Modeling and Harmonic Balance Analysis of Superconducting Parametric Amplifiers for Qubit Readout:A Tutorial

The parametric modulation of reactive elements in circuits for amplification dates back to World War I, when Alexanderson and others proposed magnetic amplifiers for radio transmitters [1], [2], [3], [4]. The low-noise figure of the first parametric amplifiers, as a result of using passive elements, was on par with the standard at the time [5].</p

An Ultrasensitive Molecular Detector for Direct Sensing of Spin Currents at Room Temperature

The experimental analysis of pure spin currents at interfaces is one major goal in the field of magnonics and spintronics. Complementary to the established Spin-Hall effect using the spin-to-charge conversion in heavy metals for information processing, we present a novel approach based on spin pumping detection by an interfacial, resonantly excited molecular paramagnet adsorbed to the surface of the spin current generating magnet. Here, we show that the sensitivity of this electron paramagnetic resonance (EPR) detector can be enhanced by orders of magnitude through intramolecular transfer of spin polarization at room temperature. Our proof-of-principle sample consists of octahedral-shaped ferrimagnetic Fe3O4 nanoparticles covered by oleic acid (OA) which has two paramagnetic centers, S1 and S2. S1 arises from the chemisorption of OA and is located directly at the interface to Fe3O4. S2 originates from radical formation at the center of the molecule close to the double bond of oleic acid and is not influenced by chemisorption. Using ferromagnetic resonance (FMR) excitation of the Fe3O4 nanoparticles to pump spins into S1, a population inversion of the spin-split levels of S2 is formed, vastly enhancing the detection sensitivity on the atomic scale.</p

Comprehensive review and state of play in the use of photovoltaics in buildings

The integration of renewable energy technologies in architecture is crucial for achieving low-carbon buildings and cities. Building-integrated photovoltaics (BIPV) represent a dynamic intersection of energy technology and sustainable construction practices. Despite the numerous available products, BIPV installations remain limited, highlighting a global need for upscaling and capacity building. This paper comprehensively analyzes BIPV technology, covering advancements, challenges, and prospects. It examines BIPV integration into architectural designs, focusing on aesthetics, design flexibility, and product diversity. Key technological breakthroughs and innovative approaches are highlighted. The review also assesses the standardization and certification of BIPV systems, emphasizing standardized practices for quality and safety. Economic feasibility is a crucial focus, with an in-depth examination of factors influencing BIPV costs. The paper synthesizes existing literature to analyze the cost-effectiveness and economic sustainability of BIPV systems through life cycle cost analyses. Additionally, it explores novel integration options offered by digital design processes. This review stands out by providing an in-depth synthesis of technological advancements, market scenarios, and regulatory environments affecting BIPV. It integrates a multidisciplinary perspective, encompassing technological, economic, and policy dimensions from applied-oriented research and industry experience. The main contributions emphasize the importance of BIPV in architectural designs, economic viability, and digital design benefits. Overall, this review is a valuable resource for understanding BIPV's role in sustainable buildings, guiding future research, and informing policymakers, practitioners, and researchers in renewable energy, architecture, and sustainable construction.</p

Tailoring the properties of carbon molecular sieves membranes for the separation of propionic acid from aqueous solutions

In the fermentative production of propionic acid (PA), the major problem with batch fermentation systems is the strong inhibitory effect of PA on the production yield; one way to increase the yield is the in-situ removal of PA by using pervaporation. Acetic acid (AA) is the most important by-product in the fermentation; therefore, the membrane should be able to remove selectively PA from an aqueous solution containing AA. Considering that PA is more hydrophobic than AA and their kinetic diameter are 0.480 and 0.436 nm respectively, hydrophobic membranes with main pores in the range of around 0.5–0.6 nm with high permeation are required. Supported thin Carbon Molecular Sieve Membranes (CMSM) were prepared by the dip coating a porous alumina support into a solution containing resorcinol phenolic resin as carbon source. The hydrophobicity was obtained by carbonizing the polymer at temperatures higher than 750 °C and adding polyvinyl butyral (PVB) as pore forming agent and carbon contributor. PA with 88 % of purity was obtained by pervaporation of an aqueous solution containing 5 % of PA and 5 % of AA using a CMSM carbonized at 850 °C containing 1 % of PVB in the dipping solution.</p

Guidance for goal achievement in knowledge-intensive processes using intuitionistic fuzzy sets

