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BACKGROUND Suicide is a serious public health issue. Understanding suicide methods is important in developing suicide prevention interventions with a focus on controlling access to tools used for suicide. However, little research has been done using the gun ownership rate by the country of origin to understand the relation between cultural/ethnic backgrounds and suicide methods. OBJECTIVE This study examines suicide method preferences among US-born and foreign-born suicide completers in consideration of the firearm acceptability of the decedent’s country of origin METHODS Multivariate logistic regression was used with data on 28,895 suicide completers of 37 States obtained from the 2017 National Violent Death Reporting System (NVDRS) dataset. Firearm possession rates of foreign-born suicide completers’ countries of origin were obtained from the 2017 Small Arms Survey. RESULTS The firearm suicide rate was about twice high among US-born suicide completers than among their foreign-born counterparts. Meanwhile, hanging suicide was about 75% higher among the foreign-born than among the US-born. Those from the countries with a low and mid-level gun ownership rate were significantly less likely to use firearms compared to the US-born (OR=0.45 and OR=0.46, respectively), while there is no statistical difference in firearm suicide between US-born and foreign-born individuals from countries with a high gun possession rate. CONCLUSIONS The results suggest that there is an association between preference for firearm as a suicide method and the availability and acceptability of firearms in the corresponding culture. One of the causes behind the high rate of firearm suicide in the US might be understood in the country’s sociocultural context related to firearms.

As the Framework Act on Sustainable Infrastructure Management has recently been enacted in Korea, it has become mandatory to establish a medium-and long-term plan for managing social infrastructure and evaluating the feasibility of maintenance projects. However, road agencies are experiencing problems due to a lack of deterioration models which are essential to conducting a life cycle cost analysis. Thus, this study developed deterioration models for bridge pavements as the first step to secure the power of execution of the Infrastructure Management Act. The deterioration model subdivided pavement materials into asphalt, conventional concrete, and latex-modified concrete. This study analyzed the data on diagnosis for the last 12 years in Korea by applying the Bayesian Markov Hazard Model. The average life expectancy by pavement type was analyzed as follows: 12.8 years for asphalt pavement; 23.4 years for concrete pavement; and 9.8 years for latex-modified concrete pavement. For the probabilistic life cycle cost analysis and risk management, probability distributions of life expectancy, effective range by confidence level, and Markov transition probability were presented. This study lays a foundation for deterministic and probabilistic life cycle cost analysis of bridge pavement. Future studies need to develop deterioration models standardized for all components of bridges and all types of social infrastructure.

This study aimed to assess the interrelationship (comorbidity pattern) between depression and PTSD among North Korean (NK) refugees in South Korea and examine the effects of trauma types at pre-migration, transit, and post-migration stages on both depression and PTSD. Therefore, the study analyzed the data of the 2017 Survey on the Human Right Abuses and Trauma of NK refugees (N = 299) using the Seemingly Unrelated Regression model. The results showed that depression and PTSD have a comorbidity pattern among NK refugees. In addition, the predominant death threat of trauma type at the transit stage had a positive relationship with depression and PTSD simultaneously. Based on these findings, we suggested a transdiagnostic approach and trauma-informed care for NK refugees who suffered from various traumas in the migration stage.

In this paper, we analyze the acquisition and tracking performance of signal using a tiered differential polyphase code as the secondary code. The Zadoff-Chu sequence is known to have a CAZAC (Constant Amplitude Zero Auto-Correlation) characteristics. The secondary code generated by differential encoding of the Zadoff-Chu sequence also has the same characteristics as the Zadoff-Chu sequence. Therefore, long integration will give better correlation results. We compare signal acquisition and tracking performance when using the NH sequence and Zadoff-Chu sequence as the secondary code. Monte-carlo simulation is performed using MATLAB. We use the probability of detection and the mean acquisition time for signal acquisition performance and tracking jitter for signal tracking performance.

