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The adsorption of n-alkanes onto Brønsted-acid sites is a key step in the catalytic cracking of alkanes. Employing configurational-bias Monte Carlo simulations, we have investigated how the ratio of equilibrium adsorption constants for central C-C bonds relative to terminal bonds of n-alkanes (i.e., the adsorption selectivity ratio) in Brønsted-acid zeolites is influenced by the Si/Al ratio and the Al distribution. A new computational approach was implemented, and the developed force field was validated by a comprehensive comparison between simulation results and experimental data for a number of Brønsted-acid zeolites. While the adsorption selectivity seems to be relatively insensitive to the Si/Al ratio, our results reveal that the Al distribution plays a crucial role in determining the adsorption selectivity. Changes in the Al distribution result in a change of as much as 2-fold in the adsorption selectivity ratio for n-hexane. The selectivity generally shows larger variations with respect to Al distribution in zeolites with a larger Si/Al ratio. The two factors identified by this work that substantially influence the selectivity ratio are the siting of Al atoms among T-sites and their spatial proximity, and an atomic-level understanding of each of these effects was achieved. The siting of Al atoms at more or less selective T-sites significantly influences the overall selectivity ratio, and Al atoms in close proximity can synergistically enhance the adsorption of central C-C bonds, leading to a higher selectivity ratio relative to isolated Al atoms. We anticipate that these results will have important implications for future large-scale computational screenings and the development of advanced synthesis approaches to target certain Al distributions in zeolites.

The adsorption of n-alkanes onto Brønsted-acid sites is a key step in the catalytic cracking of alkanes. Employing configurational-bias Monte Carlo simulations, we have investigated how the ratio of equilibrium adsorption constants for central C-C bonds relative to terminal bonds of n-alkanes (i.e., the adsorption selectivity ratio) in Brønsted-acid zeolites is influenced by the Si/Al ratio and the Al distribution. A new computational approach was implemented, and the developed force field was validated by a comprehensive comparison between simulation results and experimental data for a number of Brønsted-acid zeolites. While the adsorption selectivity seems to be relatively insensitive to the Si/Al ratio, our results reveal that the Al distribution plays a crucial role in determining the adsorption selectivity. Changes in the Al distribution result in a change of as much as 2-fold in the adsorption selectivity ratio for n-hexane. The selectivity generally shows larger variations with respect to Al distribution in zeolites with a larger Si/Al ratio. The two factors identified by this work that substantially influence the selectivity ratio are the siting of Al atoms among T-sites and their spatial proximity, and an atomic-level understanding of each of these effects was achieved. The siting of Al atoms at more or less selective T-sites significantly influences the overall selectivity ratio, and Al atoms in close proximity can synergistically enhance the adsorption of central C-C bonds, leading to a higher selectivity ratio relative to isolated Al atoms. We anticipate that these results will have important implications for future large-scale computational screenings and the development of advanced synthesis approaches to target certain Al distributions in zeolites.

© 2018, Springer-Verlag GmbH Austria, part of Springer Nature. With the advent of OpenFlow, the concept of Software-Defined Networking (SDN) becomes much popular. In the past, SDN had often been used for network virtualization; however, with the rise of OpenFlow, which speeds up network performance by separating the control layer from the data layer, SDN can be further used to manage physical network facilities. Currently, some OpenFlow controller providers have already provided users with load balancer packages in their controllers for virtual networks, such as the Neutron package in OpenStack; nevertheless, the existing load balancer packages work in the old fashion that causes extra delay since they poll controllers for every new coming connection. In this paper, we use the wildcard mask to implement the load balance method directly on switches or routers and add a user prediction mechanism to change the range of the wildcard mask dynamically. In this way, the load balance mechanism can be applied conforming to real service situations. In our experiment, we test the accuracies of flow prediction for different predicted algorithms and compare the delay times and balance situations of the proposed method with other load balancers. With the popularity of cloud computing, the demand for cloud infrastructure also increases. As a result, we also apply our load balance mechanism on cloud services and prove that the proposed method can be implemented to varieties of service platforms.

© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim The therapeutic ratio of radiotherapy is limited by acute or chronic side effects with often severe consequences to patients. The microvasculature is a central player involved in both tumor responses and healthy tissue/organ radiological injuries. However, current preclinical vascular models based on 2D culture offer only limited radiobiological insight due to their failure in recapitulating the 3D nature experienced by endothelial cells within the human microvasculature. To address this issue, the use of a 3D microvasculature-on-a-chip microfluidic technology is demonstrated in radiobiological studies. Within this vasculogenesis model a perfusable network that structurally mimics the human microvasculature is formed and the biological response to ionizing radiation including cellular apoptosis, vessel tight adherens junction breakage, DNA double strand break, and repair is systematically investigated. In comparison to cells grown in a 2D environment, human umbilical vein endothelial cells in the 3D microvasculature-on-a-chip displays significant differences in biological responses, especially at high X-ray dose. This data confirms the feasibility of using microvascular-on-a-chip models for radiobiological studies. Such vasculogenesis models have strong potential to yield more accurate prediction of healthy tissue responses to ionizing radiation as well as to guide the development of risk-reducing strategies to prevent radiation-induced acute and long-term side-effects.

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. Surface plasmon enhanced light scattering (SP-LS) is a powerful new sensing SPR modality that yields excellent sensitivity in sandwich immunoassay using spherical gold nanoparticle (AuNP) tags. Towards further improving the performance of SP-LS, we systematically investigated the AuNP size effect. Simulation results indicated an AuNP size-dependent scattered power, and predicted the optimized AuNPs sizes (i.e., 100 and 130 nm) that afford extremely high signal enhancement in SP-LS. The maximum scattered power from a 130 nm AuNP is about 1700-fold higher than that obtained from a 17 nm AuNP. Experimentally, a bio-conjugation protocol was developed by coating the AuNPs with mixture of low and high molecular weight PEG molecules. Optimal IgG antibody bioconjugation conditions were identified using physicochemical characterization and a model dot-blot assay. Aggregation prevented the use of the larger AuNPs in SP-LS experiments. As predicted by simulation, AuNPs with diameters of 50 and 64 nm yielded significantly higher SP-LS signal enhancement in comparison to the smaller particles. Finally, we demonstrated the feasibility of a two-step SP-LS protocol based on a gold enhancement step, aimed at enlarging 36 nm AuNPs tags. This study provides a blue-print for the further development of SP-LS biosensing and its translation in the bioanalytical field.

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. Surface plasmon enhanced light scattering (SP-LS) is a powerful new sensing SPR modality that yields excellent sensitivity in sandwich immunoassay using spherical gold nanoparticle (AuNP) tags. Towards further improving the performance of SP-LS, we systematically investigated the AuNP size effect. Simulation results indicated an AuNP size-dependent scattered power, and predicted the optimized AuNPs sizes (i.e., 100 and 130 nm) that afford extremely high signal enhancement in SP-LS. The maximum scattered power from a 130 nm AuNP is about 1700-fold higher than that obtained from a 17 nm AuNP. Experimentally, a bio-conjugation protocol was developed by coating the AuNPs with mixture of low and high molecular weight PEG molecules. Optimal IgG antibody bioconjugation conditions were identified using physicochemical characterization and a model dot-blot assay. Aggregation prevented the use of the larger AuNPs in SP-LS experiments. As predicted by simulation, AuNPs with diameters of 50 and 64 nm yielded significantly higher SP-LS signal enhancement in comparison to the smaller particles. Finally, we demonstrated the feasibility of a two-step SP-LS protocol based on a gold enhancement step, aimed at enlarging 36 nm AuNPs tags. This study provides a blue-print for the further development of SP-LS biosensing and its translation in the bioanalytical field.

