Large-scale discovery and validation of functional elements in the human genome

A report on the genomics workshop 'Identification of Functional Elements in Mammalian Genomes', Cold Spring Harbor, New York, 11-13 November 2004

Dynamic Analysis of Buried Structures

The transient response of a circular cylindrical cavity in a linear elastic or viscoelastic infinite or semi-infinite medium under conditions of plane strain is examined. The method employed is the dynamic Boundary Integral Equation Method in conjunction with the Laplace Transform. The results obtained are compared with results stemming from analytical solutions, where available, and numerical solutions to assess the accuracy, efficiency and applicability of the method

Synthesis and Structural Characterization of a Metal Cluster and a Coordination Polymer Based on the [Mn6(μ4-O)2]10+ Unit

A new 1-D coordination polymer {[Mn6O2(O2CMe)10(H2O)4]·2.5H2O}∞ (1·2.5H2O)∞ and the cluster [Mn6O2(O2(O2CPh)10 (py)2(MeCN)(H2O)]·2MeCN (2·2MeCN) are reported. Both compounds were synthesized by room temperature reactions of [Mn3(μ3-O)(O2CR)6(L)2(L′)] (R = Me, L = L′ = py, (1·2.5H2O)∞; R = Ph, L = py, L′ = H2O, 2·2MeCN) in the presence of 3-hydroxymethylpyridine (3hmpH) in acetonitrile. The structures of these complexes are based on hexanuclear mixed-valent manganese carboxylate clusters containing the [Mn4IIMn2III(μ4-O)2]10+ structural core. (1·2.5H2O)∞ consists of zigzag chain polymers constructed from [Mn6O2(O2CMe)10(H2O)4] repeating units linked through acetate ligands, whereas 2·2MeCN comprises a discrete Mn6-benzoate cluster

ChatGPT and Persuasive Technologies for the Management and Delivery of Personalized Recommendations in Hotel Hospitality

Recommender systems have become indispensable tools in the hotel hospitality industry, enabling personalized and tailored experiences for guests. Recent advancements in large language models (LLMs), such as ChatGPT, and persuasive technologies, have opened new avenues for enhancing the effectiveness of those systems. This paper explores the potential of integrating ChatGPT and persuasive technologies for automating and improving hotel hospitality recommender systems. First, we delve into the capabilities of ChatGPT, which can understand and generate human-like text, enabling more accurate and context-aware recommendations. We discuss the integration of ChatGPT into recommender systems, highlighting the ability to analyze user preferences, extract valuable insights from online reviews, and generate personalized recommendations based on guest profiles. Second, we investigate the role of persuasive technology in influencing user behavior and enhancing the persuasive impact of hotel recommendations. By incorporating persuasive techniques, such as social proof, scarcity and personalization, recommender systems can effectively influence user decision-making and encourage desired actions, such as booking a specific hotel or upgrading their room. To investigate the efficacy of ChatGPT and persuasive technologies, we present a pilot experi-ment with a case study involving a hotel recommender system. We aim to study the impact of integrating ChatGPT and persua-sive techniques on user engagement, satisfaction, and conversion rates. The preliminary results demonstrate the potential of these technologies in enhancing the overall guest experience and business performance. Overall, this paper contributes to the field of hotel hospitality by exploring the synergistic relationship between LLMs and persuasive technology in recommender systems, ultimately influencing guest satisfaction and hotel revenue.Comment: 17 pages, 12 figure

Evaluating Conjunctive Triple Pattern Queries over Large Structured Overlay Networks

We study the problem of evaluating conjunctive queries com- posed of triple patterns over RDF data stored in distributed hash tables. Our goal is to develop algorithms that scale to large amounts of RDF data, distribute the query processing load evenly and incur little network traffic. We present and evaluate two novel query processing algorithms with these possibly conflicting goals in mind. We discuss the various tradeoffs that occur in our setting through a detailed experimental eval- uation of the proposed algorithms