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The accurate estimation of time delays is crucial in traffic congestion analysis, as this information can be used to address fundamental questions regarding the origin and propagation of traffic congestion. However, the exact measurement of time delays during congestion remains a challenge owing to the complex propagation process between roads and high uncertainty regarding future behavior. To overcome this challenge, we propose a novel time delay estimation method for the propagation of traffic congestion due to accidents using lag-specific transfer entropy (TE). The proposed method adopts Markov bootstrap techniques to quantify uncertainty in the time delay estimator. To the best of our knowledge, our proposed method is the first to estimate time delays based on causal relationships between adjacent roads. We validated the method's efficacy using simulated data, as well as real user trajectory data obtained from a major GPS navigation system in South Korea.

Spray drying process, widely used in industrial applications, is highly energy-consuming. To reduce the energy consumption under the secured yield, optimizing the operating conditions of drying process or utilizing heat from the exhaust air of dryers has been carried out. Albeit their successful demonstration of energy saving, securing the yield of drying process still requires high temperature processing. Since high temperature processing often degrades or denatures the quality of dried materials in specific applications, further development of diverse strategies is necessitated for yield enhancement in low temperature environments. Herein, a spray drying method with low-adhesive surfaces is proposed to considerably increase the yield of drying process with no additional energy consumption or devices to accomplish the equivalent effects of the enhanced energy saving of spray dryers under the identical operating condition. The experimental results reveal that due to the significant reduction of the particle accumulations in the chamber of the spray dryer, equipping the low-adhesive surfaces to the conical walls of the pilot-scale spray dryer achieves up to 16.5% higher yield than control surfaces under identical operating conditions. This brings about the enhanced energy saving up to 14.2% to acquire the identical powder collection by the spray drying process.

Selective laser etching is a promising candidate for the mass production of glass interposers. It comprises two steps: local modification by an ultrashort-pulsed laser and chemical etching of the modified volume. According to previous studies, when an ultrashort-pulsed laser beam is irradiated on the sample, electron excitation occurs, followed by phonon vibration. In general, the electron excitation occurs for less than a few tens of picoseconds and phonon vibration occurs for more than 100 picoseconds. Thus, in order to compare the electric absorption and thermal absorption of photons in the commercial glass, we attempt to implement an additional laser pulse of 213 ps and 10 ns after the first pulse. The modified glass sample is etched with 8 mol/L KOH solution with 110 °C to verify the effect. Here, we found that the electric absorption of photons is more effective than the thermal absorption of them. We can claim that this result helps to enhance the process speed of TGV generation.

AbstractWe calculated the correlation between the doping concentration of the charge layer and the multiplication layer for separate absorption, grading, charge, and multiplication InGaAs/InAlAs avalanche photodiodes (APDs). For this purpose, a predictable program was developed according to the concentration and thickness of the charge layer and the multiplication layer. We also optimized the design, fabrication, and characteristics of an APD for 20 Gbps application. The punch‐through voltage and breakdown voltage of the fabricated device were 10 V and 33 V, respectively, and it was confirmed that these almost matched the designed values. The 3‐dB bandwidth of the APD was 10.4 GHz, and the bit rate was approximately 20.8 Gbps.

Abstract We present the selective laser-induced etching (SLE) process and design guidelines for the fabrication of three-dimensional (3D) microfluidic channels in a glass. The SLE process consisting of laser direct patterning and wet chemical etching uses different etch rates between the laser modified area and the unmodified area. The etch selectivity is an important factor for the processing speed and the fabrication resolution of the 3D structures. In order to obtain the maximum etching selectivity, we investigated the process window of the SLE process: the laser pulse energy, pulse repetition rate, and scan speed. When using potassium hydroxide (KOH) as a wet etchant, the maximum etch rate of the laser-modified glass was obtained to be 166 μm/h, exhibiting the highest selectivity about 333 respect to the pristine glass. Based on the optimized process window, a 3D microfluidic channel branching to three multilayered channels was successfully fabricated in a 4 mm-thick glass. In addition, appropriate design guidelines for preventing cracks in a glass and calibrating the position of the dimension of the hollow channels were studied.

