This study is concerned with the performance modeling of a fault tolerant system consisting of operating units supported by a combination of warm and cold spares. The on-line as well as warm standby units are subject to failures and are send for the repair to a repair facility having single repairman which is prone to failure. If the failed unit is not detected, the system enters into an unsafe state from which it is cleared by the reboot and recovery action. The server is allowed to go for vacation if there is no failed unit present in the system. Markov model is developed to obtain the transient probabilities associated with the system states. Runge–Kutta method is used to evaluate the system state probabilities and queueing measures. To explore the sensitivity and cost associated with the system, numerical simulation is conducted.

In the present investigation, we deal with the reliability characteristics of a repairable system consisting of two independent operating units, by incorporating the coverage factor. The probability of the successful detection, location and recovery from a failure is known as the coverage probability. The reboot delay and common cause shock failure are also considered. The times to failure of the components, time to failure due to common cause, time to repair and time to reboot are assumed to follow exponential distributions. The Markov model of the system is developed and the system state transition probabilities are determined which are further used to evaluate some reliability indices such as availability and mean time to failure. We use fuzzy logic approach for analyzing the system performance by assuming the trapezoidal membership functions of the system descriptors viz. failure rates and repair rates. The fuzzy mean time to failure and fuzzy availability have been established. A numerical experiment has been performed to validate the analytical results.

Background and Aims: Metal-free and fiber-reinforced composite (FRC) restorations have drawn considerable attention and interest in recent years for restoring in the posterior area due to their improved esthetics. Fracture resistance is one of the most important mechanical properties of materials because of 500-600 N load of occlusion. The restorations should tolerate this load. The purpose of this in vitro study was to compare the fracture resistance of FRC with that of zirconia all-ceramic for posterior partial dentures.Materials and Methods: Forty extracted human intact teeth (20 first premolars and 20 first molars) selected for fabricating 10 pairs of fiber-reinforced composite and 10 pairs of zirconia all-ceramic bridges. After receiving standard tooth preparation, the teeth were mounted with 7.5 mm distance between each other. The bridges were made and cemented on the teeth. Then the restorations were stored in 37oC water for 30 days. The fracture resistance was measured using mechanical testing machine with cross-head speed of 1mm/min. Data were evaluated by Independent sample t test.Results: The mean fracture resistance in the ceramic group was 1329.41 N and for the F.R.C group was 1118.528 N with significant differences between them (P=0.034). The failure modes were mainly cohesive at pontic area for ceramic samples, but adhesive for FRC samples at pontic area.Conclusion: Both systems showed sufficient fracture resistance for using in posterior area.

We address the throughput maximization problem for downlink transmission in DF-relay-assisted cognitive radio networks (CRNs) based on simultaneous wireless information and power transfer (SWIPT) capability. In this envisioned network, multiple-input multiple-output (MIMO) relay and secondary user (SU) equipment are designed to handle both radio frequency (RF) signal energy harvesting and SWIPT functional tasks. Additionally, the cognitive base station (CBS) communicates with the SU only via the MIMO relay. Based on the considered network model, several combined constraints of the main problem complicate the solution. Therefore, in this paper, we apply heuristic guidelines within the convex optimization framework to handle this complexity. First, consider the problem of maximizing throughput on both sides of the relay separately. Second, each side progresses to solve the complex problem optimally by adopting strategies for solving sub-problems. Finally, these optimal solutions are synthesized by proposing a heuristic iterative power allocation algorithm that satisfies the combinatorial constraints with short convergence times. The performance of the optimal proposed algorithm (OPA) is evaluated against benchmark algorithms via numerical results on optimality, convergence time, constraints’ compliance, and imperfect channel state information (CSI) on the CBS-PU link.

Introduction & Objective: Osteogenesis imperfecta (OI) is a generalized connective tissue disorder and it is mostly inherited as an autosomal dominant trait. It may present with fragile bones, blue sclerae and hearing loss. We present two cases of OI. Case presentation: First case is a 18 Y/O man referred with bilateral hearing loss, who had 40db hearing loss in left side (operated ear) and underwent stapedotomy and received a good hearing. Second case is a 13 Y/O boy referred with bilateral hearing loss, who had 45db hearing loss in left side (operated ear) and underwent stapedotomy and received a good hearing with SRT less than 15.  Conclusion: If the patient is selected properly, good result is achieved.

It is possible to noticeably increase the capacity of wireless communication systems through the use of multiple antennas both in the transmitter and in the receiver. In such systems, which are referred to in short as MIMO, the receiver uses its knowledge of the channel to detect the transmitted signal. Different methods have been proposed for optimal and sub-optimal detection of the transmitted signals. Recently, principles of deep learning and implementing neural networks have been employed as a near optimal approach for MIMO detection with fewer calculations during the testing process compared to traditional methods. In the event an error occurs in the receiver’ s channel estimation process, this type of detector suffers a drop in performance and as a result, BER will increase. Given that in practice, the receiver only has an estimation of the CSI instead of the exact values, the current study presents an enhanced detection method based on deep learning, which is also robust against channel estimation error. In this detection method, by using the covariance matrix of the channel estimator and the principles of deep learning, a robust detector against channel estimation error is proposed and comprehensively evaluated. Numerical simulations confirm the performance of the proposed method.