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The concept of the paper is how the library can be a useful framework for designing nurses' computer skills. The overarching aim of the research is to focus on how the computer skills involved in nurses' increasing access to health informatics can be used to improve nursing practice, consequently leading to better health care delivery. The objectives involved in achieving this aim include: finding out the level of nurses' computer literacy; finding out nurses' quest for electronic information for problem‐based nursing practice; investigating nurses' level of awareness of research‐based nursing practice; and finding out areas of desirability of informatics in nursing practice.

The measuring instrument used was a self‐administered questionnaire to senior nursing cadres in the Teaching Hospitals Complex, Ile‐Ife, Nigeria. There were 230 nurses in these categories, of whom 180 were given questionnaires to fill in. The questionnaire was pre‐tested and validated. A total of 167 copies were returned and found to be usable. Simple percentages and a summation weighted index were used to analyse the data.

The paper provides empirical insight into nurses' computer skills and the library's role. The majority of the respondents did not have knowledge of computers; in the School of Nursing they learned it through various means while practising, while a few could access and retrieve information from the available databases. Some difficulties were expressed, such as workload, lack of skills, location of the library with regard to the hospital, etc. The desirability of the introduction of health informatics to the profession is high.

The study is limited to a teaching hospital and the results may not be generalisable to non‐teaching hospitals, hence the need for further studies.

The impact of health informatics on nurses' computer skills and the library's role will save nurses from routine work, enhance their productivity, and will equip them better for the challenges that information technology presents for health professionals.

This paper fulfils and identifies the need to introduce health informatics to nursing practice in order to improve patient care.

This study aims to investigate research scholars’ and faculty members’ perception, participation in collection development, satisfaction with the adequacy of the library collection and challenges faced during the recommendation of resources in selected university libraries of Northern India.

The data was collected with the help of a structured questionnaire from the research scholars and faculty members from all disciplines of five universities of Northern India. The comparison between the researchers and faculty members was carried out within the university, as well as across the universities. A total of 652 questionnaires were distributed, out of which 465 filled questionnaires were finally selected for data analysis. The collected data was analyzed with the help of SPSS and the hypotheses were tested using Chi-square (χ²) test.

The survey results found significant differences in awareness of collection development policy (CDP), as well as the recommendation of resources (i.e. textbooks, reference books, journals and magazines and non-book materials) between the research scholars and faculty members across the libraries. However, no significant difference was found between the opinion of the research scholars and faculty members on the adequacy of library collection across the libraries.

The study was limited to five university libraries of North India which included Maharishi Dayanand University (Rohtak) and Kurukshetra University (Kurukshetra) from the State of Haryana Panjab University from Union Territory of Chandigarh and Punjabi University (Patiala) and Guru Nanak Dev University (Amritsar) from the state of Punjab.

The outcomes of this study will undoubtedly help the library authorities and management to understand the awareness of users (i.e. research scholars and faculty members) about the collection development process such as CDP of the library, kind of resources recommend, their assessment on adequacy of different kind of resources and their ultimate satisfaction from it.

The study is an extensive survey about the perception and participation of research scholars and faculty members in the collection development process of their respective libraries and indicates their satisfaction from the library collection.

The purpose of the present study is to determine levels of computer literacy adequate for searching academic information from electronic resources and databases. The study also examines whether or not student demographics influence this level of computer literacy in a university scenario in India, a developing nation.

The primary data were collected through a questionnaire-based survey on a sample of 329 respondents from three major universities located in North India. The study focused on only postgraduate students and research scholars. The data, thus collected, were analyzed with the aid of the SPSS statistical software package. χ2 test was also applied to determine significant comparability among student demographics and their acquired computer usage competencies.

The study discovered that among the respondents less than half reported that they had acquired adequate computer competence to search for information from electronic resources or databases. However, no significant differences were found for computer skills with respect to students having different demographic characteristics. There were observed significant differences among academic majors and the use of internet and OPAC. Significant differences were also observable between academic use of internet and students varying age groups.

This study is one of the few research studies carried out to examine computer literacy among university students especially in relation to their demographics. The results of the study will prove useful for improving computer literacy in university library systems in India and other developing nations.