Research on precise positioning is being actively carried out to provide accurate position information for land transportation. The most significant problem when performing precise positioning in urban canyon is the degradation of performance due to the lack of visible satellites. Prior to open service of BDS, most of the studies on positioning were focused on using GPS/GLONASS integrated navigation system. Since BDS began open service, studies using GPS/BDS have been actively performed in the Asia-Pacific region as it became possible to acquire enough available BDS satellites. The average number of visible satellites in Korea is 9 for GPS and 14 for BDS. In this paper, we analyze the availability of precise positioning using BDS in urban canyon. To do this, we simulate the urban canyon environment by applying the mask to the azimuth and the elevation. We analyze the positioning accuracy using two simulation scenarios. From the results, it is shown that the accuracy of precise positioning in the case where the satellites in the east-west direction are blocked is lowered than that in the case where the satellites in the south-north direction are blocked for the same elevation mask angle. This result comes from the fact that the PDOP increases when the satellites are blocked in the east-west direction. Also, it can be confirmed that the GPS/BDS integrated positioning is available for the high mask angle while the GPS-only positioning is not possible continuously.

Dexterous object manipulation using anthropomorphic robot hands is of great interest for natural object manipulations across the areas of healthcare, smart homes, and smart factories. Deep reinforcement learning (DRL) is a particularly promising approach to solving dexterous manipulation tasks with five-fingered robot hands. Yet, controlling an anthropomorphic robot hand via DRL in order to obtain natural, human-like object manipulation with high dexterity remains a challenging task in the current robotic field. Previous studies have utilized some predefined human hand poses to control the robot hand’s movements for successful object-grasping. However, the hand poses derived from these grasping taxonomies are limited to a partial range of adaptability that could be performed by the robot hand. In this work, we propose a combinatory approach of a deep transformer network which produces a wider range of natural hand poses to configure the robot hand’s movements, and an adaptive DRL to control the movements of an anthropomorphic robot hand according to these natural hand poses. The transformer network learns and infers the natural robot hand poses according to the object affordance. Then, DRL trains a policy using the transformer output to grasp and relocate the object to the designated target location. Our proposed transformer-based DRL (T-DRL) has been tested using various objects, such as an apple, a banana, a light bulb, a camera, a hammer, and a bottle. Additionally, its performance is compared with a baseline DRL model via natural policy gradient (NPG). The results demonstrate that our T-DRL achieved an average manipulation success rate of 90.1% for object manipulation and outperformed NPG by 24.8%.

Background: Multiple Imputation (MI) is known as an effective method for handling missing data in public health research. However, it is not clear that the method will be effective when the data contain a high percentage of missing observations on a variable. Methods: Using data from “Predictive Study of Coronary Heart Disease” study, this study examined the effectiveness of multiple imputation in data with 20% missing to 80% missing observations using absolute bias (|bias|) and Root Mean Square Error (RMSE) of MI measured under Missing Completely at Random (MCAR), Missing at Random (MAR), and Not Missing at Random (NMAR) assumptions. Results: The |bias| and RMSE of MI was much smaller than of the results of CCA under all missing mechanisms, especially with a high percentage of missing. In addition, the |bias| and RMSE of MI were consistent regardless of increasing imputation numbers from M=10 to M=50. Moreover, when comparing imputation mechanisms, MCMC method had universally smaller |bias| and RMSE than those of Regression method and Predictive Mean Matching method under all missing mechanisms. Conclusion: As missing percentages become higher, using MI is recommended, because MI produced less biased estimates under all missing mechanisms. However, when large proportions of data are missing, other things need to be considered such as the number of imputations, imputation mechanisms, and missing data mechanisms for proper imputation.

