Most research on biased public sector hiring highlights local politicians’ incentives to distribute government positions to partisan supporters. Other studies instead point to the role of bureaucratic managers in allocating government jobs to close contacts. We jointly consider the relative importance of each source of biased hiring as an allocation problem between managers and politicians who have different preferences regarding public sector hiring and different abilities to realize those preferences. We develop a theoretical model of each actor’s relative leverage and relative preferences for different types of public sector positions. We empirically examine our theory using the universe of payroll data in Kenyan local governments from 2004 to 2013. We find evidence of both patronage and bureaucratic favoritism, but with different types of bias concentrated in different types of government jobs, as our theory predicts. Our results highlight the inadequacy of examining political patronage alone without incorporating the preferences and leverage of the bureaucratic managers who are intricately involved in hiring processes.

This article examines how public opinion—notably political activism and protest, as well as threats of violence, and violence itself—shaped the eventual resolution of the 1876 election. While not discounting the bargaining or machinations of party elites in forging an ultimate compromise, the standard explanation in the scholarly literature, the emphasis here adds important texture and nuance to the conversation, and strongly suggests that public opinion (broadly construed) played a significant, if not exclusive, role in pressuring party leaders to compromise on the eventual Electoral Commission Act that resolved the crisis. In particular, a series of January 1877 demonstrations held across several key states, coupled with the threat of “menace” at the heart of the Southern rifle clubs that were prominent in the campaign and its aftermath, provided strong incentives to partisan leaders and especially members of Congress to seek compromise to resolve the electoral crisis. The article also addresses the contested nature of mass meetings and protests in this era—and in general—and how partisans seek to define terms and behaviors to suit their political positions.

This article explores the growth of abortion-related businesses in New York State that emerged to encourage Canadian women to travel across the border to access care. Referral agencies and clinics advertised their services, publicized their fees, and competed with each other. Canadian women living near the border were used to crossing to access goods and services not available in their home market. Their practice of traveling to New York for abortions was shaped by their experiences as consumers. The media used the language of commerce to explain this phenomenon, describing those involved in referral agencies as entrepreneurs and businessmen, highlighting the profits being made and evaluating the services being offered.

This study aimed to develop an efficient data collection and curation process for all drugs and natural health products (NHPs) used by participants to the Canadian Longitudinal Study on Aging (CLSA). The three-step sequential process consisted of (a) mapping drug inputs collected through the CLSA to the Health Canada Drug Product Database (DPD), (b) algorithm recoding of unmapped drug and NHP inputs, and (c) manual recoding of unmapped drug and NHP inputs. Among the 30,097 CLSA comprehensive cohort participants, 26,000 (86.4%) were using a drug or an NHP with a mean of 5.3 (SD 3.8) inputs per participant user for a total of 137,366 inputs. Of those inputs, 70,177 (51.1%) were mapped to the Health Canada DPD, 20,729 (15.1%) were recoded by algorithms, and 44,108 (32.1%) were manually recoded. The Direct algorithm correctly classified 99.4 per cent of drug inputs and 99.5 per cent of NHP inputs. We developed an efficient three-step process for drug and NHP data collection and curation for use in a longitudinal cohort.

Currently, workers in sand casting face harsh environments and the operation safety is poor. Existing pouring robots have insufficient stability and load-bearing capacity and cannot perform intelligent pouring according to the demand of pouring process. In this paper, a hybrid pouring robot is proposed to solve these limitations, and a vision-based hardware-in-the-loop (HIL) control technology is designed to achieve the real-time control problems of simulated pouring and pouring process. Firstly, based on the pouring mechanism and the motion demand of ladle, a hybrid pouring robot with a 2UPR-2RPU parallel mechanism as the main body is designed. And the equivalent hybrid kinematic model was established by using Eulerian method and differential motion. Subsequently, a motion control strategy based on HIL simulation technique was designed and presented. The working space of the robot was obtained through simulation experiments to meet the usage requirements. And the stability of the robot was tested through the key motion parameters of the robot joints. Based on the analysis of pouring quality and trajectory, optimal dynamic parameters for the experimental prototype are obtained through water simulation experiments, the pouring liquid height area is 35–40 cm, the average flow rate of pouring liquid is 112 cm3/s, and the ladle tilting speed is 0.0182 rad/s. Experimental results validate the reasonableness of the designed pouring robot structure. Its control system realizes the coordinated movement of each branch chain to complete the pouring tasks with different variable parameters. Consequently, the designed pouring robot will significantly enhance the automation level of the casting industry.

