Space Analysis Research Articles

The acoustic characteristics of Swedish vowels.

The Swedish vowel space is relatively densely populated with 21 categories that differ in quality and quantity. Existing descriptions of the entire space rest on recordings made in the late 1990s or earlier, while recent work in general has focused on subsets of the space. The present paper reports on static and dynamic acoustic analyses of the entire vowel space using a recently released database of h-VOWEL-d words (SwehVd). The results highlight the importance of static and dynamic spectral and temporal cues for Swedish vowel category distinction. The first two formants and vowel duration are the primary acoustic cues to vowel identity, however, the third formant contributes to increased category separability for neighboring contrasts presumed to differ in lip-rounding. In addition, even though all long-short vowel pairs differ systematically in duration, they also display considerable spectral differences, suggesting that quantity distinctions are not separate from quality distinctions in Swedish. The dynamic analysis further suggests formant movements in both long and short vowels, with [e:] and [o:] displaying clearer patterns of diphthongization.

تحولات الحيز في الخطاب الروائي الإماراتي النسوي في القرن الواحد والعشرين: رواية رائحة الزنجبيل لصالحة عبيد غابش أنموذجًا

This study aims to shed light on one of the aspects of Emirati novels, particularly the transformations that have emerged due to the changes undergone by the Emirati society in the 21st century. The study focuses on the element of space to highlight, critique, and analyze these transformations, since space is a crucial element in the narrative discourse. Through space, the reader can unveil the hidden meanings within the text, and a critical analysis of space allows the reader to comprehend the narrative content and its intricacies. Additionally, space often reflects the influence of the environment on the writer's psyche and emotions, which is what we aimed to address in this study. We were able to achieve this through the examination of one of the most prominent novels in the Emirati literary scene, namely: Rā’iḥa Al-Janzabīl [The Scent of Ginger] by the novelist Ṣalḥa ‘Ubayd Ġābish.

Automated volumetric analysis of the inner ear fluid space from hydrops magnetic resonance imaging using 3D neural networks

Due to the development of magnetic resonance (MR) imaging processing technology, image-based identification of endolymphatic hydrops (EH) has played an important role in understanding inner ear illnesses, such as Meniere’s disease or fluctuating sensorineural hearing loss. We segmented the inner ear, consisting of the cochlea, vestibule, and semicircular canals, using a 3D-based deep neural network model for accurate and automated EH volume ratio calculations. We built a dataset of MR cisternography (MRC) and HYDROPS-Mi2 stacks labeled with the segmentation of the perilymph fluid space and endolymph fluid space of the inner ear to devise a 3D segmentation deep neural network model. End-to-end learning was used to segment the perilymph fluid and the endolymph fluid spaces simultaneously using aligned pair data of the MRC and HYDROPS-Mi2 stacks. Consequently, the segmentation performance of the total fluid space and endolymph fluid space had Dice similarity coefficients of 0.9574 and 0.9186, respectively. In addition, the EH volume ratio calculated by experienced otologists and the EH volume ratio value predicted by the proposed deep learning model showed high agreement according to the interclass correlation coefficient (ICC) and Bland–Altman plot analysis.

Dynamical system analysis in modified Galileon cosmology

Abstract In this paper, we have investigated the phase space analysis in modified Galileon cosmology, where the Galileon term is considered a coupled scalar field, $F(\phi)$. We focus on the exponential type function of $F(\phi)$ and the three well-motivated potential functions $V(\phi)$. We obtain the critical points of the autonomous system, along with their stability conditions and cosmological properties. The critical points of the autonomous system describe different phases of the Universe. The scaling solution for critical points was found in our analysis to determine the matter-dark energy and radiation-dark energy-dominated eras of the Universe. In these scaling solutions, dark energy is typically introduced alongside another component, such as radiation or matter, and helps explain the transition between cosmological eras. The dark energy dominated critical points show stable behavior and indicate the late-time cosmic acceleration phase of the Universe. Further, the results are examined with the Hubble rate $H(z)$ and the Supernovae Ia cosmological data sets.

