Defects In Process Research Articles

A Study on the Effects of Offset and Welding Speed on Bead Shape and Welding Defects in the FCA Fillet Welding Process

Many industries, such as automobile, aerospace, heavy industries, etc, have been interested in welding automation due to cost reduction and improving productivity. For welding automation, seam tracking plays an important role for the quality of welds. If the seam is misaligned, it can cause troubles such as weld defects and poor appearance. Most of previous studies have analyzed the influence on the bead shape and weld defects by current, voltage, welding speed, but there is lack of research regarding the effects of misaligned seam, called offset. Thus, in this study, fillet welds were conducted with flux-cored arc welding, which has high welding efficiency and low cost. To find out the guideline of offset, various offsets were intentionally applied to the welding experiments, and the changes in bead shape were observed and weld defects were detected for various combination of offset and welding speed. In addition, the grades of weld defect in each condition were evaluated by ISO 5817, which illustrates the international standard of weld defects. Based on the results, the guidelines of offset was suggested to avoid weld defects.

Analysis of wrinkles and corrugations in the electrode calendering process

ABSTRACT Calendering is a crucial process in manufacturing of lithium-ion batteries electrodes. Wrinkles and corrugations are the main manufacturing defects in the calendering process. This study aims at studying the formation mechanism of wrinkles and corrugations. Corrugations and wrinkles of the electrodes were revealed to be more dependent on the difference between the front and back tensions. The tortuosity of corrugations was reduced by 34.2% when the tension difference decreased from 20 to 5 N. The increase in the tension difference led to a linear increase of the shear displacement δ, causing severe wrinkles in the uncoated area of the electrodes. The analytical prediction model for wrinkles during calendering was established based on the shearing of a rectangular membrane. The optimal web tension conditions, at a tension difference of 17 N, was achieved to obtain the optimal calendered electrodes.

Altered drug metabolism and increased susceptibility to fatty liver disease in a mouse model of myotonic dystrophy

Myotonic Dystrophy type 1 (DM1), a highly prevalent form of muscular dystrophy, is caused by (CTG)n repeat expansion in the DMPK gene. Much of DM1 research has focused on the effects within the muscle and neurological tissues; however, DM1 patients also suffer from various metabolic and liver dysfunctions such as increased susceptibility to metabolic dysfunction-associated fatty liver disease (MAFLD) and heightened sensitivity to certain drugs. Here, we generated a liver-specific DM1 mouse model that reproduces molecular and pathological features of the disease, including susceptibility to MAFLD and reduced capacity to metabolize specific analgesics and muscle relaxants. Expression of CUG-expanded (CUG)exp repeat RNA within hepatocytes sequestered muscleblind-like proteins and triggered widespread gene expression and RNA processing defects. Mechanistically, we demonstrate that increased expression and alternative splicing of acetyl-CoA carboxylase 1 drives excessive lipid accumulation in DM1 livers, which is exacerbated by high-fat, high-sugar diets. Together, these findings reveal that (CUG)exp RNA toxicity disrupts normal hepatic functions, predisposing DM1 livers to injury, MAFLD, and drug clearance pathologies that may jeopardize the health of affected individuals and complicate their treatment.

Persistent and transient inefficiencies in the U.S. airline industry

The competitive advantage of airlines depends on their technical efficiency. Efficiency analysis models now differentiate between persistent and transient inefficiencies. Persistent inefficiencies stem from fundamental flaws in production processes or ongoing managerial shortcomings, requiring long-term strategic interventions. Transient inefficiencies, however, result from irregular setbacks that can be swiftly addressed, causing temporary efficiency dips. Focusing on the decade from 2010 to 2019, we investigated how business models, mergers, and upgauging influence these inefficiencies. Our results reveal that 21.2% of the overall technical inefficiency can be accounted for by persistent inefficiency. Despite the U.S. airline industry improving transient inefficiency during the sample period, prevailing persistent inefficiency hindered any significant rise in overall efficiency. The research shows that full-service airlines (FSA) and low-cost carriers (LCC) often exhibit higher persistent and transient efficiency than ultra-low-cost carriers (ULCC). However, there seem to be no significant differences in transient and persistent inefficiency between FSA and LCC. Additionally, the results suggest that the upgauging and higher ramp-to-ramp hours may lead to higher transient efficiency. Finally, we could not find any influence of mergers and stage length on transient inefficiency.

