Shape and symmetry determine two-dimensional melting transitions of hard regular polygons

The melting transition of two-dimensional (2D) systems is a fundamental problem in condensed matter and statistical physics that has advanced significantly through the application of computational resources and algorithms. 2D systems present the opportunity for novel phases and phase transition scenarios not observed in 3D systems, but these phases depend sensitively on the system and thus predicting how any given 2D system will behave remains a challenge. Here we report a comprehensive simulation study of the phase behavior near the melting transition of all hard regular polygons with $3\leq n\leq 14$ vertices using massively parallel Monte Carlo simulations of up to one million particles. By investigating this family of shapes, we show that the melting transition depends upon both particle shape and symmetry considerations, which together can predict which of three different melting scenarios will occur for a given $n$. We show that systems of polygons with as few as seven edges behave like hard disks; they melt continuously from a solid to a hexatic fluid and then undergo a first-order transition from the hexatic phase to the fluid phase. We show that this behavior, which holds for all $7\leq n\leq 14$, arises from weak entropic forces among the particles. Strong directional entropic forces align polygons with fewer than seven edges and impose local order in the fluid. These forces can enhance or suppress the discontinuous character of the transition depending on whether the local order in the fluid is compatible with the local order in the solid. As a result, systems of triangles, squares, and hexagons exhibit a KTHNY-type continuous transition between fluid and hexatic, tetratic, and hexatic phases, respectively, and a continuous transition from the appropriate "x"-atic to the solid. [abstract truncated due to arxiv length limitations]

Low Arousal Threshold Estimation Predicts Failure of Mandibular Advancement Devices in Obstructive Sleep Apnea Syndrome

Introduction: The treatment of choice for obstructive sleep apnea syndrome (OSAS) is continuous positive airway pressure (CPAP). However, CPAP is usually poorly tolerated and mandibular advancement devices (MADs) are an alternative innovative therapeutic approach. Uncertainty still remains as to the most suitable candidates for MAD. Herein, it is hypothesized that the presence of low arousal threshold (low ArTH) could be predictive of MAD treatment failure. Methods: A total of 32 consecutive patients, with OSAS of any severity, who preferred an alternate therapy to CPAP, were treated with a tailored MAD aimed at obtaining 50% of their maximal mandibular advancement. Treatment response after 6 months of therapy was defined as AHI 58.3%. Results: There were 25 (78.1%) responders (p-value < 0.01) at 6 months. Thirteen patients (40.6%) in the non-severe group reached AHI lower than 5 events per hour. MAD treatment significantly reduced the median AHI in all patients from a median value of 22.5 to 6.5 (74.7% of reduction, p-value < 0.001). The mandibular advancement device reduced AHI, whatever the disease severity. A significant higher reduction of Delta AHI, after 6 months of treatment, was found for patients without low ArTH. Conclusions: Low ArTH at baseline was associated with a poorer response to MAD treatment and a lower AHI reduction at 6 months. A non-invasive assessment of Low ArTH can be performed through the Edwards' score, which could help to identify an endotype with a lower predicted response to oral appliances in a clinical setting

Bulk Metallic Glasses Deform via Slip Avalanches

Inelastic deformation of metallic glasses occurs via slip events with avalanche dynamics similar to those of earthquakes. For the first time in these materials, measurements have been obtained with sufficiently high temporal resolution to extract both the exponents and the scaling functions that describe the nature, statistics and dynamics of the slips according to a simple mean-field model. These slips originate from localized deformation in shear bands. The mean-field model describes the slip process as an avalanche of rearrangements of atoms in shear transformation zones (STZs). Small slips show the predicted power-law scaling and correspond to limited propagation of a shear front, while large slips are associated with uniform shear on unconstrained shear bands. The agreement between the model and data across multiple independent measures of slip statistics and dynamics provides compelling evidence for slip avalanches of STZs as the elementary mechanism of inhomogeneous deformation in metallic glasses.Comment: Article: 11 pages, 4 figures, plus Supplementary Material: 16 pages, 8 figure

Monitoring of total positive end-expiratory pressure during mechanical ventilation by artificial neural networks

Ventilation treatment of acute lung injury (ALI) requires the application of positive airway pressure at the end of expiration (PEEPapp) to avoid lung collapse. However, the total pressure exerted on the alveolar walls (PEEPtot) is the sum of PEEPapp and intrinsic PEEP (PEEPi), a hidden component. To measure PEEPtot, ventilation must be discontinued with an end-expiratory hold maneuver (EEHM). We hypothesized that artificial neural networks (ANN) could estimate the PEEPtot from flow and pressure tracings during ongoing mechanical ventilation. Ten pigs were mechanically ventilated, and the time constant of their respiratory system (τRS) was measured. We shortened their expiratory time (TE) according to multiples of τRS, obtaining different respiratory patterns (Rpat). Pressure (PAW) and flow (V′AW) at the airway opening during ongoing mechanical ventilation were simultaneously recorded, with and without the addition of external resistance. The last breath of each Rpat included an EEHM, which was used to compute the reference PEEPtot. The entire protocol was repeated after the induction of ALI with i.v. injection of oleic acid, and 382 tracings were obtained. The ANN had to extract the PEEPtot, from the tracings without an EEHM. ANN agreement with reference PEEPtot was assessed with the Bland–Altman method. Bland Altman analysis of estimation error by ANN showed −0.40 ± 2.84 (expressed as bias ± precision) and ±5.58 as limits of agreement (data expressed as cmH2O). The ANNs estimated the PEEPtot well at different levels of PEEPapp under dynamic conditions, opening up new possibilities in monitoring PEEPi in critically ill patients who require ventilator treatment