Space Techniques Research Articles

Investigating Object Translation in Room-scale, Handheld Virtual Reality.

Handheld devices have become an inclusive alternative to head-mounted displays in virtual reality (VR) environments, enhancing accessibility and allowing cross-device collaboration. Object manipulation techniques in 3D space with handheld devices, such as those in handheld augmented reality (AR), have been typically evaluated in table-top scale and we currently lack an understanding of how these techniques perform in larger scale environments. We conducted two studies, each with 30 participants, to investigate how different techniques impact usability and performance for room-scale handheld VR object translations. We compared three translation techniques that are similar to commonly studied techniques in handheld AR: 3DSlide, VirtualGrasp, and Joystick. We also examined the effects of target size, target distance, and user mobility conditions (stationary vs. moving). Results indicated that the Joystick technique, which allowed translation in relation to the user's perspective, was the fastest and most preferred, without difference in precision. Our findings provide insights for designing room-scale handheld VR systems, with potential implications for mixed reality systems involving handheld devices.

Hadamard and Riemann Matrix‐Based SLM for PAPR Reduction in OTFS Signal

ABSTRACTIn this letter, we study the peak‐to‐average power ratio (PAPR) reduction and bit error rate (BER) performances of the Hadamard and Riemann matrix‐based selected mapping (SLM) technique for Orthogonal Time Frequency Space (OTFS) signals. Unlike the conventional phase sequence based on , which requires the entire sequence to extract the original signal at the receiver, the Hadamard and Riemann matrix‐based SLM techniques require only the row index of the matrix, reducing the additional information necessary to extract the signal. Simulation results are presented to verify the PAPR and BER performance. The results are also compared with the existing normalized μ‐law, rooting μ‐law, and conventional SLM methods. The results demonstrate that the Riemann‐based SLM technique shows significant performance improvement.

Fungal microbiota of biodamages of various polymeric materials.

Data on microbial fouling of various synthetic polymer materials, including those used in space technology, are summarized. It has been established that the dominant groups of microbiota of polymer fouling are the genera of mitosporous fungi Aspergillus, Penicillium, Alternaria, Trichoderma. The enzymatic properties of fungal strains from the collection of microbial cultures of the Microbial Depository Center of the National Academy of Sciences of Armenia were studied. It has been shown that Aspergillus fumigatus, Penicillium chrysogenum, P. steckii, Juxtiphoma eupyrena and a number of other fungi have biofouling activity towards polyethylene, polyethylene terephthalate and some other synthetic polymers. New fungal kits have been developed and proposed to evaluate the fungal resistance of polymeric materials. They include fungi isolated from bio-damaged polymers used in space technology and contain 2 to 5 fungal strains instead of 7 to 9 strains in previously used kits. Taking into account the obtained data, a comparative assessment of the fungal resistance of samples of synthetic polymeric materials of various classes that passed accelerated climatic tests has been carried out. It has been established that the kits of biodegradant fungi, composed of cultures of bio-damaged space technology, generally exceeded the activity of the previously used kits, based on which one can judge the obvious advantages of strains isolated from bio-damaged space technology. In the future, these kits could find application not only for biodegradation of polymers, but also for testing the biostability of various polymers, to use for the construction of aviation and space techniques. Moreover, new optimized kits may be developed based on the strains involved in this study.

Soft limits of gluon and graviton correlators in Anti-de Sitter space

We derive formulae for the soft limit of tree-level gluon and graviton correlators in Anti-de Sitter space, which arise from Feynman diagrams encoding the Weinberg soft theorems in flat space. Other types of diagrams can also contribute to the soft limit at leading order in the soft momentum, but have a different pole structure. We derive these results at four points using explicit formulae recently obtained from the cosmological bootstrap and double copy, and extend them to any multiplicity using bootstrap techniques in Mellin-momentum space.

A survey of 3D Space Path-Planning Methods and Algorithms

Due to their agility, cost-effectiveness, and high maneuverability, Unmanned Aerial Vehicles (UAVs) have attracted considerable attention from researchers and investors alike. Path planning is one of the practical subsets of motion planning for UAVs. It prevents collisions and ensures complete coverage of an area. This study provides a structured review of applicable algorithms and coverage path planning solutions in Three-Dimensional (3D) space, presenting state-of-the-art technologies related to heuristic decomposition approaches for UAVs and the forefront challenges. Additionally, it introduces a comprehensive and novel classification of practical methods and representational techniques for path-planning algorithms. This depends on environmental characteristics and optimal parameters in the real world. The first category presents a classification of semi-accurate decomposition approaches as the most practical decomposition method, along with the data structure of these practices, categorized by phases. The second category illustrates path-planning processes based on symbolic techniques in 3D space. Additionally, it provides a critical analysis of crucial influential approaches based on their importance in path quality and researchers’ attention, highlighting their limitations and research gaps. Furthermore, it will provide the most pertinent recommendations for future work for researchers. The studies demonstrate an apparent inclination among experimenters toward using the semi-accurate cellular decomposition approach to improve 3D path planning.

