Fingerprint Groups Research Articles

Determination and Correlation of Finger Print Pattern and Blood Grouping in Diabetes Mellitus: An Analytical Study

BACKGROUND: To Determine and correlate between fingerprint patterns and blood group in type II diabetes mellitus. RESULTS: One hundred individuals with type II diabetes mellitus and One hundred normal individuals as controls were selected for study. Fingerprints were obtained by dactyloscopy. Various fingerprint patterns like arches, whorls, loops were studied and correlated with the respective blood group based on ABO system among the volunteer controls and diabetics. After obtaining results, the analysis was made using Chi square test and One way ANOVA which showed, the most predominant fingerprint pattern among the 100 diabetic volunteers is “Arch pattern” and the most predominant blood group was “O” positive among the diabetic volunteers. Also correlating both finger print and blood group “Whorl pattern” with “B” blood group showed higher predominance among the healthy volunteers. CONCLUSIONS: This is the first study correlating finger print pattern with blood grouping in subjects of Diabetes mellitus. Our study findings showed that O positive blood group with arch pattern type of finger print was the most common findings among diabetes. Thus correlating fingerprint pattern and blood grouping act as a early predictor tool for diagnosing Type II Diabetes mellitus.

How can machine learning be used for accurate representations and predictions of fracture nucleation in zirconium alloys with hydride populations?

Zirconium alloys are critical material components of systems subjected to harsh environments such as high temperatures, irradiation, and corrosion. When exposed to water in high temperature environments, these alloys can thermo-mechanically degrade by forming hydrides that have a crystalline structure that is different from that of zirconium. Cracks can nucleate near these hydrides; hence, these hydrides are a direct link to fracture failure and overall large inelastic strain deformation modes. To fundamentally understand and predict these microstructural failure modes, we interrogated a finite-element database that was deterministically tailored and generated for large strain-dislocation-density crystalline plasticity and fracture modes. A database of 210 simulations was created to randomly sample from a group of microstructural fingerprints that encompass hydride volume fraction, hydride orientation, grain orientation, hydride length, and hydride spacing for a hydride that is physically representative of an aggregate of a hydride population. Machine learning approaches were then used to understand, identify, and characterize the dominant microstructural mechanisms and characteristics. We first used fat-tailed Cauchy distributions to determine the extreme events. A multilayer perceptron was used to learn the mechanistic characteristics of the material response to predefined strain levels and accurately determine the critical fracture stress response and the accumulated shear slips in critical regions. The predictions indicate that hydride volume fraction, a population-level parameter, had a significant effect on localized parameters, such as fracture stress distribution regions, and on the accumulated immobile dislocation densities both within the face centered cubic hydrides and the hexagonal cubic packed h.c.p. matrix.

A novel repeat sequence-based PCR (rep-PCR) using specific repeat sequences of Mycobacterium intracellulare as a DNA fingerprinting.

Repetitive sequence-based PCR (rep-PCR) is a potential epidemiological technique that can provide high-throughput genotype fingerprints of heterogeneous Mycobacterium strains rapidly. Previously published rep-PCR primers, which are based on nucleotide sequences of Gram-negative bacteria may have low specificity for mycobacteria. Moreover, it was difficult to ensure the continuity of the study after the commercial rep-PCR kit was discontinued. Here, we designed a novel rep-PCR for Mycobacterium intracellulare, a major cause of nontuberculous mycobacterial pulmonary disease with frequent recurrence. We screened the 7,645 repeat sequences for 200 fragments from the genome of M. intracellulare ATCC 13950 in silico, finally generating five primers with more than 90% identity for a total of 226 loci in the genome. The five primers could make different band patterns depending on the genome of three different M. intracellulare strains using an in silico test. The novel rep-PCR with the five primers was conducted using 34 bacterial samples of 7 species containing 25 M. intracellulare clinical isolates, compared with previous published rep-PCRs. This shows distinguished patterns depending on species and blotting assay for 6 species implied the sequence specificity of the five primers. The Designed rep-PCR had a 95-98% of similarity value in the reproducibility test and showed 7 groups of fingerprints in M. intracellulare strains. Designed rep-PCR had a correlation value of 0.814 with VNTR, reference epidemiological method. This study provides a promising genotype fingerprinting method for tracing the recurrence of heterogeneous M. intracellulare.

