Hidden Structures Research Articles

Hidden Structures: Gap Junctions, the Claustrum, and Anesthesia.

Hidden Structures: Gap Junctions, the Claustrum, and Anesthesia.

Preliminary trials of trackerless augmented reality in endoscopic endonasal surgery.

We present a novel method for augmented reality in endoscopic endonasal surgery. Our method does not require the use of external tracking devices and can show hidden anatomical structures relevant to the surgical intervention. Our method registers a preoperative 3D model of the nasal cavity to an intraoperative 3D model by estimating a scaled-rigid transformation. Registration is based on a two-stage ICP approach on the reconstructed nasal cavity. The hidden structures are then transferred from the preoperative 3D model to the intraoperative one using the estimated transformation, projected and overlaid into the endoscopic images to obtain the augmented reality. We performed qualitative and quantitative validation of our method on 12 clinical cases. Qualitative results were obtained from an ENT surgeon from visual inspection of the hidden structures in the augmented images. Quantitative results were obtained by measuring a target registration error using a novel transillumination-based approach. The results show that the hidden structures of interest are augmented at the expected locations in most cases. Our method was able to augment the endoscopic images in a sufficiently precise manner when the intraoperative nasal cavity did not deform considerably with respect to its preoperative state. This is a promising step towards trackerless augmented reality in endonasal surgery.

LTCC-based Ceramic Substrates for Identification of Trustworthy Electronics

The technology of low temperature cofired ceramics (LTCC) is already established in the production of reliable and robust electronic assemblies (e.g., multi-chip modules, system-in-package) as well as in safety-critical areas. With the incorporation of additional features based on surface profiles modular protection functions and barriers will be developed as a contribution to trustworthy electronics. Both obvious and hidden structures are investigated in a modular way, which in combination ensures a one-to-one tamper-proof identification.

Hidden Tensor Structures.

Any single system whose space of states is given by a separable Hilbert space is automatically equipped with infinitely many hidden tensor-like structures. This includes all quantum mechanical systems as well as classical field theories and classical signal analysis. Accordingly, systems as simple as a single one-dimensional harmonic oscillator, an infinite potential well, or a classical finite-amplitude signal of finite duration can be decomposed into an arbitrary number of subsystems. The resulting structure is rich enough to enable quantum computation, violation of Bell's inequalities, and formulation of universal quantum gates. Less standard quantum applications involve a distinction between position and hidden position. The hidden position can be accompanied by a hidden spin, even if the particle is spinless. Hidden degrees of freedom are, in many respects, analogous to modular variables. Moreover, it is shown that these hidden structures are at the roots of some well-known theoretical constructions, such as the Brandt-Greenberg multi-boson representation of creation-annihilation operators, intensively investigated in the context of higher-order or fractional-order squeezing. In the context of classical signal analysis, the discussed structures explain why it is possible to emulate a quantum computer by classical analog circuit devices.

Hidden structures: a driving factor to achieve low thermal conductivity and high thermoelectric performance.

