ACP Model-Assertiveness, Clarity, and Positivity

The level of understanding of health instructions by patients remains low, that is, most patients have difficulty understanding the indications of their health professional to continue to treat their health after leaving the consultation. The professional oversees the challenge of validating the understanding of the message by the patient. The aim of this study was to demonstrate how verbal and non-verbal communication integrated into an interdependent and aggregated model of specific communication skills—assertiveness, language clarity, and positivity—allow the health professional to be further strengthened and trained to obtain a better understanding of the patient health instructions. The mixed method with a qualitative and quantitative approach was used in a non-probabilistic study with a convenience sample of 484 health professionals, based on a questionnaire survey, 14 focus groups, and 7 in-depth interviews. The results obtained validated the communication model for health literacy, which the author calls the ACP model – assertiveness, clarity, and positivity.

Hybrid Physics-Constrained and Data-Driven Approach for Interwell Saturation Estimation from Well Logs

Combining physics-based models for well log analysis with artificial intelligence (AI) advanced algorithms is crucial for wellbore studies. Data-driven methods do not generalize well and lack theoretical knowledge accumulated in the field. Estimating well saturation significantly improves if predictions from physical models are used to constrain data-driven algorithms in outlined primary fluid channels and other important points of interest. Saturation propagations in the reservoirs interwell region also generalize better under using combination of models. This work addresses combined usage of theoretical and data-driven models by aggregating them into single hybrid model. Multiple physical and data-driven models are under study, their parameters are optimized using observations. Weighted sum is used to predict water saturation at every point with weights being recomputed at each step. Model outputs are compared in terms accuracy and cumulative loss. A synthesized reservoir box model encompassing volumetric interwell porosity, resistivity and saturation data is used for the validation of the algorithms. Aggregated model for estimating interwell saturation shows improved prediction accuracy compared both to physics-based or data-driven approaches separately.

Aggregation of Radial Distribution System Bus with Volt-Var Control

The high penetration of the distributed energy resources (DERs) encourages themselves to implement grid-supporting functions, such as volt-var control. The quasi-static time-series (QSTS) simulation is an essential technique to evaluate the impact of active DERs on the grid. Meanwhile, the increase of complexity on the circuit model causes a heavy computational burden of QSTS simulation. Although circuit reduction methods have been proposed, there have been few methods that can appropriately handle the distribution system (DS) with multiple voltage control devices, such as DERs implementing volt-var control. To address the remaining issues, this paper proposes an offline bus aggregation method for DS with volt-var control. The method determines the volt-var curve for the aggregated bus on the basis of historical data to reduce error in the aggregated model, and its offline process solves the computational convergence issue concerned in the online one. The effectiveness of the proposed method is validated in the simulation using a Japanese low-voltage DS model. The simulation results show that the proposed method can reduce the voltage error and computational time. Furthermore, the versatility of the proposed method is verified to show the performance does not heavily depend on how to select historical data for model-building.

Efficient path planning of drone swarms over clustered human crowds in social events

PurposeIn this study, supervision and control of the possible problems among people over a large area with a limited number of drone cameras and security staff is established.Design/methodology/approachThese drones, namely unmanned aerial vehicles (UAVs) will be adaptively and automatically distributed over the crowds to control and track the communities by the proposed system. Since crowds are mobile, the design of the drone clusters will be simultaneously re-organized according to densities and distributions of people. An adaptive and dynamic distribution and routing mechanism of UAV fleets for crowds is implemented to control a specific given region. The nine popular clustering algorithms have been used and tested in the presented mechanism to gain better performance.FindingsThe nine popular clustering algorithms have been used and tested in the presented mechanism to gain better performance. An outperformed clustering performance from the aggregated model has been received when compared with a singular clustering method over five different test cases about crowds of human distributions. This study has three basic components. The first one is to divide the human crowds into clusters. The second one is to determine an optimum route of UAVs over clusters. The last one is to direct the most appropriate security personnel to the events that occurred.Originality/valueThis study has three basic components. The first one is to divide the human crowds into clusters. The second one is to determine an optimum route of UAVs over clusters. The last one is to direct the most appropriate security personnel to the events that occurred.

Federated Learning using Peer-to-peer Network for Decentralized Orchestration of Model Weights

<div>In recent times, Machine learning and Artificial intelligence have become one of the key emerging fields of computer science. Many researchers and businesses are benefited by machine learning models that are trained by data processing at scale. However, machine learning, and particularly Deep Learning requires large amounts of data, that in several instances are proprietary and confidential to many businesses. In order to respect individual organization’s privacy in collaborative machine learning, federated learning could play a crucial role. Such implementations of privacy preserving federated learning find applicability in various ecosystems like finance, health care, legal, research and other fields that require preservation of privacy. However, many such implementations are driven by a centralized architecture in the network, where the aggregator node becomes the single point of failure, and is also expected with lots of computing resources at its disposal. In this paper, we propose an approach of implementing a decentralized, peer-topeer federated learning framework, that leverages RAFT based aggregator selection. The proposal hinges on that fact that there is no one permanent aggregator, but instead a transient, time based elected leader, which will aggregate the models from all the peers in the network. The leader ( aggregator) publishes the aggregated model on the network, for everyone to consume. Along with peer-to-peer network and RAFT based aggregator selection, the framework uses dynamic generation of cryptographic keys, to create a more secure mechanism for delivery of models within the network. The key rotation also ensures anonymity of the sender on the network too. Experiments conducted in the paper, verifies the usage of peer-to-peer network for creating a resilient federated learning network. Although the proposed solution uses an artificial neural network in it’s reference implementation, the generic design of the framework can accommodate any federated learning model within the network.</div>

