The effects of space-particle bombardment on solar-cell materials

Electron microscopy and X ray topography analysis of proton bombardment effect on silicon solar cell

Impedance-Based Miniaturized Biosensor for Ultrasensitive and Fast Prostate-Specific Antigen Detection

This paper reports the successful fabrication of an impedance-based miniaturized biosensor and its application for ultrasensitive Prostate-Specific Antigen (PSA) detection in standard and real human plasma solution, spiked with different PSA concentrations. The sensor was fabricated using photolithographic techniques, while monoclonal antibodies specific to human PSA were used as primary capture antibodies. Electrochemical impedance spectroscopy (EIS) was employed as a detection technique. The sensor exhibited a detection limit of 1 pg/ml for PSA with minimal nonspecific binding (NSB). This detection limit is an order of magnitude lower than commercial PSA ELISA assays available on the market. The sensor can be easily modified into an array for the detection of other biomolecules of interest, enabling accurate, ultrasensitive, and inexpensive point-of-care sensing technologies

Exploring Wireless Sensor Network Technology In Sustainable Okra Garden: A Comparative Analysis Of Okra Grown In Different Fertilizer Treatments

The goal of this project was to explore commercial agricultural and irrigation sensor kits and to discern if the commercial wireless sensor network (WSN) is a viable tool for providing accurate real-time farm data at the nexus of food energy and water. The smart garden consists of two different varieties of Abelmoschus esculentus (okra) planted in raised beds, each grown under two different fertilizer treatments. Soil watermark sensors were programed to evaluate soil moisture and dictate irrigation events up to four times a day, while soil temperature and photosynthetic solar radiation sensors also recorded data every six hours. Solar panels harvested energy to power water pump and sensors. The objectives of the experiments were to evaluate and compare plant and soil parameters of the two okra varieties grown under two different fertilizer treatments. The plant parameters evaluated and compared were basal diameter, plant height, fruit production, and fruit size. Soil parameters measured were soil moisture, soil temperature, and soil nitrate concentration. The commercial sensors were evaluated on efficiency, accuracy, ease of use and overall practicality. Clemson spineless produced larger okra plants with the highest plant parameter values, followed by Emerald okra. However, they both averaged nearly the same yield and length of okra fruit. Nature’s Care fertilizer leached more in beds containing Clemson spineless, while Garden-tone leached more in beds containing Emerald okra. When the WSN is installed properly, the system’s great performance undoubtedly aides the farmer by providing real time field data. However, a properly installed apparatus does not promise a stable system. There are numerous challenges and limitations of which can diminish the performance quality of the WSN, those being battery power, data transmission, and data storage. Data storage is also an issue depending on the amount of data collected, rate of data collection, and size of storage unit. These issues can hinder the decision making for precision farmers

On the recurrence and robust properties of Lorenz'63 model

Lie-Poisson structure of the Lorenz'63 system gives a physical insight on its dynamical and statistical behavior considering the evolution of the associated Casimir functions. We study the invariant density and other recurrence features of a Markov expanding Lorenz-like map of the interval arising in the analysis of the predictability of the extreme values reached by particular physical observables evolving in time under the Lorenz'63 dynamics with the classical set of parameters. Moreover, we prove the statistical stability of such an invariant measure. This will allow us to further characterize the SRB measure of the system.Comment: 44 pages, 7 figures, revised version accepted for pubblicatio

Exactly solvable model of superstring in Ramond-Ramond plane wave background

We describe in detail the solution of type IIB superstring theory in the maximally supersymmetric plane-wave background with constant null Ramond-Ramond 5-form field strength. The corresponding light-cone Green-Schwarz action found in hep-th/0112044 is quadratic in both bosonic and fermionic coordinates. We find the spectrum of the light-cone Hamiltonian and the string representation of the supersymmetry algebra. The superstring Hamiltonian has a ``harmonic-oscillator'' form in both the string-oscillator and the zero-mode parts and thus has discrete spectrum in all 8 transverse directions. We analyze the structure of the zero-mode sector of the theory, establishing the precise correspondence between the lowest-lying ``massless'' string states and the type IIB supergravity fluctuation modes in the plane-wave background. The zero-mode spectrum has certain similarity to the supergravity spectrum in AdS_5 x S^5 of which the plane-wave background is a special limit. We also compare the plane-wave string spectrum with expected form of the light-cone gauge spectrum of superstring in AdS_5 x S^5.Comment: 33 pages, latex. v4: minor sign corrections in (1.5) and (3.62), to appear in PR

Solitary pancreatic tuberculous abscess mimicking pancreatic cystadenocarcinoma: a case report

BACKGROUND: The incidence of pancreatic tuberculosis is extremely rare, and it frequently misdiagnosed as pancreatic neoplasms. The nonsurgical diagnosis of this entity continues to be a challenge. CASE PRESENTATION: A 33 year old male with six-month history of intermittent right epigastric vague pain and weight lost had found a solitary pancreatic cystic mass and diagnosed as pancreatic cystadenocarcinoma. The chest X-ray film and physical examination revealed no abnormalities. Abdominal ultrasound (US) examination showed an irregular hypoechoic lesion of 6.6 cm × 4.4 cm in the head of pancreas, and color Doppler flow imaging did not demonstrate blood stream in the mass. The attempts to obtain pathological evidence of the lesion by US-guided percutaneous fine needle aspiration failed, an exploratory laparotomy and incisional biopsy revealed a caseous abscess of the head of pancreas without typical changes of tuberculous granuloma, but acid-fast stain was positive. CONCLUSIONS: Pancreatic tuberculosis should be considered in the differential diagnosis of focal pancreatic lesions, especially for young people in developing countries

Level-set based adaptive-active contour segmentation technique with long short-term memory for diabetic retinopathy classification

Diabetic Retinopathy (DR) is a major type of eye defect that is caused by abnormalities in the blood vessels within the retinal tissue. Early detection by automatic approach using modern methodologies helps prevent consequences like vision loss. So, this research has developed an effective segmentation approach known as Level-set Based Adaptive-active Contour Segmentation (LBACS) to segment the images by improving the boundary conditions and detecting the edges using Level Set Method with Improved Boundary Indicator Function (LSMIBIF) and Adaptive-Active Counter Model (AACM). For evaluating the DR system, the information is collected from the publically available datasets named as Indian Diabetic Retinopathy Image Dataset (IDRiD) and Diabetic Retinopathy Database 1 (DIARETDB 1). Then the collected images are pre-processed using a Gaussian filter, edge detection sharpening, Contrast enhancement, and Luminosity enhancement to eliminate the noises/interferences, and data imbalance that exists in the available dataset. After that, the noise-free data are processed for segmentation by using the Level set-based active contour segmentation technique. Then, the segmented images are given to the feature extraction stage where Gray Level Co-occurrence Matrix (GLCM), Local ternary, and binary patterns are employed to extract the features from the segmented image. Finally, extracted features are given as input to the classification stage where Long Short-Term Memory (LSTM) is utilized to categorize various classes of DR. The result analysis evidently shows that the proposed LBACS-LSTM achieved better results in overall metrics. The accuracy of the proposed LBACS-LSTM for IDRiD and DIARETDB 1 datasets is 99.43% and 97.39%, respectively which is comparably higher than the existing approaches such as Three-dimensional semantic model, Delimiting Segmentation Approach Using Knowledge Learning (DSA-KL), K-Nearest Neighbor (KNN), Computer aided method and Chronological Tunicate Swarm Algorithm with Stacked Auto Encoder (CTSA-SAE)