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In this study, we propose the use of an ionic liquid crystal (ILC) as a new resistive switching layer in nonvolatile resistive random-access memory (ReRAM) devices. The high-quality vacuum-deposited ILC films of 1-hexadecyl-3-methylimidazolium hexafluorophosphate ([C16mim][PF6]) enabled to demonstrate the first operation of ReRAM devices with a low set voltage of ∼1 V and stable switching behavior for up to ∼44 cycles. The key to the successful operation is that the ILC layer is in the liquid crystal phase (smectic A), where the electric double layers formed at the electrode-ILC interfaces play a significant role. The results of basic electrical properties and I-V curve fittings suggested the following operation principle: the formation and rupture of charge-composed filaments within the ILC film, where the current conduction is primarily governed by the trap charge limited current (TCLC) mechanism. These achievements will pave the way for advanced studies of ILC-based electronic devices.

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This study conducted a systematic review to determine the feasibility of automatic Cyclic Alternating Pattern (CAP) analysis. Specifically, this review followed the 2020 Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines to address the formulated research question: is automatic CAP analysis viable for clinical application? From the identified 1,280 articles, the review included 35 studies that proposed various methods for examining CAP, including the classification of A phase, their subtypes, or the CAP cycles. Three main trends were observed over time regarding A phase classification, starting with mathematical models or features classified with a tuned threshold, followed by using conventional machine learning models and, recently, deep learning models. Regarding the CAP cycle detection, it was observed that most studies employed a finite state machine to implement the CAP scoring rules, which depended on an initial A phase classifier, stressing the importance of developing suitable A phase detection models. The assessment of A-phase subtypes has proven challenging due to various approaches used in the state-of-the-art for their detection, ranging from multiclass models to creating a model for each subtype. The review provided a positive answer to the main research question, concluding that automatic CAP analysis can be reliably performed. The main recommended research agenda involves validating the proposed methodologies on larger datasets, including more subjects with sleep-related disorders, and providing the source code for independent confirmation.

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BACKGROUND AND OBJECTIVES: Sleep quality is associated with wellness, and its assessment can help diagnose several disorders and diseases. Sleep analysis is commonly performed based on self-rating indices, sleep duration, environmental factors, physiologically and polysomnographic-derived parameters, and the occurrence of disorders. However, the correlation that has been observed between the subjective assessment and objective measurements of sleep quality is small. Recently, a few automated systems have been suugested to measure sleep quality to address this challenge. Sleep quality can be assessed by evaluating macrostructure-based sleep analysis via the examination of sleep cycles, namely Rapid Eye Movement (REM) and Non Rapid Eye Movement (NREM) with N1, N2, and N3 stages. However, macrostructure sleep analysis does not consider transitory phenomena like K-complexes and transient fluctuations, which are indispensable in diagnosing various sleep disorders. The CAP, part of the microstructure of sleep, may offer a more precise and relevant examination of sleep and can be considered one of the candidates to measure sleep quality and identify sleep disorders such as insomnia and apnea. CAP is characterized by very subtle changes in the brain's electroencephalogram (EEG) signals that occur during the NREM stage of sleep. The variations among these patterns in healthy subjects and subjects with sleep disorders can be used to identify sleep disorders. Studying CAP is highly arduous for human experts; thus, developing automated systems for assessing CAP is gaining momentum. Developing new techniques for automated CAP detection installed in clinical setups is essential. This paper aims to analyze the algorithms and methods presented in the literature for the automatic assessment of CAP and the development of CAP-based sleep markers that may enhance sleep quality assessment, helping diagnose sleep disorders. METHODS: This literature survey examined the automated assessment of CAP and related parameters. We have reviewed 34 research articles, including fourteen ML, nine DL, and ten based on some other techniques. RESULTS: The review includes various algorithms, databases, features, classifiers, and classification performances and their comparisons, advantages, and limitations of automated systems for CAP assessment. CONCLUSION: A detailed description of state-of-the-art research findings on automated CAP assessment and associated challenges has been presented. Also, the research gaps have been identified based on our review. Further, future research directions are suggested for sleep quality assessment using CAP.

