Does Institutional Context Affect CSR Disclosure? A Study on Eurostoxx 50

We propose to investigate the relationship between corporate social responsibility disclosure and institutional/environmental factors among a sample of European listed companies. We find that, by using several traditional explicative variables, institutional factors affect the level of CSR disclosure, in a context where the EU Commission has been paying growing attention to social and environmental accountability of listed companies (see the EU Dir. 95/2014). Our findings are further supported by multivariate regression, where ESG score (measure of CSR disclosure) is regressed on nine variables which represent the expression of institutional factors. By looking at the institutional determinants of CSR disclosure, we are seeking to pose a challenge for future research agenda, in order to understand whether CSR does actually reflect an effective commitment of firms to accounting practices and rules, as a form of social behavior, or whether it is just a tool to manage stakeholders’ perception and to comply with regulation

Understanding Changes in Mortality with Implementation of Safe Injection Sites

The purpose of this study is to analyze mortality due to overdose in areas that have implemented supervised injection sites and understand what factors these facilities affect that can lead to a change in outcomes

Barriers to Alternative Narcotic Education and Treatment in Young Adults in New Jersey

The purpose of this study is to determine alternative methods and programs that result in better outcomes for young adults dealing with opioid addiction in New Jersey while identifying potential barriers that prevent the implementation of these programs

Character Recognition System using Radial Features

Extraction of text from documented images finds application in maximum entries which are document related in offices. The most of the popular applications which we find in public or college libraries where the entries of number of books are done by manually typing the title of book along with other credentials like name of the author and other attributes. The complete process can be made effortless with the application of a suitable algorithm or application software which can be extract the documented part from the cover of book and other parts of the book thereby reducing the manual job like typing of user. Which reduces the overall job to only arranging the book title etc.by formatting the material

Multi-Level Compositional Reasoning for Interactive Instruction Following

Robotic agents performing domestic chores by natural language directives are required to master the complex job of navigating environment and interacting with objects in the environments. The tasks given to the agents are often composite thus are challenging as completing them require to reason about multiple subtasks, e.g., bring a cup of coffee. To address the challenge, we propose to divide and conquer it by breaking the task into multiple subgoals and attend to them individually for better navigation and interaction. We call it Multi-level Compositional Reasoning Agent (MCR-Agent). Specifically, we learn a three-level action policy. At the highest level, we infer a sequence of human-interpretable subgoals to be executed based on language instructions by a high-level policy composition controller. At the middle level, we discriminatively control the agent's navigation by a master policy by alternating between a navigation policy and various independent interaction policies. Finally, at the lowest level, we infer manipulation actions with the corresponding object masks using the appropriate interaction policy. Our approach not only generates human interpretable subgoals but also achieves 2.03% absolute gain to comparable state of the arts in the efficiency metric (PLWSR in unseen set) without using rule-based planning or a semantic spatial memory.Comment: AAAI 2023 (Oral) (Project page: https://bhkim94.github.io/projects/MCR-Agent

To Determine and Compare the Antibacterial Efficacy of Hiora Mouthwash with 0.2% Chlorhexidine Gluconate in Orthodontic Patients

Aim and objectives: To determine and compare the antibacterial efficacy of Hiora mouthwash with 0.2% chlorhexidine gluconate in orthodontic patients after bonding at different time intervals for a period of 3 months of each patient. Materials and Methods: A total of 60 orthodontic patients were selected for the study. Auxillaries used were test tubes, periodontal probe (Williams probe), Wilkins probe, mouth mirror, cheek retractor and icebox. Saliva sample was collected at base line, 1st day of bonding (T0), 30th day (T1) and 90th day (T2) of 3 groups which were Group A (control group),Group B (Chlorhexidine group) and Group C (Hiora  Group) for the microbial count of mouth and gingival assessment was also done at the same time points. Microbial counts were done by conventional culture method. Paired t-test, t-test, chi square test followed by post hoc Tukey test were done to compare the groups statistically. Results: The study revealed that the microbial count of mouth increases after bonding, 0.2% Chlorhexidine gluconate mouthwash and Hiora mouthwash reduced the microbial count and gingival inflammation significantly at T2. Conclusion: 0.2% Chlorhexidine gluconate mouthwash is more effective in terms of reduction of microbial count and gingival inflammation

On the Brittle Foundations of ReAct Prompting for Agentic Large Language Models

The reasoning abilities of Large Language Models (LLMs) remain a topic of debate. Some methods such as ReAct-based prompting, have gained popularity for claiming to enhance sequential decision-making abilities of agentic LLMs. However, it is unclear what is the source of improvement in LLM reasoning with ReAct based prompting. In this paper we examine these claims of ReAct based prompting in improving agentic LLMs for sequential decision-making. By introducing systematic variations to the input prompt we perform a sensitivity analysis along the claims of ReAct and find that the performance is minimally influenced by the "interleaving reasoning trace with action execution" or the content of the generated reasoning traces in ReAct, contrary to original claims and common usage. Instead, the performance of LLMs is driven by the similarity between input example tasks and queries, implicitly forcing the prompt designer to provide instance-specific examples which significantly increases the cognitive burden on the human. Our investigation shows that the perceived reasoning abilities of LLMs stem from the exemplar-query similarity and approximate retrieval rather than any inherent reasoning abilities