Abstract Background Methane, a main component of natural gas and biogas, has gained much attention as an abundant and low-cost carbon source. Methanotrophs, which can use methane as a sole carbon and energy source, are promising hosts to produce value-added chemicals from methane, but their metabolic engineering is still challenging. In previous attempts to produce lactic acid (LA) from methane, LA production levels were limited in part due to LA toxicity. We solved this problem by generating an LA-tolerant strain, which also contributes to understanding novel LA tolerance mechanisms. Results In this study, we engineered a methanotroph strain Methylomonas sp. DH-1 to produce d-lactic acid (d-LA) from methane. LA toxicity is one of the limiting factors for high-level production of LA. Therefore, we first performed adaptive laboratory evolution of Methylomonas sp. DH-1, generating an LA-tolerant strain JHM80. Genome sequencing of JHM80 revealed the causal gene watR, encoding a LysR-type transcription factor, whose overexpression due to a 2-bp (TT) deletion in the promoter region is partly responsible for the LA tolerance of JHM80. Overexpression of the watR gene in wild-type strain also led to an increase in LA tolerance. When d form-specific lactate dehydrogenase gene from Leuconostoc mesenteroides subsp. mesenteroides ATCC 8293 was introduced into the genome while deleting the glgA gene encoding glycogen synthase, JHM80 produced about 7.5-fold higher level of d-LA from methane than wild type, suggesting that LA tolerance is a critical limiting factor for LA production in this host. d-LA production was further enhanced by optimization of the medium, resulting in a titer of 1.19 g/L and a yield of 0.245 g/g CH4. Conclusions JHM80, an LA-tolerant strain of Methylomonas sp. DH-1, generated by adaptive laboratory evolution was effective in LA production from methane. Characterization of the mutated genes in JHM80 revealed that overexpression of the watR gene, encoding a LysR-type transcription factor, is responsible for LA tolerance. By introducing a heterologous lactate dehydrogenase gene into the genome of JHM80 strain while deleting the glgA gene, high d-LA production titer and yield were achieved from methane.

This study proposes a deep-learning-based model to generate synthetic compressional wave velocity (Vp) from well-logging data with application to the Ulleung Basin Gas Hydrate (UBGH) in the East Sea, Republic of Korea. Because a bottom-simulating reflector (BSR) is a key indicator to define the presence of gas hydrate, this study generates the Vp for identifying the BSR by detecting the morphology of the hydrate in terms of the change in acoustic velocity. Conventional easy-to-acquire logging parameters, such as gamma-ray, neutron porosity, bulk density, and photoelectric absorption, were selected as model inputs based on a sensitivity analysis. Long short-term memory (LSTM) and an artificial neural network (ANN) were used to design an efficient learning-based predictive model with sensitivity analysis for hyperparameters. The LSTM model outperforms the ANN model by preserving the geological sequence of the well-logging data. Ten-fold cross-validation was conducted to verify the consistency of the LSTM model and yielded satisfactory results, with an average coefficient of determination greater than 0.8. These numerical results imply that generating synthetic well-logging via deep learning can accurately estimate missing well-logging data, contributing to the reservoir characterization of gas-hydrate-bearing sediments.

A miniaturized pump to manipulate liquid flow in microchannels is the key component of microfluidic devices. Many researchers have demonstrated active microfluidic pumps, but most of them still required additional large peripherals to operate their micropumps. In addition, those micropumps were made of polymer materials so that their application may be limited to a variety of fields that require harsh conditions at high pressures and temperatures or organic solvents and acid/base. In this work, we present a 3D miniaturized magnetic-driven glass centrifugal pump for microfluidic devices. The pump consists of a volute structure and a 3D impeller integrated with two magnet disks of Φ1 mm. The 3D pump structure was 13 mm × 10.5 mm × 3 mm, and it was monolithically fabricated in a fused silica sheet by selective laser-induced etching (SLE) technology using a femtosecond laser. The pump operation requires only one motor rotating two magnets. It was Φ42 mm × 54 mm and powered by a battery. To align the shaft of the motor to the center of the 3D glass pump chip, a housing containing the motor and the chip was fabricated, and the overall size of the proposed micropump device was 95 mm × 70 mm × 75 mm. Compared with other miniaturized pumps, ours was more compact and portable. The output pressure of the fabricated micropump was between 215 Pa and 3104 Pa, and the volumetric flow rate range was 0.55 mL/min and 7.88 mL/min. The relationship between the motor RPM and the impeller RPM was analyzed, and the flow rate was able to be controlled by the RPM. With its portability, the proposed pump can be applied to produce an integrated and portable microfluidic device for point-of-care analysis.