The long-range periodic atomic arrangement or the lack thereof in solids typically dictates the magnitude and temperature dependence of their lattice thermal conductivity (κlat). Compared to crystalline materials, glasses exhibit a much-suppressed κlat across all temperatures as the phonon mean free path reaches parity with the interatomic distances therein. While the occurrence of such glass-like thermal transport in crystalline solids captivates the scientific community with its fundamental inquiry, it also holds the potential for profoundly impacting the field of thermoelectric energy conversion. Therefore, efficient manipulation of thermal transport and comprehension of the microscopic mechanisms dictating phonon scattering in crystalline solids are paramount. As quantized lattice vibrations (i.e., phonons) drive κlat, atomistic insights into the chemical bonding characteristics are crucial to have informed knowledge about their origins. Recently, it has been observed that within the highly symmetric 'averaged' crystal structures, often there are hidden locally asymmetric atomic motifs (within a few Å), which exert far-reaching influence on phonon transport. Phenomena such as local atomic off-centering, atomic rattling or tunneling, liquid-like atomic motion, site splitting, local ordering, etc., which arise within a few Å scales, are generally found to drastically disrupt the passage of heat carrying phonons. Despite their profound implication(s) for phonon dynamics, they are often overlooked by traditional crystallographic techniques. In this review, we provide a brief overview of the fundamental aspects of heat transport and explore the status quo of innately low thermally conductive crystalline solids, wherein the phonon dynamics is majorly governed by local structural phenomena. We also discuss advanced techniques capable of characterizing the crystal structure at the sub-atomic level. Subsequently, we delve into the emergent new ideas with examples linked to local crystal structure and lattice dynamics. While discussing the implications of the local structure for thermal conductivity, we provide the state-of-the-art examples of high-performance thermoelectric materials. Finally, we offer our viewpoint on the experimental and theoretical challenges, potential new paths, and the integration of novel strategies with material synthesis to achieve low κlat and realize high thermoelectric performance in crystalline solids via local structure designing.

Topic Modelling of Web Pages with Latent Dirichlet Allocation Methods

Topic modelling with Latent Dirichlet Allocation (LDA) is a popular technique used in natural language processing to uncover hidden thematic structures within a collection of documents. When applied to web pages, LDA can help in identifying prevalent topics or themes across these pages.. This study delves into the utilization of Latent Dirichlet Allocation (LDA) methods to extract underlying topics within web pages, a fundamental pursuit in understanding the multifaceted landscape of online information. Web content analysis presents unique challenges owing to its diverse nature—comprising text, images, videos, and structured HTML elements—mandating rigorous preprocessing strategies to homogenize the data. By adapting the LDA model to accommodate these challenges, this research tackles the task of uncovering latent thematic structures prevalent across web content. Methodologically, the study explores parameter tuning and model adaptation to optimize LDA for web page analysis, navigating complexities such as varied content formats, noise, and inherent biases in web data. Addressing these intricacies involves parsing HTML, extracting meaningful textual information, and refining tokenization processes. Evaluating the fidelity and interpretability of discovered topics becomes pivotal, prompting the utilization of coherence scores, perplexity metrics, and human assessment to gauge the quality of generated topics. Additionally, this research confronts the dynamic nature of web content, proposing strategies like continuous model retraining and dynamic topic modeling to accommodate evolving trends and updates. Practical applications of the extracted topics span a spectrum of domains, encompassing content recommendation systems, user behavior analysis, sentiment analysis, targeted advertising, and the enhancement of search algorithms for improved relevance and user engagement. Supported by illustrative case studies, this study elucidates how LDA serves as a potent mechanism to distill coherent and meaningful topics from web pages, offering invaluable insights into the hidden structures within the vast expanse of online information. St This comprehensive abstract encapsulates the depth and breadth of employing LDA for the analysis of web content, encompassing challenges, methodologies, evaluations, applications, and real-world implications.

William Shakespeare’s Macbeth in Adaptation: A Cultural Materialist Reading of Bollywood Movie Maqbool

The present research article focuses on Bollywood movie Maqbool (2004), the Hindi language movie adaptation of Shakespeare’s famous play Macbeth (1606). Shakespeare’s plays have become available in the cinematic adaptations in many different languages across the globe. All these adaptations focus on different aspects of the original texts, and come up with some entirely new movies which at times have the Shakespearean elements in them. The plots, settings and the timelines are of course different, but they make Shakespeare’s dramas available to a wider audience across different cultures. Maqbool is such a movie adaptation in Hindi, directed by Indian film-director, Vishal Bhardwaj. The movie relates to the incidents and events from Shakespeare’s play Macbeth, though it has entirely new flavours of its own as a typical Bollywood movie. Like Shakespeare’s plays, these movies also have something to offer to the critics. A close analysis of such adaptations, in relation to the original textual creations, can bring forth many new critical perspectives. This article aims at analysing the movie, Maqbool from the perspective of cultural materialism where it tries to bring out that how the movie interrelates with the contemporary social and political situations of the time which it depicts. There are certain hidden structures in the movie which remain unearthed when watched without critical eyes. The paper attempts a cultural materialist reading of Maqbool to bring out such hidden aspects of the movie.

