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This article investigates the politics of the image and montage in the literature and filmmaking as an experimentation with an ethical bet on research in Human Sciences. In the light of Walter Benjamin’s concept of montage, as well as DidiHuberman’s analysis of image as taking a position, it problematizes the insurmountable distance between the subject and the object, the fracture between form and content, ethics and aesthetics in research in the face of the intolerable of an ordinary world. The questions are presented through fragments that deal with the intertwining between body, memory, revolt, and creation, to emphasize the ethical bet of the montage not only as a balm for the body suffocated by the abject, but as the oxygen necessary for the multiplication of paths, senses, overcoming limits for the creation of resistance to the numbness of thought.

Simple assembly rules applied recursively in a multistage assembly process enable the creation of DNA origami arrays with sizes of up to 0.5 square micrometres and with arbitrary patterns. Microscopic DNA origami DNA nanostructures are made of precisely arranged DNA strands and, if used as addressable pixels, can be used to create random patterns with nanometre precision. However, these two-dimensional DNA arrays are usually too small for many applications and for integration with more conventional patterning methods. Lulu Qian and colleagues now use a small set of unique DNA strands and apply simple assembly rules recursively throughout a multistage assembly process. They use this so-called 'fractal' assembly method to create two-dimensional arrays of up to 0.5 square micrometres in size and carrying up to 8,704 pixels patterned to render images, such as the Mona Lisa. Together with a software tool for converting desired patterns into the DNA sequences and experimental protocols needed to create them, this assembly technique could help to create larger and more useful DNA materials and devices. Three related papers is this issue report further advances in DNA origami, and all four are summarized in a News & Views. Self-assembled DNA nanostructures 1 enable nanometre-precise patterning that can be used to create programmable molecular machines 2 , 3 , 4 , 5 , 6 and arrays of functional materials 7 , 8 , 9 . DNA origami 10 is particularly versatile in this context because each DNA strand in the origami nanostructure occupies a unique position and can serve as a uniquely addressable pixel. However, the scale of such structures 11 , 12 , 13 , 14 has been limited to about 0.05 square micrometres, hindering applications that demand a larger layout 15 and integration with more conventional patterning methods. Hierarchical multistage assembly of simple sets of tiles 16 , 17 can in principle overcome this limitation, but so far has not been sufficiently robust to enable successful implementation of larger structures using DNA origami tiles. Here we show that by using simple local assembly rules 18 that are modified and applied recursively throughout a hierarchical, multistage assembly process, a small and constant set of unique DNA strands can be used to create DNA origami arrays of increasing size and with arbitrary patterns. We illustrate this method, which we term ‘fractal assembly’, by producing DNA origami arrays with sizes of up to 0.5 square micrometres and with up to 8,704 pixels, allowing us to render images such as the Mona Lisa and a rooster. We find that self-assembly of the tiles into arrays is unaffected by changes in surface patterns on the tiles, and that the yield of the fractal assembly process corresponds to about 0.95 m  − 1 for arrays containing m tiles. When used in conjunction with a software tool that we developed that converts an arbitrary pattern into DNA sequences and experimental protocols, our assembly method is readily accessible and will facilitate the construction of sophisticated materials and devices with sizes similar to that of a bacterium using DNA nanostructures.

Assemblies of metal oxide nanowires in 3D stacks can enable the realization of nanodevices with tailored conductivity, porous structure and a high surface area. Current fabrication methods require complicated multistep procedures that involve the initial preparation of nanowires followed by manual assembly or transfer printing, and thus lack synthesis flexibility and controllability. Here we report a general synthetic orthogonal assembly approach to controllably construct 3D multilayer-crossed metal oxide nanowire arrays. Taking tungsten oxide semiconducting nanowires as an example, we show the spontaneous orthogonal packing of composite nanorods of poly(ethylene oxide)-block-polystyrene and silicotungstic acid; the following calcination gives rise to 3D cross-stacked nanowire arrays of Si-doped metastable ε-phase WO 3 . This nanowire stack framework was also tested as a gas detector for the selective sensing of acetone. By using other polyoxometallates, this fabrication method for woodpile-like 3D nanostructures can also be generalized to different doped metal oxide nanowires, which provides a way to manipulate their physical properties for various applications. Orthogonal self-assembly of amphiphilic diblock copolymers and polyoxometallates followed by calcination allows the formation of cross-stacked multilayer 3D arrays of tungsten oxide nanowires.

