Over the past two decades, business process reengineering (BPR) has become a popular approach to improve the efficiency and effectiveness of organizations. An examination of relevant BPR literature reveals that available BPR models that have been widely in use have some serious limitations and fail to take into consideration human factors and change management aspects. Both academic researchers and industrial practitioners have acknowledged the existence of this theoretical gap. This article proposes a BPR model that is named process reengineering integrated spiral model (PRISM). PRISM is a systematic agile model that would improve the chances for organizations to successfully carry out BPR initiatives and projects.

Purpose: There is a growing interest in extending the axial fields-of-view (AFOV) of PET scanners. One major limitation for the widespread clinical adoption of such systems is the multifold increase in the associated material costs. In this study, we propose a cost-effective solution to extend the PET AFOV using a sparse detector rings configuration. The corresponding physical performance was validated using Monte Carlo simulations. Methods: Monte Carlo model of the Siemens Biograph TM mCT PET/CT, with a 21.8 cm AFOV and a set of compact rings of LSO crystals was developed as a gold standard. The mCT configuration was then modified by interleaving the LSO crystals in the axial direction within each detector block with 4 mm physical gaps (equivalent to the LSO crystal axial dimension) thus extending the AFOV to 43.6 cm (Ex-mCT). The physical performances of the two MC models were assessed and then compared using NEMA NU 2-2007 standards. Results: Ex-mCT showed <0.2 mm difference in transaxial spatial resolution, and, 0.8 mm and 0.3 mm deterioration in axial spatial resolution, compared to the mCT, at 1 and 10 cm off-center of the transaxial field-of-view respectively. The system sensitivities for the mCT and Ex-mCT models were 9.4 AE 0.2 and 10.75 AE 0.2 cps/kBq respectively. The higher sensitivity of Ex-mCT was due to four additional detector rings required to double the mCT AFOV. PET images of the NEMA Image Quality (IQ) phantom showed no artifacts due to detector rings sparsity, and all spheres were visible in both configurations. Ex-mCT achieved percent contrast recoveries within 5.6% of those of the mCT for all spheres and a maximum of 36% higher background variability at the center of the AFOV. The Ex-mCT, however, showed a more uniform noise distribution over an axial range of almost twice the length of the mCT AFOV. Conclusions: Using the proposed sparse detector-ring configuration, the AFOV of current generation PET systems can be doubled while maintaining the original number and volume of detector crystal elements, and without jeopardizing the system's overall physical performance. Despite an increase in the noise level, the Ex-mCT exhibited an improved noise uniformity.

MDRT has been shown to be accurate in tracking breathing motion and assisted in implementing a smart-gating PET acquisition technique that allowed to acquire prospectively motion-free PET images.

Nowadays VANETs are becoming a dominating technology in automotive industries where vehicles communicate with each other to deliver safety messages or any type of information to other vehicles. However, the increasing numbers of vehicles on the roads poses a challenge on designing an efficient communication protocol for VANETs. The scalability issue in VANETs has a deteriorating effect on latency and on the stability of the network. Clustering is one technique used for solving this issue. In this work, we propose a clustering technique that creates mini clusters that are in the same communication range of the vehicles with the help of Adaptive resonance theory (ART). These mini clusters are created based on speed where it categorizes the vehicle in one of three levels: high, medium or low speeds. ART is an unsupervised neural network model that classifies inputs based on the degree of similarities of the input. By carefully tuning ART, three clusters are always obtained corresponding to the above speed classifications. The proposed work was simulated and compared against traditional clustering methods where our work presented a 50% advantage over these techniques.

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