Fuzzy Logic and Singular Value Decomposition based Through Wall Image Enhancement

Singular value decomposition based through wall image enhancement is proposed which is capable of discriminating target, noise and clutter signals. The overlapping boundaries of clutter, noise and target signals are separated using fuzzy logic. Fuzzy inference engine is used to assign weights to different spectral components. K-means clustering is used for suitable selection of fuzzy parameters. Proposed scheme significantly works well for extracting multiple targets in heavy cluttered through wall images. Simulation results are compared on the basis of mean square error, peak signal to noise ratio and visual inspection

Multi-view convolutional recurrent neural networks for lung cancer nodule identification

Screening via low-dose Computer Tomography (CT) has been shown to reduce lung cancer mortality rates by at least 20%. However, the assessment of large numbers of CT scans by radiologists is cost intensive, and potentially produces varying and inconsistent results for differing radiologists (and also for temporally-separated assessments by the same radiologist). To overcome these challenges, computer aided diagnosis systems based on deep learning methods have proved an effective in automatic detection and classification of lung cancer. Latterly, interest has focused on the full utilization of the 3D information in CT scans using 3D-CNNs and related approaches. However, such approaches do not intrinsically correlate size and shape information between slices. In this work, an innovative approach to Multi-view Convolutional Recurrent Neural Networks (MV-CRecNet) is proposed that exploits shape, size and cross-slice variations while learning to identify lung cancer nodules from CT scans. The multiple-views that are passed to the model ensure better generalization and the learning of robust features. We evaluate the proposed MV-CRecNet model on the reference Lung Image Database Consortium and Image Database Resource Initiative and Early Lung Cancer Action Program datasets; six evaluation metrics are applied to eleven comparison models for testing. Results demonstrate that proposed methodology outperforms all of the models against all of the evaluation metrics

Antecedents and Consequences of Work Family Conflicts

This study is conducted to examine the impact of supportive supervisor and emotional labor on work family conflicts and also the impact of work family conflict on job satisfaction and organizational commitment among female teachers of Punjab (Pakistan a) as well. A sample of 138 female teachers was randomly selected. The results of the study indicate that emotional labor has a strong positive impact on work family conflicts while supportive supervisor effects negatively to work family conflicts. Further, Work family conflict has strong and negative influence on organizational commitment, whereas, negative but weak influence on job satisfaction. Moreover this research is an insight for the management of colleges of Pakistan by reducing the work family conflict between female lecturers, they can enhance the level of job satisfaction and organizational commitment among them. Key words: Emotional Labor, Supportive Supervisor, Work Family Conflicts, Job Satisfaction, Organizational Commitmen

Шляхи удосконалення підготовки учнів до олімпіад з фізики

(uk) Стаття присвячена проблемі формування основ методики складання задач та завдань для олімпіад з фізики учнів середніх навчальних закладів освіти.(ru) Статья посвящена проблеме формирования основ методики составления задач и заданий для олимпиад по физике учащихся средних учебных заведений образования

Plant Disease Diagnosing Based on Deep Learning Techniques: A Survey and Research Challenges

Agriculture crops are highly significant for the sustenance of human life and act as an essential source for national income development worldwide. Plant diseases and pests are considered one of the most imperative factors influencing food production, quality, and minimize losses in production. Farmers are currently facing difficulty in identifying various plant diseases and pests, which are important to prevent plant diseases effectively in a complicated environment. The recent development of deep learning techniques has found use in the diagnosis of plant diseases and pests, providing a robust tool with highly accurate results. In this context, this paper presents a comprehensive review of the literature that aims to identify the state of the art of the use of convolutional neural networks (CNNs) in the process of diagnosing and identification of plant pest and diseases. In addition, it presents some issues that are facing the models performance, and also indicates gaps that should be addressed in the future. In this regard, we review studies with various methods that addressed plant disease detection, dataset characteristics, the crops, and pathogens. Moreover, it discusses the commonly employed five-step methodology for plant disease recognition, involving data acquisition, preprocessing, segmentation, feature extraction, and classification. It discusses various deep learning architecture-based solutions that have a faster convergence rate of plant disease recognition. From this review, it is possible to understand the innovative trends regarding the use of CNN’s algorithms in the plant diseases diagnosis and to recognize the gaps that need the attention of the research community

Effect of ketoprofen on immune cells in mice

Purpose: To study the immunosuppressant and immunopotentiating effects of ketoprofen on antibody producing cells.Methods: Mice were given ketoprofen at doses of 1 mg/kg/day and 5 mg/kg/day for seven days. Similarly polyinosinic–polycytidylic acid (Poly IC) and phosphate buffer saline (PBS) were used as positive and negative control, respectively, for seven days. After seven days, the mice were sacrificed and their spleens removed. Simultaneously, blood was withdrawn from the hearts of the mice and serum was separated from the blood. The spleen cells were analyzed by enzyme-linked immunospot (ELISPOT) while the serum was investigated by enzyme-linked immunosorbant (ELISA) to evaluate the effects of ketoprofen on the ability of individual cell to produce antibodies and antibody- mediated immune responses.Results: Ketoprofen significantly (p < 0.001) reduced the ability of individual cells to produce antibodies. There was a significant difference (p < 0.001) in % of spot forming cells of PBS treated negative control group (0.045 %) as against positive control (0.058 %), 1 mg ketoprofen /kg/day (0.037 %) and 5 mg ketoprofen/kg/day (0.032 %) treated groups. The results of ELISA showed a significant (p < 0.005) difference in the absorbance values between negative control, positive control, ELISA control and ketoprofen treated groups. Absorbance was significantly (p < 0.005) reduced in ketoprofen-treated groups.Conclusion: The ability of an individual cell to produce antibodies and antibody-mediated immune responses is suppressed by ketoprofen, suggesting that it is immunosuppressive, and thus indicating its potential application in patients with auto-immune disorders.Keywords: Ketoprofen, Immunomodulatory, Immunosupressive, Antibody, Spot-forming cells, Polyinosinic–polycytidylic aci