A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs

Closing the Teacher Quality Gap in Philadelphia: New Hope and Old Hurdles

This study of teacher staffing issues in the School District of Philadelphia, the third in a series, outlines the degree to which the district has succeeded in upgrading teachers' professional credentials, recruiting and retaining them, and equitably distributing experienced and credentialed teachers across all types of schools. Since the passage of NCLB and the state's takeover of the district in 2001, the district has succeeded in improving the certification rates of its teachers, especially new teachers, and in drastically cutting the number of emergency-certified teachers and classroom vacancies. It has also improved new teacher retention and has modernized and decentralized its hiring process. At the same time, it has not been able to change the pattern of having the least qualified teachers in schools serving the highest percentages of poor and minority students nor its poor long-term rate of teacher retention. The district is also challenged to speed up and simplify its hiring and school placement process and to hire more minority teachers

On two-echelon inventory systems with Poisson demand and lost sales

We derive approximations for the service levels of two-echelon inventory systems with lost sales and Poisson demand. Our method is simple and accurate for a very broad range of problem instances, including cases with both high and low service levels. In contrast, existing methods only perform well for limited problem settings, or under restrictive assumptions.\u

Hybrid-Vehfog: A Robust Approach for Reliable Dissemination of Critical Messages in Connected Vehicles

Vehicular Ad-hoc Networks (VANET) enable efficient communication between vehicles with the aim of improving road safety. However, the growing number of vehicles in dense regions and obstacle shadowing regions like Manhattan and other downtown areas leads to frequent disconnection problems resulting in disrupted radio wave propagation between vehicles. To address this issue and to transmit critical messages between vehicles and drones deployed from service vehicles to overcome road incidents and obstacles, we proposed a hybrid technique based on fog computing called Hybrid-Vehfog to disseminate messages in obstacle shadowing regions, and multi-hop technique to disseminate messages in non-obstacle shadowing regions. Our proposed algorithm dynamically adapts to changes in an environment and benefits in efficiency with robust drone deployment capability as needed. Performance of Hybrid-Vehfog is carried out in Network Simulator (NS-2) and Simulation of Urban Mobility (SUMO) simulators. The results showed that Hybrid-Vehfog outperformed Cloud-assisted Message Downlink Dissemination Scheme (CMDS), Cross-Layer Broadcast Protocol (CLBP), PEer-to-Peer protocol for Allocated REsource (PrEPARE), Fog-Named Data Networking (NDN) with mobility, and flooding schemes at all vehicle densities and simulation times

Horizontal Violence Effect on Nurse Retention

Horizontal violence is known by a variety of terms such as lateral violence, bullying, and incivility. Christie and Jones (2014) describe lateral violence as a problem in nursing where a behavior is demonstrated through harmful actions that occur between nurses. Studies have revealed how horizontal violence affects nurse retention. Horizontal violence is a relevant issue in the healthcare community, yet often goes undiscussed. Walrafen (2012) explains that an outcome of horizontal violence in nursing is directly proportional to a decrease in retention of nurses. Sherman (2012) proclaimed that nurses who are subjected to horizontal violence have low self-esteem, depression, excessive sick leave, and poor morale. As Wilson (2011) identified nurses, who witness or experience horizontal violence have an increased desire to leave the organization where the bullying takes place. Horizontal violence is a pervasive source of occupational stress with physical, psychological, and organizational consequences (Hauge, et al, 2010). Roy (2007) describes this as an unkind, discourteous manner in which nurses relate to their colleagues. As nurses seek to perform their daily tasks, other co-workers may embarrass them for their lack of knowledge, tease them as they participate in informal cliques, or demean them for their technique (Bakker, 2012). Creating excuses, taunting, and refusing to share information, nursing education or knowledge are examples of horizontal violence (Ball, 1996)

Driving with Style: Inverse Reinforcement Learning in General-Purpose Planning for Automated Driving

Behavior and motion planning play an important role in automated driving. Traditionally, behavior planners instruct local motion planners with predefined behaviors. Due to the high scene complexity in urban environments, unpredictable situations may occur in which behavior planners fail to match predefined behavior templates. Recently, general-purpose planners have been introduced, combining behavior and local motion planning. These general-purpose planners allow behavior-aware motion planning given a single reward function. However, two challenges arise: First, this function has to map a complex feature space into rewards. Second, the reward function has to be manually tuned by an expert. Manually tuning this reward function becomes a tedious task. In this paper, we propose an approach that relies on human driving demonstrations to automatically tune reward functions. This study offers important insights into the driving style optimization of general-purpose planners with maximum entropy inverse reinforcement learning. We evaluate our approach based on the expected value difference between learned and demonstrated policies. Furthermore, we compare the similarity of human driven trajectories with optimal policies of our planner under learned and expert-tuned reward functions. Our experiments show that we are able to learn reward functions exceeding the level of manual expert tuning without prior domain knowledge.Comment: Appeared at IROS 2019. Accepted version. Added/updated footnote, minor correction in preliminarie