In this study, the hull form optimization process to minimize resistance of KCS (KRISO containership) at Fn=0.26 is described. The bow hull form of KCS was modified by varying such design parameters as sectional area curve (SAC), section shape, bulb breadth, and bulb height using multiple parametric modification curves devised by the authors. The resistance performances of modified hull forms were analysed by the viscous flow Reynolds-Averaged Navier–Stokes (RANS) solver of WAVIS ver.2.2. With a view to saving computational time during iterative analyses in the optimization process, the sinkage and trim were set to the fixed values which had been obtained for the original hull form with free condition. The validity of such constant sinkage/trim was then verified by conducting analysis for the optimal hull form with free condition. Optimization to minimize the cost function of the total resistance coefficient of model CTM was performed by sequential quadratic programming (SQP), which is one of the gradient-based local optimization methods. Utilization of parallel computing led to the simultaneous calculation of the gradient, thereby speeding up the whole optimization process. At the design speed of 24 knots, the optimal hull yielded CTM reduction by 1.8%, which is extrapolated to 3.1% reduction of effective power PE in full scale.
The Smart factory is currently being proposed as solutions to rising manufacturing costs, shrinking labor populations, and energy problems in global manufacturing. Examples of improvement in quality and productivity through smart factory construction are can be found out in many domestic and foreign enterprises. However, small and medium-sized enterprises are having difficulty to construct smart factory due to problem with an analysis of expected effects and burden of investment costs. In addition, while research on the development of smart factory technologies is relatively active, research regarding quantitative performance prediction of introduction of smart factory technologies is lacking. In this paper, a framework is designed to analyze the effects of introducting smart factory technologies, and the framework consist of the selection of technologies to be introduced, quantitative analysis of expected effects and qualitative analysis phases of expected effects. A quantitative analysis phase of expected effects uses simulation and present simulation model construction procedures and methodologies. A qualitative analysis phase of expected effects predicts the level of effectiveness of each technology through a case study of the application of smart factory technologies.
Despite the numerous applications of eosin Y as an organic photoredox catalyst, substantial mechanistic aspects of the photoredox process have remained elusive. Through deductive, steady-state kinetic studies, we first propose a mechanism for alkaline, aqueous photoredox catalysis using eosin Y, triethanolamine, and oxygen, integrating photo- and nonphotochemical steps. The photoredox cycle begins with a single-electron transfer (SET) induced when eosin Y absorbs green light. This photoinduced SET leads to the formation of a metastable radical trianion that can be fully reduced to inactivated leuco eosin Y via H+/e–/H+ transfer or regenerated to eosin Y via ground-state SET to oxygen. Since the radical trianion absorbs violet light, we tested the effect of radical trianion photoexcitation on catalyst regeneration. We found that excitation of the metastable radical trianion in the presence of a threshold concentration of oxygen enabled ∼100% regeneration of eosin Y. The response to violet light supports the important role of the metastable radical trianion and indicates that the photoredox cycle can be closed via a secondary photoinduced SET event. The idea of photoredox cycles with two consecutive photoinduced electron transfer (PET) steps is not intuitive and is introduced as a tool to increase photocatalyst turnover by selectively favoring regeneration over "death". This alludes to the Z-scheme in biological photosynthesis, where multiple PET reactions, often triggered by different frequencies, promote highly selective biochemical transformations by precluding unproductive SET events in plants and bacteria. We expect that the simple Z-scheme model introduced here will enable more efficient use of organic photoredox catalysts in organic and materials chemistry.
Implicit user feedback is a fundamental dataset for personalized recommendation models. Because of its inherent characteristics of sparse one-class values, it is challenging to uncover meaningful user/item representations. In this paper, we propose dual neural personalized ranking (DualNPR), which fully exploits both user- and item-side pairwise rankings in a unified manner. The key novelties of the proposed model are three-fold: (1) DualNPR discovers mutual correlation among users and items by utilizing both user- and item-side pairwise rankings, alleviating the data sparsity problem. We stress that, unlike existing models that require extra information, DualNPR naturally augments both user- and item-side pairwise rankings from a user-item interaction matrix. (2) DualNPR is built upon deep matrix factorization to capture the variability of user/item representations. In particular, it chooses raw user/item vectors as an input and learns latent user/item representations effectively. (3) DualNPR employs a dynamic negative sampling method using an exponential function, further improving the accuracy of top-N recommendation. In experimental results over three benchmark datasets, DualNPR outperforms baseline models by 21.9-86.7% in hit rate, 14.5-105.8% in normalized discounted cumulative gain, and 5.1-23.3% in the area under the ROC curve.
The purpose of this study was to compare metabolic energy expenditure with the computed kinetic energy for different speeds of walking and running over the treadmill and to find the relevance for individual and group equation by performing a statistical analysis, Bland-Altman plot. Seven male subjects participated, and they were required to walk and run on the treadmill with the gas analyzer and triaxial accelerometer. Walking speeds were 3.0, 4.0, 5.0 and 6.0 km/h and running speeds were 7.0, 8.0 and 9.0 km/h respectively. Kinetic energy was calculated by the integration of acceleration data and compared with the metabolic energy measured by a gas analyzer. Correlation coefficients showed relatively good between the measured metabolic energy and the calculated kinetic energy. In addition, a dramatic increase in kinetic energy was also observed at the transition speed of walking and running, and two standard deviations in Bland-Altman plot, derived from the difference between measured and predicted values, were 1.14, 2.53, 2.93, 1.80, 2.80, 0.60 and 2.48 respectively. It was showed that there is no difference for methods of how to predict the kinetic energy expenditure for individual and group even though people had each different physical characteristic.
