1,348 research outputs found
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
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