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Summers osteotomy is a technique used to increase bone height and to improve bone density in dental implant surgery. The two main risks of this surgery, which is done by impacting an osteotome in bone tissue, are i) to perforate the sinus membrane and ii) the occurrence of benign paroxysmal vertigo, which are both related to excessive impacts during the osteotomy. Therefore, impacts must be carefully modulated. The aim of this study is to determine whether an instrumented hammer can predict bone damage before the total osteotome protrusion. 35 osteotomies were performed in 9 lamb palate samples using a hammer instrumented with a force sensor to record the variation of the force as a function of time s(t). A signal processing was developed to determine the parameter $τ$ corresponding to the time between the first two peaks of s(t). A camera was used to determine the impact number for damage: NVideo. The surgeon determined when damage occurred, leading to NSurg. An algorithm was developed to detect bone damage based on the variation of $τ$ as a function of the impact number, leading to Ncrit. The algorithm was always able to detect bone damage before total protrusion of the osteotome

The deployment of PV inverters is rapidly expanding across Europe, where these devices must increasingly comply with stringent grid requirements.This study presents a benchmark analysis of four PV inverter manufacturers, focusing on their Fault Ride Through capabilities under varying grid strengths, voltage dips, and fault durations, parameters critical for grid operators during fault conditions.The findings highlight the influence of different inverter controls on key metrics such as total harmonic distortion of current and voltage signals, as well as system stability following grid faults.Additionally, the study evaluates transient stability using two distinct testing approaches.The first approach employs the current standard method, which is testing with an ideal voltage source. The second utilizes a Power Hardware in the Loop methodology with a benchmark CIGRE grid model.The results reveal that while testing with an ideal voltage source is cost-effective and convenient in the short term, it lacks the ability to capture the dynamic interactions and feedback loops of physical grid components.This limitation can obscure critical real world factors, potentially leading to unexpecte

Attention-deficit/hyperactivity disorder (ADHD) is characterized by executive dysfunction and difficulties in processing emotional facial expressions, yet the large-scale neural dynamics underlying these impairments remain insufficiently understood. This study applied network-based EEG source analysis to examine oscillatory cortical activity during cognitive and emotional Go/NoGo tasks in individuals with ADHD. EEG data from 272 participants (ADHD n equals 102, controls n equals 170, age range 6 to 60 years) were analyzed using exact low-resolution brain electromagnetic tomography combined with functional independent component analysis, yielding ten frequency-resolved cortical networks. Mixed-effects ANCOVAs were conducted on independent component loadings with Group, Task, and Condition as factors and age and sex as covariates. ADHD participants showed statistically significant but small increases in activation across several networks, including a gamma-dominant inferior temporal component showing a Group effect and a Group by Condition interaction with stronger NoGo-related activation in ADHD. Two additional components showed similar but weaker NoGo-selective patterns. A main eff

Today's mechanical tools for bone cutting (osteotomy) cause mechanical trauma that prolongs the healing process. Medical device manufacturers aim to minimize this trauma, with minimally invasive surgery using laser cutting as one innovation. This method ablates tissue using laser light instead of mechanical tools, reducing post-surgery healing time. A reliable feedback system is crucial during laser surgery to prevent damage to surrounding tissues. We propose a tissue classification method analyzing acoustic waves generated during laser ablation, demonstrating its applicability in an ex-vivo experiment. The ablation process with a microsecond pulsed Er:YAG laser produces acoustic waves, acquired with an air-coupled transducer. These waves were used to classify five porcine tissue types: hard bone, soft bone, muscle, fat, and skin. For automated tissue classification, we compared five Neural Network (NN) approaches: a one-dimensional Convolutional Neural Network (CNN) with time-dependent input, a Fully-connected Neural Network (FcNN) with either the frequency spectrum or principal components of the frequency spectrum as input, and a combination of a CNN and an FcNN with time-depende

Multiple studies in the past have analyzed the role and dynamics of the Twitter social network during real world events. However, little work has explored the content of other social media services, or compared content across two networks during real world events. We believe that social media platforms like Facebook also play a vital role in disseminating information on the Internet during real world events. In this work, we study and characterize the content posted on the world's biggest social network, Facebook, and present a comparative analysis of Facebook and Twitter content posted during 16 real world events. Contrary to existing notion that Facebook is used mostly as a private network, our findings reveal that more than 30% of public content that was present on Facebook during these events, was also present on Twitter. We then performed qualitative analysis on the content spread by the most active users during these events, and found that over 10% of the most active users on both networks post spam content. We used stylometric features from Facebook posts and tweet text to classify this spam content, and were able to achieve an accuracy of over 99% for Facebook, and over 98%

