Engineering the system of communication for safer surgery

Communication failures are a leading cause of error in surgery. Researchers and practitioners have therefore developed different interventions to improve communications, such as team briefing and pre-operative patient checklists. These different methods have clear merit. However, they have only dealt with portions of a complex system. Consequently, disparate interventions of varying kinds may not integrate and build an effective system of communication. We argue that a new view of communication is needed to improve safety in surgery; the view that communication is more fundamentally as a property of the whole system of work rather than confined to interpersonal exchanges. Rather than simply add an intervention to the system, interventions should integrate into the system. To achieve this, we propose a practical strategy to re-engineer the system of communication for surgery. This demands an analysis of the immediate informational needs within the system of interest, and an account of the wider system and those ergonomic and human factors shaping the performance of communicators. We illustrate the application of the method and refer to potential improvements in safety.     

A review study on blockchain-based IoT security and forensics

The term Internet of Things (IoT) represents all communicating countless heterogeneous devices to share data and resources via the internet. The speedy advance of IoT devices proposes limitless benefits, but it also brings new challenges regarding security and forensics. Likewise, IoT devices can generate a massive amount of data that desires integrity and security during its handling and processing in an efficient way. IoT devices and data can be vulnerable to various types of cyber-crimes at each IoT layer. For combating these cyber-crimes in IoT infrastructure, IoT forensic term has shown up. The IoT forensic is the process of performing digital forensic investigation in the IoT environment in a forensically sound and timely fashion manner. Sundry challenges face the IoT forensics that requires urgent solutions and mitigation methods; digital evidence needs to be collected, preserved, analyzed, processed, and reported in a trusted manner to be acceptable for presenting in the court of law. Preserving the evidence unchanged or tampered with is the most critical challenge in digital forensics. Authentication is another challenge facing digital forensics; who is allowed to deal with the evidence? One of the most recent solutions for supporting IoT forensics is the use of Blockchain. Using Blockchain in digital forensics guarantees data integrity, immutability, scalability, and security. Therefore, this paper presents a comprehensive review of IoT security and forensics with the integration with Blockchain technology. It begins by providing an inclusive discussion of IoT security, as well as the need for IoT forensics, and the concepts of Blockchain. Then, a review of Blockchain-based IoT security and forensics issues is presented. Finally, a discussion of open research directions is provided.

     

A systematic review on emperor penguin optimizer

Neural Computing and Applications - Emperor Penguin Optimizer (EPO) is a recently developed metaheuristic algorithm to solve general optimization problems. The main strength of EPO is twofold....     

Optimal coverage of an infrastructure network using sensors with distance-decaying sensing quality

Motivated by recent applications of wireless sensor networks in monitoring infrastructure networks, we address the problem of optimal coverage of infrastructure networks using sensors whose sensing performance decays with distance. We show that this problem can be formulated as a continuous p-median problem on networks. The literature has addressed the discrete p-median problem   on networks and in continuum domains, and the continuous p$p$-median problem in continuum domains extensively. However, in-depth analysis of the continuous p$p$-median problem on networks has been lacking. With the sensing performance model that decays with distance, each sensor covers a region equivalent to its Voronoi partition on the network in terms of the shortest path distance metric. Using Voronoi partitions, we define a directional partial derivative of the coverage metric with respect to a sensor’s location. We then propose a gradient descent algorithm to obtain a locally optimal solution with guaranteed convergence. The quality of an optimal solution depends on the choice of the initial configuration of sensors. We obtain an initial configuration using two approaches: by solving the discrete p$p$-median problem on a lumped   network and by random sampling. We consider two methods of random sampling: uniform sampling and D²$D^2$-sampling. The first approach with the initial solution of the discrete p$p$-median problem leads to the best coverage performance for large networks, but at the cost of high running time. We also observe that the gradient descent on the initial solution with the D²$D^2$-sampling method yields a solution that is within at most 7% of the previous solution and with much shorter running time.     

