Automated diagnosis and treatment planning in dentistry

This study aims to investigate the perceptions and experiences regarding automated diagnosis and treatment planning in dentistry. The field of automated diagnosis and treatment planning is rapidly evolving, leveraging advanced technologies such as artificial intelligence (AI) and machine learning to enhance patient care and outcomes. However, there is a need to understand the perspectives of dental professionals regarding the adoption and implementation of these automated systems. A questionnaire survey was conducted among 100 dentists from various dental practices to gather data on their familiarity, usage, and perceptions of automated diagnosis and treatment planning. The survey also explored the perceived benefits, challenges, and future implications of automated systems in dental care. Preliminary findings indicate that the majority of dentists in the sample (80%) have some level of familiarity with automated diagnosis and treatment planning. However, only 45% reported actively using such systems in their practice. Among the dentists using automated systems, the most commonly cited benefits include time-saving (60%), enhanced accuracy (55%), and improved treatment planning (50%). Challenges associated with the adoption of automated systems were also identified.&nbsp

Optimization of Crop Establishment Methods and Phosphorus Fertilizer Levels on Growth and Economic Efficiency of Groundnut under Semi-Arid Region of Afghanistan

An experiment was conducted at the farm of Afghanistan National Agricultural Science and Technology University (ANASTU), Kandahar Province, Afghanistan in cropping season of 2020 to investigate the Optimization of Crop Establishment methods and Phosphorus Fertilizer levels on Growth and Economic Efficiency of Groundnut under Semi-arid region of Afghanistan. The experimentation was conducted in split-plot design with 15 treatment combinations and replicated thrice. main-plot consisted of crop establishment methods, viz. ridge and furrow (RF), broad bed and furrow (BBF) and flatbed (FB), while the sub-plots comprised of phosphorus levels, viz. absolute control, 20, 40, 60 and 80 kg P2O5/ ha. The results of the investigation revealed that the growth parameters in terms of plant height in and leave area were highest in BBF, followed by FB and minimum was in RF. Adoption of BBF recorded significantly higher gross return (238928 AFN/ha), net return (202728 AFN/ha) and net benefit cost of ratio (5.2) were in Broad Bed and Furrow, followed in ridged and furrow and the minimum was in flat bed method. It can be concluded, cultivating of groundnut with Broad Bed and Furrow with application of 60 kg P2O5/ha was found beneficial for reaching higher productivity and profitability under semi-arid region of Afghanistan

Student’s perspective on NLE examination

This study aims to investigate the students' perspective on the National Licensure Examination (NLE). The NLE serves as a crucial evaluation tool for students, assessing their knowledge, skills, and competency in their respective fields. However, little research has focused on understanding the students' perceptions and experiences regarding this examination. The study employs a quantitative research design with a sample size of 300 students. The participants are selected from various educational institutions from Lahore who have recently taken the NLE examination. A questionnaire consisting of Likert-scale and open-ended questions is used to collect data on students' perceptions, attitudes, and experiences related to the NLE examination. Preliminary findings from the study indicate that a majority of students perceive the NLE examination as a highly challenging and stressful experience. Many students reported feeling anxious and pressured due to the significance of the examination for their academic and professional careers. However, despite the stress, a significant number of students also acknowledged the importance of the NLE examination in ensuring the quality and standardization of professionals in their field. Furthermore, the study explores the students' perceptions regarding the content and format of the NLE examination

Transmission expansion planning integrated with wind farms

This paper develops a novel hybrid algorithm for solving transmission expansion planning (TEP) problems in electric power networks. Raising the awareness about immense contaminants produced by fossil fuels as well as depleting these resources have pushed energy companies toward considering more renewable energy resources (RERs). The RESs are beneficial for the society and the power system utility, however, taking into account the uncertainties, which are inherent in RERs, increase the complexity of the optimization problems. In this work, a Monte-Carlo simulation (MCS) is used to address the intermittent nature of wind energy. To handle the resulted model, by modifying and combining three well-known evolutionary algorithms such as shuffled frog leaping algorithm (SFLA), particle swarm optimization (PSO), and teaching learning-based optimization (TLBO), a potent hybrid MSFLA-MPSO-MTLBO, namely combinatorial heuristic-based profound-search algorithm (CHPSA), is proposed. A self-adaptive probabilistic mutation operator (SAPMO) is employed to enhance the effectiveness and computational efficiency of the CHPSA. Ten commonly-used benchmark problems are introduced to corroborate the performance of the CHPSA, while the IEEE RTS 24-bus test system is used to validate the model. Results show that the proposed CHPSA is capable of obtaining better solutions than other algorithms, either implemented in this paper or borrowed from the literature.Peer reviewe