Genetic diversity and iron metabolism of Staphylococcus hominis isolates originating from bovine quarter milk, rectal feces, and teat apices.

Staphylococcus hominis, a member of the non-aureus staphylococci (NAS) group, is part of the human and animal microbiota. Although it has been isolated from multiple bovine-associated habitats, its relevance as a cause of bovine mastitis is currently not well described. To successfully colonize and proliferate in the bovine mammary gland, a bacterial species must be able to acquire iron from host iron-binding proteins. The aims of this study were (1) to assess the genetic diversity of S. hominis isolated from bovine quarter milk, rectal feces, and teat apices, and (2) to investigate the capacity of bovine S. hominis isolates belonging to these different habitats to utilize ferritin and lactoferrin as iron sources. To expand on an available collection of bovine S. hominis isolates (2 from quarter milk, 8 from rectal feces, and 19 from teat apices) from one commercial dairy herd, a subsequent single cross-sectional quarter milk sampling (n = 360) was performed on all lactating cows (n = 90) of the same herd. In total, 514 NAS isolates were recovered and identified by MALDI-TOF mass spectrometry; the 6 most prevalent NAS species were S. cohnii (33.9%), S. sciuri (16.7%), S. haemolyticus (16.3%), S. xylosus (9.6%), S. equorum (9.4%), and S. hominis (3.5%). A random amplified polymorphic DNA (RAPD) analysis was performed on 46 S. hominis isolates (19 from quarter milk, 8 from rectal feces, and 19 from teat apices). Eighteen distinct RAPD fingerprint groups were distinguished although we were unable to detect the presence of the same RAPD type in all 3 habitats. One S. hominis isolate of a distinct RAPD type unique to a specific habitat (8 from quarter milk, 3 from rectal feces, and 4 from teat apices) along with the quality control strain Staphylococcus aureus ATCC 25923 and 2 well-studied Staphylococcus chromogenes isolates ("IM" and "TA") were included in the phenotypical iron test. All isolates were grown in 4 types of media: iron-rich tryptic soy broth, iron-rich tryptic soy broth deferrated by 2,2'-bipyridyl, and deferrated tryptic soy broth supplemented with human recombinant lactoferrin or equine spleen-derived ferritin. The growth of the different strains was modified by the medium in which they were grown. Staphylococcus chromogenes TA showed significantly lower growth under iron-deprived conditions, and adding an iron supplement (lactoferrin or ferritin) resulted in no improvement in growth; in contrast, growth of S. chromogenes IM was significantly recovered with iron supplementation. Staphylococcus hominis strains from all 3 habitats were able to significantly utilize ferritin but not lactoferrin as an iron source to reverse the growth inhibition, in varying degrees, caused by the chelating agent 2,2'-bipyridyl.

DYNAMICS OF LACTIC ACID BACTERIA DURING PASTIRMA PRODUCTION

Pastirma is a traditional dry-cured meat product of Turkey, produced from whole beef or muscles of water buffalo. Studies on the microbial diversity of pastirma generally focus on the final product. In this study, we aimed to determine the lactic acid bacteria (LAB) dynamics during the pastirma production process. Samples were obtained from a commercial producer at four different production stages (after curing, after first drying, before çemen coating, and final product). During the production, the pH level slightly increased from ~5.6 to 5.8, while the water activity (aw) decreased to ~0.86 until çemen addition, after which it increased to 0.89. Total mesophilic aerobic bacteria (TMAB) and LAB counts increased during the production stages reaching 7.15 and 6.64 log cfu/g, respectively. The most dominant LAB for all stages was Latilactobacillus sakei group with a relative abundance (RA) of 52-73% RA. Weissella species W. viridescens and W. halotolerans followed the L. sakei group. Phylogenetic analysis of 16S rRNA gene indicated that all L. sakei isolates were of subsp. carnosus, however, (GTG)5 fingerprinting demonstrated a high degree of intraspecies variation. Moreover, fingerprinting analysis showed that L. sakei isolates of specific fingerprinting groups were selected towards the final production stages. The present study elucidates how the LAB diversity changes both at the species and intraspecies level during pastirma production.