UAV-Based Remote Sensing for Detection and Visualization of Partially-Exposed Underground Structures in Complex Archaeological Sites

The utilization of remote sensing technologies for archaeology was motivated by their ability to map large areas within a short time at a reasonable cost. With recent advances in platform and sensing technologies, uncrewed aerial vehicles (UAV) equipped with imaging and Light Detection and Ranging (LiDAR) systems have emerged as a promising tool due to their low cost, ease of deployment/operation, and ability to provide high-resolution geospatial data. In some cases, archaeological sites might be covered with vegetation, which makes the identification of below-canopy structures quite challenging. The ability of LiDAR energy to travel through gaps within vegetation allows for the derivation of returns from hidden structures below the canopy. This study deals with the development and deployment of a UAV system equipped with imaging and LiDAR sensing technologies assisted by an integrated Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) for the archaeological mapping of Dana Island, Turkey. Data processing strategies are also introduced for the detection and visualization of underground structures. More specifically, a strategy has been developed for the robust identification of ground/terrain surface in a site characterized by steep slopes and dense vegetation, as well as the presence of numerous underground structures. The derived terrain surface is then used for the automated detection/localization of underground structures, which are then visualized through a web portal. The proposed strategy has shown a promising detection ability with an F1-score of approximately 92%.

Hidden Structures: How Knowledge of New Practices Moves Among Educators in One Rural School District

Background/Context: Rural districts often struggle to provide organizational structures that support knowledge-building and sharing among educators, contributing to the challenge of change implementation in rural contexts. Although prior scholarship identifies social relationships centered on trust as important for fostering educator learning, we know little about how knowledge regarding a change initiative moves through a district lacking formal organizational learning supports and the role that informal relationships may play in fostering educator knowledge-building for organizational learning in an under-resourced rural context. Purpose/Objective/Research Question/Focus of Study: This article explores the network of knowledge related to a recent change initiative in a rural district striving to implement new instructional practices. Of particular interest is how knowledge is created and shared among educators and the role that organizational structures and informal relationships play in educators’ knowledge-building and sharing processes. Research Design: I present a conceptual model that brings together knowledge-creation and structuration theories. Using a sequential mixed methods design, I examine the information and advice interactions and knowledge-building experiences of educators in one rural school district. Analytical methods include social network analysis of survey data and thematic analysis of interview data. Conclusions/Recommendations: Findings suggest that informal relationships were consequential to organizational learning in this district with few formally designated learning supports, though friendship relationships were not consequential. Instead, knowledge-building interactions occurred when individual educators had a positive prior relationship; when mundane organizational structures, such as the master schedule and the assignment of informal or extracurricular roles, facilitated informal interactions; and when educators were motivated to engage in such interactions. This article makes three important contributions to our understandings of organizational learning in this under-resourced rural district context: (1) through mundane organizational structures, principals influence informal knowledge interactions; (2) educator agency is critical for change implementation; and (3) recursive educator interactions may constitute an informal organizational learning structure. This informal structure supporting organizational learning that was invisible to school leadership—fed by recursive informal interactions and supported by mundane organizational structures—represents a fertile area for future research.

Unsupervised machine learning methods and emerging applications in healthcare.

Unsupervised machine learning methods are important analytical tools that can facilitate the analysis and interpretation of high-dimensional data. Unsupervised machine learning methods identify latent patterns and hidden structures in high-dimensional data and can help simplify complex datasets. This article provides an overview of key unsupervised machine learning techniques including K-means clustering, hierarchical clustering, principal component analysis, and factor analysis. With a deeper understanding of these analytical tools, unsupervised machine learning methods can be incorporated into health sciences research to identify novel risk factors, improve prevention strategies, and facilitate delivery of personalized therapies and targeted patient care.Level of evidence: I.

