Hasil untuk "Art"

D. Pashley, F. Tay, L. Breschi et al.

R. Baetens, B. P. Jelle, A. Gustavsen

Abstract Aerogels are regarded as one of the most promising high performance thermal insulation materials for building applications today. With a thermal conductivity down to 13 mW/(m K) for commercial products they show remarkable characteristics compared to traditional thermal insulation materials. Also the possibility of high transmittances in the solar spectrum is of high interest for the construction sector. With the proper knowledge they give both the architect and engineer the opportunity of re-inventing architectural solutions. Within this work, a review is given on the knowledge of aerogel insulation in general and for building applications in particular.

A. P. James, B. Dasarathy

Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multi-modal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. This review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion. We characterize the medical image fusion research based on (1) the widely used image fusion methods, (2) imaging modalities, and (3) imaging of organs that are under study. This review concludes that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.

Pengxiang Zhao, Na Li, D. Astruc

Sibel A. Alumur, B. Kara

E. S. Gardner

Lane Gormley

H. Raiffa

P. Schwartz

T. Soong, B. Spencer

D. Knuth

E. Tylor

G. Carpenter, S. Grossberg

A. Gell

L. Comtet, J. Nienhuys

W. Press

Ismail Kimuli, John Baptist Kirabira

Kampala faces increasing congestion, air pollution, and dependence on fossil fuels, driven by widespread reliance on diesel minibuses and motorcycle taxis. Existing models—KAMPALA-TIMES, KLAP-TIMES, and GKMA-TIMES–CGE—show strong potential for electrified mass transit to reduce emissions, change commuter behavior, and boost macroeconomic welfare. However, these studies assume electric-bus reliability without examining the mechanical conditions needed to achieve their projected outcomes. This study combines system-level modeling insights with vehicle-level engineering analysis to identify key mechanical factors necessary for the successful deployment of electric Bus Rapid Transit (e-BRT) in Kampala. It considers drivetrain torque for steep gradients, battery thermal management in hot equatorial climates, and regenerative braking efficiency in traffic congestion, alongside policy, infrastructure, and grid readiness. Mechanical performance links modeling to implementation—adequate torque, thermal stability, and regenerative braking efficiency directly affect service reliability, headway adherence, fleet uptime, and lifecycle costs. These operational factors influence commuter mode choices, the realism of bottom-up pathways, and the broader economic benefits predicted in top-down scenarios. Engineering reliability must be a core policy consideration, guiding procurement standards, charging infrastructure design, and multisector coordination among KCCA, MoWT, MEMD, and Uganda’s power utilities. Incorporating mechanical parameters into future bottom-up or hybrid models, combined with digital-twin testing and degradation-aware analytics, will enable Kampala to serve as a living laboratory for low-carbon mobility transitions across Sub-Saharan Africa.

Dongli Deng, Sen Zhao, Meixia Miao

Local Differential Privacy (LDP) and its personalized variants (PLDP) have been widely used for privacy-preserving data analytics. However, existing schemes often enforce a uniform indistinguishability level among users, failing to accommodate the nuanced privacy needs of diverse individuals. To address this, we propose User-Distinguished Local Differential Privacy (UDPLDP), a novel framework that formalizes user-level distinguishability to support more flexible, non-uniform privacy budgets. Under this framework, we tackle the fundamental task of frequency range queries, namely UDPLDP-Tree, which overcomes the challenge due to limited user-level distinguishability, insufficient robustness in estimation under complex data distributions, and the assumption of uniform privacy requirements across different attributes in existing multi-dimensional schemes. To demonstrate the effectiveness, we conduct extensive experiments and the results show that UDPLDP-Tree reduces the mean squared error (MSE) by about 30–50% compared with a recent state-of-the-art baseline.

Qi Cao, Jianwen Tao, Yufang Dan et al.

Abstract Unsupervised domain adaptive object detection (UDA-OD) aims to deploy a detector trained on source domain(s) to a new, unlabeled target domain. Carrying out mean-teacher self-training for UDA-OD poses a significant challenge, given that its success depends heavily on the quality of pseudo boxes. While many earlier researches have mainly centered on cross-domain transferability, they often neglect the rich intra- and inter-domain semantic structures. As a result, this neglect empirically restricts the discriminative abilities of the learning model. In our study, we have found a notable alignment and synergy across contrastive learning, prototype learning, and mean-teacher self-training. Building on this insight, we introduce the Prototype-oriented C ontrastive Mean Teacher (PoCoMT) for UDA-OD, a thorough and flexible framework that seamlessly integrates these three techniques to extract the most beneficial learning signals. Specifically, PoCoMT firstly generate more diverse and reliable probabilistic outputs from self-training through maximizing information entropy and maintaining semantic consistency; secondly, PoCoMT strives to reduce both intra-domain and inter-domain prototypical contrastive learning losses by elaborately designing a Prototype Alignment Network (ProtoAN) module, which fosters intra-domain feature aggregation, aligns inter-domain class structures, and reduces semantic loss between weak and strong augmentations of target domain data. Our ProtoAN can serve as a plugin module for traditional self-training frameworks to tackle the key problem of semantic loss in UDA-OD. Extensive experiments demonstrate that PoCoMT attains new state-of-the-art performance.

Dongyu Lu, Haiyan Yan, Guodong Yuan et al.

Abstract Human psychological adaptation to indoor thermal conditions, particularly through perceived control, is increasingly recognized for its potential to influence building energy consumption. Despite this, its quantifiable impact remains unexplored. Here, we conducted a field survey (n = 922) using air conditioning (AC) access as a variable to examine how perceived control mediates occupants’ thermal adaptation during summer. The perceived control levels of occupants were collected in the form of voting. Before the installation of AC, improved perceived control mitigated thermal sensation in warm environments (> 30.5 ℃). After being granted operational rights to AC, the tolerance of the occupants to the warm environment increased. Using DesignBuilder and EnergyPlus, we modeled and simulated building energy consumption, revealing that increased perceived control could reduce cooling-related carbon emissions by up to 46.6% per day. This study highlights the role of psychological factors in energy sustainability within built environments.