Search Results (1,692)

An adult female bottlenose dolphin (Tursiops truncatus) housed at a public oceanarium presented with acute anorexia and lethargy. A blood analysis demonstrated mild leukocytosis, marked azotemia, hyperkalemia, and hyperphosphatemia suggestive of acute kidney injury or renal insufficiency. Ultrasound examination of the dolphin revealed ascites, pleural effusion, bilateral nephrolithiasis, mild hydronephrosis, and bilateral hydroureter consistent with bilateral post-renal obstruction. Initial treatment consisted of antibiotics, oral fluids, and anti-inflammatory treatment. Further imaging diagnosed bilateral obstructing ureteroliths at both ureteral orifice junctions of the urinary bladder. The dolphin’s azotemia and hyperkalemia were nonresponsive to traditional medical management; therefore, peritoneal dialysis was performed for emergent clinical stabilization. Peritoneal dialysis was conducted over 3 days and facilitated the patient to undergo laser lithotripsy of the offending ureteral obstruction. The dolphin made a full recovery following months of intensive medical treatment for complications from peritoneal dialysis and secondary peritonitis. This is the first documented case of successful, though complicated, peritoneal dialysis in a cetacean. Full article

This article presents the results of the tribological performance of 20MnCr5 (1.7147) tool steel countersamples produced by Direct Metal Laser Sintering (DMLS), as a potential material for inserts or working layers of sheet metal forming tools. Tribological tests were performed using a roller-block tribotester. The samples were sheet metals made of materials with significantly different properties: Inconel 625, titanium-stabilised stainless steel 321, EN AW-6061 T0 aluminium alloy, and pure copper. The samples and countersamples were analysed in terms of their wear resistance, coefficient of friction (COF), changes in friction force during testing, and surface morphology after tribological contact under dry friction conditions. The tests were performed on DMLSed countersamples in the as-received state. The largest gain of countersample mass was observed for the 20MnCr5/EN AW-6061 T0 friction pair. The sample mass loss in this combination was also the largest, amounting to 19.96% of the initial mass. On the other hand, in the 20MnCr5/Inconel 625 friction pair, no significant changes in the mass of materials were recorded. For the Inconel 625 sample, a mass loss of 0.04% was observed. The basic wear mechanism of the samples was identified as abrasive wear. The highest friction forces were observed in the 20MnCr5/Cu friction pair (COF = 0.913) and 20MnCr5/EN AW-6061 T0 friction pair (COF = 1.234). The other two samples (Inconel 625, 321 steel) showed a very stable value of the friction force during the roller-block test resulting in a COF between 0.194 and 0.213. Based on the changes in friction force, COFs, and mass changes in friction pair components during wear tests, it can be concluded that potential tools in the form of inserts or working layers manufactured using 3D printing technology, the DMLS method, without additional surface treatment can be successfully used for forming sheets of 321 steel and Inconel 625. Full article

We report on the welding of optical borosilicate glass to an unpolished copper substrate (surface Ra of 0.27 µm and Rz of 1.89 µm) using bursts of femtosecond laser pulses. The present paper puts forth the hypothesis that glass–metal welding with a gap is contingent upon the ejection of molten jets of glass. We have ascertained the impact of pulse energy and focal position on weldability. This finding serves to substantiate our initial hypothesis and provides a framework for understanding the conditions under which this hypothesis is applicable. Under optimal conditions, but without the assistance of any clamping system, our welded samples maintained a breaking resistance of up to 10.9 MPa. Full article

To solve the low efficiency of traditional sheet metal measurement, this paper proposes a digital inspection method for sheet metal parts based on 3D point clouds. The 3D point cloud data of sheet metal parts are collected using a 3D laser scanner, and the topological relationship is established by using a K-dimensional tree (KD tree). The pass-through filtering method is adopted to denoise the point cloud data. To preserve the fine features of the parts, an improved voxel grid method is proposed for the downsampling of the point cloud data. Feature points are extracted via the intrinsic shape signatures (ISS) algorithm and described using the fast point feature histograms (FPFH) algorithm. After rough registration with the sample consensus initial alignment (SAC-IA) algorithm, an initial position is provided for fine registration. The improved iterative closest point (ICP) algorithm, used for fine registration, can enhance the registration accuracy and efficiency. The greedy projection triangulation algorithm optimized by moving least squares (MLS) smoothing ensures surface smoothness and geometric accuracy. The reconstructed 3D model is projected onto a 2D plane, and the actual dimensions of the parts are calculated based on the pixel values of the sheet metal parts and the conversion scale. Experimental results show that the measurement error of this inspection system for three sheet metal workpieces ranges from 0.1416 mm to 0.2684 mm, meeting the accuracy requirement of ±0.3 mm. This method provides a reliable digital inspection solution for sheet metal parts. Full article

