Optimization Technology (United States)

Amidst the time-consuming process of conducting laboratory tests to confirm contamination incidents, utilities must promptly implement measures to mitigate the impact of potential contamination without undue overreaction. This paper proposes a novel approach for mitigating the impact of contamination threats (an unconfirmed contamination event) in smart water networks. By leveraging smart valves, the consumer flows are regulated to create a decreasing pressure gradient from the contamination source to a flushing point, facilitating efficient contamination flushing with minimal consumer inconvenience. The decreasing pressure gradients are formulated as constraints within an online weighted sum multiobjective optimization problem. Additionally, two methods for identifying these pressure constraints are presented. To assess the efficacy of the proposed framework, testing is conducted using a realistic water network model.

ABSTRACT Neurodiverse children do not always benefit from a typical learning environment and therefore may be at a disadvantage when learning alongside their peers. Many of these children have different preferences when it comes to the formatting of learning materials, which may impact their performance. The current study examined the preferences of 204 adults with diagnosed ADHD, autism, dyslexia, dyscalculia, and dyspraxia to identify formatting preferences for formatting variables previously shown to influence performance: font style, font size, character spacing, line spacing, title design, background colour, reward icon and instruction layout. Preferences were obtained by means of a survey, where participants rated their preferences on 5-point, Likert-type scales (1 = strongly disagree to 5 = strongly agree). Participants were also asked to rank the options provided for each formatting variable from least favourite to favourite. Results indicated consistent preference across all neurodiverse groups, with one category in each being significantly preferred across all groups. The exception to this was background colour, in which each neurodiverse group preferred a different colour.

Abstract. Estimating 3D deformation with high spatial resolution from TLS point clouds is beneficial for geomonitoring. Existing methods for this task primarily rely on geometric data. They do not use radiometric information although it is often available as well. This leaves potential for improvement. To address this, we propose an approach that utilizes RGB images—captured by built-in cameras of TLS scanners and co-registered with TLS point clouds—to generate dense 3D displacement vector fields for deformation analysis. Our method comprises three main steps: (1) applying the Efficient-LoFTR algorithm to establish dense 2D pixel correspondences on RGB images across two epochs; (2) projecting 3D points from both epochs onto RGB images and establishing 3D point correspondences by matching the projected pixels with the established 2D correspondences; (3) clustering the point cloud of one epoch and refining the 3D point correspondences within each cluster to produce the final displacement vector fields. Experiments on real measurements obtained from a rockfall simulator and from a real-world landslide demonstrate that our method achieves comparable accuracy to state-of-the-art geometry-based methods, with improved density and computational efficiency. By using radiometric features, our approach complements geometry-based methods, suggesting that combining both will enhance coverage and/or accuracy for geomonitoring applications.

Machine learning models were applied to predict the scintillation performances of organic–inorganic hybrid metal halides (OIMHs), focusing on their photoluminescent quantum yield (PLQY). Random Forest and Decision Tree algorithms identified the most critical structural parameter of organic molecules influencing the M···M distance between metal ions and correlated PLQY value, with an optimal distance of approximately 8 Å correlating with enhanced luminescence efficiency. This prediction was experimentally validated through the synthesis of several OIMH compounds, demonstrating strong agreement between predicted and measured PLQY values. The machine learning approach not only enabled the screening of efficient compounds but also deepened the understanding of how structural factors, such as the structure of organic molecules, govern scintillation properties. These findings underscore the potential of machine learning in accelerating the development of next-generation luminescent materials with improved performance, offering a powerful tool for future material design and optimization.

This paper examines the transformative potential of fifth-generation (5G) wireless technology, analyzing its impact on various sectors, emerging opportunities, and implementation challenges. Through a comprehensive review of recent literature and empirical data from early deployments, this study investigates the technological advancements, economic implications, and societal benefits of 5G networks. The research employs a mixed-methods approach, combining systematic literature review with case study analysis of successful 5G implementations across different regions. Key findings indicate that while 5G technology promises revolutionary improvements in connectivity, speed, and latency, significant challenges persist in infrastructure development, spectrum allocation, and regulatory frameworks. The study reveals that successful 5G deployment requires coordinated efforts among stakeholders, substantial capital investment, and addressing cybersecurity concerns. This research contributes to understanding the complex landscape of 5G technology adoption and provides practical insights for policymakers, telecommunications companies, and technology implementers navigating the transition to next-generation networks.

Muitas iniciativas de mudanças organizacionais e pedagógicas da Educação nas últimas décadas tiveram como foco a análise e adequação de seus processos internos ampliando as abordagens de ensino em tempos de transformações tecnológicas na nossa sociedade, como as que presenciamos atualmente. O presente artigo, tem como objetivo, propor um modelo de aplicação computacional utilizando o conceito de Machine Learning, dinâmicas envolvendo Teorias da Aprendizagem, Estilos de Aprendizagem e neurociências para mapear estrategicamente perfis de aprendizagem para a Educação a Distância (EaD), com o propósito de conceber um novo meio de medição de perfil de aprendizagem personalizado disponibilizados para educadores em geral.

This paper presents a method for forecasting reliability of distribution network based on data mining technology and BP neural network. According to principal component analysis, the effect factors of distribution network big data are found. And a BP neural network model is established to forecast supply reliability of target year. The proposed method is illustrated using the data of a city distribution network in China for nearly 8 years.