Gender Specific Correlation between Lip Print, Fingerprint and Blood Groups among Adults aged 20-30 Years attending a Tertiary Health Care Centre

A cross-sectional study was conducted to determine gender specific correlation between lip print, finger print and blood group among 120 adult clinical cases in a tertiary hospital in Kathmandu. Lip prints were collected using lipstick and cellophane sheet. Thumb print was collected using commercially available ink pad and white paper and analyzed using hand held magnifying lens. Blood group was noted at the time of recording lip and thumb print. A statistically significant association of gender with fingerprint (p=<0.001) and lip print (p=0.001) was found. A strong co-existing relation have been found between gender and the loop fingerprint-lip print type I’- blood group O and arch fingerprint–lip print type I’– blood group ‘O’.

Assessment of the Relation Between Craniofacial Tissue Profile-Morphology and Dermatoglyphics.

This study aims to evaluate the relationship between the convexity angle of facial tissue and fingerprint types by analyzing the connection between dermatoglyphic prints and facial structure. A total of 65 adults (24 females, 41 males) participated in the study. Each participant's fingerprint was analyzed using conventional methods of observation upon the index finger of the dominant hand. Fingerprints were classified into 3 fundamental types: arch, whorl, or loop. Craniofacial profiling was performed with measurements of hard tissue (Gl'-A-Pg') and soft tissue facial convexity (Gl-Sn-Pg) and total facial convexity (Gl-Prn-Pg) angles between 3 anthropological points determined on the lateral direct radiographs of the participants. Soft tissue convexity facial angle (Gl-Sn-Pg) values were found to be an average of 166.83° in females and an average of 162.05° in males. (Gl-Prn-Pg) values were found to be an average of 138.54° in females and 135.27° in males. The soft tissue profile of males showed a significantly convex structure compared with females. Also, the "whorl" type of fingerprint was found to be dominant in both sexes. There is no statistically significant difference in terms of soft tissue profile convexity in individuals belonging to the 3 different fingerprint groups. No statistically significant difference in soft tissue profile angular values between right-hand thumb and index fingerprint type groups were found among and between sexes. However, a comprehensive analysis of the relationships between the nose, lips, mouth, ear, and eyes and fingerprints of the same person may prove to be beneficial.

MALDI-TOF-MS analysis in low molecular weight serum peptidome biomarkers for NSCLC.

ObjectsLung cancer is one of the leading causes of death from cancer in the world. Screening new serum biomarkers is important for the early detection of lung cancer. The purpose of this study was to investigate the serum peptide model between non‐small cell lung cancer (NSCLC) patients and healthy controls, as well as between paired pre‐ and postoperative NSCLC patients, and to find the low molecular weight (LMW) potential tumor markers for NSCLC.Methods56 serum samples from NSCLC patients, 56 controls, and 20 matched pre‐ and postoperative patients were analyzed using magnetic‐bead (MB)‐based purification technique combined with MALDI‐TOF‐MS. To distinguish NSCLC from cancer‐free controls, three models were established. Finally, comparing the three groups of serum protein fingerprints, nano‐liquid chromatography–electrospray ionization tandem mass spectrometry was used to further identify the differential peptides.ResultsAmong the three models constructed, the GA model had the best diagnostic efficacy. Five differential peaks were screened by combining the case group, healthy controls, and postoperative group analysis, which were up‐regulated in the case group and showed a tendency to return to healthy control values after surgery. The protein matching the mass spectrometry peak m/z 2953.73 was identified as fibrinogen α chain.ConclusionThis study shows that the application of MALDI‐TOF‐MS is a promising approach for the identification of potential serum biomarkers for NSCLC, which is potentially valuable for establishing a new diagnostic method for lung cancer. In addition, we found that fibrinogen α chain may be an auxiliary diagnostic indicator for NSCLC.