Quantitative analysis of islet prohormone convertase 1/3 expression in human pancreas donors with diabetes.

Islet prohormone-processing enzymes convert peptide hormone precursors to mature hormones. Defective beta cell prohormone processing and the release of incompletely processed peptide hormones are observed prior to the onset of diabetes, yet molecular mechanisms underlying impaired prohormone processing during the development of diabetes remains largely unknown. Previous studies have shown that prohormone convertase 1/3 (PC1/3) protein and mRNA expression levels are reduced in whole islets from donors with type 1 diabetes, although whether PC1/3-mediated prohormone processing in alpha and beta cells is disrupted in type 1 diabetes remained to be explored. Herein, we aimed to analyse the expression of PC1/3 in islets from non-diabetic donors, autoantibody-positive donors and donors diagnosed with type 1 diabetes or type 2 diabetes. Immunostaining and high-dimensional image analysis were performed on pancreatic sections from a cross-sectional cohort of 54 donors obtained from the Network for Pancreatic Organ Donors with Diabetes (nPOD) repository, to evaluate PC1/3 expression patterns in islet alpha, beta and delta cells at different stages of diabetes. Alpha and beta cell morphology were altered in donors with type 1 diabetes, including decreased alpha and beta cell size. As expected, the insulin-positive and PC1/3-positive areas in the islets were both reduced, and this was accompanied by a reduced percentage of PC1/3-positive and insulin-positive/PC1/3-positive cells in islets. PC1/3 and insulin co-localisation was also reduced. The glucagon-positive area, as well as the percentage of glucagon-positive and glucagon-positive/PC1/3-positive cells in islets, was increased. PC1/3 and glucagon co-localisation was also increased in donors with type 1 diabetes. The somatostatin-positive cell area and somatostatin staining intensity were elevated in islets from donors with recent-onset type 1 diabetes. Our high-resolution histomorphological analysis of human pancreatic islets from donors with and without diabetes has uncovered details of the cellular origin of islet prohormone peptide processing defects. Reduced beta cell PC1/3 and increased alpha cell PC1/3 in islets from donors with type 1 diabetes pinpointed the functional deterioration of beta cells and the concomitant potential increase in PC1/3 usage for prohormone processing in alpha cells during the pathogenesis of type 1 diabetes. Our finding of PC1/3 loss in beta cells may inform the discovery of new prohormone biomarkers as indicators of beta cell dysfunction, and the finding of elevated PC1/3 expression in alpha cells may encourage the design of therapeutic targets via leveraging alpha cell adaptation in diabetes.

К вопросу о совершенствовании государственного регулирования развития овощеводства

The article presents the results of the analysis of key indicators characterizing the development of the protected soil vegetable growing industry in the Central Asian Republic. Based on the analysis carried out, exponential forecasting of changes in key indicators of the vegetable growing industry for a three-year period was carried out. An integrated score assessment of the categorization of the regions of the region under consideration according to the dynamics of the development of protected soil vegetable growing was carried out. The priority branch of the protected soil vegetable farming has been established, the features of its functioning at various stages of the production cycle have been determined, provided that the desire for an expanded type of reproduction is realized. The current measures of state and regional support for producers of vegetable products are analyzed, the specifics of providing such support measures are established, a process flaw regarding the organization of the production cycle is revealed when attracting state support measures at the stage its implementation. A hypothesis has been put forward about the possibility of improving state support by distributing it differently relative to the production cycle. Based on the analysis, specific ways have been identified for the implementation of scientific research to determine the features of the functioning of specialized vegetable growing enterprises and further develop methods for integrating such features with government support to form an effective concept for improving the state development of protected soil vegetable growing.

