Space Analysis Research Articles

Biquaternion linear canonical stockwell transform and associated uncertainty principles

The intersection of geometric methods and signal processing has led to significant advancements in various fields, including physics, engineering, and mathematics. In recent years, the biquaternion domain, a generalization of complex numbers with a geometric interpretation, has emerged as a promising tool for signal analysis. This paper introduces the Biquaternion Linear Canonical Stockwell Transform (BiQLCST), a novel fusion of advanced mathematical frameworks that offers enhanced signal analysis capabilities and unveils new uncertainty principles, bridging the gap between complex transformations and uncertainty analysis in high-dimensional spaces. First, we established the various fundamental properties, including linearity, shift, modulation, parity, orthogonality relation, reconstruction formula and Plancherel’s theorem. Heisenberg uncertainty principle associated with Biquaternion Linear Canonical Stockwell Transform (BiQLCST) is also established. Toward the end, some potential applications of the BiQLCST are presented.

A Novel Machine Learning Model and a Web Portal for Predicting the Human Skin Sensitization Effects of Chemical Agents

Skin sensitization is a significant concern for chemical safety assessments. Traditional animal assays often fail to predict human responses accurately, and ethical constraints limit the collection of human data, necessitating a need for reliable in silico models of skin sensitization prediction. This study introduces HuSSPred, an in silico tool based on the Human Predictive Patch Test (HPPT). HuSSPred aims to enhance the reliability of predicting human skin sensitization effects for chemical agents to support their regulatory assessment. We have curated an extensive HPPT database and performed chemical space analysis and grouping. Binary and multiclass QSAR models were developed with Bayesian hyperparameter optimization. Model performance was evaluated via five-fold cross-validation. We performed model validation with reference data from the Defined Approaches for Skin Sensitization (DASS) app. HuSSPred models demonstrated strong predictive performance with CCR ranging from 55 to 88%, sensitivity between 48 and 89%, and specificity between 37 and 92%. The positive predictive value (PPV) ranged from 84 to 97%, versus negative predictive value (NPV) from 22 to 65%, and coverage was between 75 and 93%. Our models exhibited comparable or improved performance compared to existing tools, and the external validation showed the high accuracy and sensitivity of the developed models. HuSSPred provides a reliable, open-access, and ethical alternative to traditional testing for skin sensitization. Its high accuracy and reasonable coverage make it a valuable resource for regulatory assessments, aligning with the 3Rs principles. The publicly accessible HuSSPred web tool offers a user-friendly interface for predicting skin sensitization based on chemical structure.

A Matrix-Based Approach to Unified Synthesis of Planar Four-Bar Mechanisms for Motion Generation With Position, Velocity, and Acceleration Constraints

Abstract This paper introduces a novel matrix-based approach for the simultaneous type and dimensional synthesis of planar four-bar linkage mechanisms, accommodating various practical constraints, including position, velocity, acceleration, and joint placements. Traditional design processes segregate type synthesis, the determination of joint and link configurations, from dimensional synthesis, which involves specifying link sizes and pivot locations. This segregation often leads to complexities in addressing the complete design challenge. The novel methodology proposed in this paper departs from the conventional sequential design approach by concurrently evaluating type and dimensional parameters using a data-driven matrix formulation. The crux of the paper’s methodology involves formulating a singular design equation through a transformation matrix, parameterized by the Cartesian parameters of the mechanism’s dyads. This formulation linearly expresses a broad range of constraints, facilitating the identification of viable solutions through singular value decomposition and null space analysis. This integrated approach not only simplifies the synthesis process but also provides direct insights into the mechanism’s parameters, encompassing both type and dimensions, thereby obviating the need for further interpretative steps common to the use of quaternions and kinematic mapping. In essence, the paper presents two main contributions: the development of a unified design equation capable of encompassing a wide array of constraints within the mechanism synthesis process, and the introduction of an algorithm that effectively identifies all potential planar four-bar linkage mechanisms by accurately satisfying up to five constraints. This approach promises to enhance the design and optimization of mechanical systems by offering a more holistic and efficient pathway to mechanism synthesis.

