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As technology increasingly aligns with users' personal values, traditional models of usability, focused on functionality and specifically effectiveness, efficiency, and satisfaction, may not fully capture how people perceive and evaluate it. This study investigates how the warm-glow phenomenon, the positive feeling associated with doing good, shapes perceived usability. An experimental approach was taken in which participants evaluated a hypothetical technology under conditions designed to evoke either the intrinsic (i.e., personal fulfillment) or extrinsic (i.e., social recognition) dimensions of warm-glow. A Multivariate Analysis of Variance as well as subsequent follow-up analyses revealed that intrinsic warm-glow significantly enhances all dimensions of perceived usability, while extrinsic warm-glow selectively influences perceived effectiveness and satisfaction. These findings suggest that perceptions of usability extend beyond functionality and are shaped by how technology resonates with users' broader sense of purpose. We conclude by proposing that designers consider incorporating warm-glow into technology as a strategic design decision.

Fully decentralized learning algorithms are still in an early stage of development. Creating modular Gossip Learning strategies is not trivial due to convergence challenges and Byzantine faults intrinsic in systems of decentralized nature. Our contribution provides a novel means to simulate custom Gossip Learning systems by leveraging the state-of-the-art Flower Framework. Specifically, we introduce GLow, which will allow researchers to train and assess scalability and convergence of devices, across custom network topologies, before making a physical deployment. The Flower Framework is selected for being a simulation featured library with a very active community on Federated Learning research. However, Flower exclusively includes vanilla Federated Learning strategies and, thus, is not originally designed to perform simulations without a centralized authority. GLow is presented to fill this gap and make simulation of Gossip Learning systems possible. Results achieved by GLow in the MNIST and CIFAR10 datasets, show accuracies over 0.98 and 0.75 respectively. More importantly, GLow performs similarly in terms of accuracy and convergence to its analogous Centralized and Federated appro

Considering the effects of glow discharge plasmas on plasma-facing materials and the applications such as coating, cleaning and surface treating, this work has been done to investigate the energy of ions of dc glow discharge plasmas. On the way towards this goal, a plasma chamber has been simulated via COMSOL Multiphysics software. Then, a gridded energy analyzer has been simulated and designed. In the next step, the analyzer has been constructed and tested to measure the energy of plasma ions. The devise contains a grid which is negatively biased to the same potential as the glow discharge cathode electrode. It discriminates plasma ions based on their energies, which are accelerated due to sheath potential drop before colliding with the cathode. The obtained energy distribution function from experiments has been compared to that of simulated plasma. The experimental results show that there are different groups of ions each in local thermal equilibrium in dc glow discharge plasmas.

Read noise in infrared sensor arrays remains a major obstacle for ground- and space-based astronomy. It has long been recognized that the upcoming extremely large telescopes cannot meet their full potential unless read noise is significantly improved, and it is also a prohibitive constraint on the Habitable Worlds Observatory, a space telescope with the goal of detection and characterization of nearby Earth-like exoplanets. The main strategy for lowering read noise is averaging through multiple non-destructive reads. However, this typically results in less noise reduction than the 1/$\sqrt{N}$ scaling predicted by theory. In this work, we show the poor averaging behavior can largely be explained by readout glow, photon emission from the sensor electronics that generates photoelectrons in the pixels during readout. Because glow accumulates with reads rather than averaging, this imposes a fundamental noise floor of σ_{\rm min} ~ 1.5 sigma_RN^(1/2)G^(1/4). This limits averaging in HxRG-like sensors to about 2-3 e- of noise, and linear-mode avalanche photodiodes (LmAPDs) to about 0.5 e-. We present laboratory data using both sensor architectures, with the LmAPD following the predicted

The results of an experiment with a generator of a stream of charged drops are reported. The glow of subjects placed in the stream is observed. The volume of the glowing region reaches 20 cm$^3$ at a current less than 20 $μ$A through the object. Ideas are expressed concerning the connection between St$.$Elmo's fire and the observed glow.

Inverse rendering of indoor scenes remains challenging due to the ambiguity between reflectance and lighting, exacerbated by inter-reflections among multiple objects. While natural illumination-based methods struggle to resolve this ambiguity, co-located light-camera setups offer better disentanglement as lighting can be easily calibrated via Structure-from-Motion. However, such setups introduce additional complexities like strong inter-reflections, dynamic shadows, near-field lighting, and moving specular highlights, which existing approaches fail to handle. We present GLOW, a Global Illumination-aware Inverse Rendering framework designed to address these challenges. GLOW integrates a neural implicit surface representation with a neural radiance cache to approximate global illumination, jointly optimizing geometry and reflectance through carefully designed regularization and initialization. We then introduce a dynamic radiance cache that adapts to sharp lighting discontinuities from near-field motion, and a surface-angle-weighted radiometric loss to suppress specular artifacts common in flashlight captures. Experiments show that GLOW substantially outperforms prior methods in mate

