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

Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis

Super-resolution microscopy provides direct insight into fundamental biological processes occurring at length scales smaller than light’s diffraction limit. The analysis of data at such scales has brought statistical and machine learning methods into the mainstream. Here we provide a survey of data analysis methods starting from an overview of basic statistical techniques underlying the analysis of super-resolution and, more broadly, imaging data. We subsequently break down the analysis of super-resolution data into four problems: the localization problem, the counting problem, the linking problem, and what we’ve termed the interpretation problem

Quantitative Kinetic Models from Intravital Microscopy: A Case Study Using Hepatic Transport

The liver performs critical physiological functions, including metabolizing and removing substances, such as toxins and drugs, from the bloodstream. Hepatotoxicity itself is intimately linked to abnormal hepatic transport, and hepatotoxicity remains the primary reason drugs in development fail and approved drugs are withdrawn from the market. For this reason, we propose to analyze, across liver compartments, the transport kinetics of fluorescein-a fluorescent marker used as a proxy for drug molecules-using intravital microscopy data. To resolve the transport kinetics quantitatively from fluorescence data, we account for the effect that different liver compartments (with different chemical properties) have on fluorescein's emission rate. To do so, we develop ordinary differential equation transport models from the data where the kinetics is related to the observable fluorescence levels by "measurement parameters" that vary across different liver compartments. On account of the steep non-linearities in the kinetics and stochasticity inherent to the model, we infer kinetic and measurement parameters by generalizing the method of parameter cascades. For this application, the method of parameter cascades ensures fast and precise parameter estimates from noisy time traces

A novel method to accurately locate and count large numbers of steps by photobleaching

Photobleaching event counting is a single-molecule fluorescence technique that is increasingly being used to determine the stoichiometry of protein and RNA complexes composed of many subunits in vivo as well as in vitro. By tagging protein or RNA subunits with fluorophores, activating them, and subsequently observing as the fluorophores photobleach, one obtains information on the number of subunits in a complex. The noise properties in a photobleaching time trace depend on the number of active fluorescent subunits. Thus, as fluorophores stochastically photobleach, noise properties of the time trace change stochastically, and these varying noise properties have created a challenge in identifying photobleaching steps in a time trace. Although photobleaching steps are often detected by eye, this method only works for high individual fluorophore emission signal-to-noise ratios and small numbers of fluorophores. With filtering methods or currently available algorithms, it is possible to reliably identify photobleaching steps for up to 20-30 fluorophores and signal-to-noise ratios down to ∼1. Here we present a new Bayesian method of counting steps in photobleaching time traces that takes into account stochastic noise variation in addition to complications such as overlapping photobleaching events that may arise from fluorophore interactions, as well as on-off blinking. Our method is capable of detecting ≥50 photobleaching steps even for signal-to-noise ratios as low as 0.1, can find up to ≥500 steps for more favorable noise profiles, and is computationally inexpensive

Quantitative Mapping of Endosomal DNA Processing by Single Molecule Counting

Extracellular DNA is engulfed by innate immune cells and digested by endosomal DNase II to generate an immune response. Quantitative information on endosomal stage‐specific cargo processing is a critical parameter to predict and model the innate immune response. Biochemical assays quantify endosomal processing but lack organelle‐specific information, while fluorescence microscopy has provided the latter without the former. Herein, we report a single molecule counting method based on fluorescence imaging that quantitatively maps endosomal processing of cargo DNA in innate immune cells with organelle‐specific resolution. Our studies reveal that endosomal DNA degradation occurs mainly in lysosomes and is negligible in late endosomes. This method can be used to study cargo processing in diverse endocytic pathways and measure stage‐specific activity of processing factors in endosomes.Eine bildgebende Fluoreszenzmethode zur quantitativen Kartierung der endosomalen Prozessierung von Fracht‐DNA in Zellen des angeborenen Immunsystems mit Organellen‐spezifischer Auflösung (organellar single‐molecule, high‐resolution localization and counting; oSHiRLoC) wurde entwickelt. Mithilfe dieser Methode wurde gezeigt, dass die endosomale DNA‐Zersetzung hauptsächlich in Lysosomen erfolgt und in späten Endosomen zu vernachlässigen ist.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148254/1/ange201811746_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148254/2/ange201811746.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148254/3/ange201811746-sup-0001-misc_information.pd

