Surgical performance of dental students using computer-assisted dynamic navigation and freehand approaches.

INTRODUCTION: Nowadays, the training of implant placement has shifted from once entirely instructor-student teaching to the increasing use of computer-assisted simulation. Based on computerized virtual planning, dynamic navigation has been used for implant placement with higher accuracy than the traditional freehand protocol. However, whether dynamic navigation benefits to the training of dental students in implant placement remains controversial. This study aimed to compare the surgical performance of dental students in implant placement using computer-assisted dynamic navigation and freehand approaches. MATERIALS AND METHODS: A total of 20 dental students (6 males, 14 females, age: 25.6 ± 0.5 years) were enrolled in this study. With the traditional freehand approach (training 1) as the control protocol, computer-assisted dynamic navigation (training 2) was used in the training of dental students in implant placement. For each training, both the operating time (OT) of students and placement accuracy represented by the linear (at the implant platform, Dpl, and apex, Dap) and angular (Dan) deviations between the virtually planned and placed implants were recorded. Statistical comparisons were made between the two training protocols as well as male and female surgeons. RESULTS: OT2 was around twice of OT1 (p < .0001), whereas Dan1 was almost three times of Dan2 (p < .0001). Dap1 and Dpl1 were significantly higher than Dap2 (p = .014) and Dpl2 (p = .033) respectively. Besides, male students showed statistically higher Dpl1 (p = .033) and Dan1 (p = .002) than females. No significant difference was found between male and female students in OT1, OT2, Dpl2, Dap1, Dap2 and Dan2. CONCLUSIONS: Within the limitations of this study, the use of computer-assisted dynamic navigation in the preclinical training could improve the surgical performance of the dental students in implant placement. The combination of dynamic navigation with the traditional preclinical surgical training may benefit to dental students and could be applied in dental education.

Balance between the CMC/ACP Nanocomplex and Blood Assimilation Orchestrates Immunomodulation of the Biomineralized Collagen Matrix.

Calcium phosphate-based biomineralized biomaterials have broad application prospects. However, the immune response and foreign body reactions elicited by biomineralized materials have drawn substantial attention recently, contrary to the immune microenvironment optimization concept. Therefore, it is important to clarify the immunomodulation properties of biomineralized materials. Herein, we prepared the biomineralized collagen matrix (BCM) and screened the key immunomodulation factor carboxymethyl chitosan/amorphous calcium phosphate (CMC/ACP) nanocomplex. The immunomodulation effect of the BCM was investigated in vitro and in vivo. The BCM triggered evident inflammatory responses and cascade foreign body reactions by releasing the CMC/ACP nanocomplex, which activated the potential TLR4-MAPK/NF-κB pathway, compromising the collagen matrix biocompatibility. By contrast, blocking the CMC/ACP nanocomplex release via the blood assimilation process of the BCM mitigated the inflammation and foreign body reactions, enhancing biocompatibility. Hence, the immunomodulation of the BCM was orchestrated by the balance between the CMC/ACP nanocomplex and the blood assimilation process. Controlling the release of the CMC/ACP nanocomplex to accord the biological effects of ACP with the temporal regenerative demands is key to developing advanced biomineralized materials.

Prediction of death rates for cardiovascular diseases and cancers.

Background: To estimate cardiovascular and cancer death rates by regions and time periods. Design: Novel statistical methods were used to analyze clinical surveillance data. Methods: A multicenter, population-based medical survey was performed. Annual recorded deaths from cardiovascular diseases were analyzed for all 195 countries of the world. It is challenging to model such data; few mathematical models can be applied because cardiovascular disease and cancer data are generally not normally distributed. Results: A novel approach to assessing the biosystem reliability is introduced and has been found to be particularly suitable for analyzing multiregion environmental and healthcare systems. While traditional methods for analyzing temporal observations of multiregion processes do not deal with dimensionality efficiently, our methodology has been shown to be able to cope with this challenge. Conclusions: Our novel methodology can be applied to public health and clinical survey data.