Three-dimensional computer holography with phase space tailoring

Computer holography is a prominent technique for reconstructing customized three-dimensional (3D) diffraction fields. However, the quality of optical reconstruction remains a fundamental challenge in 3D computer holography, especially for the 3D diffraction fields with physically continuous and extensive depth range. Here, we propose a 3D computer-generated hologram (CGH) optimization framework with phase space tailoring. Based on phase space analysis of the space and frequency properties in both lateral and axial directions, the intensity of the 3D diffraction field is adequately sampled across a large depth range. This sampling ensures the reconstructed intensity distribution to be comprehensively constrained with physical consistency. A physics-informed loss function is constructed based on the phase space tailoring to optimize the CGH with suppression of vortex stagnation. Numerical and optical experiments demonstrate the proposed method significantly enhances the 3D optical reconstructions with suppressed speckle noise across a continuous and extensive depth range.

An analysis of the potential for military zones to be used as green spaces in the Ankara metropolitan area (Türkiye)

Urban periphery belts play a crucial role in preserving the morphological characteristics of cities. These belts often encompass various zones, including designated Military Zones, each of which contributes to structural integrity of urban spaces. Due to the vast expanse of military areas and associated security concerns, the relocation of these zones to the city’s outskirts is a recurring topic of discussion in Türkiye. Presuming the occurrence of such a possibility, how these zones will transform into functional urban spaces in terms of urban morphology is going to be vital. This study focuses on the potential of converting military zones in the urban periphery of Ankara into green spaces, highlighting the importance of such transformations for urban ecology and the preservation of green areas. To identify the existing green spaces within the military zones of the study area, vegetation indices such as Sentinel-2’s NDVI, NDMI, GCI, RVI, NDBI, and NISI were utilized, and comprehensive green space analyses were conducted. The findings indicate that the current green space allocation per capita in the region is 5 m2. However, when military zones are incorporated as regional parks alongside existing urban green spaces, this figure increases significantly to 14.4 m² per person. Additionally, to determine the accessibility of military zones converted into regional parks in terms of walking distance and their serviceable radius, several analyses have also been conducted. The conversion of military zones into regional parks and the development of related projects in Ankara would provide substantial benefits from a public policy perspective.

The well-posedness analysis in Besov-type spaces for multi-term time-fractional wave equations

The well-posedness analysis in Besov-type spaces for multi-term time-fractional wave equations

Image encryption algorithm based on matrix projective combination-combination synchronization of an 11-dimensional time delayed hyperchaotic system

Abstract Secure image transmission is critical to protect sensitive data from unauthorized access, especially in an era of increasing digital threats. Chaotic systems with their inherent complexity and unpredictability, provide a promising solution for enhancing encryption security. To contribute to this field, we investigate a new 11-dimensional hyperchaotic system by taking advantage of its complex dynamical properties to strengthen security. The high dimensional of the system intensifies chaotic behaviors such as stability, attractors and sensitive to initial conditions, making it particularly suitable for encrypted transmission.
 Time delay is an important factor to be considered affecting the control and synchronization in nonlinear system. Additionally, time delays include the effects of past states, further increasing the unpredictability of the system. To explore these dynamics, we analyze the Lyapunov exponents, stability of equilibrium points, symmetry and dissipation. A matrix projective combination-combination synchronization scheme is proposed to synchronize four identical 11-dimensional hyperchaotic systems with time delay. Nonlinear active controllers designed based on Lyapunov stability theory are used to achieve this synchronization. This work advances an important idea for encryption and decryption algorithms, which is the secure transmission of images using affine encryption. In the affine encryption algorithm, the key is based on the solution of synchronized chaotic delayed systems and the private message of the sender and receiver. This proposed encryption and decryption algorithms have been applied on plain images. Numerical simulations and security analysis including key space, histogram, information entropy and correlation analysis are conducted to validate the theoretical results and encryption algorithm. Experimental analysis and comparisons with existing literature confirm the effectiveness and security of the proposed approach for cryptographic purposes.

In-Depth Chemical Analysis of the Brain Extracellular Space Using In Vivo Microdialysis with Liquid Chromatography-Tandem Mass Spectrometry.

