Search

The recent rise in the prevalence of chronic allergic diseases among children has increased disease burden and reduced quality of life, especially for children with comorbid allergic diseases. Predicting the occurrence of allergic diseases can help prevent its onset for those in high risk groups. Herein, we aimed to construct prediction models for asthma, atopic dermatitis (AD), and asthma-AD comorbidity (also known as atopic march) using a genome-wide association study (GWAS) and family history data from patients of Korean heritage. Among 973 patients and 481 healthy controls, we evaluated single nucleotide polymorphism (SNP) heritability for each disease using genome-based restricted maximum likelihood (GREML) analysis. We then compared the performance of prediction models constructed using Least Absolute Shrinkage and Selection Operator (LASSO) and penalized ridge regression methods. Our results indicate that the addition of family history risk scores to the prediction model greatly increase the predictability of asthma and asthma-AD comorbidity. However, prediction of AD was mostly attributable to GWAS SNPs.

A real-time streaming feedforward active-noise-cancellation (ANC) system for an in-ear headphone was demonstrated in a real application scenario, by implementing a 10-layer dilated convolutional-neural-network (CNN) on a field programmable gate array (FPGA). A 16 × 16 systolic array was used in the FPGA, to speed up the model computation. The system latency was 170.6 μs, at the system clock frequency of 120 MHz. The CNN model used 3232 parameters. Due to the large input receptive field, of 327 ms, this work achieved total power reduction, of 14.8 dB and 14.3 dB at the noise incident direction of 0∘ and 90∘, respectively, and the noise attenuation bandwidth was 2000 Hz at both angles; all results were superior to those of the conventional FxLMS algorithm.

Abstract Upconversion nanocrystals (UCNs)‐embedded microarchitectures with luminescence color transition capability and enhanced luminescence intensity under extreme conditions are suitable for developing a robust labeling system in a high‐temperature thermal industrial process. However, most UCNs based labeling systems are limited by the loss of luminescence owing to the destruction of the crystalline phase or by a predetermined luminescence color without color transition capability. Herein, an unusual crystal phase transition of UCNs to a hexagonal apatite phase in the presence of SiO2 nanoparticles is reported with the enhancements of 130‐fold green luminescence and 52‐fold luminance as compared to that of the SiO2‐free counterpart. By rationally combining this strategy with an additive color mixing method using a mask‐less flow lithography technique, single to multiple luminescence color transition, scalable labeling systems with hidden letters‐, and multi‐luminescence colored microparticles are demonstrated for a UCNs luminescence color change‐based high temperature labeling system.

Recently, the deep learning algorithm has received considerable attention and is influencing different fields including human-computer interaction (HCI). The purpose of this study is to maximize accuracy by applying deep learning to the classification of body movements. An experiment was performed to collect acceleration information on the wrist while performing seven workouts: pull up, row-barbell, bench press, dips, squat, deadlift, and military press. Participants were asked to perform each workout for ten sets repeated ten times per set. Experimental results confirm that one-dimensional convolutional neural network was the best among different algorithms including support vector machine, multi-layer perceptron, long short-term memory, and other deep convolutional neural networks. The accuracy was extremely high, 96%. The results of this experiment are applicable not only to the classification of fitness activities but also to the classification of different motions in numerous sporting events.

A MEMS directional acoustic sensor housed in an air cavity and operated underwater in a near-neutral buoyancy configuration is demonstrated. The sensor consists of two wings connected by a bridge and attached to a substrate by two centrally mounted torsional legs. The frequency response showed two resonant peaks corresponding to a rocking mode (wings moving in opposite directions) and a bending mode (wings moving in the same direction). Initial tests of the sensor using a shaker table showed that the response is highly dependent on the vibration direction. In air, the sensor showed a maximum sensitivity of about 95 mV/Pa with a cosine directional response. Underwater, the maximum sensitivity was about 37 mV/Pa with a similar cosine directional response. The measured maximum SNR was about 38 dB for a signal generated by a sound stimulus of 1 Pa when the sensor is operated near the bending resonance. The results indicate that this type of MEMS sensor can be operated in a near-neutral buoyant configuration and achieve a good directional response.

