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The Impact of Gestational Diabetes Screening on Maternal and Neonatal Complications during the Perinatal Period
No full textThis study aimed to investigate the association between gestational diabetes mellitus (GDM) screening and perinatal complications in pregnant women and newborns. With the increasing prevalence of diabetes in Taiwan, GDM has become a significant risk factor for pregnant women, leading to various complications during pregnancy and adverse outcomes for newborns. These complications include maternal gestational hypertension, preeclampsia, macrosomia, congenital anomalies, shoulder dystocia, and Apgar scores below seven at one and five minutes after birth.
The study included first-time, singleton pregnant women from 2016 to 2019, excluding those with preexisting diabetes. Among the eligible subjects, 378,285 received GDM screening during pregnancy, while 257,580 did not undergo screening. Among those screened, 10,500 were diagnosed with GDM. Additionally, among women with GDM, 4,419 underwent screening in the first trimester, 2,094 in the second trimester, and 3,987 in the third trimester. The remaining 625,635 pregnant women did not have GDM. The findings revealed that maternal age, obesity, polycystic ovary syndrome, heart disease, and kidney disease increased the willingness of pregnant women to undergo GDM screening. After controlling for confounding variables, mothers with GDM had significantly higher risks of developing gestational hypertension (OR=6.88) and preeclampsia (OR=4.46) compared to those without GDM. Moreover, the risk of adverse perinatal outcomes significantly increased in mothers with GDM, except for Apgar scores below seven at five minutes after birth. Furthermore, compared to low- risk GDM pregnant women, high-risk GDM pregnant women had an increased risk of developing preeclampsia if screened in the first trimester.
In the context of controlling for confounding variables, using the second trimester as the reference, high-risk GDM pregnant women had an increased risk of having newborns with Apgar scores below seven at one minute after birth if screened in the third trimester. However, overall, the risk of Apgar scores below seven at one minute after birth was significantly lower in GDM pregnant women detected through screening in the third trimester compared to those detected in the second trimester (OR=0.67).
Furthermore, the study revealed that high-risk pregnant women were at a higher risk of having neonates with congenital defects than low-risk women when screened during the third trimester compared to the second trimester. Consequently, for future clinical care, it is recommended that high-risk pregnant women undergo early screening and receive intervention after GDM diagnosis. Prolonged intervention may reduce the incidence of congenital defects in newborns. Thus, this study provides valuable insights for future clinical considerations when determining optimal screening timing
The Marketing Strategy Research of Breakfast Industry \ue2A Case Study of Lao Chiang Black Tea Company
No full textTaiwan's food culture is known internationally for its diversity and deliciousness. In addition, with the change of Taiwanese eating habits, Taiwan's catering business have occupied a huge proportion of the domestic consumption market. Under the limited consumption demand, the large amount of the catering company has also led to more intense competition among the whole industry . Due to the low entry barrier of Taiwan\ue2s brunch industry and the low capital requirements for opening a store, making the market is flooded with many kinds of brunch restaurants. Therefore, how to make your own brand create differentiation in the market and successfully attract consumers will become a major focus to make a brand success.
This research will take the 60-year-old local brunch restaurant in Kaohsiung - "Lao Chiang Black Tea" as a case study to understand the operation and marketing strategy of this brunch industry. Research methods such as SWOT analysis,4P analysis, five forces analysis, and questionnaire surveys have finally completed the dual purpose of this research:
(1) to discuss the current development of Taiwan's catering industry, and
(2) to analyze the operation and marketing strategies of case companies.
Through this research, it was found that in recent years, the case company has started to experience problems such as brand aging, the proportion of young consumers, and consumers\ue2 perception that product prices are too high. image, etc., resulting in negative online voice, and external factors, mainly because the entry threshold of Taiwan\ue2s catering industry is low, leading to fierce market competition. Therefore, in the future, how should the case company reposition and upgrade its brand and establish consistent external services? Creating consumer confidence and satisfaction with the brand will be a top priority
Studying mechanical properties and the sodium ion diffusion mechanism of Olivine-type sodium iron phosphate batteries using deep learning molecular dynamics simulation
No full textThis master's thesis aims to investigate the mechanical properties and diffusion mechanisms of olivine-type iron phosphate sodium batteries. Firstly, a deep potential energy model (DP) is employed to predict energy and forces, which are then compared with density functional theory (DFT) results. The results demonstrate that the energy and force predictions of the DP model agree with DFT calculations, with a maximum root mean square error (RMSE) of 2.198 meV/atom for the training set and a maximum RMSE of 44.3 meV/\uc3 for forces. This confirms that the predictive accuracy of the DP model in this study falls within an acceptable error range and is capable of achieving the same precision as DFT. Hence, it can be used to accurately explore the local structure and mechanical properties of olivine-type iron phosphate sodium.
