Comparison of Qualitative and Quantitative Methods for Isolation of Phosphate Solubilizing Microorganisms

Phosphate solubilising bacteria possess the ability to solubilise insoluble phosphate to soluble forms enhancing the nutrient status of the soil. This process not only compensates increasing cost of phosphatic fertilisers but also minimises the negative environmental impacts associated with the application of inorganic fertilisers. Phosphate solubilising bacteria (PSB) were screened based on the size of a halo/ clear zone around the colony (NBRIP agar plate assay) and by measuring solubilise phosphorous content (colorimetric method). The aim of this work was to assess the comparative reliability of quantitative and qualitative methods of isolation of phosphate solubilising bacteria. Bacterial strains which showed very poor performance in qualitative method were proven to be good phosphates solubilisers in quantitative method and vice versa. Therefore no positive relationship among the values obtained from qualitative and quantitative methods could be observed. Furthermore qualitative method did not reflect the real ability of the phosphate solubilising bacteria to solubilise insoluble phosphates. From the results of the present study, it can be concluded that isolation of efficient phosphate solubilising bacteria through quantitative method could give better results than that of qualitative method.Keywords: clear zone, insoluble phosphates, phosphate solubilising bacteri

Processing History: Potentials of Transformers for 3D Reconstruction of Historical Objects with the Help of Artifcial Intelligence

The digital preservation of cultural heritage is an important and challenging task for the research community. Reconstructing historical objects, which do not exist anymore, in the form of digital 3D models makes it possible to visualize them and present them to the public. The reconstruction process as well as the visualization lead to a deeper understanding of the lost historical objects. But the process of the digitalreconstruction is complex and time consuming as diverse sources have to be consulted and interpreted. Therefore, in this paper the latest technology in the feld of artifcial intelligence (AI) is used to support researchers in the feld of Digital Humanities: A Transformer deep learning model based on questions answering methods is introduced to assist to digitally reconstruct historical objects in 3D. It implies a new dimension of data availability, which supports the knowledge process by making large amounts of data qualitatively accessible. [Aus: Einleitung

Detecting Treasures in Museums with Artificial Intelligence

Museums around the world possess hundreds of thousands of priceless objects, which have stories to tell about human history. While students and scholars study them, even the general public is interested in these stories. If there is a way to automate the information delivery system about these objects it will be of immense value, e.g. it will support students to study these objects and speed up research. Adaptive blended learning options are conceivable, which can perfectly merge digital analysis and onsite viewing. Thus, the preparation and post-processing of studied objects is just as conceivable as the adequate acquisition of information for on-site studies. Examples of such solutions would be mobile apps and computer software that can be used for history and archaeology education as well. However, it is important to identify these objects correctly in order to build such solutions. Computer vision technologies in artificial intelligence (AI) can be used for this. Therefore, this paper will show how AI-algorithms can be used for digital humanities in novel ways, such as for detecting museum treasures

Energy-Efficient Self-Organization of Wireless Acoustic Sensor Networks for Ground Target Tracking

With the developments in computing and communication technologies, wireless sensor networks have become popular in wide range of application areas such as health, military, environment and habitant monitoring. Moreover, wireless acoustic sensor networks have been widely used for target tracking applications due to their passive nature, reliability and low cost. Traditionally, acoustic sensor arrays built in linear, circular or other regular shapes are used for tracking acoustic sources. The maintaining of relative geometry of the acoustic sensors in the array is vital for accurate target tracking, which greatly reduces the flexibility of the sensor network. To overcome this limitation, we propose using only a single acoustic sensor at each sensor node. This design greatly improves the flexibility of the sensor network and makes it possible to deploy the sensor network in remote or hostile regions through air-drop or other stealth approaches. Acoustic arrays are capable of performing the target localization or generating the bearing estimations on their own. However, with only a single acoustic sensor, the sensor nodes will not be able to generate such measurements. Thus, self-organization of sensor nodes into virtual arrays to perform the target localization is essential. We developed an energy-efficient and distributed self-organization algorithm for target tracking using wireless acoustic sensor networks. The major error sources of the localization process were studied, and an energy-aware node selection criterion was developed to minimize the target localization errors. Using this node selection criterion, the self-organization algorithm selects a near-optimal localization sensor group to minimize the target tracking errors. In addition, a message passing protocol was developed to implement the self-organization algorithm in a distributed manner. In order to achieve extended sensor network lifetime, energy conservation was incorporated into the self-organization algorithm by incorporating a sleep-wakeup management mechanism with a novel cross layer adaptive wakeup probability adjustment scheme. The simulation results confirm that the developed self-organization algorithm provides satisfactory target tracking performance. Moreover, the energy saving analysis confirms the effectiveness of the cross layer power management scheme in achieving extended sensor network lifetime without degrading the target tracking performance