Transient model of induction machine using rotating magnetic field approach

Most simulation models of electric machines use the coupled circuit approach, where the machine is considered as an electric circuit element with time-varying inductances (abc model) or with constant inductances (dq0 model). On the other hand, the rotating magnetic field approach, which considers the electric machine as two groups of windings producing rotating magnetic fields and can give insight into internal phenomena of the machines, has not yet received much attention in electric machines modeling, especially for machine transient analysis. Based on the rotating magnetic field approach, this paper presents a transient model of the induction machine including main flux saturation effect. Based on the direct computation of the magnetizing fluxes of all machine windings, the model represents instantaneous main flux saturation by simply introducing a main flux saturation factor. No iteration process is involved to incorporate the saturation effects. The model combines the advantages of the dq0 and abc models advantages, such as rapid computation time and nonsymmetrical conditions simulation, respectively. The simulation results and the experimental tests show advantages and verification of the model.     

Correction to: Automatic COVID-19 detection mechanisms and approaches from medical images: a systematic review

Multimedia Tools and Applications -     

Integrating Collaborative and Reclusive Methods for Effective Recommendations: A Fuzzy Bayesian Approach

Collaborative filtering (CF), the most successful and widely used technique, recommends items based on the preferences of similar users. The main potentials of CF are its cross‐genre recommendation ability, and that it is completely independent of representation of the items being recommended. However, CF suffers from sparsity and cold start problems. On the other hand, a highly effective variant of content‐based filtering (CBF), reclusive methods (RMs) based on the preference of the single individual for whom recommendations to be made, provides a methodology that considers uncertainty and the multivalued nature of item features as well as user preferences in a content‐based framework using fuzzy logic approaches. The adoption of RM paradigm has several advantages when compared to CF such as sparsity and new item problem, but it suffers from overspecialization and limited content analysis. In view of the complementary nature of CF and RM, we develop a hybrid recommender system (RS) that helps in alleviating aforementioned problems in each approach. First, we propose fuzzy naïve Bayesian classifier based CF (FNB‐CF) and RM (FNB‐RM) for handling correlation‐based similarity problems. To overcome individual weaknesses of FNB‐CF and FNB‐RM, we develop a hybrid RS, FNB‐CF‐RM. Effectiveness of our proposed hybrid RS is demonstrated through experimental results using the MovieLens and IMDb data sets.     

Shear effect on dynamic behavior of microcantilever beam with manufacturing process defects

This paper is concerned with the investigation of the shear effect on the dynamic behavior of a thin microcantilever beam with manufacturing process defects. Unlike the Rayleigh beam model (RBM), the Timoshenko beam model (TBM) takes in consideration the shear effect on the resonance frequency. This effect become significant for thin microcantilever beams with larger slenderness ratios that are normally encountered in MEMS devices such as sensors. The TBM model is presented and analyzed by numerical simulation using Finite Element Method (FEM) to determine corrective factors for the correction of the effect of manufacturing process defects like the underetching at the clamped end of the microbeam and the nonrectangular cross section of the area. A semi-analytical approach is proposed for the extraction of the Young’s modulus from 3D FEM simulation with COMSOL Multiphysics software. This model was tested on measurements of a thin chromium microcantilever beam of dimensions (80 × 2 × 0.95 μm³). Final results indicate that the correction of the effect of manufacturing process defects is significant where the corrected value of Young’s modulus is very close to the experimental results and it is about 280.81 GPa.

     

Managing People to Promote Innovation

There is growing evidence available to suggest that Human Resource Management (HRM) practice is an important predictor of organizational performance. Drawing upon organizational learning perspectives, we argue that HRM systems also have the potential to promote organizational innovation. We present longitudinal data from thirty‐five UK manufacturing organizations to suggest that effective HRM systems – incorporating sophisticated approaches to recruitment and selection, induction, appraisal and training – predict organizational innovation in products and production technology. We further show that organizational innovation is enhanced where there is a supportive learning climate, and inhibited (for innovation in production processes) where there is a link between appraisal and remuneration.     

Intermediate temperature oxidation in silicon nitride yttria ceramics

Silicon nitride billets with both 4% and 8% Y₂O₃ additives have been subjected to oxidation treatments for up to 300 h, in air, in the temperature range 700 to 1000° C. Flexure strength and weight gain measurements together with both scanning and transmission electron microscopy and X-ray diffraction studies were conducted on these billets in an effort to understand the oxidation process. It appears that the degradation phenomena is associated with both the formation of phases outside the Si₃N₄-Si₂ON₂-Y₂Si₂O₇ compatibility triangle of the system Si₃N₄-SiO₂-Y₂O₃ and with the decomposition of W-containing phases at and near the grain boundaries.