All existing secured loans, including crypto-secured loans, are provided under the condition that the collateral entrusted by the borrower is kept safe during the loan term. In other words, they use a one-way collateral function. Thus, a frequent drawback of these loans is that the collateral value increases if and only if the collateral price increases. To resolve this problem, this paper proposes a new crypto-secured lending system incorporating a new two-way collateral function. It would allow a borrower to invest proportions of their own collateral by predicting the market in both directions to make profits irrespective of whether the price of the collateral increases or decreases. This benefits the borrower since profit can be made even if the price of the collateral drops, by betting on the price decrease. This new lending system could include a new hedged portion, unlike traditional secured lending systems. As a result, larger loans can be made under this arrangement; further, this portion provides the advantage of reducing the underlying collateral price volatility risk.

This paper addresses the protection capability of a runway pavement by executing a field blast test on an airfield pavement subjected to blast loading from a CBU (cluster bomb unit), and by confirming the numerical simulation of warhead penetration and the form of damage. The CBU’s blast loading applies the BAP 100 of an air-to-ground munition in a similar scale. Penetration depth is calculated by a formula which incorporates the terminal speed of a free-falling cluster munition dispersed 20 km above the ground. According to the result of the calculation, the penetration depth by a cluster munition is 33 cm from the surface of the pavement. The field blast test was conducted based on this result. Furthermore, LS-DYNA software simulation was used to assess the condition of damage, determined by the depth of penetration and explosive pressure from a free-falling CBU landing on the airfield pavement from 20 km above the ground. The condition was ultimately used to verify the result of field testing and to confirm the scale of damage repair. The depth of explosion was 78 cm, from the surface to the crushed stone and sand layer below the pavement, and the diameter was 30 cm. The size of the crushed concrete that needed to be removed was an average diameter of 156 cm. The simulation result confirms that the diameter and depth of the crater are 67.6 cm and 67 cm, respectively, when the CBU is detonated under the same depth as the field testing, and the height of upheaval is 12 cm. The most appropriate method for repair, after a series of reviews, is to cut and remove a concrete slab 1.8 m × 1.8 m and cast the crushed stone layer disrupted from the explosion, followed by repairing the removed damaged concrete slab sections using rapid hardening concrete.

The smartphone is a must-have mobile device for the military forces to accomplish critical missions and protect critical infrastructures. This paper explores the applicability of a technology prediction methodology to manage technological obsolescence while pursuing the acquisition of advanced commercial technology for military use. It reviews the Technology Forecasting using Data Envelopment Analysis (TFDEA) methodology and applies an author-written Stata program for smartphone technology forecasting using TFDEA. We analyzed smartphone launch data from 2005 to 2020 to predict the adoption of smartphone technology and discuss the pace of technological change. The study identifies that the market is undergoing reorganization as new smartphone models expand the market and increase their technical performance. The average rate of technological change, the efficiency change, and the technology change were 1.079, 1.004, and 1.011 each, respectively, which means that the technology progressed over the period. When dividing before and after 2017, technological change and efficiency change generally regressed except for Huawei, Xiaomi, and Oppo. This means that Chinese smartphones are expanding the global market in all directions and the technology is reaching maturity and market competition is accelerating.

Abstract Most inverse reservoir modeling techniques require many forward simulations, and the posterior models cannot preserve geological features of prior models. This study proposes an iterative static modeling approach that utilizes dynamic data for rejecting an unsuitable training image (TI) among a set of TI candidates and for synthesizing history-matched pseudo-soft data. The proposed method is applied to two cases of channelized reservoirs, which have uncertainty in channel geometry such as direction, amplitude, and width. Distance-based clustering is applied to the initial models in total to select the qualified models efficiently. The mean of the qualified models is employed as a history-matched facies probability map in the next iteration of static models. Also, the most plausible TI is determined among TI candidates by rejecting other TIs during the iteration. The posterior models of the proposed method outperform updated models of ensemble Kalman filter (EnKF) and ensemble smoother (ES) because they describe the true facies connectivity with bimodal distribution and predict oil and water production with a reasonable range of uncertainty. In terms of simulation time, it requires 30 times of forward simulation in history matching, while the EnKF and ES need 9000 times and 200 times, respectively.

