Satellite-based and mesoscale regression modeling of monthly air and soil temperatures over complex terrain in Turkey

Simple regression algorithms were developed to quantify spatio-temporal dynamics of minimum and maximum air temperatures (T_min and T_max, respectively) and soil temperature for a depth of 0-5 cm (T_soil-5cm) across complex terrain in Turkey using Moderate Resolution Imaging Spectroradiometer (MODIS) data at a 500-m resolution. A total of 762 16-day MODIS composites (127 images × 6 bands) between 2000 and 2005 were averaged over a monthly basis to temporally match monthly T_min, T_max, and T_soil-5cm from 83 meteorological stations. A total of 60 (28 temporally averaged plus 32 time series-based) linear regression models of T_min, T_max, and T_soil-5cm were developed using best subsets procedure as a function of a combination of 12 explanatory variables: six MODIS bands of blue, red, near infrared (NIR), middle infrared (MIR), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI); four geographical variables of latitude, longitude, altitude, and distance to sea (DtS); and two temporal variables of month, and year. The best multiple linear regression models elucidated 65% (RMSE = 5.9 °C), 65% (RMSE = 5.1 °C), and 57% (RMSE = 6.9 °C) of variations in T_min, T_max, and T_soil-5cm, respectively, under a wide range of T_min (−34 to 25 °C), T_max (0.2-47 °C) and T_soil-5cm (−9 to 40 °C) observed at the 83 stations.     

The m-pancycle-connectivity of a WK-Recursive network

In this paper, we study the m-pancycle-connectivity of a WK-Recursive network. We show that a WK-Recursive network with amplitude W and level L is strictly (5 × 2^L−1 − 2)-pancycle-connected for W ? 3. That is, each pair of vertices in a WK-recursive network with amplitude greater than or equal to 3 resides in a common cycle of every length ranging from 5 × 2^L−1 − 2 to N, where N is the size of the interconnection network; and the value 5 × 2^L−1 − 2 reaches the lower bound of the problem.     

Longest fault-free paths in hypercubes with vertex faults

The hypercube is one of the most versatile and efficient interconnection networks (networks for short) so far discovered for parallel computation. Let f denote the number of faulty vertices in an n-cube. This study demonstrates that when f ? n − 2, the n-cube contains a fault-free path with length at least 2ⁿ − 2f − 1 (or 2ⁿ − 2f − 2) between two arbitrary vertices of odd (or even) distance. Since an n-cube is a bipartite graph with two partite sets of equal size, the path is longest in the worst-case. Furthermore, since the connectivity of an n-cube is n, the n-cube cannot tolerate n − 1 faulty vertices. Hence, our result is optimal.     

A (4n − 9)/3 diagnosis algorithm on n-dimensional cube network

As a generalization of the precise and pessimistic diagnosis strategies of system-level diagnosis of multicomputers, the t/k diagnosis strategy can significantly improve the self-diagnosing capability of a system at the expense of no more than k fault-free processors (nodes) being mistakenly diagnosed as faulty. In the case k ? 2, to our knowledge, there is no known t/k diagnosis algorithm for general diagnosable system or for any specific system. Hypercube is a popular topology for interconnecting processors of multicomputers. It is known that an n-dimensional cube is (4n − 9)/3-diagnosable. This paper addresses the (4n − 9)/3 diagnosis of n-dimensional cube. By exploring the relationship between a largest connected component of the 0-test subgraph of a faulty hypercube and the distribution of the faulty nodes over the network, the fault diagnosis of an n-dimensional cube can be reduced to those of two constituent (n − 1)-dimensional cubes. On this basis, a diagnosis algorithm is presented. Given that there are no more than 4n − 9 faulty nodes, this algorithm can isolate all faulty nodes to within a set in which at most three nodes are fault-free. The proposed algorithm can operate in O(N log₂ N) time, where N = 2ⁿ is the total number of nodes of the hypercube. The work of this paper provides insight into developing efficient t/k diagnosis algorithms for larger k value and for other types of interconnection networks.     

