Synthesis,Characterization and Catalytic Activities of Palladium(II) Nitroaryl Complexes

Two palladium(II) nitroaryl complexes trans-[bromo(p-nitrophenyl)bis(triphenylphosphine)palladium(II)] 1 and trans-[bromo(2,4-dinitrophenyl)bis(triphenylphosphine)palladium(II)] 2 have been synthesized. The complexes were characterized by FTIR and NMR (¹H, ¹³C and ³¹P) spectroscopy and elemental analysis. The molecular structure of complex 2, as confirmed by X-ray crystallography, reveals that the Pd atom and its neighboring groups (two PPh₃, Br and phenylene group) lie in a slightly distorted square plane. In the UV–Vis spectra of the complexes 1 and 2, the palladium to aryl charge transfer bands were observed. The emission peaks from the singlet excited states (S₁ → S₀) were observed in the photoluminescence spectra of the complexes. The thermal stability of the complexes has been studied by thermal gravimetric analysis (TGA). TGA data showed that both complexes are thermally stable up to 200 °C, and complex 1 is more stable than 2. The catalytic efficiency of the new palladium(II) complexes was studied as demonstrated using the Sonogashira coupling reactions with good yields. The experimental results suggest that the Sonogashira coupling reactions can be performed at moderate temperature (50 °C) using these new palladium(II) complexes as catalysts.     

Network coding for unicast in a WiFi hotspot: Promises,challenges, and testbed implementation

In offices and residential buildings, WiFi networks have become a primary means for providing Internet access to wireless devices whose dominant traffic pattern is unicast. In the meantime, the emergence of network coding has brought about great promises for multicast in communication networks where intermediate nodes are allowed to process independent incoming information flows. Little is known about network coding for unicast, however. The objective of this paper is thus to depart from multicast scenarios and shed light on several possible unicast scenarios to which network coding may be applied in a WiFi hotspot in order to obtain communication benefits such as throughput gain, fairness, and reduced protocol complexity. We identify five representative scenarios in which network coding may be used to benefit unicasting in a WiFi hotspot. Several open research issues and practical challenges related to each scenario are discussed individually. To illustrate the benefits of network coding for unicast in a WiFi hotspot, we propose and implement iCORE: The interface COoperation Repeater-aided network coding Engine. iCORE is a practical system in which multi-channel multi-radio repeaters are used to relay unicast traffic for those terminals sitting far away from an access point and suffering from weak signals at a WiFi hotspot. It is based on our last scenario which illustrates the synergy among network coding, opportunistic routing, and interface management. Utilizing idle wireless interfaces and listening to traffic opportunistically, iCORE allows packets to move across the interfaces and to be coded across flows whenever it sees more transmission opportunities. We evaluate iCORE on a four-node chain-like topology testbed implemented using IEEE 802.11b/g radios and compare it to MORE – the state-of-art intra-flow network coding implementation based on opportunistic routing. Our experimental results reveal promising gains in terms of throughput over MORE.     

Approximate methods in Bayesian point process spatial models

A range of point process models which are commonly used in spatial epidemiology applications for the increased incidence of disease are compared. The models considered vary from approximate methods to an exact method. The approximate methods include the Poisson process model and methods that are based on discretization of the study window. The exact method includes a marked point process model, i.e., the conditional logistic model. Apart from analyzing a real dataset (Lancashire larynx cancer data), a small simulation study is also carried out to examine the ability of these methods to recover known parameter values. The main results are as follows. In estimating the distance effect of larynx cancer incidences from the incinerator, the conditional logistic model and the binomial model for the discretized window perform relatively well. In explaining the spatial heterogeneity, the Poisson model (or the log Gaussian Cox process model) for the discretized window produces the best estimate.     

A new blend of polymeric encapsulation of azithromycin by spray-drying with a pH responsive in drug release

The microencapsulation of macrolides with polymers has been reported to retard the release of the drug in oral cavity. However, these methods are unable to control drug release in gastrointestinal tract. The aim of the present study was to investigate the effect of formulation of a new polymeric encapsulation of azithromycin which is suitable for both masking and sustained release usage. Eudragit E100 and polyethylene glycol (PEG) 4000 were chosen as the barrier coatings. The spray drying technique was used to obtain the microcapsules containing azithromycin. To obtain the initial results, the effects of several parameters were evaluated. A 3:2:1 ratio of E100:PEG 4000:azithromycin at pH 6 gave the best coating condition. Thermogravimetric analysis and IR analysis data confirmed the encapsulation of azithromycin inside polymers. The encapsulated drug was released in different rates from the particles by changing the pH (1.7 and 7.4). An analysis of the kinetic release properties indicates that the release of the drug is a combination of swelling and diffusion mechanism. The synergistic cooperation between polymers and drug due to the existence of several hydrogen bonding is supposed to influence the pH-responsive property of the encapsulated drug. Moreover, the use of mixtures of E100 and PEG 4000 appears to offer a good balance between cost and efficiency.     

