Evidence on the Value of Strategic Planning in Marketing: How Much Planning Should a Marketing Planner Plan?

What evidence exists on the value of formal planning for strategic decision-making in marketing? This paper reviews the evidence. This includes two tests of face validity. First, we use the market test: Are formal procedures used for marketing planning? Next, we examine expert prescriptions: What do they say is the best way to plan? More important than face validity, however, are tests of construct or predictive validity: What empirical evidence exists on the relative value of formal and informal approaches to marketing planning? The paper concludes with suggestions on the types of research that would be most useful for measuring the value of formal marketing planning. Before reviewing the evidence, we present a framework for the formal planning process.strategic planning, marketing

Social networks, social support and issues of aging: survey of three rural New England communities/

This Masters Thesis is actually a combination of two separate but related works, A comprehensive literature review of research pertaining to social networks and elders is presented here alongside a description and analysis of a much more limited and focused original research project

Evidence on the value of strategic planning in marketing: how much planning should a marketing planner plan?

What evidence exists on the value of formal planning for strategic decision-making in marketing? This paper reviews the evidence. This includes two tests of face validity. First, we use the market test: Are formal procedures used for marketing planning? Next, we examine expert prescriptions: What do they say is the best way to plan? More important than face validity, however, are tests of construct or predictive validity: What empirical evidence exists on the relative value of formal and informal approaches to marketing planning? The paper concludes with suggestions on the types of research that would be most useful for measuring the value of formal marketing planning. Before reviewing the evidence, we present a framework for the formal planning process

The positional probability and true host star identification of TESS exoplanet candidates

We present a method for deriving a probabilistic estimate of the true source of a detected TESS transiting event. Our method relies on comparing the observed photometric centroid offset for the target star with models of the offset that would occur if the event was either on the target or any of the Gaia identified nearby sources. The comparison is done probabilistically, allowing us to incorporate the uncertainties of the observed and modelled offsets in our result. The method was developed for TESS Full Frame Image lightcurves produced from the SPOC pipeline, but could be easily adapted to lightcurves from other sources. We applied the method on 3226 TESS Objects of Interest (TOIs), with a released lightcurve from SPOC. The method correctly identified 96.5% of 655 known exoplanet hosts as the most likely source of the eclipse. For 142 confirmed Nearby Eclipsing Binaries (NEBs) and Nearby Planet Candidates (NPCs), a nearby source was found to be the most likely in 96.5% of the cases. For 40 NEBs and NPCs where the true source is known, it was correctly designated as the most likely in 38 of those. Finally, for 2365 active planet candidates, the method suggests that 2072 are most likely on-target and 293 on a nearby source. The method forms a part of an in-development vetting and validation pipeline, called RAVEN, and is released as a standalone tool.Comment: Accepted for publication in MNRA

The Phenomenology of Cushing\u27s Syndrome: One Patient\u27s Account

Cushing\u27s syndrome caused by ectopic secretion of adrenocorticotropic hormone (ACTH) is often a serious disease and a diagnostic dilemma. In the reported patient, the source of ACTH proved to he a benign pulmonary carcinoid tumor. The patient describes his trying experiences through the six months from initial diagnosis to definitive therapy

Transit shapes and self organising maps as a tool for ranking planetary candidates : application to Kepler and K2

A crucial step in planet hunting surveys is to select the best candidates for follow up observations, given limited telescope resources. This is often performed by human ‘eyeballing’, a time consuming and statistically awkward process. Here we present a new, fast machine learning technique to separate true planet signals from astrophysical false positives. We use Self Organising Maps (SOMs) to study the transit shapes of Kepler and K2 known and candidate planets. We find that SOMs are capable of distinguishing known planets from known false positives with a success rate of 87.0%, using the transit shape alone. Furthermore, they do not require any candidates to be dispositioned prior to use, meaning that they can be used early in a mission’s lifetime. A method for classifying candidates using a SOM is developed, and applied to previously unclassified members of the Kepler KOI list as well as candidates from the K2 mission. The method is extremely fast, taking minutes to run the entire KOI list on a typical laptop. We make Python code for performing classifications publicly available, using either new SOMs or those created in this work. The SOM technique represents a novel method for ranking planetary candidate lists, and can be used both alone or as part of a larger autovetting code

A counterfactual study of the Charge of the Light Brigade

We use a mathematical model to perform a counterfactual study of the 1854 Charge of the Light Brigade. We first calibrate the model with historical data so that it reproduces the actual charge’s outcome. We then adjust the model to see how that outcome might have changed if the Heavy Brigade had joined the charge, and/or if the charge had targeted the Russian forces on the heights instead of those in the valley. The results suggest that all of the counterfactual attacks would have led to heavier British casualties. However, a charge by both brigades along the valley might plausibly have yielded a British victory

Virtual Fly Brain: An ontology-linked schema of the Drosophila Brain

Drosophila neuro-anatomical data is scattered across a large, diverse literature dating back over 75 years and a growing number of community databases. Lack of a standardized nomenclature for neuro-anatomy makes comparison and searching this growing data-set extremely arduous. 

A recent standardization effort (BrainName; Manuscript in preparation) has produced a segmented, 3D model of the Drosophila brain annotated with a controlled vocabulary. We are formalizing these developments to produce a web-based ontology-linked atlas in which gross brain anatomy is defined, in part, by labeled volumes in a standard reference brain.

We have developed new relations that allow us to use this well-defined gross anatomy as a substrate to define neuronal types according to where they fasciculate and innervate as well as to record the neurotransmitters they release, their lineage and functions. The resulting ontology will provide a vocabulary for annotation and a means for integrative queries of neurobiological data.

The ontology and associated images, queries and annotations will be integrated into the Virtual Fly Brain website. This will provide a resource that biologists can use to browse annotated images of Drosophila neuro-anatomy and to get answers to questions about that anatomy and related data, without any need for ontology expertise.
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Exoplanet validation with machine learning : 50 new validated Kepler planets

Over 30% of the ∼4000 known exoplanets to date have been discovered using ‘validation’, where the statistical likelihood of a transit arising from a false positive (FP), non-planetary scenario is calculated. For the large majority of these validated planets calculations were performed using the vespa algorithm (Morton et al. 2016). Regardless of the strengths and weaknesses of vespa, it is highly desirable for the catalogue of known planets not to be dependent on a single method. We demonstrate the use of machine learning algorithms, specifically a gaussian process classifier (GPC) reinforced by other models, to perform probabilistic planet validation incorporating prior probabilities for possible FP scenarios. The GPC can attain a mean log-loss per sample of 0.54 when separating confirmed planets from FPs in the Kepler threshold crossing event (TCE) catalogue. Our models can validate thousands of unseen candidates in seconds once applicable vetting metrics are calculated, and can be adapted to work with the active TESS mission, where the large number of observed targets necessitates the use of automated algorithms. We discuss the limitations and caveats of this methodology, and after accounting for possible failure modes newly validate 50 Kepler candidates as planets, sanity checking the validations by confirming them with vespa using up to date stellar information. Concerning discrepancies with vespa arise for many other candidates, which typically resolve in favour of our models. Given such issues, we caution against using single-method planet validation with either method until the discrepancies are fully understood