Hedgehog (Hh) pathway plays a key role in development from invertebrate to vertebrate. It is known to be involved in cell differentiation, polarity, proliferation, including the development of vertebrate limb and the establishment of flies’ body plan. To investigate how the regulation of Hh pathway affects the development of parthenogenetic murine embryos, the parthenogenetically activated murine embryos were treated with either cyclopamine (Cyc), an antagonist of Hh pathway, or purmorphamine, an agonist of Hh pathway. While Cyc did not affect the blastocyst formation and its total cell number, the chemical reduced the hatching rate of embryos and the expression levels of Fn1 mRNA. The results of the present study show the possibility that Cyc may affect the development of embryos at blastocyst stage by blocking Hh pathway and this may cause detrimental effect to the embryos at peri-, and post-implantation stages.

This paper proposes an FPGA-based network intrusion detection system for the IEC 61850-based industrial network that is specially designed for substation automation. The proposed system uses the Shift-And algorithm for detecting malicious network packets within IEC 61850 messages. To implement a complex rule matching module with a limited memory size of FPGA, a specially designed rule matching module was proposed in this paper. For feasibility evaluation, a prototype with 265 regular expression matching modules was implemented using Xilinx Zynq-7030 FPGA and its performance is presented in this paper. Keywords: Network intrusion detection system (NIDS), IEC 61850, Regular expression, Substation automation, FPGA

The X-ray free-electron laser of the Pohang Accelerator Laboratory (PAL-XFEL) was opened to users in 2017. Since then, significant progress has been made in PAL-XFEL operation and beamline experiments. This includes increasing the FEL pulse energy, increasing the FEL photon energy, generating self-seeding FEL, and trials of two-color operation. In the beamline, new instruments or endstations have been added or are being prepared. Overall, beamline operation has been stabilized since its initiation, which has enabled excellent scientific results through efficient user experiments. In this paper, we describe details of the recent progress of the PAL-XFEL.

To estimate range and angle information of multiple targets, FMCW MIMO radars have been exploited with 2D MUSIC algorithms. To improve estimation accuracy, received signals from multiple FMCW MIMO radars are collected at the data fusion center and processed coherently, which increases data communication overhead and implementation complexity. To resolve them, we propose the distributed 2D MUSIC algorithm with coordinate transformation, in which 2D MUSIC algorithm is operated with respect to the reference radar’s coordinate at each radar in a distributed way. Rather than forwarding the raw data of received signal to the fusion center, each radar performs 2D MUSIC with its own received signal in the transformed coordinates. Accordingly, the distributed radars do not need to report all their measured signals to the data fusion center, but they forward their local cost function values of 2D MUSIC for the radar image region of interest. The data fusion center can then estimate the range and angle information of targets jointly from the aggregated cost function. By applying the proposed scheme to the experimentally measured data, its performance is verified in the real environment test.

To increase the flexibility of network architecture, a mobile X-haul network (MXN) structure has been considered, in which the wireless front-haul, mid-haul, and backhaul links are integrated into a wireless X-haul link. Accordingly, the mobile station can be served by flexible transmission reception point (f-TRP) or remote radio head through the access link and the associated data should be delivered from XDU to the f-TRP through the X-haul link. Accordingly, the system throughput is determined by the minimum value of the access link capacity and the X-haul link capacity. In this paper, when millimeter-wave (mmWave) is exploited in such MXN with in-band full duplex, the optimal beamforming strategy is proposed, in which the receive beamforming for the X-haul link and the transmit beamforming for the access link are jointly designed to maximize the system throughput. Furthermore, we investigate the rate loss due to the quantization error of the channel state information at f-TRP with limited feedback, in which its upper bound is analytically derived and exploited in the determination of the number of feedback bits for a given finite rate loss.