The purpose of this paper is to identify the information needs of nurses in two hospitals in Nigeria and the ways in which they went about attempting to meet those needs.

The study is a descriptive survey of nurses at the Federal Medical Center (FMC), Yenagoa, and Niger Delta University Teaching Hospital (NDU‐TH), Okolobiri, in Bayelsa State, Nigeria.

The main reasons that motivated nurses to search for information were better patient care, improved medication administration, and better job performance. The main types of information needed by the nurses were how to avoid contracting HIV‐AIDS from patients, information about the outbreak of diseases and new discoveries in nursing. They were hampered by their inability to access foreign nursing journals and a lack of awareness of how to use medical databases. This could be due to a lack of funding to subscribe to local and foreign journals, and lack of education and training in the skills of using medical databases.

This type of information may prove useful to decision makers such as unit managers, directors of nursing services, and hospital executives.

Three Coupled Model Intercomparison Project Phase 5 models involved in the G4 experiment of the Geoengineering Model Inter-comparison Project (GeoMIP) project were used to investigate the impact of stratospheric aerosol injection (SAI) on the mean surface air temperature and precipitation extremes in Africa.

This impact was examined under G4 and Representative Concentration Pathway (RCP) 4.5 scenarios on the total precipitation, the number of rainy days (RR1) and of days with heavy rainfall (R20 mm), the rainfall intensity (SDII), the maximum length of consecutive wet (CWD) and dry (CDD) days and on the maximum rainfall in five consecutive days (Rx5day) across four regions: Western Africa (WAF), Eastern Africa (EAF), Northern Africa and Southern Africa (SAF).

During the 50 years (2020–2069) of SAI, mean continental warming is −0.40°C lower in G4 than under RCP4.5. During the post-injection period (2070–2090), the temperature continues to increase, but at a lower rate (−0.19°C) than in RCP4.5. During SAI, annual rainfall in G4 is significantly greater than in RCP4.5 over the high latitudes (especially over SAF) and lower over the tropics. The termination of SAI leads to a significant increase of rainfall over Sahel and EAF and a decrease over SAF and Guinea Coast (WAF).

Compared to RCP4.5, SAI will contribute to reducing significantly regional warming but with a significant decrease of rainfall in the tropics where rainfed agriculture account for a large part of the economies. After the SAI period, the risk of drought over the extratropical regions (especially in SAF) will be mitigated, while the risk of floods will be exacerbated in the Central Sahel.

To meet the Paris Agreement, African countries will implement mitigation measures to contribute to keep the surface air temperature below 2°C. Geoengineering with SAI is suggested as an option to meet this challenge, but its implication on the African climate system needs a deep investigation in the aim to understand the impacts on temperature and precipitation extremes. To the best of the authors’ knowledge, this study is the first to investigate the potential impact of SAI using the G4 experiment of GeoMIP on temperature and precipitation extremes of the African continent.

The purpose of this study is to analyze the growth of academic librarians’ research output in Nigeria; it examined their research productivity and determined the authorship pattern and degree of collaboration.

A total of 1,106 articles were retrieved from Current index to Journals in Education and Library, Information Science and Technology Abstract databases for the period 2000-March 2018.

The study revealed that only few authors are productive in the field of Library and Information Science in Nigeria during the period under study. The author productivity pattern is, therefore, in agreement with Lotka’s Law of inverse square. Top journals in which the academic librarians in Nigeria publish their works were identified. Of the 153 recognized universities in Nigeria, the study revealed that only few universities are productive. The years 2011 and 2012 recorded the highest contributions by the academic librarians. The findings also showed a high level of teamwork with most publications being produced jointly.

The limitation of this study is that it only retrieved articles that were indexed by Current index to Journals in Education and Library, Information Science and Technology Abstract. Secondly, articles published by the academic librarians in local journals in Nigeria that are not indexed and not visible are not included in the study.

The findings call for researchers in developing countries to recognize that it is important to publish a substantial number of papers in journals that are indexed and are widely visible.

Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully investigated. This paper aims to investigate the effects traffic data set have on the performance of machine learning (ML) predictive models in AQ prediction.