Optimized osteogenesis of porcine bone-derived xenograft through surface coating of magnesium-doped nanohydroxyapatite.

As one of the key factors influencing the outcome of guided bone regeneration, the currently used xenografts possess insufficient capability in osteogenesis. With the aim of improving the osteogenic performance of xenografts, porcine bone-derived hydroxyapatite (PHA) was prepared and subsequently coated by magnesium-doped nano hydroxyapatite (nMgHA, 10%, 20%, and 30% of Mg/Ca + Mg) through a straightforward and cost-efficient approach. The physiochemical and biological properties of nMgHA/PHAs were examinedin vitroandin vivo. The inherent three-dimensional (3D) porous framework with the average pore size of 300 µm was well preserved in nMgHA/PHAs. Meanwhile, excess magnesium released from the so-called 'surface pool' of PHA was verified. In contrast, slower release of magnesium at lower concentrations was detected for nMgHA/PHAs. Significantly more newly-formed bone and microvessels were observed in 20%nMgHA/PHA than the other specimens. With the limitations of the present study, it could be concluded that PHA coated by 20%nMgHA may have the optimized osteogenic performance due to the elimination of the excess magnesium from the 'surface pool', the preservation of the inherent 3D porous framework with the favorable pore size, and the release of magnesium at an appropriate concentration that possessed osteoimmunomodulatory effects on macrophages.

Gaidai-Xing reliability method validation for 10-MW floating wind turbines.

In contrast to well-known bivariate statistical approach, which is known to properly forecast extreme response levels for two-dimensional systems, the research validates innovative structural reliability method, which is particularly appropriate for multi-dimensional structural responses. The disadvantage of dealing with large system dimensionality and cross-correlation across multiple dimensions is not a benefit of traditional dependability approaches that deal with time series. Since offshore constructions are built to handle extremely high wind and wave loads, understanding these severe stresses is essential, e.g. wind turbines should be built and operated with the least amount of inconvenience. In the first scenario, the blade root flapwise bending moment is examined, whereas in the second, the tower bottom fore-aft bending moment is examined. The FAST simulation program was utilized to generate the empirical bending moments for this investigation with the load instances activated at under-rated, rated, and above-rated speeds. The novel reliability approach, in contrast to conventional reliability methods, does not call for the study of a multi-dimensional reliability function in the case of numerical simulation. As demonstrated in this work, it is now possible to assess multi-degree-of-freedom nonlinear system failure probability, in the case when only limited system measurements are available.

Oil tanker under ice loadings.

As a result of global warming, the area of the polar pack ice is diminishing, making merchant travel more practical. Even if Arctic ice thickness reduced in the summer, fractured ice is still presenting operational risks to the future navigation. The intricate process of ship-ice interaction includes stochastic ice loading on the vessel hull. In order to properly construct a vessel, the severe bow forces that arise must be accurately anticipated using statistical extrapolation techniques. This study examines the severe bow forces that an oil tanker encounters when sailing in the Arctic Ocean. Two stages are taken in the analysis. Then, using the FEM program ANSYS/LS-DYNA, the oil tanker bow force distribution is estimated. Second, in order to estimate the bow force levels connected with extended return periods, the average conditional exceedance rate approach is used to anticipate severe bow forces. The vessel's itinerary was planned to take advantage of the weaker ice. As a result, the Arctic Ocean passage took a meandering route rather than a linear one. As a result, the ship route data that was investigated was inaccurate with regard to the ice thickness data encountered by a vessel yet skewed with regard to the ice thickness distribution in the region. This research intends to demonstrate the effective application of an exact reliability approach to an oil tanker with severe bow forces on a particular route.

COVID-19 Epidemic Forecast in Brazil.

This study advocates a novel spatio-temporal method for accurate prediction of COVID-19 epidemic occurrence probability at any time in any Brazil state of interest, and raw clinical observational data have been used. This article describes a novel bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient time period, resulting in robust long-term forecast of the virus outbreak probability. COVID-19 daily numbers of recorded patients in all affected Brazil states were taken into account. This work aimed to benchmark novel state-of-the-art methods, making it possible to analyse dynamically observed patient numbers while taking into account relevant regional mapping. Advocated approach may help to monitor and predict possible future epidemic outbreaks within a large variety of multi-regional biological systems. Suggested methodology may be used in various modern public health applications, efficiently using their clinical survey data.