Substrate accessibility is a key limiting factor for the efficiency of heterogeneous photoredox catalysis. Recently, a high photoactive surface area of conjugated microporous polymer nanoparticles (CMP NPs) has made them promising candidates for overcoming the mass transfer limitation to achieve high photocatalytic efficiency. However, this potential has not been realized due to limited dispersibility of CMP NPs in many solvents, particularly in water. Here, we report a polymer grafting strategy that furnishes versatile hairy CMP NPs with enhanced solvent-specific dispersibility. The method associates hundreds of solvent-miscible repeating units with one chain end of the photocatalyst surface, allowing minimal modification to the CMP network that preserves its photocatalytic activity. Therefore, the enhanced dispersibility of hairy CMP NPs in organic solvents or aqueous solutions affords high efficiency in various photocatalytic organic transformations.
For the treatment of nasopharyngeal carcinoma (NPC), radiation therapy is a primary option. Be cause of distant metastasis and the high incidence of a locoregional failure following radiotherapy, the combined treatment modality with chemotherapy is applied, although resistance to chemotherapy makes chemotherapy less effective. The Cisplatin-based chemotherapy has been widely used in the field of nasopharyngeal cancer. The Cisplatin resistance is known to be caused by the multidrug resistance-associated protein (MRP), which is one of the drug-export pumps and the glutathione S-transferase (GST)-pi which catalyzes the conjugation of the GSH (glutathione) and the cisplatin. The aim of this study is to determine the predictive value of GST-pi and MRP upon the response to cisplatin in nasopharyngeal carcinoma. Subjects and Method：We analyzed tumor tissues from 49 cases of paraffin block specimens which were diagnosed with NPC and treated at Chonnam National University Hospital. The immunohistochemical study for the GST-pi and the MRP was performed with paraffin block specimens of nasopharyngeal cancers. Results：In the GST-pi, the relationship between the early stage (64.3%) and the advanced stage (91.4%) was statistically significant (p＝0.020). The expression of the GST-pi and the MRP had no relationship with the clinical factor, the response to chemotherpy and the survival rate. Conclusion：Because the expression of the GST-pi and the MRP in the nasopharyngeal carcinoma could not predict the response to chemotherapy. So the efforts to find the predictive value of the chemotherapy are needed. (Korean J Otolaryngol 2002;45:791-5)
We conducted a survey of open clusters within 1 kpc from the Sun using the astrometric and photometric data of the Gaia Data Release 2. We found 655 cluster candidates by visual inspection of the stellar distributions in proper motion space and spatial distributions in l-b space. All of the 655 cluster candidates have a well defined main-sequence except for two candidates if we consider that the main sequence of very young clusters is somewhat broad due to differential extinction. Cross-matching of our 653 open clusters with known open clusters in various catalogs resulted in 207 new open clusters. We present the physical properties of the newly discovered open clusters. The majority of the newly discovered open clusters are of young to intermediate age and have less than ~50 member stars.
The ongoing COVID-19 pandemic has clearly established how vital rapid, widely accessible diagnostic tests are in controlling infectious diseases and how difficult and slow it is to scale existing technologies. Here, we demonstrate the use of the rapid affinity pair identification via directed selection (RAPIDS) method to discover multiple affinity pairs for SARS-CoV-2 nucleocapsid protein (N-protein), a biomarker of COVID-19, from in vitro libraries in 10 weeks. The pair with the highest biomarker sensitivity was then integrated into a 10 min, vertical-flow cellulose paper test. Notably, the as-identified affinity proteins were compatible with a roll-to-roll printing process for large-scale manufacturing of tests. The test achieved 40 and 80 pM limits of detection in 1× phosphate-buffered saline (mock swab) and saliva matrices spiked with cell-culture-generated SARS-CoV-2 viruses and is also capable of detection of N-protein from characterized clinical swab samples. Hence, this work paves the way toward the mass production of cellulose paper-based assays which can address the shortages faced due to dependence on nitrocellulose and current manufacturing techniques. Further, the results reported herein indicate the promise of RAPIDS and engineered binder proteins for the timely and flexible development of clinically relevant diagnostic tests in response to emerging infectious diseases.
Rapid diagnostic tests (RDTs) have shown to be instrumental in healthcare and disease control. However, they have been plagued by many inefficiencies in the laborious empirical development and optimization process for the attainment of clinically relevant sensitivity. While various studies have sought to model paper-based RDTs, most have relied on continuum-based models that are not necessarily applicable to all operation regimes, and have solely focused on predicting the specific interactions between the antigen and binders. It is also unclear how the model predictions may be utilized for optimizing assay performance. Here, we propose a streamlined and simplified model-based framework, only relying on calibration with a minimal experimental dataset, for the acceleration of assay optimization. We show that our models are capable of recapitulating experimental data across different formats and antigen-binder-matrix combinations. By predicting signals due to both specific and background interactions, our facile approach enables the estimation of several pertinent assay performance metrics such as limit-of-detection, sensitivity, signal-to-noise ratio and difference. We believe that our proposed workflow would be a valuable addition to the toolset of any assay developer, regardless of the amount of resources they have in their arsenal, and aid assay optimization at any stage in their assay development process.
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