Search

Malaria is among the most devastating and widespread tropical parasitic diseases in developing countries. To prevent a potential public health emergency, there is an urgent need for new antimalarial drugs, with single-dose cures, broad therapeutic potential, and novel mechanism of action. We synthesized HCl salt of SKM13 (SKM13-2HCl) based on the modification of SKM13 to improve solubility in water. The anti-malarial activity of the synthesized drug was evaluated in both in vitro and in vivo models. The selective index indicated that SKM13-2HCl showed the same effectiveness with SKM13 in Plasmodium falciparum in in-vitro. Even though, in vivo mouse study demonstrated that SKM13 (20 mg/kg) at single dose could not completely inhibit P. berghei growth in blood. The survival rate increased from 33 to 90% at 15 days after infection. However, SKM13-2HCl (20 mg/kg) at a single dose increased the survival rate up to 100% at the same duration. Ultra-High-Performance Liquid Chromatography (UHPLC) showed that solubility in water of SKM13 and SKM13-2HCL was 0.389 mg/mL and 417 mg/mL, respectively. Pharmacokinetics (PK) analysis corresponded to the increased solubility of SKM13-2HCl over SKM13. Haematological parameters [red blood cell (RBC) count, haemoglobin level, and haematocrit level] supported the comparable efficacy of SKM13 and SKM13-2HCl in a 4-day suppression test. One mode of these drugs was found to be activating phosphorylation of eIF2α, hallmark of ER-stress, to kill parasite. Novel salt derivative of SKM13 (SKM13-2HCl) have enhanced anti-malarial activity against P. falciparum with endoplasmic reticulum (ER)-stress and salt form of SKM13 is an excellent direction to develop anti-malarial drug candidate in mice model. [Display omitted] [ABSTRACT FROM AUTHOR]

The particulate matter (PM 10) trend has decreased at a growing rate in South Korea, which is on the downwind side in the East Asian region over the last five-year period of 2014–2018. Nevertheless, nationwide, days of high PM 10 levels and haze frequently occurred over the period of February–March in 2019. During the period of February–March 2019, two major atmospheric blockings were identified over the Alaskan region in the northeast Pacific over the periods of February 21-March 1 and March 18–29. Warm and stagnant synoptic meteorological conditions favorable for high-PM 10 haze days in the East Asian region were accompanied 4–5 days later by the remote Alaskan atmospheric blockings. The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) was used, applying zero-out emission to analyze the regional PM 10 contribution on severe haze days exceeding a daily average of 100 μg m−3 over South Korea during March 3–6, 2019. The daily average regional PM 10 contribution indicated a rate of 57% compared to rates of 23% before and 29% after severe haze days. Furthermore, the major regional PM 10 components were nitrates (49%), other inorganics (28%), and sulphates (9%) during the severe haze days. Among inorganic PM 10 aerosols, other inorganics mainly composed of anthropogenic fine dust were not a major contribution component in the South Korean case due to fuel consumption changes in China. However, higher nitrate flux from sources in eastern China remained in South Korea, though in slightly reduced amounts. The contribution rate of NO X discharged from automobiles has gradually increased in China, and the level of SO 2 remained on a low level through emission control technologies and regulatory options. The warm and stagnant atmosphere in the East Asian region caused the flux of relatively more nitrates were likely transported, with ammonium nitrate in the atmosphere and with lower sulphate levels. • Warm and stagnant conditions favorable for haze were accompanied by blocking 4–5 days later. • WRF-Chem was used, applying zero-out emission to analyze regional PM10 contribution. • Regional PM10 contribution indicated 57% compared to 23% before and 29% after haze. • The warm and stagnant atmosphere caused the flux of more nitrates likely transported. [ABSTRACT FROM AUTHOR]

