Spatio‐temporal trends of nutrients and physico‐chemical parameters on lake ecosystem and fisheries prior to onset of cage farming and re‐opening of the Mbita passage in the Nyanza Gulf of Lake Victoria

Many large lake ecosystems are experiencing increasing eutrophication and persistent cyanobacteria‐dominated algal blooms affecting their water quality and ecosystem productivity because of widespread non‐point and point nutrient sources. Accordingly, the present study utilized data of July 2003 and January–February 2004, as well as previous measurements of nutrients and physico‐chemical variables (electrical conductivity, dissolved oxygen, temperature, pH, turbidity and chlorophyll‐a), to characterize the spatial and temporal trends, as a means of better understanding the factors influencing lake environmental conditions, as support tools for long‐term ecosystem management and for better understanding the long‐term trends and effects. Inshore gulf areas were found to represent zones of maximum nutrient concentrations, compared to the deep main lake zones, with significant inter‐parameter correlations. Phosphorus, silicon and chlorophyll‐a concentrations were significantly correlated. Water electrical conductivity was also significantly and positively correlated with soluble reactive silicon (SRSi), alkalinity hardness DO, while exhibiting a negative association with water transparency. Water turbidity and transparency, electrical conductivity, and SRSi concentrations clearly describe a gradient from the gulf into the main lake. For such a shallow gulf, these findings suggest primary productivity is influenced mainly by the availability of nutrients, light transparency and the extent of availability of resuspended nutrients. The increasing eutrophic state of Lake Victoria is a serious concern since it contributes to an increased potential of more frequent occurrences of cyanobacterial blooms, a potential public health risk to both humans and wildlife. Improved understanding of influences from previous fish species introductions and concomitant changes in indigenous fish species, increased lake basin population and anthropogenic activities, water hyacinth resurgences, sustainability of biodiversity, and current interests in cage farming, are among the major concerns and challenges facing the contemporary Lake Victoria. The trends regarding nutrients and physico‐chemical characteristics are intended to support better monitoring efforts and data to promote the lake's ecosystem services and the sustainable management of the lake ecosystem.     

Chemically functionalized carbon nanotubes and their characterization using thermogravimetric analysis, fourier transform infrared, and raman spectroscopy

This article reports key findings on the chemical functionalization of carbon nanotubes (CNT). The functionalization of chemical vapor-deposited CNT was carried out by treating tubes with polyvinyl alcohol through ultrasonication in water with the aid of a surfactant. The surfactant is expected to promote the unbundling of aggregated CNT. The characterization of functionalized samples using thermogravimetric analysis, Fourier transform infrared spectroscopy, and Raman spectroscopy revealed that the CNT were functionalized by the interaction of carboxylic acid and hydroxyl groups. From the characterization studies, it is apparent that there is a strong interaction between these functional groups and the covalently bonded carbon in the CNT network. The functionalization process enabled good CNT dispersion in the solution, and the CNT remained in suspension for many days. To support the effective functionalization of the tubes, the interaction of functionalized CNT with Ni ions is also demonstrated. This paper was presented at the fourth International Surface Engineering Congress and Exposition held August 1–3, 2005 in St. Paul, MN.     

Generalized differentiability and integrability for fuzzy set-valued functions on time scales

This paper deals with the fuzzy set-valued functions of real variables on time scale whose values are normal, convex, upper semicontinuous and compactly supported fuzzy sets in \(\mathbb {R}^{n}\). We introduce and study the fundamental properties of new class of derivative called generalized delta derivative (\(\Delta _{g}\)-derivative) and generalized delta integral (\(\Delta _{g}\)-integral) for such fuzzy functions.     

Chemical control of superconducting properties of (Y, Ca)(Sr, Ba)2Cu2.7Ga0.3O7?δ

The partial substitution of Ga at the Cu(1) (chain) sites of the YBa₂Cu₃O₇ structure allows synthesis at ambient pressure of Ba-free analogs, e.g., YSr₂Cu_2.7Ga_0.3O_7– . Materials with this composition have been found to be nonsuperconducting, but superconductivity has been induced by one or more of the following methods: Ca substitutions at the Y site; Ba substitutions at the Sr site; annealing in high-pressure oxygen. The influence of these chemical manipulations onT _c has been monitored and all methods have been found to enhanceT _c . The electronic effects of Ba substitutions have been deduced indirectly using powder neutron diffraction, and such substitutions appear to result in a redistribution of hole density into the Cu(1) sites from the superconducting CuO₂ planes.     