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Isolated chiral skyrmions are investigated within the phenomenological Dzyaloshinskii model near the ordering temperatures of quasi-two-dimensional chiral magnets with Cnv symmetry and three-dimensional cubic helimagnets. In the former case, isolated skyrmions (IS) perfectly blend into the homogeneously magnetized state. The interaction between these particle-like states, being repulsive in a broad low-temperature (LT) range, is found to switch into attraction at high temperatures (HT). This leads to a remarkable confinement effect: near the ordering temperature, skyrmions exist only as bound states. This is a consequence of the coupling between the magnitude and the angular part of the order parameter, which becomes pronounced at HT. The nascent conical state in bulk cubic helimagnets, on the contrary, is shown to shape skyrmion internal structure and to substantiate the attraction between them. Although the attracting skyrmion interaction in this case is explained by the reduction of the total pair energy due to the overlap of skyrmion shells, which are circular domain boundaries with the positive energy density formed with respect to the surrounding host phase, additional magnetization "ripples" at the skyrmion outskirt may lead to attraction also at larger length scales. The present work provides fundamental insights into the mechanism for complex mesophase formation near the ordering temperatures and constitutes a first step to explain the phenomenon of multifarious precursor effects in that temperature region.

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STUDY OBJECTIVES: Sleep stability can be studied by evaluating the cyclic alternating pattern (CAP) in electroencephalogram (EEG) signals. The present study presents a novel approach for assessing sleep stability, developing an index based on the CAP A-phase characteristics to display a sleep stability profile for a whole night's sleep. METHODS: Two ensemble classifiers were developed to automatically score the signals, one for "A-phase" and the other for "non-rapid eye movement" estimation. Both were based on three one-dimension convolutional neural networks. Six different inputs were produced from the EEG signal to feed the ensembles' classifiers. A proposed heuristic-oriented search algorithm individually tuned the classifiers' structures. The outputs of the two ensembles were combined to estimate the A-phase index (API). The models can also assess the A-phase subtypes, their API, and the CAP cycles and rate. RESULTS: Four dataset variations were considered, examining healthy and sleep-disordered subjects. The A-phase average estimation's accuracy, sensitivity, and specificity range was 82%-87%, 72%-80%, and 82%-88%, respectively. A similar performance was attained for the A-phase subtype's assessments, with an accuracy range of 82%-88%. Furthermore, in the examined dataset's variations, the API metric's average error varied from 0.15 to 0.25 (with a median range of 0.11-0.24). These results were attained without manually removing wake or rapid eye movement periods, leading to a methodology suitable to produce a fully automatic CAP scoring algorithm. CONCLUSIONS: Metrics based on API can be understood as a new view for CAP analysis, where the goal is to produce and examine a sleep stability profile.

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The Cyclic Alternating Pattern (CAP) is a periodic activity detected in the electroencephalogram (EEG) signals. This pattern was identified as a marker of unstable sleep with several possible clinical applications; however, there is a need to develop automatic methodologies to facilitate real-world applications based on CAP assessment. Therefore, a deep learning-based EEG channels' feature level fusion was proposed in this work and employed for the CAP A phase classification. Two optimization algorithms optimized the channel selection, fusion, and classification procedures. The developed methodologies were evaluated by fusing the information from multiple EEG channels for patients with nocturnal frontal lobe epilepsy and patients without neurological disorders. Results showed that both optimization algorithms selected a comparable structure with similar feature level fusion, consisting of three electroencephalogram channels (Fp2-F4, C4-A1, F4-C4), which is in line with the CAP protocol to ensure multiple channels' arousals for CAP detection. Moreover, the two optimized models reached an area under the receiver operating characteristic curve of 0.82, with average accuracy ranging from 77% to 79%, a result in the upper range of the specialist agreement and best state-of-the-art works, despite a challenging dataset. The proposed methodology also has the advantage of providing a fully automatic analysis without requiring any manual procedure. Ultimately, the models were revealed to be noise-resistant and resilient to multiple channel loss, being thus suitable for real-world application.

