Search Results (3,176)

Impellers are critical components in industrial applications, requiring smooth surfaces and precise dimensions. Traditional investment casting methods are often time-consuming and costly. Fused filament fabrication (FFF), an additive manufacturing (AM) technology, offers a faster, more cost-effective alternative. FFF produces 3D-printed sacrificial patterns directly from a CAD file, making it ideal for low-volume and complex patterns. Unlike wax patterns, which can shrink or distort, 3D-printed patterns offer precise tolerances and allow for thin-walled geometries. FFF also eliminates the need for tooling, reducing capital investment. However, achieving the desired surface finish and accuracy remains a challenge. In this study, a semi-open, single-shrouded centrifugal pump impeller was fabricated using FFF with acrylonitrile butadiene styrene (ABS). A Taguchi L9 (3³) design of experiments was employed to investigate the influence of layer thickness (0.08–0.24 mm), extrusion temperature (260–280 °C), and infill density (30–70%) on dimensional accuracy and surface roughness. Dimensional deviations were evaluated for critical features, including outer diameter (OD), inner diameter (ID), blade thickness (BT), shroud thickness (ST), and blade height (BH). Results show that small and thin features (BT, ST, BH) exhibited deviations with standard deviations below 0.08 mm, whereas OD was the most affected feature with a maximum standard deviation of 0.362 mm due to dominant shrinkage effects. The optimal parameter combination for minimum dimensional deviation was identified as 0.08 mm layer thickness, 280 °C extrusion temperature, and 70% infill density. Surface roughness analysis revealed that layer thickness was the most significant factor, with Ra values ranging from 4 to 7 µm, which falls within acceptable limits for investment casting. Surfaces parallel to the XY plane demonstrated superior surface quality compared with XZ/YZ planes, highlighting the feasibility of FFF-printed ABS patterns for investment casting of complex impellers. Full article

Background: Human milk is considered an ideal diet for neonates, and every effort should be made to promote breastfeeding. Donor human milk (DHM) remains the best alternative for neonates when their mother’s own milk (MOM) is not available. We tried to determine whether having easy access to DHM from a Human Milk Bank (HMB) would reduce the pursuit to feed neonates MOM. Methods: A retrospective study was conducted on data from neonates consecutively admitted to the Neonatal Intensive and Intermediate Care Units of the Department of Neonatology of the Medical University of Bialystok between 1 January 2022 and 31 March 2025. The study period covered 2 years before the opening of the HMB and 1 year of its operation. No specific changes in feeding practices occurred simultaneously during the HMB’s first year of operation. Results: In the first year of operation of the HMB, we observed an increase in the percentage of neonates who (1) received mother’s own colostrum (71.88% vs. 52.28% (2023) and 52.05% (2022); p < 0.001), (2) were fed human milk during hospitalization (24.38% vs. 3.57% (2023) and 4.09% (2022); p < 0.001) and (3) were fed MOM at discharge (43.86% vs. 56.25%, p = 0.024). In total, 53.06% of neonates who received DHM were fed MOM at discharge. Conclusions: The possibility of using milk from the HMB did not limit the desire to feed neonates MOM but intensified it. Neonates were more likely to be fed MOM during the first feeding, throughout their hospitalization, and at discharge. It shows the strong potential of HMBs in improving feeding practices in Neonatal Intensive and Intermediate Care Units. Full article

This essay examines William Makepeace Thackeray’s Vanity Fair (1847–48) through the lens of nineteenth-century Anglican Evangelicalism, arguing that Thackeray’s portrayal of Pitt Crawley (Junior) crystallizes the paradoxes of Evangelical reform culture in early Victorian Britain. Building on the critical foundations laid by Elisabeth Jay, Mark Knight, and Catherine Hall, the study situates Pitt within the historical context of Evangelical social reform, moral discipline, and political conservatism. Through close examination of Pitt Crawley’s domestic reforms at Queen’s Crawley and his engagement in politics, it demonstrates how Thackeray appropriates Evangelical ideals—domestic propriety, moral earnestness, and reformist zeal—only to expose their susceptibility to self-interest and hypocrisy. In exploring Pitt’s political ascent and eventual collapse following the 1832 Reform Act, the essay further interprets his career as an allegory for the decline of the Evangelical Tory aristocracy amid the rise of bourgeois liberalism. As a disillusioned Evangelical, Thackeray renders Vanity Fair not simply a critique of religious hypocrisy but a meditation on the limits of moral reform in a self-interested age. Full article