Red tide detection and tracing using MODIS fluorescence data: A regional example in SW Florida coastal waters

Near real-time data from the MODIS satellite sensor was used to detect and trace a harmful algal bloom (HAB), or red tide, in SW Florida coastal waters from October to December 2004. MODIS fluorescence line height (FLH in W m^− 2 μm^− 1 sr^− 1) data showed the highest correlation with near-concurrent in situ chlorophyll-a concentration (Chl in mg m^− 3). For Chl ranging between 0.4 to 4 mg m^− 3 the ratio between MODIS FLH and in situ Chl is about 0.1 W m^− 2 μm^− 1 sr^− 1 per mg m^− 3 chlorophyll (Chl = 1.255 (FLH × 10)^0.86, r = 0.92, n = 77). In contrast, the band-ratio chlorophyll product of either MODIS or SeaWiFS in this complex coastal environment provided false information. Errors in the satellite Chl data can be both negative and positive (3-15 times higher than in situ Chl) and these data are often inconsistent either spatially or temporally, due to interferences of other water constituents. The red tide that formed from November to December 2004 off SW Florida was revealed by MODIS FLH imagery, and was confirmed by field sampling to contain medium (10⁴ to 10⁵ cells L^− 1) to high (> 10⁵ cells L^− 1) concentrations of the toxic dinoflagellate Karenia brevis. The FLH imagery also showed that the bloom started in mid-October south of Charlotte Harbor, and that it developed and moved to the south and southwest in the subsequent weeks. Despite some artifacts in the data and uncertainty caused by factors such as unknown fluorescence efficiency, our results show that the MODIS FLH data provide an unprecedented tool for research and managers to study and monitor algal blooms in coastal environments.     

A four-band semi-analytical model for estimating chlorophyll a in highly turbid lakes: The case of Taihu Lake, China

Accurate estimation of phytoplankton chlorophyll a (Chla) concentration from remotely sensed data is particularly challenging in turbid, productive waters. The objectives of this study are to validate the applicability of a semi-analytical three-band algorithm in estimating Chla concentration in the highly turbid, widely variable waters of Taihu Lake, China, and to improve the algorithm using a proposed four-band algorithm. The improved algorithm is expressed as [Rrs(λ₁)^− 1 − Rrs(λ₂)^− 1][Rrs(λ₄)^− 1 − Rrs(λ₃)^− 1]^− 1. The two semi-analytical algorithms are calibrated and evaluated against two independent datasets collected from 2007 and 2005 in Taihu Lake. Strong linear relationships were established between measured Chla concentration and that derived from the three-band algorithm of [Rrs^− 1(660) − Rrs^− 1(692)]Rrs(740) and the four-band algorithm of [Rrs^− 1(662) − Rrs^− 1(693)][Rrs^− 1(740) − Rrs^− 1(705)]^− 1. The first algorithm accounts for 87% and 80% variation in Chla concentration in the 2007 and 2005 datasets, respectively. The second algorithm accounts for 97% of variability in Chla concentration for the 2007 dataset and 87% of variation in the 2005 dataset. The three-band algorithm has a mean relative error (MRE) of 43.9% and 34.7% for the 2007 and 2005 datasets. The corresponding figures for the four-band algorithm are 26.7% and 28.4%. This study demonstrates the potential of the four-band model in estimating Chla even in highly turbid case 2 waters.     

Electrochemical genosensor based on modified octadecanethiol self-assembled monolayer for Escherichia coli detection

An electrochemical genosensor based on 1-fluoro-2-nitro-4-azidobenzene (FNAB) modified octadecanethiol (ODT) self-assembled monolayer (SAM) has been fabricated for Escherichia coli detection. The results of electrochemical response measurements investigated using methylene blue (MB) as a redox indicator reveal that this nucleic acid sensor has 60 s of response time, high sensitivity (0.5 × 10⁻¹⁸ M) and linearity as 0.5 × 10⁻¹⁸-1 × 10⁻⁶ M. The sensor has been found to be stable for about four months and can be used about ten times. It is shown that water borne pathogens like Klebsiella pneumonia, Salmonella typhimurium and other gram-negative bacterial samples has no significant effects in the response of this sensor.     

