Search Results (8)

Background: Dental implants are popular among both healthy and medically complex patients. Patients prone to thromboembolic events receive anticoagulant treatment, like direct oral anticoagulants (DOACs). When those patients undergo dental implantation, the risk of bleeding is higher. Objectives: This study primarily aims to describe and analyze bleeding events following dental implantation. The secondary aim is to identify risk factors for bleeding events in patients receiving DOACs, and to suggest precautions. Methods: A case–control retrospective study was carried out on patients who underwent dental implant insertion in a single dental clinic. The experimental group comprised patients on DOACs, while the control group consisted of healthy patients without anticoagulant treatment. The study was submitted and approved by the Helsinki committee of the medical center (ASF-205-23). Results: Out of 100 patients initially screened for the study, 80 were included: 41 in the experimental group and 39 in the control group. A total of 11 patients presented with bleeding: 7 in the experimental group and 4 in the control group. A higher incidence of bleeding was observed in the experimental group (17%) compared with the control group (10%). The odds of bleeding in patients receiving DOACs were 1.8 times higher than for those not receiving anticoagulant therapy. Bleeding risk was also elevated among patients who underwent bone grafting procedures, with an odds ratio (OR) of 3.47, and among patients aged over 75 years, with an OR of 3.92. All bleeding events were successfully managed using local hemostasis. Conclusions: Despite the study’s limitations, it can be concluded that surgeons should consider complex surgical intervention, but should be careful and aware of several risk factors and precautions regarding bleeding. Patients on DOACs with additional bone grafting and medically compromised patients over 75 years old should be monitored in the first postoperative days, and the postoperative guidelines should be refined to address the risks effectively. Full article

This paper presents an advanced time-series forecasting methodology that integrates multiple machine learning models to improve data prediction in enhanced long-range navigation (eLoran) systems. The analysis evaluates five forecasting approaches: multivariate linear regression, long short-term memory (LSTM) networks, random forest (RF), a fusion model combining LSTM and RF, and a dynamic weighting (DW) model. The results demonstrate that the DW model achieves the highest prediction accuracy while maintaining strong computational efficiency, making it particularly suitable for real-time applications with stringent performance requirements. Although the LSTM model effectively captures temporal dependencies, it demands considerable computational resources. The hybrid model utilises the strengths of LSTM and RF to enhance the accuracy but involves extended training times. By contrast, the DW model dynamically adjusts the relative contributions of LSTM and RF on the basis of the data characteristics, thereby enhancing the accuracy while significantly reducing the computational demands. Demonstrating exceptional performance on the ASF2 dataset, the DW model provides a well-balanced solution that combines precision with operational efficiency. This research offers valuable insights into optimising additional secondary phase factor (ASF) prediction in eLoran systems and highlights the broader applicability of real-time forecasting models. Full article

Loran is a crucial maritime navigation system and is also considered a key backup for satellite navigation systems. To enhance positioning and timing services, improving the accuracy of the Loran system is essential. This paper discusses the factors affecting Loran’s positioning and timing performance, with a focus on ASF (additional secondary factor) measurement techniques and filtering methods. This study specifically addresses challenges in maritime navigation and employs a first-order Gauss–Markov process to simulate ASF+SF values. This approach eliminates the need for precise geodetic distances or real-time GNSS corrections. The research included experimental tests conducted along the eastern coast of China, evaluating environmental conditions and the positioning station’s location data. Positioning calculations were performed under maritime navigation conditions. The experimental results demonstrate that when satellite navigation systems are unavailable, the proposed model significantly enhances navigation accuracy. The accuracy, previously at the level of several hundred meters, was improved to approximately 40 m, making Loran a more reliable alternative for maritime applications. Full article

With the increasing utilization of location information, attempts to improve the safety of absolute positioning coordinates, which have depended on global navigation satellite systems (GNSSs), such as the Global Positioning System (GPS), are underway. Among these, enhanced long range navigation (eLoran) is the most technically developed system. In Korea, related technologies have been developed since 2016, and a testbed for eLoran performance evaluation, which is currently in operation as a pilot service, was completed in 2021. We analyze the position accuracy of the eLoran pilot service to use it as an alternative when GNSS usage is challenging within Korea’s eLoran testbed. We evaluated the accuracy of the absolute position using the eLoran system by sailing up to 160 km away from the Incheon testbed transmitter according to four navigation stages (inland waterway, port approach, coastal, and ocean) classified by the International Maritime Organization (IMO). To validate the eLoran positioning performance in which an additional secondary factor (ASF) map is not provided, we propose a differential GPS (DGPS) position-based ASF estimation technique. Based on this study, Korea’s eLoran system can calculate the absolute position with an accuracy of approximately 15 m with 95% probability at the port-approach stage. Full article