Natural disasters can cause substantial negative socio-economic impacts around the world, due to mortality, relocation, rates, and reconstruction decisions. Robotics has been successfully applied to identify and rescue victims during the occurrence of a natural hazard. However, little effort has been taken to deploy solutions where an autonomous robot can save the life of a citizen by itself relocating it, without the need to wait for a rescue team composed of humans. Reinforcement learning approaches can be used to deploy such a solution, however, one of the most famous algorithms to deploy it, the Q-learning, suffers from biased results generated when performing its learning routines. In this research a solution for citizen relocation based on Partially Observable Markov Decision Processes is adopted, where the capability of the Double Q-learning in relocating citizens during a natural hazard is evaluated under a proposed hazard simulation engine based on a grid world. The performance of the solution was measured as a success rate of a citizen relocation procedure, where the results show that the technique portrays a performance above 100% for easy scenarios and near 50% for hard

We study the cosmological evolution of some nonlocal gravity models, when the initial conditions are set during a phase of primordial inflation. We examine in particular three models, the so-called RT, RR and $Δ_4$ models, previously introduced by our group. We find that the RR and $Δ_4$ models have a stable evolution also during inflation. The RT model has an apparent instability, but we show that, because of the smallness of the scale associated to the nonlocal term compared to the inflationary scale, this instability is innocuous and also the RT model has a viable evolution even when its initial conditions are set during a phase of primordial inflation.

Inflation typically predicts a quasi scale-invariant spectrum of gravitational waves. In models of slow-roll inflation, the amplitude of such a background is too small to allow direct detection without a dedicated space-based experiment such as the proposed BBO or DECIGO. In this paper we note that particle production during inflation can generate a feature in the spectrum of primordial gravitational waves. We discuss the possibility that such a feature might be detected by ground-based laser interferometers such as Advanced LIGO and Advanced Virgo, which will become operational in the next few years. We also discuss the prospects of detection by a space interferometer like LISA. We first study gravitational waves induced by nonperturbative, explosive particle production during inflation: while explosive production of scalar quanta does not generate a significant bump in the primordial tensor spectrum, production of vectors can. We also show that chiral gravitational waves produced by electromagnetic fields amplified by an axion-like inflaton could be detectable by Advanced LIGO.

Global and regional annual mean temperature data have been analysed by many groups to determine the linear trends of temperature over climatological time scales. The near consistent results generally show an increase of about 0.07 °C per decade during the 20th Century. But many basic questions including spatial and temporal data gaps, non-uniform distribution of observing sites, superposition of internal/natural variations at different scales with parametric feedbacks etc., still remain unresolved. This paper mainly deals with a detailed study of the climatological variations of surface-air temperatures over the Indian region using a well-tested, verified, researched and gridded (1°x1°) daily mean temperature data set for the period 1970-2009. The annual mean temperatures estimated with different spatial integration show linear trends with an increase of about 0.4 °C during this period. A detailed error analysis of the voluminous data shows that the statistical errors at 95% confidence interval are lower than that of the increase in temperatures determined from its linear trend. The annual mean temperature time series also shows consistent and nearly phase coherent periodic variati

A significant difference in Titan's ionospheric electron density is observed between the T118 and T119 Cassini nightside flybys. These flybys had similar geometry, occurred at the same Saturn local time and while Titan was exposed to similar EUV and ambient magnetic field conditions. Despite these similarities, the RPWS/LP measured density differed a factor of 5 between the passes. This difference was present, and similar, both inbound and outbound. Two distinct electron peaks were present during T118, at 1150 km and 1200 km, suggesting very localised plasma production. During T118, from 1200-1350 km and below 1100 km, the lowest electron density ever observed in Titan's ionosphere are reported. We suggest that an exceptionally low rate of particle impact ionisation in combination with increased dynamics in the ionosphere could be the cause. This is, however, not verified by measurements and the measured ambient high energy particle pressure is in fact higher during T118 than during T119.

