The efficiencies and performance of gas turbine cycles are highly dependent on parameters such as the turbine inlet temperature (TIT), compressor inlet temperature (T1), and pressure ratio (Rc). This study analyzed the effects of these parameters on the energy efficiency, exergy efficiency, and specific fuel consumption (SFC) of a simple gas turbine cycle. The analysis found that increasing the TIT leads to higher efficiencies and lower SFC, while increasing the To or Rc results in lower efficiencies and higher SFC. For a TIT of 1400 ℃, T1 of 20 ℃, and Rc of 8, the energy and exergy efficiencies were 32.75% and 30.9%, respectively, with an SFC of 187.9 g/kWh. However, for a TIT of 900 ℃, T1 of 30 ℃, and Rc of 30, the energy and exergy efficiencies dropped to 13.18% and 12.44%, respectively, while the SFC increased to 570.3 g/kWh. The results show that there are optimal combinations of TIT, To, and Rc that maximize performance for a given application. Designers must consider trade-offs between efficiency, emissions, cost, and other factors to optimize gas turbine cycles. Overall, this study provides data and insights to improve the design and operation of simple gas turbine cycles.

Vehicle detection stands out as a rapidly developing technology today and is further strengthened by deep learning algorithms. This technology is critical in traffic management, automated driving systems, security, urban planning, environmental impacts, transportation, and emergency response applications. Vehicle detection, which is used in many application areas such as monitoring traffic flow, assessing density, increasing security, and vehicle detection in automatic driving systems, makes an effective contribution to a wide range of areas, from urban planning to security measures. Moreover, the integration of this technology represents an important step for the development of smart cities and sustainable urban life. Deep learning models, especially algorithms such as You Only Look Once version 5 (YOLOv5) and You Only Look Once version 8 (YOLOv8), show effective vehicle detection results with satellite image data. According to the comparisons, the precision and recall values of the YOLOv5 model are 1.63% and 2.49% higher, respectively, than the YOLOv8 model. The reason for this difference is that the YOLOv8 model makes more sensitive vehicle detection than the YOLOv5. In the comparison based on the F1 score, the F1 score of YOLOv5 was measured as 0.958, while the F1 score of YOLOv8 was measured as 0.938. Ignoring sensitivity amounts, the increase in F1 score of YOLOv8 compared to YOLOv5 was found to be 0.06%.

This study explores the determinants of auditor performance, focusing on the moderating role of organizational commitment within the Tangerang City Inspectorate. Employing stratified random sampling, a sample of 250 auditors was chosen to ensure diversity across experience, departmental affiliation, and roles. Quantitative analysis used SPSS to examine the relationships between auditor performance, organizational commitment, and other relevant variables. Findings indicated that organizational commitment significantly moderates the effects of various social pressures on auditor performance. This underscores the necessity for auditing organizations to foster organizational commitment to enhance auditor efficacy and uphold ethical standards. These results hold substantial implications for governance and audit quality assurance, suggesting that reinforced organizational commitment could lead to more robust auditor performance and ethical conduct within similar urban governance settings. This study contributes valuable insights into the influence of organizational dynamics on auditor behaviour and performance outcomes.

Studies to evaluate the response of passion fruit seedlings in terms of emergence, nursery, and early field growth to growing media and mulching were carried out at the Teaching and Research Farm of Joseph Sarwuan Tarka University Makurdi between July and December 2018. Treatments consisted of five media, composted from readily available substrates. The five nursery media were; medium 1:1:2:3 (SB) composed of top soil + poultry manure + river sand; medium 2:1:2:3 (RHB) – rice hull + poultry manure + river sand; medium 3:2:3:1 (RHB) – rice hull + poultry manure + river sand; medium 4:1:4:3 (SDB) – sawdust + poultry manure + river sand and medium 5:1:2:3 (SDB) – sawdust + poultry manure + river sand. For the nursery experiment, treatments were the five potting media, while the field trial was a 5 × 2 factorial arrangement consisting of the five growing media and mulching status (mulch and no mulch). In both cases, treatments were laid out in randomized designs that were replicated three times. Results showed that there were no significant differences in all the emergence traits evaluated. However, medium M5 (sawdust based) showed superior performance in most of the seedling characters evaluated. Under field conditions, the sawdust based media (M4 and M5) gave the best growth of passion fruit seedlings compared to the other potting media. Application of mulch, however, did not elicit any significant response in plant growth. It is therefore conclusive that sawdust based growing media could be used to produce high quality passion fruit seedlings with the prospect of excellent performance under field conditions.