Evaluation of pipelined dilated banyan switch architectures for ATMnetworks

In the pipeline banyan (PB), the reservation cycle in the control plane is made several times faster than payload transmission in data plane. This enables pipelining multiple banyans. It is observed that the ratio of throughput to switching delay (service rate) is relatively low in the PB due to the banyan. For this, we present a scalable pipelined asynchronous transfer mode (ATM) switch architecture employing a family of dilated banyan (DB) networks together with their complexity analysis and performance. A DB can be engineered between two extremes: (1) a low-cost banyan with internal and external conflicts, or (2) a high-cost conflict-free fully connected network with multiple outlets. Between the two extremes lies a family of DBs having different switching delays and throughputs. Increasing the dilation degree reduces path conflicts, which produces noticeable increase in service rate due to increase in throughput and decrease in path delay. Compared to PB, the pipelined dilated banyan (PDB) requires smaller number of data planes for the same throughput, or provides higher throughput for a given number of data planes. Simulation of PDB is carded out under uniform traffic and simulated ATM traffic. We study the switch performance while varying the load, buffer size, and number of data planes. To analyze the robustness of the switch, we show that performance is not degradable under ATM traffic with temporal and spatial burstiness generated using the on-off model. The PDB is scalable with respect to service rate and can be engineered with respect to: (1) cell loss rate; (2) hardware resources; (3) size of buffers; (4) switching delays; and (5) delay incurred to higher priority traffic. The PDB can deliver up to 3.5 times the service rate of the PB with only linear increase in hardware cost     

Sustainable management and design of the energy-water-food nexus using a mathematical programming approach

Global primary and secondary resources are important for economic growth. Resource management and environment conservation are currently frequently discussed topics worldwide. In this study, a discrete optimization model formulation is presented for an integrated energy, water, and food (EWF) supply chain problem. The optimization model examines the temporal and spatial integration of the EWF supply chain elements to provide optimal infrastructure capacity expansion of essential commodities within the EWF system, and their corresponding periodic optimal supply for a given region. Furthermore, the model considers endogenous demand between the EWF elements that reflect the interdependency of nexus elements. A mixed integer linear programming model is developed to assist in the process of optimal infrastructure capacity expansion and operation of the EWF system. A case study is given to show the application of the proposed mathematical programming model. Several scenarios are assumed for the case study under different commodity prices and climate change conditions. In addition, diversification in the energy and agriculture sectors is examined by shifting from international refined sugar trading to bioethanol production. The results show economic gains of ~10% under the emergence of bioethanol production compared with the business-as-usual scenario. Production dynamic exits for the production of refined sugar, bioethanol, and power from sugarcane and bagasse resources over time in the considered sale price range for the refined sugar and bioethanol products.     

A turbulent air-flow corrugating photovoltaic model to enhance power output

This paper investigated the influence of the cooling of turbulent blowing air on the photovoltaic (PV) modules using the corrugation technique. The well-known five-parameter diode equation side by side with conservation principles were used to study the effect of corrugating lower PV sides to cool PV's and enhance efficiency. As the main restriction of PV systems is low efficiency that is powerfully counting on its operational temperature, therefore, reducing the operating temperature of PV cells is critical for the PV panel to work. To achieve this aim, a mathematical model describing PV temperatures and efficiency using continuity, momentum, and energy side by side with the Shockley diode equation was built. It was found that the corrugating of lower sides of PV's up to 1 mm in a distance of 20 mm considering turbulent flow conditions considerably reduced temperature and consequently enhanced thermal efficiency from 13% to 15%; the eddy viscosity and Reynolds shear stress increased boundary layer thickness and velocities, so increased the coefficient of heat transfer and consequently electrical power output and thermal efficiency.     

Nano-Ceramic Coating: A Novel Idea to Reduce Laparoscope Lens Fogging – Experimental Study

Silicon - One of the basic principles of a successful laparoscopic procedure is a clear operating area. The endoscopic lens should be able to visualize the internal organs as clearly as possible....     

Impacts of alginate–basil seed mucilage–prebiotic microencapsulation on the survival rate of the potential probiotic Leuconostoc mesenteroides ABRIINW.N18 in yogurt

We aimed at improving probiotic survival, stability and release in digestive settings during yogurt storage. Alginate–basil seed mucilage was formulated with various concentrations of prebiotics (inulin, fructooligosaccharides and fenugreek) and selected for microencapsulation. When compared with uncapsulated probiotic cells, all examined formulations had high encapsulation effectiveness of over 98.2% and a viable potential probiotic cell survival (62%) when studied under simulated settings. These findings indicate the use of home-grown-based gums such as basil seed adhesive and fenugreek in combination with alginate are good media for probiotic encapsulation.     