Hybrid discrete‐continuum modeling of tumor‐immune interactions: Fractional time and space analysis with immunotherapy

In this paper, we introduce an innovative hybrid discrete‐continuum model that provides a comprehensive framework for understanding the intricate interactions between tumor cells, immune cells, and the effects of immunotherapy. This study distinguishes itself by addressing the limitations of traditional diffusion models, which often fail to capture the irregular and nonlocal movements of cells within the complex tumor microenvironment. To overcome these challenges, we employ fractional time derivatives and the fractional Laplacian operator, offering a more accurate representation of anomalous diffusion processes that are critical in cancer dynamics. Our research begins with a rigorous mathematical analysis, where we establish the global existence of a unique mild solution, laying a solid theoretical foundation for the model. A key innovation of our study is the introduction of the “invasion threshold,” a critical parameter inspired by the next‐generation operator used in epidemiological modeling. This threshold provides a powerful tool for determining the existence and stability of equilibrium points, offering deep insights into the conditions that either promote or inhibit tumor growth under immunotherapeutic interventions. By integrating a hybrid discrete‐continuum approach, we capture the individual behavior of each cell, allowing for a more detailed exploration of the dynamic interplay within the tumor microenvironment. This model not only advances our understanding of tumor‐immune interactions but also holds potential for informing more effective therapeutic strategies, making a significant contribution to the field of mathematical oncology.

Deep Learning Assessment of Disproportionately Enlarged Subarachnoid-Space Hydrocephalus in Hakim’s Disease or Idiopathic Normal Pressure Hydrocephalus

Abstract Background Disproportionately enlarged subarachnoid space hydrocephalus (DESH) is a key feature of Hakim’s disease (synonymous with idiopathic normal pressure hydrocephalus; iNPH). However, it previously had been only subjectively evaluated. Purpose This study aims to evaluate the usefulness of MRI indices, derived from deep learning segmentation of CSF spaces, for DESH detection and to establish their optimal thresholds. Materials and Methods This study retrospectively enrolled a total of 1009 participants, including 77 patients diagnosed with Hakim’s disease, 380 healthy volunteers, 163 with mild cognitive impairment, 256 with Alzheimer’s disease, and 217 with other types of neurodegenerative diseases. DESH, ventriculomegaly, tightened sulci in the high convexities, and Sylvian fissure dilatation were evaluated on three-dimensional T1-weighted MRI by radiologists. The total ventricles, high-convexity part of the subarachnoid space, and Sylvian fissure and basal cistern were automatically segmented using the CSF Space Analysis application (FUJIFILM Corporation). Moreover, DESH, Venthi, and Sylhi indices were calculated based on these three regions. The area under the receiver-operating characteristic curves of these indices and region volumes (volume ratios) for DESH detection were calculated. Results Of the 1009 participants, 101 (10%) presented with DESH. The DESH, Venthi, and Sylhi indices performed well with 95.0-96.0% sensitivity and 91.5-96.8% specificity at optimal thresholds. All patients with Hakim’s disease were diagnosed with DESH, despite variations in severity. In patients with Hakim’s disease, with or without Alzheimer’s disease, the DESH index and total ventricular volume were significantly higher compared to patients with Alzheimer's disease, although the total intracranial cerebrospinal fluid volume was significantly lower. Conclusion DESH, Venthi, and Sylhi indices, and the volumes and volume ratios of the ventricle and high-convexity part of the subarachnoid space computed using deep learning were useful for the DESH detection that may help to improve the diagnosis of Hakim’s disease (ie, iNPH).

