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Objectives. Branchio-oto syndrome (BOS) primarily manifests as hearing loss, preauricular pits, and branchial defects. EYA1 is the most common pathogenic gene, and splicing mutations account for a substantial proportion of cases. However, few studies have addressed the structural changes in the protein caused by splicing mutations and potential pathogenic factors, and several studies have shown that middle-ear surgery has limited effectiveness in improving hearing in these patients. BOS has also been relatively infrequently reported in the Chinese population. This study explored the genetic etiology in the family of a proband with BOS and provided clinical treatment to improve the patient’s hearing. Methods. We collected detailed clinical features and peripheral blood samples from the patients and unaffected individuals within the family. Pathogenic mutations were identified by whole-exome sequencing and cosegregation analysis and classified according to the American College of Medical Genetics and Genomics guidelines. Alternative splicing was verified through a minigene assay. The predicted three-dimensional protein structure and biochemical experiments were used to investigate the pathogenicity of the mutation. The proband underwent middle-ear surgery and was followed up at 1 month and 6 months postoperatively to monitor auditory improvement. Results. A novel heterozygous EYA1 splicing variant (c.1050+4 A>C) was identified and classified as pathogenic (PVS1(RNA), PM2, PP1). Skipping of exon 11 of the EYA1 pre-mRNA was confirmed using a minigene assay. This mutation may impair EYA1-SIX1 interactions, as shown by an immunoprecipitation assay. The EYA1-Mut protein exhibited cellular mislocalization and decreased protein expression in cytological experiments. Middle-ear surgery significantly improved hearing loss caused by bone-conduction abnormalities in the proband. Conclusion. We reported a novel splicing variant of EYA1 in a Chinese family with BOS and revealed the potential molecular pathogenic mechanism. The significant hearing improvement observed in the proband after middle-ear surgery provides a reference for auditory rehabilitation in similar patients.

Galvanic corrosion of AZ31B joined with bare or Zn-coated DP590 steel by ultrasonic spot welding or linear friction stir welding was quantitatively studied by pre-defining anode and cathode in the lap joint samples. Corrosion volume and depth from Mg anode surfaces exposed to 0.1 M sodium chloride solution was analyzed as functions of cathode surface type and welding method. Characterization of as-welded joints was performed to identify any microstructural feature of the bonding zone that could impact galvanic corrosion behavior. COMSOL modeling with modified user subroutine was conducted to simulate the progression of Mg corrosion in the same joint and electrode configurations used for the corrosion experiments. The experimental results indicated that Zn-coated cathode surface can reduce Mg galvanic corrosion significantly as galvanic polarization and cathodic current on Zn-coated surface remained relatively low for Mg in the weld joints. COMSOL modeling described the growth of Mg galvanic corrosion in a reasonable manner but showed limitation by underestimating the corrosion volume as it did not capture self-corrosion.

Carbon fiber reinforced polymer (CFRP) and AZ31B Mg alloy were joined by the friction self-piercing riveting (F-SPR) with different steel rivet shank sizes. With the increase of rivet shank size, lap shear fracture load and mechanical interlock distance increased. Ultrafine grains were formed at the joint in AZ31B as a result of dynamic recrystallization, which contributed to the higher hardness. Fatigue life of the CFRP-AZ31B joint was studied at various peak loads of 0.5, 1, 2, and 3 kN and compared with the resistance spot welded AZ31B-AZ31B from the open literature. The fatigue performance was better at higher peak load (>2 kN) and comparable to that of resistance spot welding of AZ31B to AZ31B at lower peak loads (

Unique friction-based self-piercing riveting (F-SPR) was employed to join high-strength, low-ductility aluminum alloy 7055 for lightweight vehicle applications. This study aimed to maximize the joint strength of the AA7055 F-SPR joint while avoiding cracking issues due to low ductility at room temperature. A fully coupled Eulerian–Lagrangian (CEL) model was employed to predict the process temperature during F-SPR, and the temperature field was then mapped onto a 2D axisymmetric equivalent model for accelerated numerical analysis. The geometry, dimensions, and material strength of the rivet, as well as the depth of the die cavity and plunging depth, were investigated to enhance joint formation. Also, a static finite-element analysis model was developed to predict and analyze the stress distribution in the rivet under different mechanical testing loading conditions. Overall, the numerical model showed good agreement with the experiment results, such as joint formation and mechanical joint strength. With the aid of virtual fabrication through numerical modeling, the joint design iterations and process development time of F-SPR were greatly reduced regarding the goal of lightweight, high-strength aluminum joining.