Next, the DP model's results from the Climbing Image-Nudged Elastic Band (CI-NEB) calculations are compared to DFT results to evaluate the accuracy of the DP model in describing the diffusion behavior of sodium ions. Initially, DFT is employed for energy minimization calculations to obtain initial and final structures. Subsequently, the DP model is used for CI-NEB calculations, yielding the corresponding energy potential surface and minimum energy pathway. By comparing the results of DFT calculations with those of the DP model, the accuracy of the DP model in describing the diffusion behavior of sodium ions can be assessed.
Additionally, molecular dynamics simulation methods are utilized to investigate the tensile behavior of olivine-type iron phosphate sodium materials at different ratios. Focus is placed on the stress-strain curve results. Furthermore, the non-equilibrium molecular dynamics (NEMD) simulation method is employed to compute the thermal conductivity of the material. The results are derived from the microscopic to macroscopic scale, providing insights into the thermal conductivity of the material. These studies are of significant importance for an in-depth understanding of the mechanical properties and thermal conductivity performance of olivine-type iron phosphate sodium materials.
Lastly, this research conducts an in-depth analysis of the thermodynamic behavior of olivine-type iron phosphate sodium materials, particularly focusing on parameters such as mean square displacement (MSD) and diffusion rate. These research findings hold crucial implications for further studying and optimizing the performance of battery materials
Studying the impact of solvents on the surface chemistry of perovskite nanowires using atomic force microscopy
No full textIn recent years, perovskite materials have gained attention due to their excellent optoelectronic properties, making it suitable for various devices such as highly efficient solar cells, LEDs, and photodetectors. However, during the synthesis and device fabrication processes of perovskite, it's common to use solvents for further purification to remove unwanted impurities. The choice of different purification solvents and methods can affect the characteristics and performance of perovskite, potentially leading to a decrease in optoelectronic properties and surface ligand stability.
This report aims to analyze the influence of different polarity purification solvents (ranging from high to low polarity: ethanol, chlorobenzene, toluene, and n-octane) on the surface chemistry of one-dimensional perovskite nanowires. We provide a measurement method using Scanning Kelvin Probe Microscopy (SKPM), where the surface potential variation is observed by illuminating the perovskite nanowire surface with light of different wavelengths, to confirm the impact of solvent purification on defect formation and its influence. Additionally, we conducted X-ray diffractometer (XRD) and X-ray photoelectron spectroscopy (XPS) material structure analysis and discussed the results in comparison with SKPM. After purification with n-octane, the perovskite exhibits reduced defect formation, leading to a 21% increase in Photoluminescence Quantum Yield (PLQY) compared to the toluene-purified sample. Finally, we used the Force-distance curve mode of Atomic Force Microscopy (AFM) and Fourier-transform infrared spectroscopy (FTIR) to investigate differences in ligand arrangement and surface adhesion on the perovskite surface. Through these studies, we gain a deeper understanding of the interactions between perovskite and purification solvents, contributing to enhancing the performance of perovskite
Kinematic Compensation and Verification for a Dual-Axes Rotary Table
No full textRoller gear cam (RGC) has the advantage of low backlash and high rigidity. It\ue2s usually used in indexing mechanisms, tool changers, and speed reducers. Previously, using the generative method on the specialized machines is commonly used to manufacture RGC. With the maturity of servo motors and control technology, now research compensated for tool and linear axis machining of RGC using the generative method on a 5-axis machine. Polyoxymethylene (POM) has features like shock absorption and self-lubrication. When applied in light loading, POM cams have advantages such as cost-effectiveness and high processing efficiency. Therefore, this study focuses on the development of the dual-axis rotary table for use in light loading. However, this study used rotary encoders to form a closed-loop system for the dual-axis rotary table. Kinematic analysis is employed to rotational speeds and average rotational speeds. The real-time speed compensation is achieved by SR method and MT method with the closed-loop system. This method can automatically calculate compensation values through a program, reducing assembly errors and motion errors. In this study, Microsoft Visual Studio C# will be used as a programming language to compensate for the speed errors of the dual-axis rotary table, and apply it to RGC machining, verifying the feasibility of the proposed real-time speed compensation
Machine Learning-based Classification of Antenna Defects using Far-field Measurements and S-parameter Analysis
No full textWith the rapid advancement of wireless communication technology, 5G millimeter-wave communication technology has brought more convenient experiences to our lives. However, in 5G communication technology, the performance of antennas plays a crucial role in communication quality. Therefore, researching the design and optimization of 5G millimeter-wave antennas has become a current hot topic.