When Al alloy sheet is reduced in thickness by 84% with asymmetric rolling and heat treatment, this sheet simultaneously develops a {001}〈110〉 texture, which decreases the R-value, and a 〈111〉//normal direction (ND) texture, which enhances the R-value. To control the intensities of these {001}〈110〉 and 〈111〉//ND texture components, an 84% heavy asymmetrically rolled and heat-treated AA1050 Al alloy sheet was 20% light asymmetrically rolled and heat-treated at 500 °C for 1 h. This process increased the amount of 〈111〉//ND texture component and reduced the amount of the {001}〈110〉 texture component, resulting in an $$\bar{R}$$ -value of 1.3. This was 2.13 times higher than the $$\bar{R}$$ -value of 0.61 obtained for the initial specimen that had {001}〈100〉 component.

In-situ catalytic hydrodeoxygenation (HDO) of bio-tar in supercritical ethanol for upgraded biofuel was investigated. Highly mesoporous KOH-treated AC (KOH-AC) was synthesized by chemical activation and used as the support for Ni-based catalysts. Among the tested catalysts (AC, KOH-AC, Mg-Ni-Mo/AC, and Mg-Ni-Mo/KOH-AC), Mg-Ni-Mo/KOH-AC with a high surface area of 1310.1 m2/g and a well-developed mesoporous structure exhibited much higher catalytic performance for the HDO of bio-tar. The effects of different reaction temperatures (300–400 °C) and residence time (0–120 min) on the HDO of bio-tar over Mg-Ni-Mo/KOH-AC were also examined. Enhanced properties of liquid fuel with a higher heating value (HHV) of 36.2 MJkg−1, an oxygen content of 11.7 wt%, and a total acid number (TAN) value of 8.6 mgKOHg−1 were obtained from bio-tar over a Mg-Ni-Mo/KOH-AC at 400 °C for 120 min. In these conditions, acids, aldehydes, and oxygenated phenols present in bio-tar (>67 area%) were effectively converted to high value-added species including aromatics, hydrocarbons, and alkyl phenols in upgraded bio-tar (>77 area%) via esterification, hydrogenation, deoxygenation, and ring-alkylation reactions. A series of Mg-Ni-Mo/KOH-AC catalyst recycle test showed the deposition of coke on the catalyst, which became a major reason for the catalyst deactivation.

An efficient method to upgrade bio-oil derived from fast pyrolysis of woody biomass was investigated in supercritical ethanol without external hydrogen supply. A set of Ni-based catalysts (Ni, Ni-Mo, and Mg-Ni-Mo) supported on activated charcoal (AC) was tested to understand their catalytic effect on the yield and the quality of upgraded liquid fuel in supercritical ethanol. Mg-Ni-Mo/AC was found to be the most effective catalyst among the tested Ni-based catalysts; the liquid product obtained at 350 °C showed the highest yield (70.5 wt%) and the lowest oxygen-to-carbon (O/C) molar ratio (0.19). The catalyst also formed the lowest amount of solid product (9.8 wt%) at 350 °C by suppressing coke formation. At 350 °C, a higher quality liquid product with a total acid number (TAN) value of 6.2 mgKOHg−1 and a higher heating value (HHV) of 33.4 MJkg−1 was obtained compared with bio-oil feed (TAN: 48.0 mgKOHg−1 and HHV: 19.5 MJkg−1). A gas chromatography–mass spectrometer (GC–MS) analysis of the liquid product demonstrated that major oxygenated components in the bio-oil feed such as acids, aldehydes, and levoglucosan were effectively destroyed through hydrogenation, esterification, and deoxygenation reactions in the catalytic upgrading process.

The global R&D of functional materials have become more prominent and are expected to increase with a high growth rate through 2022. High demand due to current and emerging applications, superior structural properties, and the development of advanced fabrication methods is accelerating and driving the functional materials market. The functional materials are segmented based on application into construction composites, medical technology, cosmetics & personal care, oil & gas, automotive, life science & technology, and other categories. In particular, the use of microparticles as functional materials is continually increasing in the field of medicine and the healthcare industry. Nanotechnology for diagnosis, monitoring, drug delivery, treatment and control of biological systems is the leading application in the microparticle market, causing the growing demand for new types of microparticles. Furthermore, growing medical and healthcare industries will increase the worldwide demand for new functional microparticles in biomedical technology applications. This review paper will discuss the main aspects of state-of-the-art functional microparticles research, while demonstrating the broad variety of applications in the fields of diagnostics and therapy, especially in large-scale commercialized products related to magnetic microparticles in IVD and to microcarriers in cell therapeutics.