This paper explores the feasibility of employing small language models (SLMs) on edge devices powered by batteries in environments with limited/no internet connectivity. SLMs in fact offer significant advantages in such scenarios due to their lower resource requirements with respect to large language models. The use case in this study is maritime navigation—in particular, the documentation on Sailing Directions (Enroutd) of the World Port Index (WPI) provided by the National Geospatial-Intelligence Agency (NGA), which provides information that cannot be shown graphically on nautical charts and is not readily available elsewhere. In this environment, response immediacy is not critical, as users have sufficient time to query information while navigating and planning activities, making edge devices ideal for running these models. On the contrary, the response quality is fundamental. For this reason, given the constrained knowledge of SLMs in maritime contexts, we investigate the use of the retrieval-augmented generation (RAG) methodology, integrating external information from sailing directions. A comparative analysis is presented to evaluate the performance of various state-of-the-art SLMs, focusing on response quality, the effectiveness of the RAG component, and inference times. Full article

To address the limitations of existing quarter-vehicle models in capturing pitch motion and front-rear coupling effects, this paper proposes a half-vehicle mechatronic suspension system based on the electromechanical analogy. Traditional methods often overlook non-ideal effects and the dynamic interaction between the front and rear wheels. This paper constructs an equivalent electrical network model for the half-vehicle suspension system. To ensure the physical realizability of the system, parameter optimization is performed under positive-real constraints using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). This approach achieves an optimal trade-off between vertical vibration suppression and pitch control. Simulation results under random road input at a vehicle speed of 20 m/s indicate that while the unconstrained mechatronic suspension improves ride comfort, it increases the dynamic tire load by 19.18%. In contrast, the constrained mechatronic suspension reduces RMS vertical body acceleration by 19.54% and pitch angular acceleration by 2.22% compared to the standard passive suspension. Additionally, a reduction of 8.29% was observed in the suspension working space RMS, alongside a 1.26% decrease in the dynamic tire load. These results demonstrate that introducing appropriate positive-real constraints effectively balances ride comfort and road-holding performance, providing a systematic modeling and optimization framework for half-vehicle mechatronic suspensions. Full article

Accurately predicting the ultimate tensile strain of full-scale pipelines with unequal wall thickness containing cracked girth weld joints is essential for strain-based design, structural integrity assessment, and safe operation. However, many existing limit state prediction methods for full-scale girth welds are developed for equal wall thickness configurations or idealized geometries, and their applicability to unequal wall thickness conditions remains limited. To address this gap, this paper develops a limit state prediction model for the ultimate tensile strain of cracked girth welded joints in full-scale pipelines with unequal wall thickness. The model is established using a numerical database generated from finite element simulations, incorporating realistic pipe geometry, material properties, wall thickness mismatch, and representative crack defect characteristics. By considering the stress and strain concentration effects induced by geometric non-uniformity in the weld region, the proposed model provides a practical and efficient tool for limit state evaluation. During pipeline construction, it supports the formulation of quantitative requirements for key design and fabrication parameters, such as the strength matching level. During stable operation, it enables reliable prediction of the strain capacity of existing girth welds in pipelines with unequal wall thickness, thereby supporting integrity management and decision making for safe service. Full article

Most existing studies on the cost competitiveness of self-consumption PV systems fail to jointly consider key technical, economic, and user-specific factors—such as the share of PV electricity self-consumed, energy exported or imported from the grid, and time-of-use electricity pricing—all of which significantly influence investment viability. To address these gaps, this study introduces a novel method based on a new model to calculate the unit cost of electricity consumption from the user’s perspective (C_EC, in €·kWh⁻¹). The array DC power rating is then optimally sized—assuming ideal orientation and tilt—to minimize C_EC. A self-consumption PV system is considered cost-competitive when the annualized minimized C_EC is lower than the applicable regulated electricity tariff. Colombia is selected as a case study to demonstrate the novel method due to the limited deployment and analysis of self-consumption PV systems in the country. The method is applied across residential, commercial, and industrial sectors in various locations. The resulting annualized minimized C_EC values (0.35–8.85 c€/kWh) are consistently below the corresponding regulated tariffs, demonstrating the economic viability of properly sized PV systems. The method’s adaptability to international tariff frameworks makes it a valuable tool for global application and a useful resource for policymakers and stakeholders. Full article

In the era of Industry 4.0, artificial intelligence technology is experiencing rapid development, and the integration of artificial intelligence (AI) with flexible sensors has emerged as a transformative approach for human motion and posture sensing. This paper explores the advancements in AI-enhanced flexible sensors, focusing on the application of flexible sensors on various parts of the human body. Flexible sensors, due to their conformability and sensitivity, are ideal for capturing the dynamic and subtle movements of the human body. AI algorithms, particularly machine learning and deep learning techniques are employed to process the complex data streams from these sensors, enabling the accurate recognition and prediction of various human postures and motions. The combination of these technologies overcomes the limitations of traditional sensing systems, offering higher precision, adaptability, and real-time feedback. It can be applied to healthcare for rehabilitation monitoring, sports for performance enhancement, and human–computer interaction for intuitive control. This review also discusses the challenges such as sensor reliability, data privacy, and power management. The future outlook emphasizes more sophisticated AI models and deeper technology integration, promising a seamless integration into everyday life for enhanced human–machine interaction and health monitoring. Full article

