Objective. The objectives of this study were to evaluate the linear and volumetric measurements of the maxillary sinus in relation to sex and side on cone beam computer tomographic (CBCT) images in a Sri Lankan population. Methods. A total of 20 sets of CBCT images selected from the database at the Division of Oral Medicine and Radiology, Faculty of Dental Sciences, University of Peradeniya, Sri Lanka, were evaluated. Linear measurements were obtained in a craniocaudal (height), anteroposterior (length), and mediolateral (width) dimensions. Volume was computed by using the same data using a computerized 3D modeling software developed for 3D measurements and calculations. Results. The maximum mean craniocaudal dimension was at the level of the 1st and the 2nd molar tooth bilaterally. The largest average craniocaudal, mediolateral, and anteroposterior extensions of the maxillary sinus using CBCT were 31.71 ± 5.44   mm , 21.28 ± 5.09   mm , and 32.92 ± 4.31   mm , respectively. The differences between the sides and sex showed no statistical significance ( P > 0.05 ), except for the maximum average value in craniocaudal dimension which showed a statistically significant difference in relation to gender ( P = 0.02 ). Conclusion. There is no significant difference in the largest average craniocaudal, mediolateral, and anteroposterior extensions of the maxillary sinus when gender and side were compared. However, the maximum average value in craniocaudal dimension had a statistically significant difference in relation to gender. This study provides valuable knowledge of the anatomical dimensions of the maxillary sinus which may help clinicians in treatment planning.

Rapid bone substitutes manufacturing is highly important due to a vast number casualties are stepped to the society as a result of mainly traffic accidents, natural disasters and civil wars. Though casualties are grouped into several categories, a considerable number of patients is fallen into the bone associated injuries. It is also notable that especially the traffic related accidents and natural disasters may occur in populated regions. Due to financial reasons all the hospitals in the developing countries cannot maintain sophisticated scanning equipments along with their software solutions. Therefore having a lightweight software solution that facilitates bone profiling will be beneficial for patients and it also helps surgeons to prepare a care plan depending on the disorder. However, the artificial tools that are inserted to the human body can vary upon the injury. Hence, they should be highly customizable. Though computerized 3D modeling started around two decades ago, a few tools are available to assist surgeons in such situations. The available applications and techniques have limited functionalities thus, the manufactured bone grafts may not perfectly be suited to the lesion or injury. In this paper we propose a minimally invasive procedures to model bone grafts. In which, quality control methods for noise removals and 3D data compression mechanisms are coupled to the software solution that runs even on typical personal computer systems. The end result of the 3D modeled bone can be employed to extract the cavity, clip regions of interest and even to test the manufactured bone graft before the surgical procedure. Thereby, the process of manufacturing the prosthetic and the clinical procedures will be efficient and reliable.

Internet of things (IoT) applications in smart agricultural systems vary from monitoring climate conditions, automating irrigation systems, greenhouse automation, crop monitoring and management, and crop prediction, up to end-to-end autonomous farm management systems. One of the main challenges to the advancement of IoT systems for the agricultural domain is the lack of training data under operational environmental conditions. Most of the current designs are based on simulations and artificially generated data. Therefore, the essential first step is studying and understanding the finely tuned and highly sensitive mechanism plants have developed to sense, respond, and adapt to changes in their environment, and their behavior under field and controlled systems. Therefore, this study was designed to achieve two specific objectives; to develop low-cost IoT components from basic building blocks, and to study the performance of the developed systems, and generate real-time experimental data, with and without plants. Low-cost IoT devices developed locally were used to convert existing basic polytunnels to semi-controlled and monitoring-only polytunnels. Their performances were analyzed and compared with each other based on several matrices while maintaining the planted tomato variety and agronomic practices similar. The developed system performed as expected suggesting the possibility of commercial applications and research purposes.