Recently, social interest in the deteriorated bridges and infrastructure has been increasing. Also, studies on performance prediction and maintenance decision making of structures are underway. In this study, it was proposed an algorithm to create an optimal maintenance scenario that takes into consideration the relationship between the members of bridge that affect each other, rather than the conventional maintenance that is focused on single members of the bridge.

Recently, the GNSS signal has tended to utilize the data channel and the pilot channel, and the pilot channel uses a secondary code as well as a primary code. In general, a long secondary code is used for frame synchronization and a short secondary code is used for data symbol synchronization. So far, most of GNSS signals use NH code as the short secondary code. However, the NH code may decrease the ratio of the maximum peak to the side peak as the residual Doppler frequency increases. In this paper, we analyze the effect of residual Doppler frequency and propose a new secondary code candidate which has similar performance to NH code. Finally, the ratio of the maximum peak to the side peak according to the residual Doppler frequency is presented for each NH code and proposed secondary code candidate.

Upgrading of bio-tar derived from fast pyrolysis of sawdust was performed over activated charcoal supported nickel-based catalysts (Ni/AC, NiMo/AC, MgNiMo/AC) under supercritical ethanol conditions. The catalytic activities of the catalysts was compared with those of representative bio-oil upgrading catalysts (Ru/AC, HZSM-5). The MgNiMo/AC catalyst exhibited the highest catalytic performance among the catalysts with a decrease of oxygen content in bio-tar from 31.3 wt% up to 10.8 wt% and the yield of liquid product (75 wt%). The effects of reaction temperature (275–350 °C), reaction time (0–120 min) and bio-tar to ethanol ratio (1/9–4/6) were also studied over the MgNiMo/AC catalyst. In addition, reaction of vanillin which is one of major compounds present in bio-tar was carried out under the same bio-tar upgrading conditions in order to understand the production mechanism of phenolic chemicals.

A deterioration model plays an important role to predict the valid total maintenance cost for sustainable maintenance of bridges. In the current state-of-the-art, the deterioration model has regression parameters as a probabilistic process by an initially determined mean and standard deviation, called an existing model. However, the existing model has difficulty to predict maintenance costs accurately, because it cannot reflect an information based on structural damage at an operational stage. In this research, updating the probabilistic deterioration model is presented for the prediction of pre-stressed concrete I-type (PSCI) girder bridges using a particle filtering technique which is an advanced Bayesian updating method based on big data analysis. The method enables predicting maintenance cost fitted in the current structural status, which includes the recent information by inspection with bridge-monitoring. The method is adapted in the Mokdo Bridge which is currently being used for evaluating the efficiency of maintenance cost by effects on updated probabilistic values with two different scenarios. As the result, it is shown that the proposed method is effective in predicting maintenance costs.

This paper presents the idealization of a mechanically coupled acoustic sensor mechanism for directivity with the use of polysilicon, which has ideal mechanical and electrical material properties in terms of micro-fabrication. A mathematical model related to mechanical sensitivity is developed as a function of the material properties and geometry of the sensor, which evaluates the characteristics of a two-degree-of-freedom-based lumped parameter model. A challenge in such a study is that the model needs to be simple but sufficiently sophisticated to capture the characteristics of the sensor mechanism. Eigen modes and frequencies of the mechanically coupled acoustic sensor mechanism are determined by an energy method using mode functions applicable to the complete system that are from admissible the mode functions chosen for the component elements of the system. The synthesis is accomplished by using equations of constraint that follow conditions imposed by force equilibrium and deflection compatibility at the junctions. Finally, the results are compared with those obtained by a full-scale finite element model developed in a commercial software package. The predicted first and second natural frequencies differed by less than 10% and 3%, respectively, in all test cases.

