Complex Space Research Articles

DIGITAL TRANSFORMATION POTENTIAL: THE ROLE OF ARTIFICIAL INTELLIGENCE IN BUSINESS

Purpose: The integration of Artificial Intelligence (AI) into business operations has become a pivotal driver of innovation and efficiency. This research paper explores the multifaceted landscape of AI implementation in businesses, examining the benefits of AI implementation. Theoretical Framework: The methodology adopted for this research comprises Semi-structured interviews with six key stakeholders’ executives, AI project managers, and digital transformation staff. Six additional globally businesses were chosen for case studies based on AI adoption maturity and digital transformation performance. Additionally, a qualitative content study of AI and digital transformation literature was conducted. Results and Discussion: The study revealed that as businesses continue to integrate AI, a balanced approach considering workforce implications is crucial to realizing the full potential of AI. The study also found out that the adoption of Artificial Intelligence in business is a nuanced process shaped by a confluence of factors. Organizational leadership, culture, resource availability, perceived benefits, regulatory considerations, data security, technology evaluation, and workforce readiness all play intricate roles. Thus, a holistic understanding of these factors empowers organizations to navigate the complex space of AI adoption, unlocking its transformative potential. Research Implications: The study's findings have many practical implications to businesses adoption of AI. AI, managers must promote innovation and adaptability. AI implementation requires human and technology capital. To reduce AI risks, legal and data security measures must be followed. To prepare employees for an AI-enabled workplace, organizations should adopt comprehensive training and development programs. Businesses may overcome AI implementation challenges and capitalize on its disruptive potential by addressing these five pragmatic elements. Originality and Value: This study contributes to the literature by offering insights into the nuanced landscape of AI adoption in businesses, emphasizing the crucial role of workforce considerations. It provides a comprehensive understanding of the multifaceted factors shaping AI implementation. The relevance and value of this research are evidenced by the holistic approach and contributes valuable guidance for organizations navigating the complexities of AI adoption, fostering innovation and efficiency while prioritizing workforce integration.

MorphVAE: Advancing Morphological Design of Voxel-Based Soft Robots with Variational Autoencoders

Soft robot design is an intricate field with unique challenges due to its complex and vast search space. In the past literature, evolutionary computation algorithms, including novel probabilistic generative models (PGMs), have shown potential in this realm. However, these methods are sample inefficient and predominantly focus on rigid robots in locomotion tasks, which limit their performance and application in robot design automation. In this work, we propose MorphVAE, an innovative PGM that incorporates a multi-task training scheme and a meticulously crafted sampling technique termed ``continuous natural selection'', aimed at bolstering sample efficiency. This method empowers us to gain insights from assessed samples across diverse tasks and temporal evolutionary stages, while simultaneously maintaining a delicate balance between optimization efficiency and biodiversity. Through extensive experiments in various locomotion and manipulation tasks, we substantiate the efficiency of MorphVAE in generating high-performing and diverse designs, surpassing the performance of competitive baselines.

HGE: Embedding Temporal Knowledge Graphs in a Product Space of Heterogeneous Geometric Subspaces

Temporal knowledge graphs represent temporal facts (s,p,o,?) relating a subject s and an object o via a relation label p at time ?, where ? could be a time point or time interval. Temporal knowledge graphs may exhibit static temporal patterns at distinct points in time and dynamic temporal patterns between different timestamps. In order to learn a rich set of static and dynamic temporal patterns and apply them for inference, several embedding approaches have been suggested in the literature. However, as most of them resort to single underlying embedding spaces, their capability to model all kinds of temporal patterns was severely limited by having to adhere to the geometric property of their one embedding space. We lift this limitation by an embedding approach that maps temporal facts into a product space of several heterogeneous geometric subspaces with distinct geometric properties, i.e.\ Complex, Dual, and Split-complex spaces. In addition, we propose a temporal-geometric attention mechanism to integrate information from different geometric subspaces conveniently according to the captured relational and temporal information. Experimental results on standard temporal benchmark datasets favorably evaluate our approach against state-of-the-art models.

