A study to determine the factors to improve group and team effectiveness in Transnet Engineering

Teams have increasingly become the means for completing tasks in many organisations, and organisations have turned to teams as a better way to use employee talents. Many South African companies have established work teams to solve both complex and minor problems, and some companies’ performance has increased due to the implementation of work teams. The fact that organisations are using teams does not necessarily mean they are always effective, there are many factors that contribute to team effectiveness in an organisation, and these factors need to be identified and managed properly so that the team can remain effective and produce the results that are expected. Management of most companies is unaware of the factors that contribute to group and team effectiveness, and most teams are ineffective because of the lack of focus on the factors that improve group and team effectiveness. This is the reason or objective why this study was conducted at Transnet Engineering, to identify the factors that are critical to improving team effectiveness. The researcher conducted a literature review in order to determine the factors that improve group and team effectiveness. Some of the factors deal with organisational culture, motivation (monetary and non-monetary motivation), diversity in teams, size of teams, formulation of teams, team leadership, team goals, team structures, team member training, trust in teams, etc. An empirical study with the use of a questionnaire was also conducted to determine the perceptions that supervisors, superintendents, foremen and managers have at Transnet Engineering with regards to factors that improve group and team effectiveness. The research instrument was grouped into five categories; organisational context, individual context, team context, management support and team effectiveness. More than 50 percent of the respondents agreed with the organisational and individual context factors that were tested, around 75 percent of the respondents agreed with team context factors that were tested, almost 60 percent of respondents agreed with management support factors, and more than 60 percent of respondents indicated that their teams are effective. Although there is general agreement between most factors identified in the literature study and the empirical study, the following will need more focus: Offering of team resources Leadership support from executive committee members (EXCO) Proper reward and recognition systems Conducting research to identify employee satisfaction levels Team development Diversity management Talent management Team siz

Historical and projected trends in nearsurface temperature indices for 22 locations in South Africa

Motivated by the risks posed by global warming, historical trends and future projections of near-surface temperature in South Africa have been investigated in a number of previous studies. These studies included the assessment of trends in average temperatures as well as extremes. In this study, historical trends in near-surface minimum and maximum temperatures as well as extreme temperature indices in South Africa were critically investigated by comparing quality-controlled station observations with downscaled model projections. Because climate models are the only means of generating future global warming projections, this critical point comparison between observed and downscaled model simulated time series can provide valuable information regarding the interpretation of model-generated projections. Over the historical 1951–2005 period, both observed data and downscaled model projections were compared at 22 point locations in South Africa. An analysis of model projection trends was conducted over the period 2006–2095. The results from the historical analysis show that model outputs tend to simulate the historical trends well for annual means of daily maximum and minimum temperatures. However, noteworthy discrepancies exist in the assessment of temperature extremes. While both the historical model simulations and observations show a general warming trend in the extreme indices, the observational data show appreciably more spatial and temporal variability. On the other hand, model projections for the period 2006–2095 show that for the medium-to-low concentration Representative Concentration Pathway (RCP) 4.5, the projected decrease in cold nights is not as strong as is the case for the historically observed trends. However, the upward trends in warm nights for both the RCP4.5 and the high concentration RCP8.5 pathways are noticeably stronger than the historically observed trends. For cool days, future projections are comparable to the historically observed trends, but for hot days noticeably higher. Decreases in cold spells and increases in warm spells are expected to continue in future, with relatively strong positive trends on a regional basis. It is shown that projected trends are not expected to be constant into the future, in particular trends generated from the RCP8.5 pathway that show a strong increase in warming towards the end of the projection period. SIGNIFICANCE : • Comparison between the observed and simulated trends emphasises the necessity to assess the reliability of the output of climate models which have a bearing on the credibility of projections. • The limitation of the models to adequately simulate the climate extremes, renders the projections conservative, which is an important result in the light of climate change adaptation.http://www.sajs.co.zaam2019Geography, Geoinformatics and MeteorologySchool of Health Systems and Public Health (SHSPH

Primary healthcare and school health service utilisation by adolescents and young adults in KwaZulu-Natal, South Africa.

