Since the early 2000s, the US Government has made purposeful investments to help ensure medical preparedness should a radiological or nuclear incident occur within its borders. This focused support of products to diagnose, mitigate, and treat radiation-induced bodily injuries that would be anticipated during a radiation public health emergency has involved many departments, ranging from multiple agencies within the Department of Health and Human Services to the Department of Defense. The intent of this manuscript is to convey information both on products that have been approved by the US Food and Drug Administration for radiation injuries during a radiation incident, as well as promising approaches under advanced stages of development. These products impact multiple organ systems (e.g., bone marrow, gastrointestinal tract, lungs, kidneys, skin) and have been tested for efficacy in a number of different small and large preclinical animal models. The successful development of these models, methods, products, and devices discussed herein demonstrate the importance of an intentionally collaborative, “one-government” approach to fostering radiation research, while also showcasing the need for critical public-private partnerships – all to ensure the safety of the public should the unthinkable occur.

The adoption of a feminist foreign policy (FFP) by Mexico took Mexican feminist civil society by surprise. In September 2019, without prior consultations or dialogues with local feminist civil society, the Mexican Foreign Affairs Secretary announced its FFP during the 74th session of the UN General Assembly. While the UN New York City headquarters roared with applause in response to the Mexican Foreign Affairs Secretary’s claim that the “Mexican government is feminist,” many feminists in Mexico interpreted the statement as an effort to pinkwash the government’s poor track record on gender equality within its borders. Moreover, Mexico’s FFP was charged with racial and colonial undertones since its inception, colloquially known as the “first FFP from the Global South” (Thompson 2020; Thompson, Ahmed, and Khokhar 2021).

Sub-clinical ketosis (SCK) significantly affects post-partum dairy cow performance and welfare. A total of 11,327,959 test-day (TD) records over two years on 1.76 million Holstein cow lactations and 2840 farms were processed to ascertain thresholds for milk acetone (mACE) and β-hydroxybutyrate (mBHB) as indicators of SCK on the basis of a significant milk yield loss at the TD. The set thresholds for mACE and mBHB were 0.10 mmol/L and 0.14 mmol/L, respectively. The prevalence of SCK in the population during the first 60 days in milk (DIM) was estimated based on herd size and milk yields, utilizing one or both of these metabolites surpassing their respective thresholds. Analyzing both mACE and mBHB together revealed a higher occurrence of SCK in small herds (fewer than 100 cows) and a lower occurrence in the two most productive milk categories. The prevalence had an inverse relationship with the daily milk yield at 60 DIM, indicating a surprisingly high frequency of low-productivity herds in the risk classes exceeding 30%. These results suggest that assessing SCK prevalence through the combined evaluation of mACE and mBHB is a more effective approach than using the milk fat to protein ratio, especially when considering different herd sizes and daily milk yield at 60 DIM.

To understand the requirements of animals their calls can be analysed. This potentially enables specific and more precise individual care under different emotional and physiological conditions. This study was conducted to identify three different conditions (oestrus, delayed milking and isolation) of buffaloes based on vocalization patterns. A total of 600 acoustic samples of buffaloes for each condition were collected under different conditions consisting of 300 records for confirming and 300 for non-confirming of a particular condition. Important acoustic features like amplitude (P), total energy (P2s), pitch (Hz), intensity (dB), formants (Hz), number of pulses, number of periods, mean period (sec) and unvoiced frames (%) were extracted using the MFCC (mel frequency cepstrum coefficients) technique. Algorithms (model) were trained by partitioning the acoustic data into training and validation sets to develop predictive models. Three different ratios were assessed: 60%-40%, 70%-30% and 80%-20%. Decision tree models were optimized based on decision and average square error (probability) options and other parameters were set to default values of the software package to deveop the best model. The performance of algorithms was evaluated on the parameter accuracy rate. Decision tree models predicted the physiological conditions oestrus, isolation and delayed milking with an accuracy of 66.1, 84.3 and 71.3%, respectively, while the logistic regression model predicted with an accuracy rate of 59.5, 71.1 and 65.7%, respectively, and the artificial neural network (ANN) model predicted these three conditions with 77.7, 85.2 and 79.4% accuracy, respectively. The ANN model was found to be best on the basis of minimum misclassification rate (on 80%-20% portioning). However, decision tree algorithms also provided the additional information that intensity (maximum), amplitude (minimum) and formant (F1) are the most important features of vocal signals to identify physiological conditions like oestrus, isolation and delayed milking respectively in dairy buffalo.

We consider the problem of sequential matching in a stochastic block model with several classes of nodes and generic compatibility constraints. When the probabilities of connections do not scale with the size of the graph, we show that under the Ncond condition, a simple max-weight type policy allows us to attain an asymptotically perfect matching while no sequential algorithm attains perfect matching otherwise. The proof relies on a specific Markovian representation of the dynamics associated with Lyapunov techniques.

Since the late 2010s, Rwanda has advertised its Visit Rwanda logo on the jerseys of prominent European football teams and has built new sports stadiums to host international sports competitions. Such strategies reflect the practice of sportswashing, which refers to the utilization of sports by political actors to gain global legitimacy while diverting attention from unjust processes occurring in their home countries. Dubinsky analyzes the effectiveness of Rwanda’s sportswashing through the concept of authoritarian image management, arguing that the mutual interests shared between authoritarian and Western actors facilitate the country’s sportswashing, despite the critiques it attracts.

States frequently use leadership decapitation in their domestic and cross-border counter-insurgency/terrorism operations, yet the literature is far from having a consensus regarding its effects. I argue that literature focuses on the military implications of decapitation (its implications for the organisation’s operational capabilities/ability to generate violence) at the expense of its implications for negotiations between insurgents and the state. Second, I argue that leadership arrest and killing are analytically distinct categories of leadership decapitation that can trigger different processes and outcomes and that an arrested leader’s possible role from the prison should be considered in the analysis since leadership arrest alters the leader’s incentives, resulting in a new bargaining game between the leader, the state, and the organisation. I empirically illustrate these arguments using the arrest of the leader of the Kurdistan Workers’ Party (PKK) Abdullah Öcalan as a theory-building case study. In the case study, I show that Abdullah Öcalan’s arrest was productive for terminating the conflict in the short run, whereas it was counter-productive in the medium and long run. These findings suggest that the literature may benefit from tracing the process closely, considering the dynamic nature of conflicts and the impact of decapitation on bargaining processes, without limiting the temporal scope of inquiry.