Federated Learning using Peer-to-peer Network for Decentralized Orchestration of Model Weights

<div>In recent times, Machine learning and Artificial intelligence have become one of the key emerging fields of computer science. Many researchers and businesses are benefited by machine learning models that are trained by data processing at scale. However, machine learning, and particularly Deep Learning requires large amounts of data, that in several instances are proprietary and confidential to many businesses. In order to respect individual organization’s privacy in collaborative machine learning, federated learning could play a crucial role. Such implementations of privacy preserving federated learning find applicability in various ecosystems like finance, health care, legal, research and other fields that require preservation of privacy. However, many such implementations are driven by a centralized architecture in the network, where the aggregator node becomes the single point of failure, and is also expected with lots of computing resources at its disposal. In this paper, we propose an approach of implementing a decentralized, peer-topeer federated learning framework, that leverages RAFT based aggregator selection. The proposal hinges on that fact that there is no one permanent aggregator, but instead a transient, time based elected leader, which will aggregate the models from all the peers in the network. The leader ( aggregator) publishes the aggregated model on the network, for everyone to consume. Along with peer-to-peer network and RAFT based aggregator selection, the framework uses dynamic generation of cryptographic keys, to create a more secure mechanism for delivery of models within the network. The key rotation also ensures anonymity of the sender on the network too. Experiments conducted in the paper, verifies the usage of peer-to-peer network for creating a resilient federated learning network. Although the proposed solution uses an artificial neural network in it’s reference implementation, the generic design of the framework can accommodate any federated learning model within the network.</div>

Perceptions of Hospitality and Safety Are Two Sides of the Same Coin

Entering a building is a ‘moment of truth’ and may invoke feelings of hospitableness. Physical environments and staff behaviour deliver ‘clues’ that may result in the experience of hospitality. The focus in a reception area may be on mitigation of risks, or on a hospitable atmosphere, with either a host or a security officer at the entrance. However, the division of tasks to either the pleasing host or the controlling security officer to a certain extent disavows the overlap between perceptions of hospitality and safety. This exploratory qualitative study combines a group interview with three managers responsible for hospitality and security in reception areas and Critical Incidents by staff and visitors (N = 51). Thematic coding was based on The Egg Aggregated Model and the Experience of Hospitality Scale. Results show that hospitality and safety are indeed two sides of the same coin. Usually people do accept security measures, provided that staff act in a hospitable way. A lack of security measures may seem ‘inviting’, but also decreases the perception of care for your visitor, and may cause uncertainty and therefore decrease comfort. A correct risk perception, flexible appliance of security measures, and a friendly approach connect aspects of ‘safe’ and ‘hospitable’ sentiments.

A combined fault- and catalog-based hazard assessment for Central Zagros, Iran

&lt;p&gt;The Zagros mountains is a tectonically active Arabian-Eurasian plate convergence zone. The convergence direction changes along the strike of the belt, results in oblique faulting in the North-Western Zagros (NWZ) and the prevalence of pure reverse faulting in the South-Eastern Zagros (SEZ). The two regions undergo different convergence rates, (4 &amp;#177; 2 mm yr &amp;#8722;1) in NWZ and (9 &amp;#177; 2 mm yr &amp;#8722;1) in SEZ. These differences is partially accommodated by right-lateral strike-slip faulting throughout the Central Zagros (CZ), resulting in catastrophic earthquakes like 1972 Mw = 6.7 Qir and 1934 Mw = 6.3 Kazerun. This study presents the Probabilistic Seismic Hazard Assessment (PSHA) for the CZ region by integrating fault sources and seismological data. The seismological catalog data consists of 6504 events (2.5 &lt; Mw &lt; 6.7) during 1925-2020 and was compiled from the International Seismological Center (ISC) and the Iranian Seismological Center (IRSC). The faults with the history of Mw &gt; 5.5 or geometrical potential of producing such an event were modeled. A Truncated Gutenberg&amp;#8211;Richter (TGR) Magnitude-Frequency Distribution (MFD) for a range of magnitudes (5.5 &lt; Mw &lt; Mmax ) is evaluated by processing the geometrical parameters and slip rate of each fault source using the FiSH code. The Mmax is computed for each source by combining various Mmax estimates based on the faults geometry and observed Mmax if it is available. The catalog data was modeled as a grid source. A unique set of seismic activity rate parameters (for Mw &gt; 4) in each grid is obtained by applying a modified smoothed seismicity approach. More precisely, a penalized likelihood-based methodwas utilized for the spatial estimation of the b-values, and a weighted smoothing method was implemented to calculate the spatial distribution of the a-values. The catalog events with Mw &gt; 5.5 were excluded to avoid duplicated hazard estimation (modified earthquake catalog). Compiling the source models, the hazard computations were performed using the OpenQuake Engine. The Peak Ground Acceleration (PGA) is computed for the Probability Of Exceedance (POE) of 10% over 50 years for distributed seismicity obtained by the full catalog, and an aggregated model of active faults and distributed seismicity with the modified earthquake catalog. The distributed model produces an approximately uniform PGA with a maximum value of 0.185 g over CZ, while the aggregated model accents the PGA in the vicinity of the faults the maximum of 0.319 g observed around the Kazerun fault. The results show the competence of aggregating fault-based and distributed seismicity hazard assessments for applying comprehensive PSHA studies.&lt;/p&gt;