Influential Article Review - Competitive Demand Assessment in Companies: A Structural Comparison

This paper examines management. We present insights from a highly influential paper. Here are the highlights from this paper: Organizational scholars have shown increasing interest in the ways in which managers enact and respond to competing demands and the tensions they prompt as constitutive elements of their organizations. There is now a proliferation of conceptualizations of such competing demands that can be somewhat confusing. We will enhance conceptual clarity by identifying seven constitutive empirical characteristics of competing demands: these consist of the existence of dyadic relations, contradiction, interrelatedness, complementarity, compatibility, simultaneity, and the existence of push-pull forces. We construct a comparative classification of competing demands using these characteristics as our distinguishing features. The result is a more nuanced understanding of how managers approach competing demands that can help scholars to minimize arbitrariness, interpret results, and compare contributions in the area in a much-needed step toward understanding and designing organizations. For our overseas readers, we then present the insights from this paper in Spanish, French, Portuguese, and German

Managing Patient Expectations with Implant Treatment

This patient came to University of the Pacific, Arthur A. Dugoni School of Dentistry to get implants and implant crowns to improve his smile, replacing his upper partial denture. Different approaches were made by the faculty to arrive at best outcomes, which took four years for the patient to receive the treatment he wanted. This abstract will further detail how the treatment was conducted and the results afterwards

Bayesian modelling of high-throughput sequencing assays with malacoda.

NGS studies have uncovered an ever-growing catalog of human variation while leaving an enormous gap between observed variation and experimental characterization of variant function. High-throughput screens powered by NGS have greatly increased the rate of variant functionalization, but the development of comprehensive statistical methods to analyze screen data has lagged. In the massively parallel reporter assay (MPRA), short barcodes are counted by sequencing DNA libraries transfected into cells and the cell\u27s output RNA in order to simultaneously measure the shifts in transcription induced by thousands of genetic variants. These counts present many statistical challenges, including overdispersion, depth dependence, and uncertain DNA concentrations. So far, the statistical methods used have been rudimentary, employing transformations on count level data and disregarding experimental and technical structure while failing to quantify uncertainty in the statistical model. We have developed an extensive framework for the analysis of NGS functionalization screens available as an R package called malacoda (available from github.com/andrewGhazi/malacoda). Our software implements a probabilistic, fully Bayesian model of screen data. The model uses the negative binomial distribution with gamma priors to model sequencing counts while accounting for effects from input library preparation and sequencing depth. The method leverages the high-throughput nature of the assay to estimate the priors empirically. External annotations such as ENCODE data or DeepSea predictions can also be incorporated to obtain more informative priors-a transformative capability for data integration. The package also includes quality control and utility functions, including automated barcode counting and visualization methods. To validate our method, we analyzed several datasets using malacoda and alternative MPRA analysis methods. These data include experiments from the literature, simulated assays, and primary MPRA data. We also used luciferase assays to experimentally validate several hits from our primary data, as well as variants for which the various methods disagree and variants detectable only with the aid of external annotations

A Multistage Stochastic Programming Approach to the Dynamic and Stochastic VRPTW - Extended version

We consider a dynamic vehicle routing problem with time windows and stochastic customers (DS-VRPTW), such that customers may request for services as vehicles have already started their tours. To solve this problem, the goal is to provide a decision rule for choosing, at each time step, the next action to perform in light of known requests and probabilistic knowledge on requests likelihood. We introduce a new decision rule, called Global Stochastic Assessment (GSA) rule for the DS-VRPTW, and we compare it with existing decision rules, such as MSA. In particular, we show that GSA fully integrates nonanticipativity constraints so that it leads to better decisions in our stochastic context. We describe a new heuristic approach for efficiently approximating our GSA rule. We introduce a new waiting strategy. Experiments on dynamic and stochastic benchmarks, which include instances of different degrees of dynamism, show that not only our approach is competitive with state-of-the-art methods, but also enables to compute meaningful offline solutions to fully dynamic problems where absolutely no a priori customer request is provided.Comment: Extended version of the same-name study submitted for publication in conference CPAIOR201