In this research project a large linear electromagnetic actuator (LLEA) was designed and manufactured. The electromagnetic performance was published in previous works, but in this paper we focus on the technological challenges related to the manufacturing in particular. This LLEA was based on the magnet-free switched-reluctance principle, having six effective energised stator “teeth” and four passive mover parts (4:6 ratio). Various aspects and challenges encountered during the manufacturing, transport, and assembly are discussed. Thermal expansion of steel contributed to the decision of the modular design, with each module having 1.3 m in length, with a 2 mm longitudinal dilatation gap. The initial prototype was tested with a 10.6 m length, with plans to extend the test track to 60 m, which was fully achievable due to the modular design and required 29 tons of electrical steel to be built. The stator laminations were cut by a bespoke progressive tool with stamping, and other parts by a CO₂ laser. Mounting was based on welding (back of the stator) and clamping plates (through insulated bolts). The linear longitudinal force was on the order of 8 kN, with the main air gap of 7.5–10 mm on either side of the mover. The lateral forces could exceed 40 kN and were supported by appropriate construction steel members bolted to the concrete floor. The overall mechanical tolerances after installation remained below 0.5 mm. The technology used for constructing this prototype demonstrated the cost-effective way for a semi-industrial manufacturing scale. Full article

This study systematically investigates the homogenization-induced Laves phase dissolution kinetics and recrystallization mechanisms in laser powder bed fusion (L-PBF) processed IN718 superalloy. The as-built material exhibits a characteristic fine dendritic microstructure with interdendritic Laves phase segregation and high dislocation density, featuring directional sub-grain boundaries aligned with the build direction. Laves phase dissolution demonstrates dual-stage kinetics: initial rapid dissolution (0–15 min) governed by bulk atomic diffusion, followed by interface reaction-controlled deceleration (15–60 min) after 1 h at 1150 °C. Complete dissolution of the Laves phase is achieved after 3.7 h at 1150 °C. Recrystallization initiates preferentially at serrated grain boundaries through boundary bulging mechanisms, driven by localized orientation gradients and stored energy differentials. Grain growth kinetics obey a fourth-power time dependence, confirming Ostwald ripening-controlled boundary migration via grain boundary diffusion. Such a study is expected to be helpful in understanding the microstructural development of L-PBF-built IN718 under heat treatments. Full article

To capture the complex metallic spatter and melt pool behavior during the rapid interaction between the laser and metal material, high-speed cameras are applied to record the laser powder bed fusion process and generate a large volume of image data. In this study, four deep learning algorithms are applied: YOLOv5, Fast R-CNN, RetinaNet, and EfficientDet. They are trained by the recorded videos to learn and extract information on spatter and melt pool behavior during the laser powder bed fusion process. The well-trained models achieved high accuracy and low loss, demonstrating strong capability in accurately detecting and tracking spatter and melt pool dynamics. A stability index is proposed and calculated based on the melt pool length change rate. Greater index value reflects a more stable melt pool. We found that more spatters were detected for the unstable melt pool, while fewer spatters were found for the stable melt pool. The spatter’s size can affect its initial ejection speed, and large spatters are ejected slowly while small spatters are ejected rapidly. In addition, more than 58% of detected spatters have their initial ejection angle in the range of 60–120$°$. These findings provide a better understanding of spatter and melt pool dynamics and behavior, uncover the influence of melt pool stability on spatter formation, and demonstrate the correlation between the spatter size and its initial ejection speed. This work will contribute to the extraction of important information from high-speed recorded videos for additive manufacturing to reduce waste, lower cost, enhance part quality, and increase process reliability. Full article

This paper, based on the Johnson–Cook damage model, investigates the damage mechanism of high-strength steel tailor welded blanks (TWBs) (Usibor1500P and Ductibor500) during the forming process. Initially, specimens with varying notch sizes were designed and fabricated to perform uniaxial tensile tests to determine their mechanical properties. Then, the deformation process of the notched specimens was simulated using finite element software, revealing the distribution and variation of stress triaxiality at the fracture surface. By combining both experimental and simulation data, the parameters of the Johnson–Cook (J–C) damage model were calibrated, and the effects of temperature, strain rate, and stress triaxiality on material fracture behavior were further analyzed. Based on finite element analysis, the relevant coefficients for stress triaxiality, strain rate, and temperature were systematically calibrated, successfully establishing a J–C fracture criterion for TWB welds, Usibor1500P, and Ductibor500 high-strength steels. Finally, the calibrated damage model was further validated through the Nakajima-type bulge test, and the simulated Forming Limit Diagram (FLD) closely matched the experimental data. The results show that the analysis based on the J–C damage model can effectively predict the fracture behavior of tailor welded blanks (TWB) during the forming process. This study provides reliable numerical predictions for the damage behavior of high-strength steel laser-customized welded sheets and offers a theoretical basis for engineering design and material performance optimization. Full article