Ultrasonic spot welding (USW) represents one of the unique solid-state joining methods for lightweight materials such as magnesium alloy and aluminum alloy. However, the sonotrode vibration may have a detrimental impact on the sheet material and the existing welds, depending on the component geometry and vibration frequency. In this study, a modal analysis tool based on steady-state dynamics was developed for ultrasonic spot welding which features a cyclic load applied to the sheets during the joining process. Through predicting relative motion and shear stress at the faying surfaces, coupon geometry and weld spacing are identified as two major factors that affect the welding reliability and joint quality in USW. The model was validated via welding experiments on aluminum alloy and magnesium alloy and relevant characterization of temperature distribution, joint strength as well as fracture location.

The ELMOD3 gene is implicated in causing autosomal recessive/dominant non-syndromic hearing loss in humans. However, the etiology has yet to be completely elucidated. In this study, we generated a patient-derived iPSC line carrying ELMOD3 c.512A>G mutation. In addition, the patient-derived iPSC line was corrected by CRISPR/Cas9 genome editing system. Then we applied RNA sequencing profiling to compare the patient-derived iPSC line with different controls, respectively (the healthy sibling-derived iPSCs and the CRISPR/Cas9 corrected iPSCs). Functional enrichment and PPI network analysis revealed that differentially expressed genes (DEGs) were enriched in the gene ontology, such as sensory epithelial development, intermediate filament cytoskeleton organization, and the regulation of ion transmembrane transport. Our current work provided a new tool for studying how disruption of ELMOD3 mechanistically drives hearing loss.

Condition assessment of welded steam components is a crucial activity for safe power plant operations. In this work, remaining lifetime of a 141,000-hour service-aged large bore girth weld between an SA-182 forged 91 (F91) reducer and an SA-335 pipe 91 (P91) header was assessed with a specially designed in-situ high temperature digital image correlation (DIC) system. Nonuniform creep deformation/damages in this service-aged multi-pass F91-P91 welds were quantified and characterized. The in-situ DIC strain measurements reveal the nominal creep strain averaged over the gauge length was consistently below 3%, and the localized creep strain value at failure in the HAZ was ∼10%. Advanced metallurgical characterization was executed in the as-received and posttest conditions to determine the contributions of preexisting creep damage, the so-called “soft zone” in the HAZ, and microstructural variations in the HAZ to the observed Type IV failure. Creep failure occurred in the most predamaged region and this location is adjacent to, but not inside, the documented soft zone before the test. The higher fraction of inclusions in F91 steel contributed significantly to the lower creep resistance observed in both base metal and HAZ.

In manufacturing, convolutional neural networks (CNNs) are widely used on image sensor data for data-driven process monitoring and quality prediction. However, as purely data-driven models, CNNs do not integrate physical measures or practical considerations into the model structure or training procedure. Consequently, CNNs’ prediction accuracy can be limited, and model outputs may be hard to interpret practically. This study aims to leverage manufacturing domain knowledge to improve the accuracy and interpretability of CNNs in quality prediction. A novel CNN model, named Di-CNN, was developed that learns from both design-stage information (such as working condition and operational mode) and real-time sensor data, and adaptively weighs these data sources during model training. It exploits domain knowledge to guide model training, thus improving prediction accuracy and model interpretability. A case study on resistance spot welding, a popular lightweight metal-joining process for automotive manufacturing, compared the performance of (1) a Di-CNN with adaptive weights (the proposed model), (2) a Di-CNN without adaptive weights, and (3) a conventional CNN. The quality prediction results were measured with the mean squared error (MSE) over sixfold cross-validation. Model (1) achieved a mean MSE of 6.8866 and a median MSE of 6.1916, Model (2) achieved 13.6171 and 13.1343, and Model (3) achieved 27.2935 and 25.6117, demonstrating the superior performance of the proposed model.

In this paper, a solution based on an improved particle swarm algorithm is proposed for the path planning problem without a road network in forest fire rescue scenarios. The algorithm adopts an adaptive inertia weight and a dynamically updated learning factor strategy to enhance the global and local search capabilities of the algorithm. In terms of cost function design, the article considers three factors: path length, terrain slope, and obstacle avoidance ability to ensure the safety and effectiveness of the path. The experimental results show that: (1) the path planning algorithm based on improved particle swarm optimization can effectively avoid spreading wildfire and reach the designated target point with a good “detour” effect; (2) the path planned by the improved PSO algorithm performs better than the original PSO algorithm in terms of fitness evaluation and average slope; and (3) changes in the particle population, dimensions, and learning factors in the particle swarm optimization algorithm can affect the convergence of the final path. Increasing the particle dimensions can bring more reasonable and specific paths; decreasing the learning factor increases the convergence iterations, but also obtains a better path planning solution and higher fitness.

