Mapping the Intellectual Structure of Social Entrepreneurship Research: A Citation/Co-citation Analysis

In this paper, we employ bibliometric analysis to empirically analyse the research on social entrepreneurship published between 1996 and 2017. By employing methods of citation analysis, document co-citation analysis, and social network analysis, we analyse 1296 papers containing 74,237 cited references and uncover the structure, or intellectual base, of research on social entrepreneurship. We identify nine distinct clusters of social entrepreneurship research that depict the intellectual structure of the field. The results provide an overall perspective of the social entrepreneurship field, identifying its influential works and analysing scholarly communication between these works. The results further aid in clarifying the overall centrality features of the social entrepreneurship research network. We also examine the integration of ethics into social entrepreneurship literature. We conclude with a discussion on the structure and evolution of the social entrepreneurship field

Rapid synthesis of biocompatible silver nanoparticles using aqueous extract of Rosa damascena petals and evaluation of their anticancer activity

AbstractObjectiveTo optimize the process parameters involved in the green synthesis of silver nanoparticles (G-SNPs) by aqueous extract of Rosa damascena petals and to evaluate the biocompatibility and anti cancer activity of the synthesized silver nanoparticles against human lung adenocarcinoma (A549).MethodsThe process variables that include concentration of extract, mixing ratio of reactants, silver salt concentration and interaction time were analyzed. The compatibility of the G-SNPs was verified by incubating with erythrocytes and the anticancer property of the G-SNPs against A549 cells was performed by MTT assay.ResultsFormation of G-SNPs was confirmed by the visual change in the colour of the reaction mixture from pale yellow to brown yellow. Surface plasmon resonance of synthesized G-SNPs was observed at 420 nm; the size of G-SNPs were analyzed by DLS and found to be in the range of (84.00±10.08) nm. Field emission scanning electron microscope and high resolution transmission electron microscopy analysis confirmed that the G-SNPs were fairly spherical. Fourier transform infrared spectroscopy spectroscopy and X-ray diffraction revealed the characteristic peaks of G-SNPs. Energy dispersive X-ray analysis showed a signal of silver around 3 keV. The synthesized G-SNPs exhibited anticancer activity as evidenced by the MTT assay. IC50 value of G-SNPs was found to be 80 μg/mL.ConclusionThe results of the present study suggest that G-SNPs can be synthesized rapidly within first minute of the reaction; they are biocompatible and possess anticancer activity against human lung adenocarcinoma

SARC: Soft Actor Retrospective Critic

The two-time scale nature of SAC, which is an actor-critic algorithm, is characterised by the fact that the critic estimate has not converged for the actor at any given time, but since the critic learns faster than the actor, it ensures eventual consistency between the two. Various strategies have been introduced in literature to learn better gradient estimates to help achieve better convergence. Since gradient estimates depend upon the critic, we posit that improving the critic can provide a better gradient estimate for the actor at each time. Utilizing this, we propose Soft Actor Retrospective Critic (SARC), where we augment the SAC critic loss with another loss term - retrospective loss - leading to faster critic convergence and consequently, better policy gradient estimates for the actor. An existing implementation of SAC can be easily adapted to SARC with minimal modifications. Through extensive experimentation and analysis, we show that SARC provides consistent improvement over SAC on benchmark environments. We plan to open-source the code and all experiment data at: https://github.com/sukritiverma1996/SARC.Comment: Accepted at RLDM 202

Explaining RL Decisions with Trajectories

Explanation is a key component for the adoption of reinforcement learning (RL) in many real-world decision-making problems. In the literature, the explanation is often provided by saliency attribution to the features of the RL agent's state. In this work, we propose a complementary approach to these explanations, particularly for offline RL, where we attribute the policy decisions of a trained RL agent to the trajectories encountered by it during training. To do so, we encode trajectories in offline training data individually as well as collectively (encoding a set of trajectories). We then attribute policy decisions to a set of trajectories in this encoded space by estimating the sensitivity of the decision with respect to that set. Further, we demonstrate the effectiveness of the proposed approach in terms of quality of attributions as well as practical scalability in diverse environments that involve both discrete and continuous state and action spaces such as grid-worlds, video games (Atari) and continuous control (MuJoCo). We also conduct a human study on a simple navigation task to observe how their understanding of the task compares with data attributed for a trained RL policy. Keywords -- Explainable AI, Verifiability of AI Decisions, Explainable RL.Comment: Published at International Conference on Learning Representations (ICLR), 202