Synthesis, composition, and structure of superconducting (Y, Ca)Sr, Ba)2Cu2GaO7?δ

The superconducting properties of Y_1–y Ca _y Sr₂Cu₂GaO_7– have been examined and related to the Ca content,y, and the use of annealing treatments at 350 bar oxygen. Superconductivity withT _c up to 41 K was found only for high-pressure-annealed samples, and the structural effects of Ca substitution and high-pressure treatment were examined using powder neutron diffraction. Small but significant changes in Cu-Cu and Cu-O distances were found and suggest that the Cu ions are more highly charged in superconducting samples. Partial substitution of Ba for Sr was found to be possible (up to 20%) to give samples which, after annealing in high-pressure oxygen, were superconducting at temperatures up to 68 K.     

Association Rule Mining Frequent-Pattern-Based Intrusion Detection in Network

In the network security system, intrusion detection plays a significant role. The network security system detects the malicious actions in the network and also conforms the availability, integrity and confidentiality of data information resources. Intrusion identification system can easily detect the false positive alerts. If large number of false positive alerts are created then it makes intrusion detection system as difficult to differentiate the false positive alerts from genuine attacks. Many research works have been done. The issues in the existing algorithms are more memory space and need more time to execute the transactions of records. This paper proposes a novel framework of network security Intrusion Detection System (IDS) using Modified Frequent Pattern (MFP-Tree) via K-means algorithm. The accuracy rate of Modified Frequent Pattern Tree (MFPT)-K means method in finding the various attacks are Normal 94.89%, for DoS based attack 98.34%, for User to Root (U2R) attacks got 96.73%, Remote to Local (R2L) got 95.89% and Probe attack got 92.67% and is optimal when it is compared with other existing algorithms of K-Means and APRIORI.     

Product beamforming and nested array in tandem for enhanced sonar performance

Multidimensional Systems and Signal Processing - High-performance sonar systems mostly combine a large sensor array and an efficient beamformer for enhancing the overall detection capabilities and...     

A Dynamic Hybrid Decoder Apprroach Using EG-LDPC Codes for Signal Processing Applications

The Low-Density Parity Check (LDPC) codes of Euclidean Geometry (EG) are encrypted and decrypted in numerous ways, namely Soft Bit Flipping (SBF), Sequential Peeling Decoder (SPD), Belief Propagation Decoder (BPD), Majority Logic Decoder/Detector (MLDD), and Parallel Peeling Decoder (PPD) decoding algorithms. These algorithms provide aextensive range of trade-offs between latency decoding, power consumption, hardware complexity-required resources, and error rate performance. Therefore, the problem is to communicate a sophisticated technique specifying the both soft and burst errors for effective information transmission. In this research, projected a technique named as Hybrid SBF (HSBF) decoder for EG-LDPC codes, which reduces the decoding complexity and maximizes the signal transmission and reception. In this paper, HSBF is also known as Self Reliability based Weighted Soft Bit Flipping (SRWSBF) Decoder. It is obvious from the outcomes that the proposed technique is better than the decoding algorithms SBF, MLDD, BPD, SPD and PPD. Using Xilinx synthesis and SPARTAN 3e, a simulation model is designed to investigate latency, hardware utilization and power consumption. Average latency of 16.65 percent is found to be reduced. It is observed that in considered synthesis parameters such as number of 4-input LUTs, number of slices, and number of bonded IOBs, excluding number of slice Flip-Flops, hardware utilization is minimized to an average of 4.25 percent. The number of slices Flip-Flops resource use in the proposed HSBF decoding algorithm is slightly higher than other decoding algorithms, i.e. 1.85%. It is noted that, over the decoding algorithms considered in this study, the proposed research study minimizes power consumption by an average of 41.68%. These algorithms are used in multimedia applications, processing systems for security and information.