[Display omitted] • Developed compression molding monitoring system reveals cure shrinkage behavior of epoxy molding compound in curing stages. • FEA considering viscoelastic properties and cure shrinkage accurately predicts warpage behavior of bi-material dummy package. • Optimized post-cure temperature profile reduces bi-material dummy package warpage threefold, enhancing package reliability. Warpage in semiconductor packages is a critical issue that affects their reliability and performance. This study aims to minimize the warpage of a bi-material dummy package by optimizing the post-mold curing (PMC) temperature profile. A warpage simulation model was developed considering the viscoelastic properties and cure shrinkage behavior of the epoxy molding compound (EMC). A compression molding monitoring system was also developed to analyze the cure behavior of the EMC. The gelation point of the EMC during compression molding was determined using a dielectric sensor, while cure shrinkage behaviors were analyzed using a fiber Bragg grating sensor. Viscoelastic properties were characterized through stress relaxation tests, and thermal properties were measured using three-dimensional digital image correlation. The developed simulation model was used to optimize the PMC temperature profile using a genetic algorithm. Experimental validation showed that the optimized temperature profile reduced the warpage from ∼1519 μm to 486 μm compared to a linear profile. The findings of this study have broader implications for the design and manufacturing of semiconductor packages, as the proposed approach can be applied to minimize warpage and improve package reliability. [ABSTRACT FROM AUTHOR]

Airbags are essential automotive components that protect occupants from collisions. Airbag covers made of thermoplastic olefin (TPO) material should be deployed quickly without debris under various environments. To accurately predict airbag deployment during a collision, the mechanical properties of polymer materials at high strain rates according to temperature should be considered. In this study, a quasi-static test in the range of 0.0083, 0.0083 s − 1 and a medium strain rate tensile test were performed at strain rates of 1, 10, and 100 s − 1 . Additionally, a split-Hopkinson Pressure bar test was performed to conduct a high strain rate tensile test at 1000, 1500, and 2000 s − 1 . Through this, tensile strength and failure strain were derived for each strain rate. As the polymer phase moves toward the high strain rate region, the β -transition becomes dominant, resulting in a non-linear increase in tensile strength in the Eyring plot. Based on the strain rate dependent mechanical behavior, an airbag module impact simulation was conducted to verify the effects of strain rate on airbag deployment using the LS-DYNA software. The airbag deployment analysis results showed deployment angle results similar to the actual experiment when strain rate dependent mechanical properties were applied. This is because higher strength was applied at high strain rates compared to low strain rates. Therefore, strain rate dependent mechanical properties are essential in high strain rate simulation such as collision analysis. [ABSTRACT FROM AUTHOR]

This study presents a unique application of a temperature control algorithm, specifically modified deep deterministic policy gradient (DDPG), in an actual 2.8 m2 cold storage facility, contrasting the majority of research that leverages theoretical validations using simulation tools. The primary goal was to minimize energy consumption while maintaining the desired temperature range. To achieve this, thermocouples and a watt-hour meter were installed to collect real-time data on temperature and power consumption, subsequently transmitted to a deep-learning computing and control system for processing. Utilizing the gathered data, the algorithm was trained to simultaneously maintain the temperature and minimize power consumption. The temperature setting served as a control variable, and a deep deterministic policy gradient algorithm was used. A hyperparameter with a dominant influence on learning outcomes was optimized. Furthermore, the algorithm was exposed to various complex scenarios that occur during actual cold storage operations, such as door opening, reinforcing its practical viability. The study findings revealed that our real-world application of the DDPG algorithm significantly reduced energy consumption by 47.64% compared to conventional proportional-integral-derivative control algorithms, whilst maintaining the target temperature range. • Developed a temperature control algorithm based on deep reinforcement learning to optimize energy use in real cold storage facilities. • Utilized real-time data collection and communication for temperature and power consumption, integrated into a deep learning system. • Leveraged the Deep Deterministic Policy Gradient algorithm, using temperature settings as a control variable. • Achieved a remarkable 47.64% reduction in energy use while maintaining the target temperature range. • Demonstrated the practicality and efficiency of deep reinforcement learning methods in real-world energy conservation scenarios. [ABSTRACT FROM AUTHOR]