The blind HR: Why do people leave managers not companies? A case of inconsistency in performance appraisal and performance outcome

Learning outcomes The learning outcomes are as follows: to benchmark and compare the theoretical models of the performance management and appraisal processes. (Questions 1 and 2) Remembering-in Bloom’s Taxonomy; to understand the importance of practicing fair performance appraisal process. (Question 4) Understanding-in Bloom’s Taxonomy; to analyze the implementation and effectiveness of 180-degree performance appraisal method and rating system prevalent in the IT Sector. (Question 1) Applying and Analyzing-in Bloom’s Taxonomy; to assess the impact of perceptual biases on human behavior and performance (Questions 2 and 3) Evaluating-in Bloom’s Taxonomy. Case overview/synopsis The case study entitled “Is HR Blind? Why do People Leave Managers Not Companies? A Case of Unfair Performance Appraisal and Biases” is a classic example of a flawed and biased performance appraisal process and perceptual biasness, which resulted in the loss of a valuable and talented resource in a leading Indian IT MNC. The present case had been based upon the real-life experience of an employee (i.e. Rahul Verma), who worked with the company from year 2010 to 2021. It was among the top ten IT MNCs employing about 0.1 million people. The objective of the case was to highlight real time issues existing with HR practices, mainly in IT sector organizations. For example, in the present case, do the HR seek proper justification from the manager before taking a harsh decision like forcibly asking an employee to sign a termination contract without looking at the contributions of his qualitative performance or even performance rating (refer to the transcript) for that matter? Was the job of the HR to only ensure how to fit in employees in the faulty bell curve system? Whether the performance appraisal system being followed at the company is adequately capable of identifying and recognizing the talent. Do the different functions really work cohesively and organically toward achieving the intended goals and objectives of the organization? Was this a failure of the manager in recognizing talent or something went wrong at the employee’s part? Was this a failure of the entire HR system or performance management process at the organization that was unable to filter out the capable and skilled resources out of the crowd? Was this a problem of organizational culture that put on stake its most critical resource – the human capital – by allowing the appraisers to evaluate them just because of the hierarchical structure, and not because they are not being competent enough to perform this most critical job objectively? Who ensures the appraiser is free from any kind of prejudice or bias and is capable of fairly assessing the talent resource? So, the present case was a deliberate attempt to throw out these burning questions to the practitioners and students to ponder upon. Does HR really follow the blind process merely acting on the feedback received from the different units of the organization? With the help of strong theoretical foundation and practical applications, the following objectives and questions have been framed to deliberate and propose the workable solutions for the benefits of the relevant stakeholders. Complexity academic level HR practitioners, HR managers, supervisors, senior management and HR students, IT heads, project managers. Supplementary material Teaching notes are available for educators only. Subject code CSS 6: Human Resource Management.

Disinfectants efficacy in reducing pathogens related to health-care infection associated in universities hospitals of Gorgan, North of Iran

BackgroundDisinfection has a fundamental role in the control of pathogens in the hospital environment. This study was designed to assess the efficacy and functional impact of disinfectants in reducing pathogens related to healthcare associated infections (HAIs) in hospitals.MethodsThis observation study was conducted at three university hospitals in Gorgan, Iran, from May to Oct 2023. The data including used disinfectants and microbiological examination were obtained from the infection control unit of each hospital.ResultsThe results showed that a variety of disinfectants from intermediate to high levels were employed in accordance with the World Health Organization (WHO) protocols. The microbial result revealed that 31.6% (286 out of 906) of the sample had at least one microorganism. Among identified organisms, Bacillus spp. were the predominant species followed by Staphylococcus epidermis, fungus genera, Enterobacter spp., Enterococcus spp., Pseudomonas spp., Escherichia coli, Alcaligenes spp., Staphylococcus aureus, Citrobacter spp., Corynebacterium spp., Klebsiella spp., Acinetobacter spp., Micrococcus spp., Staphylococcus saprophyticus, and Serratias spp. The highest prevalence rates of microorganisms were observed in the wards of ICU, emergency, internal medicine, and women’s ward. The chi-square test revealed a significant relationship between the presence of organisms and hospital wards (P < 0.05).ConclusionThe presence of pathogens indicates a defect in the disinfection process, probably due to both little attention to disinfection protocols and multidrug resistance. It is not yet possible to eliminate pathogens from the hospital environment, but it can be minimized by education intervention, standardizing disinfecting processes, and monitoring by the infection control committee.