BACKGROUND: Young people aged 10-24 years are a vulnerable group with poor health service access relative to other populations. Recent South African initiatives, the She Conquers campaign, the Integrated School Health Policy and the Adolescent & Youth Health Policy, include a focus on improving the breadth and quality of youth-friendly health service delivery. However, in some settings the provision and impact of scaled-up youth friendly health services has been limited indicating a gap between policy and implementation. In this study we reviewed existing sources of data on health service utilisation to answer the following question: 'What health conditions do young people present with and what services do they receive at public health clinics, mobile clinics and school health services?' METHODS: We conducted a retrospective register review in three purposively selected primary healthcare clinics (PHCC), one mobile clinic, and one school health team in Hlabisa and Mtubatuba sub-districts of uMkhanyakude District, KwaZulu-Natal, South Africa. The focus was service utilisation for any reason by 10-24 year olds. We also conducted descriptive analysis of pre-existing data on service utilisation by young people available from the District Health Information System for all 17 PHCC in the study sub-districts. RESULTS: Three quarters of 4121 recorded young person visits in the register review were by females, and 40% of all young person visits were by females aged 20-24 years. The most common presenting conditions were HIV-related, antenatal care, family planning, general non-specific complaints and respiratory problems (excluding TB). There were relatively few recorded consultations for other common conditions affecting young people such as mental health and nutritional problems. Antibiotics, antiretrovirals, contraceptives, vitamins/supplements, and analgesics were most commonly provided. Routine health registers recorded limited information, were often incomplete and/or inconsistent, and age was not routinely recorded. CONCLUSIONS: Measuring morbidity and service provision are fundamental to informing policy and promoting responsive health systems. Efforts should be intensified to improve the quality and completeness of health registers, with attention to the documentation of important, and currently poorly documented, young people's health issues such as mental health and nutrition

Meteorological influences on malaria transmission in Limpopo Province, South Africa

MENVSC (Geography)Department of Geography and Geo-Information SciencesSemi-arid regions of Africa are prone to epidemics of malaria. Epidemic malaria occurs along the geographical margins of endemic regions, when the equilibrium between the human, parasite and mosquito vector populations are occasionally disturbed by changes in one or more meteorological factors and a sharp but temporary increase in disease incidence results. Monthly rainfall and temperature data from the South African Weather Service and malaria incidence data from Department of Health were used to determine the influence of meteorological variables on malaria transmission in Limpopo from 1998-2014. Meteorological influences on malaria transmission were analyzed using time series analysis techniques. Climate suitability for malaria transmission was determined using MARA distribution model. There are three distinct modes of rainfall variability over Limpopo which can be associated with land falling tropical cyclones, cloud bands and intensity of the Botswana upper high. ENSO and ENSO-Modoki explains about 58% of this variability. Malaria epidemics were identified using a standardized index, where cases greater than two standard deviations from the mean are identified as epidemics. Significant positive correlations between meteorological variables and monthly malaria incidence is observed at least one month lag time, except for rainfall which shows positive correlation at three months lag time. Malaria transmission appears to be strongly influenced by minimum temperature and relative humidity (R = 0.52, p<0.001). A SARIMA (2, 1, 2) (1, 0, 0)12 model fitted with only malaria cases has prediction performance of about 53%. Warm SSTs of the SWIO and Benguela Niño region west of Angola are the dominant predictors of malaria epidemics in Limpopo in the absence of La Niña. Warm SSTs over the equatorial Atlantic and Benguela Niño region results in the relaxation of the St. Helena high thus shifting the rainy weather to south-east Africa. La Niña have been linked with increased malaria cases in south-east Africa. During El Niño when rain bearing systems have migrated east of Madagascar ridging of the St. Helena high may produce conducive conditions for malaria transmission. Anomalously warmer and moist winters preceding the malaria transmission season are likely to allow for high mosquito survival and the availability of the breeding sites thus high population in the beginning of the transmission season hence resulting in increased epidemics.NR

Historical and projected trends in near-surface temperature indices for 22 locations in South Africa