Behavior Optimized Image Generation

The last few years have witnessed great success on image generation, which has crossed the acceptance thresholds of aesthetics, making it directly applicable to personal and commercial applications. However, images, especially in marketing and advertising applications, are often created as a means to an end as opposed to just aesthetic concerns. The goal can be increasing sales, getting more clicks, likes, or image sales (in the case of stock businesses). Therefore, the generated images need to perform well on these key performance indicators (KPIs), in addition to being aesthetically good. In this paper, we make the first endeavor to answer the question of "How can one infuse the knowledge of the end-goal within the image generation process itself to create not just better-looking images but also "better-performing'' images?''. We propose BoigLLM, an LLM that understands both image content and user behavior. BoigLLM knows how an image should look to get a certain required KPI. We show that BoigLLM outperforms 13x larger models such as GPT-3.5 and GPT-4 in this task, demonstrating that while these state-of-the-art models can understand images, they lack information on how these images perform in the real world. To generate actual pixels of behavior-conditioned images, we train a diffusion-based model (BoigSD) to align with a proposed BoigLLM-defined reward. We show the performance of the overall pipeline on two datasets covering two different behaviors: a stock dataset with the number of forward actions as the KPI and a dataset containing tweets with the total likes as the KPI, denoted as BoigBench. To advance research in the direction of utility-driven image generation and understanding, we release BoigBench, a benchmark dataset containing 168 million enterprise tweets with their media, brand account names, time of post, and total likes

Cloning and expression of S-Adenosyl Methionine Synthetase gene in recombinant E. coli strain for large scale production of SAMe

S-Adenosyl Methionine (SAMe) Synthetase is an enzyme which catalyses the synthesis of S-Adenosyl Methionine using methionine and ATP. It is also known as AdoMet which is well known methyl donor, which modifies DNA, RNA, histones and other proteins, dictating replicational, transcriptional and translational fidelity, mismatch repair, chromatin modeling, epigenetic modifications and imprinting. The objective of the present work is to clone the SAMe Synthetase gene in recombinant E. coli strain in order to express, characterize and purify it for further synthesis of SAMe in a large scale fermentation. Expression was induced by 1 mM IPTG and expressed protein was characterized by SDS-PAGE. The recombinant E. coli cells were used for the production of SAMe through batch and fed batch fermentation operations. The produced SAMe was purified through paper chromatography in order to use it in our future studies

An insight into the putative role of victuals like honey and its polyphenols in breast cancer

Diet plays a crucial role in cancer advancement as well as prevention. Breast cancer is the second leading cause of cancer death among women. Recent research links breast cancer with diet and some evidence for the preventive effect of diet against breast cancer was also documented. The growth of cancer cells is influenced by natural sweetener honey and its multitude of phenolic phytochemical components. Honey has been used medicinally by ancient Greeks and Egyptians and also traditionally exploited in Ayurveda and Chinese medicine. In this paper, the anti-cancer properties of honey and its phytochemical's action against breast cancer have been summarized. They result in apoptosis by enhancing reactive oxygen species level, activating mitochondrial pathway, initiation of pro-apoptotic and anti-apoptotic proteins, induction of p53 pathway that finally cause DNA fragmentation. However, there is a necessity for more proteomic and genetic-based experiments to understand its molecular mechanism to promote honey and its phenolic markers as plausible candidates for breast cancer treatment. Further, there is a need for quality check of honey available in the market, which warrants significant investigation by researchers in the food industry to ensure their attributes