This paper presents a study of the dynamic behavior of a planar four-bar mechanism with clearance. A spring was added to improve the dynamic response of the mechanism with clearance. Simulations and experimental tests were carried out to show the improvement of the dynamic behavior using this simple yet effective solution. The simulation was performed under MATLAB and ADAMS. An experimental set-up was designed and built to achieve the experimental validations. The presented results show that the added spring improved the dynamic behavior of the mechanism by keeping a continuous contact between the journal and the sleeve despite the presence of the clearance. The effect of the spring on the input torque was also investigated. It is shown that the spring increases the maximum value of the torque. Optimization was performed to determine the best parameters of the spring that reduce the effect of the clearance while minimizing the increase of the input torque. The optimized system was tested at different speeds to show the effect of the speed on the dynamic behavior of the optimized system.

GPS is used in various navigation fields. However, visibility is lowered in such an environment as urban canyon, and navigation performance deteriorates due to the visibility, multipath effect. So, in order to improve navigation performance in this environment, methods of integration with other sensors have been studied. In this paper, we use radar to measure the range of the landmark with known position. An integration algorithm is designed using GPS pseudorange and radar measurement.

Various methods have been studied to mitigate the influence of multipath signals, representative methods focused the correlator structure are the Narrow Correlator and the Multipath Elimination Technique (MET). It is known that the MET has better performance than Narrow Correlator but it requires more complexity. In this paper, we propose a technique that has similar performance to the MET and it uses only three correlators like the Narrow Correlator. This technique switches the chip spacing of the correlators for each Predetection Integration Time (PIT) and applies it to the MET. For the performance analysis, we implemented a software platform and compared the code tracking error of the proposed technique with that of the Narrow Correlator and the MET.

For the last ten years, the number of cases of large-scale fires which occur on bridges, tunnels, and underpasses has increased. Such fires cause primary and secondary damage, including loss of human life, traffic congestion, and extensive financial damage. Therefore, a risk grade model and effective response plan need to be established for such cases in order to minimize the social and economic costs of bridge fires. In this study, the hazard factors contributing to bridge fires were selected to apply a risk grade model. A total of 144 bridge fire simulations were performed to calculate a surface temperature based on time by using Fire Dynamics Simulation (FDS). A risk grade in accordance with the degree of surface damage state caused by temperature of bridges was presented, and the mobilization time criteria for fire suppression were proposed. The surface temperatures based on time can be classified according to the vertical clearance and mobilization time criteria for fire suppression. Through the classified maximum surface temperatures based on time for bridges, the risk grade can be estimated according to the degree of surface damage state caused by temperature. In order to evaluate the applicability of the established risk grade model to the actual bridge, the arrival time taken from the bridge to the fire station was calculated through a Geographic Information System (GIS) network analysis, and the grades for actual bridge cases were assessed. The purpose of this bridge fire risk grade model is to establish a disaster prevention strategy based on risk grades and to minimize the subsequent social damage by determining a priori the disaster scale.

AA7075 Al alloy sheet shows low r-value (plastic strain ratio) and low deep drawability. To increase average r-value ( -value) of AA7075 Al alloy sheets, the sheets were asymmetrically rolled and subsequently heat treated in a salt bath as the first action, followed by the second asymmetry rolling and subsequent heat treatment in a salt bath. The second 22% asymmetrically rolled and subsequent heat-treated AA7075 Al sheet shows 1·41 times higher average r-value (-value) than that of initial specimen. This result could be explained by the texture changes of AA7075 Al alloy sheet after two steps asymmetry rolling and subsequent heat treatment.