Supporting Learning in Art History – Artificial Intelligence in Digital Humanities Education

In recent years and especially in the context of the coronavirus pandemic, digital distance learning increases. But for academic students, the selection of adequate learning materials for educational purposes is becoming more and more complex. This marks only one starting point where the use of artificial intelligence (AI) offers additional value. AI has a great potential to enhance and support research and education in the field of digital humanities (DH). As international organisations have just expressed their thoughts on the subject, AI is the topic par excellence and will decisively shape the future development of educational processes

Predicting and Comparing the Retention and Turnover Intention of Generations X and Y at Selected Service Companies in Sri Lanka

The generation gap has impacted a much higher turnover in the generation Y than in generation X during the previous years. It has impacted in achieving a healthy working environment at organizations to achieve the organizational goals. Hence it was needed to identify on what factors does the turnover rate of generation ‘y’ has increased than in generation ‘x’ within organizations and to predict which employees will retain and leave from the organizations during the next year. This study is based on a quantitative research type. A survey was used as the main research strategy and the study is based on deductive research approach. The population of the study was 1298 employees who belong to the two generations ‘x’ and ‘y’ of selected private companies which operate under the service category in Sri Lanka. The target sample of the study was 297 and the researchers were able to fulfil their requirement. The collected data were analyzed using descriptive analysis, multiple linear regression and binary logistic regression using SPSS. It was found that differences in characteristics of the two generations and the behaviors of them had influenced a higher turnover intention in generation ‘y’ than in generation ‘x’. It was specifically noted that the three independent variables had a positive impact on the retention and intention to leave of the two generations at workplaces separately. The results will be of utmost importance for employers to predict the retention and turnover intention of employees and for employees to have faith and continue the career providing the best to fulfill the organization’s needs. Hence, this concept could be recognized as a key factor to drive the quality in both employers and employees while achieving sustainability to have a healthy working environment. The main limitation of the study was that only two generations were taken into consideration. Therefore, it is recommended for future researchers to have research studies on upcoming generations at workplaces to identify the generational behavior. Keywords: Sustainable HRM, Generation ‘X’, Generation ‘Y’, Predictive Analysi

Building a Generic Value Creation Model For the Sri Lankan National Education System

This research was an attempt to build a generic value creation model architecture which can be used by any organisation without business v. public or profit v non-profit differences, by way of: a synthesis of literature in 6 streams of management related to value creation; operationalise it using data collected through an exploratory study in the System of General School Education in Sri Lanka; and, test the operationalised model in the same context through a confirmatory study. The study was a mixed-method one, using in its exploratory phase interviews as its data collection instrument, and in its subsequent confirmatory phase, questionnaires as its data collection instruments. Data analysis methodologies used to test hypotheses were structured equation modelling and multiple regression analysis. The operationalisation validated the model building assumptions, and the final research results showed that the proposed model can be used in a national-scale public education context to measure value creation

Improving primary health care quality for refugees and asylum seekers: A systematic review of interventional approaches

Background: It has been widely acknowledged that refugees are at risk of poorer health outcomes, spanning mental health and general well-being. A common point of access to health care for the migrant population is via the primary health care network in the country of resettlement. This review aims to synthesize the evidence of primary health care interventions to improve the quality of health care provided to refugees and asylum seekers. Methods: A systematic review was undertaken, and 55 articles were included in the final review. The Preferred Reporting Items for Systematic Reviews was used to guide the reporting of the review, and articles were managed using a reference-management software (Covidence). The findings were analysed using a narrative empirical synthesis. A quality assessment was conducted for all the studies included. Results: The interventions within the broad primary care setting could be organized into four categories, that is, those that focused on developing the skills of individual refugees/asylum seekers and their families; skills of primary health care workers; system and/or service integration models and structures; and lastly, interventions enhancing communication services. Promoting effective health care delivery for refugees, asylum seekers and their families is a complex challenge faced by primary care professionals, the patients themselves and the communication between them. Conclusion: This review highlights the innovative interventions in primary care promoting refugee health. Primary care interventions mostly focused on upskilling doctors, with a paucity of research exploring the involvement of other health care members. Further research can explore the involvement of interprofessional team members in providing effective refugee/migrant health. Patient or Public Contribution: Patient and public involvement was explored in terms of interventions designed to improve health care delivery for the humanitarian migrant population, that is, specifically refugees and asylum seekers