This study proposes a rapid and inexpensive thermocycler that enables rapid heating of samples using a thin glass chip and a cheap chip resistor to overcome the on-site diagnostic limitations of polymerase chain reaction (PCR). Microchip PCR devices have emerged to miniaturize conventional PCR systems and reduce operation time and cost. In general, PCR microchips require a thin-film heater fabricated through a semiconductor process, which is a complicated process, resulting in high costs. Therefore, this investigation substituted a general chip resistor for a thin-film heater. The proposed thermocycler consists of a compact glass microchip of 12.5 mm × 12.5 mm × 2 mm that could hold a 2 μL PCR sample and a surface-mounted chip resistor of 6432 size (6.4 mm × 3.2 mm). Improving heat transfer from the chip resistor heater to the PCR reaction chamber in the microchip was accomplished via the design and fabrication of a three-dimensional chip structure using selective laser-induced etching, a rapid prototyping technique that allowed to be embedded. The fabricated PCR microchip was combined with a thermistor temperature sensor, a blower fan, and a microcontroller. The assembled thermocycler could heat the sample at a maximum rate of 28.8 °C/s per second. When compared with a commercially available PCR apparatus running the same PCR protocol, the total PCR operating time with a DNA sample was reduced by about 20%.

This study proposes a deep neural network- (DNN-) based prediction model for creating synthetic log. Unlike previous studies, it focuses on building a reliable prediction model based on two criteria: fit-for-purpose of a target field (the Golden field in Alberta) and compliance with domain knowledge. First, in the target field, the density log has advantages over the sonic log for porosity analysis because of the carbonate depositional environment. Considering the correlation between the density and sonic logs, we determine the sonic log as input and the density log as output for the DNN. Although only five wells have a pair of training data in the field (i.e., sonic and density logs), we obtain, based on geological knowledge, 29 additional wells sharing the same depositional setting in the Slave Point Formation. After securing the data, 5 wells among the 29 wells are excluded from dataset during preprocessing procedures (elimination of abnormal data and min–max normalisation) to improve the prediction model. Two cases are designed according to usage of the well information at the target field. Case 1 uses only 23 of the surrounding wells to train the prediction model, and another surrounding well is used for model testing. In Case 1, the Levenberg–Marquardt algorithm shows a fast and reliable performance and the numbers of neurons in the two hidden layers are of 45 and 14, respectively. In Case 2, the 24 surrounding wells and four wells from the target field are used to train the DNN with the optimised parameters from Case 1. The synthetic density logs from Case 2 mitigate an underestimation problem in Case 1 and follow the overall trend of the true density logs. The developed prediction model utilises the sonic log for generating the synthetic density log, and a reliable porosity model will be created by combining the given and the synthetic density logs.

It is necessary to monitor, acquire, preprocess, and classify microseismic data to understand active faults or other causes of earthquakes, thereby facilitating the preparation of early-warning earthquake systems. Accordingly, this study proposes the application of machine learning for signal–noise classification of microseismic data from Pohang, South Korea. For the first time, unique microseismic data were obtained from the monitoring system of the borehole station PHBS8 located in Yongcheon-ri, Pohang region, while hydraulic stimulation was being conducted. The collected data were properly preprocessed and utilized as training and test data for supervised and unsupervised learning methods: random forest, convolutional neural network, and K-medoids clustering with fast Fourier transform. The supervised learning methods showed 100% and 97.4% of accuracy for the training and test data, respectively. The unsupervised method showed 97.0% accuracy. Consequently, the results from machine learning validated that automation based on the proposed supervised and unsupervised learning applications can classify the acquired microseismic data in real time.

This study proposes three-phase saturation identification using X-ray computerized tomography (CT) images of gas hydrate (GH) experiments considering critical GH saturation (SGH,C) based on the machine-learning method of random forest. Eight GH samples were categorized into three low and five high GH saturation (SGH) groups. Mean square error of test results in the low and the high groups showed decreases of 37% and 33%, respectively, compared to that of the total eight. Additionally, a universal test set was configured from the total eight and tested with two trained machines for the low and high GH groups. Results revealed a boundary at ~50% of SGH signifying different saturation identification performance and the ~50% was estimated as SGH,C in this study. The trained machines for the low and high SGH groups had less performance on the larger and smaller values, respectively, of SGH,C. These findings conclude that we can take advantage of suitable separation of obtained training data, such as GH CT images, under the criteria of SGH,C. Moreover, the proposed data-driven method not only serves as a saturation identification method for GH samples in real time, but also provides a guideline to make decisions for data acquirement priorities.

