Moment-Based Calculation of Parameters for the Storage Zone Model for River Dispersion

Theoretical methods have been developed to calculate values of parameters of the storage zone model for river mixing. Analytical solutions of the Laplace-transformed equations of the storage zone model are related to the observed concentration distribution in order to determine model parameters in both the moment matching method and the maximum likelihood method, which were developed in this study. The results obtained by comparison with experimental data show that the parameters calculated by the moment matching method are in good agreement with the observed values of storage zone model parameters, whereas results from the maximum likelihood method and several existing methods are not in good agreement with the experimentally observed values. Dispersion data from natural streams show that the calculated concentration curves from the numerical solutions of the storage zone model with the parameters calculated by the moment matching method fit the observed concentration curves very well. It can be concluded that parameters of the storage zone model calculated using the moment matching method can properly explain the natural dispersion processes in real streams.     

Estimation of CO2 Emissions in Life Cycle of Residential Buildings

A method for estimating CO2 emissions given off over the entire life cycle of various types of residential buildings was examined. In the method for estimating CO2 emissions, the life cycle of a residential building was divided into four stages (manufacturing, construction, operation, and demolition). The result showed that CO2 emissions resulting from construction, including manufacturing building materials for residential building, were in the range of 381.1–620.1 kg-C∕10 m2 for each building type. However, most CO2 emissions given off during the residential building life cycle were due to the building operation having 87.5–96.9% of total CO2 emissions. And when comparing residential building types, single-family houses using liquefied petroleum gas as the main heating energy source had the lowest CO2 emissions, with 5,260 kg-C∕10 m2 of floor area during their life cycles. On the other hand, single-family houses using briquette as the main heating energy source had the highest CO2 emissions (15,100 kg-C∕10 m2), with emissions being 2.9 times more than those of the (former) single-family house using liquefied petroleum gas.     

Analysis of pollutant transport in the Han River with tidal current using a 2D finite element model

Pollutant transport in the Han River, which is one of the most important rivers in Korea, was studied using the 2D advection-dispersion model developed in this research. The numerical model was developed based on the streamline-upwind Petrov-Galerkin (SUPG) where a finite element grid was used in order to treat the complex geometry of the natural water bodies. Velocity fields for the input data of pollutant transport simulation were obtained using a 2D hydrodynamic model, RMA2. The pollutant transport simulations in both the steady state flow and in the tidal currents were carried out to analyze the effects of tidal currents on the 2D concentration distribution in the region of application. The simulation results show that, with increasing water level period under tidal conditions, very large recirculation zones occurs and water flows in the upstream direction. Therefore, within the tidal currents, the polluted water goes back and forth with variations in the flow direction. As a result of this movement of pollutant, an oscillation in the concentration appears over time, which reflects both the significantly complex periodic variation in the flow pattern and large circulations.     

Cluster-Based FAQ Retrieval Using Latent Term Weights

A high-performance FAQ retrieval system uses query-log clustering to resolve lexical-disagreement problems. The proposed system outperforms traditional information-retrieval systems in FAQ retrieval.     

Noise removal with gauss curvature-driven diffusion.

In this paper, we propose the use of the Gauss curvature in a Gauss curvature-driven diffusion equation for noise removal. The proposed scheme uses the Gauss curvature as the conductance term and controls the amount of diffusion. The main advantage of the scheme is that it preserves important structures, such as straight edges, curvy edges, ramps, corners, small-scaled features, etc.     

Ultraviolet-cured polymer microlens arrays

A method for fabricating microlens arrays that uses the contraction effect of UV-curable photopolymers is presented. Lenses with diameters ranging from 0.2 to 2 mm that were made under different conditions are optically evaluated. The optimum conditions for producing low f-number lenses are discussed.     