• Prediction of interfacial delamination including not only damage initiation but also crack propagation under arbitrary mixed mode loads for semiconductor packages. • Mixed mode fracture toughness measurement of thin EMC/PCB bilayer for a range of temperature and moisture absorption conditions. • Development of delamination prediction analysis model which was validated through different mixed mode bending test. Interfacial delamination is a critical factor for dissimilar bi-material interfaces in terms of structural mechanical behavior and reliability. In this study, the mixed mode interfacial fracture toughness of a thin epoxy molding compound/printed circuit board bilayer with different mixities was measured via the asymmetric double cantilever beam test with a reinforced metal jig. Additionally, mode II fracture toughness properties were measured through the end notched flexure test. All tests were performed under various temperature and moisture absorption conditions below 100℃/85 %RH. Then, a finite element simulation was performed with the cohesive zone model technique to validate the experimentally measured fracture toughness values by comparing the load–displacement curves from the experiment and simulation. Additionally, the single leg beam test and its finite element simulation were conducted and compared for validation of the measured mixed mode fracture toughness values. It was demonstrated that the developed mixed mode fracture toughness measurement method could provide accurate fracture toughness, which could effectively predict the overall load–displacement curve and damage propagation behavior. [ABSTRACT FROM AUTHOR]

In this work, a novel deep neural network was proposed for predicting the mechanical behavior of plain carbon fabric reinforced woven composites. The deep neural network was trained by a pre-simulated stress-strain curve database of woven composites depending on yarn structures and the mechanical properties of the fiber and matrix. Micro-mechanics-based multi-scale analyses of woven composites were conducted for progressive damage analysis. These analyses utilized the stress amplification factor to transfer stress between the micro-scale and meso-scale simulations and the respective failure criteria were applied for micro-scale stresses of the fiber and matrix, respectively. The database of stress-strain curves under tensile, compressive and shear loading was acquired for different yarn geometries and constituent properties. These variables were used as training input and the resulting stress-strain curves were used as training output of the network. To optimize the network, hyper parameters of the neural network, such as the number of layers and nodes, were determined by the Hyperband optimization algorithm. The train and test of deep neural network model was performed by TensorFlow backend using the Keras library in Python. Mechanical tests were performed to validate the predicted mechanical behavior from both simulation and the deep neural network. As a result, the stress-strain curves under tensile, compressive and shear loading of arbitrary woven carbon composites can be successfully predicted in several seconds by the deep neural network with high accuracy. [ABSTRACT FROM AUTHOR]

A terahertz time-domain spectroscopy (THz-TDS) system was used to monitor the crystallinity and moisture content of polyethylene terephthalate (PET) specimens. The crystallinity and moisture content were varied by controlling cooling rate and immersion time. Crystallinity, crystallite size, and moisture content of the PET specimens were measured through differential scanning calorimetry (DSC), x-ray diffraction (XRD), and gravimetric methods, respectively. The THz signal with regard to crystallinity and moisture content was subsequently analyzed within the time and frequency-domain. In addition, optical properties of refractive index and absorption coefficient were derived. Consequently, the crystallinity and moisture content could be simultaneously measured by analyzing the THz signals. • The THz amplitude ratio and the moisture content have a negative correlation. • The Low/high amplitude ratio and the degree of crystallinity have a negative correlation. • Degree of crystallinity and moisture content affect electro-magnetic properties. • Degree of crystallinity and moisture content of PET can be monitored in an in-situ condition using a terahertz system. [ABSTRACT FROM AUTHOR]