To achieve this, the authors have set up an experiment with the control data set having only the AQ data set and meteorological (Met) data set, while the experimental data set is made up of the AQ data set, Met data set and traffic data set. Several ML models (such as extra trees regressor, eXtreme gradient boosting regressor, random forest regressor, K-neighbors regressor and two others) were trained, tested and compared on these individual combinations of data sets to predict the volume of PM_2.5, PM₁₀, NO₂ and O₃ in the atmosphere at various times of the day.

The result obtained showed that various ML algorithms react differently to the traffic data set despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%.

This research is limited in terms of the study area, and the result cannot be generalized outside of the UK as some of the inherent conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research, therefore, leaving out a few other ML algorithms.

This study reinforces the belief that the traffic data set has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form of traffic data set in the development of an AQ prediction model. This implies that developers and researchers in AQ prediction need to identify the ML algorithms that behave in their best interest before implementation.

The result of this study will enable researchers to focus more on algorithms of benefit when using traffic data sets in AQ prediction.

The growing complexity of consumer engagement (CE) due to the impact of Internet of things (IoT) has been attracting significant attention from both academics and industry practitioners especially in recent times. Hence, understanding this phenomenon remains very crucial to the body of knowledge. This study conducted a systematic review on IoT and CE with the aim of proposing future research opportunities using the TCCM model.

Extant literature studies were systematically examined by sourcing high ranking ABS journals from EBSCO, ScienceDirect and Emerald. A total of 58 articles were included in the final analysis of this research.

The analysis established the need to conduct more research on CE due to the impact of new technological implementation in retail. The results further suggest the need for extensive research across African countries and emerging markets to enable broader empirical generalizations of research outcomes. Using the TCCM framework, the authors indicated directions for future empirical research.

This study exposes the current trends in CE and IoT. The results and analysis are both compelling and verifiable, hence, establishing a firm base of reference for future research in related fields.

The study aims to develop a multilayer high-effective ensemble of ensembles predictive model (stacking ensemble) using several hyperparameter optimized ensemble machine learning (ML) methods (bagging and boosting ensembles) trained with high-volume data points retrieved from Internet of Things (IoT) emission sensors, time-corresponding meteorology and traffic data.

For a start, the study experimented big data hypothesis theory by developing sample ensemble predictive models on different data sample sizes and compared their results. Second, it developed a standalone model and several bagging and boosting ensemble models and compared their results. Finally, it used the best performing bagging and boosting predictive models as input estimators to develop a novel multilayer high-effective stacking ensemble predictive model.

Results proved data size to be one of the main determinants to ensemble ML predictive power. Second, it proved that, as compared to using a single algorithm, the cumulative result from ensemble ML algorithms is usually always better in terms of predicted accuracy. Finally, it proved stacking ensemble to be a better model for predicting PM_2.5 concentration level than bagging and boosting ensemble models.

A limitation of this study is the trade-off between performance of this novel model and the computational time required to train it. Whether this gap can be closed remains an open research question. As a result, future research should attempt to close this gap. Also, future studies can integrate this novel model to a personal air quality messaging system to inform public of pollution levels and improve public access to air quality forecast.

The outcome of this study will aid the public to proactively identify highly polluted areas thus potentially reducing pollution-associated/ triggered COVID-19 (and other lung diseases) deaths/ complications/ transmission by encouraging avoidance behavior and support informed decision to lock down by government bodies when integrated into an air pollution monitoring system

This study fills a gap in literature by providing a justification for selecting appropriate ensemble ML algorithms for PM_2.5 concentration level predictive modeling. Second, it contributes to the big data hypothesis theory, which suggests that data size is one of the most important factors of ML predictive capability. Third, it supports the premise that when using ensemble ML algorithms, the cumulative output is usually always better in terms of predicted accuracy than using a single algorithm. Finally developing a novel multilayer high-performant hyperparameter optimized ensemble of ensembles predictive model that can accurately predict PM_2.5 concentration levels with improved model interpretability and enhanced generalizability, as well as the provision of a novel databank of historic pollution data from IoT emission sensors that can be purchased for research, consultancy and policymaking.