The root cause of post-weld baking on the mechanical performance of Al-steel dissimilar resistance spot welds (RSWs) has been determined by machine learning (ML) and finite element modeling (FEM) in this study. A deep neural network (DNN) model was constructed to associate the spot weld performance with the joint attributes, stacking materials, and other conditions, using a comprehensive experimental dataset. The DNN model positively identified that the post-weld baking reduces the joint performance, and the extent of degradation depends on the thickness of stacking materials. A three-dimensional finite element (FE) model was then used to investigate the root cause and the mechanism of the baking effect. It revealed that the formation of high thermal stresses during baking, from the mismatch of thermal expansion between steel and Al alloy, causes damage and cracking of the brittle intermetallic compound (IMC) formed at the interface of the weld nugget during welding. This in turn reduces the joint performance by promoting undesirable interfacial fracture when the welds were subjected to externally applied loads. The FEM model further revealed that increase in structural stiffness, because of increase in steel sheet thickness, reduces the thermal stresses at the interface caused by the thermal expansion mismatch and consequently lessens the detrimental effect of post-weld baking on the joint performance.

Effect of dynamic recrystallization (DRX) on the grain refinement and texture modification was studied by conducting a series of hot compression on AZ31B Mg alloy. Processing-grain size-texture maps were established as a function of temperature, strain rate, strain, and the Zener-Hollomon parameter (Z). Moreover, influence of simultaneous changes in the grain size and texture on tensile yield strength and ductility of the hot-worked Mg alloy was studied using the grain size and texture maps established. The processing-grain size map showed that the DRX grain size decreased with the increase in Z, also revealing various characteristics ranging from a grain growth, grain refinement, to a bimodal distribution of ultrafine grains and partially recrystallized grains. The effect of twinning on the grain refinement was also evident at high Z conditions. The processing-texture map revealed that the initial fiber texture was altered significantly to a shear, off-normal, or extension-twin texture with the increase in Z. The Schmid-factor maps were calculated to identify a dominant deformation mode during a subsequent tensile deformation of the hot-worked samples. Finally, a corresponding Hall-Petch relationship for each dominant deformation mode was used to establish tensile yield-strength maps, which agree well with the measured data.

In this paper, the nanostructured reactive multilayer films (RMFs) are used as the intermediate layer to assist laser welding of Ti6Al4V alloy. During the welding process, the heat of the laser ignites the RMFs, and the heat released by the RMFs reaction instantaneously reaches the liquidus temperature in the center of the joint. This new joining method is helpful in reducing the laser energy input, minimizing the increasing brittleness and reducing strength effect of heat affected zone of the titanium alloy base material, and increase the joint strength of welds. The experiment confirmed that the joint with the Ni/Al RMFs as the intermediate layer can achieve a shear strength of 396 MPa at 700 W laser power, which is 3 times as high as that of the direct laser welded joints without using Ni/Al RMFs at the same power. Moreover, the Ni/Al RMFs as an intermediate layer can reduce the laser power required for welding by 10–20% without lowering the joint strength. Keywords: Ti6Al4V, Laser welding, RMFs, Intermediate layer

Ultrasonic spot welding using different welding conditions was applied to join dissimilar metals of galvanized DP590 steel and AZ31B magnesium sheets. In situ high-speed imaging, digital image correlation, and infrared thermography were utilized to quantitatively study the interfacial relative motion, surface indentation, and heat generation across the joint faying interface and the sheet/sonotrode interfaces under the welding condition of moderate welding power and short welding time. For welds made with high power and long welding time, lap shear tensile tests as well as fatigue tests were carried out. Different fracture modes were observed after the lap shear tensile tests and fatigue tests performed under different peak loads. Post-weld cross-sectional analysis with scanning electron microscopy coupled with energy dispersive X-Ray spectroscopy revealed the variation of morphology and chemical composition at the joint interface for welds made with different welding conditions.

In the present work, joining of a carbon fiber-reinforced polymer and dual phase 980 steel was studied using the friction bit joining, adhesive bonding, and weldbonding processes. The friction bit joining process was optimized for the maximum joint strength by varying the process parameters. Then, the adhesive bonding and weld bonding (friction bit joining plus adhesive bonding) processes were further developed. Lap shear tensile and cross-tension testing were used to assess the joint integrity of each process. Fractured specimens were compared for the individual processes. The microstructures in the joining bit ranged from tempered martensite to fully martensite in the cross-section view of friction bit-joined specimens. Additionally, the thermal decomposition temperature of the as-received carbon fiber composite was studied by thermogravimetric analysis. Fourier-transform infrared–attenuated total reflectance spectroscopy and X-ray diffraction measurements showed minimal variations in the absorption peak and diffraction peak patterns, indicating insignificant thermal degradation of the carbon fiber matrix due to friction bit joining.