Digital Circuit Design Through Simulated Evolution (SimE)

Abstract- In this paper, the use of Simulated Evolution (SimE) Algorithm in the design of digital logic circuits is proposed. SimE algorithm consists of three steps: evaluation, selection and allocation. Two goodness measures are designed to guide the selection and allocation operations of SimE. Area, power and delay are considered in the optimization of circuits. Results obtained by SimE algorithm are compared to those obtained by Genetic Algorithm (CA)

ENHANCING PERFORMANCE OF ITERATIVE HEURISTICS FOR VLSI NETLIST PARTITIONING

ABSTRACT In this paper we, present a new heuristic called PowerFM which is a modification of the well-known Fidducia Mattheyeses algorithm for VLSI netlist partitioning. PowerFM considers the minimization of power consumption due to the nets cut. The advantages of using PowerFM as an initial solution generator for other iterative algorithms, in panicular Genetic Algorithm (GA) and Tabu Search (TS), for multiobjective optimization is investigated. A series of experiments are conducted on ISCAS-85/89 benchmark circuits to evaluate the efficiency of the PawerFM algorithm. Results suggest that this heuristic would provide a good starting solution for multiobjective optimization using iterative algorithms

Model Calibration and Optimization of a Protein Purification Process

Four different chromatography models were calibrated to describe the separation of a ternary protein mixture consisting of lysozyme, cytochrome C and ribonuclease A in an ion-exchange chromatography column. The models are based on the same column model, the kinetic dispersive model. Protein adsorption was described by four different adsorption models, the Langmuir model with mobile phase modulators (MPM), the steric mass action (SMA) model, the self-association (SAS) model and the generalized Langmuir (GL) model. The models were calibrated against two kind of experiments, multi-component gradient experiments at low column load and single-component gradient experiments at high column load. The models were also validated against a multi-component validation experiment. All the models, especially the Langmuir MPM model, fit the experimental profiles at low column load very well. At high column load only the SAS model and GL model could capture the behavior of the experimental profiles, but even these two models did not fit the experimental profiles so well. The thesis was concluded with an optimization of the protein purification process. Three different objective functions were optimized, productivity, yield and normalized earnings. Optimization was performed with regard to two decision variables, the variables correspond to the amount of proteins loaded and the slope of the salt gradient, and one purity constraint. Maximum productivity was obtained at high column load and steep salt gradient. Maximum yield was obtained at low column load and flat salt gradient.In this thesis four different models were calibrated to describe the separation of a ternary protein mixture in an ion-exchange chromatography column. The thesis was concluded with an optimization of the separation process

The effect of sulfate contents on the surface properties of iron–manganese doped sulfated zirconia catalysts

The iron–manganese doped sulfated zirconia catalysts were prepared via precipitation method; the sulfation was carried out by impregnation with different amounts of sulfate (4%, 10% and 16% SO4− 2 by weight) with the addition of Fe–Mn doped and calcined at 600 °C for 3 h. The prepared catalysts were characterized by TGA-DTA, XRD, BET, FT-IR, TEM, TPD-NH3 and XPS. XRD and BET results revealed that the addition of sulfate imparts special stabilization to the catalytically active tetragonal phase of zirconia. All the iron–manganese doped sulfated zirconia catalysts were found to have strong acid sites, high surface area and small crystallite size

Effect of recombinant human erythropoietin and doxorubicin in combination on the proliferation of MCF-7 and MDA-mb231 breast cancer cells

Patients with cancer often exhibit signs of anemia as the result of the disease. Thus, cancer chemotherapies often include erythropoietin (EPO) in the regime to improve the survival rate of these patients. The aim of the present study was to determine the effect of EPO on doxorubicin-treated breast cancer cells. The cytotoxicity of doxorubicin alone or in combination with EPO against the MCF-7 and MDA-MB 231 human breast cancer cells were determined using an MTT cell viability assay, neutral red (NR) uptake assay and lactate dehydrogenase (LDH) assay. The estimated half maximal inhibitory concentration values for doxorubicin and the combination of doxorubicin with EPO were between 0.140 and 0.260 µg/ml for all cells treated for 72 h. Treatment with doxorubicin in combination with EPO led to no notable difference in cytotoxicity, compared with treatment with doxorubicin alone. The antiproliferative effect of doxorubicin at a concentration of 1 µg/ml on the MDA MB 231 cells was demonstrated by the decrease in viable cells from 3.6x10(5) at 24 h to 2.1x10(5) at 72 h of treatment. In order to confirm apoptosis in the doxorubicin-treated cells, the activities of caspases-3/7 and 9 were determined using a TBE assay. The results indicated that the activities of caspases-3/7 and 9 were significantly elevated in the doxorubicin-treated MDA-MB-231 cells by 571 and 645%, respectively, and in the MCF 7 cells by 471 and 345%, respectively, compared with the control cells. EPO did not modify the effect of doxorubicin on these cell lines. The results of the present study suggested that EPO was safe for use in combination with doxorubicin in the treatment of patients with breast cancer and concurrent anemia

Effect of treated BAPCO oil refinery effluents on the marine environment in Bahrain