Electrochemical quantification of mancozeb through tungsten oxide/reduced graphene oxide nanocomposite: A potential method for environmental remediation

The extensive use of pesticides for better yield of crops have become major human concern over the decades. Pesticides are widely used in the fields to kill weeds and pests on the vegetable and crops to improve the quality and yield of the food knowing the fact that pesticides residue in food are very lethal for human being. Amongst, the hazardous pesticides, mancozeb is widely applied in the protection of crops. Thus the quantification of mancozeb residue is of great importance. This study reports the electrochemical monitoring of mancozeb through tungsten oxide reduced graphene oxide (WO3/rGO) nanocomposite. The engineered nanocomposite was characterized though different analytical tools such as FTIR, XRD and TEM to examine crystallinity, internal texture and the size. The FTIR result confirm the functionalities of GO and WO3/rGO nanocomposite in finger print and functional group region. Through XRD analysis, the size of the WO3/rGO nanocomposite was calculated as 31.6 nm. While the TEM analysis was also exploited to examine the 2D texture of GO and nanometric size of the WO3/rGO. To ensure the conductive nature of the WO3/rGO nanocomposite, the glassy carbon electrode was modified and exploited for cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). Under the optimal conditions, the modified sensor showed exceptional response for mancozeb. The linear dynamic range was set from 0.05 to 70 μM in BRB buffer of pH 4. The LOD and LOQ for proposed method was calculated as 0.0038 and 0.0115 μM. The analytical applicability of chemically modified sensor was investigated in real matrix of different vegetable samples and the recovery values were observed in acceptable range. The electrochemical examination of present work reveals that WO3/rGO nanocomposite can be an exceptional aspirant for the determination of mancozeb at commercial level.

Association between gestational period and obesity in children with the use of dermatoglyphic traits: A preliminary study.

Events occurring during the gestational period can influence the development of diseases and conditions such as obesity. This study aimed to analyze the association between events occurring in the gestational period and the occurrence of obesity in children based on dermatoglyphic traits. The sample comprised 73 children born in 2009, living in Palhoça, Santa Catarina (SC), Brazil, regularly enrolled in public and private schools in that municipality and who are participants of an ongoing major cohort study project called Coorte Brasil Sul. The results show predictive traits of obesity when comparing BMI and fingerprint groups. Obese male group, presented the figure Ulnar Loop (UL) in the right hand (MDT1) thumb and greater number of ridges in the (MDSQL1) right thumb the greater the BMI; likewise, the older the woman getting pregnant the greater the number of ridges that the child presented in the (MESQL2) left index finger and (MESQL1) right thumb. The results obtained infer the presence of predictive traits of BMI ranges and a possible association between the dermatoglyphic traits of children with obesity and late pregnancy women.

A Hybrid Fingerprint Quality Evaluation Model for WiFi Localization

The main drawback for large-scale applications of WiFi-based localization is the varying characteristics of received signal strength (RSS), which degenerates the localization performance seriously. To mitigate the variation problem, we propose a hybrid fingerprint quality evaluation model (HFQuM) for accurate WiFi localization. HFQuM can intelligently determine the location of a user by evaluating the hybrid fingerprint quality in different subareas, that is a high fingerprint quality indicates that the frequently occurred location label is more likely to be true. To achieve this, in the offline phase, instead of only collecting RSS fingerprints, we construct a WiFi-based group of fingerprints (GOOFs) consisting of RSS, signal strength difference (SSD), and hyperbolic location fingerprint (HLF). Given an RSS testing sample of a user at an unknown location in the online phase, we first construct the multiple supporting sets (MSSs), including a sample space and a label space, selected by the similarity between the online sample and the GOOF. Based on the MSS, HFQuM is able to estimate the user’s location as well as subareas and their hybrid fingerprint quality simultaneously by jointly modeling the process of generating the sample space and label space. To further reduce the computational complexity, HFQuM employs an access point (AP) selection algorithm to exclude redundancy APs. Experimental results in a typical library environment verify the superiority of HFQuM in terms of localization accuracy as compared with other existing fingerprint-based methods.