Background: The constant growth in demand for sustainable energy products and the development of the circular economy have created a critical need for an efficient supply chain for biomass. However, the inherent challenges of biomass make its harvesting, collection, storage, and transport difficult, impacting logistical efficiency and the viability of bioenergy and bioproduct production. This study analyzes how combining artificial intelligence (AI) with multimodal transport can optimize and improve efficiency, as well as reduce costs, in biomass logistics. Methods: The study uses a tiered research framework that encompasses the physical domain (biomass limitations), the structural domain (mathematical modeling for multimodal transport), the intelligence domain (AI-based decision making), and the strategic approach. Results: The outcomes indicate that while truck transport is ideal for short distances, integrating rail and water transport through AI-driven optimization reduces costs and greenhouse gas emissions for long-distance travel. AI technologies, such as digital twins and machine learning, improve demand forecasting, real-time routing, and cargo consolidation, leading to enhanced prediction accuracy for transport costs. Conclusions: The integration of AI and multimodal networks builds resilient and sustainable biomass supply chains. However, full implementation requires addressing data fragmentation and investing in digital infrastructure to enable seamless coordination between supply chain stakeholders. Full article

Reliable ultrasonic inspection of welded structures requires a quantitative understanding of how defect morphology and depth influence detectability. In this study, a simulation-based signal-response Probability of Detection (POD) framework is developed to investigate ultrasonic wave interaction with representative planar and volumetric weld defects. Two-dimensional finite-element shear-wave simulations were conducted to model wave propagation and scattering from planar flaws (toe and root cracks) and volumetric flaws (porosity) across defined inspection depth zones. Peak terminal voltage was used as a continuous response metric for regression-based POD analysis. The results demonstrate that defect morphology dominates the influence on ultrasonic detectability. Planar defects produced systematically higher signal responses than volumetric defects of comparable size, resulting in lower characteristic detection limits. The estimated a₉₀ value for planar flaws was 2.96 mm, compared to 5.64 mm for volumetric flaws under identical threshold conditions. Depth-dependent analyses further revealed morphology-specific behavior: planar defects exhibited consistently high detection probabilities across depth zones (POD > 0.98), whereas volumetric defects showed a reduction in detectability with depth, with POD decreasing from approximately 0.32 in shallow zones to 0.16 in deeper regions. The resulting POD trends are interpreted as comparative, trend-based indicators of morphology and depth-dependent ultrasonic detectability under idealized inspection conditions. These findings quantitatively demonstrate how ultrasonic detectability is governed by wave-defect interaction mechanisms associated with defect morphology and inspection depth. Full article

Three-dimensional (3D) digitization has become a central methodological pillar in cultural heritage documentation, conservation support, and dissemination. Despite the maturity of image-based photogrammetry and active sensing technologies, real-world digitization campaigns frequently diverge from idealized workflows due to constraints related to object accessibility, surface properties, lighting conditions, and operational feasibility. As a result, practitioners are often required to adapt acquisition and processing strategies dynamically, balancing geometric fidelity, visual quality, and practical limitations. This study presents a practice-oriented analysis of applied digitization workflows conducted in controlled indoor and museum environments, focusing on fragile and optically challenging cultural and paleontological objects. Structured light scanning, DSLR-based photogrammetry, and hybrid approaches were systematically explored. While structured light scanning offered high nominal resolution, its performance proved sensitive to material properties and surface behavior, leading to incomplete or unstable reconstructions in several cases. Photogrammetric workflows, when supported by controlled acquisition setups, yielded robust and visually coherent results for the majority of objects. For cases where conventional photogrammetry underperformed, alternative AI-assisted image-based reconstruction pipelines were evaluated as complementary solutions. Rather than emphasizing only successful outcomes, the paper documents recurring failure modes, decision-making trade-offs, and breakdown points across acquisition, alignment, meshing, and texturing stages. Empirical observations are synthesized into qualitative comparisons and decision-support tables, highlighting the conditions under which specific digitization strategies succeed or fail. The findings underscore that hybrid workflows, while theoretically advantageous, can amplify integration complexity and error propagation if not carefully constrained. By foregrounding practical constraints and adaptive methodological choices, this work contributes a transparent, experience-driven perspective on cultural heritage digitization, supporting more resilient planning and informed decision-making in future documentation and conservation projects. Full article

