In large-scale, non-convex problems, adaptive stochastic global optimization methodologies are frequently used. But improving these methodsâ\euro™ search effectiveness and re- peatability frequently calls for well-customized methodologies. Intensity-modulated radiation treatment (IMRT) planning faces the critical but difficult problem of automatically selecting the beam angle. Despite numerous efforts, the clinical IMRT practice continues to be not particularly satisfying for the reason of the excessive processing of the inverse circumstance. The objective problem that we would discuss in this research is the 4-Dimensional Radiation Therapy (4DRT) Inverse Planning Problem. Previous research in this domain makes use of Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). This article aims in proposing a hybrid model that combines both the Genetic Algorithm as well as the Particle Swarm Optimization and chooses the best resulting parameters. The proposition makes use of the concept of threading that allows both the GA, and the PSO to run in parallel at the same time.Â

Interferometric Synthetic Aperture Radar (InSAR) time series analysis is a powerful technique to estimate long-term water level changes in wetlands ecosystems. However, few studies have applied InSAR on wetlands that are highly segmented by canals and levees due in part to the challenge of selecting qualified reference points to minimize unwrapping errors, which, by contrast, is a relatively easy task for unsegmented wetlands. Here we developed a new method to automatically select the optimal reference point for InSAR time series analysis. The method selects reference points by considering temporal behaviors of coherence and InSAR phase connectivity from each reference point to its wetland of interest. We tested the method on six managed and highly segmented wetland units within the Sacramento National Wildlife Refuge in the Central Valley, California. We validated the InSAR measurement against water depth gauge measurements during a low water depth (< 0.05) for five out of six units and similar RMSE for the remaining one. This new automatic method enables us to maximize the performance of InSAR to predict water depth and could be applied to other types of InSAR applications as well.

 Tuning the conductance of a memristive device is a  process that requires energy and involves power dissipation. In  this letter, the role the memory state programming strategy plays  in this connection is investigated. To this end, the device model  equations representing the electron transport and metal  ion/oxygen vacancy displacement caused by the application of an external signal must be solved consistently. However, if instead of  considering the applied voltage as the model input, a memory state  trajectory is assumed, the model equations can be decoupled  allowing an analytic description of the problem. In order to  accomplish this objective a more accurate version of the dynamic  memdiode model is used which incorporates additional physical  considerations in the characteristic switching times. It is  demonstrated that alternative trajectories (concave, convex, and  sigmoidal) lead to a variety of energy consumption-maximum  dissipated power relationships indicating the key role played by  the selected programming strategy. This kind of study contributes  to the basic understanding of the writing process of memristors (synaptic weight assignment) and sheds light on its electrical  consequences. Â

Abstract: We propose two novel motion planners for a robotic manipulator with a passive end-effector that is free to sway during and after the robot’s motion. The planners utilize Dynamic Programming to generate trajectories that damp the end-effector’s residual sway while ensuring that the boom tip—the point to which the end-effector is attached—follows a collision-free path or time-dependent waypoints. Our use case is a crane of a forwarder machine, a log-loading machine in the forestry industry, with a passive grapple. In the cluttered forest environment, accurate path following and grapple sway damping are critical to increase the operation efficiency and avoid harming the machine and environment. The results of the simulation in a high-fidelity multibody-dynamics simulator showcase the effectiveness of our methodology in achieving exact path following or timed waypoints following and the residual sway damping. In particular, an average reduction of 75% in the residual sway is demonstrated, as compared to fifth, sixth, and tenth order polynomial trajectories, in six test cases, including common paths used by operators to pick and place logs. Other merits of our Dynamic Programming trajectories are that they are smooth, computationally inexpensive, and result in reduced residual sway even for nonzero initial sway conditions. Moreover, the generality of our methodology opens a new way to design anti-sway motion planners for construction cranes or quadrotors with a slung payload, in addition to serial manipulators with passive end-effectors. Note to Practicioners: This work was motivated by the problems arising in the operation of log-loading cranes in the forestry industry: the problems of the end-effectorâ\euro™s large sway during crane reconfiguration and the collision between the crane and obstacles, which are detrimental to the efficiency of the operation. Similar issues arise, for example, in construction cranes transporting large hanging objects. We propose a novel methodology to address both problems by generating smooth and computationally inexpensive trajectories for joint motion of the crane. The approach begins with the model of the sway motion and the definition of the collision-free path. Then, our Dynamic Programming algorithm generates anti-sway trajectories that satisfy the joint constraints. The results in a high-fidelity simulator show that our motion planners lead to precise path following and significant sway damping, and also confirm its superiority compared to polynomial trajectories, commonly used in industries. Our methodology is also applicable to other dynamic systems with freely hanging objects, such as multi-degree-of-freedom robotic manipulators, construction cranes, and quadrotors carrying a slung payload. A possible limitation is that the methodology necessitates finding the sway dynamics model and payload properties of these systems.