This study presents a non-destructive evaluation (NDE) method that utilizes a convolutional neural network (CNN) based terahertz (THz) signal processing to quantitively distinguish overlapping signals from micro-defects in glass fiber reinforced polymer (GFRP) composites. Theoretical electro–magnetic wave propagation models for describing the propagation of the THz signal into the GFRP composite were devised considering the interaction of THz with the micro-defects in the GFRP composite. THz signals were simulated with respect to the thickness of the defects, and transformed into two-dimensional (2D) spectrograms using a short-term Fourier transform (STFT). The converted 2D-STFT images were used to train the CNN deep learning model to classify the size and thickness of the defects. A probability map of the classification of the defect thickness was derived from the trained CNN deep learning model. Using the probability map based on the CNN model, the depth and size of the micro-defects thinner than 20 μm inside the GFRP composites could be successfully differentiated with 95% accuracy. This research proposes a promising approach for analyzing micro-defects in composite materials by quantitatively evaluating the extent of signal overlap, going beyond the traditional approach of training the model based solely on the signal. [ABSTRACT FROM AUTHOR]

Cure shrinkage of the epoxy molding compound (EMC) for semiconductor package during molding process, was precisely measured using a fiber Bragg grating (FBG) sensor by measuring a Bragg wavelength (BW) shift. A dielectric sensor was simultaneously used to monitor the curing process of the EMC and define its gelation point. The relationship between internal strain development of the EMC and its degree of cure was investigated by correlating two measured values (The internal strain from the FBG sensor and the degree of cure from the dielectrometry). The viscoelastic properties of the EMC were also measured by a three-point bending stress relaxation test. Finally, the warpage of EMC-aluminum bi-layer strip was measured with digital image correlation (DIC) method and compared with the result from the finite element analysis. As a result, it was confirmed that the cure shrinkage of the EMC should be considered as an important factor in predicting warpage behavior of the semiconductor. [ABSTRACT FROM AUTHOR]

In this work, sandwich structure with trapezoidal corrugated cores was 3D printed by fused deposition modeling (FDM) method applying to short and continuous carbon fiber (CF) reinforced nylon filament. The split core of the sandwich structure was designed and fabricated. To investigate the bending characteristics for the sandwich structures, the three-point bending (3 PB) tests were conducted for the different core types. Consequently, it was found that the mechanical behavior and failure modes were significantly changed by core materials and the split number of cores. Also, the dramatic improvements in the mechanical performances (4.1 times higher specific flexural stiffness, 2.6 times larger specific load capability and 8.6 times larger energy absorption capability than those of PET foam core sandwich structure) could be obtained in corrugated sandwich structure which was 3D printed using continuous carbon fiber. This result comes from the precise fiber alignment of continuous carbon fiber along the core, which can be realized only by continuous CF 3D printing technology. [ABSTRACT FROM AUTHOR]

Recently, the electric mobility has emerged with great prospects as there has been a growing interest in and demand for eco-friendly energy. The electric vehicle uses a large number of lithium batteries as sources of power, and the lithium battery poses a risk of fire and explosion when the external impact is loaded. Therefore, in this study, an underbody shield (UBS) was designed and manufactured using carbon fiber reinforced thermoplastic composites for battery protection. The underbody shield is composed of two parts, which are the main shield plate and collision protection bar (CPB). Mechanical behavior against impact was analyzed through finite element analysis considering the position of the CPB and the shape of the underbody shield plate. For the UBS, the hybrid composites (Carbon fiber (CF)/polyethylene terephthalate (PET) and self-reinforced polypropylene (SRPP) composites) were used and the ratio between the CF/PET and SRPP was optimized. Through a finite element analysis, the optimal underbody shield was designed with a thickness of 2.4 mm and a hybrid ratio of CF/PET:SRPP as 1:2. Finally, the designed underbody shield was manufactured using a thermo-forming process and mounted to an electric vehicle, and then a vehicle crash test was performed with a concrete obstacle. The developed underbody shield could protect the battery against the impact damage successfully, showing that the deformation of a rear part was within 5 mm and no battery leakage occurred. [ABSTRACT FROM AUTHOR]