Imagine that we are on a train, playing with some mechanical systems. Why can’t we detect any differences in their behavior when the train is parked versus when it is moving uniformly? The standard answer is that boosts are symmetries of Newtonian systems. In this article, I use the case of a spring to argue that this answer is problematic because symmetries are neither sufficient nor necessary for preserving its behavior. I also develop a new answer according to which boosts preserve the relational properties on which the behavior of a system depends, even when they are not symmetries.

The antibacterial, antifungal and antioxidant effects of halloysite nanoclay, Cloisite 10A (C10A) and Cloisite 15A (C15A) organonanoclays were examined in this study. The antimicrobial action was assessed using the agar-well method and the disc diffusion method. The free radical-scavenging effects of the clays were determined using the 2,2-diphenyl-1-picrylhydrazyl method. Halloysite showed antimicrobial activity against Pseudomonas aeruginosa, Enterococcus faecalis and Staphylococcus aureus. C10A was effective against both Gram-positive bacteria (S. aureus, Listeria monocytogenes, Bacillus subtilis and E. faecalis) and Gram-negative bacteria (Escherichia coli, Klebsiella pneumoniae and P. aeruginosa). Additionally, only C10A was found to have an antimicrobial effect on Candida glabrata of 18 mm amongst the tested clays. C15A showed an antimicrobial effect on S. aureus and K. pneumoniae. It was determined that the antifungal properties of organoclays were higher than those of halloysite. The most effective clay type was determined to be C10A. The positively charged inner surface of the halloysite nanoclay can provide a large area to which negatively charged free radicals can attach. The modified C15A used in this study has two long-chain alkyl groups attached, whereas the modified C10A has a single long-chain alkyl group and a benzyl group attached. It is proposed that the differences in these antimicrobial effects are due to the structures of the molecules. According to these results, organoclays as green source materials could be used as additives and coatings in food processing, biomedical devices, filters and paints due to their antimicrobial and antioxidant properties.

Reinforcement learning (RL) has been successfully applied to a wealth of robot manipulation tasks and continuous control problems. However, it is still limited to industrial applications and suffers from three major challenges: sample inefficiency, real data collection, and the gap between simulator and reality. In this paper, we focus on the practical application of RL for robot assembly in the real world. We apply enlightenment learning to improve the proximal policy optimization, an on-policy model-free actor-critic reinforcement learning algorithm, to train an agent in Cartesian space using the proprioceptive information. We introduce enlightenment learning incorporated via pretraining, which is beneficial to reduce the cost of policy training and improve the effectiveness of the policy. A human-like assembly trajectory is generated through a two-step method with segmenting objects by locations and iterative closest point for pretraining. We also design a sim-to-real controller to correct the error while transferring to reality. We set up the environment in the MuJoCo simulator and demonstrated the proposed method on the recently established The National Institute of Standards and Technology (NIST) gear assembly benchmark. The paper introduces a unique framework that enables a robot to learn assembly tasks efficiently using limited real-world samples by leveraging simulations and visual demonstrations. The comparative experiment results indicate that our approach surpasses other baseline methods in terms of training speed, success rate, and efficiency.

This research note contributes updated and extended point estimates of the ideological positions of Brazilian political parties and novel estimates of the positions of all presidents since redemocratization in 1985. Presidents and parties are jointly responsible for the operability of Brazil’s version of coalitional presidentialism. Locating these key political actors in a unidimensional left–right space over time reveals rising challenges to the institutional matrix, particularly since 2013. Ideological polarization among parties has sharply increased, presidents have become more distant from Congress, and the political center has become increasingly vacated. Coalitional presidentialism is being subjected to unprecedented ideological stress as President Luiz Inácio Lula da Silva begins his third term in office.