Aging railroad bridges present complex challenges due to advancing deterioration and outdated design assumptions. This study develops a comprehensive analytical approach for assessing an aging steel truss railroad bridge through finite element (FE) modeling, sensitivity analysis, and model updating, supported by field testing. An initial FE model of the bridge was created based on original drawings and field observations. Field testing using a laser Doppler vibrometer captured the bridge’s dynamic response (vibrations and deflections) under regular train traffic. Key structural parameters (material properties, section properties, support conditions) were identified and varied in a sensitivity analysis to determine their influence on model outputs. A hybrid sensitivity analysis combining log-normal sampling and a genetic algorithm (GA) was employed to explore the parameter space and calibrate the model. The GA optimization tuned the FE model parameters to minimize discrepancies between simulated results and field measurements, focusing on vertical deflections and natural frequencies. The updated FE model showed significantly improved agreement with observed behavior; for example, vertical deflections under a representative train were matched within a few percent, and natural frequencies were accurately reproduced. This validated model provides a more reliable tool for predicting structural performance and fatigue life under various loading scenarios. The results demonstrate that integrating field data, sensitivity analysis, and model updating can greatly enhance the accuracy of structural assessments for aging railroad bridges, supporting more informed maintenance and management decisions. Full article

The structural health monitoring of historic buildings represents one of the most significant challenges in contemporary structural analysis, particularly for large-scale structures with accumulated damage. Obtaining reliable diagnostics is crucial yet complex due to the inherent uncertainties in both geometric definition and material properties of historic constructions, especially when structural stability may be compromised. This study presents a comprehensive structural assessment of the Bullring of the Real Maestranza de Caballería de Ronda (Spain), an emblematic 18th-century structure, through an innovative multi-technique approach aimed at evaluating its structural stability. The methodology integrates various non-destructive techniques: 3D laser scanning for precise geometric documentation, operational modal analysis (OMA) for global dynamic characterisation, experimental modal analysis (EMA) for local assessment of critical structural elements, and sonic tests (ST) to determine the elastic moduli of the principal materials that define the historic construction. The research particularly focuses on the inner ring of sandstone columns, identified as the most vulnerable structural component through initial dynamic testing. A detailed finite-element (FE) model was developed based on high-precision laser-scanning data and calibrated using experimental dynamic properties. The model’s reliability was validated through the correlation between numerical predictions and experimental observations, enabling a thorough stability analysis of the structure. Results reveal concerning stability issues in specific columns of the inner ring, identifying elements at significant risk of collapse. This finding demonstrates the effectiveness of the proposed methodology in detecting critical structural vulnerabilities in historic buildings, providing crucial information for preservation strategies. Full article

In this note, we discuss interesting aspects of the interaction of electromagnetic waves (EMW) with gravitational waves (GWs) and how we can use them for GW detection. We show that there is (i) a deviation from the original path of the EMW, as measured by an angle of the scattered EMW, as well as (ii) a change in frequency. We show that the angular deviation is dependent on the frequency of the initial EMW and GW and suggest the use of MASERS/RASERS instead of LASERS for GW detection. We also briefly examine the influence of the Earth’s rotation and revolution, which can be sources of noise in the measurement of the angular deviation of EMW. Full article

The accurate measurement of the tree diameter is vital for forest inventories, urban tree quality assessments, the management of roadside and railway vegetation, and various other applications. It also plays a crucial role in evaluating tree growth dynamics, which are closely linked to tree health, structural stability, and vulnerability. Although a range of devices and methodologies are currently under investigation, the widespread adoption of laser scanners remains constrained by their high cost. This study therefore aimed to compare high-end laser scanners (Trimble TX8 and GeoSLAM ZEB Horizon) with cost-effective alternatives, represented by the Apple iPhone 14 Pro and the LA03 scanner developed by mapry Co., Ltd. (Tamba, Japan). It further sought to evaluate the feasibility of employing these more affordable devices, even for small-scale forest owners or managers. Given the growing availability of 3D-based forest inventory algorithms, a selection of such processing pipelines was used to assess the practical potential of the scanning devices. The tested low-cost device produced moderate results, achieving a tree detection rate of up to 78% and a relative root mean square error (rRMSE) of 19.7% in diameter at breast height (DBH) estimation. However, performance varied depending on the algorithms applied. In contrast, the high-end mobile laser scanning (MLS) and terrestrial laser scanning (TLS) systems outperformed the low-cost alternative across all metrics, with tree detection rates reaching up to 99% and DBH estimation rRMSEs as low as 5%. Nevertheless, the low-cost device may still be suitable for scanning small sample plots at a reduced cost and could potentially be deployed in larger quantities to support broader forest inventory initiatives. Full article

