Measurement of space charge generation-recombination current in Hg1−xCdxTe photodiodes by deep level transient spectroscopy

Deep Level Transient Spectroscopy (DLTS) measurements have been used to characterize n⁺ ? pHg_1?xCd_xTe junction photodiode performance. Deep level results obtained on a x = 0.320 liquid phase epitaxial grown photodiode and a x = 0.219 bulk quench anneal-grown photodiode have identified deep Shockley-Read recombination centers. Detailed characterization of trap energy, trap density, and capture cross sections for these traps located within the diode depletion region have been used to predict a space charge generation-recombination current and dynamic resistance-area product at zero bias voltage. This paper presents for the first time a direct correlation of DLTS parameters with photodiode device performance.     

Effect of chlorine exposure on the thermo-reflectance spectra of lead chalcogenide films

Small crystallites of PbCl₂ have been observed on Cl_2- and air-exposed surfaces of PbS. Identification has been made by observing the fundamental exciton of PbCl₂ in thermoreflectance, and by the EDAX technique in a scanning electron microscope. Similar effects occur on PbSe and PbTe. The phenomenon may be applicable to a low level integrating chlorine detector.     

Effect of Lead-Free Soldering on Key Material Properties of FR-4 Printed Circuit Board Laminates

The transition to lead-free soldering of printed circuit boards (PCBs) using solder alloys such as SnAgCu has resulted in higher temperature exposures during assembly compared with eutectic SnPb solders. The knowledge of PCB laminate material properties and their dependence on the material constituents, combined with their possible variations due to lead-free soldering temperature exposures, is an essential input in the laminate selection process. This paper provides laminate selection guidelines that were arrived at by assessing key material properties (glass transition temperature, coefficient of thermal expansion, decomposition temperature, and water absorption), and their responses to lead-free soldering assembly conditions. A range of commercially available FR-4 PCB laminate materials, classified on the basis of glass transition temperature (high, mid, and low), curing agents (dicyandiamide and phenolic), flame retardants (halogenated and halogen-free), and fillers (presence or absence) were studied. The laminate material properties under investigation were measured as per the IPC-TM-650 test methods before and after exposure to multiple lead-free soldering cycles. Combinatorial property analysis was conducted to investigate the causes behind variations in material properties.     

A Potent,Selective CBX2 Chromodomain Ligand and Its Cellular Activity During Prostate Cancer Neuroendocrine Differentiation

Polycomb group (PcG) proteins are epigenetic regulators that facilitate both embryonic development and cancer progression. PcG proteins form Polycomb repressive complexes 1 and 2 (PRC1 and PRC2). PRC2 trimethylates histone H3 lysine 27 (H3K27me3), a histone mark recognized by the N-terminal chromodomain (ChD) of the CBX subunit of canonical PRC1. There are five PcG CBX paralogs in humans. CBX2 in particular is upregulated in a variety of cancers, particularly in advanced prostate cancers. Using CBX2 inhibitors to understand and target CBX2 in prostate cancer is highly desirable; however, high structural similarity among the CBX ChDs has been challenging for developing selective CBX ChD inhibitors. Here, we utilize selections of focused DNA encoded libraries (DELs) for the discovery of a selective CBX2 chromodomain probe, SW2_152F. SW2_152F binds to CBX2 ChD with a K_d of 80 nM and displays 24-1000-fold selectivity for CBX2 ChD over other CBX paralogs in vitro. SW2_152F is cell permeable, selectively inhibits CBX2 chromatin binding in cells, and blocks neuroendocrine differentiation of prostate cancer cell lines in response to androgen deprivation.     

Volumetric medical image compression using inter-slice correlation switched prediction approach

With the advancement in medical data acquisition and telemedicine systems, image compression has become an important tool for image handling, as the tremendous amount of data generated in medical field needs to be stored and transmitted effectively. Volumetric MRI and CT images comprise a set of image slices that are correlated to each other. The prediction of the pixels in a slice depends not only upon the spatial information of the slice, but also the inter-slice information to achieve compression. This article proposes an inter-slice correlation switched predictor (ICSP) with block adaptive arithmetic encoding (BAAE) technique for 3D medical image data. The proposed ICSP exploits both inter-slice and intra-slice redundancies from the volumetric images efficiently. Novelty of the proposed technique is in selecting the correlation coefficient threshold (T_ϒ) for switching of ICSP. Resolution independent gradient edge detector (RIGED) at optimal prediction threshold value is proposed for intra-slice prediction. Use of RIGED, which is modality and resolution independent, brings the novelty and improved performance for 3D prediction of volumetric images. BAAE is employed for encoding of prediction error image to resulting in higher compression efficiency. The proposed technique is also extended for higher bit depth volumetric medical images (16-bit depth) presenting significant compression gain of 3D images. The performance of the proposed technique is compared with the state-of-the art techniques in terms of bits per pixel (BPP) for 8-bit depth and was found to be 31.21%, 27.55%, 21.89%, and 2.39% better than the JPEG-2000, CALIC, JPEG-LS, M-CALIC, and 3D-CALIC respectively. The proposed technique is 11.86%, 8.56%, 7.97%, 6.80%, and 4.86% better than the M-CALIC, 3D CALIC, JPEG-2000, JPEG-LS and CALIC respectively for 16-bit depth image datasets. The average value of compression ratio for 8-bit and 16-bit image dataset is obtained as 3.70 and 3.11 respectively by the proposed technique.     