HPLC fingerprinting and pattern recognition of Brazilian green propolis and Chinese propolis

In order to improve the quality control method for propolis, HPLC fingerprinting and pattern recognition of 12 batches of Brazilian green propolis and 12 batches of Chinese propolis were carried out. HPLC fingerprinting was performed on a C18 column (250 mm × 4.6 mm, 5μm) using gradient elution with methanol-water (containing 0.2% formic acid) as the mobile phase. Similarity analysis was executed on similarity evaluation system for chromatographic fingerprint of TCM (2012A). Pattern recognition was performed by SIMCA-P 13.0 software. As a result, as for the two groups of propolis samples, 21 common peaks were calibrated and 11 of them were identified as chlorogenic acid, caffeic acid, isochlorogenic acid B, isochlorogenic acid C, quercetin, kaempferol, apigenin, pinocembrin, caffeic acid phenylethyl ester, galangin and artepillin C. The similarity of the fingerprint profiles of the tested Brazilian propolis is more than 0.92, and that of the tested Chinese propolis is more than 0.91. However, the two groups of fingerprints’ similarity are from 0.523 to 0.693. The results of PCA and OPLS-DA showed that chemical profiles of the tested Brazilian green propolis were different from those of Chinese propolis. A set of compounds, namely, artepillin C, chlorogenic acid, pinocembrin, isochlorogenic acid B and caffeic acid phenylethyl ester can act as difference marker of the two groups of propolis. Moreover, HPLC fingerprint combined with pattern recognition could be developed into one new method for quality control of propolis.

Phylogenetic diversity of Bradyrhizobium strains isolated from root nodules of Lupinus angustifolius grown wild in the North East of Algeria

From a total of 80 bacterial strains isolated from root nodules of Lupinus angustifolius grown wild in the North-Eastern Algerian region of El Tarf, 64 plant host-nodulating strains clustered into 17 random amplified polymorphic DNA (RAPD) fingerprinting groups. The nearly complete 16S rRNA gene sequence from the representative strain of each group revealed they were closely related to members of the genus Bradyrhizobium of the Alphaproteobacteria, but their affiliation at the species level was not clear. Sequencing of the housekeeping genes glnII and recA, and their concatenated phylogenetic analysis, showed that 12 strains belong to B. lupini, other 2 strains affiliated with B. diazoefficiens and that 1 strain was closely related to B. japonicum. The remaining two strains showed similarity values ≤95% with B. cytisi and could represent new lineages within the genus Bradyrhizobium. Sequencing of the symbiotic nodC gene from 4 selected bradyrhizobial strains showed they were all similar to those of the species included in symbiovar genistearum.

Indoor Localization by Fusing a Group of Fingerprints Based on Random Forests

Indoor localization is becoming critical to empower Internet of Things for various applications, such as asset tracking, autonomous parking, virtual reality, context awareness, condition monitoring, geolocation, smart manufacturing, as well as smart cities. It is well known that indoor localization based on some single fingerprints is rather susceptible to the changing environment. The efficiency of building single fingerprints from one localization system is also low. Recently, we first proposed a group of fingerprints (GOOF) based localization to improve the efficiency of building fingerprints, and then proposed an efficient fusion algorithm, namely, multiple classifiers multiple samples (MUCUS), to improve the accuracy of localization. However, the main drawbacks of MUCUS are the low localization efficiency and low accuracy when all classifiers show poor performance simultaneously. In this paper, based on the aforementioned GOOF, we propose a sliding window aided mode-based (SWIM) fusion algorithm to balance the localization accuracy and efficiency. SWIM first adopts windowing and sliding techniques to improve the localization efficiency, and then obtains a more accurate estimate by minimizing the entropy of multiple classifiers or multiple samples. This can guarantee our estimator to be robust to changing environment and larger noise level. We demonstrate the performance of our algorithms through simulations and real experimental data via two universal software radio peripheral platforms.