On the Genuine Relevance of the Data-Driven Signal Decomposition-Based Multiscale Permutation Entropy.

Ordinal pattern-based approaches have great potential to capture intrinsic structures of dynamical systems, and therefore, they continue to be developed in various research fields. Among these, the permutation entropy (PE), defined as the Shannon entropy of ordinal probabilities, is an attractive time series complexity measure. Several multiscale variants (MPE) have been proposed in order to bring out hidden structures at different time scales. Multiscaling is achieved by combining linear or nonlinear preprocessing with PE calculation. However, the impact of such a preprocessing on the PE values is not fully characterized. In a previous study, we have theoretically decoupled the contribution of specific signal models to the PE values from that induced by the inner correlations of linear preprocessing filters. A variety of linear filters such as the autoregressive moving average (ARMA), Butterworth, and Chebyshev were tested. The current work is an extension to nonlinear preprocessing and especially to data-driven signal decomposition-based MPE. The empirical mode decomposition, variational mode decomposition, singular spectrum analysis-based decomposition and empirical wavelet transform are considered. We identify possible pitfalls in the interpretation of PE values induced by these nonlinear preprocessing, and hence, we contribute to improving the PE interpretation. The simulated dataset of representative processes such as white Gaussian noise, fractional Gaussian processes, ARMA models and synthetic sEMG signals as well as real-life sEMG signals are tested.

Detection of hidden faults within the Fucino basin from single-station ambient noise measurements: The case study of the Trasacco fault system

The presence of normal fault systems in central Italy, outcropping or hidden below Quaternary covers in intra-mountain basins, is the expression of the Neogene–Quaternary evolution of the area, characterized by an extensional tectonic regime following the fold and thrust structuring of the Apennine orogen. Italian urban settlements of central Italy are developed on hills or mountains but also in lowland areas, which are often set up in sedimentary basins. In this framework, urban centers found close to fault lines are common, with strong implications on the seismic risk of the area. In this work, we performed a dense seismological passive survey (88 single-station ambient noise measurements) and used the horizontal-to-vertical spectral ratio (HVNSR) technique to investigate hidden faults in the Trasacco municipality located in the southern part of the Fucino Basin (central Italy), where microzonation studies pointed out hypothetical fault lines crossing the urban area with the Apennine orientation. These hidden structures were only suggested by previous studies based on commercial seismic lines and aerial photogrammetry; their presence in the basin area is confirmed by our measurements. This case study shows the potentiality of using the HVNSR technique in fault areas to have a preliminary indication of anomalous behaviors, to be investigated later with specific geophysical techniques. Our approach can support microzonation studies whenever fault zones are involved, especially in urban areas or in places designated for future developments.

Alternate Forms of Justice and Adjudicating Trauma in 13 Reasons Why

ABSTRACT This article interrogates the production of images of sexual violence, suicide and its traumatic aftermath within the television series, 13 Reasons Why. The understanding of law as a deeply cultural enterprise, constituted and influenced by aesthetic, affect and narrative, is invaluable to revealing law’s hidden structures and techniques. Drawing on cultural legal methodology and trauma literature, this article examines the legal and narrative impetuses to articulate trauma within 13 Reasons Why. After outlining the implied failure of legal institutions to deliver satisfactory justice for the viewer, this article unpacks the animation of alternative modes of justice beyond law. This justice takes its form in the pre-recorded cassette tapes made by the deceased protagonist, Hannah Baker, which transform her private trauma into a public one by naming those who she believed to be accountable for her death. In analysing the content and medium of the tapes, and the unfinished business implied by Hannah’s ghostly haunting, this article considers the excess of trauma in relation to the distinct concepts of law and justice. Through its analysis of 13 Reasons Why, this article contributes to the ongoing tensions in trauma articulation and the perceived disparity between legal and just outcomes.