Simulation Research on the Control Method of Bow-Collapse in Gear Cold Roll-Beating

Bow-collapse is a type of geometric defect in the gear cold roll-beating process. In order to effectively control bow-collapse and improve material utilization, this paper calculates the cross-section radius of the blank according to the volume invariance principle of metal plastic deformation, and then performs FE simulation of the cold roll-beating process by using the cyclic beating of adjacent tooth spaces and intermittent blank feeding method. The flow state of metal particles is investigated, revealing that the movement of metal particles along the axial direction of the blank is the main reason for the formation of bow-collapse. This paper proposes a method to correct the cross-section radius of the blank and thus control for bow-collapse, where the loss coefficient K characterizes the state of metal particle loss in an infinitesimal thickness region on the cross-section. The analytical equation for calculating the corrected value of the cross-section radius is derived, and the corrected value is calculated. Conducted on the modified blank, cold roll-beating FE simulation results show that bow-collapse is effectively controlled, and the loss coefficient K correctly reflects the loss state of metal particles on the cross-section. The simulation results also show that after cold roll-beating, the gear teeth with standard face width and tooth depth can be obtained after two turning end faces and turning tip circle processes. The bow-collapse control method proposed in this paper effectively improves material utilization.

Digital Engineering in Photonics: Optimizing Laser Processing

This article explores the transformative impact of digital engineering on photonic technologies, emphasizing advancements in laser processing through digital models, artificial intelligence (AI), and freeform optics. It presents a comprehensive review of how these technologies enhance efficiency, precision, and control in manufacturing processes. Digital models are pivotal for predicting and optimizing thermal effects in laser processing, thereby reducing material deformation and defects. The integration of AI further refines these models, improving productivity and quality in applications such as micromachining and cladding. Additionally, the combination of AI with freeform optics advances laser technology by enabling real-time adjustments and customizable beam profiles, which enhance processing versatility and reduce material damage. The use of digital twins is also examined as a key development in laser-based manufacturing, offering significant improvements in process optimization, defect reduction, and system efficiency. By incorporating real-time monitoring, machine learning, and physics-based modeling, digital twins facilitate precise simulations and predictions, leading to more effective and reliable manufacturing practices. Overall, the integration of digital twins, AI, and freeform optics into laser processing marks a significant progression in manufacturing technology. These advancements collectively enhance precision, efficiency, and adaptability, resulting in improved product quality and reduced operational costs. The continued evolution of these technologies is expected to drive further advancements in manufacturing practices, offering more robust solutions for complex production environments.

A new control chart based on proportional hazards and frailty regression models for monitoring high‐quality processes with censored observations

AbstractImproving quality and reducing defects in various processes have led to the design of numerous control charts for monitoring count data. These charts are characterized by a large number of zero defects and are modeled with zero‐inflated distributions. Besides, it is important to consider the influence of key factors on the number of defects in some processes. This paper presents three monitoring scenarios that utilize cumulative sum (CUSUM) control charts in Phase II. These scenarios are valuable for monitoring high‐quality processes, particularly with censored observations. The first scenario uses a zero‐inflated Poisson (ZIP) distribution while the second one employs a proportional hazards regression model to account for observed variables. The third scenario combines the proportional hazards and frailty regression models to consider both observed and unobserved variables. The performance of all three control charts is evaluated by applying shifts to both parameters of the ZIP distribution separately and simultaneously. Extensive simulation studies are conducted and the effects of low, moderate, and high censoring rates are thoroughly studied. The results highlight the importance of considering influential variables for detecting early shifts, as neglecting them significantly impairs the control chart performance. Finally, a real case study, involving defect counts in printed rolls at a label printing factory, has been provided and discussed to show the application of the monitoring procedures.

A defect classification algorithm for gas tungsten arc welding process based on unsupervised learning and few-shot learning strategy

Welding defect prediction is the foundation for ensuring welding quality in gas tungsten arc welding (GTAW). In the prediction process, method based on molten pool vision is the most effective. Since the classification of molten pool defects relies on a substantial volume of labeled data, it is challenging for the models to be applied industrially. This paper presents an algorithm, FS-Classifier, that can achieve high prediction accuracy based on a limited amount of labeled data. The FS-Classifier comprises two stages: Firstly, an unsupervised training approach named RaP is designed to pre-train the feature extractor using extensive unlabeled daily datasets. The RaP consists of a rotation angle prediction task and a position prediction task, which ensure that the network focuses on salient features and precise elements, respectively. Secondly, the support vectors constructed from limited labeled data are used for the feature classifier. The input data is classified to certain class by computing its distances to support vector. The model achieves an accuracy of 94.5 % on the private dataset and 92.8 % on the public dataset for the six classes of defects using 5 % of labeled data volume. In addition, comparative experiments show that our method only requires 5 % of labeled data to achieve accuracy comparable to traditional supervised learning methods. The proposed algorithm addresses the issue of relying on a substantial amount of labeled data in welding process defect classification.