This paper provides a review of Extended Reality (ER) technologies and applications. The different technologies related to ER, such as Virtual Reality, Augmented Reality and Mixed Reality are introduced and explained, following an historical perspective. The basic technical concepts behind these technologies are briefly explained. Current applications are presented, including references to actual tools and systems, in order to clarify the concepts and the potential of the technologies.Â

If tokenized assets are to be a reality in the future decentralized Web3 then transaction keys need to be distributed and under the control of the asset-owners. This requires a careful design of wallet systems based on trusted hardware. A core feature needed for wallet systems is the attestation of the state of the transaction keys in the wallet without disclosure of the keys. This feature is relevant for relying parties such as insurance providers who need to perform risk assessment based on the security quality of the environment inside the wallet system that is protecting the transaction keys. In the longer term, all key-bearing devices that participate in a decentralized tokenized asset network will need to be hardened using trusted hardware, with attestation capabilities for detecting and countering cyberattacks.

This paper presents a homogenized electro-thermal model for high-temperature superconductor (HTS) stacks made of multiple REBCO tapes, which reduces the computational complexity of their thermal analysis. The electromagnetic analysis is performed in both the H- and Tâ\euro“A-formulation. An average heat capacity and an orthotropic heat conductivity are derived for the equivalent thermal properties of the HTS coil. The proposed homogenization method is verified to produce the same results as the full model with an error below 5% for a range of scenarios, including transport losses, magnetization losses, AC frequencies from 50 to 300 Hz and a temperature range from 25 to 80 K. Finally, the model is applied to study a permanent magnet motor with HTS armature windings, and is verified to produce accurate results as long as the stabilizer eddy current losses are low. Compared to the full multilayer model, the newly proposed model allows for the use of a significantly coarser mesh, which reduces the computation time by an order of magnitude.

Deepfakes the term was coined in 2018 by a Reddit user who created a Reddit forum dedicated to the creation and use of deep learning software for synthetically face swapping female celebrities into pornographic videos. According to Sumsub’s research in 2023, the top-5 identity fraud types in 2023 are AI-powered fraud, money muling networks, fake IDs, account takeovers, and forced verification. The country most attacked by deepfakes is Spain; the most forged document worldwide is the UAE passport, whereas Latin America is the region where fraud has increased in every country. On November 24, 2023, the Union Government of India issued an advisory to social media intermediaries to identify misinformation and deepfakes. A deepfake refers to a specific kind of synthetic media where a person in an image or video is swapped with another person’s likeness. AI-generated deepfakes have emerged as a complex and pervasive challenge in today’s digital landscape, enabling the creation of remarkably convincing yet falsified multimedia content. This review paper examines the multifaceted landscape of deepfakes, encompassing their technological underpinnings, societal implications, detection methodologies, and ethical considerations. The review aggregates and synthesizes a broad array of scholarly articles, studies, and reports to elucidate the diverse typologies of deepfakes, including face-swapping, voice cloning, and synthetic media, while delineating the intricate methodologies employed in their fabrication. This review culminates in an overview of future directions and recommendations, advocating for proactive measures to counter the escalating threat posed by AI-generated deepfakes.

In recent years, the likelihood of wildfire occurrence has increased in many North American communities as changes in climate have led to longer, more deadly fire seasons.  Many Americans, especially those living in Western states, have reported frequent drought and wildfire conditions, leading to an increased need for a modeling program to assess wildfire risk at a low computational cost. The research objective of this paper was to develop a machine learning model capable of producing real-time wildfire risk assessments using five geospatial datasets: Land Fire Mean Return, Annual Precipitation, Sentinel-2 Imagery, Land Cover, and Moisture Deficit & Surplus. To create the model, three separate machine learning architectures were implemented (U-Net, DeepLabV3, and the Pyramid Scene Parsing Network) and then applied to the study area of San Bernardino County, CA for the year 2020. In addition, this study demonstrated a proof of concept for further inquiry into combining artificial intelligence and geospatial datasets to create useful insights. Â