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I take non-locality to be the Michelson-Morley experiment of the early 21st century, assume its universal validity, and try to derive its consequences. Spacetime, with its locality, cannot be fundamental, but must somehow be emergent from entangled coherent quantum variables and their behaviors. There are, then, two immediate consequences: (i). if we start with non-locality, we need not explain non-locality. We must instead explain an emergence of locality and spacetime. (ii). There can be no emergence of spacetime without matter. These propositions flatly contradict General Relativity, which is foundationally local, can be formulated without matter, and in which there is no "emergence" of spacetime. If these be true, then quantum gravity cannot be a minor alteration of General Relativity but must demand its deep reformulation. This will almost inevitably lead to: matter not only curves spacetime, but "creates" spacetime. We will see independent grounds for the assertion that matter both curves and creates spacetime that may invite a new union of quantum gravity and General Relativity. This quantum creation of spacetime consists of: (i) fully non-local entangled coherent quantum variables. (ii) The onset of locality via decoherence. (iii) A metric in Hilbert space among entangled quantum variables by the sub-additive von Neumann entropy between pairs of variables. (iv) Mapping from metric distances in Hilbert space to metric distances in classical spacetime by episodic actualization events. (v) Discrete spacetime is the relations among these discrete actualization events. (vi) "Now" is the shared moment of actualization of one among the entangled variables when the amplitudes of the remaining entangled variables change instantaneously. (vii) The discrete, successive, episodic, irreversible actualization events constitute a quantum arrow of time. (viii) The arrow of time history of these events is recorded in the very structure of the spacetime constructed. (ix) Actual Time is a succession of two or more actual events. The theory inevitably yields a UV cutoff of a new type. The cutoff is a phase transition between continuous spacetime before the transition and discontinuous spacetime beyond the phase transition. This quantum creation of spacetime modifies General Relativity and may account for Dark Energy, Dark Matter, and the possible elimination of the singularities of General Relativity. Relations to Causal Set Theory, faithful Lorentzian manifolds, and past and future light cones joined at "Actual Now" are discussed. Possible observational and experimental tests based on: (i). the existence of Sub- Planckian photons, (ii). knee and ankle discontinuities in the high-energy gamma ray spectrum, and (iii). possible experiments to detect a creation of spacetime in the Casimir system are discussed. A quantum actualization enhancement of repulsive Casimir effect would be anti-gravitational and of possible practical use. The ideas and concepts discussed here are not yet a theory, but at most the start of a framework that may be useful.

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In this work, we are interested in the nucleation of bâtonnets at the Isotropic/Smectic A phase transition of 10CB liquid crystal. Very often, these bâtonnets are decorated with a large number of focal conics. We present here an example of a bâtonnet obtained by optical crossed polarized microscopy in a frequently observed particular area of the sample. This bâtonnet presents bulges and one of them consists of a tessellation of ellipses. These ellipses are two by two tangent, one to each other, and their confocal hyperbolas merge at the apex of the bâtonnet. We propose a numerical simulation with Python software to reproduce this tiling of ellipses as well as the shape of the smectic layers taking the well-known shape of Dupin cyclides within this particular bâtonnet area.

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Objective. The cyclic alternating pattern is a marker of sleep instability identified in the electroencephalogram signals whose sequence of transient variations compose the A phases. These phases are divided into three subtypes (A1, A2, and A3) according to the presented patterns. The traditional approach of manually scoring the cyclic alternating pattern events for the full night is unpractical, with a high probability of miss classification, due to the large quantity of information that is produced during a full night recording. To address this concern, automatic methodologies were proposed using a long short-term memory to perform the classification of one electroencephalogram monopolar derivation signal.Approach. The proposed model is composed of three classifiers, one for each subtype, performing binary classification in a one versus all procedure. Two methodologies were tested: feed the pre-processed electroencephalogram signal to the classifiers; create features from the pre-processed electroencephalogram signal which were fed to the classifiers (feature-based methods).Main results. It was verified that the A1 subtype classification performance was similar for both methods and the A2 subtype classification was higher for the feature-based methods. However, the A3 subtype classification was found to be the most challenging to be performed, and for this classification, the feature-based methods were superior. A characterization analysis was also performed using a recurrence quantification analysis to further examine the subtypes characteristics.Significance. The average accuracy and area under the receiver operating characteristic curve for the A1, A2, and A3 subtypes of the feature-based methods were respectively: 82% and 0.92; 80% and 0.88; 85% and 0.86.