Predicting the colour properties of viscose knitted fabrics using soft computing approaches

The aim of this paper was to predict the colour strength of viscose knitted fabrics by using fuzzy logic (FL) model based on dye concentration, salt concentration and alkali concentration as input variables. Moreover, the performance of fuzzy logic (FL) model is compared with that of artificial neural network (ANN) model. In addition, same parameters and data have been used in ANN model. From the experimental study, it was found that dye concentration has the main and greatest effects on the colour strength of viscose knitted fabrics. The coefficient of determination (R²), root mean square (RMS) and mean absolute errors (MAE) between the experimental colour strength and that predicted by FL model are found to be 0.977, 1.025 and 4.61%, respectively. Further, the coefficient of determination (R²), root mean square (RMS) and mean absolute errors (MAE) between the experimental colour strength and that predicted by ANN model are found to be 0.992, 0.726 and 3.28%, respectively. It was found that both ANN and FL models have ability and accuracy to predict the fabric colour strength effectively in non-linear domain. However, ANN prediction model shows higher prediction accuracy than that of Fuzzy model.     

A committee machine with intelligent systems for estimation of total organic carbon content from petrophysical data: An example from Kangan and Dalan reservoirs in South Pars Gas Field,Iran

Total organic carbon (TOC) content present in reservoir rocks is one of the important parameters, which could be used for evaluation of residual production potential and geochemical characterization of hydrocarbon-bearing units. In general, organic-rich rocks are characterized by higher porosity, higher sonic transit time, lower density, higher γ-ray, and higher resistivity than other rocks. Current study suggests an improved and optimal model for TOC estimation by integration of intelligent systems and the concept of committee machine with an example from Kangan and Dalan Formations, in South Pars Gas Field, Iran. This committee machine with intelligent systems (CMIS) combines the results of TOC predicted from intelligent systems including fuzzy logic (FL), neuro-fuzzy (NF), and neural network (NN), each of them has a weight factor showing its contribution in overall prediction. The optimal combination of weights is derived by a genetic algorithm (GA). This method is illustrated using a case study. One hundred twenty-four data points including petrophysical data and measured TOC from three wells of South Pars Gas Field were divided into 87 training sets to build the CMIS model and 37 testing sets to evaluate the reliability of the developed model. The results show that the CMIS performs better than any one of the individual intelligent systems acting alone for predicting TOC.     

Sustainability of Groundwater Resources in the North-Eastern Region of Bangladesh

Water is essential for economic, social, and environmental development. Global water resources are vulnerable due to increasing demand related to population growth, pollution potential, and climate change. Competition for water between different sectors is increasing. To meet the increasing demand, the use of groundwater is increasing worldwide. In this paper, the water-table dynamics of the north-eastern region of Bangladesh were studied using the MEKESENS software. This study reveals that the depth to water-table (WT) of almost all the wells is declining slowly. In many cases, the depth will approximately double by the year 2040, and almost all will double by 2060, if the present trend continues. If the decline of the water-table is allowed to continue in the long run, the result could be a serious threat to the ecology and to the sustainability of food production, which is vital for the nation’s food security. Therefore, necessary measures should be taken to sustain water resources and thereby agricultural production. Demand-side management of water and the development of alternative surface water sources seem to be viable strategies for the area. These strategies could be employed to reduce pressure on groundwater and thus maintain the sustainability of the resource.     

Mapping the dynamics of knowledge base of innovations of R&;D in Bangladesh: triple helix perspective

Triple helix (TH) collaborations involving university, industry and government provide a networked infrastructure for shaping the dynamic fluxes of knowledge base of innovations locally and these fluxes remain emergent within the domains. This study maps these emergence dynamics of the knowledge base of innovations of Research & Development (R&D) by exploring the longitudinal trend of systemness within the networked research relations in Bangladesh on the TH model. The bibliometric data of publications collected from the Science Citation Index (SCI), the social sciences and the arts and humanities for analysis of science indicators and the patent data collected from the US Patent Office to analyze the patent success ratio as a measure of innovation within TH domains. The findings show that the network dynamics have varied considerably according to the R&D policies of the government. The collaboration patterns of co-authorship relations in the SCI publications prominently increased, with some variation, from 1996 to 2006. Nevertheless, inter-institutional collaboration negatively influenced by the national science and technology (S&T) research policies in the last 5 years due to their evaluation criteria. Finally, the findings reveal that the R&D system of Bangladesh is still undergoing a process of institutionalizing S&T and has failed to boost its research capacity for building the knowledge base of innovations by neglecting the network effects of TH dynamics.