The periodicity and initial evolution of micro-mobility systems: a case study of the docked bike-sharing system in New York City, USA

ObjectivesThis study developed an analytical framework that aims at understanding the evolutionary processes of a micro-mobility system (for example, bike-sharing), which offers insights into the transforming nature of a city transport system.MethodsFirstly, the framework applied a Gaussian Mixture Model to examine the long-term fluctuations of travel demands. Secondly, it investigated the growth trajectories of service points via exponential and logistic growth models. Cumulative connections with other points represented the growth of a service location. An eigendecomposition approach was used to uncover the hidden structures behind the growth curves.ResultsThis framework was applied in the docked bike-sharing program in New York City, USA. The results show that there existed periodic patterns of travel demands in the long term. The majority of stations grew rapidly after they began to operate. However, the temporal signatures of stations’ growth displayed some variations across different locations.ConclusionThis proposed workflow can be employed in other cities with similar context to better investigate how micro-mobility systems evolve.

Estimation of milk yield based on udder measures of Pelibuey sheep using artificial neural networks

Udder measures have been used to assess milk yield of sheep through classical methods of estimation. Artificial neural networks (ANN) can deal with complex non-linear relationships between input and output variables. In the current study, ANN were applied to udder measures from Pelibuey ewes to estimate their milk yield and this was compared with linear regression. A total of 357 milk yield records with its corresponding udder measures were used. A supervised learning was used to train and teach the network using a two-layer ANN with seven hidden structures. The globally convergent algorithm based on the resilient backpropagation was used to calculate ANN. Goodness of fit was evaluated using the mean square prediction error (MSPE), root MSPE (RMSPE), correlation coefficient (r), Bayesian’s Information Criterion (BIC), Akaike’s Information Criterion (AIC) and accuracy. The 15–15 ANN architecture showed that the best predictive milk yield performance achieved an accuracy of 97.9% and the highest values of r2 (0.93), and the lowest values of MSPE (0.0023), RMSPE (0.04), AIC (− 2088.81) and BIC (− 2069.56). The study revealed that ANN is a powerful tool to estimate milk yield when udder measures are used as input variables and showed better goodness of fit in comparison with classical regression methods.

Multiple metals influence distinct properties of the Arabidopsis circadian clock.

Circadian rhythms coordinate endogenous events with external signals, and are essential to biological function. When environmental contaminants affect these rhythms, the organism may experience fitness consequences such as reduced growth or increased susceptibility to pathogens. In their natural environment plants may be exposed to a wide range of industrial and agricultural soil pollutants. Here, we investigate how the addition of various metal salts to the root-interaction environment can impact rhythms, measured via the promoter:luciferase system. The consequences of these environmental changes were found to be varied and complex. Therefore, in addition to traditional Fourier-based analyses, we additionally apply novel wavelet-based spectral hypothesis testing and clustering methodologies to organize and understand the data. We are able to classify broad sets of responses to these metal salts, including those that increase, and those that decrease, the period, or which induce a lack of precision or disrupt any meaningful periodicity. Our methods are general, and may be applied to discover common responses and hidden structures within a wide range of biological time series data.