Fingerprinting sediment sources in the water-wind erosion crisscross region on the Chinese Loess Plateau

Sand sheet sources in the wind-water erosion crisscross region of the Chinese Loess Plateau have not been specifically identified. Spatially, we selected a transect that cuts across the eastern edge of the Mu Us Sandy Land, sand sheets distributed loess hills (sand-loess hills), loess hills, and adjacent rocky mountains to collect surface sediment samples. Temporally, we selected a site on a sampling sand-loess hill to collected depth incremental samples in a 320 cm depth soil profile to analyze sediment sources using the multiple composite fingerprinting approach. We screened out a group of optimal composite fingerprints that correctly classified sediment sources at 100%. Greater than half of all sediment on sand-loess hills originated from the sandy land area on the sampling transect, and roughly half from the sandy land in the soil profile, while the remaining sediment all originated from the loess hills, and rocky mountains had no contributing effect. Coarse particles (>100 μm) on sand-loess hills were mainly transported from the Mu Us Sandy Land, while fine particles were mainly from loess hills, indicating the importance of aeolian activity in the transport of sand from the Mu Us Sandy Land to loess hills. Environmental changes chronicled by sediment deposition at approximately 12.85 ka BP in the soil profile were primarily attributed to climate change. This study confirms that the composite fingerprinting approach is a feasible method for future studies on sediment sources in areas influenced by complex erosion agencies, landforms and soil types.

Accurate WiFi Localization by Fusing a Group of Fingerprints via a Global Fusion Profile

The existing indoor localization approaches based on single fingerprints, such as received signal strength (RSS) and channel impulse response, are rather susceptible to the changing environment, multipath, and nonline-of-sight. It is well known that indoor localization can obtain higher positioning accuracy than the single-fingerprint-based methods by fusing multiple information sources (fingerprints/fingerprint functions). However, the existing fusion methods cannot fully exploit the intrinsic complementarity among multiple information sources and thus show lower accuracy. In this paper, we propose an accurate WiFi localization approach by Fusing A Group Of fingerprinTs (WiFi-FAGOT) via a global fusion profile (GFP). WiFi-FAGOT first constructs a WiFi-based GrOup Of Fingerprints (GOOF) in the offline phase, which consists of RSS, signal strength difference, and hyperbolic location fingerprint. Then, instead of direct localization by using the WiFi-based GOOF, we design multiple classifiers by training each fingerprint in the WiFi-based GOOF, namely GOOF classifiers. To fully leverage the intrinsic complementarity among different kinds of fingerprints, we propose a GFP construction algorithm by minimizing the average positioning error over the space of all GOOF classifiers. Finally, in the online phase, we derive a grid-dependent matching algorithm, namely, optimal classifier selection, to intelligently choose a fusion profile in the GFP for more accurate localization. Experimental results demonstrate that WiFi-FAGOT performs better than other systems in real complex indoor environments.

RSRP difference elimination and motion state classification for fingerprint-based cellular network positioning system

In recent years, the widespread availability of wireless communication has stimulated research of positioning service for cellular networks. Based on the minimization of drive-test introduced in 3GPP R10, it not only can be used to collect radio measurements and associated location information for network performance assessment, but also be used to build radio map for the fingerprint-based cellular network positioning system. However, due to the UE diversity, reference signal receiving power (RSRP) difference seriously degrades the positioning performance. In addition, UE motion changes the fingerprint length, which leads to fingerprint misalignment. Therefore, in this paper we propose to use multi-dimensional scaling algorithm to eliminate UE RSRP difference. By analyzing the relative RSRP difference of each pair of UE, we can eliminate RSRP difference in both offline and online. We also use the pattern recognition theory to classify UE motion for fingerprint alignment. We separated RSRP into two groups according to the UE motion state. The two groups of fingerprints are classified separately in offline and online. We implemented the proposed method in a real urban area and evaluated its positioning performance. The experiment results indicated our method can achieve a better positioning performance in cellular network positioning system.