Abstract Background The construction of microbial cell factories requires cost-effective and rapid strain development through metabolic engineering. Recently, RNA-guided CRISPR technologies have been developed for metabolic engineering of industrially-relevant host. Results To demonstrate the application of the CRISPR interference (CRISPRi), we developed two-plasmid CRISPRi vectors and applied the CRISPRi in Corynebacterium glutamicum to repress single target genes and double target genes simultaneously. Four-different single genes (the pyc, gltA, idsA, and glgC genes) repressions were successfully performed using the CRISPRi vectors, resulting significant mRNA reductions of the targets compared to a control. Subsequently, the phenotypes for the target gene-repressed strains were analyzed, showing the expected cell growth behaviors with different carbon sources. In addition, double gene repression (the idsA and glgC genes in a different order) by the CRISPRi resulted in an independent gene repression to each target gene simultaneously. To demonstrate an industrial application of the CRISPRi, citrate synthase (CS)-targeting DM1919 (l-lysine producer) strains with a sgRNA-gltA-r showed reduced CS activity, resulting in the improvement of l-lysine yield by 1.39-fold than the parental DM1919 (a lysine producer). Conclusions Single or double gene repression were successfully performed using the CRISPRi vectors and sequence specific sgRNAs. The CRISPRi can be applied for multiplex metabolic engineering to enhanced lysine production and it will promote the further rapid development of microbial cell factories of C. glutamicum.

In this paper, efficient gradient updating strategies are developed for the federated learning when distributed clients are connected to the server via a wireless backhaul link. Specifically, a common convolutional neural network (CNN) module is shared for all the distributed clients and it is trained through the federated learning over wireless backhaul connected to the main server. However, during the training phase, local gradients need to be transferred from multiple clients to the server over wireless backhaul link and can be distorted due to wireless channel fading. To overcome it, an efficient gradient updating method is proposed, in which the gradients are combined such that the effective SNR is maximized at the server. In addition, when the backhaul links for all clients have small channel gain simultaneously, the server may have severely distorted gradient vectors. Accordingly, we also propose a binary gradient updating strategy based on thresholding in which the round associated with all channels having small channel gains is excluded from federated learning. Because each client has limited transmission power, it is effective to allocate more power on the channel slots carrying specific important information, rather than allocating power equally to all channel resources (equivalently, slots). Accordingly, we also propose an adaptive power allocation method, in which each client allocates its transmit power proportionally to the magnitude of the gradient information. This is because, when training a deep learning model, the gradient elements with large values imply the large change of weight to decrease the loss function.

Although new virtual reality (VR) devices and their contents are actively being released, there are still not enough studies to prepare its interface/interaction standard. In this study, it was investigated whether specific interaction factors influenced task performance and the degree of virtual reality sickness when performing pointing tasks in immersive virtual reality. A smartphone-based VR device was used, and twenty-five targets were placed in a 5 × 5 layout on the VR experimental area that extended to a range similar to the human viewing angle. Task completion time (TCT) was significantly affected by target selection method (p < 0.001) and target size (p < 0.001), whereas the error rate (ER) significantly differed for the target selection method (p < 0.001) and not for the target size (p = 0.057). Target location was observed to be a factor affecting TCT (p < 0.001), but it did not affect the ER (p = 0.876). VR sickness was more severe when the target size was smaller. Gaze selection was found to be more efficient when accuracy is demanded, and manual selection is more efficient for quick selection. Moreover, applying these experimental data to Fitts’ Law showed that the movement time was found to be less affected by the device when using the gaze-selection method. Virtual reality provides a three-dimensional visual environment, but a one-dimensional formula can sufficiently predict the movement time. The result of this study is expected to be a reference for preparing interface/interaction design standards for virtual reality.

Abstract Although surgery and radiation are beneficial for treating cancer, they can also lead to malfunctions of the lymphatic system such as secondary lymphedema. This abnormality of the lymphatic system is characterized by severe swelling, adipogenesis, inflammation, and fibrosis in the lymphedematous region. Moreover, the proliferation of fibrotic tissue in the lymphedematous region generates edema that is no longer spontaneously reversible. No treatment for fibrosis has been validated in patients with lymphedema. In our efforts to develop a therapeutic agent for lymphedema fibrosis, we used a newly established mouse hind limb model. Previous studies have demonstrated that hyaluronic acid accumulates in the lymphedematous region. Thus, we challenged mice with of hyaluronidase (HYAL), with the aim of reducing fibrogenesis. After subcutaneous injections in the lymphedematous mouse leg every two days, the volume of lymphedema had reduced significantly by 7 days post-operation. Histochemical analysis indicated that collagen accumulation and myofibroblast differentiation were decreased in epidermal tissues after HYAL injection. Moreover, it was associated with upregulation of interferon-gamma, increased numbers of Th1 cells, and downregulation of interleukin-4 and interleukin-6 in the lymphedematous region and spleen. These results indicate that hydrolysis of hyaluronic acid can boost an anti-fibrotic immune response in the mouse lymphedema model.