Swirl spray combustion has attracted significant attention due to its common usage in gas turbines. However, the high pressure in many practical applications remains a major obstacle to the deep understanding of flame stability and pollutant formation. To address this concern, this study investigated a swirl spray flame fueled with n-decane at elevated pressure. Planar laser-induced fluorescence (PLIF) of OH and polycyclic aromatic hydrocarbons (PAHs) were used simultaneously, enabling the distinction of the locations of OH, PAHs, and mixtures of them, providing detailed information on flame structure and evolution of PAHs. The effects of swirl number and ambient pressure on reaction zone characteristics and PAHs’ formation were studied, with the swirl number ranging from 0.30 to 1.18 and the pressure ranging from 1 to 3 bar. The data suggest that the swirl number changes the flame structure from V-shaped to crown-shaped, as observed at both atmospheric and elevated pressures. Additionally, varying swirl numbers lead to the initiation of flame divergence at distinct pressure levels. Moreover, PAHs of different molecular sizes exhibit significant overlap, with larger PAHs able to further extend downstream. The relative concentration of PAH increased with pressure, and the promoting effect of pressure on producing larger PAHs was significant. Full article

Autosomal recessive types of both syndromic and non-syndromic inherited myopia are common in Saudi Arabia (SA) because many people marry their relatives. The prevalence of syndromic myopathies in SA, like Stickler syndrome (SS), Knobloch syndrome (KS), and Marfan syndrome (MFS), further complicates the disease spectrum. The causative genes linked to the Knobloch, Marfan, and Pierson syndromes are COL18A1, FBN1, and LAMB2, respectively. Additionally, we found recessive types of non-syndromic high myopia that have a high chance of causing retinal detachment, like those linked to LRPAP1 and LEPREL1. In these cases, regular evaluation and early intervention, including prophylactic laser photocoagulation and pars plana vitrectomy, may improve the outcome. Advancements in genetic testing for diagnosis and prevention accelerate detection, facilitate early interventions, and provide genetic counseling. The utilization of artificial intelligence (AI), machine learning (ML), and the advancement of gene therapy offer promising avenues for personalized care. We place a high value on using genetic knowledge to create a national screening program and patient registry aimed at understanding the national burden of myopia, knowing that we have a high rate of consanguinity, which reflects pathogenic homozygous alleles and founder mutations. This initiative will incorporate genetic counseling and leverage innovative technologies, which are crucial for disease management, early identification, and prevention in Saudi Arabia’s healthcare system. Full article

Detecting leakage using a point cloud acquired by mobile laser scanning (MLS) presents significant challenges, particularly from within three-dimensional space. These challenges primarily arise from the prevalence of noise in tunnel point clouds and the difficulty in accurately capturing the three-dimensional morphological characteristics of leakage patterns. To address these limitations, this study proposes a classification method based on XGBoost classifier, integrating both intensity and geometric features. The proposed methodology comprises the following steps: First, a RANSAC algorithm is employed to filter out noise from tunnel objects, such as facilities, tracks, and bolt holes, which exhibit intensity values similar to leakage. Next, intensity features are extracted to facilitate the initial separation of leakage regions from the tunnel lining. Subsequently, geometric features derived from the k neighborhood are incorporated to complement the intensity features, enabling more effective segmentation of leakage from the lining structures. The optimal neighborhood scale is determined by selecting the scale that yields the highest F_1-score for leakage across various multiple evaluated scales. Finally, the XGBoost classifier is applied to the binary classification to distinguish leakage from tunnel lining. Experimental results demonstrate that the integration of geometric features significantly enhances leakage detection accuracy, achieving an F_1-score of 91.18% and 97.84% on two evaluated datasets, respectively. The consistent performance across four heterogeneous datasets indicates the robust generalization capability of the proposed methodology. Comparative analysis further shows that XGBoost outperforms other classifiers, such as Random Forest, AdaBoost, LightGBM, and CatBoost, in terms of balance of accuracy and computational efficiency. Moreover, compared to deep learning models, including PointNet, PointNet++, and DGCNN, the proposed method demonstrates superior performance in both detection accuracy and computational efficiency. Full article