Numerous industrial thermal processes and fluid processes can be described by distributed parameter systems (DPSs), wherein many process parameters and variables vary in space and time. Early internal abnormalities in the DPS may develop into uncontrollable thermal failures, causing serious safety incidents. In this study, the multiscale information fusion is proposed for internal abnormality detection and localization of DPSs under different scenarios. We introduce the dissimilarity statistic as a means to identify anomalies for lumped variables, whereas spatial and temporal statistic measures are presented for the anomaly detection for distributed variables. Through appropriate parameter optimization, these statistic functions are integrated into the comprehensive multiscale detection index, which outperforms traditional single-scale detection methods. The proposed multiscale statistic has good physical interpretability from the system disorder degree. Experiments on the internal short circuit (ISC) of a battery system have demonstrated that our proposed method can swiftly identify ISC abnormalities and accurately pinpoint problematic battery cells under various working conditions.Â

Learning in sequential decision making problems can be significantly affected by the choice of reward function. Although crucial for learning success, reward function design remains a complex task, requiring expertise and not always aligned with human preferences. Large Language Models (LLMs) offer a promising avenue for reward design through textual prompts that leverage the prior knowledge and contextual reasoning of LLMs. Despite their potential, LLM responses lack guarantees and their reasoning abilities are poorly understood. To mitigate potential LLM errors, we introduce an alternative approach: learning a new reward function utilizing LLM outputs as auxiliary rewards. This problem is tackled through a bi-level optimization framework, showcasing the method’s proficiency in both optimal reward acquisition and adaptive reward shaping. The proposed approach demonstrates robustness and effectiveness, offering a novel strategy for enhancing reinforcement learning outcomes.     Â

Wideband millimeter wave and terahertz systems face severe nonlinearity and other practical impairments such as transmitter and receiver I/Q imbalances (IQIs), carrier frequency offset, and phase noise. Based on a simplified yet effective received signal model, this paper proposes a low-complexity digital post-cancellation (DPC) framework for transmitter IQI and overall system nonlinearity mitigation. The nonlinearity parameters with reduced nonlinearity order are firstly estimated with low-complexity using a novel transmission protocol incorporating both frame rotation and preamble power scaling. Through widely linear system equalization and interference cancellation, the signal distortion caused by frequency-dependent IQI and nonlinearity are then mitigated with significant performance improvement. The mean-squared-error measurement of the EMP-modelled signals also provides a practical means for the nonlinear system identification and characterization. Both simulation and experiment results obtained from a millimeter wave system with 2.5 GHz bandwidth and 73.5 GHz carrier frequency are presented to verify the theoretical analyses and demonstrate the effectiveness of the DPC technology.

Artificial Intelligence (AI) stands out as a revolutionary force driving profound shifts in strategic decision-making processes. This paper embarks on a comprehensive exploration of the transformative role of AI in strategic management. Beginning with an analysis of AI’s evolution, we delve into its integration into business strategy formulation, juxtaposing traditional strategic paradigms with AI-infused approaches. A sector-specific investigation highlights industries significantly altered by AI, underscoring its pervasive influence across diverse sectors. We also explore real-world scenarios, recording AI-enhanced success stories and revealing the inherent challenges businesses face during AI’s integration. To effectively navigate this terrain, it’s crucial for businesses to ensure transparency and explainability in AI systems (Castelvecchi, 2016) [9], maintain rigorous audit trails (Brundage et al., 2018) [4], actively avoid biases (Buolamwini & Gebru, 2018) [7], and promote inclusive development (Knight, 2017) [18]; (O’Neil, 2017) [24]; (West, 2018) [23]; (Wirtz, Weyerer, & Geyer, 2019) [14]. Understanding AI’s limitations (Brynjolfsson & McAfee, 2014) [5], championing continuous learning (Davenport & Ronanki, 2018) [11], adopting a collaborative approach (Agrawal, Gans, & Goldfarb, 2022) [1], prioritizing security (Zhu & Liu, 2018) [12], and engaging stakeholders are key in AI-driven strategic formulation (Fountaine, McCarthy, & Saleh, 2019) [13]. Beyond its practical implications, AI’s strategic dominance raises complex ethical dilemmas. This research clarifies the multifaceted ethical challenges posed by AI-driven decision-making, with a focus on biases inherent to algorithmic predictions and regulatory challenges. By integrating AI’s evolutionary path, tangible business impacts, ethical considerations, and best practices, this paper offers an enlightening journey through the AI-augmented strategic landscapes of contemporary businesses.

