Mother-to-child transmission of human immunodeficiency virus in Italy : temporal trends and determinants of infection

In order to analyse temporal trends in vertical transmission rates of human immunodeficiency virus (HIV) and determinant of congenital HIV infection in Italy, we have considered data from a network of hospitals co-operating in the Italian Collaborative Study on HIV infection in pregnancy, conducted between 1988 and 1995. A total of 1040 women entered the study. The HIV-1 status of the babies was known in 848 cases (81.5%). Transmission rates were highest in the period 1988\u20131991, then tended to decrease and in 1995 the rate was 9.7 per 100 children (this finding, however, was based on only six infected children and the trend was not statistically significant). Considering the overall series, the risk of vertical HIV transmission was higher in women with low CD4 count in pregnancy [odds ratio (OR) <400 versus \u2a7e400 1.8, 95% confidence interval (CI) 1.1\u20132.9]. In comparison with vaginal delivery the risk of transmission was 0.3 (95% CI 0.1\u20130.5) and 0.6 (95% CI 0.3\u20131.2) respectively for elective and emergency delivery. In comparison with women who delivered at term (\u2a7e37 gestation weeks) the OR of HIV infection of the babies for the whole series was 2.2 (95% CI 1.3\u20133.6) in women who delivered preterm. Similar findings emerged when the analysis was conducted considering, separately, subjects observed in the period 1988\u20131991 and 1992\u20131995. The frequency of Caesarean section increased from 26.5% of deliveries in 1988\u20131991 to 36.2% in 1992\u20131995. Consequently, most temporal differences disappeared after standardization for mode of delivery, but the rate in 1995 was still lower than in 1988\u20131994

Design and development of a factory of the future in Turkey.

The Factory of the Future programme of work embraces a number of collaborative research projects primarily concerned with factory automation. The current research encompasses the development of a laser device for machine tool calibration and a wireless network for application in manufacturing factories. A further work concerns research into design of a knowledge-based-system (KBS) for information automation as the basis for automating an entire manufacturing enterprise. The latter work is hoped to lead to the introduction of Intelligent Integrated Product Cycle (I2PC). This approach involves the development of a self-learning automated management system, which can be applied in a manufacturing enterprise, large or small. The paper makes special references to the I2 PC approach and the proposed neural network system and their application in automation of information and production processes within an enterprise

Developing a mechanism for learning in engineering environments.

American Society for Engineering Education, ASEEThe concept of learning has invariably been related to a classroom environment and/or industrial seminars, workshops, etc. The recent development in Artificial Intelligence, particularly in Neural Network applications offer interesting opportunities in developing continuous learning mechanisms for industrial applications in specific sectors. This paper gives information about neural models and an application example elucidating how a learning system can be developed for determining and forecasting parts quantities in a supply chain. If a continuous system can reliably predict numbers of parts required at the right time and at the right place, then the entire production schedule throughout the entire supply chain and within each organisation within it can be planned. All information flow routes and material flow paths can be optimised. The possibilities are very promising. The challenge, however, is as to how these learning systems can be validated and used with Computerised Enterprise Resource Planning (CERP) packages already used in industry

Design and development of material and information flow for supply chaıns using genetic cellular networks.

In a recent paper by authors (Ziarati and Ucan, January 2001) a Back Propagation-Artificial Neural Network (BP-ANN) was adapted for predicting the required car parts quantities in a real and major auto parts supplier chain. It was argued that due to the learning ability of neural networks, their speed and capacity to handle large amount of data, they have a potential for predicting components requirements and establishing associated scheduling throughout a given supply chain system.This paper should be considered a continuation of the first paper as the neural network approach introduced in this paper replaces the BP-ANN by a new method viz., Genetic Cellular Neural Network (GCNN). The latter approach requires by far less stability parameters and hence better suited to fast changing scenarios as in real supply chain applications.The model has shown promising outcomes in learning and predicting material demand in a supply chain, with high degree of accuracy

Investigating nursing students� needs and data sources in the emergency wards

Background: Data demand deals with queries and answers accustomed treat and lookout of patients in emergency cases. In addition, analyzing patients' needs helps with nursing practice and developing an appropriate care plan. On the opposite hand, acceptable knowledge sources in clinical for designation, care and therapeutic functions are supported patients� scientific criteria and correct info. Purpose: This study was performed to meet the data needs and resources of nursing students in the emergency department of a teaching hospital affiliated to Hormozgan University of Medical Sciences in 2018. Methods: This descriptive-analytical have a look at become performed on one hundred forty nursing students who were educated at the least as soon as within the emergency branch of Shahid Mohammadi medical institution. The sampling approach turn out to be census.The records series instrument become a questionnaire evolved through the researcher which blanketed domains: wishes and information assets. The reliability and validity of the questionnaire were established. The facts were analyzed via SPSS v.19 software program. Findings: The scholar members in this examine had been 43.6 men and 56.4 ladies. Their age ranged among 19-35 years. in line with the findings, 50.7 of the scholars strongly wanted medical records. want for diagnostic and management facts became common. A two-manner and bivariate correlation take a look at showed the best correlation among diagnostic and control needs (p<.001, r=.594). facts wishes confirmed that the most massive correlation amongst guys belonged to diagnostic/therapeutic information wishes and among girls belonged to diagnostic/control information desires. the very best facts need among men turned into diagnostic/therapeutic and amongst ladies become diagnostic/management. The consequences related to information resources discovered that scholars� facts resources cited protected, respectively: patient�s document, patient, patient�s caregiver and nurses. Conclusion: According to the findings, college students were found to heed a tremendous deal of to diagnostic information desires. college students� data sources had been pretty their files. consequently, it's entreated to maneuver students in the direction of new medical sources to recognise a outstanding deal of statistics. © 2020, Advanced Scientific Research. All rights reserved

Application of polysaccharide-based biopolymers as supports in photocatalytic treatment of water and wastewater: a review

Rising health issues of Worldwide pollution by fossil fuel products are Fostering the development of safer materials such as biopolymers in many sectors such as food, pharmaceutical, medical and environmental industries. Indeed, biopolymers are often safe, biodegradable, cheap and easy to modify. Here we review photocatalysts based on polysaccharides for wastewater treatment. Polysaccharides include starch, cellulose, carrageenan, alginate, chitin, chitosan and gum. The main reasons for using biodegradable biopolymers are their ability to adsorb pollutants and to be modified with nanoparticles and semiconductors. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG