Sanskrit text with verse translation in English and Hindi.

Study on the philosophy of R̥gveda; includes related mantras also.

On Ambedkar's philosophy and views, and Indian philosophy on dalits; includes quotation extracted from Buddhist scriptures and literature.

. Software testing is a very important phase in the development of software. Testing includes the generation of test cases which, if done manually, is time consuming. To automate this process and generate optimal test cases, several meta-heuristic techniques have been developed. These approaches include genetic algorithm, cuckoo search, tabu search, intelligent water drop, etc. This paper presents an effective approach for test data generation using the cuckoo search and tabu search algorithms. It combines the cuckoo algorithm's strength of converging (...) to the solution in minimal time along with the tabu mechanism of backtracking from local optima by Lévy flight. The experimental results show that the algorithm is effective in generating test cases optimally and its performance is better than various earlier proposed approaches. (shrink)

A case of a particularly severe misbehavior in a review process is described. Two reviewers simply copied and pasted their critical comments from their previous reviews without reading the reviewed manuscript. The editor readily accepted the reviewers’ opinion and rejected the manuscript. These facts give rise to some general questions about possible factors affecting the ethical behavior of reviewers and editors, as well as possible countermeasures to prevent ethical violations.

Most clinical neurofeedback studies based on functional magnetic resonance imaging use the patient's own neural activity as feedback. The objective of this study was to create a subject-independent brain state classifier as part of a real-time fMRI neurofeedback system that can guide patients with depression in achieving a healthy brain state, and then to examine subsequent clinical changes. In a first step, a brain classifier based on a support vector machine was trained from the neural information of happy autobiographical imagery (...) and motor imagery blocks received from a healthy female participant during an MRI session. In the second step, 7 right-handed female patients with mild or moderate depressive symptoms were trained to match their own neural activity with the neural activity corresponding to the “happiness emotional brain state” of the healthy participant. The training was carried out using the rt-fMRI NF system guided by the brain-state classifier we had created. Thus, the informative voxels previously obtained in the first step, using SVM classification and Effect Mapping, were used to classify the Blood-Oxygen-Level Dependent activity of the patients and converted into real-time visual feedback during the neurofeedback training runs. Improvements in the classifier accuracy toward the end of the training were observed in all the patients [Session 4–1 Median = 6.563%; Range = 4.10–27.34; Wilcoxon Test, 2-tailed p = 0.031]. Clinical improvement also was observed in a blind standardized clinical evaluation [HDRS CE2-1 Median = 7; Range 2 to 15; Wilcoxon Test, 2-tailed p = 0.016], and in self-report assessments [BDI-II CE2-1 Median = 8; Range 1–15; Wilcoxon Test, 2-tailed p = 0.031]. In addition, the clinical improvement was still present 10 days after the intervention [BDI-II CE3-2_Median = 0; Range −1 to 2; Wilcoxon Test, 2-tailed p = 0.50/ HDRS CE3-2 Median = 0; Range −1 to 2; Wilcoxon Test, 2-tailed p = 0.625]. Although the number of participants needs to be increased and a control group included to confirm these findings, the results suggest a novel option for neural modulation and clinical alleviation in depression using noninvasive stimulation technologies. (shrink)