The African swine fever virus (ASFV) has been circulating in and ravaging the swine industry in Cameroon for decades. Annual ASFV epidemics occur in Cameroon from April to August. With the absence of vaccines and antiviral drugs against this virus, biosecurity has been the only effective control tool available. When properly applied, biosecurity measures allow control of the spread of ASFV and the eventual eradication of this virus. Many outbreak investigations by PCR were effected in Cameroon, with ASFV prevalences ranging from 15.23% to 42.80%. Considering that pre-outbreak studies are not available for Cameroon, the present study aimed at assessing the status of the animals before an outbreak. A two-stage cluster sampling study was conducted from January to March 2020. In this study, the primary unit was the farm and the secondary unit was the individual animals. In all, 97 farms were visited and 277 samples were collected. Pre-outbreak ASFV prevalence, as assessed by PCR, was 9.75%. In parallel, data were collected using a survey of farm characteristics, awareness and attitude of the farmers toward ASF. The survey results showed that 34.1% of the farms were backyard cemented piggeries with the majority having less than 10 pigs (54.1%). The majority of farmers (90.6%) had heard of and knew about the disease caused by ASF. Though 69.4% of the farmers were uninformed on the clinical signs of ASF, 73.6% of them did treat sick pigs presenting similar clinical signs to ASF (with no risk of mortality) with antibiotics, while 79.4% did not treat but sold the pigs presenting clinical signs similar to ASF with an increased risk of mortality. Sixty-three percent of the farmers had reported a case of ASF in the past and believed that reporting was useful and had no negative consequences on other farmers or third parties. We established that poor implementation of biosecurity measures in addition to poor training are contributing factors to the enzootic nature of ASFV in Cameroon and, thus, the spread of ASFV. Hence, pig farmers in Cameroon must be properly trained in ASFV awareness and the impact thereof on pig production. Furthermore, training will also facilitate the successful implementation of biosecurity measures to contain ASFV outbreaks. Full article

This paper investigates the Additional Secondary Phase Factor (ASF) characteristics of Automatic Identification System (AIS) signals spreading over a rough sea surface. According to the change of the ASFs for AIS signals in different signal form, the influences of the different propagation conditions on the ASFs are analyzed. The expression, numerical calculation, and simulation analysis of the ASFs of AIS signal are performed in the rough sea surface. The results contribute to the high-accuracy propagation delay measurement of AIS signals spreading over the rough sea surface as, well as providing a reference for reliable communication link design in marine engineering for Very High Frequency (VHF) signals. Full article

This paper looks at the development and implementation of the additional secondary phase factor (ASF) real-time correction system for the Automatic Identification System (AIS) signal. A large number of test data were collected using the developed ASF correction system and the propagation characteristics of the AIS signal that transmits at sea and the ASF real-time correction algorithm of the AIS signal were analyzed and verified. Accounting for the different hardware of the receivers in the land-based positioning system and the variation of the actual environmental factors, the ASF correction system corrects original measurements of positioning receivers in real time and provides corrected positioning accuracy within 10 m. Full article

In order to overcome the vulnerability of the global navigation satellite system (GNSS) and provide robust position, navigation and time (PNT) information in marine navigation, the autonomous positioning system based on ranging-mode Automatic Identification System (AIS) is presented in the paper. The principle of the AIS autonomous positioning system (AAPS) is investigated, including the position algorithm, the signal measurement technique, the geometric dilution of precision, the time synchronization technique and the additional secondary factor correction technique. In order to validate the proposed AAPS, a verification system has been established in the Xinghai sea region of Dalian (China). Static and dynamic positioning experiments are performed. The original function of the AIS in the AAPS is not influenced. The experimental results show that the positioning precision of the AAPS is better than 10 m in the area with good geometric dilution of precision (GDOP) by the additional secondary factor correction technology. This is the most economical solution for a land-based positioning system to complement the GNSS for the navigation safety of vessels sailing along coasts. Full article