Remarkable Electrical Conductivity Increase and Pure Metallic Properties from Semiconducting Colloidal Nanocrystals by Cation Exchange for Solution-Processable Optoelectronic Applications

The authors report a strategic approach to achieve metallic properties from semiconducting Cu Fe S colloidal nanocrystal (NC) solids through cation exchange method. An unprecedentedly high electrical conductivity is realized by the efficient generation of charge carriers onto a semiconducting Cu S NC template via minimal Fe exchange. An electrical conductivity exceeding 10 500 S cm⁻¹ (13 400 S cm⁻¹ at 2 K) and a sheet resistance of 17 Ω/sq at room temperature, which are among the highest values for solution-processable semiconducting NCs, are achieved successfully from bornite-phase Cu Fe S NC films possessing 10% Fe atom. The temperature dependence of the corresponding films exhibits pure metallic characteristics. Highly conducting NCs are demonstrated for a thermoelectric layer exhibiting a high power factor over 1.2 mW m⁻¹K⁻² at room temperature, electrical wires for switching on light emitting diods (LEDs), and source–drain electrodes for p- and n-type organic field-effect transistors. Ambient stability, eco-friendly composition, and solution-processability further validate their sustainable and practical applicability. The present study provides a simple but very effective method for significantly increasing charge carrier concentrations in semiconducting colloidal NCs to achieve metallic properties, which is applicable to various optoelectronic devices.     

Deep chest X‐ray: Detection and classification of lesions based on deep convolutional neural networks

We investigated whether a convolutional neural network (CNN) can enhance the usability of computer‐aided detection (CAD) of chest radiographs for various pulmonary abnormal lesions. The numbers of normal and abnormal patients were 6055 and 3463, respectively. Two radiologists delineated regions of interest for lesions and labeled the disease types as ground truths. The datasets were split into training, tuning, and testing as 7:1: 2. Total test sets were randomly selected in 1214 normal and 690 abnormal. A 5‐fold, cross‐validation was performed on our datasets. For the classification of normal and abnormal, we developed a CNN based on DenseNet169; for abnormal detection, The You Only Look Once (YOLO) v2 with DenseNet was used. Detection and classification of normal and five classes of diseases (nodule[s], consolidation, interstitial opacity, pleural effusion, and pneumothorax) on chest radiographs were analyzed. Our CNN model classified chest radiographs as normal or abnormal with an accuracy of 97.8%. For the results of the abnormal, F1 score, was 75.2 ± 2.28% for nodules, 55.0 ± 4.3% for consolidation, 78.2 ± 7.85% for interstitial opacity, 81.6 ± 2.07% for pleural effusion, and 70.0 ± 7.97% for pneumothorax, respectively. In addition, we conducted the experiments between our method and RetinaNet with only nodules. The results of our method and RetinaNet at cutoff‐0.5 in the free response operating characteristic curve were 83.45% and 80.55%, respectively. Our algorithm demonstrated viable detection and disease classification capacity and could be used for CAD of lung diseases on chest radiographs.     

Phase Engineering of Transition Metal Dichalcogenides with Unprecedentedly High Phase Purity,Stability, and Scalability via Molten-Metal-Assisted Intercalation

The crystalline phase of layered transition metal dichalcogenides (TMDs) directly determines their material property. The most thermodynamically stable phase structures in TMDs are the semiconducting 2H and metastable metallic 1T phases. To overcome the low phase purity and instability of 1T-TMDs, which limits the utilization of their intrinsic properties, various synthesis strategies for 1T-TMDs have been proposed in phase-engineering studies. Herein, a facile and scalable synthesis of 1T-phase molybdenum disulfide (MoS₂) via the molten-metal-assisted intercalation (MMI) approach is introduced, which exploits the capillary action of molten potassium and the difference between the electron affinity of MoS₂ and the ionization potential of potassium. Highly reactive molten potassium metal can readily intercalate into the MoS₂ interlayers, inducing an efficient phase transition from the 2H to 1T crystal structure. The ionic bonding between the intercalated potassium and sulfur lowers the energy barrier of the 1T-phase transition, enhancing the phase stability of the 1T crystals. Owing to the high purity and stability of the 1T phase, the electrocatalytic performance for the hydrogen evolution reaction is significantly higher in 1T-MoS₂ (MMI) than in 2H-MoS₂ and even in 1T-MoS₂ synthesized using n-butyllithium.