Hard, Harder, and the Hardest Problem: The Society of Cognitive Selves

The hard problem of consciousness is explicating how moving matter becomes thinking matter. Harder yet is the problem of spelling out the mutual determinations of individual experiences and the experiencing self. Determining how the collective social consciousness influences and is influenced by the individual selves constituting the society is the hardest problem. Drawing parallels between individual cognition and the collective knowing of mathematical science, here we present a conceptualization of the cognitive dimension of the self. Our abstraction of the relations between the physical world, biological brain, mind, intuition, consciousness, cognitive self, and the society can facilitate the construction of the conceptual repertoire required for an explicit science of the self within human society

Conscious Experience and Designing User Experiences

Neuroscientific discourse on consciousness often resorts to "collection of elements", notwithstanding the Gestalt demonstrations against representing conscious experience as a collection of sensory elements. Here I show that defining conscious experience as an object of the category of conscious experiences, instead of as cohesion-less set of structure-less elements, provides the conceptual repertoire—basic shapes, figures, and incidence relations—needed to reason about the essence of conscious experiences and the essence-preserving transformations of conscious experiences. Viewed in light of the category of conscious experiences, designers of user experience—in designing pre-conceptualized user experiences—are well situated to contribute to the development of the science of consciousness

Location-Aware Keyword Query Proposal Based On File Proximity

Web search query suggestions aid users in finding relevant content without requiring them to know how to search for it exactly. Existing keyword suggestion approaches do not take into account user locations and query results; i.e. the geographic proximity of a user to the results found is not taken as a consideration in the recommendation. However, the relevancy of search results is known to be connected to their geographic proximity to the query emitter in many applications (e.g. location-based services). We build a keyword query suggestion framework that is aware of location. We offer a weighted keyword-document graph capturing both the semitone significance between keyword searches and the geographic distance between the documents generated and the user location. To choose the highest-scoring keyword queries as suggestions, the graph is viewed in a random-walk-with-restart method. A partition-based technique that's up to an order of magnitude better than the baseline beats the baseline method. To assess the performance of our framework and algorithms, we use real data

Load Flow Solution of Distribution Systems - A Bibliometric Survey

In this paper, Bibliometric Survey has been carried out on ‘Load Flow Solution of Distribution Systems’ from 2012 to 2021. Scopus database has been used for the analysis. There were total 1711 documents found on this topic. The statistical analysis is carried out source wise, year wise, area wise, Country wise, University wise, author wise, and based on funding agency. Network analysis is also carried out based on Co-authorship, Co-occurrence. Results are presented. During 2020 and 2018, there were 263 documents published which is the highest. ‘IEEE Transactions on Power Systems’ has published 90 documents during the period of study which is the highest in terms of articles under the category of sources. Highest citations were received by the article authored by Hung and Mithulanathan with 484 citations in the collected database with the chosen key words. VOSviewer 1.6.16 is the software that is used for the statistical analysis and network analysis on the database. It provides a very effective way to analyze the co-authorship, co-occurrences, citation and bibliometric analysis etc. The Source for all Tables and figures is www.scopus.com, The data is assessed on 6th July, 2021

Hypericin: a potential antiglioma therapy

Journal ArticleHYPERICIN, A POLYCYCLIC aromatic dione isolated from plants, is presently being clinically evaluated as an antiviral agent in the treatment of human immunodeficiency virus (HIV) infection. In addition, it is known to be a potent protein kinase C inhibitor. To evaluate its potential as an inhibitor of glioma growth, an established (U87) and low-passage glioma line (93-492) were treated with hypericin in tissue culture for a period of 48 hours after passage. Hypericin inhibited the glioma growth in a dose-related manner, with a marked inhibition of growth in the low-micromolar concentration range (e.g., in line U87 and low-passage line 93-492 , a concentration of hypericin of 10 pmol/L produced 62 and 76% decreases in [3H]thymidine uptake, respectively). Because the reported inhibitory effects of protein kinase Care enhanced by visible light, [3H]thymidine uptake was measured in both the presence and the absence of visible light. In glioma line A172, the presence of light slightly increased the inhibitory effect of hypericin. Moreover, an apoptosis (i.e., programmed cell death) assay was performed to determine whether the treatment of glioma cells with hypericin was cytostatic or cytocidal. Cells were harvested, and purified deoxyribonucleic acid (DNA) was analyzed by agarose gel electrophoresis. DNA from cells treated with hypericin for 48 hours exhibited a classical "ladder" pattern of oligonucleosome-sized fragments characteristic of apoptosis. These data suggest that the proven safe drug hypericin may have potential as an antiglioma agent; we suggest clinical trials

Prediction Of Diabetic Retinopathy Using Weighted Fusion Deep Learning Model

Diabetes arises from consistently elevated blood glucose levels, which can lead to vascular complications and vision loss. Timely diagnosis signifies  a crucial role in minimizing risk of advanced disorder of blood vessels of retina  and associated severe visual impairment. Hence, the classification of DR stages holds significant importance. This proposed novel study introduces a weighted fusion deep learning network designed for exigently extracting essential features and characterize  DR  stages using retinal images. The suggested system intends to iidentify retinopathy symptoms present in these images. Fundus-related features are extracted by fine-tuning the Inception V4 and VGG-19 models. The outputs of these fine-tuned models are combined utilizing a weighted fusion methodology and the ultimate recognition outcome is calculated by using softmax classifier. The suggested network exhibits an elevated degree of accuracy for recognizing DR phases, based on experimental results. The suggested approach specifically obtains an accuracy score of 99.18% and sensitivity of 97.5% when assessed on the Messidor dataset. Our suggested novel weighted fusion deep learning model  network has equivalent performance when compared to other models, thus supporting its efficiency