This study conducts saturation modeling in a gas hydrate (GH) sand sample with X-ray CT images using the following machine learning algorithms: random forest (RF), convolutional neural network (CNN), and support vector machine (SVM). The RF yields the best prediction performance for water, gas, and GH saturation in the samples among the three methods. The CNN and SVM also exhibit sufficient performances under the restricted conditions, but require improvements to their reliability and overall prediction performance. Furthermore, the RF yields the lowest mean square error and highest correlation coefficient between the original and predicted datasets. Although the GH CT images aid in approximately understanding how fluids act in a GH sample, difficulties were encountered in accurately understanding the behavior of GH in a GH sample during the experiments owing to limited physical conditions. Therefore, the proposed saturation modeling method can aid in understanding the behavior of GH in a GH sample in real-time with the use of an appropriate machine learning method. Furthermore, highly accurate descriptions of each saturation, obtained from the proposed method, lead to an accurate resource evaluation and well-guided optimal depressurization for a target GH field production.

For an ensemble-based history matching of a channelized reservoir, loss of geological plausibility is challenging because of pixel-based manipulation of channel shape and connectivity despite sufficient conditioning to dynamic observations. Regarding the loss as artificial noise, this study designs a serial denoising autoencoder (SDAE) composed of two neural network filters, utilizes this machine learning algorithm for relieving noise effects in the process of ensemble smoother with multiple data assimilation (ES-MDA), and improves the overall history matching performance. As a training dataset of the SDAE, the static reservoir models are realized based on multipoint geostatistics and contaminated with two types of noise: salt and pepper noise and Gaussian noise. The SDAE learns how to eliminate the noise and restore the clean reservoir models. It does this through encoding and decoding processes using the noise realizations as inputs and the original realizations as outputs of the SDAE. The trained SDAE is embedded in the ES-MDA. The posterior reservoir models updated using Kalman gain are imported to the SDAE which then exports the purified prior models of the next assimilation. In this manner, a clear contrast among rock facies parameters during multiple data assimilations is maintained. A case study at a gas reservoir indicates that ES-MDA coupled with the noise remover outperforms a conventional ES-MDA. Improvement in the history matching performance resulting from denoising is also observed for ES-MDA algorithms combined with dimension reduction approaches such as discrete cosine transform, K-singular vector decomposition, and a stacked autoencoder. The results of this study imply that a well-trained SDAE has the potential to be a reliable auxiliary method for enhancing the performance of data assimilation algorithms if the computational cost required for machine learning is affordable.

This study couples an iterative sparse coding in a transformed space with an ensemble smoother with multiple data assimilation (ES-MDA) for providing a set of geologically plausible models that preserve the non-Gaussian distribution of lithofacies in a channelized reservoir. Discrete cosine transform (DCT) of sand-shale facies is followed by the repetition of K-singular value decomposition (K-SVD) in order to construct sparse geologic dictionaries that archive geologic features of the channelized reservoir such as pattern and continuity. Integration of ES-MDA, DCT, and K-SVD is conducted in a complementary way as the initially static dictionaries are updated with dynamic data in each assimilation of ES-MDA. This update of dictionaries allows the coupled algorithm to yield an ensemble well conditioned to static and dynamic data at affordable computational costs. Applications of the proposed algorithm to history matching of two channelized gas reservoirs show that the hybridization of DCT and iterative K-SVD enhances the matching performance of gas rate, water rate, bottomhole pressure, and channel properties with geological plausibility.

History matching is a calibration of reservoir models according to their production history. Although ensemble-based methods (EBMs) have been researched as promising history matching methods, reservoir parameters updated using EBMs do not have ideal geological features because of a Gaussian assumption. This study proposes an application of spectral clustering algorithm (SCA) on ensemble smoother with multiple data assimilation (ES-MDA) as a parameterization technique for non-Gaussian model parameters. The proposed method combines discrete cosine transform (DCT), SCA, and ES-MDA. After DCT is used to parameterize reservoir facies to conserve their connectivity and geometry, ES-MDA updates the coefficients of DCT. Then, SCA conducts a post-process of rock facies assignment to let the updated model have discrete values. The proposed ES-MDA with SCA and DCT gives a more trustworthy history matching performance than the preservation of facies ratio (PFR), which was utilized in previous studies. The SCA considers a trend of assimilated facies index fields, although the PFR classifies facies through a cut-off with a pre-determined facies ratio. The SCA properly decreases uncertainty of the dynamic prediction. The error rate of ES-MDA with SCA was reduced by 42% compared to the ES-MDA with PFR, although it required an extra computational cost of about 9 min for each calibration of an ensemble. Consequently, the SCA can be proposed as a reliable post-process method for ES-MDA with DCT instead of PFR.

