Strategic Management and Stakeholder Engagement: A Case for Environmental Sustainability

There has been an increase in industrial activity in the last few decades, but we must not lose sight of the importance of protecting the natural world. Industry stakeholders are concerned because we have yet to determine the long-term effects of this significant issue. Environmental sustainability is crucial given the world’s increasing population, industrialization, and mounting evidence of climate change, environmental degradation, and global warming. Organizations have made significant efforts to reduce their environmental impact, and this study will explore the obstacles they must overcome to become environmentally sustainable. The study’s findings may have important implications for businesses, clients, stakeholders, and the environment. The application of stakeholder theory has improved our understanding of company behavior, particularly in a dynamic and rapidly changing business environment, where environmental concerns are gaining traction. The purpose of expanding stakeholder theory could be to create fair value for all stakeholders while benefiting society and the environment

Psychology: Examining the relationship between tuition payment and academic performance through GPA.

Abstract The purpose of this study is to evaluate the correlation between whether paying for college tuition can affect individual\u27s grade point average (GPA). In previous studies, research has shown that individuals tend to perform better academically when receiving financial help vs. Individuals funded by their parents (Faulk et al., 2012). Following previous studies, most of the studies found do not focus specifically on the correlation between financial dependency when paying for college and GPA. Our study strictly focuses on the relationship between those students who do not pay for college and those who do pay for college and how those effects someone\u27s GPA. We hypothesize that participants who pay for any part of their tuition themselves will have a higher GPA and spend more time studying than those who have third parties paying for their tuition, such as scholarships, family, and grants. The individuals involved in the sample are introductory psychology students who attend Belmont University and are participating for a class credit. Participants were asked to answer specific questions regarding financial status, GPA, time spent studying, and who is currently paying for their college tuition. Results will be presented at SURS

Cytological dilemma of a rare subcutaneous fungal infection: Phaeoacremonium griseorubrum

Human disease caused by Phaeoacremonium species is rare. It was first reported in 1974 to cause subcutaneous tissue infection in the renal transplant recipient. Since then, only a few cases have been reported in the literature; however, underreporting is common in such cases due to incomplete or incorrect identification. Furthermore, some cases of subcutaneous infection in otherwise healthy patients may be asymptomatic and definitive identification of the etiological agent is not pursued. Identification of fungi at the species level is challenging by conventional methods; hence, final identification of fungi is based on culture. Phaeoacremonium has a very wide spectrum of presentation ranging from subcutaneous infections to fungemia and disseminated disease. We report a case of subcutaneous tissue swelling in an immunocompetent 55-year-old male, in whom etiology was traced to Phaeoacremonium griseorubrum. To the best of our knowledge, this report of P. griseorubrum causing human infection is second of its kind and the first from India. The patient did not respond well to oral itraconazole therapy and was advised surgical debridement with amphotericin B therapy

The Trans-Pacific Partnership agreement and public health: Why we should be concerned

In October 2012, Canada became a negotiating member of the Trans-Pacific Partnership (TPP) agreement along with 11 other Pacific Rim countries. Widely touted as "a model for 21st-century trade agreements,"1 it extends well beyond traditional trade issues into domestic policy, creating a number of concerns about its implications for public health. These concerns include potential increases in pharmaceutical costs, the undermining of Canadian patent law, and strengthened investor rights over public health regulations to limit the consumption of products harmful to health. The Comprehensive Economic and Trade Agreement (CETA) currently being negotiated between Canada and the European Union has already been forecast to increase Canadian drug costs by between $850 million and$1.6 billion annually by extending patent protection; leaked text of the TPP suggests that its provisions would increase these costs further.2 (See also Box 1.) The TPP's draft chapter on intellectual property rights goes beyond CETA, allowing the patenting of new forms and uses of old drugs regardless of efficacy, and introducing the patenting of diagnostic, therapeutic, and surgical methods. Although the rationale for extending patents is that it will lead to increased research and development spending in Canada, brand-name pharmaceutical companies have failed to comply with similar commitments in the pas

A Hybrid Convolutional Network and Long Short-Term Memory (HBCNLS) model for Sentiment Analysis on Movie Reviews

This paper proposes a hybrid model (HBCNLS) for sentiment analysis that combines the strengths of multiple machine learning approaches. The model consists of a convolutional neural network (CNN) for feature extraction, a long short-term memory (LSTM) network for capturing sequential dependencies, and a fully connected layer for classification on movie review dataset. We evaluate the performance of the HBCNLS on the IMDb movie review dataset and compare it to other state-of-the-art models, including BERT. Our results show that the hybrid model outperforms the other models in terms of accuracy, precision, and recall, demonstrating the effectiveness of the hybrid approach. The research work also compares the performance of BERT, a pre-trained transformer model, with long short-term memory (LSTM) networks and convolutional neural networks (CNNs) for the task of sentiment analysis on a movie review dataset.