This paper aims to use two different sets of data, S-parameter measurements obtained through Lookback testing and far-field patterns measured through Over The Air (OTA) testing, to perform machine learning-based identification of antenna defect types. By analyzing the variations in the Return Loss curve of S-parameters for different defect severity levels, we can effectively identify outlier defect severity S-parameters and determine them as defective antennas.
The paper proposes a novel approach based on machine learning techniques, utilizing S-parameters and far-field patterns as the machine learning data, to enhance the accuracy and efficiency of defect classification. This approach aids in assessing and improving the manufacturing quality of patch antennas, enhancing the performance and stability of 5G millimeter-wave communication systems, and achieving automated defect classification. Simulation results demonstrate that both S-parameters and far-field patterns are effective in defect detection using machine learning, with the latter exhibiting higher complexity
Exploration of Skin Metabolites Related to Sleep with Ambient Ionization Mass Spectrometry
No full textMetabolomics investigates the metabolites produced by biological tissues and organs, providing a comprehensive study of these metabolites. Analyzing metabolites using mass spectrometry allows for the acquisition of critical information about the biological system, offering robust assistance in studying metabolic states and physiological functions. In this study, Thermal Desorption Electrospray Ionization Mass Spectrometry (TD-ESI/MS) was employed for long-term monitoring of skin surface metabolites, supplemented by Liquid Chromatography-Mass Spectrometry (LC-MS) for validation purposes. The TD-ESI/MS system facilitates desorption of samples through heating, with subsequent ionization of analytes using an electrospray system. The LC-MS system combines liquid chromatography and mass spectrometry, enabling effective separation and identification of target analytes. The former allows for real-time detection of metabolites, complemented by validation using LC-MS. Experimental results confirm that the detected skin metabolites correspond to the targeted compounds under investigation in this study.
Sleep constitutes approximately 30% of human life and its quality significantly impacts both physical health and behavioral abilities. As a prevalent physiological state, sleep aids in the restoration of biological functions, contributing to the normal operation of various physiological processes such as the immune system, cognition, and memory. When the body is in a state of sleep, metabolic activity differs from that during wakefulness. In this study, successful observation of certain endogenous substances on the skin, such as adenosine and histamine, revealed significant signal variations between subjects with normal and abnormal sleep patterns.
In subjects with normal sleep, the adenosine signal displayed a relatively steady trend, whereas in subjects experiencing wakefulness during sleep periods (abnormal sleep), the signal exhibited a notable increase. Furthermore, the study delved into substances linked to adenosine metabolism, such as guanosine and xanthine, analyzing the changes in signal trends between wakefulness and sleep phases. In subjects with normal sleep patterns, the average signal peak areas of guanosine and xanthine displayed in their respective time-period trend plots were lower during sleep periods compared to wakefulness, akin to the behavior of adenosine. This trend persisted during prolonged monitoring, distinguishing sleep from active periods.
The human sleep process is influenced by various exogenous factors, such as diet and environment. This study examined caffeine and its metabolite- paraxanthine in different specimens. The results revealed the presence of caffeine and paraxanthine in specimens from various parts of the human body. The utilization of TD-ESI/MS enabled effective and real-time monitoring of caffeine and paraxanthine signals in five different biological specimens: skin, tears, saliva, urine, and stools. Under the influence of an exogenous factor - partial sleep deprivation, the examination of metabolites on the skin surface indicated that during the period of partial sleep deprivation, endogenous substances such as adenosine, histamine, and glutamine exhibited an increasing trend in signal peak area. Conversely, substances with less relevance did not display significant signal trend changes.
Research demonstrates a certain degree of correlation between skin metabolites and sleep, whereby analyzing metabolites in the skin yields information about sleep status. In this study, different sleep states were targeted, and the skin metabolites were extensively monitored using the TD-ESI/MS and LC-MS systems. The aim is to utilize these results to further investigate endogenous substances related to sleep within skin samples, and comprehend the association between endogenous substances and the circadian rhythm of the human body.