Applying Low-Impact Development Techniques for Improved Water Management in Urban Areas

Worldwide, the increase in impervious surfaces due to urbanization has led to significant water cycle issues such as groundwater depletion, urban heat islands, and flooding. To address these challenges, Low-Impact Development (LID) techniques are increasingly being applied in stormwater management. This study focuses on Ulsan, designated as a water cycle model city in South Korea, with a particular emphasis on the highly urbanized Okgyo drainage watershed. Using the Stormwater Management Model (SWMM) version 5.1, long-term runoff simulations were conducted to evaluate the effects of LID implementation on water cycle change rates and recovery rates. The model incorporates detailed hydrological and hydraulic parameters, including inflow, runoff, infiltration, and evapotranspiration for six subcatchments within the watershed. The SWMM was calibrated and validated using 30 years of historical rainfall data (1987–2016) from the Ulsan weather station. Calibration and validation processes used the NRCS-CN (Curve Number) method to ensure accuracy in simulating runoff patterns and water balance. The study specifically evaluated the effectiveness of two LID techniques: bioretention and permeable pavements. Three scenarios were modeled: bioretention applied to 5% of the area, permeable pavements applied to 5% of the area, and a combined application of both techniques. The results showed that the combined scenario provided the best outcome, with a 7.80% reduction in surface runoff and a 14.56% improvement in water cycle health. The LID application scenario confirmed the potential to achieve the water cycle management target of handling 25.5 mm of rainfall. These findings demonstrate that the introduction of LID techniques in public spaces can significantly enhance water management. This research provides insights into effective water cycle management methods tailored to specific urban land uses, laying a foundation for future urban planning and sustainable development.

A novel transformation free nonuniform higher order compact finite difference scheme for solving incompressible flows on circular geometries

This paper presents a newly developed higher order compact scheme that is designed on a polar grid by using an implicit form of first order derivatives on nonuniform grids. These derivatives are formulated compactly and implicitly with a relationship of the coefficients in terms of the unknown variables. The proposed scheme is free from transformation technique and third order accurate in space. The objective is to solve Stokes equations and Navier–Stokes equations on curvilinear grids using the polar nature of the coordinate system. Our newly developed scheme is used to solve several problems namely, a problem having an analytical solution, Stokes flow with different orientations of the lids for the half filled annular and wedge cavity, the lid driven polar cavity flow and flow past an impulsively started circular cylinder. Our newly developed scheme is used to analyze the flow structures for the flow governed by different physical control parameters: the cavity radius ratio, the cavity angle and the ratio of the upper and lower lid speeds with rotating coaxial cylinders and Reynolds number for the impulsively started circular cylinder. The Stokes equations and the Navier–Stokes equations are efficiently solved with Dirichlet as well as Neumann boundary conditions. The efficacy and robustness of our proposed scheme are shown through its applicability in all the complex fluid flow problems.

The Aesthetic Expression of Abstract Calligraphy: Emotion and Form between Brush and Ink