Operational reliability study of ice loads acting on oil tanker bow.

As a result of climate change, the Arctic glaciers start to melt, and the summer season arrives, making it acceptable for trade ships. There is still shattered ice in the saltwater even though the Arctic glaciers melt in the summer. The stochastic ice loading on the ship's hull is a complex ship-ice interaction. In order to properly build a vessel, it is necessary to reliably estimate the consequent high bow stresses using statistical extrapolation techniques. The bivariate reliability approach is used in this study to compute the excessive bow forces that an oil tanker encounters while sailing in the Arctic Ocean. Two stages are taken in the analysis. First, ANSYS/LS-DYNA is used to compute the oil tanker's bow stress distribution. Second, high bow stresses are projected utilizing a unique dependability methodology to evaluate return levels associated with extended return times. This research focuses on bow loads of an oil tanker travelling in the Artic Ocean using the recorded ice thickness distribution. To take advantage of weaker ice, the vessel's itinerary across the Arctic Ocean was windy (not the shortest straight path). This results in the ship route data used being inaccurate concerning the ice thickness statistics for the area yet skewed concerning the ice thickness data that was particular to a vessel's path. Therefore, this work aims to present a quick and precise approach for estimating the high bow stresses experienced by oil tankers along a given path. Most designs incorporate univariate characteristic values, while this study advocates a bivariate reliability approach for a safer and better design.

Characterisation of extreme load responses of a 10-MW floating semi-submersible type wind turbine.

The global average size of offshore wind turbines has increased steadily from 1.5 MW to 6 MW from 2000 to 2020. With this backdrop, the research community has recently looked at huge 10-15 MW class floating offshore wind turbines (FOWTs). The larger rotor, nacelle structure and tower have more significant structural flexibility. The larger structural flexibility, controller dynamics, aerodynamics, hydrodynamics, and various environmental conditions result in complex structural responses. The structural load effects of a very large FOWT could be more severe than that of the lower MW classes. Accurate quantification of the extreme dynamic responses of FOWT systems is essential in the design of the Ultimate Limit State (ULS) due to the fully-coupled interaction between the FOWT system and environmental conditions. Motivated by this, extreme responses of the 10 MW semi-submersible type FOWT are investigated using the average conditional exceedance rate (ACER) and Gumbel methods. Three operating conditions representing below-rated (U = 8 m/s), rated (U = 12 m/s) and above-rated (U = 16 m/s) regions were considered. The aim is to guide future research on large FOWTs by indicating the expected ULS loads.

Novel methods for reliability study of multi-dimensional non-linear dynamic systems.

This research presents two unique techniques for engineering system reliability analysis of multi-dimensional non-linear dynamic structures. First, the structural reliability technique works best for multi-dimensional structural responses that have been either numerically simulated or measured over a long enough length to produce an ergodic time series. Second, a novel extreme value prediction method that can be used in various engineering applications is proposed. In contrast to those currently used in engineering reliability methodologies, the novel method is easy to use, and even a limited amount of data can still be used to obtain robust system failure estimates. As demonstrated in this work, proposed methods also provide accurate confidence bands for system failure levels in the case of real-life measured structural response. Additionally, traditional reliability approaches that deal with time series do not have the benefit of being able to handle a system's high dimensionality and cross-correlation across several dimensions readily. Container ship that experiences significant deck panel pressures and high roll angles when travelling in bad weather was selected as the example for this study. The main concern for ship transportation is the potential loss of cargo owing to violent movements. Simulating such a situation is difficult since waves and ship motions are non-stationary and complicatedly non-linear. Extreme movements greatly enhance the role of nonlinearities, activating effects of second and higher order. Furthermore, laboratory testing may also be called into doubt due to the scale and the choice of the sea state. Therefore, data collected from actual ships during difficult weather journeys offer a unique perspective on the statistics of ship movements. This work aims to benchmark state-of-the-art methods, making it possible to extract necessary information about the extreme response from available on-board measured time histories. Both suggested methods can be used in combination, making them attractive and ready to use for engineers. Methods proposed in this paper open up possibilities to predict simply yet efficiently system failure probability for non-linear multi-dimensional dynamic structure.