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Sleep is one of the most important body mechanisms responsible for the proper functioning of human body. Cyclic alternating patterns (CAP) play an indispensable role in the analysis of sleep quality and related disorders like nocturnal front lobe epilepsy, insomnia, narcolepsy etc. The traditional manual segregation methods of CAP phases by the medical experts are prone to human fatigue and errors which may lead to inaccurate diagnosis of sleep stages. In this paper, we present an automated approach for the classification of CAP phases (A and B) using Wigner-Ville Distribution (WVD) and Rényi entropy (RE) features. The WVD provides a high-resolution time-frequency analysis of the signals whereas RE provides least time-frequency uncertainty with WVD. The classification is performed using medium Gaussian kernel-based support vector machine with 10-fold cross-validation technique. We have presented the results for randomly sampled balanced data sets. The proposed approach does not require any pre-processing or post-processing stages, making it simple as compared to the existing techniques. The proposed method is able to achieve an average classification accuracy of 72.35% and 87.45% for balanced and unbalanced data sets respectively. The proposed method can aid the medical experts to analyze the cerebral stability as well as the sleep quality of a person.

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BACKGROUND: This study was performed to evaluate the amount of apically extruded debris removed from a root canal filled with cold lateral condensation (CLC), and warm vertical compaction (WVC) techniques, using b or a phase gutta-percha with AH-Plus (Dentsply DeTrey, Konstanz, Germany) or Resilon (Resilon Research LLC, Madison, WI) with RealSeal SE (SybronEndo, Amersfoort, The Netherlands). MATERIALS AND METHODS: About 100 human incisor teeth were prepared with a #25.06 NiTi rotary system and divided into five groups according to the filling material used: Group 1: CLC (gutta-percha, AH-Plus); Group 2: WVC (b phase gutta-percha, AH-Plus); Group 3: WVC (a phase gutta-percha, AH-Plus); Group 4: CLC (Resilon, RealSeal SE); and Group 5: WVC (Resilon, RealSeal SE). Extruded debris during the retreatment procedure was collected in preweighed Eppendorf tubes. The times required for retreatment were recorded. RESULTS: The amount of debris extrusion was significantly greater with WVC than CLC in the gutta-percha and Resilon groups (P < 0.001). Using a phase gutta-percha resulted in significantly more debris extrusion than b phase gutta-percha (P < 0.001). In the WVC groups, Resilon caused significantly more debris extrusion than gutta-percha (P < 0.05). Retreatment was faster for CLC than WVC (P < 0.05). CONCLUSIONS: In the retreatment procedure, the amount of apically extruded debris and retreatment duration were dependent on the type of obturation material and technique used.

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Magnetic separation has gained new popularity as a versatile partitioning method with the recent growth in nanotechnology and related biotechnology applications. In this study, iron oxide magnetic nanoparticles were synthesized via solvothermal methods and directly coated with gold to form core-shell gold-coated magnetic nanoparticles (Fe3O4-AuNPs). High-resolution transmission electron microscopy with Energy dispersive X-ray spectroscopy results suggests that temperature and reaction time play an important role in the formation of small, monodisperse Fe3O4-AuNPs. We also demonstrate that increased 4- dimethyl(amino)pyridine (DMAP) concentrations and vigorous stirring were required to successfully transfer Fe3O4-AuNPs into aqueous solution. The structure and morphology of the synthesized and transferred Fe3O4-AuNPs was further confirmed by UV-vis absorption spectroscopy and solubility experiments. •Direct coating of Fe3O4 with Au: Slowly heating by (10 °C/ min) until 180-190 °C without exceeding this reaction temperature and increasing the reaction time to 3 h from 1.5 h•High yield transfer of Fe3O4-AuNPs was achieved using 4- dimethyl(amino)pyridine (DMAP) as phase transfer catalyst.