The power-shift system employed in agricultural multi-range continuously variable transmissions (CVTs) features a complex structure and control logic, presenting significant challenges to the reliability of agricultural machinery. To enable timely detection of faults, constructing an intelligent fault diagnosis classifier to monitor the system’s health status is essential. Typically, fault samples utilized for classifier development originate from ideal bench tests, characterized by uniform patterns and limited diversity, thereby hindering the algorithm’s generalization capability. This study addresses this issue by proposing a generative adversarial network (GAN) model, integrated with a triple loss function and a novel generator architecture, to augment the fault dataset under laboratory conditions. The generator architecture comprises a variational autoencoder module and an oil pressure point attention mechanism, enabling the generation of diverse and fluctuating virtual samples. Building on this augmented dataset, a fault classifier based on one-dimensional ConvNeXt was developed. Experimental results indicate that the classifier achieves an accuracy of 99.73%. While classifier accuracy decreases with increasing noise levels, the GAN-generated dataset provides more comprehensive training, resulting in an accuracy approximately 3% higher than that achieved using the original dataset. Full article

Background: Chronic kidney disease (CKD) continues to place immense strain on health systems globally, with nurses at the centre of care delivery physically, emotionally, and relationally. In dialysis units, nurses form long-term therapeutic relationships with patients who depend on life-sustaining treatment several times a week. Objective: This study explored the lived experiences of professional nurses caring for patients with CKD in a dialysis unit, using Watson’s Theory of Human Caring as a guiding framework. Methods: A qualitative, exploratory, descriptive design was employed. Data were collected through in-depth face-to-face interviews with twelve professional nurses and analyzed using thematic analysis. Trustworthiness was ensured through credibility, dependability, confirmability, transferability, and authenticity. Ethical approval and informed consent were obtained. Results: Three themes emerged: (1) emotional and professional experiences, (2) systemic resource constraints, and (3) recommendations for practice improvement. These findings highlight the tension between caring ideals and systemic limitations. Conclusions: The study concludes that dialysis nursing is profoundly meaningful yet emotionally demanding. Strengthened emotional support, improved leadership visibility, consistent resource allocation, and enhanced nephrology nursing education are critical to sustaining compassionate care. The findings offer important insights for policy, workforce development, and quality improvement efforts in CKD care. Full article

Configuring a battery energy storage system (BESS) is an effective approach to alleviating the peak shaving and valley filling burden on conventional thermal power units. However, excessive capacity increases investment cost, whereas insufficient capacity limits operational effectiveness. To address this trade-off, a multi-objective optimization framework is proposed to simultaneously maximize annual economic revenue and minimize load variance. The model comprehensively incorporates investment, operation and maintenance, decommissioning, environmental benefits, and deferred grid investment revenue, together with practical operational constraints on power limits, state of charge (SOC), charge/discharge states, and daily energy balance. A multi-objective particle swarm optimization (MOPSO) algorithm is employed to obtain the Pareto frontier, and the technique for order preference by similarity to ideal solution (TOPSIS) is applied to select the final optimal configuration. Simulation results based on a typical 24 h load profile indicate that the optimal BESS configuration is 27.7 MW/78.3 MWh, which reduces load variance by 32.15% and peak demand by 13.5%, while achieving an average annual revenue of 5.73 million CNY. Comparative analysis shows that the proposed method outperforms the traditional weighted-sum approach in both economic and technical indicators. Furthermore, the framework is extended to a WSCC nine-bus system with photovoltaic (PV) integration by introducing node voltage fluctuation as an additional objective. The results verify that the optimized BESS configuration can effectively mitigate voltage fluctuations under high PV penetration, demonstrating the scalability and applicability of the proposed method in renewable-energy integrated power systems. Full article

Remotely operated vehicles (ROVs) equipped with multibeam forward-looking sonar are widely used for underwater object search in environments where visibility is limited. Ensuring complete three-dimensional (3D) scan coverage within a bounded mission duration remains a challenging planning problem due to sonar beam geometry and vehicle motion constraints. This study presents a deterministic, geometry-driven framework for volumetric path planning of ROV-based forward-looking sonar scanning in predefined circular and rectangular underwater volumes. The proposed approach constructs layered planar scan trajectories by explicitly incorporating sonar detection range, horizontal and vertical beamwidths, and scan volume geometry. Mission duration is analytically estimated from path length and vehicle kinematic parameters, enabling systematic comparison among multiple planning strategies. To support qualitative interpretation of scan effectiveness, a distance-based target position certainty metric is introduced and combined with the active sonar equation to estimate likely target locations within the scanned volume. Simulation results under idealized sensing and motion assumptions demonstrate that the corrected zigzag pattern for rectangular scan areas, as well as the corrected zigzag-II and corrected arithmetic spiral-III patterns for circular scan areas, achieve complete volumetric coverage with bounded mission duration and consistent localization performance. The proposed framework provides a transparent analytical baseline for evaluating volumetric scan path planning strategies for forward-looking sonar–equipped ROVs. Full article