Calligraphy is one of the most important visual art forms inherited from China for thousands of years. The visual formal resources contributed by the dots and structures of Chinese calligraphy are one of the wonders in the world art history. Among them, abstract calligraphy, as a unique art form, contains rich emotional expression and formal aesthetic exploration between brush and ink. This study adopts a qualitative research paradigm, through in-depth interviews with senior abstract calligraphers, art critics and scholars, combined with on-site observation of the creative process of calligraphy, literature analysis and interpretation of works, in order to comprehensively analyze the emotional connotation and formal vocabulary of abstract calligraphy, and to clarify the intrinsic connection and tension between the two. The results of the study show that in abstract calligraphy, the subject's emotional experience is the root power of modeling. Through the refinement and sublimation of life experience, the calligrapher injects the vibration of individual consciousness and philosophical thoughts into the brush and ink with the help of visual elements such as brush strokes, lines, and compositions. The brush and ink become the direct speech of the mind, and the viewer can feel the rhythm and bitterness of life, the struggle and emptiness of desire, the pursuit of tranquility and other rich emotional experiences in the works. At the same time, the careful construction of formal language is also the core of abstract calligraphy exploration. Through the mastery of brush and ink materials and writing momentum, the use of wet and dry contrast, white space, texture and other techniques, to create a shocking visual tension, giving people a strong experience of formal aesthetics. The study emphasizes that abstract calligraphy realizes a high degree of unity between emotion and form. On the one hand, the form itself carries the spiritual experience and inner emotion of the calligrapher; on the other hand, the emotion gives life and inner tension to the form. The two integrate and echo each other, forming an organic whole. It is this perfect combination of mind and form that makes abstract calligraphy become the visualization of human emotion and spiritual world, and triggers endless thoughts and resonance in the viewers. The researcher comprehensively analyzes the emotional core and formal qualities of abstract calligraphy from both theoretical and empirical levels, providing a new perspective for a deeper understanding of the unique aesthetic value of this art form.

Control barrier function based visual servoing for Mobile Manipulator Systems under functional limitations

This paper proposes a new control strategy for Mobile Manipulator Systems (MMSs) that integrates image-based visual servoing (IBVS) to address operational limitations and safety constraints. The proposed approach based on the concept of control barrier functions (CBFs), provides a solution to address various operational challenges including visibility constraints, manipulator joint limits, predefined system velocity bounds, and system dynamic uncertainties. The proposed control strategy is a two-tiered structure, wherein the first level, a CBF-IBVS controller calculates control commands, taking into account the Field of View (FoV) constraints. By leveraging null space techniques, these commands are transposed to the joint-level configuration of the MMS, while considering system operational limits. Subsequently, in the second level, a CBF velocity controller employed for the entire MMS undertakes the tracking of the commands at the joint level, ensuring compliance with the predefined system’s velocity limitations as well as the safety of the whole combined system dynamics. The proposed control strategy offers superior transient and steady-state responses and heightened resilience to disturbances and modeling uncertainties. Furthermore, due to its low computational complexity, it can be easily implemented on an onboard computing system, facilitating real-time operation. The proposed strategy’s effectiveness is illustrated via simulation outcomes, which reveal enhanced performance and system safety compared to conventional IBVS methods. The results indicate that the proposed approach is effective in addressing the challenging operational limitations and safety constraints of mobile manipulator systems, making it suitable for practical applications.

Antenna optimization using machine learning with reduced-dimensionality surrogates

In modern times, antenna design has become more demanding than ever. The escalating requirements for performance and functionality drive the development of intricately structured antennas, where parameters must be meticulously adjusted to achieve peak performance. Often, global adjustments to geometry are necessary for optimal results. However, direct manipulation of antenna responses evaluated with full-wave electromagnetic (EM) simulation models using conventional nature-inspired methods entails significant computational costs. Alternatively, surrogate-based techniques show promise but are impeded by dimensionality-related challenges and nonlinearity of antenna outputs. This study introduces an innovative technique for swiftly optimizing antennas. It leverages a machine learning framework with an infill criterion employing predicted enhancement of the merit function, utilizing a particle swarm optimizer as the primary search engine, and employs kriging for constructing the underlying surrogate model. The surrogate model operates within a reduced-dimensionality domain, guided by directions corresponding to maximum antenna response variability identified through fast global sensitivity analysis, tailored explicitly for domain determination. Operating within this reduced domain enables building dependable metamodels at a significantly lower computational cost. To address accuracy loss resulting from dimensionality reduction, the global optimization phase is supplemented by local sensitivity-based parameter adjustment. Extensive comparative experiments involving various planar antennas demonstrate the competitive operation of the presented technique over machine learning algorithms operating in full-dimensionality space and direct EM-driven bio-inspired optimization techniques.

Identification of gamma irradiated polycarbonate-polybutylene terephthalate stiff polymer blend products using HS-GC/MS, FTIR and UV/Vis spectroscopy