Background: Asymmetry in repeated motion may lead to dyskinesia through imbalance in the involved musculoskeletal structures. The dominance sides are also involved greater movement involvement over the non-dominant sides. The upper limbs with multiple joints and largest range of motion are prone for unsynchronized coordination. Natural movement analysis is required for application to everyday activities. Methods: Thirty participants were first recruited from Inha University, Incheon, Korea in 2019. Twenty subjects were assessed for comparisons of asymmetrical motion between the dominant and non-dominant arms during the abduction and adduction lateral raises after excluding ten subjects for shoulder pain and left-handedness. Results: The abduction and adduction motions of the bilateral arms were compared for the angular locations, velocity, and acceleration for every 10 degrees. The angular locations of the dominant side occurred significant earlier in the initial (10°, 20°, 30°) phase and later in the last (10°, 20°) phase of abduction and adduction in comparison to the non-dominant side (P

Background: The clinical gender-dependent characteristics of visuospatial neglect between men and women have not been elucidated in Korean patients with cognitive impairment. The goal of this study was to observe the asymmetric lateralization in patients using a novel e-pen based cognitive assessment system. Methods: A total of 31 patients, 16 men and 15 women, with early stage hemispheric cerebral dysfunction were recruited for the assessment of unilateral neglect suing a novice paper-and-pencil based electronic evaluation system from a rehabilitation center of Inha University hospital in 2016. Results were assessed for degrees of deviations, and numbers of neglected lines. Degree of deviation was assessed using the positions and distances from the horizontal line centers. Effect sizes were calculated to assess proximities between the assessed results. Results: Comparatively greater left and rightward biasness for the right-sided and left-sided horizontal lines were observed for the male patients, respectively. Moreover, greater degree of left to rightward biasness was observed as the horizontal lines shortened in both groups. However, the magnitude of biasness in female patients showed comparatively less directional bias, indicating greater prevalence for the center of mass effect in male patients. Conclusion: Gender difference in visuospatial neglect seems to exist with less accuracy in recognition for the bisecting center for the female and asymmetrical lateralization and magnitude of deviation for the male patients.

Smart service systems have been dealing with various wicked social and cultural problems by providing diverse solutions for society. Cargo transportation is one of the most challenging issues in logistics; in this paper, we explore multiple stakeholders’ information environments surrounding containerized cargo transportation by a design thinking approach. Throughout the analysis, the latent problems are summarized in the following two manners: (1) miscommunication with inefficient information flow among multiple stakeholders; and (2) a lack of resource management coordination among service providers. With these two identified problems, the objective of this work is to develop a prototype information and communication technology-based service application for both trucking companies and customers. This work makes two contributions. First, it methodologically proposes a customer-centered design approach for logistics management by adopting a design thinking approach. Second, it suggests a practical ICT-based solution for the mentioned problems.

In this paper, we propose a cooperative linear discriminant analysis (LDA)-based motion classification algorithm for distributed micro-Doppler (MD) radars which are connected to a data fusion center through the limited backhaul. Due to the limited backhaul, each radar cannot report the high-dimensional data of a multi-aspect angle MD signature to the fusion center. Instead, at each radar, the dimensionality of the MD signature is reduced by using the LDA algorithm and the dimensionally-reduced MD signature can be collected at the data fusion center. To further reduce the burden of backhaul, we also propose the softmax processing method in which the distances of the sensed MD signatures from the centers of clusters for all motion candidates are computed at each radar. The output of the softmax process at each radar is quantized through the pyramid vector quantization with a finite number of bits and is reported to the data fusion center. To improve the classification performance at the fusion center, the channel resources of the backhaul are adaptively allocated based on the classification separability at each radar. The proposed classification performance was assessed with synthetic simulation data as well as experimental data measured through the USRP-based MD radar.