Development of an optical sensor for determination of zinc by application of PC-ANN

A very sensitive and reversible optical chemical sensor based on dithizone as chromoionophore immobilized within a plasticized carboxylated PVC film for Zn²⁺ determination is described. At optimum conditions (i.e. pH 5.0), the proposed sensor displays a linear response to Zn²⁺ over 5.0 × 10⁻⁸-5.0 × 10⁻⁶ mol L⁻¹ range. This range was improved to 2.5 × 10⁻⁸-5.8 × 10⁻⁵ mol L⁻¹ range by applying principle component-feed forward artificial neural network with back-propagation training algorithm (PC-ANNB). Detection limit of 8.0 × 10⁻⁹ mol L⁻¹ was obtained. The sensor is fully reversible within the dynamic range and the response time (t_95%) is approximately 4 min under batch conditions. In addition to its high stability and reproducibility, the sensor shows good selectivity towards Zn²⁺ ion with respect to common metal cations. The sensor was successfully applied for determination of Zn²⁺ ion in hair sample.     

An assessment of chlorophyll-a algorithms available for SeaWiFS in coastal and open areas of the Bay of Bengal and Arabian Sea

Three ocean colour algorithms, OC4v6, Carder and OC5 were tested for retrieving Chlorophyll-a (Chla) in coastal areas of the Bay of Bengal and open ocean areas of the Arabian Sea. Firstly, the algorithms were run using ~ 80 in situ Remote Sensing Reflectance, (R_rs(λ)) data collected from coastal areas during eight cruises from January 2000 to March 2002 and the output was compared to in situ Chla. Secondly, the algorithms were run with ~ 20 SeaWiFS R_rs(λ) and the results were compared with coincident in situ Chla. In both cases, OC5 exhibited the lowest log₁₀-RMS, bias, had a slope close to 1 and this algorithm appears to be the most accurate for both coastal and open ocean areas. Thirdly the error in the algorithms was regressed against Total Suspended Material (TSM) and Coloured Dissolved Organic Material (CDOM) data to assess the co-variance with these parameters. The OC5 error did not co-vary with TSM and CDOM. OC4v6 tended to over-estimate Chla > 2 mg m⁻³ and the error in OC4v6 co-varied with TSM. OC4v6 was more accurate than the Carder algorithm, which over-estimated Chla at concentrations > 1 mg m⁻³ and under-estimated Chla at values < 0.5 mg m⁻³. The error in Carder Chla also co-varied with TSM. The algorithms were inter-compared using > 5500 SeaWiFS R_rs(λ) data from coastal to offshore transects in the Northern Bay of Bengal. There was good agreement between OC4v6 and OC5 in open ocean waters and in coastal areas up to 2 mg m⁻³. There was a strong divergence between Carder and OC5 in open ocean and coastal waters. OC4v6 and Carder tended to over-estimate Chla in coastal areas by a factor of 2 to 3 when TSM > 25 g m⁻³. We strongly recommend the use of OC5 for coastal and open ocean waters of the Bay of Bengal and Arabian Sea. A Chla time series was generated using OC5 from 2000 to 2003, which showed that concentrations at the mouths of the Ganges reach a maxima (~ 5 mg m⁻³) in October and November and were 0.08 mg m⁻³ further offshore increasing to 0.2 mg m⁻³ during December. Similarly in early spring from February to March, Chla was 0.08 to 0.2 mg m⁻³ on the east coast of the Bay.     

Remote estimation of chl-a concentration in turbid productive waters — Return to a simple two-band NIR-red model?