Machine learning predictions of spatter behavior in LPBF additive manufacturing

The adoption of additive manufacturing (AM) technologies, particularly Laser Powder Bed Fusion (LPBF), has been rapidly increasing in industries requiring high precision and complex geometries. Despite its advantages, LPBF faces challenges related to defects that affect material quality, with spatter formation being a significant concern. Spatters—tiny particles ejected during the printing process—can adversely affect the final product’s integrity by altering surface roughness and contributing to defects. This study introduces a comprehensive approach to predict the ejection velocity and direction of spatter particles using a suite of machine learning (ML) algorithms, including Random Forest, Gaussian Process Regression, Support Vector Machine, Regularized Linear Regressions, Gradient Boosting Trees, and Neural Networks. Our analysis reveals that the Neural Network model outperforms others, achieving prediction accuracies of 97.58% for spatter velocity and 88.22% for ejection direction, thus offering a substantial improvement in understanding and controlling spatter-related defects in LPBF processes. The practical implications of these predictions are profound, enabling manufacturers to adjust AM parameters in real time to minimize defects and enhance product quality. This study not only fills a gap in the current literature by providing a detailed comparative analysis of multiple ML algorithms for spatter ejection prediction but also paves the way for future research into real-time monitoring and control systems in AM.

Mechanical property improvements of LPBF-AlSi10Mg via forging to modify microstructure and defect characteristics

Additive Manufacturing (AM) processes have versatile capabilities but are susceptible to the formation of as-cast non-equilibrium microstructures, process-induced defects, and porosity, which have deleterious effects on the mechanical performance. As part of our NSF-ERC-HAMMER program, isothermal forging was investigated as a novel post-processing technique for refining microstructure, reducing process defect severity, and thereby improving mechanical properties. Specimens of Laser Powderbed Fusion (LPBF) AlSi10Mg were fabricated over a range of process parameters and tensile tested as a baseline. Initial work focused on duplicate AM material that was then hot forged with 20 % strain to investigate the effects of isothermal forging at one temperature and strain rate on the microstructure, tensile, and fatigue properties of the as-deposited materials. The microstructures, process-induced defect populations, and tensile/fatigue properties of both as-deposited and forged materials were quantified and analysed by OM, EBSD, XCT, and SEM by various NSF-ERC-HAMMER team members. Isothermal hot forging was found to induce recrystallisation and modify process-induced defect geometry along with increasing tensile ductility. The effects of AM deposition parameters and forge post-processing conditions on LPBF AlSi10Mg will be discussed in terms of microstructure, mechanical properties, and fractography.

Selection Process Based on Advancing the Development of a Science and Technology Law System with Chinese Features

The significance of advancing socialist S&amp;T legal system building with Chinese features is examined in this paper, along with the extensive effects it has on S&amp;T innovation and economic growth. Against the backdrop of an increasingly competitive global scientific and technology landscape, China’s ability to innovate and achieve sustainable growth hinges on the establishment of an ideal science and technology legislative framework. This essay first examines the primary obstacles to China’s development of a science, technology, and innovation (S&amp;T) legal system, including inadequate protection for intellectual property rights, a flawed process for transforming scientific and technological advancements, and an insufficient system for encouraging enterprise innovation. Then, this research presents a quantitative analysis model to optimize the path of science and technology legal building by applying the improved particle swarm optimization method (PSO). The model takes into account a wide range of variables, including the degree of intellectual property protection, the strength of legal backing, the pace at which scientific and technological advancements are transformed, etc. Through the analysis of simulation data, the model also confirms the promotion effect of the legal system construction on the quantity of patent applications, the success rate of innovation projects, the enterprise R&amp;D expenditure, and the expansion of the local economy. The study’s findings demonstrate that bolstering the science and technology legal system can effectively encourage businesses to boost R&amp;D investment and foster regional economic development in addition to greatly raising the quantity of patent applications and the success rate of innovation projects. The rigorous intellectual property protection laws and ideal legal framework for the conversion of accomplishments greatly boost the regional innovation vitality and economic efficiency, particularly in the case study of Zhongguancun in Beijing and East China. Moreover, adaptive weighting is used to enhance the PSO algorithm and optimize the development of science and technology legal system’s comprehensive performance index, thereby confirming the model’s viability and efficacy. The study’s findings offer theoretical justification and helpful advice for China’s development of a science and technology legal framework, which is crucial for fostering innovation in these fields and boosting the country’s competitiveness.