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BACKGROUND: Detection and quantification of cyclic alternating patterns (CAP) components has the potential to serve as a disease bio-marker. Few methods exist to discriminate all the different CAP components, they do not present appropriate sensitivities, and often they are evaluated based on accuracy (AC) that is not an appropriate measure for imbalanced datasets. METHODS: We describe a knowledge discovery methodology in data (KDD) aiming the development of automatic CAP scoring approaches. Automatic CAP scoring was faced from two perspectives: the binary distinction between A-phases and B-phases, and also for multi-class classification of the different CAP components. The most important KDD stages are: extraction of 55 features, feature ranking/transformation, and classification. Classification is performed by (i) support vector machine (SVM), (ii) k-nearest neighbors (k-NN), and (iii) discriminant analysis. We report the weighted accuracy (WAC) that accounts for class imbalance. RESULTS: The study includes 30 subjects from the CAP Sleep Database of Physionet. The best alternative for the discrimination of the different A-phase subtypes involved feature ranking by the minimum redundancy maximum relevance algorithm (mRMR) and classification by SVM, with a WAC of 51%. Concerning the binary discrimination between A-phases and B-phases, k-NN with mRMR ranking achieved the best WAC of 80%. CONCLUSIONS: We describe a KDD that, to the best of our knowledge, was for the first time applied to CAP scoring. In particular, the fully discrimination of the three different A-phases subtypes is a new perspective, since past works tried multi-class approaches but based on grouping of different sub-types. We also considered the weighted accuracy, in addition to simple accuracy, resulting in a more trustworthy performance assessment. Globally, better subtype sensitivities than other published approaches were achieved.

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The free boundary of smectic A (SmA), nematic and isotropic liquid phases were studied using a polarized optical microscope, an interferometric surface structure analyzer (ISSA), an atomic force microscope (AFM) and a scanning near-field optical microscope (SNOM). Images of the SmA phase free surface obtained by the polarized microscope and ISSA are in good correlation and show a well-known focal domain structure. The new periodic stripe structure was observed by scanning near-field optical microscopy on the surface of the smectic A, nematic and isotropic phases. The properties of this periodic structure are similar to the charged liquid helium surface and can be explained by nonlinear electrostatic instabilities previously described.

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This work presents the first high-resolution nanoprofilometry study consisting of nanoscale resolution surface profile measurements and high-quality visualization of a the free surface of a liquid crystal-air boundary. The capabilities of this new experimental method, as applied for liquid crystal free boundaries, are discussed. The formation of focal conic domain structures at the smectic-A-air free boundary was detected and studied.

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The purpose of this study was to evaluate the bioactivity and cell response of a well-characterized Nurse's A-phase (7CaO·P2O5·2SiO2) ceramic and its effect compared to a control (tissue culture polystyrene-TCPS) on the adhesion, viability, proliferation, and osteogenic differentiation of ahMSCs in vitro. Cell proliferation (Alamar Blue Assay), Alizarin Red-S (AR-s) staining, alkaline phosphatase (ALP) activity, osteocalcin (OCN), and collagen I (Col I) were evaluated. Also, field emission scanning electron microscopy (FESEM) images were acquired in order to visualise the cells and the topography of the material. The proliferation of cells growing in a direct contact with the material was slower at early stages of the study because of the new environmental conditions. However, the entire surface was colonized after 28 days of culture in growth medium (GM). Osteoblastic differentiation markers were significantly enhanced in cells growing on Nurse's A phase ceramic and cultured with osteogenic medium (OM), probably due to the role of silica to stimulate the differentiation of ahMSCs. Moreover, calcium nodules were formed under the influence of ceramic material. Therefore, it is predicted that Nurse's A-phase ceramic would present high biocompatibility and osteoinductive properties and would be a good candidate to be used as a biomaterial for bone tissue engineering.

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The possibility of using the acoustic sensor on the basis of a two-channel delay line for rapid analysis of bacterial cells in the conductive suspensions was investigated. The dependencies of change in phase and insertion loss of output signal of the sensor on conductivity of buffer solution with various concentrations of cells due to a specific interaction "bacterial cells - mini-antibodies" for electrically open and electrically shorted channels of delay line were measured. It has been found that these changes have the most values for the electrically open channel. It has been also shown that the sensor rapidly responds to the specific interaction and the time stabilization of the phase and insertion loss of output signal is less than 10min.

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While present in large numbers in nature, studies on the physical chemical aspects of glycosteroids are quite rare and focused on cholesterol, and all compounds studied thus far have shown liquid crystalline properties in a narrow temperature range. New glycosteroids composed by cholesterol or diosgenin and different glycosidic moieties have been synthesized here in order to analyze the influence of the structure on the formation of mesophases. These compounds have been studied by crossed polarized optical microscopy. These studies have revealed that these new glycosteroids form Smetic A liquid crystals in a broad temperature range.

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