On the M 2,A,A -numerical range and the M 2,A,A -maximal numerical range of the basic elementary operator M 2,B,C

Let A be a positive bounded operator acting on a complex Hilbert space H . For two bounded operators B and C on H , we denote by M 2 , B , C the basic elementary operator on the class of Hilbert–Schmidt operators C 2 ( H ) , i.e. M 2 , B , C ( X ) = BXC for all X ∈ C 2 ( H ) . In this paper, we investigate the A -numerical range W A ( M 2 , B , ( C ♯ A ) ∗ ) and the A -maximal numerical range W max A ( M 2 , B , ( C ♯ A ) ∗ ) , where A = M 2 , A , A , C ♯ A is the reduced solution of the equation AX = C ∗ A and C ∗ is the adjoint of C. Within this framework, we show, under some A-hyponormality conditions, the following two equalities: W A ( M 2 , B , ( C ♯ A ) ∗ ) ¯ = co ( W A ( B ) ¯ ⋅ W A ( C ) ¯ ) and W max A ( M 2 , B , ( C ♯ A ) ∗ ) = co ( W max A ( B ) ⋅ W max A ( C ) ) , where W A ( ⋅ ) , W max A ( ⋅ ) and co ( ⋅ ) denote respectively the A-numerical range, the A-maximal numerical range and the convex hull. Here, the bar stands for the closure. The first equality allows us to establish that ‖ M 2 , B , ( C ♯ A ) ∗ ‖ A = ‖ B ‖ A ‖ C ‖ A , where ‖ ⋅ ‖ A and ‖ ⋅ ‖ A designate the A -operator seminorm and the A-operator seminorm, respectively.

‘I want to make a difference’: Students co-researching school cultures of gender and sexuality

Research on violence in schools has been dominated by ‘bullying’ discourse and methodologies that place individualised pathos at the centre of problematic behaviours. This focus has resulted in the neglect of broader structures of power such as gender, sexuality, race, ethnicity, and class in the violence enacted and encountered by young people. As a result, institutional solutions to school violence have often failed to acknowledge and respond to the ways that gender and sexuality might operate in the lives of young people, instead offering simplistic strategies to reduce ubiquitous forms of ‘bullying’. Simultaneously, political discourses and research challenges further prohibit young people from contributing to targeted and critical interventions into the socio-cultural practices of gender- and sexuality-making in schools. As outsiders to these nuanced, complex, and dynamic spaces, university-based researchers often lack vocabulary and literacy, especially in relation to always-shifting meanings around gender and sexuality. In this paper, I argue for the need to actively include young people in research praxis to co-investigate school cultures of gender and sexuality using a Community-Led Research (CLR) approach. Reporting on work with ten student co-researchers that formed two groups in two schools in Australia, the paper details some of the ways that they impacted and benefitted the early phases of a three-year project, including through impacting methods, participant engagement, data analysis and the implementation of new strategies to improve school cultures of gender and sexuality. This article also illustrates some of the challenges and opportunities that CLR methodologies produce when working in less structured settings, including ethical and social challenges that emerge in iterative research praxis. Findings argue for the rights of young people to exert their hopes for their school cultures that relate to gender and sexuality, particularly through research-based empowerment. While often slower and more difficult, co-researching with young people can produce opportunities for transformation and change not only of research processes, but also of researchers and their communities.

Some inequalities for bi-slant Riemannian submersions in complex space forms

The goal of this paper is to analyze sharp-type inequalities including the scalar and Ricci curvatures of bi-slant Riemannian submersions in complex space forms. Then, for bi-slant Riemannian submersion between a complex space form and a Riemannian manifold, we give inequalities involving the Casorati curvature of the space ker [Formula: see text]. Also, we mention some examples.

Foundational aspects of a new matrix holomorphic structure

PurposeIn this paper we talk about complex matrix quaternions (biquaternions) and we deal with some abstract methods in mathematical complex matrix analysis.Design/methodology/approachWe introduce and investigate the complex space HC consisting of all 2 × 2 complex matrices of the form ξ=z1+iw1z2+iw2−z‾2−iw‾2z‾1+iw‾1, (z1,w1,z2,w2)∈C4.FindingsWe develop on HC a new matrix holomorphic structure for which we provide the fundamental operational calculus properties.Originality/valueWe give sufficient and necessary conditions in terms of Cauchy–Riemann type quaternionic differential equations for holomorphicity of a function of one complex matrix variable ξ∈HC. In particular, we show that we have a lot of holomorphic functions of one matrix quaternion variable.

Kłopotliwa wielka płyta. Sąsiedzkie i artystyczne praktyki transformowania przestrzeni sofijskich kompleksów mieszkaniowych

The aim of the article is to show social practices (neighbourly and artistic) activated around the example of Sofia’s dissonant heritage, which is the space of a housing complex. The author interprets the capital’s block housing estates as zones of transition between what is public and what is private, where specialized urban planning meets the everyday experience of residents, and the dehumanizing uniformity of the landscape clashes with the effects of amateur activities aimed at personalizing the environment. Using the methodological tools of anthropology of visual culture and arts and cultural studies, the author considers grassroots ways of adapting housing estate space to the residents’ needs, as well as attempts towards its visual revitalization using street art. The analysis shows that apartment blocks — as dissonant heritage, and at the same time a personal space — are today a field of activities of local communities and the artistic community, exploring the aesthetic and social potential of housing complexes.

