Identification of Nursing Management Planning Standards in Iran‏

BACKGROUND: In recent century, planning is one of the most important care skills for health service development. Nurses should be ready scientifically for managing situations in order to develop and perform appropriate models for patient care. It is necessary for manager to know about the process and standards of planning and how to apply them in real conditions. With regard to importance of health care planning and lack of nursing management planning accreditation in Iran. METHODS: This triangulating research was carried out between 2004 and 2006. Fifty professional nursing managers in different level of Iran medical universities and central hospitals, having experience in nursing management at least for five years, and an MS or BS degree in nursing management, were included in a study through purposed sampling. At first, a pilot study with an open questionnaire was conducted in Isfahan and Shiraz and then the study went on in 3 phases including searching for international standards in method by 15 professional nursing manager after consensus on 70% and sending the final was used for data analysis. At last standards for nursing management planning were obtained in Iran. RESULTS: 48 standards of nursing management planning were obtained. The findings showed that most of the standards were accepted (90%) and there were not any standards with agreement lower than 70%. These standards, accompanied with the standards of other nursing units, can be used for quality improvement. The researcher suggests the ministry of health to use these findings and other related ones to accredit the nursing system and to identify its deficiencies

Prevalence and Predictors of Cesarean Section in Zanjan-Iran during 2014-2016

Background: The increased prevalence of cesarean section (C–section) is a global epidemic. Objectives: The aim of this study was to determine the prevalence and demographic, fertility, and childbirth-related factors of C–section in Zanjan province, Iran,-from 21 March 2014 to 19 March 2016. Methods: This study was a descriptive analytic study, carried out in 2014–2016, which gathered 41, 265 registered childbirth data in Zanjan province hospitals and from country electronic childbirth register system. Data were analyzed using descriptive, univariate and multivariate logistic binominal regression. Results: according to the findings, the prevalence of C–section was 40.1%. The odds of having C–section went up with increasing maternal age (OR=1.026), gravidity (OR=0.670), and gestational age (OR=0.093), while it decreased with an increased parity, end educational level up to high school graduate. In contrast, higher educational (OR=3.064) level increased the odds of having C–section. Living in the urban areas (OR=1.855) also increased the oddsof C–section. Diabetes (OR=1.990), preeclampsia or eclampsia (OR=2.350), hypertension (OR=1.983), and thyroid disorders (OR=2.289) increased the odds of having C–section. Newborns with low birth weight (OR=1) and macrosomia (OR=2.663), and boys (OR=1.107) were delivered more via C–section. Among the interventions during labor, induction (OR=1.131) and stimulation of labor (OR=0.269) reduced the odds of C–section (P<0.05). Conclusion: C–section rate is very high in Iran and its association with different variables can be a basis for planning and policymaking in order to reduce the C–section rate, particularly in Zanjan province

Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing

Widespread adoption of cloud computing has increased the attractiveness of such services to cybercriminals. Distributed denial of service (DDoS) attacks targeting the cloud’s bandwidth, services and resources to render the cloud unavailable to both cloud providers, and users are a common form of attacks. In recent times, feature selection has been identified as a pre-processing phase in cloud DDoS attack defence which can potentially increase classification accuracy and reduce computational complexity by identifying important features from the original dataset during supervised learning. In this work, we propose an ensemble-based multi-filter feature selection method that combines the output of four filter methods to achieve an optimum selection. We then perform an extensive experimental evaluation of our proposed method using intrusion detection benchmark dataset, NSL-KDD and decision tree classifier. The findings show that our proposed method can effectively reduce the number of features from 41 to 13 and has a high detection rate and classification accuracy when compared to other classification techniques

Intelligent mining of large-scale bio-data: bioinformatics applications

Today, there is a collection of a tremendous amount of bio-data because of the computerized applications worldwide. Therefore, scholars have been encouraged to develop effective methods to extract the hidden knowledge in these data. Consequently, a challenging and valuable area for research in artificial intelligence has been created. Bioinformatics creates heuristic approaches and complex algorithms using artificial intelligence and information technology in order to solve biological problems. Intelligent implication of the data can accelerate biological knowledge discovery. Data mining, as biology intelligence, attempts to find reliable, new, useful and meaningful patterns in huge amounts of data. Hence, there is a high potential to raise the interaction between artificial intelligence and bio-data mining. The present paper argues how artificial intelligence can assist bio-data analysis and gives an up-to-date review of different applications of bio-data mining. It also highlights some future perspectives of data mining in bioinformatics that can inspire further developments of data mining instruments. Important and new techniques are critically discussed for intelligent knowledge discovery of different types of row datasets with applicable examples in human, plant and animal sciences. Finally, a broad perception of this hot topic in data science is given

Women in Higher Education: Social Sciences at Land Grant Universities in the U.S.

Labor and Human Capital, Teaching/Communication/Extension/Profession,

AN ECONOMIC ANALYSIS OF UNIT-TRAIN FACILITY INVESTMENT

Country elevators competed chiefly through increased efficiency in grain handling and transportation. The development by the railroads of more favorable rates for multi-car shipments (unit train) has led grain cooperatives and other agribusiness firms to invest in high speed rail load out facilities. In this study the feasibility of an investment in a unit-train load out facility is analyzed. The impact of grain through put volume, unit rate transportation savings, discount rates, and grain-cleaning costs is also determined

Cmp-Pim: An Energy-Efficient Comparator-Based Processing-In-Memory Neural Network Accelerator

In this paper, an energy-efficient and high-speed comparator-based processing-in-memory accelerator (CMP-PIM) is proposed to efficiently execute a novel hardware-oriented comparator-based deep neural network called CMPNET. Inspired by local binary pattern feature extraction method combined with depthwise separable convolution, we first modify the existing Convolutional Neural Network (CNN) algorithm by replacing the computationally-intensive multiplications in convolution layers with more efficient and less complex comparison and addition. Then, we propose a CMP-PIM that employs parallel computational memory sub-array as a fundamental processing unit based on SOT-MRAM. We compare CMP-PIM accelerator performance on different data-sets with recent CNN accelerator designs. With the close inference accuracy on SVHN data-set, CMP-PIM can get ∼ 94× and 3× better energy efficiency compared to CNN and Local Binary CNN (LBCNN), respectively. Besides, it achieves 4.3× speed-up compared to CNN-baseline with identical network configuration