Metabolomic analysis of samples acquired in vivo from the brain extracellular space by microdialysis sampling can provide insights into chemical underpinnings of a given brain state and how it changes over time. Small sample volumes and low physiological concentrations have limited the identification of compounds from this compartment, so at present, we have scant knowledge of its composition. As a result, most in vivo measurements have limited depth of analysis. Here, we describe an approach to (1) identify hundreds of compounds in brain dialysate and (2) routinely detect many of these compounds in 5 μL microdialysis samples to enable deep monitoring of brain chemistry in time-resolved studies. Dialysate samples collected over 12 h were concentrated 10-fold and then analyzed using liquid chromatography with iterative tandem mass spectrometry (LC-MS/MS). Using this approach on dialysate from the rat striatum with both reversed-phase and hydrophilic interaction liquid chromatography yielded 479 unique compound identifications. 60% of the identified compounds could be detected in 5 μL of dialysate without further concentration using a single 20 min LC-MS analysis, showing that once identified, most compounds can be detected using small sample volumes and shorter analysis times compatible with routine in vivo monitoring. To detect more neurochemicals, LC-MS analysis of dialysate derivatized with light and isotopically labeled benzoyl chloride was employed. 872 nondegenerate benzoylated features were detected with this approach, including most small-molecule neurotransmitters and several metabolites involved in dopamine metabolism. This strategy allows deeper annotation of the brain extracellular space than previously possible and provides a launching point for defining the chemistry underlying brain states.

Morphological Analysis of Public Spaces and Their Contribution to Urban Resilience in Guelma, Algeria

Modern Algerian cities are facing various uncertainties, both natural and human-made. To address these risks, urban areas need to be more adaptable and responsive. The increasing impact of climate change and the recent pandemic have emphasized the importance of outdoor spaces. As people spend a significant amount of time in these areas, public spaces have become essential for urban life. This study conducts a comparative analysis of public spaces, using a grid-based methodology to identify similarities and differences between two resilient public spaces and the square of Guelma. The analysis is preceded by a literature review that establishes key conceptual frameworks. The main objective is to develop recommendations for resilient public spaces and their contribution to a city's environmental resilience, particularly its capacity to withstand climate change. By understanding how these spaces can mitigate the effects of climate change, this research aims to guide the design and management of urban environments.

Numerical study of phase space analysis for the development of subsurface crack detection method using pulsed eddy current

Root-deck cracks, a prominent form of subsurface damage observed in orthotropic steel decks (OSDs), pose significant challenges to maintaining structural integrity. Early detection of these cracks is crucial to prevent further deterioration. This paper introduces novel and robust techniques for processing response signals obtained from the Pulsed Eddy Current Technique (PECT) using phase space analysis. The proposed methods aim to improve sensitivity and noise handling capabilities by employing an appropriate damage index derived from comprehensive phase space analysis, enabling effective detection of subsurface cracks in OSDs. A numerical study demonstrates the effectiveness of the proposed methods in detecting crack length change as small as 1 mm from the bottom of a 12 mm steel plate, with the Change in Phase Space Topology (CPST) identified as a suitable damage index. Furthermore, the newly proposed signal processing techniques allow for a more comprehensive interpretation of response signals for subsurface damage detection, encapsulating the extent of damage within a single value, even in the presence of small crack lengths near the bottom of the steel plate and noise. As a result, the proposed methods overcome the limitations of conventional approaches and offer promising prospects for enhanced subsurface crack detection in OSDs.

Extrauterine Causes of Severe and Concealed Postpartum Bleeding: Clinical Aspects and Challenges