Federated Learning with Efficient Aggregation via Markov Decision Process in Edge Networks

Federated Learning (FL), as an emerging paradigm in distributed machine learning, has received extensive research attention. However, few works consider the impact of device mobility on the learning efficiency of FL. In fact, it is detrimental to the training result if heterogeneous clients undergo migration or are in an offline state during the global aggregation process. To address this issue, the Optimal Global Aggregation strategy (OGAs) is proposed. The OGAs first models the interaction between clients and servers of the FL as a Markov Decision Process (MDP) model, which jointly considers device mobility and data heterogeneity to determine local participants that are conducive to global aggregation. To obtain the optimal client participation strategy, an improved σ-value iteration method is utilized to solve the MDP, ensuring that the number of participating clients is maintained within an optimal interval in each global round. Furthermore, the Principal Component Analysis (PCA) is used to reduce the dimensionality of the original features to deal with the complex state space in the MDP. The experimental results demonstrate that, compared with other existing aggregation strategies, the OGAs has the faster convergence speed and the higher training accuracy, which significantly improves the learning efficiency of the FL.

Further results on the concavity of operator means

In this work, we provide some further refinements for the following concavity inequalityAσB+CσD≤(A+C)σ(B+D) for positive invertible bounded linear operators A,B,C,D on a complex Hilbert space and for an operator mean σ. To this end, we use the operator majorization intended for comparing two n+1-tuples of pairs of operators, and prove a Hardy-Littlewood-Pólya-Karamata (HLPK) type theorem for the triangle map induced by σ. Next, we apply a specification of the HLPK Theorem for n=2 in order to obtain the required refinements.

Experimental investigation on adaptive grasping of a novel 3D-MSSPA gripper in complex space

Many achievements for soft grippers are made in the structure design and grasping of target objects. However, there are limited studies on grasping objects with different attributes by soft grippers with omnidirectional bending capabilities within environments containing obstacles. In this paper, drawing inspiration from the multi-segment structure of the biological finger, a 3D-multi-segment soft pneumatic actuator (3D-MSSPA) with a strong envelope and omnidirectional independent bending is designed and manufactured. Additionally, we developed a novel prototype for a soft gripper prototype. A quasi-static model is proposed to effectively describe the bending deformation of 3D-MSSPA. The experimental results show that, for an applied pressure of 40kPa, the single segment SPA achieves a maximum bending angle of 175.2°, aligning closely with the theoretical prediction. Furthermore, the omnidirectional bending ability of multi-segments is analyzed through the finite element method, and a position compensation method for gravity deviation is presented. Through experiments, the pressure fore and lifting force of the soft gripper are investigated, yielding maximum values of 11N and 4.08N, respectively. These results serve as a benchmark for the mass grasping range of the target objects. Finally, the experiments are conducted to demonstrate the soft gripper’s adaptive and flexible grasping capabilities for objects with varying attributes, in both free space and complex space with obstacles. These experiments showcase their ability to avoid obstacles, adaptively grasp objects, and their good envelope ability. The proposed 3D-MSSPA can provide good inspiration and reference for flexible applications, particularly in fields such as post-disaster rescue and rehabilitation medicine.

The emergence of synergistic complexities in Russia: A request for new university courses

The article explains the request for new approaches in the higher education as determined by the emergence of the “order out of chaos” (I. Prigogine) in the form of synergistic complexities. Their underestimation during perestroika and liberal reforms led to the crisis in the educational system and society. Until now, these actual issues have not been sufficiently represented in the educational programs of universities: students need synergistic knowledge that connects the achievements of social sciences, humanities and natural sciences and allows to analyze the nature of complex, non-linearly realities and manage them. The author considers the following synergistic realities: social-techno/digital-natural hybrids, complex spaces and time, interference risks and hybrid vulnerabilities, complex social identity, complexities of social consciousness, good and evil, scientific knowledge and education. To analyze the nature of synergistic realities, the author suggests a theoretical-methodological combination of three meta-paradigms: the theory of synergetics, which includes social-humanitarian and natural-scientific knowledge; theories that study the mutual influence of global and national-local factors; theories of nonlinear development and metamorphization. The author makes conclusions about the need for interdisciplinary and post-disciplinary courses for the study of synergistically complex realities, focusing on the Russian cultural specifics, which would allow to equip students with knowledge about complex causality; to form a creative personality with a set of cultural and professional competencies necessary for the preservation and reproduction of the Russian synergistic complexities with their national-cultural specifics; to develop a humanistic worldview and a socially responsible attitude towards society, macroand micro-worlds of nature and technological innovation, thus, contributing to the sovereign development of the country.

