Phosphido pincer complexes of platinum: synthesis, structure and reactivity

A series of platinum(II) complexes supported by the tridentate bis(phosphine)phosphido ligand bis(2-diisopropylphosphinophenyl)phosphide) [iPr–PPP] have been synthesized and characterized (1–4). X-Ray structural studies of [iPr–PPP]PtCl (1) and [iPr–PPP]PtCH3 (3) complexes show meridional [iPr–PPP] ligands around approximately square-planar platinum centers. Structural data and NMR analysis highlight a strong trans influence for the phosphido phosphorous donor, comparable to that of the anionic aryl carbon of the classic PCP pincer complexes. A series of thermally stable [PPP]Pt(IV) compounds, including [PPP]Pt(CH_3)_2X [X = I (5) and SbF_6 (6)], were also synthesized. The study of the binding affinity of SO_2 and NO to complex 1 has also been addressed

Complete and Voluntary Starvation of 50 days

A 34-year-old obese male (96.8 kg; BMI, 30.2 kg m⁻¹) volitionally undertook a 50-day fast with the stated goal of losing body mass. During this time, only tea, coffee, water, and a daily multivitamin were consumed. Severe and linear loss of body mass is recorded during these 50 days (final 75.4 kg; BMI, 23.5 kg m⁻¹). A surprising resilience to effects of fasting on activity levels and physical function is noted. Plasma samples are suggestive of early impairment of liver function, and perturbations to cardiovascular dynamics are also noted. One month following resumption of feeding behavior, body weight was maintained (75.0 kg; BMI, 23.4 kg m⁻¹). Evidence-based decision-making with the fasting or hunger striking patient is limited by a lack of evidence. This case report suggests that total body mass, not mass lost, may be a key observation in clinical decision-making during fasting and starvation

Prototype of Fault Adaptive Embedded Software for Large-Scale Real-Time Systems

This paper describes a comprehensive prototype of large-scale fault adaptive embedded software developed for the proposed Fermilab BTeV high energy physics experiment. Lightweight self-optimizing agents embedded within Level 1 of the prototype are responsible for proactive and reactive monitoring and mitigation based on specified layers of competence. The agents are self-protecting, detecting cascading failures using a distributed approach. Adaptive, reconfigurable, and mobile objects for reliablility are designed to be self-configuring to adapt automatically to dynamically changing environments. These objects provide a self-healing layer with the ability to discover, diagnose, and react to discontinuities in real-time processing. A generic modeling environment was developed to facilitate design and implementation of hardware resource specifications, application data flow, and failure mitigation strategies. Level 1 of the planned BTeV trigger system alone will consist of 2500 DSPs, so the number of components and intractable fault scenarios involved make it impossible to design an `expert system' that applies traditional centralized mitigative strategies based on rules capturing every possible system state. Instead, a distributed reactive approach is implemented using the tools and methodologies developed by the Real-Time Embedded Systems group.Comment: 2nd Workshop on Engineering of Autonomic Systems (EASe), in the 12th Annual IEEE International Conference and Workshop on the Engineering of Computer Based Systems (ECBS), Washington, DC, April, 200

Making the right real! : A case study on the implementation of the right to sport for persons with disabilities in Ethiopia

Even though the right to participate in sport, recreation and play is stipulated in the United Nations' Convention on the Rights of Persons with Disabilities as a stand-alone provision, it is often treated as a second class right'. This paper critically investigates challenges of realizing this right in the context of Ethiopia. Findings are based on wheelchair basketball trainings held in Ethiopia for persons with physical disabilities in 2015 as a case study and from follow-up data to assess the impact of the trainings. Firstly, inequalities in structures related to disability sports between the Global North and South are described. Secondly, examples of discrimination between groups within disability communities are shown. Lastly, the complex nature of realizing the rights of persons with disabilities is examined in the context of accessibility and sports. In conclusion, we summarize the key components for genuine implementation of Sports for all'.Peer reviewe

A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions

Robots operating alongside humans in diverse, stochastic environments must be able to accurately interpret natural language commands. These instructions often fall into one of two categories: those that specify a goal condition or target state, and those that specify explicit actions, or how to perform a given task. Recent approaches have used reward functions as a semantic representation of goal-based commands, which allows for the use of a state-of-the-art planner to find a policy for the given task. However, these reward functions cannot be directly used to represent action-oriented commands. We introduce a new hybrid approach, the Deep Recurrent Action-Goal Grounding Network (DRAGGN), for task grounding and execution that handles natural language from either category as input, and generalizes to unseen environments. Our robot-simulation results demonstrate that a system successfully interpreting both goal-oriented and action-oriented task specifications brings us closer to robust natural language understanding for human-robot interaction.Comment: Accepted at the 1st Workshop on Language Grounding for Robotics at ACL 201

A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions

Robots operating alongside humans in diverse, stochastic environments must be able to accurately interpret natural language commands. These instructions often fall into one of two categories: those that specify a goal condition or target state, and those that specify explicit actions, or how to perform a given task. Recent approaches have used reward functions as a semantic representation of goal-based commands, which allows for the use of a state-of-the-art planner to find a policy for the given task. However, these reward functions cannot be directly used to represent action-oriented commands. We introduce a new hybrid approach, the Deep Recurrent Action-Goal Grounding Network (DRAGGN), for task grounding and execution that handles natural language from either category as input, and generalizes to unseen environments. Our robot-simulation results demonstrate that a system successfully interpreting both goal-oriented and action-oriented task specifications brings us closer to robust natural language understanding for human-robot interaction.Comment: Accepted at the 1st Workshop on Language Grounding for Robotics at ACL 201