In this study, the fragmentation products of Makro-blend stiff polymer due to gamma exposure were identified using Head space (HS), Gas Chromatography, and Mass Spectrometry (HS-GC/MS) techniques and Fourier Transform Infra-Red (FTIR) spectroscopy. As well the electronic transitions due to gamma exposure were examined using UV/Vis spectroscopy. For the un-irradiated and 10 kGy irradiated samples, one product was detected by (HS-GC/MS) method with a clear peak of Dimethoxyethane (MW = 90), which has a peak area percentage of 98.58% and 97.13%, respectively. With increasing gamma doses the number of identified components was increased. At 1200 kGy, ten components were detected: Biethylene (MW = 54), Dimethoxyethane (MW = 90), 1,1-Dimethoxy-2-propanone (MW = 118), 1,1-Dimethoxy-2-propanol (MW = 120), 1,1-Dimethoxycyclopentane (MW = 130), 1,1-Dimethoxyheptane (MW = 160), 1,1-Dimethoxyoctane (MW = 174), Dimethyl terephthalate (MW = 194), 4-(Diethoxymethyl) benzaldehyde (MW = 208), and Terephthalate dihexyl (MW = 334), which have peaks area percentages of 4.22, 2.98, 25.84, 2.2, 10.65, 38.86, 1.57, 5.91, 1.22, and 6.48, respectively. The recorded mass spectrum of each component confirms the molecular ion peak. The FTIR measurements demonstrate the decrease in the detected band intensities following gamma radiation. Accordingly, a chain scission occurs, which causes the carbonate bond to scission, resulting in the creation of a hydroxyl group and the (–H) abstraction from the backbone of the irradiation blend samples. The absorbance edge of the gamma-exposed samples shifted towards a higher wavelength. This pattern shows that the band gap energy is declining. The use of FTIR and HS-GC/MS in mass spectrometry analysis will be useful tools for examining radiation-induced polymeric fragments and advancing our understanding of the process underlying the creation of these fragments in polymers.

Residual-enhanced graph convolutional networks with hypersphere mapping for anomaly detection in attributed networks

In the burgeoning field of anomaly detection within attributed networks, traditional methodologies often encounter the intricacies of network complexity, particularly in capturing nonlinearity and sparsity. This study introduces an innovative approach that synergizes the strengths of Graph Convolutional Networks (GCNs) with advanced deep residual learning and a unique residual-based attention mechanism, thereby creating a more nuanced and efficient method for anomaly detection in complex networks. The heart of our model lies in the integration of GCNs that capture complex structural relationships within the network data. This is further bolstered by deep residual learning, which is employed to model intricate nonlinear connections directly from input data. A pivotal innovation in our approach is the incorporation of a residual-based attention mechanism. This mechanism dynamically adjusts the importance of nodes based on their residual information, thereby significantly enhancing the sensitivity of the model to subtle anomalies. Furthermore, we introduce a novel hypersphere mapping technique in the latent space to distinctly separate normal and anomalous data. This mapping is the key to our model’s ability to pinpoint anomalies with greater precision. An extensive experimental setup was used to validate the efficacy of the proposed model. Using attributed social network datasets, we demonstrate that our model not only competes with but also surpasses existing state-of-the-art methods in anomaly detection. The results show the exceptional capability of our model to handle the multifaceted nature of real-world networks.

A reproducing kernel Hilbert space approach to singular local stochastic volatility McKean–Vlasov models

Motivated by the challenges related to the calibration of financial models, we consider the problem of numerically solving a singular McKean–Vlasov equation dXt=σ(t,Xt)XtvtE[vt|Xt]dWt,\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ d X_{t}= \\sigma (t,X_{t}) X_{t} \\frac{\\sqrt{v}_{t}}{\\sqrt{\\mathbb{E}[v_{t}|X_{t}]}}dW_{t}, $$\\end{document} where W\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$W$\\end{document} is a Brownian motion and v\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$v$\\end{document} is an adapted diffusion process. This equation can be considered as a singular local stochastic volatility model. While such models are quite popular among practitioners, its well-posedness has unfortunately not yet been fully understood and in general is possibly not guaranteed at all. We develop a novel regularisation approach based on the reproducing kernel Hilbert space (RKHS) technique and show that the regularised model is well posed. Furthermore, we prove propagation of chaos. We demonstrate numerically that a thus regularised model is able to perfectly replicate option prices coming from typical local volatility models. Our results are also applicable to more general McKean–Vlasov equations.

Endoscopic ultrasound-guided hepaticojejunostomy for complete biliary anastomotic stricture: the echo-free space technique for scope insertion in surgically altered anatomy.

Endoscopic ultrasound-guided hepaticojejunostomy for complete biliary anastomotic stricture: the echo-free space technique for scope insertion in surgically altered anatomy.