The catalytic upgrading of biomass pyrolysis oil (bio-oil) was performed under supercritical conditions. The behaviors of solvents with MgNiMo/AC were investigated for use of methanol, ethanol, butanol, hexanol, and formic acid as supercritical fluids. The production of gases including hydrogen, carbon monoxide, carbon dioxide, and methane was analyzed, and changes in the compositions of esters, alcohols, and phenols were examined. When the catalyst dosage and temperature were increased, the production of hydrogen increased. Among the alcohols, methanol produced the highest amount of hydrogen, and butanol produced the lowest amount of hydrogen. Formic acid showed good hydrogen-production ability. After catalytic upgrading of bio-oil, there was a decrease in oxygen and moisture content and an increase in the higher heating value (HHV). With an extended reaction time, the oxygen content was lowered further. During the supercritical ethanol reaction at 340 °C under an initial 30 bar of N2 gas for 2 h the oxygen content decreased from 33.9 to 14.6 wt% and the HHV increased from 25.2 to 35.3 MJ/kg.

For the efficient life-cycle maintenance of bridge, prediction of future bridge performance based on the current performance must be required and it is possible more rational decision-making through the higher accuracy of the prediction model of bridge deterioration. In other words, predicting a performance of bridges is important to reduce the costs of maintenance, repair, rehabilitation and replacement of bridges. To establish the optimal maintenance strategy and planning, there are needs to consider bridge performance history and prediction of maintenance time. In this study, performance models of bridges are developed on the basis of condition index of the bridges by a statistical and probabilistic method. The condition index(or grade) resulted from visual inspection and are associated with bridge damage. (corrosion, fatigue, crack, water leak, scaling, etc.) Factors causing these defects are considered in bridge element performance model. Also, the predicted performance includes a lot of uncertain factors because the condition index of bridge elements were observed by person. So, when inspector determine the bridge condition index(or grade), it is essential to perform In-depth inspection and monitoring using device. While performing a detailed inspection of all parts of a bridge and/or assigning an experienced inspector have reduced a significant part of errors contained in the prediction model, such personnel-based existing maintenance may result in enormous maintenance costs since it is difficult for a bridge administrator to estimate the bridge performance exactly at a targeting management level, thereby disrupting a rational decision making for bridge maintenance. In this study, to solve this problem, a Bayesian updating method which is related to updating prior information of bridge deterioration is used to the optimal maintenance strategy in Bridge Management System(BMS) considering the uncertainty of inspection data. Also, examples of application are presented, showing the effects of inspection and updating on domestic(Korea) bridge maintenance strategies. That is, we propose a bridge maintenance scenario model based on Bayesian updating and discuss the uncertainty of inspection data obtained from a bridge. The main purpose of this research is to verify advantages of the Bayesian-updating-driven preventive maintenance in terms of the cost efficiency in contrast to the conventional periodic maintenance.

This work aims to derive the kinematic and dynamic models governing the motion of an Autonomous Underwater Vehicle (AUV) and identify the hydrodynamic parameters of a prototype AUV. Good knowledge of such models enable the design of controllers for AUVs to perform path following or trajectory tracking tasks. The modeling process takes into account the primary hydrodynamic phenomena that affect the vehicle, including drag, restoring forces, and added inertia. Quaternions are employed to describe the attitude of the vehicle to avoid the singularity associated with the Euler angles based formulation. The finite element analysis package ANSYS is used to identify the drag and added inertia parameters that affect the vehicle. The approach taken in the system identification task yields results that match well with published analytical and empirical results.

This paper deals with the control design of a nonlinear Sliding Mode Controller (SMC) for an underactuated mechanical system under external disturbance. The Furuta Pendulum represents a testbed on which control methodology is tested because it exhibits chaotic behavior, it involves highly nonlinear dynamics, and it is underactuated. The aforementioned remarks make the control of the Furuta pendulum a nontrivial task. The SMC methodology which uses the full model of the system, applied here, is pegged against a linear state feedback control law which is based on the linearized model around the control objective. The control laws are tested in a simulated environment in which external disturbances are injected, and the performance of the approaches is then compared.