Multi-regional COVID-19 epidemic forecast in Sweden.

The novel coronavirus disease 2019 (COVID-19) is a contagious disease with high transmissibility to spread worldwide, reported to present a certain burden on worldwide public health. This study aimed to determine epidemic occurrence probability at any reasonable time horizon in any region of interest by applying modern novel statistical methods directly to raw clinical data. This paper describes a novel bio-system reliability approach, particularly suitable for multi-regional health and stationary environmental systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of the highly pathogenic virus outbreak probability. For this study, COVID-19 daily recorded patient numbers in most affected Sweden regions were chosen. This work aims to benchmark state-of-the-art methods, making it possible to extract necessary information from dynamically observed patient numbers while considering relevant territorial mapping. The method proposed in this paper opens up the possibility of accurately predicting epidemic outbreak probability for multi-regional biological systems. Based on their clinical survey data, the suggested methodology can be used in various public health applications. Key findings are: A novel spatiotemporal health system reliability method has been developed and applied to COVID-19 epidemic data.Accurate multi-regional epidemic occurrence prediction is made.Epidemic threshold confidence bands given.

Optimizing the biodegradability and osteogenesis of biogenic collagen membrane via fluoride-modified polymer-induced liquid precursor process.

Biogenic collagen membranes (BCM) have been widely used in guided bone regeneration (GBR) owing to their biodegradability during tissue integration. However, their relatively high degradation rate and lack of pro-osteogenic properties limit their clinical outcomes. It is of great importance to endow BCM with tailored degradation as well as pro-osteogenic properties. In this study, a fluoride-modified polymer-induced liquid precursor (PILP) based biomineralization strategy was used to convert the collagen membrane from an organic phase to an apatite-based inorganic phase, thus achieving enhanced anti-degradation performance as well as osteogenesis. As a result, three phases of collagen membranes were prepared. The original BCM in the organic phase induced the mildest inflammatory response and was mostly degraded after 4 weeks. The organic-inorganic mixture phase of the collagen membrane evoked a prominent inflammatory response owing to the fluoride-containing amorphous calcium phosphate (F-ACP) nanoparticles, resulting in active angiogenesis and fibrous encapsulation, whereas the inorganic phase induced a mild inflammatory response and degraded the least owing to the transition of F-ACP particles into calcium phosphate with high crystallinity. Effective control of ACP is key to building novel apatite-based barrier membranes. The current results may pave the way for the development of advanced apatite-based membranes with enhanced barrier performances.

Offloading operation bivariate extreme response statistics for FPSO vessel.

The Floating Production Storage and Offloading unit (FPSO) is an offshore unit producing and storing crude oil prior to tanker transport. An important design concern is an accurate prediction of risky dynamic hawser tensions during FPSO offloading operations. Bivariate extreme hawser tension contours are important for selecting proper design values. This paper employed the AQWA hydrodynamic software to analyze vessel hydrodynamic wave loads dynamic response, acting on FPSO vessels under realistic sea state conditions. This paper presents an efficient method for estimating FPSO bivariate response statistics based on numerical simulations validated by various experiments. The bivariate Average Conditional Exceedance Rate (ACER2D) method offers an accurate bivariate extreme value probability distribution and return period contours estimation, utilizing available data efficiently. The two-dimensional probability contours, corresponding to low probability return periods, are easily obtained by the ACER2D method. The performance of the presented method has shown that the ACER2D method provides an efficient and accurate prediction of extreme return period contours. The suggested approach may be used for FPSO vessel design, minimizing potential FPSO hawser damage. Bivariate contours yield bivariate design points, as opposed to a pair of uncoupled univariate design points with the same return period as currently adopted in the industry.