A Critical Phenomenology of Walking: Footpaths and Flightways

It is hardly difficult to imagine writing about critical phenomenology and walking. One might pause over the method of critical phenomenology as a meta-odos, a thinking of the path. Or consider the steps critical phenomenology takes and the unique pitch of its gait as it traverses the borderlands between phenomenology and critical theory. One might query how these two have the capacity to walk so well side by side, so much so that they can become as one, barely distinguishable against an open sky. Such an inquiry would no doubt track how it is that phenomenology walks toward things, through things, into things, suspending the eye of the natural attitude and proceeding ever so carefully and yet bluntly in search of what springs toward it. But such an inquiry would also track how that very process is a scripted processual, notwithstanding all the suspensions upon which it steps. Who and what writes and rewrites the script of what appears, when, and how? What inscriptions define appearances in advance and diaeretically cut them clean from one another? And what are the unscripted forces still at work? Ferreting out the work of scripts and inscriptions, such an inquiry would pause over the hidden structures that constrict what might feel like a free flight of the mind, a bit of unfettered rambling in the fields of consciousness. Thinking critical phenomenology as walking, then, means tracking the two moving in tandem. Phenomenology pulls toward the horizon of experience, while critical theory veers toward structural analyses. Together, they tread a uniquely illuminating path.

Toward a Foucauldian Literary Criticism

AbstractThe article argues for the renewed relevance of Foucault's early essays on literature, written throughout the 1960s, given a return to anthropological reflection in so much literary theory today (especially through affect theory and “new” phenomenologies—both of which rely on older categories supplied by psychoanalysis). On one hand, Foucault reminds us of all the “warped and twisted forms of reflection” that arise from anthropological thought, with its assumptions regarding the “unthought” and the hidden structures of sense and perception. This same Foucault, on the other hand, is deeply engaged with literature; his writings on a range of authors—from Homer and Cervantes, to Friedrich Hölderlin and the Marquis de Sade, to Georges Bataille and Maurice Blanchot—constitute nothing less than an oeuvre. And yet, despite proposals to move beyond Foucauldian critique and its orthodoxy in literary studies today, hardly anything has been thought or said about this body of work in which Foucault, as David Carroll points out, “has the most to say about literature and language.” This lacuna is all the more surprising, since Foucault's early essays offer a rich and fruitful understanding of the being of literature as more than a limpid reflection of the body. In his reading of Bataille and Blanchot in particular, Foucault offers a unique vision of literature that is neither suspicious nor negative but that, in connection with his well-known critique of finitude, culminates in a hopeful call for openness.

Practical Multi-Keyword and Boolean Search Over Encrypted E-mail in Cloud Server

With the outbreak of e-mail message leakage events, such as the Hillary Clinton’s Email Controversy, privacy and security of sensitive e-mail information have become users’ primary concern. Encrypted email seems to be a viable solution for providing security, but it will greatly limit their operations. Public encryption with keyword search (PEKS) scheme is a popular technology to incorporate security protection and favorable operability functions together, which can play an important role in searching over encrypted email in a cloud server. In this paper, we propose a practical PEKS scheme named as public-key multi-keyword searchable encryption with hidden structures (PMSEHS). It could enable e-mail receivers to do the multi-keyword and boolean search in the large encrypted email database as fast as possible, without revealing more information to the cloud server. We also give comparative experiments, which demonstrate that our scheme has a higher efficiency in multi-keyword search for encrypted emails.

FEA and Machine Learning Techniques for Hidden Structure Analysis.

This study focuses on investigating and predicting two hidden structures: plant root system architecture and non-visible bubbles in plexiglass. Current approaches are damaging, expensive, or time-consuming. Infrared imaging was used to study the root structure and depth of small plants and to detect the diameter and depth of bubbles in plexiglass. A finite element analysis (FEA) model was built to simulate the infrared imaging process to realize the detection and prediction given the amount of heat flux required to obtain thermal images and data. For the root system, based on a tree structure thermal profile over time derived from the FEA model, a line scan method was developed to predict root structure. The main root branches can be viewed from the detection results. Polynomial regression, support vector machine (SVM), and artificial neural network (ANN) models were designed to predict root depth. For bubble defects, three ANN models were developed to predict bubble size using temperature data generated by the FEA model. Results indicated that these models provide valid predictions. Statistical tests were applied to evaluate and compare the above predictive models. Results suggest that infrared imaging and machine learning models can be used to provide information on both hidden structures.