Mathematics is the foundational discipline for all sciences, engineering, and technology, and the pursuit of a normed division algebra of all finite dimensions represents a paramount mathematical objective. In the quest for a real three-dimensional, normed, associative division algebra, Hamilton discovered quaternions, constituting a non-commutative division algebra of quadruples. Subsequent investigations revealed the existence of only four division algebras over reals, each with dimensions 1, 2, 4, and 8. This study transcends such limitations by introducing generalized hypercomplex numbers extending across all dimensions, serving as extensions of traditional complex numbers. The space formed by these numbers constitutes a non-distributive normed division algebra extendable to all finite dimensions. The derivation of these extensions involves the definitions of two new $\pi$-periodic functions and a unified multiplication operation, designated as spherical multiplication, that is fully compatible with the existing multiplication structures. Importantly, these new hypercomplex numbers and their associated algebras are compatible with the existing real and complex number systems, ensuring continuity across dimensionalities. Most importantly, like the addition operation, the proposed multiplication in all dimensions forms an Abelian group while simultaneously preserving the norm. In summary, this study presents a comprehensive generalization of complex numbers and the Euler identity in higher dimensions, shedding light on the geometric properties of vectors within these extended spaces. Finally, we elucidate the practical applications of the proposed methodology as a viable alternative for expressing a quantum state through the multiplication of specified quantum states, thereby offering a potential complement to the established superposition paradigm. Additionally, we explore its utility in point cloud image processing.

The material is dedicated to various aspects of graphene metasurface implementation of the Reconfigurable Intelligent Surface (RIS) in the framework of the incoherent paradigm for NOMA-RIS-MIMO transmission design. Digital coded model of the metasurface is considered in the framework of the general representation of the graphene metasurface as a planar antenna array (PA). The simplification of the PA in the form of the non-uniform ULA (n-ULA) array and uniform linear array (ULA) is explained as well. In this regard array orthogonalization was applied in the form of the universal Prolate Spheroidal Wave Functions (PSWF) eigenbasis and it was used for the formulation of the RIS controller algorithm.

For wideband transceivers operating at millimeter wave and terahertz frequencies, the implementation of conventional digital predistortion for nonlinearity mitigation faces significant challenges due to the limited availability and/or complexity of high-speed digital signal processing. In this paper, a simple received signal model is proposed for wideband system with nonlinearity and other practical impairments, such as transmitter (Tx) and receiver (Rx) I/Q imbalances (IQIs), carrier frequency offset (CFO), and phase noise, to enable low-complexity impairment mitigation. An expanded memory polynomial (EMP) model is firstly proposed to capture Tx IQI and the nonlinearity over the entire transceiver chain. Exploiting the CFO and a novel transmission protocol, a blind Rx IQI estimation is also proposed. The noise enhancement after Rx IQI and CFO compensation is then evaluated as a noise factor related to the mean-square-error of the Rx IQI estimation. As a result, the received signal of the wideband system is finally modelled as an EMP plus additive noises followed by a band-limited noisy receiver filter. Simulation results using a millimeter wave system with 2.5 GHz bandwidth and 73.5 GHz carrier frequency are presented to verify the accuracy of the EMP modelling and validate the theoretical analyses.

In this paper, we develop an economic mechanism for the DSO to exploit the flexibility of the LEM and integrate it into the techno-economic framework for coordinated procurement of DF supported by the novel active network technologies, in this case SOPs. The LEM flexibility mechanism independently clears the LEM, while respecting the network constraints and optimizing network operation cost by leveraging it with the social welfare of the LEM participants. The coordinated procurement framework is based on the bi-level programming approach, where the lower level ensures unconditional clearing of the LEM bids and offers, while the upper level minimizes network operational cost, subject to network constraints and DF originating from all three types of flexibility (i.e., network, contracted, and LEM). Both, the mechanism and the framework can be applied by the DSO to ensure secure and economic network operation while facilitating LEM clearing in the independent manner. In the numerical study, the framework has been applied to IEEE 33-bus benchmark system, where we investigate the performance of the coordination method proposed, evaluate the value of the LEM flexibility mechanism to the DSO, and the value of the novel active network technology (i.e., SOP). Using realistic flexibility demand and supply data, the numerical study showed that the optimal utilization of network and LEM flexibilities can lead to the DSO contracted flexibility service to become unnecessary. While the estimated value of coordinated procurement of the LEM and DSO contracted flexibilities accounts for 26.6% of the network operation cost, technology supported procurement of the LEM flexibility only can reduce the original network operation cost almost twice (by 48.9%).

In this study, we introduce an innovative off-tracking optimisation-based framework designed for Generalised N-Trailer vehicles. The task of generating feasible references for the segments of the interconnected vehicle has historically been burdensome. This challenge becomes even more pronounced as the number of trailers increases, particularly when the quantity of trailers fluctuates during operations. Our proposed method address this complexity by autonomously generating pose references for both the tractor and each trailer of the vehicle. This is achieved through a parameterisation of the desired path and attitudes to be traversed. Notably, our approach incorporates the system's model into the optimisation process as a constraint. This guarantees that the generated reference remains within the bounds of feasibility, even when the nominal path does not. Several simulated and field experiments with a tractor pulling 2 trailers show the effectiveness of the proposed method.