The aim of this thesis was to study the effect of the treated oil refinery effluents of Bahrain Petroleum Company ( BAPCO ) on the marine environment. The study has been carried out in seven chapters, the first de~ling with general introduction about the physical environment of the Arabian Gulf and its oil industry in addition to marine pollution and fishery in Bahrain. The second chapter was aimed at studying the quality of water at different areas in Bahrain to compare it with the quality of water at an area adjacent to the refinery outfalls. The third chapter deals with the impact of the refinery on the marine environment. The research approach adopted was ecological and observational I involving water and sediment analysis. The fourth chapter was aimed at studying the effects of the refinery effluents on the fish through toxicity tests and estimation of trace metals and hydrocarbons in the fish tissues. The studies were conducted with several effluents from the main and side streams of the refinery, outlets of the induced air flotation unit and Sitra separator. The fifth chapter was aimed at studying the effect of Sitra oil storage tanks treated effluent on the intertidal fauna of the adjacent area. The sixth chapter deals with a general discussion on the whole studies. While the seventh chapter deals with sions and recommendations. From the study conducted , it could be said that in general the water quality of the western and northern regions of Bahrain is better than the water quality of the eastern region. The east has in general higher pH, higher ammonia, higher nitrite, lower nitrate, and higher phosphate levels than the other regions. The higher concentrations of all these substances reflects the urbanisation and industrialisation of this part of Bahrain and the consequent discharges of waste material. The fish, safee, were tested with different concentrations of the refinery main stream effluent using both short and long term exposures. The same experiment was set up using effluents from the side stream, the I.A.F. discharge of the refinery and Sitra separator outlet. The results indicated a greater toxicity of pollutants in the I.A.F. and Sitar separator effluents than in the main and the side stream effluents. The lethal concentration of I.A.F. and Sitra separator effluents which brought death to fifty percent of the test population of fish (safee) within 96 hours was approximately twenty percent. Fish from the experimental media showed a higner concentration of heavy metals. The study of the effect of Sitra oil storage tanks treated effluent on the intertidal fauna of the adjacent area revealed that no fauna was found at stations located just outside the effluent outlet. The sediment of these stations was black, slimy, heavily oiled with a bad smell and contains dead shells and turrets. Away from the effluent, a diverse fauna was found. It is concluded that the BAPCO refinery has a measurable impact on the marine environment of the east coast of Bahrain, and recommenqations are made tor the development of future policies with regard to Bahraini coastal waters

Cytotoxicity of nickel zinc ferrite nanoparticles on cancer cells of epithelial origin

In this study, in vitro cytotoxicity of nickel zinc (NiZn) ferrite nanoparticles against human colon cancer HT29, breast cancer MCF7, and liver cancer HepG2 cells was examined. The morphology, homogeneity, and elemental composition of NiZn ferrite nanoparticles were investigated by scanning electron microscopy, transmission electron microscopy, and energy dispersive X-ray spectroscopy, respectively. The exposure of cancer cells to NiZn ferrite nano-particles (15.6-1,000 μg/mL; 72 hours) has resulted in a dose-dependent inhibition of cell growth determined by MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay. The quantification of caspase-3 and -9 activities and DNA fragmentation to assess the cell death pathway of the treated cells showed that both were stimulated when exposed to NiZn ferrite nanoparticles. Light microscopy examination of the cells exposed to NiZn ferrite nanoparticles demonstrated significant changes in cellular morphology. The HepG2 cells were most prone to apoptosis among the three cells lines examined, as the result of treatment with NiZn nanoparticles. In conclusion, NiZn ferrite nanoparticles are suggested to have potential cytotoxicity against cancer cells

Software-Defined Networking-Based Campus Networks Via Deep Reinforcement Learning Algorithms: The Case of University of Technology

As a consequence of the COVID-19 pandemic, networks need to be adopted to satisfy the new situation. People have been introduced to new modes of working from home, attending teleconferences, and taking part in e-learning. Other technologies, including smart cities, the Internet of Things, and simulation tools, have also seen a rise in demand. In the new situation, the network most affected is the campus network. Fortunately, a powerful and flexible network model called the software-defined network (SDN) is currently being standardized. SDN can significantly improve the performance of campus networks. Consequently, many scholars and experts have focused on enhancing campus networks via SDN technology. Integrating deep reinforcement learning (DRL) with SDN is pivotal for advancing the quality of service (QoS) of contemporary networks. Their integration enables real-time collaboration, intelligent decision making, and optimized traffic flow and resource allocation. The system proposed in this research is a DRL algorithm applied to a campus network—the University of Technology—and investigated as a case study. The proposed system employs a two-method approach for optimizing the QoS of a network. First, the system classifies service types and directs TCP traffic by using a deep Q-network (DQN) for intelligent routing; then, UDP traffic is managed using the Dijkstra algorithm for shortest-path selection. This hybrid model leverages the strengths of machine learning and classical algorithms to ensure efficient resource allocation and high-quality data transmission. The system combines the adaptability of DQN with the proven reliability of the Dijkstra algorithm to enhance dynamically the network performance. The proposed hybrid system, which used DQN for TCP traffic and the Dijkstra algorithm for UDP traffic, was benchmarked against two other algorithms. The first algorithm was an advanced version of the Dijkstra algorithm that was designed specifically for this study. The second algorithm involved a Q-learning (QL)-based approach. The evaluation metrics included throughput and latency. Tests were conducted under various topologies and load conditions. The research findings revealed a clear advantage of the hybrid system in complex network topologies under heavy-load conditions. The throughput of the proposed system was 30% higher than the advanced Dijkstra and QL algorithms. The latency benefits were pronounced, with a 50% improvement over the competing algorithms

Some of the methods used to solve complete and incomplete differential equations

This paper studies the methods used to solve complete and in complete differential equations and types of first order and second order and Exact differential equation to solve integration general in This equation Fur there more, and the Special cases to find the integration factor use solve those types of equations is use as well,supported by a relevant variety of examples