The novel coronavirus (COVID-19) pandemic has posed unprecedented challenges for the utilities and grid operators around the world. In this work, we focus on the problem of load forecasting. With strict social distancing restrictions, power consumption profiles around the world have shifted both in magnitude and daily patterns. These changes have caused significant difficulties in short-term load forecasting. Typically algorithms use weather, timing information and previous consumption levels as input variables, yet they cannot capture large and sudden changes in socioeconomic behavior during the pandemic. In this paper, we introduce mobility as a measure of economic activities to complement existing building blocks of forecasting algorithms. Mobility data acts as good proxies for the population-level behaviors during the implementation and subsequent easing of social distancing measures. The major challenge with such dataset is that only limited mobility records are associated with the recent pandemic. To overcome this small data problem, we design a transfer learning scheme that enables knowledge transfer between several different geographical regions. This architecture leverages

A cassette of uncured composite materials with an epoxy resin matrix was exposed in the stratosphere (40 km altitude) over 3 days. Temperature variations of -76...+32.50C and pressure up to 2.1 Torr were recorded during flight. An analysis of the chemical structure of the composites showed, that the polymer matrix exposed in the stratosphere becomes crosslinked, while the ground control materials react by way of polycondensation reaction of epoxy groups. The space irradiations are considered to be responsible for crosslinking of the uncured polymers exposed in the stratosphere. The composites were cured on Earth after landing. Analysis of the cured composites showed, that the polymer matrix remains active under stratospheric conditions. The results can be used for predicting curing processes of polymer composite in a free space environment during an orbital space flight.

Aims. To determine the solar transition region and coronal radius at EUV wavelengths and its time evolution during Solar Cycle XXIII. Methods. We use daily 30.4 and 17.1 nm images obtained by the Extreme Ultraviolet Imager (EIT) aboard the SoHO satellite and derive the solar radius by fitting a circle to the limb brightness ring. Results. The weighted mean of the temporal series gives (967''.56 +/- 0''.04) and (969''.54 +/- 0''.02) at 30.4 and 17.1 nm respectively. No significant correlation was found with the solar cycle at any of the two wavelengths. Conclusions. Since the temperature formation of the 30.4 nm line is between (60 - 80) 10^3 K (Transition Region), the obtained result is bigger than that derived from present atmospheric models. On the contrary this height is compatible with radio models.

An increasing number of cybersecurity incidents prompts organizations to explore alternative security solutions, such as threat intelligence programs. For such programs to succeed, data needs to be collected, validated, and recorded in relevant datastores. One potential source supplying these datastores is an organization's security incident response team. However, researchers have argued that these teams focus more on eradication and recovery and less on providing feedback to enhance organizational security. This prompts the idea that data collected during security incident investigations may be of insufficient quality for threat intelligence analysis. While previous discussions focus on data quality issues from threat intelligence sharing perspectives, minimal research examines the data generated during incident response investigations. This paper presents the results of a case study identifying data quality challenges in a Fortune 500 organization's incident response team. Furthermore, the paper provides the foundation for future research regarding data quality concerns in security incident response.

NASA’s Psyche spacecraft skimmed past Mars in a precision flyby that helped catapult it deeper into space toward its ultimate target: the bizarre metal-rich asteroid Psyche。 During the encounter, it snapped detailed images of heavily cratered Martian terrain, including the striking double-ring Huygens crater。 The flyby gave the spacecraft a critica

In order to figure out and to forecast the emergence phenomena of social systems, we propose several probabilistic models for the analysis of financial markets, especially around a crisis. We first attempt to visualize the collective behaviour of markets during a financial crisis through cross-correlations between typical Japanese daily stocks by making use of multi- dimensional scaling. We find that all the two-dimensional points (stocks) shrink into a single small region when a economic crisis takes place. By using the properties of cross-correlations in financial markets especially during a crisis, we next propose a theoretical framework to predict several time-series simultaneously. Our model system is basically described by a variant of the multi-layered Ising model with random fields as non-stationary time series. Hyper-parameters appearing in the probabilistic model are estimated by means of minimizing the 'cumulative error' in the past market history. The justification and validity of our approaches are numerically examined for several empirical data sets.

We present high speed video of Cassie-Baxter to Wenzel drop transition during gentle deposition of droplets where the modest amount of energy is channeled via rapid deceleration into a high water hammer pressure.

Quantum operations represented by completely positive maps encompass many of the physical processes and have been very powerful in describing quantum computation and information processing tasks. We introduce the notion of relative phase change for a quantum system undergoing quantum operation. We find that the relative phase shift of a system not only depends on the state of the system, but also depends on the initial state of the ancilla with which it might have interacted in the past. The relative phase change during a sequence of quantum operations is shown to be non-additive in nature. This property can attribute a `memory' to a quantum channel. Also the notion of relative phase shift helps us to define what we call `in-phase quantum channels'. We will present the relative phase shift for a qubit undergoing depolarizing channel and complete randomization and discuss their implications.