335598 research outputs found
Sort by
CO Cycle: Experimentally-informed Simulation Workflows for Injection, Storage, and Utilization in a Circular Economy
No full textRevealing Protein–Protein Interactions Using a Graph Theory‐Augmented Deep Learning Approach
No full text
Flexibility at a cost: Industrial battery storage and the breakdown of grid fee fairness in Germany
No full textThe transformation of the electricity system towards higher shares of renewable energy necessitates increased flexibility on the demand side. The industrial sector, which accounts for over 40% of Germany’s total electricity demand, exhibits significant heterogeneity in terms of production processes, load characteristics and grid use patterns. Consequently, it is considered a primary source of such flexibility. This study examines the economic and regulatory implications of deploying battery energy storage systems (BESS) in industrial settings, focusing on how different grid fee policy frameworks influence operational strategies, total costs of companies as well as their impact on grid operators. Using a mixed-integer linear programming model applied to more than 800 real-world industrial load profiles, we explicitly capture the diversity of industrial electricity demand and assess three regulatory scenarios: the current framework with incentives for atypical and intensive grid usage, and two proposed reforms incorporating dynamic energy prices and revised capacity prices. The objective of the model is a minimization of each company’s annual total costs, with the model deciding how many BESS modules should be built. Results show that BESS adoption leads to an average cost reduction in electricity procurement costs of 14.3%, with some companies reaching cost reductions of over 30%. At the same time companies that make use of atypical grid usage increase their maximum peak load on average by 51.7%, while the average grid fee payments are reduced by 41.6%. It is shown that by removing capacity price components of grid fees and by fully dynamizing energy price components, cost and grid fee payment reductions as well as peak loads are increased significantly. This raises concerns about unintended system effects such as increased network congestion or transformer overloading. In contrast, retaining capacity components supports more equitable cost allocation and reduces the risk of cross-subsidization, whereby smaller consumers subsidize larger, more flexible ones. These findings underscore the importance of cost-reflective grid tariffs that align network charges with underlying system costs, and incentivize grid-friendly behavior. From a policy perspective, economic efficiency ought to be balanced with potential distributional effects, taking into account temporal and locational price signals enabling the efficient deployment of industrial flexibility
Ev2Gray: event-only intensity imaging – occlusion-referenced log-intensity reconstruction
No full textDiese Arbeit stellt Ev2Gray vor, eine Methode zur Erzeugung von Pseudo-Grauwertbildern aus reinen Event-Kamera-Daten, die eine zentrale Limitation ereignisbasierter Sensoren adressiert: fehlende Erscheinungsinformation in statischen Szenen. Durch Überstreichen des Sichtfelds mit einem bewegten opaken Streifen wird kontrollierte Verdeckung und Aufdeckung erzwungen, sodass alle Pixel kurzzeitig dieselbe Intensität beobachten. Dies etabliert einen Intensitätsanker, der pixelweise Werte aus polaritätsaufgelösten Events ermöglicht, die relative Helligkeitsstrukturen ohne Frames, Training oder Zusatzsensorik erfassen. Ev2Gray demonstriert einen transparenten Weg zur Rekonstruktion von Strukturen, Tonwertverläufen und Details aus Event-Streams. Die physikalische Fundierung unterstreicht die Relevanz für Vision-Pipelines, die Erscheinungsinformation aus reinen Event-Strömen ableiten
Structure and Composition of a Novel Refractory Ni-Containing CrMoNbTaVW High-Entropy-Alloy Thin Film
No full textMechanism of Catalytic Thermal Decomposition of Ammonium Perchlorate in the Presence of TiCT
No full textThe thermal decomposition of ammonium perchlorate (AP) proceeds via a well-established stepwise mechanism. However, its catalytic decomposition under combustion conditions is not yet fully understood. This study investigates and clarifies the catalytic decomposition pathway of AP in the presence of TiCT, a novel two-dimensional (2D) material with unique structural properties. MXene was chosen for its exceptional conductivity, large surface area, and layered architecture, which provide active sites for redox interactions and enhance the thermal decomposition rate of AP. During combustion of AP-based solid rocket propellants, MXene acts as a catalyst, promoting more complete and rapid oxidation reactions. The combustion products were thoroughly analyzed using X-ray phase analysis, and based on the obtained data, stoichiometric equations for the potential reaction pathways were proposed. These equations highlight the formation of metal oxides and intermediate chlorinated compounds. Furthermore, a schematic model illustrating the catalytic action of TiCT was developed, showing the interaction between AP molecules and MXene’s surface functional groups. These findings advance understanding of nanocatalyst behavior in energetic materials and offer insights for improving solid-propellant performance via MXene incorporation