These findings are expected to contribute to a deeper understanding of variations in sleep and the occurrence of relevant sleep disorders. They could potentially provide partial scientific foundation for future sleep research as well as the diagnosis and treatment of sleep-related issues
Molecular Approach Toward High-resolution Electronic Circuit using Silver-organic Complex
No full textThe development trend of today's technology products is gradually moving towards miniaturization and multi-functionality. If these two conditions must be met at the same time, it means that more electronic components must be placed in the same substrate area. Therefore, the electronic circuits must be in high-resolution.
The current mature processes for conducting metal line patterns are screen printing and photolithography, but the former has a resolution limit of 100 \uc2\ub5m, which is difficult to be further reduced. Although the latter can achieve a much higher resolution, the process is more complicated. This research attempts to combine the features of the both processes and to yield a new procedure capable of adopting simple operation together with a higher resolution, than screen printing.
With the addition of silver-organic complex (i.e., silver oxalate), the undercut was mitigated from 1.74 to 1.005, which is much superior to the case of adding silver nano powder. The highest resolution, defined by line width/line gap (Lwid/Lgap) is reduced to be 8/15 \uc2\ub5m. The adding amounts of silver oxalate can directly manipulate the final resolution, while the four tested adding amounts (0%, 13%, 25%, 50%) exhibit the optimal resolution in the 25% sample. Finally, due to the improvement of the undercut phenomenon, a conductive line with an aspect ratio of 0.64 was successfully fabricated in this study. After testing the volume resistivity, the resistance value can reach 10-5 \uce\ua9. cm, has met the needs of the industry
Numerical modeling of surf zone hydrodynamic under plunging breakers using Reynolds stress models
No full textNumerous previous studies have utilized computational fluid dynamics (CFD) to investigate breaking waves. Among these studies, the Reynolds-averaged Navier-Stokes (RANS) model has commonly beenemployed, while the use of the Reynolds stress model (RSM) is gradually increasing. However, as of yet, there is no model that can accurately and efficiently simulate the entire process of wave breaking. Therefore, this study aims to employ different RSM models to simulate plunging breakers, aiming to comprehend the characteristics and accuracy of these models.
In this study, the Reynolds stress transport equation with different dissipation equations was used to investigate the evolution of turbulent kinetic energy (k) and turbulence dissipation rate (epsilon) under linear and nonlinear velocity gradients by using the simple model. The various RSM models are then applied to large-scale regular wave-breaking experiments to compare the differences in water surface elevation, significant wave height, velocity, and turbulence intensity.
The simulation results reveal that using the standard epsilon equation under linear velocity gradients, k and epsilon follow a power-law decrease similar to grid turbulence. Under nonlinear velocity gradients, k and epsilon initially follow the power-law decrease and later exhibit an exponential growth similar to homogeneous shear flow. Moreover, the transition point and magnitude of the growth increase with wave steepness (H/L). On the other hand, using the epsilon equation from the realizable k \ue2 epsilon model can suppress the nonphysical exponential growth of k. When applied to the surf zone, the epsilon equation from the realizable k \ue2 epsilon model provides more accurate results for water surface elevation, significant wave height, velocity, and turbulence intensity.
The simulation results also observe that the residual turbulence swept into the roller, resulting in higher turbulence levels and transport seaward by the undertow. Furthermore, the turbulence near the water surface after wave breaking is constrained to the upper water column, slowly dissipating while being transported towards the shore, resulting in incomplete dissipation before the arrival of the next wave, ultimately forming \ue2residual turbulence\ue2 in the surf zone
Improving The Even Swap Decision-Making System by Visualization Techniques
No full textThe purpose of this study is to establish an improved even swap method decision system and explore whether this system can assist decision-makers in making more confident and data-driven decisions. When faced with complex decisions, even swap can help resolve more intricate dilemmas. However, it may encounter two challenges. The first issue lies in the difficulty of quantifying the value differences between target conditions during the execution of even swap (e.g., comparing traffic time with meal service). To address this, we introduce the concept of money, measuring the modifications required for even swap in monetary terms (e.g., comparing traffic time with compensation funds). This monetary compensation concept facilitates decision-makers in assessing the value disparities between objectives more easily. In this study, we refer to this compensation as "sacrifice value."
The second challenge with traditional even swap lies in the difficulty of comparison using tabular formats. To improve upon this limitation, our system offers a presentation through visualized charts, mitigating the shortcomings of tabular comparison. Therefore, to address the two aforementioned challenges, we have enhanced the even swap method and introduced the concept of "sacrifice value," combining it with an interactive radar chart presentation. This improved even swap method decision system aims to assist decision-makers in the decision-making process, enabling them to make more optimal choices