Today the water quality of many inland and coastal waters is compromised by cultural eutrophication in consequence of increased human agricultural and industrial activities. Remote sensing is widely applied to monitor the trophic state of these waters. This study investigates the performance of near infrared-red models for the remote estimation of chlorophyll-a concentrations in turbid productive waters and evaluates several near infrared-red models developed within the last 34 years. Three models were calibrated for a dataset with chlorophyll-a concentrations from 0 to 100 mg m⁻³ and validated for independent and statistically different datasets with chlorophyll-a concentrations from 0 to 100 mg m⁻³ and 0 to 25 mg m⁻³ for the spectral bands of the MEdium Resolution Imaging Spectrometer (MERIS) and MODerate resolution Imaging Spectroradiometer (MODIS). The MERIS two-band model estimated chlorophyll-a concentrations slightly more accurately than the more complex models, with mean absolute errors of 2.3 mg m⁻³ for chlorophyll-a concentrations from 0 to 100 mg m⁻³ and 1.2 mg m⁻³ for chlorophyll-a concentrations from 0 to 25 mg m⁻³. Comparable results from several near infrared-red models with different levels of complexity, calibrated for inland and coastal waters around the world, indicate a high potential for the development of a simple universally applicable near infrared-red algorithm.     

Edge-bipancyclicity of the k-ary n-cubes with faulty nodes and edges

The k-ary n-cube has been one of the most popular interconnection networks for massively parallel systems. In this paper, we investigate the edge-bipancyclicity of k-ary n-cubes with faulty nodes and edges. It is proved that every healthy edge of the faulty k-ary n-cube with f_v faulty nodes and f_e faulty edges lies in a fault-free cycle of every even length from 4 to kⁿ − 2f_v (resp. kⁿ − f_v) if k ? 4 is even (resp. k ? 3 is odd) and f_v + f_e ? 2n − 3. The results are optimal with respect to the number of node and edge faults tolerated.     

High sensitive optode for selective determination of Ni based on the covalently immobilized thionine in agarose membrane

A novel Ni²⁺ optode was prepared by covalent immobilization of thionine, 3,7-diamine-5-phenothiazoniom thionineacetate, in a transparent agarose membrane. Influences of various experimental parameters on Ni²⁺ sensing, including the reaction time, the solution pH and the concentration of reagents were investigated. Under the optimized conditions, a linear response was obtained for Ni²⁺ concentrations ranging from 1.00 × 10⁻¹⁰ to 1.00 × 10⁻⁷ mol l⁻¹ with an R² value of 0.9985. The detection limit (3σ) of the method for Ni²⁺ was 9.30 × 10⁻¹¹ mol l⁻¹. The influence of several potentially interfering ions such as Ag⁺, Hg²⁺, Cd²⁺, Zn²⁺, Pb²⁺, Cu²⁺, Mn²⁺, Co³⁺, Cr³⁺, Al³⁺ and Fe³⁺ on the determination of Ni²⁺ was studied and no significant interference was observed. The membrane showed a good durability and short response time with no evidence of reagent leaching. The membrane was successfully applied for the determination of Ni²⁺ in environmental water samples.     

A sensitive hydrazine electrochemical sensor based on electrodeposition of gold nanoparticles on choline film modified glassy carbon electrode

A highly sensitive hydrazine sensor was developed based on the electrodeposition of gold nanoparticles onto the choline film modified glassy carbon electrode (GNPs/Ch/GCE). The electrochemical experiments showed that the GNPs/Ch film exhibited a distinctly higher activity for the electro-oxidation of hydrazine than GNPs with 3.4-fold enhancement of peak current. The kinetic parameters such as the electron transfer coefficient (α) and the rate of electron exchange (k) for the oxidation of hydrazine were determined. The diffusion coefficient (D) of hydrazine in solution was also calculated by chronoamperometry. The sensor exhibited two wide linear ranges of 5.0 × 10⁻⁷-5.0 × 10⁻⁴ and 5.0 × 10⁻⁴-9.3 × 10⁻³ M with the detection limit of 1.0 × 10⁻⁷ M (s/n = 3). The proposed electrode presented excellent operational and storage stability for the determination of hydrazine. Moreover, the sensor showed outstanding sensitivity, selectivity and reproducibility properties. All the results indicated a good potential application of this sensor in the detection of hydrazine.     

