Factor market oligopsony and the location decision of free entry oligopoly

This paper examines the impact of oligopsony power on the location decision of undifferentiated oligopolistic firms with free entry. In the case where the distance of an oligopolistic firm from the output market is held constant, it shows that the optimum location moves away from the oligopsonistic input market if the demand function and the labor supply function are linear. In the case where the distance of an oligopolistic firm from the output market is a decision variable, it shows that the optimum location may not move toward the output market as demand increases if the demand function is convex. These results are significantly different from the conventional results based on the perfectly competitive factor market. It indicates that the presence of oligopsony power has important influence on the location decision of oligopolistic firms.Free entry oligopoly

Pollution Taxes and Location Decision under Free Entry Oligopoly

This paper examines the impact of a pollution tax as a pollution control device on the output and location decisions of undifferentiated oligopolistic firms with free entry. It shows that the optimum output and location of an oligopolistic firm is independent of a change in the pollution tax if the demand function is linear. Furthermore, an increase in the pollution tax will increase (decrease) output and move the plant location toward (away from) the CBD if the demand function is concave (convex). It also shows that a higher pollution tax will increase the pollution damage if the demand function is linear and the location effect dominates the demand effect. These results are significantly different from the conventional results based on the monopolistic location model. It indicates that the demand condition plays an important role in the determination of the impact of a pollution tax on the location decision of an oligopolistic firm and the pollution damage to the CBD residents.

How the presentation of patient information and decision-support advisories influences opioid prescribing behavior: A simulation study

ObjectiveThe United States faces an opioid crisis. Integrating prescription drug monitoring programs into electronic health records offers promise to improve opioid prescribing practices. This study aimed to evaluate 2 different user interface designs for prescription drug monitoring program and electronic health record integration.Materials and MethodsTwenty-four resident physicians participated in a randomized controlled experiment using 4 simulated patient cases. In the conventional condition, prescription opioid histories were presented in tabular format, and computerized clinical decision support (CDS) was provided via interruptive modal dialogs (ie, pop-ups). The alternative condition featured a graphical opioid history, a cue to visit that history, and noninterruptive CDS. Two attending pain specialists judged prescription appropriateness.ResultsParticipants in the alternative condition wrote more appropriate prescriptions. When asked after the experiment, most participants stated that they preferred the alternative design to the conventional design.ConclusionsHow patient information and CDS are presented appears to have a significant influence on opioid prescribing behavior

Semi-Automated Identification of Motor Units Concurrently Recorded in High-Density Surface and Intramuscular Electromyography

: An increasing focus on extending automated surface electromyography (EMG) decomposition algorithms to operate under non-stationary conditions requires rigorous and robust validation. However, relevant benchmarks derived manually from iEMG are laborsome to obtain and this is further exacerbated by the need to consider multiple contraction conditions. This work demonstrates a semi-automatic technique for extracting motor units (MUs) whose activities are present in concurrently recorded high-density surface EMG (HD-sEMG) and intramuscular EMG (iEMG) during isometric contractions. We leverage existing automatic surface decomposition algorithms for initial identification of active MUs. Resulting spike times are then used to identify (trigger) the sources that are concurrently detectable in iEMG. We demonstrate this technique on recordings targeting the extensor carpi radialis brevis in five human subjects. This dataset consists of 117 trials across different force levels and wrist angles, from which the presented method yielded a set of 367 high-confidence decompositions. Thus, our approach effectively alleviates the overhead of manual decomposition as it efficiently produces reliable benchmarks under different conditions.Clinical Relevance- We present an efficient method for obtaining high-quality in-vivo decomposition particularly useful in the verification of new surface decomposition approaches

Optimal Motor Unit Subset Selection for Accurate Motor Intention Decoding: Towards Dexterous Real-Time Interfacing

Objective: Motor unit (MU) discharge timings encode human motor intentions to the finest degree. Whilst tapping into such information can bring significant gains to a range of applications, current approaches to MU decoding from surface signals do not scale well with the demands of dexterous human-machine interfacing (HMI). To optimize the forward estimation accuracy and time-efficiency of such systems, we propose the inclusion of task-wise initialization and MU subset selection. Methods: Offline analyses were conducted on data recorded from 11 non-disabled subjects. Task-wise decomposition was applied to identify MUs from high-density surface electromyography (HD-sEMG) pertaining to 18 wrist/forearm motor tasks. The activities of a selected subset of MUs were extracted from test data and used for forward estimation of intended motor tasks and joint kinematics. To that end, various combinations of subset selection and estimation algorithms (both regression and classification-based) were tested for a range of subset sizes. Results: The mutual information-based minimum Redundancy Maximum Relevance (mRMR-MI) criterion retained MUs with the highest predicative power. When the portion of tracked MUs was reduced down to 25%, the regression performance decreased only by 3% (R2=0.79) while classification accuracy dropped by 2.7% (accuracy = 74%) when kernel-based estimators were considered. Conclusion and Significance: Careful selection of tracked MUs can optimize the efficiency of MU-driven interfacing. In particular, prioritization of MUs exhibiting strong nonlinear relationships with target motions is best leveraged by kernel-based estimators. Hence, this frees resources for more robust and adaptive MU decoding techniques to be implemented in future

Evaluating the impact of AMPK activation, a target of metformin, on risk of cardiovascular diseases and cancer in the UK Biobank: A Mendelian randomization study

Aims/hypothesis: Whether metformin reduces cardiovascular or cancer risk is unclear owing to concerns over immortal time bias and confounding in observational studies. This study evaluated the effect of AMP-activated protein kinase (AMPK), the target of metformin, on risk of cardiovascular disease and cancer. Methods: This is a Mendelian randomisation design, using AMPK, the pharmacological target of metformin, to infer the AMPK pathway-dependent effects of metformin on risk of cardiovascular disease and cancer in participants of white British ancestry in the UK Biobank. Results: A total of 391,199 participants were included (mean age 56.9 years; 54.1% women), including 26,690 cases of type 2 diabetes, 38,098 cases of coronary artery disease and 80,941 cases of overall cancer. Genetically predicted reduction in HbA1c (%) instrumented by AMPK variants was associated with a 61% reduction in risk of type 2 diabetes (OR 0.39; 95% CI 0.20, 0.78; p = 7.69 × 10−3), a 53% decrease in the risk of coronary artery disease (OR 0.47; 95% CI 0.26, 0.84; p = 0.01) and a 44% decrease in the risk of overall cancer (OR 0.56; 95% CI 0.36, 0.85; p = 7.23 × 10−3). Results were similar using median or quartiles of AMPK score, with dose–response effects (p for trend = 4.18 × 10−3 for type 2 diabetes, 4.37 × 10−3 for coronary artery disease and 4.04 × 10−3 for overall cancer). Conclusions/interpretation: This study provides some genetic evidence that AMPK activation by metformin may protect against cardiovascular disease and cancer, which needs to be confirmed by randomised controlled trials