Field cancerization: why late "recurrent" ovarian cancer is not recurrent

OBJECTIVE: Late "recurrence" of ovarian cancer may result from either regrowth of dormant tumor cells or from development of a new cancer caused by the phenomenon of field cancerization. Clinically, some recurrent ovarian cancers show the same therapeutic sensitivities to chemotherapy and surgery as did the primary disease, whereas others are refractory to all therapy. We hypothesize that recurrent ovarian cancers are distinguishable on the basis of a molecular genetic fingerprint and that some are actually new primary cancers of the peritoneum rather than recurrent ovarian cancer. STUDY DESIGN: We constructed molecular genetic fingerprints of 13 paired primary and late recurrent ovarian cancers to study their clonal relationships. The tumor pairs were analyzed for p53 mutations and allelotypes, patterns of X-chromosome inactivation, loss of heterozygosity, and microsatellite instability at 12 different loci on 6 different chromosomes. Techniques used included single-strand conformational polymorphism mutation screening and polymerase chain reaction-based sequence analysis of the p53 locus, restriction digestion of the androgen receptor locus to determine X-chromosome inactivation, and polyacrylamide gel electrophoresis of highly polymorphic dinucleotide, trinucleotide, and tetranucleotide repeats. RESULTS: The average age at initial diagnosis for this cohort was 54.7 years (range 45.3 to 65.5). Mean interval to recurrence was 42.7 months (range 28 to 62). Molecular fingerprints were characterized for 4 to 8 informative loci per tumor pair. The fingerprints of 10 (77%) differed significantly, strongly suggesting that a second primary cancer had developed. The remaining 3 tumor pairs demonstrated identical allelotypes consistent with regrowth of dormant tumor cells. CONCLUSION: Our results are consistent with the "field cancerization" hypothesis of ovarian carcinogenesis but could also be explained by a polyclonal tumor origin, which contrasts with the currently accepted monoclonal theory of ovarian carcinogenesis. Late development of a new primary cancer may herald the proband as a member of a familial cancer phenotype. These studies provide a molecular genetic rationale that both explains and prognosticates the clinical course of recurrent ovarian cancer.     

SNA based QoS and reliability in fog and cloud framework

Fog and Cloud computing are ubiquitous computing paradigms based on the concepts of utility and grid computing. Cloud service providers permit flexible and dynamic access to virtualized computing resources on pay-per-use basis to the end users. The users having mobile device will like to process maximum number of applications locally by defining fog layer to provide infrastructure for storage and processing of applications. In case demands for resources are not being satisfied by fog layer of mobile device then job is transferred to cloud for processing. Due to large number of jobs and limited resources, fog is prone to deadlock at very large scale. Therefore, Quality of Service (QoS) and reliability are important aspects for heterogeneous fog and cloud framework. In this paper, Social Network Analysis (SNA) technique is used to detect deadlock for resources in fog layer of mobile device. A new concept of free space fog is proposed which helps to remove deadlock by collecting available free resource from all allocated jobs. A set of rules are proposed for a deadlock manager to increase the utilization of resources in fog layer and decrease the response time of request in case deadlock is detected by the system. Two different clouds (public cloud and virtual private cloud) apart from fog layer and free space fog are used to manage deadlock effectively. Selection among them is being done by assigning priorities to the requests and providing resources accordingly from fog and cloud. Therefore, QoS as well as reliability to users can be provided using proposed framework. Cloudsim is used to evaluate resource utilization using Resource Pool Manager (RPM). The results show the effectiveness of proposed technique.     

Internet of Things-based student performance evaluation framework

In recent years, solutions based on Internet of Things (IoT) are gaining impetus in educational institutions. It is observed that student performance evaluation system in education institutions is still manual. The performance score of student in traditional evaluation system is confined to its academic achievements while activity-based performance attributes are overlooked. Moreover, the traditional system fails to capitalise information of each student related to different activities in learning environment. In relation to this context, we propose to facilitate automated student performance evaluation system by exploring ubiquitous sensing capabilities of IoT. The system deduces important results about the performance of the students by discovering daily spatial–temporal patterns. These patterns are based on the data collected by the sensory nodes (objects) in the institution learning environment. The information is generated by applying data mining algorithms for each concerned activity. The automated decisions are taken by management authority for each student using game theory. In addition, to effectively manage IoT-based activity data, tensor-based storage mechanism is proposed. The experimental evaluation compares the student performance score generated by the proposed system with the manual student performance evaluation system. The results depict that the proposed system evaluates the performance of the student efficiently.