Abstract Objective To describe the challenges in diagnosing concealed postpartum bleeding, a rare but potentially life-threatening condition, and to highlight key factors in identifying and managing this condition. Methods We reviewed clinical cases and diagnostic approaches where concealed postpartum bleeding was suspected, focusing on the role of imaging, serologic markers, and angiography. We examined instances of hemodynamic instability, the utility of pelvic space analysis via computed tomography, and the limitations of digital angiography, particularly in identifying bleeding sources in the posterior iliac internal artery division. Results Concealed postpartum bleeding often originates from venous damage or, less commonly, arterial injury. Hemodynamic instability was noted to occur periodically, despite negative findings on vaginal examination and ultrasound, leading to diagnostic delays. Digital angiography often yields negative results when the focus is limited to the anterior division of the internal iliac artery. Bleeding typically arises from damage to the vaginal muscular layer or levator ani muscle, both supplied by branches of the internal pudendal artery (posterior iliac division). In some cases, laparotomy also failed to locate the source due to the bleeding being in the subperitoneal spaces. Conclusion Concealed postpartum bleeding presents a diagnostic challenge due to intermittent hemodynamic instability and often negative imaging results. A detailed pelvic space analysis and awareness of posterior iliac internal artery involvement are crucial. Advanced expertise, along with serologic markers of hidden bleeding, is essential for timely diagnosis and management.

Exploring consumer acceptability of leafy greens in earth and space immersive environments using biometrics

Novel research on food perception is required for long-term space exploration. There is limited research on food/beverage sensory analysis in space and space-simulated conditions, with many studies presenting biases in sensory and statistical methods. This study used univariate and multivariate analysis on data from pick-and-eat leafy greens to assess self-reported and biometric consumer sensory analysis in simulated microgravity using reclining chairs and space-immersive environments. According to ANOVA (p < 0.05), there were significant differences between interaction room × position for head movements; besides, there were non-significant differences in the interaction samples × environment. On the other hand, there were significant differences in the sample×position interaction for all liking attributes. Results from multivariate analysis showed effects on self-reported, physiological, and emotional responses of samples in space-related positions and environments related to sensory perception changes. Non-invasive biometrics could offer a powerful tool for developing digital twins to assess genetically modified plants and plant-based food/beverages for long-term space exploration.

Synchrotron X-ray radiation study of retained austenite stability and the effect on fatigue behavior of bainitic railway steels

The effect of retained austenite stability on the fatigue performance was studied in bainitic rail steel subjected to different heat treatments, including normalizing at 900 °C and normalizing at 900 °C combined with tempering at 320 °C. The normalized steel had yield strength of 851 MPa, tensile strength of 1131 MPa, uniform elongation 7.0 %, and total elongation 17.5 %. Tempering the steel at 320 °C resulted in similar strength and plasticity compared to the normalized condition. However, the fatigue performance of steel was improved significantly after tempering. The ultimate fatigue stress (endurance limit) after 1 × 107 cycles was 671 MPa, which was 38 MPa greater than the normalized steel. Microstructural analysis revealed similarities between normalized and tempered matrices at crystallographic interfaces and grain boundaries. Synchrotron X-ray radiation indicated that the initial retained austenite content was ∼15.5 % in both normalized and tempered conditions. However, at tensile stress of 800 MPa, the amount of retained austenite transformed in the normalized steel was ∼4 %, which was twice that of the tempered steel. The enhancement of retained austenite stability was an important aspect in improving the fatigue performance. The analysis of interphase spacing showed that the strain in the normalized matrix synchronized with the retained austenite, and the stress was concentrated in the retained austenite, which promoted transformation of retained austenite. The matrix of tempered steel was strained first, which prevented stress concentration in the retained austenite and rendered the retained austenite stable.

Instance Space Analysis of Testing of Autonomous Vehicles in Critical Scenarios

Before being deployed on roads, Autonomous Vehicles (AVs) must undergo comprehensive testing. Safety-critical situations, however, are infrequent in usual driving conditions, so simulated scenarios are used to create them. A test scenario comprises static and dynamic features related to the AV and the test environment; the representation of these features is complex and makes testing a heavy process. A test scenario is effective if it identifies incorrect behaviors of the AV. In this article, we present a technique for identifying key features of test scenarios associated with their effectiveness using Instance Space Analysis (ISA). ISA generates a ( \(2D\) ) representation of test scenarios and their features. This visualization helps to identify combinations of features that make a test scenario effective. We present a graphical representation of each feature that helps identify how well each testing technique explores the search space. While identifying key features is a primary goal, this study specifically seeks to determine the critical features that differentiate the performance of algorithms. Finally, we present metrics to assess the robustness of testing algorithms and the scenarios generated. Collecting essential features in combination with their values associated with effectiveness can be used for selection and prioritization of effective test cases.

