indicates that layered earthwork solutions may introduce additional deformation at internal material interfaces. Three complementary definitions of differential settlement are proposed to support interpretation of field observations and maintenance-oriented assessment of railway transition zones.
This article proposes an Adaptive Coding and Modulation (ACM) scheme to improve the throughput of Long-Range Wide Area Network (LoRaWAN) in a maritime environment where wireless communication faces severe challenges from multipath propagation, interference, and Rayleigh fading. Unlike conventional LoRaWAN systems that use fixed physical layer configurations, dynamic adjustment of spreading factor (SF), coding rate (CR), and bandwidth (BW), according to channel conditions based on real-time Signal-to-Noise Ratio (SNR), is another solution to respond to communication challenges in the maritime environment. Simulations using a Rayleigh fading model with six different configuration levels demonstrate that the proposed ACM can achieve a gain of up to 103 times the throughput improvement compared to fixed SF7 under low SNR conditions while maintaining link reliability. This work establishes the foundation for implementing efficient and reliable LoRaWAN networks in maritime Internet of Things (IoT) applications.
The widespread integration of Internet of Things devices in modern healthcare systems has significantly increased the demand for secure and efficient mechanisms to safeguard sensitive medical information. Ensuring data confidentiality, integrity, and trustworthy collaboration in federated learning environments necessitates strong authentication protocols founded on robust cryptographic primitives. This work presents a comprehensive assessment of authentication schemes applicable to federated learning-based healthcare systems, with a particular focus on the elliptic curve digital signature algorithm in conjunction with state-of-the-art hash functions, namely BLAKE2b and BLAKE3. The study systematically investigates the influence of different elliptic curves - SECP256K1, NIST256p, NIST384p and NIST521p - on the performance of key cryptographic operations such as key generation, signing and verification. Experimental results reveal that the choice of curve-hash pair directly affects the trade-off between computational efficiency, scalability and security robustness. These findings provide valuable design insights for developing lightweight yet secure authentication frameworks suited to federated learning deployments in healthcare, particularly under resource-constrained and latency-sensitive operating conditions.
The reliability and sustainability of civil infrastructure depend on high-resolution subsurface characterization, yet conventional site investigation is often limited by labor-intensive fieldwork and sparse measurements. This critical systematic review links the digital transformation of geological surveying to geotechnical site characterization, treating remote sensing, geophysics, and digital geological mapping as upstream evidence that constrains engineering interpretation. We synthesize how multi-source observations are converted into engineering-ready site models and decision-facing uncertainty for design and risk management. Through analyzing the intellectual structure of the field, we identify a shift toward integrated workflows that combine multi-platform sensing with physics-informed and explainable AI. We discuss where these workflows are mature enough to support engineering decisions (e.g. ground model updating, hazard screening for slopes, and construction-phase monitoring), and where they remain limited by transferability, registration errors, and validation constraints. The analysis reveals that the frontier is shifting beyond data acquisition toward four-dimensional geological digital twins that can be updated through monitoring and consumed within BIM-centered delivery. This review offers a roadmap for integrating smart sensing and AI into routine engineering practice, highlighting the necessity of explainable algorithms to ensure safety and resilience in the built environment.
When analysing the slope stability of levees, the soil mechanical parameters of the saturated and unsaturated cross-sectional areas are the key influencing factors. Thus, prediction of the seepage line is necessary. In practice, steady-state boundary conditions are typically assumed, where the seepage line reaches the maximum elevation within the levee. This scenario leads to conservative, potentially oversimplified safety assessments, reflecting the awareness that the actual level of safety is not fully known and acknowledges the inherently dynamic rather than steady-state nature of flood events. This study proposes a novel approach to enhance the safety assessment of homogeneous levees by accounting for the dynamic evolution of phreatic lines over time. By considering transient seepage flow and incorporating key flood hydrograph parameters, such as rising or peak duration, and using an analytical estimate of the transient seepage line, this method allows the practical application of risk-based design in flood protection contexts. An equation grounded in numerical principles was developed to empirically derive the transient behaviour of the seepage line, significantly reducing the computational effort compared with numerical methods. This equation can be subjected to a probabilistically supported assessment of homogeneous levees, contributing to an improved understanding of transient geohydraulic conditions.
This study aims to investigate the transient thermal characteristics of brake linings and uniformly decelerating brake discs through numerical and experimental methods. It is found that there is a strong correlation between theory and experiment, and data variations are within +/- 5%. The results confirm the accuracy of the developed thermal model. The temperature range for the braking process is found to be within 82.1 degrees C and 296.4 degrees C. It is found from this analysis that heat is efficiently dissipated in disc brakes, and optimal designs can achieve thermal stability for vehicles running between 80 and 100 km/h. High-performance brakes require vehicles running at more than 150 km/h for effective heat management during a single brake application. Transient temperature distribution is found to have high thermal gradients in brake disc thickness, away from the mid-plane. Furthermore, the study also quantifies the effect of various parameters on the system, and it has been shown how pad pressure and disc tightness considerably increase interface temperatures, whereas numerical accuracy is highly influenced by mesh density, time step size, and material properties. Experimental validation of the system, using a brake test rig, has also been performed and has shown improved heat dissipation characteristics of the proposed design. Overall, the results of the study provide quantitative information on thermal distribution, validation accuracy, and performance limits, which would help in optimizing the design of a brake system for improved safety and high-temperature performance.
In today’s global economy, characterised by variable demand and complex project environments, project managers face significant challenges in assessing project complexity and mitigating risks. This study addresses the challenge of measuring project complexity effectively, offering an innovative methodology that combines Structural Equation Modelling (SEM) with the Fuzzy Analytical Hierarchy Process (FAHP). The research follows a two-stage process. First, SEM is used to model the relationships between key organisational complexity factors. Second, the Project Complexity Rank (PCR) is developed using FAHP to address uncertainty in project assessments. To validate the SEM model, a survey was conducted with 150 project management professionals. The SEM outcomes were then used as inputs to the FAHP, generating PCR-based rankings to assess and compare project complexity. The methodology was applied at an educational institute to evaluate ongoing projects. Judgements from the Project Director, Manager, and team, based on SEM-derived criteria, were collected to calculate the PCR rankings. The findings reveal that project size, variety, and interdependencies are critical factors influencing project complexity. This study contributes a novel hybrid methodology for assessing complexity in project management. This ranking mechanism helps guide organisational structure changes and improve complex risk management.