This paper presents a review of 71 research papers related to a distance-based clustering (DBC) technique for efficiently assessing reservoir uncertainty. The key to DBC is to select a few models that can represent hundreds of possible reservoir models. DBC is defined as a combination of four technical processes: distance definition, distance matrix construction, dimensional reduction, and clustering. In this paper, we review the algorithms employed in each step. For distance calculation, Minkowski distance is recommended with even order due to sign problem. In the case of clustering, K-means algorithm has been commonly used. DBC has been applied to various reservoir types from channel to unconventional reservoirs. DBC is effective for unconventional resources and enhanced oil recovery projects that have a significant advantage of reducing the number of reservoir simulations. Recently, DBC studies have been performed with deep learning algorithms for feature extraction to define a distance and for effective clustering.

We propose a new packaging process for an implantable blood pressure sensor using ultrafast laser micro-welding. The sensor is a membrane type, passive device that uses the change in the capacitance caused by the membrane deformation due to applied pressure. Components of the sensor such as inductors and capacitors were fabricated on two glass (quartz) wafers and the two wafers were bonded into a single package. Conventional bonding methods such as adhesive bonding, thermal bonding, and anodic bonding require considerable effort and cost. Therefore CO2 laser cutting was used due to its fast and easy operation providing melting and bonding of the interface at the same time. However, a severe heat process leading to a large temperature gradient by rapid heating and quenching at the interface causes microcracks in brittle glass and results in low durability and production yield. In this paper, we introduce an ultrafast laser process for glass bonding because it can optimize the heat accumulation inside the glass by a short pulse width within a few picoseconds and a high pulse repetition rate. As a result, the ultrafast laser welding provides microscale bonding for glass pressure sensor packaging. The packaging process was performed with a minimized welding seam width of 100 μm with a minute. The minimized welding seam allows a drastic reduction of the sensor size, which is a significant benefit for implantable sensors. The fabricated pressure sensor was operated with resonance frequencies corresponding to applied pressures and there was no air leakage through the welded interface. In addition, in vitro cytotoxicity tests with the sensor showed that there was no elution of inner components and the ultrafast laser packaged sensor is non-toxic. The ultrafast laser welding provides a fast and robust glass chip packaging, which has advantages in hermeticity, bio-compatibility, and cost-effectiveness in the manufacturing of compact implantable sensors.

We present a rapid and highly reliable glass (fused silica) microfluidic device fabrication process using various laser processes, including maskless microchannel formation and packaging. Femtosecond laser assisted selective etching was adopted to pattern microfluidic channels on a glass substrate and direct welding was applied for local melting of the glass interface in the vicinity of the microchannels. To pattern channels, a pulse energy of 10 μJ was used with a scanning speed of 100 mm/s at a pulse repetition rate of 500 kHz. After 20⁻30 min of etching in hydrofluoric acid (HF), the glass was welded with a pulse energy of 2.7 μJ and a speed of 20 mm/s. The developed process was as simple as drawing, but powerful enough to reduce the entire production time to an hour. To investigate the welding strength of the fabricated glass device, we increased the hydraulic pressure inside the microchannel of the glass device integrated into a custom-built pressure measurement system and monitored the internal pressure. The glass device showed extremely reliable bonding by enduring internal pressure up to at least 1.4 MPa without any leakage or breakage. The measured pressure is 3.5-fold higher than the maximum internal pressure of the conventional polydimethylsiloxane (PDMS)⁻glass or PDMS⁻PDMS bonding. The demonstrated laser process can be applied to produce a new class of glass devices with reliability in a high pressure environment, which cannot be achieved by PDMS devices or ultraviolet (UV) glued glass devices.

In a fixed-end arch railway bridge restraining the displacement and rotation at the support to constrain the longitudinal deformation of the superstructure, vertical deformation occurs according to temperature change. Due to such deformation, periodic change in long-wavelength track irregularity occurs, which, by increasing the vertical train body acceleration, degrades ride comfort. In the present study, the vertical deformation of a fixed-end arch railway bridge and the accompanying track irregularity changes were measured during the summer and winter, respectively. Based on the measured data, the relationships among the ambient temperature, the temperature of the bridge members, the deformation of the bridge, and the track irregularity were investigated. Additionally, the correlation between the train body acceleration and the long-wavelength track irregularity was examined, and a method of controlling long-wavelength track irregularity considering seasonal temperature change was discussed.