Characteristics of Spatiotemporal Changes in China's Carbon Budget at Different Administrative Scales

Currently, scientifically and reasonably specifying carbon emission reduction measures in the context of "double carbon" has become a common concern worldwide. China's administrative divisions have a notable impact on the formulation and implementation of relevant policies. Therefore the carbon emissions must be calculated accurately under China's administrative divisions at different scales. The spatiotemporal change characteristics of absorption and carbon emissions can provide scientific basis for the formulation of reasonable and differentiated carbon emission reduction policies in different administrative regions in China. To this end, this study used multi-source data such as remote sensing and statistics and integrated ecological models, statistics, and GIS space analysis and other methods to analyze the spatiotemporal dynamic change characteristics of carbon emissions and carbon absorption at different administrative scales （provinces, cities, and counties） in China. The results showed that： ① The total carbon absorption of vegetation in China continued to increase from 2000 to 2021 and the average value gradually increased. Differences were observed in spatiotemporal changes in carbon emissions at different administrative scales. The spatiotemporal changes at smaller scales were more evident. Carbon emissions showed obvious spatial differences of "high in the north and low in the south, high in the east and low in the west." ② The spatiotemporal distribution of CPI at the administrative scale was similar to that of carbon emissions and the overall trend was increasing annually. The pressure of carbon emissions on carbon absorption gradually weakened from the east to the central and western regions. ③ Spatiotemporal hotspot analysis showed that the overall spatial distribution of cold and hot spots in China's carbon absorption was as follows： In the spatial pattern of "hot in the east and cold in the west," the spatial distribution of cold and hot spots of carbon emissions showed agglomeration characteristics. The provincial scale was primarily oscillating hotspot whereas municipal and county scales were majorly continuous hot spots. Further results revealed that： ① Carbon absorption in different regions and periods in China showed significant variability, especially in the central and eastern regions. The possibility of offsetting carbon emissions by increasing carbon absorption remains. ② At the same scale, administrative regions （such as different provinces） and lower-level administrative regions at another scale （such as different cities in the same province） showed varying degrees of variability in carbon absorption and carbon emissions. Therefore, taking provincial administrative regions as an example for subsequent formulation considering carbon trading, emission reduction, and other policies, we should first consider the coordination of emissions between different cities in the province and then consider the coordination between provinces, which is expected to better promote the implementation of relevant policies.

A Deep Learning-Driven Sampling Technique to Explore the Phase Space of an RNA Stem-Loop.

The folding and unfolding of RNA stem-loops are critical biological processes; however, their computational studies are often hampered by the ruggedness of their folding landscape, necessitating long simulation times at the atomistic scale. Here, we adapted DeepDriveMD (DDMD), an advanced deep learning-driven sampling technique originally developed for protein folding, to address the challenges of RNA stem-loop folding. Although tempering- and order parameter-based techniques are commonly used for similar rare-event problems, the computational costs or the need for a priori knowledge about the system often present a challenge in their effective use. DDMD overcomes these challenges by adaptively learning from an ensemble of running MD simulations using generic contact maps as the raw input. DeepDriveMD enables on-the-fly learning of a low-dimensional latent representation and guides the simulation toward the undersampled regions while optimizing the resources to explore the relevant parts of the phase space. We showed that DDMD estimates the free energy landscape of the RNA stem-loop reasonably well at room temperature. Our simulation framework runs at a constant temperature without external biasing potential, hence preserving the information on transition rates, with a computational cost much lower than that of the simulations performed with external biasing potentials. We also introduced a reweighting strategy for obtaining unbiased free energy surfaces and presented a qualitative analysis of the latent space. This analysis showed that the latent space captures the relevant slow degrees of freedom for the RNA folding problem of interest. Finally, throughout the manuscript, we outlined how different parameters are selected and optimized to adapt DDMD for this system. We believe this compendium of decision-making processes will help new users adapt this technique for the rare-event sampling problems of their interest.