Pengembangan Potensi Petani Jagung Melalui Edukasi Budidaya Jagung Hibrida Di Desa Kesiut, Kabupaten Tabanan, Bali

Kesiut Village is located in the lowlands area of 266 hectares, with a population of 2,203 people. Most people in the village of Kesiut are corn farmer. The results of the conversation with the chief of the village of Kesiut on May 25, 2022, counseling and outreach were needed as well as direct demonstrations of how to cultivate hybrid corn to farmers in Kesiut Village. The activity was conducted for 3 months, from August 15 to November 17, 2022. The method of integrated community service activities is counseling. Activities carried out to educate how to grow hybrid corn, discuss problems and provide solutions related to hybrid corn cultivation. Initial interviews revealed that 60% of farmers rarely participated in agricultural training activities. Regarding crop failure experienced, 40% often experienced crop failure. The problem often faced by corn farmers in Kesiut Village is the management of downy mildew caused by Peronosclerospora spp. Based on questions by farmers in the activity, farmer in the village of Kesiut already have a basic understanding of hybrid corn cultivation. However, it is necessary to enrich their knowledge about good and correct cultivation techniques, especially seeds requirements, spacing, pest and disease control techniques. Farmers' response to this activity was very positive.

Linear versus nonlinear forms of partial unconditionality of bases

The main results in this paper contribute to bringing to the fore novel underlying connections between the contemporary concepts and methods springing from greedy approximation theory with the well-established techniques of classical Banach spaces. We do that by showing that bounded-oscillation unconditional bases, introduced by Dilworth et al. in 2009 in the setting of their search for extraction principles of subsequences verifying partial forms of unconditionality, are the same as truncation quasi-greedy bases, a new breed of bases that appear naturally in the study of the performance of the thresholding greedy algorithm in Banach spaces. We use this identification to provide examples of bases that exhibit that bounded-oscillation unconditionality is a stronger condition than Elton's near unconditionality. We also take advantage of our arguments to provide examples that allow us to tell apart certain types of bases that verify either debilitated unconditionality conditions or weaker forms of quasi-greediness in the context of abstract approximation theory.

Association of Extent of Transverse Sinus Stenosis With Cerebral Glymphatic Clearance in Patients With Idiopathic Intracranial Hypertension.

Idiopathic intracranial hypertension (IIH) is a neurologic disorder characterized by symptoms of elevated intracranial pressure in the absence of a clear cause. There is a developing theory that IIH may, in part, be related to abnormal cerebral glymphatic clearance. In addition, transverse sinus stenosis (TSS) is a common finding in IIH of unclear pathophysiologic significance. Similarly, whether or not TSS is associated with glymphatic outflow in IIH is unknown. The aim of this investigation was to explore the possible association between glymphatic outflow and extent of TSS in patients with IIH. The study cohort consisted of patients with IIH and healthy controls who were retrospectively identified from our tertiary care institution located in upstate New York from 2016 to 2023. Patients with IIH were included if they had brain MRIs completed with sufficient sequences for analysis. Brain MRIs were computationally analyzed using diffusion tensor imaging analysis along the perivascular space technique to quantify the glymphatic function in patients with IIH. Glymphatic clearance, the primary outcome, was then correlated with the degree of TSS on MR venography using 2 different scoring systems, the 'Farb score' and 'Carvalho score.' Overall, 81 patients with IIH (70 [86%] female, mean age 29.8 years [SD: 8.2 years], mean BMI 41 [SD: 8.4]) and 10 normal controls were identified with sufficient imaging. Based on the Carvalho TSS score, IIH patients without TSS had significantly lower glymphatic clearance than healthy controls (mean ALPS index: 1.196 [SD: 0.05] vs 1.238 [SD: 0.04], respectively; p = 0.018). Furthermore, IIH patients with TSS had significantly lower glymphatic outflow than healthy controls (1.129 [SD: 0.07] vs 1.238 [SD: 0.04], respectively; p < 0.0001) and IIH patients without TSS (1.129 [SD: 0.07] vs 1.196 [SD: 0.05], respectively; p < 0.0001). In addition, there was a significant association between increasing extent of TSS and declining glymphatic clearance (p < 0.0001, R = 0.62). Finally, IIH patients with severe TSS had significantly lower glymphatic flow than IIH patients with mild stenosis (1.121 [SD: 0.07] vs 1.178 [SD: 0.05], respectively; p < 0.0001). These findings were similarly recapitulated using the Farb TSS scoring system. These preliminary findings suggest that the extent of TSS is associated with the degree of glymphatic clearance in IIH, providing novel insights into IIH pathophysiology. Further research is required to clarify the possible causal relationship between TSS and impaired glymphatic clearance in IIH.