In order to break the limitation of plasma nitriding technology,which can be applied to a few nonmetallic gaseous elements, the "Double Glow Discharge Phenomenon" was found and then invented the "Double Glow Plasma Surface Metallurgy Technology". This double glow plasma surface metallurgy technology can use any element in the periodic table of chemical elements for surface alloying of metal materials. Countless surface alloys with special physical and chemical properties have been produced on the surfaces of conductive materials.By using double glow discharge phenomenon,a series of new plasma technologies,such as the double glow plasma graphene technology, double glow plasma brazing technology,double glow plasma sintering technology, double glow plasma nanotechnology,double glow plasma cleaning technology, double glow plasma carburizing without hydrogen and so on, have been invented.A very simple phenomenon of double glow discharge can generate about 10 plasma innovation technologies, which fully shows that there is still a lot of innovation space on the basis of classical physics.This paper briefly introduces the basic principles,functions and characteristics of each technology. T

Most existing Low-Light Image Enhancement (LLIE) methods are primarily designed to improve brightness in dark regions, which suffer from severe degradation in nighttime images. However, these methods have limited exploration in another major visibility damage, the glow effects in real night scenes. Glow effects are inevitable in the presence of artificial light sources and cause further diffused blurring when directly enhanced. To settle this issue, we innovatively consider the glow suppression task as learning physical glow generation via multiple scattering estimation according to the Atmospheric Point Spread Function (APSF). In response to the challenges posed by uneven glow intensity and varying source shapes, an APSF-based Nighttime Imaging Model with Near-field Light Sources (NIM-NLS) is specifically derived to design a scalable Light-aware Blind Deconvolution Network (LBDN). The glow-suppressed result is then brightened via a Retinex-based Enhancement Module (REM). Remarkably, the proposed glow suppression method is based on zero-shot learning and does not rely on any paired or unpaired training data. Empirical evaluations demonstrate the effectiveness of the proposed method

Self-organized patterns (SOP) in plasma discharges arise from the complex interplay of electric field, reactive species and charged particles, driven by non-linear plasma dynamics. While studies have explored SOP formation in various configurations, no systematic comparison of positive and negative DC glow discharges has been conducted to explain why SOP form exclusively when polarization is negative. This study aims to analyze SOP formation mechanisms by comparing electrical, optical and spectral properties of positive and negative DC glow discharges interacting with a grounded water surface. Key differences in gas temperature, electric field and reactive species distribution are hence identified. For positive DC glow discharges (PGD), the gas temperature remains in the 350-370 K range, while the reduced electric field remains below 100 Td across the gap. The plasma is dominated by OH and N2* species, whose excitation results from direct electron impact and energy transfer in a low-field environment. The absence of strong ionization and electric field gradients leads to a spatially homogeneous emission layer on the liquid surface, resulting in a circular uniform plasma (CUP) patte

The glow-to-arc transition is a critical phenomenon in plasma discharges, commonly leading to detrimental effects. The physical mechanisms triggering this transition remain poorly understood. The advent of a discharge called Hyper-Power Impulse Magnetron has opened possibilities. Hyper-Power Impulse Magnetron allows the glow mode to be maintained over long periods (1 ms) and at high-current densities (> 5 A/cm$^2$), which has unveiled certain features in the glow-to-arc transition. This work focuses on a graphite target that transits easily in the arc regime. The high-speed video-camera analysis revealed specific properties of graphite in ExB discharges, and the statistical study of the arc transition revealed differences from other refractory target materials. The early stage of cathodic spot formation, observed as bright dots, will be presented and analyzed within the known "ecton" and "vaporization" models for spot formation. This experimental study highlights the role of luminous spot formation prior to arc transition, with possible optimization on the stability of magnetron discharges.

Agentic Workflows (AWs) have emerged as a promising paradigm for solving complex tasks. However, the scalability of automating their generation is severely constrained by the high cost and latency of execution-based evaluation. Existing AW performance prediction methods act as surrogates but fail to simultaneously capture the intricate topological dependencies and the deep semantic logic embedded in AWs. To address this limitation, we propose GLOW, a unified framework for AW performance prediction that combines the graph-structure modeling capabilities of GNNs with the reasoning power of LLMs. Specifically, we introduce a graph-oriented LLM, instruction-tuned on graph tasks, to extract topologically aware semantic features, which are fused with GNN-encoded structural representations. A contrastive alignment strategy further refines the latent space to distinguish high-quality AWs. Extensive experiments on FLORA-Bench show that GLOW outperforms state-of-the-art baselines in prediction accuracy and ranking utility.

We present GLOW, a transformer-based particle flow model that combines incidence matrix supervision from HGPflow with a MaskFormer architecture. Evaluated on CLIC detector simulations, GLOW achieves state-of-the-art performance and, together with prior work, demonstrates that a single unified transformer architecture can effectively address diverse reconstruction tasks in particle physics.

We present Gaze and Glow, an interactive installation that reveals the often-invisible efforts of social media editing. Through narrative personas, experimental videos, and sensor-based interactions, the installation explores how audience attention shapes users' editing practices and emotional experiences. Deployed in a two-month public exhibition, Gaze and Glow engaged viewers and elicited responses. Reflexive thematic analysis of audience feedback highlights how making editing visible prompts new reflections on authenticity, agency, and performativity. We discuss implications for designing interactive systems that support selective memory, user-controlled visibility, and critical engagement with everyday digital self-presentation.

