The Impact of the Performance Appraisal Process on Job Satisfaction of the Academic Staff in Higher Educational Institutions

From MDPI via Jisc Publications RouterHistory: accepted 2021-10-02, pub-electronic 2021-10-11Publication status: PublishedPerformance appraisal is one of the key management tools which identifies employees’ strengths and weaknesses. Usually, this is the major mechanism of gathering information for rewarding/training employees based on their performance, and hence a key to achieve organisational goals by creating a satisfied workforce. Therefore, this study was aimed at examining the effects of the Performance Appraisal Process on job satisfaction of the university academic staff. The information collected within one of the largest universities in the UK via questionnaires and semi-structured interviews showed that the existing appraisal process majorly aligned with the requirements of the research-excellence-framework of the UK, which is greatly concerned with research rather than teaching. Furthermore, it was found that there is no clear link between promotions, salary increments, and rewards, etc. with staff performance within the current appraisal process. Eventually, it was realised that the majority of the academic staff of the source university were dissatisfied with the current performance appraisal process, and this could be the situation in the majority of universities in the UK. Therefore, further research in this area is highly recommended to explore extensive information to create a favourable work/study environment for both staff and students within the universities

Sustainable development through proper land utilization Case study Ratnapura, Sri Lanka

Since the land is a scarce resource, the utilization of land effectively would be directly affected to thesustainable development of any country. A proper system ofland uti Iization is very essential especiallyfor a country like Sri Lanka which is very small in size and the majority of the people are dependingon the land related activities such as farming, grazing etc. as their major source of income. Unfortunately,land is in the state of under utilization in Sri Lanka. This paper attempts to study the importance of theeffective land utilization using Geographical Information Systems (GIS) for the sustainable development.The study was mainly based on the data gathered from Ratnapura Divisional Secretary area, Thearea is consisting with natural vegetations such as forests, shrubs. and man -rnade cultivation such astea, rubber, paddy. Also the area has a high economic value and it is well known for natural disasterssuch as floods and earth slips. Primary data was collected using semi-structured questionnaires,interviewing people and relevant officers and field surveying using hand held Global Positioning System(GPS). The study supplemented with secondary data such as remote sensing satellite images, existingmaps. GIS technique was used for the data analyses.The study identified 4 major issues in the specified areaI. 20 % of the total area is under utilization2. 25 % of the total area is misused3. 05 % of the total area is over utilized4. Continuous loss of land due to the natural disastersAccording to the results, the land has not used effectively in the area. 45% of the area is not usedeffectively. Since the country is very small, proper land utilization is very important. The study suggestthat an immediate action should be taken to overcome the matter. And also it is very important to getthe active participation of the general public to stop the loss ofland due to natural disasters and forfuture decision making. 

SDI and crowdsourced spatial information management automation for disaster management

Modern disaster reporting is becoming increasingly sophisticated with the ready access to social media and user-friendly online mapping tools. Citizen engagement in location enabled disaster reporting is more obvious, and the availability of crowd generated geospatial data is higher than ever before. Crowd generated geospatial content is current and more diverse than conventional geographic information; however, quality and credibility issues exist. Although spatial data infrastructures (SDIs) have proven to be successful in supporting disaster management activities in the past, delays in providing public mapping portals and gaps in data are common. Crowd support and crowd generated spatial data have the potential to speed up disaster management actions and disaster mitigations. Within the study, crowd communications that occurred during the 2011 Queensland floods through the Australian Broadcasting Corporation's (ABC's) QLD flood crisis map were critically analysed to investigate the readiness of current information sources to support disaster management. The accuracy of the reported event locations were compared to the authoritative Queensland Government street network, Open Street Map's (OSM's) streets and Google streets to compare the accuracy of the street and address names provided through the crowdsourced data. The study reveals that several issues exist regarding the quality of the data provided and the intent of the data provider. Moreover, the results indicate that the direct usage of reported location is problematic and that the semantic processing of the information location along with available spatial data may be required to improve data quality

Quality of surface and ground waters for domestic and irrigation purposes in CKD/CKDu prevalent areas in Moneragala District, Sri Lanka

Total of 152 water samples from CKD/CKDu endemic areas and 30 water samples from non-CKDu prevalent areas in the Moneragala District were collected separately. pH values of water samples varied from 5.04 to 8.37 which are within the permissible limit prescribed by World Health Organization. More than 15% of water samples showed low Dissolved Oxygen (DO) values indicating heavy contamination by organic matter. Electrical conductivity was high ranging between 32 and 2865 μS/cm indicating higher mineralization in groundwater. The major ion chemistry reveals that the array of abundance of cations was variable as Na+ > Ca2+ > Mg2+ > K+ in dug wells, Ca2+ > Na+ > Mg2+ > K2+ in tube wells and Ca2+> Na+ > K+> Mg2+ in surface waters, while anions varied as HCO3->Cl->SO42->F- in water samples in both seasons. Computed WQI, 43%, 33%, and 82% for dug wells, tube wells and surface water respectively were in good water quality in this area, while waters in 30% and 25% of dug and tube wells respectively exceeded the value of 100 showing not suitable category. Considering irrigation quality, 88%, 93% and 96% of dug, tube and surface waters respectively were less than the permissible level. SAR values in the study area ranged from 0.08 to 4.91 meq/L by showing no danger of sodium as per SAR. The bulk of studied waters are appropriate for irrigation according to Residual Sodium Carbonate (RSC). Considering KR and PI, majority of water samples of the study area are suitable for irrigation purposes

Assimilation of Streamflow Observations

Streamflow is arguably the most important predictor in operational hydrologic forecasting and water resources management. Assimilation of streamflow observations into hydrologic models has received growing attention in recent decades as a cost-effective means to improve prediction accuracy. Whereas the methods used for streamflow data assimilation (DA) originated and were popularized in atmospheric and ocean sciences, the nature of streamflow DA is significantly different from that of atmospheric or oceanic DA. Compared to the atmospheric processes modeled in weather forecasting, the hydrologic processes for surface and groundwater flow operate over a much wider range of time scales. Also, most hydrologic systems are severely under-observed. The purpose of this chapter is to provide a review on streamflow measurements and associated uncertainty and to share the latest advances, experiences gained, and science issues and challenges in streamflow DA. Toward this end, we discuss the following aspects of streamflow observations and assimilation methods: (1) measurement methods and uncertainty of streamflow observations, (2) streamflow assimilation applications, and (3) benefits and challenges streamflow DA with regard to large-scale DA, multi-data assimilation, and dealing with timing errors