Casein kinase II (CK2) inhibitors suppress cancer cell growth. In this study, we examined the inhibitory effects of a novel CK2 inhibitor, hematein, on tumor growth in a murine xenograft model. We found that in lung cancer cells, hematein inhibited cancer

Casein kinase II (CK2) inhibitors suppress cancer cell growth. In this study, we examined the inhibitory effects of a novel CK2 inhibitor, hematein, on tumor growth in a murine xenograft model. We found that in lung cancer cells, hematein inhibited cancer

We present in this paper a high detection rate of Boosted vehicle detection. The positive database of Boosted training usually consists of similar contour or lighting of vehicle image, but in our work the training data is extracted from both daytime and evening images, it means different in lighting, and we are forced to stop Boosted training toward a high detection rate, which results in a relatively high false alarm rate. Therefore, the Boosted detector will detect vehicle candidate as much as possible. Moreover, we develop two false alarm eliminating methods to eliminate the false vehicle candidates. The algorithm consists of edge complexity for daytime case, and combination histogram matching and intensity complexity for evening case. Each case is chosen by automatic switcher algorithm. To provide real-time detection, we also proposed position based sliding window detector. The idea is based on sliding windows size selection relative to the position of vehicle candidate in an image. Thus, we do not need to apply various size of detector size as traditional Boosted algorithm. Finally, our experimental results show that our proposed system can operate in a real-world environment, while providing realtime detection. © 2013 ICIC International.

Vertical Collaborative Fuzzy C-Means (VC-FCM) is a clustering method that performs clustering on a data set of having some set of patterns with the collaboration of some knowledge which is obtained from other data set having the same number of features but different set of patterns. Uncertain relationship lies in data between the data sets as well as within a dataset. Practically data of the same group of objects are usually stored in different datasets; in each data set, the data dimensions are not necessarily the same and unreal data may exist. Fuzzy clustering of a single data set would bring about less reliable results. And these data sets cannot be integrated for some reasons. An interesting application of vertical clustering occurs when dealing with huge data sets. Instead of clustering them in a single pass, we split them into individual data sets, cluster each of them separately, and reconcile the results through the collaborative exchange of prototypes. Vertical collaborative fuzzy C-Means is a useful tool for dealing collaborative clustering problems where a feature space is described in different pattern-sets. In this paper we use collaborative fuzzy clustering, first we cluster each data set individually and then optimize in accordance with the dependency of these datasets is adopted so as to improve the quality of fuzzy clustering of a single data set with the help of other data sets, taking personal privacy and security of data into consideration. © 2013 Springer-Verlag Berlin Heidelberg.

We present in this paper a high detection rate of Boosted vehicle detection. The positive database of Boosted training usually consists of similar contour or lighting of vehicle image, but in our work the training data is extracted from both daytime and evening images, it means different in lighting, and we are forced to stop Boosted training toward a high detection rate, which results in a relatively high false alarm rate. Therefore, the Boosted detector will detect vehicle candidate as much as possible. Moreover, we develop two false alarm eliminating methods to eliminate the false vehicle candidates. The algorithm consists of edge complexity for daytime case, and combination histogram matching and intensity complexity for evening case. Each case is chosen by automatic switcher algorithm. To provide real-time detection, we also proposed position based sliding window detector. The idea is based on sliding windows size selection relative to the position of vehicle candidate in an image. Thus, we do not need to apply various size of detector size as traditional Boosted algorithm. Finally, our experimental results show that our proposed system can operate in a real-world environment, while providing realtime detection. © 2013 ICIC International.

Vertical Collaborative Fuzzy C-Means (VC-FCM) is a clustering method that performs clustering on a data set of having some set of patterns with the collaboration of some knowledge which is obtained from other data set having the same number of features but different set of patterns. Uncertain relationship lies in data between the data sets as well as within a dataset. Practically data of the same group of objects are usually stored in different datasets; in each data set, the data dimensions are not necessarily the same and unreal data may exist. Fuzzy clustering of a single data set would bring about less reliable results. And these data sets cannot be integrated for some reasons. An interesting application of vertical clustering occurs when dealing with huge data sets. Instead of clustering them in a single pass, we split them into individual data sets, cluster each of them separately, and reconcile the results through the collaborative exchange of prototypes. Vertical collaborative fuzzy C-Means is a useful tool for dealing collaborative clustering problems where a feature space is described in different pattern-sets. In this paper we use collaborative fuzzy clustering, first we cluster each data set individually and then optimize in accordance with the dependency of these datasets is adopted so as to improve the quality of fuzzy clustering of a single data set with the help of other data sets, taking personal privacy and security of data into consideration. © 2013 Springer-Verlag Berlin Heidelberg.