Penicillin vs. erythromycin in the treatment of diphtheria

PURPOSE: To review the University of Florida experience in treating ependymomas, analyze prognostic factors, and provide treatment recommendations. METHODS AND MATERIALS: Forty-one patients with ependymoma and no metastases outside the central nervous system received postoperative radiotherapy with curative intent between 1966 and 1989. Ten patients had supratentorial lesions, 22 had infratentorial lesions, and 9 had spinal cord lesions. All patients had surgery (stereotactic biopsy, subtotal resection, or gross total resection). Most patients with high-grade lesions received radiotherapy to the craniospinal axis. Low-grade intracranial lesions received more limited treatment. Spinal cord lesions were treated using either partial spine or whole spine fields. RESULTS: Of 32 intracranial tumors, 21 recurred, all at the primary site; no spinal cord tumors recurred. Overall 10-year survival rates were 51% (absolute) and 46% (relapse-free); by tumor site: spinal cord, 100%; infratentorial, 45%; supratentorial, 20% (p = 0.002). On multivariate analysis, tumor site was the only factor that influenced absolute survival (p = 0.0004); other factors evaluated included grade, gender, age, duration of symptoms, resection extent, primary tumor dose, treatment field extent, surgery-to-radiotherapy interval, and days under radiotherapy treatment. CONCLUSIONS: Patients with supratentorial or infratentorial tumors receive irradiation, regardless of grade. Craniospinal-axis fields are used when spinal seeding is radiographically or pathologically evident. Spinal cord tumors are treated using localized fields to the primary site if not completely resected. Failure to control disease at the primary site remains the main impediment to cure.     

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.     

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.

     

Deep neural network-based fusion model for emotion recognition using visual data

The Journal of Supercomputing - In this study, we present a fusion model for emotion recognition based on visual data. The proposed model uses video information as its input and generates emotion...     

Noise-adaptive synthetic oversampling technique

Applied Intelligence - In the field of supervised learning, the problem of class imbalance is one of the most difficult problems, and has attracted a great deal of research attention in recent...     

A self-learning particle swarm optimizer for global optimization problems

Particle swarm optimization (PSO) has been shown as an effective tool for solving global optimization problems. So far, most PSO algorithms use a single learning pattern for all particles, which means that all particles in a swarm use the same strategy. This monotonic learning pattern may cause the lack of intelligence for a particular particle, which makes it unable to deal with different complex situations. This paper presents a novel algorithm, called self-learning particle swarm optimizer (SLPSO), for global optimization problems. In SLPSO, each particle has a set of four strategies to cope with different situations in the search space. The cooperation of the four strategies is implemented by an adaptive learning framework at the individual level, which can enable a particle to choose the optimal strategy according to its own local fitness landscape. The experimental study on a set of 45 test functions and two real-world problems show that SLPSO has a superior performance in comparison with several other peer algorithms.