A Surface Defect Detection Method for Cold-rolled Strip Steel Based on Improved YOLOv8

Cold-rolled strip steel has excellent mechanical properties, and it is widely used in the fields such as automotive manufacturing, construction industry, aircraft manufacturing and electronic product manufacturing. However, many different processing defects occur in the manufacturing process of cold-rolled strip steel, and this article proposes a visual detection method based on YOLOv8 to identify the surface defects on cold-rolled strip steel more accurately. Firstly, several common types of defects in cold-rolled strip steel were analyzed. Then, in order to enrich the dataset for subsequent detection, wavelet transform was used for filtering, denoising, and canny edge detection in the image processing stage for obtaining the clearer input model image with the more prominent graphics. Lastly, the CA attention mechanism was added into the YOLOv8 which is useful to improve the features recognition ability of the model, at the same time, the original standard convolution was replaced by DCS convolution and the loss function was optimized which is useful to get the lightweight models. The improved YOLOv8 model was validated on the NEU-DET dataset, the overall detection performance of the model such as the recall, the accuracy, and the average precision has significantly improved.

Mapping and engineering RNA-controlled architecture of the multiphase nucleolus.

Biomolecular condensates are key features of intracellular compartmentalization. As the most prominent nuclear condensate in eukaryotes, the nucleolus is a layered multiphase liquid-like structure and the site of ribosome biogenesis. In the nucleolus, ribosomal RNAs (rRNAs) are transcribed and processed, undergoing multiple maturation steps that ultimately result in formation of the ribosomal small subunit (SSU) and large subunit (LSU). However, how rRNA processing is coupled to the layered nucleolar organization is poorly understood due to a lack of tools to precisely monitor and perturb nucleolar rRNA processing dynamics. Here, we developed two complementary approaches to spatiotemporally map rRNA processing and engineer de novo nucleoli. Using sequencing in parallel with imaging, we found that rRNA processing steps are spatially segregated, with sequential maturation of rRNA required for its outward movement through nucleolar phases. Furthermore, by generating synthetic de novo nucleoli through an engineered rDNA plasmid system in cells, we show that defects in SSU processing can alter the ordering of nucleolar phases, resulting in inside-out nucleoli and preventing rRNA outflux, while LSU precursors are necessary to build the outermost layer of the nucleolus. These findings demonstrate how rRNA is both a scaffold and substrate for the nucleolus, with rRNA acting as a programmable blueprint for the multiphase architecture that facilitates assembly of an essential molecular machine.

New insights into the ovulatory process in the human ovary.

