The stability of duplexes involving AT and/or G4EtC base pairs is not dependent on their AT/G4EtC ratio content. Implication for DNA sequencing by hybridization

Sequencing by the recently reported hybridization technique requires the formation of DNA duplexes with similar stabilities. In this paper we describe a new strategy to obtain DNA duplexes with a thermal stability independent of their AT/GC ratio content. Melting data were acquired on 35 natural and 27 modified duplexes of a given length and of varying base compositions. Duplexes built with AT and/or G4EtC base pairs exhibit a thermal stability restrained to a lower range of temperature than that of the corresponding natural compounds (16 instead of 51 degrees C). The 16 degrees C difference in thermal stability observed between the least stable and the most stable duplex built with AT and/or G4EtC base pairs is mainly due to the sequence effect and not to their AT/G4EtC ratio content. Thus N -4-ethyl-2'-deoxycytidine (d4EtC) hybridizes specifically with natural deoxyguanosine leading to a G4EtC base pair whose stability is very close to that of the natural AT base pair. Oligonucleotide probes involving d4EtC can be easily prepared by chemical synthesis with phosphoramidite chemistry. Modified DNA targets were successfully amplified by random priming or PCR techniques using d4EtCTP, dATP, dGTP and dTTP in the presence of DNA polymerase. This new system might be very useful for DNA sequencing by hybridization.     

Medullary thyroid carcinoma: genetic advances, treatment recommendations, and the approach to the patient with persistent hypercalcitoninemia

Medullary thyroid cancer is a tumor of the thyroid C cells that occurs in sporadic and hereditary clinical settings. Genetic testing of at-risk individuals is available and has been applied to patient management. Plasma calcitonin levels are a sensitive marker for the presence of disease. Surgery offers the best hope for cure and also is an effective modality for managing metastatic and recurrent disease.     

Industrial Policies In A Maturing Taiwan Economy

Taiwan's successful industrialisation process with significant structural change now spans four decades. This paper analyses the role of small and medium enterprises and the development of secondary import substitution industries in Taiwan in relation to some policy debates. It is argued that market friendly and self-restrained industrial policies were key to the success of Taiwan's fast industrialisation. However, when Taiwan entered a more matured economy in the 1980s, a more liberal industrial policy was introduced.     

基于组反馈融合机制的视频超分辨率模型

视频超分辨率（video super-resolution,VSR）的目的是利用多个相邻帧的信息来生成参考帧的高分辨率版本。现有的许多VSR工作都集中在如何有效地对齐相邻帧以更好地融合相邻帧信息,而很少在相邻帧信息融合这一重要步骤上进行研究。针对该问题,提出了基于组反馈融合机制的视频超分辩模型（GFFMVSR）。具体来说,在相邻帧对齐后,将对齐视频序列输入第一重时间注意力模块;然后,将序列分成几个小组,各小组依次通过组内融合模块实现初步融合。不同小组的融合结果经过第二重时间注意力模块;然后,各小组逐组输入反馈融合模块,利用反馈机制反馈融合不同组别的信息,最后将融合结果输出重建。经验证,该模型具有较强的信息融合能力,在客观评价指标和主观视觉效果上都优于现有的模型。     

A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques

Shear connectors play a prominent role in the design of steel-concrete composite systems. The behavior of shear connectors is generally determined through conducting push-out tests. However, these tests are costly and require plenty of time. As an alternative approach, soft computing (SC) can be used to eliminate the need for conducting push-out tests. This study aims to investigate the application of artificial intelligence (AI) techniques, as sub-branches of SC methods, in the behavior prediction of an innovative type of C-shaped shear connectors, called Tilted Angle Connectors. For this purpose, several push-out tests are conducted on these connectors and the required data for the AI models are collected. Then, an adaptive neuro-fuzzy inference system (ANFIS) is developed to identify the most influencing parameters on the shear strength of the tilted angle connectors. Totally, six different models are created based on the ANFIS results. Finally, AI techniques such as an artificial neural network (ANN), an extreme learning machine (ELM), and another ANFIS are employed to predict the shear strength of the connectors in each of the six models. The results of the paper show that slip is the most influential factor in the shear strength of tilted connectors and after that, the inclination angle is the most effective one. Moreover, it is deducted that considering only four parameters in the predictive models is enough to have a very accurate prediction. It is also demonstrated that ELM needs less time and it can reach slightly better performance indices than those of ANN and ANFIS.

     

Application of genetic algorithm-based intuitionistic fuzzy weighted c-ordered-means algorithm to cluster analysis

Knowledge and Information Systems - With the advance of information technology, many fields have begun using data clustering to reveal data structures and obtain useful information. Most of the...     

Nonlinear evolutionary swarm intelligence of grasshopper optimization algorithm and gray wolf optimization for weight adjustment of neural network

Engineering with Computers - The advent of new data-mining techniques and, more recently, swarm-based optimization algorithms have antiquated traditional models in the field of energy performance...     

Scheduling Optimization Modelling: A Case Study of a Woven Label Manufacturing Company

Production scheduling involves all activities of building production schedules, including coordinating and assigning activities to each person, group of people, or machine and arranging work orders in each workplace. Production scheduling must solve all problems such as minimizing customer wait time, storage costs, and production time; and effectively using the enterprise’s human resources. This paper studies the application of flexible job shop modelling on scheduling a woven labelling process. The labelling process includes several steps which are handled in different work-stations. Each workstation is also comprised of several identical parallel machines. In this study, job splitting is allowed so that the power of work stations can be utilized better. The final objective is to minimize the total completion time of all jobs. The results show a significant improvement since the new planning may save more than 60% of lead time compared to the current schedule. The contribution of this research is to propose a flexible job shop model for scheduling a woven labelling process. The proposed approach can also be applied to support complex production scheduling processes under fuzzy environments in different industries. A practical case study demonstrates the effectiveness of the proposed model.     

Energy efficiency in cloud computing based on mixture power spectral density prediction

Due to the budget and environmental issues, adaptive energy efficiency receives a lot of attention these days, especially for cloud computing. In the previous research, we developed a combined methodology based on nonparametric prediction and convex optimization to produce proactive energy efficiency-oriented solution. In this work, the predictive analysis was further enhanced by deriving the mixture power spectral density to model the complex cloud monitoring statistics. By engaging the improved technique to the predictive analysis, the prediction process was more adaptive to handle the fluctuation in system utilization. As a consequence, the optimization process could subsequently produce more appropriate setting for energy savings. After the infrastructure setting has been made available, the instruction of virtual machine migration was created and implemented by the cloud orchestrator. This instruction condensed the services into the pool of active facilities, satisfying the objective of power efficiency. Eventually, any physical machine out of the power configuration would be gradually terminated. Compared to our former method, the effectiveness of the proposed technique has been proven by cutting down 4.92% of energy consumption, while still maintaining a similar quality of services.