Light Reflections Detection And Correction For Robotic Volumetric PIV

Laser light reflection mitigation in Particle Image Velocimetry (PIV) is crucial for accurate flow field measurements. While numerous methods exist for planar PIV, fewer have been developed for volumetric PIV systems, and in particular for coaxial setups like robotic volumetric PIV. Light reflections in volumetric PIV experiments result in high-intensity regions that corrupt particle detection and analysis. This study presents a novel approach for treating light reflections in robotic volumetric PIV experiments. The proposed method uses image filtering and masking techniques in the wavenumber space to separate particle images from reflection regions. The process involves decomposing the image signal into low- and high-wavenumber components using the 2D discrete Fourier transform (DFT) to then use a high-pass filter to attenuate the intensity of the reflection regions. Finally, a step of automated adaptive masking is applied to remove residual reflection areas that the filtering is not able to eliminate. The proposed approach is tested on experimental data obtained from experiments performed using robotic volumetric PIV on two different geometries: a side-view mirror and a rotating two-blade propeller. Comparison between raw and pre-processed images, as well as particle tracking results, is presented. The results from this data comparison show successful removal of reflection-induced artifacts in instantaneous images by using the spatial Fourier filter automated masking approach. The developed image pre-processing strategy effectively removes unsteady light reflection regions in robotic volumetric PIV images, preventing the appearance of spurious particle tracks and improving the accuracy of flow field measurements. The spatial gaps introduced by the masking procedure can be easily filled in via measurements from multiple directions, which are promptly achieved via the robotic volumetric PIV approach.

Intuitionistic Fuzzy Gram-Schmidt Orthogonalized Artificial Neural Network for Solving MAGDM Problems

Objectives: To propose a suitable decision-making model based on Intuitionistic Fuzzy sets (IFSs) and Gram-Schmidt orthogonalization process for Artificial Neural Network (ANN). Methods: The IFS data sets appearing in the form of matrices are aggregated using the available aggregation operators in the literature and then the collective aggregated information is processed through Gram-Schmidt orthogonalization for the revised input vectors which is then fed into the ANN algorithm following Delta Learning Rule for the next phase. The weight updation is performed through the ANN and the output is improvised. Findings: The proposed Gram-Scmidt Orthogonalization process is utilized in Intuitionistic Fuzzy Artificial Neural Network model. The Delta learning rule is utilized in the process of the Neural Network, where the Intuitionistic Fuzzy nature of the input data is transformed into a fuzzy data and then the ranking of the alternatives is done based on the weights updation through the learning phase of the ANN. Once the vector is trained out of the learning phase, it is then processed through the activation function for the final selection of the best alternative required of the Multiple Attribute Group Decision Making (MAGDM) problem posed in this work. To demonstrate the usefulness and applicability of this new method with the Gram-Schmidt process, the numerical example also adds more insight to the proposed methodology of ANN with the application of some Linear Space techniques. Novelty: Most of the research done on Intuitionistic Fuzzy Artificial Neural Network model are based on learning rules or using some other calculations. The proposed Gram-Schmidt Orthogonalization process is used to find the orthogonal basis that are used as input training vectors in the Delta learning rule for ANN. Keywords: MAGDM, ANN, Aggregation operators, Learning Rules, Intuitionistic Fuzzy sets, Gram-Scmidt Orthogonalization

A Novel Construction of the g-Riesz Decomposition in Hilbert Spaces

The concept of the g-frame, a generalized frame in Hilbert spaces, has garnered attention in recent research. While numerous properties of g-frames have been explored, certain aspects remain unexamined, including a novel construction approach for the g-Riesz decomposition in Hilbert spaces. While prior works such as [12] presented the equivalence conditions for g-Riesz decomposition and Khosravi [10] proposed a new construction method for g-frames, neither addressed a new construction method for g-Riesz decomposition. This paper aims to fill this gap by investigating a novel construction method for the specialized g-frame-g-Riesz decomposition. Leveraging operator theory from generalized functional analysis and function space techniques in complex Hilbert spaces, we establish necessary and sufficient conditions for constructing g-Riesz decompositions, an area insufficiently explored by Khosravi and [12]. Furthermore, we introduce two annotations and provide proofs demonstrating that g-Riesz bases are equivalent to Riesz bases, aligning with the findings of W.C. Sun in [6]. This underscores the significance of our research. The newly proposed g-Riesz decomposition not only contributes to mathematical inquiry but also holds promise for various applications, particularly in signal and image processing.