Piezoelectric Energy Harvester Response Statistics.

Safety and reliability are essential engineering concerns for energy-harvesting installations. In the case of the piezoelectric galloping energy harvester, there is a risk that excessive wake galloping may lead to instability, overload, and thus damage. With this in mind, this paper studies bivariate statistics of the extreme, experimental galloping energy harvester dynamic response under realistic environmental conditions. The bivariate statistics were extracted from experimental wind tunnel results, specifically for the voltage-force data set. Authors advocate a novel general-purpose reliability approach that may be applied to a wide range of dynamic systems, including micro-machines. Both experimental and numerically simulated dynamic responses can be used as input for the suggested structural reliability analysis. The statistical analysis proposed in this study may be used at the design stage, supplying proper characteristic values and safeguarding the dynamic system from overload, thus extending the machine's lifetime. This work introduces a novel bivariate technique for reliability analysis instead of the more general univariate design approaches.

Improving extreme offshore wind speed prediction by using deconvolution.

This study proposes an innovative method for predicting extreme values in offshore engineering. This includes and is not limited to environmental loads due to offshore wind and waves and related structural reliability issues. Traditional extreme value predictions are frequently constructed using certain statistical distribution functional classes. The proposed method differs from this as it does not assume any extrapolation-specific functional class and is based on the data set's intrinsic qualities. To demonstrate the method's effectiveness, two wind speed data sets were analysed and the forecast accuracy of the suggested technique has been compared to the Naess-Gaidai extrapolation method. The original batch of data consisted of simulated wind speeds. The second data related to wind speed was recorded at an offshore Norwegian meteorological station.

Future world cancer death rate prediction.

Cancer is a worldwide illness that causes significant morbidity and death and imposes an immense cost on global public health. Modelling such a phenomenon is complex because of the non-stationarity and complexity of cancer waves. Apply modern novel statistical methods directly to raw clinical data. To estimate extreme cancer death rate likelihood at any period in any location of interest. Traditional statistical methodologies that deal with temporal observations of multi-regional processes cannot adequately deal with substantial regional dimensionality and cross-correlation of various regional variables. Setting: multicenter, population-based, medical survey data-based biostatistical approach. Due to the non-stationarity and complicated nature of cancer, it is challenging to model such a phenomenon. This paper offers a unique bio-system dependability technique suited for multi-regional environmental and health systems. When monitored over a significant period, it yields a reliable long-term projection of the chance of an exceptional cancer mortality rate. Traditional statistical approaches dealing with temporal observations of multi-regional processes cannot effectively deal with large regional dimensionality and cross-correlation between multiple regional data. The provided approach may be employed in numerous public health applications, depending on their clinical survey data.

Novel methods for coupled prediction of extreme wind speeds and wave heights.

Two novel methods are being outlined that, when combined, can be used for spatiotemporal analysis of wind speeds and wave heights, thus contributing to global climate studies. First, the authors provide a unique reliability approach that is especially suited for multi-dimensional structural and environmental dynamic system responses that have been numerically simulated or observed over a substantial time range, yielding representative ergodic time series. Next, this work introduces a novel deconvolution extrapolation technique applicable to a wide range of environmental and engineering applications. Classical reliability approaches cannot cope with dynamic systems with high dimensionality and responses with complicated cross-correlation. The combined study of wind speed and wave height is notoriously difficult, since they comprise a very complex, multi-dimensional, non-linear environmental system. Additionally, global warming is a significant element influencing ocean waves throughout the years. Furthermore, the environmental system reliability method is crucial for structures working in any particular region of interest and facing actual and often harsh weather conditions. This research demonstrates the effectiveness of our approach by applying it to the concurrent prediction of wind speeds and wave heights from NOAA buoys in the North Pacific. This study aims to evaluate the state-of-the-art approach that extracts essential information about the extreme responses from observed time histories.