Purpose of Review Polysomnography (PSG) represents a fundamental diagnostic tool used in the evaluation of sleep disorders. It represents a simultaneous recording of sleep staging, eye movements, electromyographic tone, respiratory parameters, and electrocardiogram. It is particularly helpful in the assessment of sleep-disordered breathing and its management, propensity for excessive sleepiness, complex behaviors during sleep, including motor disturbances of sleep, sleep-related epilepsy, and parasomnias. This review is intended to summarize the indications for PSG, the limitations and challenges of this diagnostic tool, indications for home sleep apnea testing options, and new developments and trends in polysomnography. Recent Findings The polysomnogram is fundamentally important in the evaluation of sleep-disordered breathing in the setting of cardiovascular comorbidities and neurologic conditions such as neuromuscular disease, stroke, and epilepsy and in the evaluation of dream enactment behavior in the setting of REM sleep behavior disorder (RBD). Because RBD is predictive of neurodegenerative disorders, recent data highlights the importance of PSG in corroborating the diagnosis of RBD and identifying people who may be at risk. However, due to cost as well as limitations in access to care, further testing has been developed and implemented including the home sleep apnea test (HSAT). The evolution of consumer wearable devices has also been a growing trend in sleep medicine; however, few have received appropriate validation. Summary PSG has been used in both the clinical and research settings and remains the gold standard clinical diagnostic test for suspected obstructive sleep apnea (OSA) or central sleep apnea (CSA). Clinicians must be familiar with the basic indications for a PSG but also recognize when it is absolutely required. At this time, the PSG is essential in the evaluation of nocturnal hypoventilation disorders of sleep, periodic limb movements of sleep, and central nervous system hypersomnia (in the absence of CSF hypocretin) when combined with the multiple sleep latency test (MSLT) and is probably the only way to help differentiate among complex behaviors during sleep, especially in the setting of RBD. The capacity to establish an early diagnostic risk of potential dementia would be of critical importance once neuroprotective agents become available.

Epilepsy is a chronic neurological disorder caused by abnormal neuronal activity that is diagnosed visually by analyzing electroencephalography (EEG) signals. Background: Surgical operations are the only option for epilepsy treatment when patients are refractory to treatment, which highlights the role of classifying focal and generalized epilepsy syndrome. Therefore, developing a model to be used for diagnosing focal and generalized epilepsy automatically is important. Methods: A classification model based on longitudinal bipolar montage (LB), discrete wavelet transform (DWT), feature extraction techniques, and statistical analysis in feature selection for RNN combined with long short-term memory (LSTM) is proposed in this work for identifying epilepsy. Initially, normal and epileptic LB channels were decomposed into three levels, and 15 various features were extracted. The selected features were extracted from each segment of the signals and fed into LSTM for the classification approach. Results: The proposed algorithm achieved a 96.1% accuracy, a 96.8% sensitivity, and a 97.4% specificity in distinguishing normal subjects from subjects with epilepsy. This optimal model was used to analyze the channels of subjects with focal and generalized epilepsy for diagnosing purposes, relying on statistical parameters. Conclusions: The proposed approach is promising, as it can be used to detect epilepsy with satisfactory classification performance and diagnose focal and generalized epilepsy.

Sound Documents, Biographical Resonances and Montage for the Configuration of Idiorrhythmic Constellations: the mutual influence between Lola Arias and Ulises Conti – This article explores the scenic and sonic dimensions of works resulting from the collaboration between director Lola Arias and composer Ulises Conti. Their partnership reflects an exploration of discursive universes as dynamic entities in continuous flux. We aim to map the creations that emerged from this collaboration and analyze key features of their works, focusing on the mutual influences at play. Drawing on the theories of Roland Barthes, we suggest that these creations configure idiorrhythmic constellations that merge personal narratives with collective experiences.