Screen-printed carbon electrode for choline based on MnO2 nanoparticles and choline oxidase/polyelectrolyte layers

This paper presents the amperometric biosensor that determines choline and cholinesterase activity using a screen printed graphite electrode. In order to detect H₂O₂ we have blanket modified the electrode material with manganese dioxide nanoparticles layer. Using layer-by-layer technique on the developed hydrogen peroxide sensitive electrode surface choline oxidase was incorporated into the interpolyelectrolyte nanofilm. Its ability to serve as a detector of choline in bulk analysis and cholinesterase assay was investigated. We examined the interferences from red-ox species and heavy metals in the blood and in the environmental sample matrixes. The sensor exhibited a linear increase of the amperometric signal at the concentration of choline ranging from 1.3 × 10⁻⁷ to 1.0 × 10⁻⁴ M, with a detection limit (evaluated as 3σ) of 130 nM and a sensitivity of 103 mA M⁻¹ cm⁻² under optimized potential applied (480 mV vs. Ag/AgCl). The biosensor retained its activity for more than 10 consecutive measurements and kept 75% of initial activity for three weeks of storage at 4 °C. The R.S.D. was determined as 1.9% for a choline concentration of 10⁻⁴ M (n = 10) with a typical response time of about 10 s. The developed choline biosensor was applied for butyrylcholinesterase assay showing a detection limit of 5 pM (3σ). We used the biosensor to develop the cholinesterase inhibitor assay. Detection limit for chlorpyrifos was estimated as 50 pM.     

Development of molecularly imprinted electrochemical sensor with titanium oxide and gold nanomaterials enhanced technique for determination of 4-nonylphenol

4-Nonylphenol (4-NP) was reported to affect the health of wildlife and humans through altering endocrine function. A novel electrochemical sensor for sensitive and fast determination of 4-NP was developed. Titanium oxide (TiO₂) nanoparticles and gold nanoparticles (AuNPs) were introduced for the enhancement of electron conduction and sensitivity. 4-NP-imprinted functionalized AuNPs composites with specific binding sites for 4-NP was modified on electrode. The resulting electrodes were characterized by cyclic voltammetry (CV). Rebinding experiments were carried out to determine the specific binding capacity and selective recognition. The linear range was over the range from 4.80 × 10⁻⁴ to 9.50 × 10⁻⁷ mol L⁻¹, with the detection limit of 3.20 × 10⁻⁷ mol L⁻¹ (S/N = 3). The sensor was successfully employed to detect 4-NP in real samples.     

Mobile phone platform as portable chemical analyzer

In this work, a mobile phone platform for portable chemical analysis is presented. This platform is based on the use of the built-in camera for capturing the image of a single-use colorimetric chemical sensor, while a custom-developed software application processes this image for obtaining its characteristic H (hue) value, which is related to analyte concentration. This software application is optimized for mobile phone usage, thus preserving battery life and targeting reduced computation time through a customized image processing scheme including a modified monodimensional edge detection algorithm. Meanwhile, the influence of physical and chemical factors has been characterized, with results showing that the presented platform provides accurate results even when variations on distance from phone to sensor, image focusing, or image centering are induced. In the same way, factors such as indicator concentration and membrane thickness have been shown to have negligible effects on the obtained H values. The calibration and testing procedures have shown that the presented platform is able to provide a detection limit of 3.1 × 10⁻⁵ M in a range of 3.1 × 10⁻⁵-0.1 M with a relative standard deviation for inter-membrane reproducibility lower than 1.6% for potassium concentration determination in solution.     

Microfabricated microfluidic fuel cells

We demonstrate high performance microfuel cells (μFC) operating at room temperature. The smallest μFC has a reaction surface of 0.11 cm² and has an output power density equal to 22.9 mW cm⁻². Methanol and air are supplied using microchannels etched into silicon wafers using microfabrication techniques which can accurately determine the μFC surface and the microchannel dimensions. The insertion of a novel hydrophilic fibrous layer into the anode diffusion layer stack produces 9.25 mW cm⁻² for an input fuel flow rate of 550 nL min⁻¹. The benefits of size-scaling and architecture optimization in μFC are demonstrated. Our observations and conclusions are by no means unique to methanol μFC but could be applied to other microfluidic liquid fuel μFC based on, e.g. microbial fuel cells, bio-ethanol and glucose solution.     