Optimizing biocompatible materials for personalized medical implants using reinforcement learning and Bayesian strategies

This study presents a comprehensive research methodology integrating computational approaches, statistical analysis, and visualization techniques to predict biocompatible materials for medical implants and evaluate predictive model performance (Whiting K. 2020, October). The initial phase involves data acquisition and preprocessing, organizing a representative dataset into a pandas data frame. Visualization of the dataset through bar, pie, and line charts provides insights into relationships between materials and functional attributes. The subsequent phase focuses on evaluating a predictive model using simulated datasets and key metrics such as accuracy, precision, recall, F1 score, and the receiver operating characteristics (ROC) curve with an area under the curve (AUC) value. Performance metrics are visually represented through bar charts and ROC curves, aiding stakeholders in understanding the model’s strengths and areas for improvement. The confusion matrix offers a granular examination of the model’s classification performance. The results and discussion section delves into graphical representations, emphasizing the material vs. strength/conductance/resistance/function chart, elucidating the diverse functional profiles of materials. The distribution of material functionality pie chart succinctly illustrates the proportional contribution of each material, aiding informed decision-making in material selection. The materials performance graph provides a nuanced understanding of material characteristics, guiding the development of personalized healthcare solutions. Model performance metrics and receiver operating characteristics graphs comprehensively assess the predictive model, while the confusion matrix details classification outcomes. This methodology and its visualizations contribute to predicting biocompatible materials, emphasizing the significance of advanced computational approaches for efficiently navigating the complex material space. The study’s outcomes inform both material scientists and healthcare professionals, guiding the development of personalized healthcare solutions tailored to specific patient needs.

Schwannoma of the pterygopalatine fossa: Transfacial approach and maxillary swing technique

The pterygopalatine fossa is a complex anatomic space related to the skull base. Primary and secondary tumors can be located in this area. The approach to this space can be challenging due to its anatomy and neurovascular relations. The aim of this paper is to describe systematically the maxillary swing technique used in the resection of a V2 Schwannoma located in the right pterygopalatine fossa. With this report we want to highlight that this classic approach should we considered in the management of Deep facial spaces when endoscopic approach cannot be performed.

Executive summary of the American Radium Society appropriate use criteria for brain metastases in epidermal growth factor receptor mutated-mutated and ALK-fusion non-small cell lung cancer.

The American Radium Society (ARS) Central Nervous System (CNS) committee reviewed literature on epidermal growth factor receptor mutated (EGFRm) and ALK-fusion (ALK+) tyrosine kinase inhibitors (TKIs) for the treatment of brain metastases (BrMs) from non-small cell lung cancers (NSCLC) to generate appropriate use guidelines addressing use of TKIs in conjunction with or in lieu of radiotherapy (RT). The panel developed three key questions to guide systematic review: can radiotherapy be deferred in patients receiving EGFR or ALK TKIs at (1) diagnosis or (2) recurrence? Should TKI be administered concurrently with RT (3)? Two literature searches were performed (May 2019 and December 2023). The panel developed 8 model cases and voted on treatment options using a 9-point scale, with 1-3, 4-6 and 7-9 corresponding to usually not appropriate, may be appropriate, and usually appropriate (respectively), per the UCLA/RAND Appropriateness Method. Consensus was achieved in only 4 treatment scenarios, all consistent with existing ARS-AUC guidelines for multiple BrM. The panel did not reach consensus that RT can be appropriately deferred in patients with BrM receiving CNS penetrant ALK or EGFR TKIs, though median scores indicated deferral may be appropriate under most circumstances. Whole brain RT with concurrent TKI generated broad disagreement except in cases with 2-4 BrM, where it was considered usually not appropriate. We identified no definitive studies dictating optimal sequencing of TKIs and RT for EGFRm and ALK+ BrM. Until such studies are completed, the committee hopes these cases guide decision- making in this complex clinical space.