Using colour, shape and radionuclide fingerprints to identify sources of sediment in an agricultural watershed in Atlantic Canada

Information on the sources of sediment is required both for the targeting, development and implementation of best management practices (BMPs) in agricultural regions and for understanding the nature of sediment dynamics within watersheds. Investigations have shown that a number of physical and biogeochemical sediment properties can be used as fingerprints to trace sediment back to its source. Three groups of fingerprints – spectral reflectance (colour), particle morphology (size and shape), and fallout radionuclides (137Cs) – were used to fingerprint the sources of sediment in a predominantly agricultural watershed (14.5 km2) in Atlantic Canada over a 7-year period. Both spectral reflectance and fallout radionuclides were successful in discriminating between sources of sediment identified within the watershed. In contrast, fingerprints based on particle morphology were not able to discriminate between any of the sources of sediment. A composite fingerprint including both spectral reflectance and radionuclide fingerprints was used to estimate the relative contributions from agricultural topsoil (i.e. rill and inter-rill erosion), agricultural subsurface (i.e. streambank and gully erosion) and forested areas. Sediment fingerprinting indicated that topsoil derived from agricultural land was the greatest contributor (77.2%; 95% credible interval (CI95%) [35.5, 92.5]) to suspended sediment collected at the outlet of the watershed. Streambanks and gullies within agricultural areas were also identified as important sources of sediment (17.4%; CI95% [3.1, 46.9]), while forested land was a minor source of sediment in the watershed (3.2%; CI95% [0.2, 18.9]). The fingerprinting results demonstrate that BMPs that target soil erosion in agricultural fields will result in the greatest reduction in the export of sediment from the watershed. This study also established that adjusting the spatial scale at which suspended sediment samples are collected would provide more detailed information on the location of the sources of sediment (i.e. identify sources at the sub-watershed level) and lead to better targeting of BMPs.

Localization by Fusing a Group of Fingerprints via Multiple Antennas in Indoor Environment

Most existing fingerprints-based indoor localization approaches are based on some single fingerprint, such as received signal strength (RSS), channel impulse response, and signal subspace. However, the localization accuracy obtained by the single fingerprint approach is rather susceptible to the changing environment, multipath, and non-line-of-sight propagation. In this paper, we propose a novel localization framework by Fusing A Group Of fingerprinTs (FAGOT) via multiple antennas for the indoor environment. We first build a GrOup Of Fingerprints (GOOF), which includes five different fingerprints, namely, RSS, covariance matrix, signal subspace, fractional low-order moment, and fourth-order cumulant, which are obtained by different transformations of the received signals from multiple antennas in the offline stage. Then, we design a parallel GOOF multiple classifiers based on AdaBoost (GOOF-AdaBoost) to train each of these fingerprints in parallel as five strong multiple classifiers. In the online stage, we input the corresponding transformations of the real measurements into these strong classifiers to obtain independent decisions. Finally, we propose an efficient combination fusion algorithm, namely, MUltiple Classifiers mUltiple Samples (MUCUS) fusion algorithm to improve the accuracy of localization by combining the predictions of multiple classifiers with different samples. As compared with the single fingerprint approaches, our proposed approach can improve the accuracy and robustness of localization significantly. We demonstrate the feasibility and performance of the proposed algorithm through extensive simulations as well as via real experimental data using a Universal Software Radio Peripheral platform with four antennas.

Novel and highly stable indigo (C.I. Vat Blue I) organic semiconductor dye: Crystal structure, optically diffused reflectance and the electrical conductivity/dielectric behaviors

The design and characterization of indigo dye (C.I. Vat Blue I) have analyzed by several techniques such as XRD, FTIR; optically diffused reflectance based on the Kubelka–Munk model and electrical conductivity/dielectric properties that can use as natural organic semiconductor dyes. The XRD measurements revealed that the indigo dye has crystallized for the first time by using Crysfire and Check-cell assignment software in orthorhombic structure with a space group (P222) and both the optimized lattice and refinement cell parameters have calculated before and after refinement. FTIR supports the fingerprint groups of indigo dye. Based on the Kubelka–Munk model, it has noted that there are two optical transition bands equal to 1.847 eV and 1.721 eV related to the multi-graded band gap of the indigo dye that enhanced the light absorption and can make the indigo material to be used in solar cell and optoelectronic applications. The AC/DC electrical conductivities as function frequency/temperatures, respectively, parallel/series dielectric properties (real/imaginary parts) have investigated and interpreted by using the two-probe electrical conductivity measurement. The AC conductivity and dielectric properties can support the charge-hopping carriers of barriers of various heights. Indigo dye is a highly stable organic dye for new organic devices and technologies.