Motivated by the risks posed by global warming, historical trends and future projections of near-surface temperature in South Africa have been investigated in a number of previous studies. These studies included the assessment of trends in average temperatures as well as extremes. In this study, historical trends in near-surface minimum and maximum temperatures as well as extreme temperature indices in South Africa were critically investigated by comparing quality-controlled station observations with downscaled model projections. Because climate models are the only means of generating future global warming projections, this critical point comparison between observed and downscaled model simulated time series can provide valuable information regarding the interpretation of model-generated projections. Over the historical 1951–2005 period, both observed data and downscaled model projections were compared at 22 point locations in South Africa. An analysis of model projection trends was conducted over the period 2006–2095. The results from the historical analysis show that model outputs tend to simulate the historical trends well for annual means of daily maximum and minimum temperatures. However, noteworthy discrepancies exist in the assessment of temperature extremes. While both the historical model simulations and observations show a general warming trend in the extreme indices, the observational data show appreciably more spatial and temporal variability. On the other hand, model projections for the period 2006–2095 show that for the medium-to-low concentration Representative Concentration Pathway (RCP) 4.5, the projected decrease in cold nights is not as strong as is the case for the historically observed trends. However, the upward trends in warm nights for both the RCP4.5 and the high concentration RCP8.5 pathways are noticeably stronger than the historically observed trends. For cool days, future projections are comparable to the historically observed trends, but for hot days noticeably higher. Decreases in cold spells and increases in warm spells are expected to continue in future, with relatively strong positive trends on a regional basis. It is shown that projected trends are not expected to be constant into the future, in particular trends generated from the RCP8.5 pathway that show a strong increase in warming towards the end of the projection period. Significance: Comparison between the observed and simulated trends emphasises the necessity to assess the reliability of the output of climate models which have a bearing on the credibility of projections. The limitation of the models to adequately simulate the climate extremes, renders the projections conservative

Development of an updated global land in situ‐based data set of temperature and precipitation extremes: HadEX3

We present the second update to a data set of gridded land‐based temperature and precipitation extremes indices: HadEX3. This consists of 17 temperature and 12 precipitation indices derived from daily, in situ observations and recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). These indices have been calculated at around 7,000 locations for temperature and 17,000 for precipitation. The annual (and monthly) indices have been interpolated on a 1.875°×1.25° longitude‐latitude grid, covering 1901–2018. We show changes in these indices by examining ”global”‐average time series in comparison with previous observational data sets and also estimating the uncertainty resulting from the nonuniform distribution of meteorological stations. Both the short and long time scale behavior of HadEX3 agrees well with existing products. Changes in the temperature indices are widespread and consistent with global‐scale warming. The extremes related to daily minimum temperatures are changing faster than the maximum. Spatial changes in the linear trends of precipitation indices over 1950–2018 are less spatially coherent than those for temperature indices. Globally, there are more heavy precipitation events that are also more intense and contribute a greater fraction to the total. Some of the indices use a reference period for calculating exceedance thresholds. We present a comparison between using 1961–1990 and 1981–2010. The differences between the time series of the temperature indices observed over longer time scales are shown to be the result of the interaction of the reference period with a warming climate. The gridded netCDF files and, where possible, underlying station indices are available from www.metoffice.gov.uk/hadobs/hadex3 and www.climdex.org.Robert Dunn was supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra (GA01101) and thanks Nick Rayner and Lizzie Good for helpful comments on the manuscript. Lisa Alexander is supported by the Australian Research Council (ARC) Grants DP160103439 and CE170100023. Markus Donat acknowledges funding by the Spanish Ministry for the Economy, Industry and Competitiveness Ramón y Cajal 2017 Grant Reference RYC‐2017‐22964. Mohd Noor'Arifin Bin Hj Yussof and Muhammad Khairul Izzat Bin Ibrahim thank the Brunei Darussalam Meteorological Department (BDMD). Ying Sun was supported by China funding agencies 2018YFA0605604 and 2018YFC1507702. Fatemeh Rahimzadeh and Mahbobeh Khoshkam thank I.R. of Iranian Meteorological Organization (IRIMO) and the Atmospheric Science and Meteorological Organization Research Center (ASMERC) for Data and also sharing their experiences, especially Abbas Rangbar. Jose Marengo was supported by the National Institute of Science and Technology for Climate Change Phase 2 under CNPq Grant 465501/2014‐1, FAPESP Grants 2014/50848‐9 and 2015/03804‐9, and the National Coordination for High Level Education and Training (CAPES) Grant 88887.136402‐00INCT. The team that worked on the data in West Africa received funding from the UK's National Environment Research Council (NERC)/Department for International Development DFID) Future Climate For Africa programme, under the AMMA‐2050 project (Grants NE/M020428/1 and NE/M019969/1). Data from Southeast Asia (excl. Indonesia) was supported by work on using ClimPACT2 during the Second Workshop on ASEAN Regional Climate Data, Analysis and Projections (ARCDAP‐2), 25–29 March 2019, Singapore, jointly funded by Meteorological Service Singapore and WMO through the Canada‐Climate Risk and Early Warning Systems (CREWS) initiative. This research was supported by Thai Meteorological Department (TMD) and Thailand Science Research and Innovation (TSRI) under Grant RDG6030003. Daily data for Mexico were provided by the Servicio Meteorológico Nacional (SMN) of Comisión Nacional del Agua (CONAGUA). We acknowledge the data providers in the ECA&D project (https://www.ecad.eu), the SACA&D project (https://saca-bmkg.knmi.nl), and the LACA&D project (https://ciifen.knmi.nl). We thank the three anonymous reviewers for their detailed comments which improved the manuscript.Peer ReviewedPostprint (published version