Optimized osteogenesis of biological hydroxyapatite-based bone grafting materials by ion doping and osteoimmunomodulation.

BACKGROUND: Biological hydroxyapatite (BHA)-based bone grafting materials have been widely used for bone regeneration in implant surgery. Much effort has been made in the improvement of their osteogenic property as it remains unsatisfactory for clinical use. Osteoimmunomodulation plays a significant role in bone regeneration, which is highly related to active inorganic ions. Therefore, attempts have been made to obtain osteoimmunomodulatory BHA-based bone grafting materials with optimized osteogenic property by ion doping. OBJECTIVE: To summarize and discuss the active inorganic ions doped into BHA and their effects on BHA-based bone grafting materials. METHOD: A literature search was performed in databases including Google Scholar, Web of Science and PubMed, with the elementary keywords of "ion doped" and "biological hydroxyapatite", as well as several supplementary keywords. All document types were included in this search. The searching period and language were not limited and kept updated to 2022. RESULTS: A total of 32 articles were finally included, of which 32 discussed the physiochemical properties of BHA-based biomaterials, while 12 investigated their biological features in vitro, and only three examined their biological performance in vivo. Various ions were doped into BHA, including fluoride, zinc, magnesium and lithium. Such ions improved the biological performance of BHA-based biomaterials, which was attributed to their osteoimmunomodulatory effect. CONCLUSION: The doping of active inorganic ions is a reliable strategy to endow BHA-based biomaterials with osteoimmunomodulatory property and promote bone regeneration. Further studies are still in need to explore more ions and their effects in the crosstalk between the skeletal and immune systems.

Offshore tethered platform springing response statistics.

This paper demonstrates the validity of the Naess-Gadai method for extrapolating extreme value statistics of second-order Volterra series processes through application on a representative model of a deep water small size tension leg platform (TLP), with specific focus on wave sum frequency effects affecting restrained modes: heave, roll and pitch. The wave loading was estimated from a second order diffraction code WAMIT, and the stochastic TLP structural response in a random sea state was calculated exactly using Volterra series representation of the TLP corner vertical displacement, chosen as a response process. Although the wave loading was assumed to be a second order (non-linear) process, the dynamic system was modelled as a linear damped mass-spring system. Next, the mean up-crossing rate based extrapolation method (Naess-Gaidai method) was applied to calculate response levels at low probability levels. Since exact solution was available via Volterra series representation, both predictions were compared in this study, namely the exact Volterra and the approximate one. The latter gave a consistent way to estimate efficiency and accuracy of Naess-Gaidai extrapolation method. Therefore the main goal of this study was to validate Naess-Gaidai extrapolation method by available analytical-based exact solution. Moreover, this paper highlights limitations of mean up-crossing rate based extrapolation methods for the case of narrow band effects, such as clustering, typically included in the springing type of response.

Novel methods for wind speeds prediction across multiple locations.

This article provides two unique methodologies that may be coupled to study the dependability of multidimensional nonlinear dynamic systems. First, the structural reliability approach is well suited for multidimensional environmental and structural reactions and is either measured or numerically simulated over sufficient time, yielding lengthy ergodic time series. Second, a unique approach to predicting extreme values has technical and environmental implications. In the event of measurable environmental loads, it is also feasible to calculate the probability of system failure, as shown in this research. In addition, traditional probability approaches for time series cannot cope effectively with the system's high dimensionality and cross-correlation across dimensions. It is common knowledge that wind speeds represent a complex, nonlinear, multidimensional, and cross-correlated dynamic environmental system that is always difficult to analyze. Additionally, global warming is a significant element influencing ocean waves throughout time. This section aims to demonstrate the efficacy of the previously mentioned technique by applying a novel method to the Norwegian offshore data set for the greatest daily wind cast speeds in the vicinity of the Landvik wind station. This study aims to evaluate the state-of-the-art approach for extracting essential information about the extreme reaction from observed time histories. The approach provided in this research enables the simple and efficient prediction of failure probability for the whole nonlinear multidimensional dynamic system.