In this paper, a joint multiple-input multiple-output (MIMO OFDM) radar and communication (RadCom) system is proposed, in which orthogonal frequency division multiplexing (OFDM) waveforms carrying data to be transmitted to the information receiver are exploited to get high-resolution radar images at the RadCom platform. Specifically, to get two-dimensional (i.e., range and azimuth angle) radar images with high resolution, a compressive sensing-based imaging algorithm is proposed that is applicable to the signal received through multiple receive antennas. Because both the radar imaging performance (i.e., the mean square error of the radar image) and the communication performance (i.e., the achievable rate) are affected by the subcarrier allocation across multiple transmit antennas, by analyzing both radar imaging and communication performances, we also propose a subcarrier allocation strategy such that a high achievable rate is obtained without sacrificing the radar imaging performance.

LiDAR-based Simultaneous Localization And Mapping (SLAM), which provides environmental information for autonomous vehicles by map building, is a major challenge for autonomous driving. In addition, the semantic information has been used for the LiDAR-based SLAM with the advent of deep neural network-based semantic segmentation algorithms. The semantic segmented point clouds provide a much greater range of functionality for autonomous vehicles than geometry alone, which can play an important role in the mapping step. However, due to the uncertainty of the semantic segmentation algorithms, the semantic segmented point clouds have limitations in being directly used for SLAM. In order to solve the limitations, this paper proposes a semantic segmentation-based LiDAR SLAM system considering the uncertainty of the semantic segmentation algorithms. The uncertainty is explicitly modeled by proposed probability models which are come from the data-driven approaches. Based on the probability models, this paper proposes semantic registration which calculates the transformation relationship of consecutive point clouds using semantic information with proposed probability models. Furthermore, the proposed probability models are used to determine the semantic class of the points when the multiple scans indicate different classes due to the uncertainty. The proposed framework is verified and evaluated by the KITTI dataset and outdoor environments. The experiment results show that the proposed semantic mapping framework reduces the errors of the mapping poses and eliminates the ambiguity of the semantic information of the generated semantic map.

Human malignant melanoma is the most aggressive type of skin cancer with high metastatic ability. Despite several traditional therapies, the mortality rate remains high. Lactobacillus plantarum (L. plantarum), a species of lactic acid bacteria (LAB), is being studied for human health, including cancer treatment. However, few studies have elucidated the relationship between L. plantarum extract and human malignant melanoma. To investigate the effects of L. plantarum on human melanoma cells, A375 human melanoma cells were used and treated with L. plantarum L-14 extract. After the treatment, viability, migration ability, molecular changes of migration- and apoptosis-related genes, and the location of cytochrome c was evaluated. The L-14 extract inhibited the viability, migration of A375 cells as well as reduced expression of migration-related genes. In addition, it was confirmed that the L-14 extract induced intrinsic apoptosis in A375 cells. This study demonstrated that the L-14 extract exerted anticancer effects on A375 cells. Therefore, these data suggest that the L-14 extract is worth studying for the development of melanoma drugs using LAB.

A thermoelectric generator (TEG) is a clean electricity generator from a heat source, usually waste heat. However, it is not as widely utilized as other electricity generators due to low conversion efficiency from heat to electricity. One approach is a system-level net power optimization for a TEG system composed of TEGs, heat sink, and fans. In this paper, we propose airflow reuse after cooling preceding TEGs to maximize system net power. For the accurate system net power, we model the TEG system, air, and heat source with proper dimension and material characteristics, and simulate with a computational fluid dynamics program. Next, the TEG power generation and the fan power consumption are calculated in consideration of the Seebeck coefficient and internal electrical resistance varying with hot and cold side temperatures. Finally, we find the optimal number of TEGs and fan speed generating the most efficient system net power in various TEG systems. The results show that the system with a side fan with a specific number of TEGs provides a system net power up to 58.6% higher than when with a top fan. The most efficient system net power with the side fan increases up to four TEGs generating 1.907 W at 13,000 RPM.