Development of an Updated Global Land In Situ‐Based Data Set of Temperature and Precipitation Extremes: HadEX3

We present the second update to a data set of gridded land‐based temperature and precipitation extremes indices: HadEX3. This consists of 17 temperature and 12 precipitation indices derived from daily, in situ observations and recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). These indices have been calculated at around 7,000 locations for temperature and 17,000 for precipitation. The annual (and monthly) indices have been interpolated on a 1.875°×1.25° longitude‐latitude grid, covering 1901–2018. We show changes in these indices by examining ”global”‐average time series in comparison with previous observational data sets and also estimating the uncertainty resulting from the nonuniform distribution of meteorological stations. Both the short and long time scale behavior of HadEX3 agrees well with existing products. Changes in the temperature indices are widespread and consistent with global‐scale warming. The extremes related to daily minimum temperatures are changing faster than the maximum. Spatial changes in the linear trends of precipitation indices over 1950–2018 are less spatially coherent than those for temperature indices. Globally, there are more heavy precipitation events that are also more intense and contribute a greater fraction to the total. Some of the indices use a reference period for calculating exceedance thresholds. We present a comparison between using 1961–1990 and 1981–2010. The differences between the time series of the temperature indices observed over longer time scales are shown to be the result of the interaction of the reference period with a warming climate. The gridded netCDF files and, where possible, underlying station indices are available from www.metoffice.gov.uk/hadobs/hadex3 and www.climdex.org.Robert Dunn was supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra (GA01101) and thanks Nick Rayner and Lizzie Good for helpful comments on the manuscript. Lisa Alexander is supported by the Australian Research Council (ARC) Grants DP160103439 and CE170100023. Markus Donat acknowledges funding by the Spanish Ministry for the Economy, Industry and Competitiveness Ramón y Cajal 2017 Grant Reference RYC‐2017‐22964. Mohd Noor'Arifin Bin Hj Yussof and Muhammad Khairul Izzat Bin Ibrahim thank the Brunei Darussalam Meteorological Department (BDMD). Ying Sun was supported by China funding agencies 2018YFA0605604 and 2018YFC1507702. Fatemeh Rahimzadeh and Mahbobeh Khoshkam thank I.R. of Iranian Meteorological Organization (IRIMO) and the Atmospheric Science and Meteorological Organization Research Center (ASMERC) for Data and also sharing their experiences, especially Abbas Rangbar. Jose Marengo was supported by the National Institute of Science and Technology for Climate Change Phase 2 under CNPq Grant 465501/2014‐1, FAPESP Grants 2014/50848‐9 and 2015/03804‐9, and the National Coordination for High Level Education and Training (CAPES) Grant 88887.136402‐00INCT. The team that worked on the data in West Africa received funding from the UK's National Environment Research Council (NERC)/Department for International Development DFID) Future Climate For Africa programme, under the AMMA‐2050 project (Grants NE/M020428/1 and NE/M019969/1). Data from Southeast Asia (excl. Indonesia) was supported by work on using ClimPACT2 during the Second Workshop on ASEAN Regional Climate Data, Analysis and Projections (ARCDAP‐2), 25–29 March 2019, Singapore, jointly funded by Meteorological Service Singapore and WMO through the Canada‐Climate Risk and Early Warning Systems (CREWS) initiative. This research was supported by Thai Meteorological Department (TMD) and Thailand Science Research and Innovation (TSRI) under Grant RDG6030003. Daily data for Mexico were provided by the Servicio Meteorológico Nacional (SMN) of Comisión Nacional del Agua (CONAGUA). We acknowledge the data providers in the ECA&D project (https://www.ecad.eu), the SACA&D project (https://saca-bmkg.knmi.nl), and the LACA&D project (https://ciifen.knmi.nl). We thank the three anonymous reviewers for their detailed comments which improved the manuscript.Peer ReviewedPostprint (published version