Analysis of modeling nasal narrow space based on visual slam

Abstract. Now, surgeries are becoming more stable, safe, efficient and low-cost. During the surgical treatment of nasal diseases, surgical robotic robotics can help operate accurately and reduce the discomfort after surgery. However, due to the internal space of the nasal cavity being relatively narrow, it is difficult for the nasal surgical robot to contain multiple vision sensors and the monocular camera could not get information about the depth of the 3D objects in the scene, so the existing surgical robots cannot accomplish the three-dimensional modeling about the internal space of nasal cavity well. In practice, doctors still have to analyze pictures from the robotic, which may decrease the efficiency of the surgery and increase the risk to patients. This article designed a SLAM algorithm framework based on a depth estimation network, it can simulate the internal structure of the nasal cavity more accurately through pictures, which come from monocular endoscopic on surgical robotic. The insights gained in this study verify that the method of image segmentation can also make the depth representation of the nasal internal space more accurate and this method may help robots realize their self-position in the narrow area of the nasal cavity, which lays the foundations for the development of fully autonomous surgical robots.

Quantum Harmonic Analysis for Polyanalytic Fock Spaces

We develop the quantum harmonic analysis framework in the reducible setting and apply our findings to polyanalytic Fock spaces. In particular, we explain some phenomena observed in recent work by the second author and answer a few related open questions. For instance, we show that there exists a symbol such that the corresponding Toeplitz operator is unitary on the analytic Fock space but vanishes completely on one of the true polyanalytic Fock spaces. This follows directly from an explicit characterization of the kernel of the Toeplitz quantization, which we derive using quantum harmonic analysis. Moreover, we show that the Berezin transform is injective on the set of of Toeplitz operators. Finally, we provide several characterizations of the C1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {C}_1$$\\end{document}-algebra in terms of integral kernel estimates and essential commutants.

Diffusion-based generative drug-like molecular editing with chemical natural language

Recently, diffusion models have emerged as a promising paradigm for molecular design and optimization. However, most diffusion-based molecular generative models focus on modeling 2D graphs or 3D geometries, with limited research on molecular sequence diffusion models. The International Union of Pure and Applied Chemistry (IUPAC) names are more akin to chemical natural language than the Simplified Molecular Input Line Entry System (SMILES) for organic compounds. In this work, we apply an IUPAC-guided conditional diffusion model to facilitate molecular editing from chemical natural language to chemical language (SMILES) and explore whether the pre-trained generative performance of diffusion models can be transferred to chemical natural language. We propose DiffIUPAC, a controllable molecular editing diffusion model that converts IUPAC names to SMILES strings. Evaluation results demonstrate that our model outperforms existing methods and successfully captures the semantic rules of both chemical languages. Chemical space and scaffold analysis show that the model can generate similar compounds with diverse scaffolds within the specified constraints. Additionally, to illustrate the model's applicability in drug design, we conducted case studies in functional group editing, analogue design and linker design.

Sequences of sickness absence, disability pension and unemployment four years before and five years after musculoskeletal diagnosis among Swedish twins.

To investigate sustainable working life via identification of time-related sequences of sickness absence (SA), disability pension (DP) and unemployment four years before and five years after the first musculoskeletal diagnosis in a Swedish twin cohort. Other aims were to account for familial confounding and to examine the associations between sequences and sociodemographic characteristics. Among 28,474 Swedish twins, the patterns of interruptions of working life four years before the first M00-M99 diagnosis (MSD) and five years after MSD diagnosis were investigated with a sequence analysis in a seven-element state space consisting of sustainable working life, unemployment >90 days, moderate SA/DP (30-179 days), almost full year of SA/DP (180-365 days), full year of SA/DP (⩾ 365 days), death, and old-age pension. The six-cluster solution had the best fit to the data. Five clusters had varying patterns of interruptions of sustainable working life (Clusters 2-6, n = 537-1949 with SA/DP, unemployment, but also accounting death and old-age pension) compared with the largest cluster with primarily sustainable working life (n = 23,316). Age, sex and familial factors affected the likelihood of belonging to the clusters with SA/DP. Most Swedish twins with or without MSD diagnosis have a sustainable working life, although MSD was both prevalent and a strong risk factor for belonging to the clusters with SA/DP. Thus, early prevention of MSD and prevention of recurrent or long sickness absences due to any cause would be merited while paying special attention to women also.