Automation aims to improve the task performance and the safety of human operators. The success of automation can be facilitated with well-designed human−automation interaction (HAI), which includes the consideration of a trade-off between the benefits of reliable automation and the cost of Failed automation. This study evaluated four different types of HAIs in order to validate the automation trade-off, and HAI types were configured by the levels and the statuses of office automation. The levels of automation were determined by information amount (i.e., Low and High), and the statues were decided by automation function (i.e., Routine and Failed). Task performance including task completion time and accuracy and subjective workload of participants were measured in the evaluation of the HAIs. Relatively better task performance (short task completion time and high accuracy) were presented in the High level in Routine automation, while no significant effects of automation level were reported in Failed automation. The subjective workload by the National Aeronautics and Space Administration (NASA) Task Load Index (TLX) showed higher workload in High and Failed automation than Low and Failed automation. The type of sub-functions and the task classification can be estimated as major causes of automation trade-off, and dissimilar results between empirical and subjective measures need to be considered in the design of effective HAI.

This study was conducted to investigate changes in blood enzyme parameters and to evaluate the relationship between insulin-like growth factor-1 (IGF-1), antler growth and body weight during the antler growth of sika deer (Cervus nippon). Serum enzyme activity and IGF-1 concentrations were measured in blood samples collected from the jugular and femoral veins at regular intervals during the antler growth period. Blood samples were taken in the morning from fasted stags (n = 12) which were healthy and showed no clinical signs of disease. Alfalfa was available ad libitum and concentrates were given at 1% of body weight to all stags. The experimental diet was provided at 9 am with water available at all times. There were no significant differences in alkaline phosphatase, aspartate aminotransferase, and alanine aminotransferase during antler growth, but alkaline phosphatase concentrations increased with antler growth progression, and the highest alkaline phosphatase concentration was obtained 55 days after antler casting. Serum IGF-1 concentrations measured from blood samples taken from the jugular vein during antler growth, determined that levels of IGF-1 was associated with body weight and antler growth patterns. Serum IGF-1 concentrations were higher at the antler cutting date than other sampling dates. Antler length increased significantly during antler growth (p

Wearable internet of things (IoT) devices can enable a variety of biomedical applications, such as gesture recognition, health monitoring, and human activity tracking. Size and weight constraints limit the battery capacity, which leads to frequent charging requirements and user dissatisfaction. Minimizing the energy consumption not only alleviates this problem, but also paves the way for self-powered devices that operate on harvested energy. This paper considers an energy-optimal gesture recognition application that runs on energy-harvesting devices. We first formulate an optimization problem for maximizing the number of recognized gestures when energy budget and accuracy constraints are given. Next, we derive an analytical energy model from the power consumption measurements using a wearable IoT device prototype. Then, we prove that maximizing the number of recognized gestures is equivalent to minimizing the duration of gesture recognition. Finally, we utilize this result to construct an optimization technique that maximizes the number of gestures recognized under the energy budget constraints while satisfying the recognition accuracy requirements. Our extensive evaluations demonstrate that the proposed analytical model is valid for wearable IoT applications, and the optimization approach increases the number of recognized gestures by up to 2.4× compared to a manual optimization.

Latent dirichlet allocation (LDA) is a representative topic model to extract keywords related to latent topics embedded in a document set. Despite its effectiveness in finding underlying topics in documents, the traditional algorithm of LDA does not have a process to reflect sentimental meanings in text for topic extraction. Focusing on this issue, this study aims to investigate the usability of both LDA and sentiment analysis (SA) algorithms based on the affective level of text. This study defines the affective level of a given set of paragraphs and attempts to analyze the perceived trust of the methodologies in regards to usability. In our experiments, the text of the college scholastic ability test was selected as the set of evaluation paragraphs, and the affective level of the paragraphs was manipulated into three levels (low, medium, and high) as an independent variable. The LDA algorithm was used to extract the keywords of the paragraph, while SA was used to identify the positive or negative mood of the extracted subject word. In addition, the perceived trust score of the algorithm was evaluated by the subjects, and this study verifies whether there is a difference in the score according to the affective levels of the paragraphs. The results show that paragraphs with low affect lead to the high perceived trust of LDA from the participants. However, the perceived trust of SA does not show a statistically significant difference between the affect levels. The findings from this study indicate that LDA is more effective to find topics in text that mainly contains objective information.