Thermoelectric performance of Ni,Co, and Fe nanoparticles incorporated into their metal borates glassy matrices

Here, we present our current attempt to intrinsically dope Ni⁰, Co⁰, and Fe⁰ nanoparticles within Ni^II-, Co^II-, and Fe^II-borate glassy matrices, respectively. The system was prepared by one-pot reaction of the desired M_T^II salt with excess NaBH₄ through an in-situ reduction and hydrolysis processes to afford metallic M_T⁰ nanoparticles dispersed into the M_T-BO₃ matrix. The composition and structural characteristics of these M_T⁰:M_T-BO₃ materials were identified by thermal oxidation, ATR-IR, X-ray powder diffraction, and magnetic techniques as glassy/amorphous borate matrices containing magnetic nanoparticles. The electrical conductivity (σ) of cold-pressed discs of these metal-doped composites shows that they behave as nonohmic semiconductors within the temperature range of 303 ≤ T ≤ 373 K suggesting a mixed electronic-ionic conduction. However, their thermal conductivity (κ) occurs through phonon lattice vibration dynamics rather than electronic. The σ/κ ratio shows a steep non-linear increase from 9.4 to 270 KV⁻² in Ni⁰:Ni-BO₃. In contrast, a moderate-weak increase is observed for Co⁰:Co-BO₃ and Fe⁰:Fe-BO₃ analogs. The obtained materials are examined for thermoelectric (TE) applications by determining their Seebeck coefficient (S) power factor (PF), figure of merit (ZT), and conversion efficiency (η%). All the TE data shows that Ni⁰:Ni-BO₃ (S, 80 μVK⁻¹; PF, 97.7 mWm⁻¹ K⁻¹; ZT 0.54; η, 2.15%) is a better TE semiconductor than the other two M_T⁰:M_T-BO₃. This finding shows that Ni⁰:Ni-BO₃ is a promising candidate to exploit low-temperature waste heat from body heat, sunshine, and small domestic devices for small-scale TE applications.     

An Improved Deep Structure for Accurately Brain Tumor Recognition

Brain neoplasms are recognized with a biopsy, which is not commonly done before decisive brain surgery. By using Convolutional Neural Networks (CNNs) and textural features, the process of diagnosing brain tumors by radiologists would be a noninvasive procedure. This paper proposes a features fusion model that can distinguish between no tumor and brain tumor types via a novel deep learning structure. The proposed model extracts Gray Level Co-occurrence Matrix (GLCM) textural features from MRI brain tumor images. Moreover, a deep neural network (DNN) model has been proposed to select the most salient features from the GLCM. Moreover, it manipulates the extraction of the additional high levels of salient features from a proposed CNN model. Finally, a fusion process has been utilized between these two types of features to form the input layer of additional proposed DNN model which is responsible for the recognition process. Two common datasets have been applied and tested, Br35H and FigShare datasets. The first dataset contains binary labels, while the second one splits the brain tumor into four classes; glioma, meningioma, pituitary, and no cancer. Moreover, several performance metrics have been evaluated from both datasets, including, accuracy, sensitivity, specificity, F-score, and training time. Experimental results show that the proposed methodology has achieved superior performance compared with the current state of art studies. The proposed system has achieved about 98.22% accuracy value in the case of the Br35H dataset however, an accuracy of 98.01% has been achieved in the case of the FigShare dataset.     

Supramolecular Self-Assembly Built by Hydrogen,Stacking and Br···Br Interactions in 4-((4-Bromobenzyl)Selanyl)Aniline: Structure,Hirshfeld Surface Analysis, 3D Energy Framework Approach and Global Reactivity Descriptors

Journal of Inorganic and Organometallic Polymers and Materials - A novel organoselenium compound named 4-((4-bromobenzyl)selanyl)aniline, C13H12BrNSe, (A), was synthesized via reduction of...     

A data-driven controller for position tracking of a long-stroke piezoelectric actuator

Microsystem Technologies - Hysteresis and other nonlinearities exhibited by piezoelectric materials significantly limit the tracking capability of piezoelectric actuators (PAs). To eliminate these...