This paper proposes a novel approach for creating an inverse electron distribution function (EDF). Based on the obtained criteria for the formation of an inverse EDF in a non-uniform plasma, studies are conducted in low- and medium-pressure glow discharges with flat and hollow cathodes. The results of the numerical modeling and theoretical analysis are used to present reliable criteria and scaling for the evaluation of the possible inversion of the EDF under specific conditions. By solving the nonlocal Boltzmann kinetic equation in energy and coordinate variables, it is shown that the simplest way to implement the inversion of the EDF is in a glow discharge with a hollow cathode. For such discharges, practical recommendations are developed and specific conditions for the experimental detection of an inverse EDF are identified.

Wave-optics phenomena in gravitational lensing occur when the signal's wavelength is commensurate to the gravitational radius of the lens. Although potentially detectable in lensed gravitational waves, fast radio bursts and pulsars, accurate numerical predictions are challenging to compute. Here we present novel methods for wave-optics lensing that allow the treatment of general lenses. In addition to a general algorithm, specialized methods optimize symmetric lenses (arbitrary number of images) and generic lenses in the single-image regime. We also develop approximations for simple lenses (point-like and singular isothermal sphere) that drastically outperform known solutions without compromising accuracy. These algorithms are implemented in Gravitational Lensing of Waves (GLoW): an accurate, flexible, and fast code. GLoW efficiently computes the frequency-dependent amplification factor for generic lens models and arbitrary impact parameters in O(1 ms) to O(10 ms) depending on the lens configuration and complexity. GLoW is readily applicable to model lensing diffraction on gravitational-wave signals, offering new means to investigate the distribution of dark-matter and large-scale

The location of surface brightness maxima (e.g. apocentre and pericentre glow) in eccentric debris discs are often used to infer the underlying orbits of the dust and planetesimals that comprise the disc. However, there is a misconception that eccentric discs have higher surface densities at apocentre and thus necessarily exhibit apocentre glow at long wavelengths. This arises from the expectation that the slower velocities at apocentre lead to a "pile up'" of dust, which fails to account for the greater area over which dust is spread at apocentre. Instead we show with theory and by modelling three different regimes that the morphology and surface brightness distributions of face-on debris discs are strongly dependent on their eccentricity profile (i.e. whether this is constant, rising or falling with distance). We demonstrate that at shorter wavelengths the classical pericentre glow effect remains true, whereas at longer wavelengths discs can either demonstrate apocentre glow or pericentre glow. We additionally show that at long wavelengths the same disc morphology can produce either apocentre glow or pericentre glow depending on the observational resolution. Finally, we show that

Adversarial attacks aim to perturb images such that a predictor outputs incorrect results. Due to the limited research in structured attacks, imposing consistency checks on natural multi-object scenes is a promising yet practical defense against conventional adversarial attacks. More desired attacks, to this end, should be able to fool defenses with such consistency checks. Therefore, we present the first approach GLOW that copes with various attack requests by generating global layout-aware adversarial attacks, in which both categorical and geometric layout constraints are explicitly established. Specifically, we focus on object detection task and given a victim image, GLOW first localizes victim objects according to target labels. And then it generates multiple attack plans, together with their context-consistency scores. Our proposed GLOW, on the one hand, is capable of handling various types of requests, including single or multiple victim objects, with or without specified victim objects. On the other hand, it produces a consistency score for each attack plan, reflecting the overall contextual consistency that both semantic category and global scene layout are considered. In e

In this paper, we address the single image haze removal problem in a nighttime scene. The night haze removal is a severely ill-posed problem especially due to the presence of various visible light sources with varying colors and non-uniform illumination. These light sources are of different shapes and introduce noticeable glow in night scenes. To address these effects we introduce a deep learning based DeGlow-DeHaze iterative architecture which accounts for varying color illumination and glows. First, our convolution neural network (CNN) based DeGlow model is able to remove the glow effect significantly and on top of it a separate DeHaze network is included to remove the haze effect. For our recurrent network training, the hazy images and the corresponding transmission maps are synthesized from the NYU depth datasets and consequently restored a high-quality haze-free image. The experimental results demonstrate that our hybrid CNN model outperforms other state-of-the-art methods in terms of computation speed and image quality. We also show the effectiveness of our model on a number of real images and compare our results with the existing night haze heuristic models.

The problem of the possibility of observing a uniform sky glow through the throat of a Morris--Thorne wormhole by an observer located in another asymptotically flat space-time is considered. It is shown that an individual star has multiple images, and the image of a luminous sky has a complex structure and contains ring structures. The reasons for the emergence of such structures are considered. The distribution of radiation intensity in the image along the radial coordinate is constructed.In addition, an image of the Morris-Thorne wormhole was constructed against the background of uniform sky radiation in the observer's space. A comparison to observations can be made by producing a synthetic Morris-Thorne type wormhole image against a CMB background. This image has been constructed by a combination of the images for inner and outer areas.