This study analyzed preference and satisfaction according to the weight and size of products in order to understand how the size-weight illusion (SWI) occurs in affect. Perceived weight is known to be affected not only by the weight of the object, but also by its size, color, and material. A total of 54 participants took part in the experiment. Nine kinds of cookie boxes were prepared by combining three sizes and three weight levels of confectionery products. Participants were asked to rate the perceived weight of the cookie box by the modulus method and evaluate the preference and the satisfaction of the weight by using the semantic differential (SD) scale of 11 points. The results showed that SWI occurred in terms of the perceived weight of cookies boxes like previous studies; however, SWI appeared only partially in affect. The preference and satisfaction did not increase after a certain weight, and the limits of weight were different according to the size of cookie box. These results can be referred to determine the weight and size of a product for affective design and especially utilized for the package design of cookie boxes.

The user experience (UX) concept has widely been adopted in the field of development and evaluation of products and services. One reason behind this emerging trend is the limitation in traditional usability evaluation methods (UEMs), which emphasizes performance measures such as task completion time and error rate. To overcome the limitation, users’ perceived value must be set to reflect users’ various experiences. In order to address this issue, this study proposes a network analysis-based method to investigate a value structure to assess the level of user experience in using a product. Gesture interaction on a smart TV is used as a case study to demonstrate the process of obtaining a quantified value structure from the transcriptions of users’ in-depth interview. A major contribution of this study lies in the transformation from qualitative data to a quantified value structure. Product or service designers, in the industrial field, can develop value structure based on network analysis when evaluating their design alternatives within a properly fitted evaluation framework.

Many previous studies have identified that physiological responses of a driver are significantly associated with driving stress. However, research is limited to identifying the effects of traffic conditions (low vs. high traffic) and road types (highway vs. city) on driving stress. The objective of this study is to quantify the relationship between driving stress and traffic conditions, and driving stress and road types, respectively. In this study, electrodermal activity (EDA) signals for a male driver were collected in real road driving conditions for 60 min a day for 21 days. To classify the levels of driving stress (low vs. high), two separate models were developed by incorporating the statistical features of the EDA signals, one for traffic conditions and the other for road types. Both models were based on the application of EDA features with the logistic regression analysis. City driving turned out to be more stressful than highway driving. Traffic conditions, defined as traffic jam also significantly affected the stress level of the driver, when using the criteria of the vehicle speed of 40 km/h and standard deviation of the speed of 20 km/h. Relevance to industry: The classification results of the two models indicate that the traffic conditions and the road types are important features for driving stress and its related applications.

Hemodialysis using hollow-fiber membranes provides life-sustaining treatment for nearly 2 million patients worldwide with end stage renal disease (ESRD). However, patients on hemodialysis have worse long-term outcomes compared to kidney transplant or other chronic illnesses. Additionally, the underlying membrane technology of polymer hollow-fiber membranes has not fundamentally changed in over four decades. Therefore, we have proposed a fundamentally different approach using microelectromechanical systems (MEMS) fabrication techniques to create thin-flat sheets of silicon-based membranes for implantable or portable hemodialysis applications. The silicon nanopore membranes (SNM) have biomimetic slit-pore geometry and uniform pores size distribution that allow for exceptional permeability and selectivity. A quantitative diffusion model identified structural limits to diffusive solute transport and motivated a new microfabrication technique to create SNM with enhanced diffusive transport. We performed in vitro testing and extracorporeal testing in pigs on prototype membranes with an effective surface area of 2.52 cm2 and 2.02 cm2, respectively. The diffusive clearance was a two-fold improvement in with the new microfabrication technique and was consistent with our mathematical model. These results establish the feasibility of using SNM for hemodialysis applications with additional scale-up.

The construction of Pohang Accelerator Laboratory X-ray Free-Electron Laser (PAL-XFEL), a 0.1-nm hard X-ray free-electron laser (FEL) facility based on a 10-GeV S-band linear accelerator (LINAC), is achieved in Pohang, Korea by the end of 2016. The construction of the 1.11 km-long building was completed by the end of 2014, and the installation of the 10-GeV LINAC and undulators started in January 2015. The installation of the 10-GeV LINAC, together with the undulators and beamlines, was completed by the end of 2015. The commissioning began in April 2016, and the first lasing of the hard X-ray FEL line was achieved on 14 June 2016. The progress of the PAL-XFEL construction and its commission are reported here.