Throughout the execution of a knowledge-intensive process (KiP), knowledge workers need to make critical decisions such as skipping a task or canceling a process instance. These decisions significantly impact the efficiency and effectiveness of KiP execution and should, therefore, be made in a well-informed manner. When historical data, such as event logs, is available, it can be leveraged to support knowledge workers in making these decisions. However, KiPs often lack useful historical data, as each KiP instance is unique and hardly repeatable. To address this issue, this paper proposes the novel concept of potential goal achievement, i.e., the extent to which a goal can be achieved at the end of the process, considering the collected (but incomplete) data, to support knowledge workers in efficiently executing KiPs. An approach based on Intuitionistic Fuzzy Sets (IFSs) is introduced to calculate the potential goal achievement without relying on historical data. The use of potential goal achievement in supporting knowledge workers’ decisions is demonstrated, and the effectiveness of the approach is evaluated through simulations. The results demonstrate that modeling and calculating potential goal achievement support knowledge workers in achieving goals more efficiently.</p

ST-DAGCN:A Spatiotemporal Dual Adaptive Graph Convolutional Network Model for Traffic Prediction

Accurately predicting traffic flow characteristics is crucial for effective urban transportation management. Emergence of artificial intelligence has led to the surge of deep learning methods for short-term traffic forecast. Notably, Graph Convolutional Neural Networks (GCN) have demonstrated remarkable prediction accuracy by incorporating road network topology into deep neural networks. However, many existing GCN-based models are based on the premise that the graph network is static, which may fail to do justice in replicatingthe situations in the real World. On one hand, real road networks are dynamic and undergo changes such as road maintenance and traffic control, leading to altered network structures over time. On the other hand, relationships between road sections can fluctuate due to factors like traffic accidents, weather conditions, and other events, which can significantly impact traffic patterns and result in inaccurate predictions if a static network and static relationshipsbetween nodes are assumed. To address these challenges, we propose the spatiotemporal dual adaptive graph convolutional network (ST-DAGCN) model for spatiotemporal traffic prediction, which utilizes a dual-adaptive adjacency matrix comprising both a static and a dynamic graph structure learning matrix. The dual-adaptive mechanism can adaptively learn the global features and the local dynamic features of the traffic states by updating the correlationsof nodes at each prediction step, while the gated recurrent unit (GRU), which is also a component of the model, extracts the temporal dependencies of traffic data. Through a comprehensive comparison analysis on two real-world traffic datasets, our model has achieved the highest prediction accuracy when compared to other advanced models.Accurately predicting traffic flow characteristics is crucial for effective urban transportation management. Emergence of artificial intelligence has led to the surge of deep learning methods for short-term traffic forecast. Notably, Graph Convolutional Neural Networks (GCN) have demonstrated remarkable prediction accuracy by incorporating road network topology into deep neural networks. However, many existing GCN-based models are based on the premise that the graph network is static, which may fail to do justice in replicating the situations in the real World. On one hand, real road networks are dynamic and undergo changes such as road maintenance and traffic control, leading to altered network structures over time. On the other hand, relationships between road sections can fluctuate due to factors like traffic accidents, weather conditions, and other events, which can significantly impact traffic patterns and result in inaccurate predictions if a static network and static relationships between nodes are assumed. To address these challenges, we propose the spatiotemporal dual adaptive graph convolutional network (ST-DAGCN) model for spatiotemporal traffic prediction, which utilizes a dual-adaptive adjacency matrix comprising both a static and a dynamic graph structure learning matrix. The dual-adaptive mechanism can adaptively learn the global features and the local dynamic features of the traffic states by updating the correlations of nodes at each prediction step, while the gated recurrent unit (GRU), which is also a component of the model, extracts the temporal dependencies of traffic data. Through a comprehensive comparison analysis on two real-world traffic datasets, our model has achieved the highest prediction accuracy when compared to other advanced models.</p

Local existence and uniqueness of solutions to the time-dependent Kohn–Sham equations coupled with classical nuclear dynamics

We prove the short-time existence and uniqueness of solutions to the initial-value problem associated with a class of time-dependent Kohn–Sham equations coupled with Newtonian nuclear dynamics, combining Yajima's theory for time-dependent Hamiltonians with Duhamel's principle, based on suitable Lipschitz estimates. We consider a pure power exchange term within a generalisation of the so-called local-density approximation, identifying a range of exponents for the existence and uniqueness of H2 solutions to the Kohn–Sham equations.</p

Spline-Based Rotor and Stator Optimization of a Permanent Magnet Synchronous Motor

This work features the magnetostatic optimization of a Permanent Magnet Synchronous Motor using 2D nonlinear simulations in an Isogeometric Analysis framework. The rotor and stator designs are optimized for both geometric parameters and surface shapes via modifications of control points. The scaling laws for magnetism are employed to allow for axial and radial scaling, enabling a thorough optimization of all critical machine parameters for multiple operating points. The process is carried out in a gradient-based fashion with the objectives of lowering motor material cost, torque ripple and losses. It is shown that the optimization can be efficiently conducted for many optimization variables and all objective values can be reduced.</p