Successful ovulation is essential for natural conception and fertility. Defects in the ovulatory process are associated with various conditions of infertility or subfertility in women. However, our understanding of the intra-ovarian biochemical mechanisms underlying this process in women has lagged compared to our understanding of animal models. This has been largely due to the limited availability of human ovarian samples that can be used to examine changes across the ovulatory period and delineate the underlying cellular/molecular mechanisms in women. Despite this challenge, steady progress has been made to improve our knowledge of the ovulatory process in women by: (i) collecting granulosa cells across the IVF interval, (ii) creating a novel approach to collecting follicular cells and tissues across the periovulatory period from normally cycling women, and (iii) developing unique in vitro models to examine the LH surge or hCG administration-induced ovulatory changes in gene expression, the regulatory mechanisms underlying the ovulatory changes, and the specific functions of the ovulatory factors. The objective of this review is to summarize findings generated using in vivo and in vitro models of human ovulation, with the goal of providing new insights into the mechanisms underlying the ovulatory process in women. This review is based on the authors' own studies and a search of the relevant literature on human ovulation to date using PubMed search terms such as 'human ovulation EGF-signaling', 'human ovulation steroidogenesis', 'human ovulation transcription factor', 'human ovulation prostaglandin', 'human ovulation proteinase', 'human ovulation angiogenesis' 'human ovulation chemokine', 'human ovulatory disorder', 'human granulosa cell culture'. Our approach includes comparing the data from the authors' studies with the existing microarray or RNA-seq datasets generated using ovarian cells obtained throughout the ovulatory period from humans, monkeys, and mice. Current findings from studies using in vivo and in vitro models demonstrate that the LH surge or hCG administration increases the expression of ovulatory mediators, including EGF-like factors, steroids, transcription factors, prostaglandins, proteolytic systems, and other autocrine and paracrine factors, similar to those observed in other animal models such as rodents, ruminants, and monkeys. However, the specific ovulatory factors induced, their expression pattern, and their regulatory mechanisms vary among different species. These species-specific differences stress the necessity of utilizing human samples to delineate the mechanisms underlying the ovulatory process in women. The data from human ovulation in vivo and in vitro models have begun to fill the gaps in our understanding of the ovulatory process in women. Further efforts are needed to discover novel ovulatory factors. One approach to address these gaps is to improve existing in vitro models to more closely mimic in vivo ovulatory conditions in humans. This is critically important as the knowledge obtained from these human studies can be translated directly to aid in the diagnosis of ovulation-associated pathological conditions, for the development of more effective treatment to help women with anovulatory infertility or, conversely, to better manage ovulation for contraceptive purposes. N/A.

Molecular Dynamics Analysis of Multi-Factor Influences on Structural Defects in Deposited Mg-Matrix Zn Atom Films.

Mg metal vascular stents not only have good mechanical support properties but also can be entirely absorbed by the human body as a trace element beneficial to the human body, but because Mg metal is quickly dissolved and absorbed in the human body, magnesium metal alone cannot be ideally used as a vascular stent. Since the dense oxide Zn film formed by Zn contact with oxygen in the air has good anti-corrosion performance, the Zn nanolayer film deposited on the Mg matrix vascular scaffold by magnetron sputtering can effectively inhibit the rapid dissolution of Mg metal. However, there are few studies on the molecular dynamic structural defects of Mg-matrix Zn atomic magnetron sputtering, and the atomic level simulation of Mg-matrix Zn thin-film depositions can comprehensively understand the atomic level structural defects in the deposition process of Zn thin films from a theoretical perspective to further guide experimental research. Based on this, this research first studied and analyzed the atomic layer structure defects, surface morphology, surface roughness, atomic density of different deposited layers, radial distribution function, and residual stress of the thin-film deposition layer of 1500 deposited Zn atoms at the initial deposition stage, during and after deposition under Mg-matrix thermal layer 500K and a deposited velocity 5 Å/ps by molecular dynamics. At the same time, the effects of temperature and deposited velocity of the Mg-matrix thermal layer on the surface morphology, roughness, and biaxial stress of the deposited films were studied.

Ultrasonic TFM imaging inspection of large complex rings using defect quantitative evaluation mappings

ABSTRACT Large complex rings are widely used in wind power energy, aerospace, large construction machinery. There are different scales of defects in the manufacturing process for large complex rings. Because of the complex cross-section and large thickness, it is difficult to quantitative evaluation of defects by conventional ultrasonic technology. Therefore, the ultrasonic total focusing method (TFM) defect quantitative inspection for large complex rings is proposed in this paper. First, the TFM inspection technology of large complex rings for simultaneous scanning of the end-face and side-face is designed. Then, the maximum amplitudes of defects are collected at each grid point of the imaging area by the TFM inspection of calibration specimens. The Φ1.5mm defect quantitative evaluation mappings are obtained by the interpolation fitting algorithm. Finally, the ultrasonic TFM and B-scan inspection experiments of the Φ3440mm large ring are carried out. The results show that the TFM quantitative evaluation mapping has higher accuracy compared to the conventional B-scan method. Especially for the Φ1.5mm defects, the average error of TFM quantitative evaluation is 2.92%. The proposed method can effectively identify defects of other sizes that exceed the calibration mapping. This work has industrial application value for developing ultrasonic TFM inspection of large complex components.