Embedding meshes into locally twisted cubes

As a newly introduced interconnection network for parallel computing, the locally twisted cube possesses many desirable properties. In this paper, mesh embeddings in locally twisted cubes are studied. Let LTQ_n(V, E) denote the n-dimensional locally twisted cube. We present three major results in this paper: (1) For any integer n ? 1, a 2 × 2ⁿ⁻¹ mesh can be embedded in LTQ_n with dilation 1 and expansion 1. (2) For any integer n ? 4, two node-disjoint 4 × 2ⁿ⁻³ meshes can be embedded in LTQ_n with dilation 1 and expansion 2. (3) For any integer n ? 3, a 4  × (2ⁿ⁻² − 1) mesh can be embedded in LTQ_n with dilation 2. The first two results are optimal in the sense that the dilations of all embeddings are 1. The embedding of the 2 × 2ⁿ⁻¹ mesh is also optimal in terms of expansion. We also present the analysis of 2p × 2q mesh embedding in locally twisted cubes.     

An electrochemical sensor for trace uranium determination based on 6-O-palmitoyl-l-ascorbic acid-modified graphite electrodes

The development of a preconcentrating sensor based on 6-O-palmitoyl-l-ascorbic acid (PAA)-modified graphite (GRA) electrodes for the determination of uranium is described. PAA, a water insoluble compound of ascorbic acid, was immobilized onto the surface of the GRA electrodes through physical adsorption from acetone solutions. Uranium was accumulated by heterogeneous complexation (10 min, in 0.1 M H₃BO₃, pH 4.3) and then, it was reduced by means of a differential pulse voltammetric scan in 0.1 M H₃BO₃, pH 3.4. Alternatively, the performance of both preconcentration and voltammetric steps in a single run, at 0.1 M H₃BO₃, pH 3.65, was also examined; however, in this case the observed current signals were lower by 30%. The experimental variables were investigated and under the selected conditions, a linear calibration curve in the range 2.7-67.5 μg L⁻¹ U(VI) was constructed (r² = 0.9981). The 3σ limit of detection and the relative standard deviation of the method were 1.8 μg L⁻¹ U(VI) and 8% (n = 5, 20 μg L⁻¹ U(VI), preconcentration time 10 min), respectively. By increasing the preconcentration time to 30 min, a limit of detection as low as 0.26 μg L⁻¹ U(VI) can be achieved. The effect of potential interferences was also examined. The accuracy of the method was established by recovery studies in inoculated tap and lake water samples. A simple and fast procedure based on filtering of the sample through a C-18 microcolumn was successfully used to remove the organic matter from the lake water samples.     

The vegetation cycle in West Africa from AVHRR-NDVI data: Horizons of predictability versus spatial scales

The predictability of the vegetation cycle is analyzed as a function of the spatial scale over West Africa during the period 1982-2004. The NDVI-AVHRR satellite data time series are spatially aggregated over windows covering a range of sizes from 8 × 8 km² to 1024 × 1024 km². The times series are then embedded in a low-dimensional pseudo-phase space using a system of time delayed coordinates. The correlation dimension (D_c) and entropy of the underlying vegetation dynamics, as well as the noise level (σ) are extracted from a nonlinear analysis of the time series. The horizon of predictability (H_P) of the vegetation cycle defined as the time interval required for an n% RMS error on the vegetation state to double (i.e. reach 2n% RMS) is estimated from the entropy production. Compared to full resolution, the intermediate scales of aggregation (in the range of 64 × 64 km² to 256 × 256 km²) provide times series with a slightly improved signal to noise ratio, longer horizon of predictability (about 2 to 5 decades) and preserve the most salient spatial patterns of the vegetation cycle. Insights on the best aggregation scale and on the expected vegetation cycle predictability over West Africa are provided by a set of maps of the correlation dimension (D_c), the horizon of predictability (H_P) and the level of noise (σ).