In this paper, we present Emotion-Aware Music Driven Movie Montage, a novel paradigm for the challenging task of generating movie montages. Specifically, given a movie and a piece of music as the guidance, our method aims to generate a montage out of the movie that is emotionally consistent with the music. Unlike previous work such as video summarization, this task requires not only video content understanding, but also emotion analysis of both the input movie and music. To this end, we propose a two-stage framework, including a learning-based module for the prediction of emotion similarity and an optimization-based module for the selection and composition of candidate movie shots. The core of our method is to align and estimate emotional similarity between music clips and movie shots in a multi-modal latent space via contrastive learning. Subsequently, the montage generation is modeled as a joint optimization of emotion similarity and additional constraints such as scene-level story completeness and shot-level rhythm synchronization. We conduct both qualitative and quantitative evaluations to demonstrate that our method can generate emotionally consistent montages and outperforms alternative baselines.

One of the most challenging tasks in the internet of things-cloud-based environment is the resource allocation for the tasks. The cloud provides various resources such as virtual machines, computational cores, networks, and other resources for the execution of the various tasks of the internet of things (IoT). Moreover, some methods are used for executing IoT tasks using an optimal resource management system but these methods are not efficient. Hence, in this research, we present a resource-efficient workload task scheduling (RWTS) model for a cloud-assisted IoT environment to execute the IoT task which utilizes few numbers of resources to bring a good tradeoff, achieve high performance using fewer resources of the cloud, compute the number of resources required for the execution of the IoT task such as bandwidth and computational core. Furthermore, this model mainly focuses to reduce energy consumption and also provides a task scheduling model to schedule the IoT tasks in an IoT-cloud-based environment. The experimentation has been done using the Montage workflow and the results have been obtained in terms of execution time, power sum, average power, and energy consumption. When compared with the existing model, the RWTS model performs better when the size of the tasks is increased.

In modern clothing design, quilting as a means of emphasizing pattern decoration and enriching fabric texture form, its application has presented a diversified and diversified trend. This paper utilizes the montage technique in the process of designing patchwork clothing. The cultural elements of quilted clothing are being innovated through the use of the unique creative art technique of montage. Patchwork clothing design and montage techniques are cross-analyzed to explore the commonality of the two in creative techniques and artistic expression. In order to objectively measure the design effect, AHP is used to determine the weights of the indicators, and then TOPSIS is used for the comprehensive evaluation, which is constructed into a comprehensive evaluation model of quilted apparel design integrating the montage technique. The design evaluation results were achieved by combining the consistency test of the model and the hierarchical total ranking. According to the hierarchical analysis method, the weights of the secondary indicators are calculated, and practicality (25.46%), comfort (20.96%), and functionality (15.37%) rank in the top three, which have a greater impact on the effect of quilting garment design incorporating the montage technique. When the two techniques are applied to apparel design, the understanding and application of practicality, comfort, and functionality should be deepened.

In the last decade, robot manufacturers have started to produce collaborative industrial robots, that can work while safely sharing the workspace with a human operator. In this way, robot repeatability, combined with human dexterity, can move automated assembly to a new level of flexibility. The aim of this paper is to investigate the conditions at which such systems, called collaborative assembly systems (CAS), can be better performing than the traditional manual or automated assembly systems. Throughput and unit direct production cost are considered for the comparison. The estimation of such performance figures, which is straightforward in traditional automated assembly systems, becomes more complex in the case of collaborative systems. In fact, both task allocation between the human and the robot, and the way they collaborate/interfere with each other during assembly, affect the throughput of CAS. With the aim of taking into account such parameters, we introduce a set of system variables and a mathematical model which allow to estimate the real convenience of the implementation of CAS in the industrial scenario. In the paper, the model is applied to compare CAS to manual assembly and to noncollaborative automated assembly, both with parameters derived from the literature and in a case study. Finally, a set of implementation conditions is derived, related to the task allocation that maximises CAS performance.