Assumption-lean and Data-adaptive Post-Prediction Inference

A primary challenge facing modern scientific research is the limited availability of gold-standard data which can be both costly and labor-intensive to obtain. With the rapid development of machine learning (ML), scientists have relied on ML algorithms to predict these gold-standard outcomes with easily obtained covariates. However, these predicted outcomes are often used directly in subsequent statistical analyses, ignoring imprecision and heterogeneity introduced by the prediction procedure. This will likely result in false positive findings and invalid scientific conclusions. In this work, we introduce an assumption-lean and data-adaptive Post-Prediction Inference (POP-Inf) procedure that allows valid and powerful inference based on ML-predicted outcomes. Its "assumption-lean" property guarantees reliable statistical inference without assumptions on the ML-prediction, for a wide range of statistical quantities. Its "data-adaptive'" feature guarantees an efficiency gain over existing post-prediction inference methods, regardless of the accuracy of ML-prediction. We demonstrate the superiority and applicability of our method through simulations and large-scale genomic data

One Experience and Multiple Reviews: The Case of Upscale U.S. Hotels

Purpose– The present study aimed to understand the relationships between the various kinds of feedback received by hospitality operators. Information from guests, experts, and internal sources are often received, valued, and processed in various ways. The researchers sought to further explore the usage of such feedback and implications for theory and practice. Design/methodology/approach– A survey was sent to hotel general managers of four‐ and five‐diamond properties around the USA using the listing of the American Automobile Association (AAA). A total of 140 responses were received. The researchers utilized correlations and canonical correlation analysis to help understand the relationships among the variables. Findings– The results of the study revealed moderate to strong correlations between improvement in consumer‐generated feedback and customer satisfaction; between improvement in AAA ratings and customer satisfaction and mystery shopping scores. There were also moderate to high correlations among value placed in consumer‐generated and that placed on other electronic forms of electronic feedback such as social networking, blogs, and online travel agency feedback. Canonical correlation was also performed among the variables in the various correlation matrices. Two statistically significant dimensions emerged. The most influential variables in the first dimension were value placed on TripAdvisor and value placed on meeting planner feedback. The second dimension featured three influential variables: value placed on reviews in TripAdvisor, value placed on social networking, and perceived improvement in consumer‐generated ratings. Practical implications– The present research revealed two distinct sets of general managers: those who have a strong preference towards online feedback and those who place greater value in traditional sources of feedback such as letters from customers. Additionally, the researchers discovered some similarities between improvement in scores of experts and consumers. This in turn, points out to the existence of some universal aspects of service that appeal to both stakeholder groups. The different levels of value placed on various kinds of feedback points out to the need for tourism and hospitality operators to adopt a more comprehensive strategy to collect, analyze, and take appropriate actions based on such information. Originality/value– The researchers contribute to the nascent literature on consumer‐generated feedback by exploring its relationship to other variables. Furthermore, the study of various sources of feedback (i.e. guests, experts, and operators) is often studied separately in the tourism literature. It was the aim of this study to explore all of these together in order to better understand their relationships, value, and uses

Isolation and Characteristics of a Bacterial Strain for Deodorization of Dimethyl Sulfide

AbstractThe removal characteristics of dimethyl sulfide (DMS) with a peat packed tower were studied. The peat itself did not remove DMS. The peat inoculated with activated sludge as a source of microorganisms showed an efficient removal of DMS. Dominant microorganisms for degradation of DMS in the peat packed tower were some chemolithotrophic and non-acidophilic sulfur-oxidizing microorganisms originating from sludge. A dominant DMS-oxidizing strain Au7 was isolated and identified as chemolithotrophic Thiobacilli. Product of DMS oxidation by strain Au7 was sulfate. The optimum pH of DMS removal by strain Au7 was 7-5.45

Searching for the determinants of climate change interest

A meaningful CO2 mitigation policy is unlikely at the national level in the United States. What is currently happening and what is much more likely to occur in the future is city and regional level efforts of mitigation and adaptation. This paper aims to understand the geographic and socioeconomic characteristics of metropolitan areas and regions that lead to engagement with the issue of climate change. We use geographically explicit, internet search data from Google to measure information seeking behavior, which we take to translate into engagement, attention and interest. Our spatial hotspot analysis creates a map that potentially could be harnessed by policymakers to gauge mitigation support or adaptation potential. The results of our multivariate analysis suggest that socioeconomic factors are the strongest determinants of search behavior and that climate and geography have little to no impact. With regard to political ideology, we find evidence of a non-linear, inverse-U relationship with maximum search activity occurring in metropolitan areas with a near even political split, suggesting parity may be good for engagement

Strong Photoluminescence Enhancement of MoS2 through Defect Engineering and Oxygen Bonding

We report on a strong photoluminescence (PL) enhancement of monolayer MoS2 through defect engineering and oxygen bonding. Micro- PL and Raman images clearly reveal that the PL enhancement occurs at cracks/defects formed during high temperature vacuum annealing. The PL enhancement at crack/defect sites could be as high as thousands of times after considering the laser spot size. The main reasons of such huge PL enhancement include: (1) the oxygen chemical adsorption induced heavy p doping and the conversion from trion to exciton; (2) the suppression of non-radiative recombination of excitons at defect sites as verified by low temperature PL measurements. First principle calculations reveal a strong binding energy of ~2.395 eV for oxygen molecule adsorbed on an S vacancy of MoS2. The chemical adsorbed oxygen also provides a much more effective charge transfer (0.997 electrons per O2) compared to physical adsorbed oxygen on ideal MoS2 surface. We also demonstrate that the defect engineering and oxygen bonding could be easily realized by oxygen plasma irradiation. X-ray photoelectron spectroscopy further confirms the formation of Mo-O bonding. Our results provide a new route for modulating the optical properties of two dimensional semiconductors. The strong and stable PL from defects sites of MoS2 may have promising applications in optoelectronic devices.Comment: 23 pages, 9 figures, to appear in ACS Nan

The Use of Consumer-Generated Feedback in the Hotel Industry: Current Practices and Their Effects on Quality

Consumer-generated feedback is hard to ignore these days. Word-of-mouth has expanded beyond a customer’s immediate friends and family; with the help of technology it reaches thousands of current and prospective guests. In light of this, scholars and practitioners are exploring the subject of consumer-generated feedback. Today, most of the research regarding this subject focuses on the use of consumer-generated feedback to make purchase decisions. In contrast, the present study explores the use of such information for the purposes of improving hotel operations. This article examines the amount of value placed on consumer-generated feedback, the relative importance placed on positive and negative feedback, and its effects on perceived quality. Furthermore, this study inquires as to the specific uses given to consumer-generated feedback in the hotel industry. It is the researchers’ contention that valuing feedback has positive effects on perceived quality. The findings conclude that hotels can use consumer-generated feedback to take actions such as modifying training programs and operating procedures, as well as identifying patterns of complaint and praise