Optimization of Installation Position for Complex Space Curve Weldments in Robotic Friction Stir Welding Based on Dynamic Dual Particle Swarm Optimization

Robotic friction stir welding (RFSW), with its wide application range, ample working space, and task flexibility, has emerged as a vital development in friction stir welding (FSW) technology. However, the low stiffness of serial industrial robots can lead to end-effector deviations and vibrations during FSW tasks, adversely affecting the weld quality. This paper proposes a dynamic dual particle swarm optimization (DDPSO) algorithm through a new comprehensive stability index that considers both the stiffness and vibration stability of the robot to optimize the installation position of complex space curve weldments, thereby enhancing the robot’s stability during the FSW process. The algorithm employs two independent particle swarms for exploration and exploitation tasks and dynamically adjusts task allocation and particle numbers based on current results to fully utilize computational resources and enhance search efficiency. Compared to the standard particle swarm optimization (PSO) algorithm, the DDPSO approach demonstrated superior search capabilities and stability of optimization results. The maximum fitness value improved by 4.2%, the average value increased by 12.74%, and the concentration level of optimization results rose by 72.91% on average. The new optimization method pioneers fresh perspectives for optimizing the stability of RFSW, providing significant grounds for the process optimization and offline programming of complex spatial curve weldments.

Few‐shot learning for satellite characterisation from synthetic inverse synthetic aperture radar images

AbstractSpace situational awareness systems primarily focus on detecting and tracking space objects, providing crucial positional data. However, understanding the complex space domain requires characterising satellites, often involving estimation of bus and solar panel sizes. While inverse synthetic aperture radar allows satellite visualisation, developing deep learning models for substructure segmentation in inverse synthetic aperture radar images is challenging due to the high costs and hardware requirements. The authors present a framework addressing the scarcity of inverse synthetic aperture radar data through synthetic training data. The authors approach utilises a few‐shot domain adaptation technique, leveraging thousands of rapidly simulated low‐fidelity inverse synthetic aperture radar images and a small set of inverse synthetic aperture radar images from the target domain. The authors validate their framework by simulating a real‐case scenario, fine‐tuning a deep learning‐based segmentation model using four inverse synthetic aperture radar images generated through the backprojection algorithm from simulated raw radar data (simulated at the analogue‐to‐digital converter level) as the target domain. The authors results demonstrate the effectiveness of the proposed framework, significantly improving inverse synthetic aperture radar image segmentation across diverse domains. This enhancement enables accurate characterisation of satellite bus and solar panel sizes as well as their orientation, even when the images are sourced from different domains.

Finite Element Method-Based Dynamic Modeling Framework for Flexible Continuum Manipulators

Abstract Flexible continuum manipulators (FCMs) are gaining importance because of their maneuverability and pliability in confined and complex spaces, where rigid link manipulators underperform. However, the dynamic behavior and control of the FCM are quite challenging due to its complex nonlinear behavior. In this study, a finite element-based dynamic model framework is derived that accounts for the geometric nonlinearities and inertial effects. An experimental setup of tendon-driven FCM, consisting of a flexible backbone, is developed to validate the model. The modal analysis of the model is in agreement with the analytical solutions, with less than 10% error. The model is also validated for various loading conditions on the tip-actuated tendon-driven FCM. The steady-state tip position predictions are within 15% of the ground truth.

A sublinear functional based approximated equivalence to optimality and duality for multiobjective programming in complex spaces

In this paper, we introduce a new approximation approach for a class of multiobjective programming problems in complex spaces and their duals. Using a sublinear functional, an approximated problem is constructed at a given feasible solution of the original problem. The equivalence between the solution of the considered multiobjective complex programming problem and its approximated problem is established by deriving the sufficiency theorem under the F-convexity assumption. An example showing this equivalence is also presented. Further, corresponding to the approximated problem, the Mond–Weir and Wolfe-type approximated dual problems are formulated. The duality results for the Mond–Weir and Wolfe-type dual problems are derived using the duality results established for the approximated Mond–Weir and Wolfe-type dual problems, respectively. The importance of the optimality conditions and proposed approximation approach is highlighted in a blind equalization problem in a communication system.

Periodic Self-Triggered Impulsive Synchronization of Hybrid Stochastic Complex-Valued Delayed Networks

This paper studies the synchronization problem for hybrid stochastic delay complex-valued networks with time-varying multi-links (HSDCNT) via periodic self-triggered impulsive control (PSIC). Therein, PSIC combines with the benefits of impulsive control and periodic self-triggered strategy. Moreover, time delays, stochastic disturbances, and time-varying multi-links are considered simultaneously. Particularly, the synchronization for HSDCNT is studied on the complex space and no split real and imaginary parts. Actually, different from the previous PSIC-based results, this paper extends the real-valued neural networks to HSSs, delayed systems, and complex-valued networks, which is significant and challenging. Then, as a practical application, we consider the synchronization of a kind of Cohen-Grossberg neural network in the complex space. Finally, a numerical example is utilized for demonstration.