The heat released during catalytic turnover enhances the diffusion of an enzyme

Recent studies have shown that the diffusivity of enzymes increases in a substrate-dependent manner during catalysis,. Although this observation has been reported and characterized for several different systems–, the precise origin of this phenomenon is unknown. Calorimetric methods are often used to determine enthalpies from enzyme-catalysed reactions and can therefore provide important insight into their reaction mechanisms,. The ensemble averages involved in traditional bulk calorimetry cannot probe the transient effects that the energy exchanged in a reaction may have on the catalyst. Here we obtain single-molecule fluorescence correlation spectroscopy data and analyse them within the framework of a stochastic theory to demonstrate a mechanistic link between the enhanced diffusion of a single enzyme molecule and the heat released in the reaction. We propose that the heat released during catalysis generates an asymmetric pressure wave that results in a differential stress at the protein–solvent interface that transiently displaces the centre-of-mass of the enzyme (chemoacoustic effect). This novel perspective on how enzymes respond to the energy released during catalysis suggests a possible effect of the heat of reaction on the structural integrity and internal degrees of freedom of the enzyme

Counting Photobleach Steps and the Dynamics of Bacterial Predators

poster abstractPhotobleach (PB) counting is used to enumerate proteins by monitoring how the light intensity in some regions decreases by quanta as individual fluorophores photobleach. While it is straightforward in theory, PB counting is often difficult because fluorescence traces are noisy. In this work, we quantify the sources of noise that arise during photobleach counting to construct a principled likelihood function of observing the data given a model. Noise in the signal could arise from background fluorescence, variable fluorophore emission, and fluorophore blinking. In addition, in a completely different direction, we explore the role of hydrodynamic interactions on the dynamics of bacterial predators. Our study shows that Bdellovibrio (BV) - a model predatory bacterium - is susceptible to self-generated hydrodynamic forces. Near surfaces and defects, these hydrodynamic interactions co-localize BV with its prey, and this may enhance BV’s hunting efficiency

Hydrodynamic Hunters

The Gram-negative Bdellovibrio bacteriovorus (BV) is a model bacterial predator that hunts other bacteria and may serve as a living antibiotic. Despite over 50 years since its discovery, it is suggested that BV probably collides into its prey at random. It remains unclear to what degree, if any, BV uses chemical cues to target its prey. The targeted search problem by the predator for its prey in three dimensions is a difficult problem: it requires the predator to sensitively detect prey and forecast its mobile prey’s future position on the basis of previously detected signal. Here instead we find that rather than chemically detecting prey, hydrodynamics forces BV into regions high in prey density, thereby improving its odds of a chance collision with prey and ultimately reducing BV’s search space for prey. We do so by showing that BV’s dynamics are strongly influenced by self-generated hydrodynamic flow fields forcing BV onto surfaces and, for large enough defects on surfaces, forcing BV in orbital motion around these defects. Key experimental controls and calculations recapitulate the hydrodynamic origin of these behaviors. While BV’s prey (Escherichia coli) are too small to trap BV in hydrodynamic orbit, the prey are also susceptible to their own hydrodynamic fields, substantially confining them to surfaces and defects where mobile predator and prey density is now dramatically enhanced. Colocalization, driven by hydrodynamics, ultimately reduces BV’s search space for prey from three to two dimensions (on surfaces) even down to a single dimension (around defects). We conclude that BV’s search for individual prey remains random, as suggested in the literature, but confined, however—by generic hydrodynamic forces—to reduced dimensionality