Terra incognita of the uncontrolled manifold (UCM).

The review addresses the central concept of the uncontrolled manifold (UCM) hypothesis, which has become a major framework for analysis of performance-stabilizing motor synergies. The major goals are to summarize the status quo in the field and to ask new questions stimulating new studies. We focus on a few main questions: What is the UCM? What are the likely neural origins of the UCM? How is the UCM reflected in movement patterns? Are properties of the UCM similar in all directions? We contrast experience-based features of movements seen very soon after the movement initiation vs. those based on on-line sensory feedback signals. Further, we address a number of poorly explored issues such as the differences in characteristic times of processes within the UCM and orthogonal to the UCM space, the interplay between movement stability and optimality, the origin of preferred sharing patterns of performance variables across abundant sets of elements and of their inter-trial variability, problems with the UCM-based analysis in different spaces, and likely neurophysiological mechanisms contributing to the UCM formation. In particular, we focus on the UCM in spaces of hypothetical neural control variables, which we associate with the reciprocal and coactivation commands to the effectors. Analysis of performance-stabilizing synergies within the UCM framework in abundant spaces of kinetic, kinematic and electromyographic variables at the selected level of analysis may be practically useful. However, mapping findings in such studies onto neural control mechanisms has been challenging.

Eco-Friendly Office Platform: Leveraging Machine Learning and GIS for Carbon Footprint Management and Green Space Analysis

This research focuses on developing an innovative platform to manage carbon footprint data related to office activities and monitor green spaces, integrating geoinformatics and machine learning technologies. The platform addresses the lack of automated systems for tracking carbon emissions, particularly in high paper consumption environments, such as those involving printing and photocopying. Additionally, it monitors green spaces within corporate settings, an aspect often overlooked in existing systems. The study demonstrates the platform’s capability to automate carbon footprint calculations and provide accurate assessments of green areas, achieving a high accuracy rate of 96.22% and a Kappa coefficient of 0.92. The results confirm the platform’s ability to deliver both comprehensive and granular environmental insights, supporting decision making for more sustainable office environments. The key novelty of this study lies in the integration of real-time data capture with geoinformatics and machine learning to efficiently track both carbon footprints and green spaces. This approach offers a practical solution to a gap in environmental data management in office settings, enabling organizations to align their practices with sustainability goals. The platform’s precise, automated system contributes significantly to the development of eco-friendly workplaces, highlighting its academic and practical value in the field of environmental sustainability.

Sustainable enhancement of wool fibers using Hymenocrater platystegius flower and natural dyes for improved Insect-Repellent and color properties

This study investigates the application of Hymenocrater platystegius flower as a natural mothproofing agent for wool fibers and examines the effects of aluminum mordanting on the dyeing properties of wool treated with various natural dyes. Wool fibers were treated with Hymenocrater platystegius flower powder and aluminum salts, followed by dyeing with Madder, Reseda lutea, Indigo, or Walnut shell dyes using different mordanting methods. FTIR analysis revealed the characteristic absorption bands and confirmed the interactions between the wool fibers and the treatment agents. The insect-repellent properties were assessed by weight loss measurements after exposure to Anthrenus verbasci larvae, showing significant improvements in treated samples, particularly with the combination of Hymenocrater platystegius and aluminum mordanting. GC–MS analysis of the essential oil identified as α-Pinene, Trans-Verbenol, Linalool, and β-Bourbonene as the main constituents, contributing to the insect-repellent effect. The L*a*b* color space analysis demonstrated that aluminum mordanting significantly enhances the color strength and stability of dyed wool, with pre-mordanting generally providing better results. The study further evaluated the washing and light fastness, as well as the mothproofing properties of the dyed wool samples. These results indicate that the weight loss percentages of treated wool fiber after washing are slightly higher than before washing, but the difference is not significant. Overall, the use of aluminum mordant in conjunction with Hymenocrater platystegius flower powder not only improves the color characteristics of wool dyed with natural dyes but also provides effective mothproofing properties, making it a viable and eco-friendly option for textile applications.

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.