Strain‐Induced Piezo‐Optoelectronic Coupling in Monolayer MoS
No full textPiezo-optoelectronic coupling, the direct modulation of photoresponse by strain-induced piezoelectric polarization, is a theoretically promising route to adaptive, programmable optoelectronics, yet it remains experimentally elusive in scalable two-dimensional systems. Here, we present large-area monolayer MoS as a robust platform for probing and controlling this coupling at the atomic limit, and a single device made on it demonstrates integrated functionality for energy generation, strain sensing, and photodetection within a unified configuration. Using dual AC resonance tracking piezoresponse force microscopy, we quantify an out-of-plane piezoelectric coefficient (d = 0.64 pm/V) and demonstrate a pronounced, strain-tunable internal piezoelectric polarization that enables exciton dissociation under low bias and illumination, distinct from conventional photodetection approaches. While absolute responsivity is modest, the observed strain-induced enhancement of photocurrent and the clear correlation with measured piezoresponse reveal that mechanical deformation can be leveraged as a precise control knob for charge separation and light–matter interaction in ultrathin semiconductors. These findings provide direct experimental insight into piezo-optoelectronic coupling, establishing monolayer MoS as a multifunctional and scalable platform for future studies of coupled electromechanical photonic phenomena and for the development of programmable optoelectronics, hybrid sensors, and next-generation flexible devices
PWNN: Power-Wasting Neural Network As Remote Fault Injector
No full textThe explosive growth of AI-driven services has led to cloud-based Field Programmable Gate Array (FPGA) accelerators as key enablers of high-performance training and inference in modern data centers. Since 2024, the demand for deploying large AI workloads, especially Large Language Model (LLM), in the cloud has increased dramatically, intensifying competition among cloud providers and increasing pressure on shared FPGA infrastructures. This increasing reliance highlights the need for robust hardware security measures for cloud FPGAs. A particularly serious threat is fault injection attacks, which exploit dynamic voltage fluctuations to induce timing faults, potentially compromising functional integrity and bypassing cryptographic protections. However, existing verification procedures and structural Design Rule Check (DRC) remain blind to attacks embedded in benign-looking circuits. In this paper, we present Power-Wasting Neural Network (PWNN), a novel adversarial technique that leverages the inherent switching behavior of neural network operations to act as a power-waster circuit under adversarial input patterns. We systematically explore network architectures, and input patterns to craft configurations that induce voltage fluctuations capable of triggering timing faults for successful Differential Fault Analysis (DFA). Our PWNN implementation uses a standard open-source tool chain and passes all pre-implementation verification checks, while covertly inducing faults at runtime. We demonstrate on both the AMD ZCU104 and PYNQ-Z2 that PWNN can reliably cause timing faults on the critical path of a co-located AES-128 block cipher, enabling the rapid collection of correct/faulty ciphertext pairs needed for DFA-based key recovery. These results show that functionally correct, DRC compliant accelerators can serve as powerful, adaptive fault injectors that invalidate assumptions about bitstream security and hardware isolation
Mitigating Label Noise using Prompt-Based Hyperbolic Meta-Learning in Open-Set Domain Generalization
No full textOpen-Set Domain Generalization (OSDG) is a challenging task requiring models to accurately predict familiar categories while minimizing confidence for unknown categories to effectively reject them in unseen domains. While the OSDG field has seen considerable advancements, the impact of label noise–a common issue in real-world datasets–has been largely overlooked. Label noise can mislead model optimization, thereby exacerbating the challenges of open-set recognition in novel domains. In this study, we take the first step towards addressing Open-Set Domain Generalization under Noisy Labels (OSDG-NL) by constructing dedicated benchmarks derived from widely used OSDG datasets, including PACS and DigitsDG. We evaluate baseline approaches by integrating techniques from both label denoising and OSDG methodologies, highlighting the limitations of existing strategies in handling label noise effectively. To address these limitations, we propose HyProMeta, a novel framework that integrates hyperbolic category prototypes for label noise-aware meta-learning alongside a learnable new-category agnostic prompt designed to enhance generalization to unseen classes. Our extensive experiments demonstrate the superior performance of HyProMeta compared to state-of-the-art methods across the newly established benchmarks. The source code of this work is released at https://github.com/KPeng9510/HyProMeta