Assessing the impact of the unfinished part of the Cascade architectural and monumental complex as a communicative core between Kentron and Kanaker-Zeytun districts in Yerevan and a proposal for solving the urban planning problem

The paper considers the problem of communication between Kentron and Kanaker-Zeytun districts in Yerevan. Particular attention is paid to the Cascade architectural and monumental complex. It obtains an important urban planning significance in the structure of public spaces in Yerevan and is considered by citizens as one of main symbols of the capital of Armenia. The study is aimed at identifying the grounds for poor pedestrian communication between the districts of the city and determining the prospects of development, transformation, and modernization of the territory. The methodology involved a comparative analysis of public spaces in the city center, publicly available GPS tracks and the results of a sociological survey. The results of the urban planning analysis outline the problem of pedestrian accessibility in the city, and demonstrate the relationship between public spaces. The sociological survey depicts the demands of the citizens and guests of Yerevan for the further development of the Cascade architectural and monumental complex. Studying the qualitative and quantitative characteristics of public spaces revealed the problems of communication between the districts of the city on the northsouth compositional axis, which are associated with the incomplete construction of the fifth terrace of the complex. Sociological research necessitates the construction to be completed. The survey shows the opinions of citizens and guests of the city about the potential development of the Cascade environment.

Perceptual evaluation of sound synthesis of wind turbine noise

This study brings together several laboratories with the aim of assessing the health effects of "audible" noise (&gt; 20 Hz) and infrasound (&lt; 20 Hz) emitted by wind turbines. To study loudness and annoyance due to this noise, perceptual tests are planned at the LMA, where a restitution cabin has been developed, specifically designed to diffuse very low frequencies and infrasound. As recording wind turbine noise is only possible at low wind speeds for a good quality sound reproduction, it would be interesting to be able to use sound synthesis of wind turbine noise. From sounds recorded in a wind farm for different meteorological conditions, the corresponding sounds have been synthesized. A physical model synthesis was performed, based on an extended-source aeroacoustic model taking into account propagation over flat ground. Dissimilarity tests including recorded and synthesized sounds enabled a 2D perceptual space to be built. Synthesized and the corresponding recorded sounds are closed together in the perceptual space, but some differences can be perceived, mainly due to difference in amplitude of fluctuation and spectral balance. The analysis of the perceptual space opens up interesting prospects for improving the sound synthesis and its use for future perceptual tests.

Analysis of electrode locations on limb condition effect for myoelectric pattern recognition

BackgroundGesture recognition using surface electromyography (sEMG) has garnered significant attention due to its potential for intuitive and natural control in wearable human–machine interfaces. However, ensuring robustness remains essential and is currently the primary challenge for practical applications.MethodsThis study investigates the impact of limb conditions and analyzes the influence of electrode placement. Both static and dynamic limb conditions were examined using electrodes positioned on the wrist, elbow, and the midpoint between them. Initially, we compared classification performance across various training conditions at these three electrode locations. Subsequently, a feature space analysis was conducted to quantify the effects of limb conditions. Finally, strategies for group training and feature selection were explored to mitigate these effects.ResultsThe results indicate that with the state-of-the-art method, classification performance at the wrist was comparable to that at the middle position, both of which outperformed the elbow, consistent with the findings from the feature space analysis. In inter-condition classification, training under dynamic limb conditions yielded better results than training under static conditions, especially at the positions covered by dynamic training. Additionally, fast and slow movement speeds produced similar performance outcomes. To mitigate the effects of limb conditions, adding more training conditions reduced classification errors; however, this reduction plateaued after four conditions, resulting in classification errors of 22.72%, 22.65%, and 26.58% for the wrist, middle, and elbow, respectively. Feature selection further improved classification performance, reducing errors to 19.98%, 19.75%, and 27.14% at the respective electrode locations, using three optimal features derived from single-condition training.ConclusionsThe study demonstrated that the impact of limb conditions was mitigated when electrodes were placed near the wrist. Dynamic limb condition training, combined with feature optimization, proved to be an effective strategy for reducing this effect. This work contributes to enhancing the robustness of myoelectric-controlled interfaces, thereby advancing the development of wearable intelligent devices.