Husson University

This research shows that droughts are robustly associated with city-level unrest in Europe over the years 900 to 1800 ce . This relationship is non-linear, with disproportionately greater increases in the probability of a conflict among droughts in the upper tail of the severity distribution. Elected city governments are relatively immune to drought-induced conflict, while those based on representation by burghers or guilds are not. These results suggest that local governments are key to maintaining social stability during economic shocks, and are most successful when they have a greater degree of democratic legitimacy.

This paper explores the dimensions of Artificial Intelligence Law and Breach of Fiduciary duty when using AI research models. The paper also explores Artificial Intelligence in the international realm providing a basis for educating international firms. Reading this paper is worth your time because: (1) you will learn about copyrights and artificial intelligence; (2) you will also learn about artificial intelligence and the fascinating developments in money and banking; (3) you will read about the best practices in the disciplines, and international firm concerns and applications of U.S. AI dispositions. This paper will give you parameters so that one may navigate the legal landscape to help you plan and avoid the courts. As we all know, inflation is prevalent, and the best use of resources is essential to stay in business avoiding closure. Those firms domestic and global, who are not aware of the dangers suffer the perils that follow poor resource management practices. Our corporate psychologists and organizational managers heartily agree.

The authors of this commentary describe efforts by the library at Husson University in Bangor, Maine, to provide all Husson students with an opportunity to build foundational understandings of generative AI.

Abstract College student mental health has been a critical concern for professional counselors. Anxiety and depressive disorders have become increasingly prevalent over the past decade. Utilizing machine learning, a subset of artificial intelligence (AI), we developed predictive models (i.e., eXtreme Gradient Boosting [XGBoost], Random Forest, Decision Tree, and Logistic Regression) to identify US college students at heightened risk of diagnosable anxiety and depressive disorders. The dataset included 61,619 students from 133 US higher education institutions and was partitioned into a 90:10 ratio for training and testing the models. We employed hyperparameter tuning and cross‐validation to optimize model performance and examined multiple measures of predictive performance (e.g., area under the receiver operating characteristic curve [AUC], accuracy, sensitivity). Results revealed strong discriminative power in our machine learning predictive models with AUC of 0.74 and 0.77, indicating current financial situation, sense of belonging on campus, disability status, and age as the top predictors of anxiety and depressive disorders. This study provides a practical tool for professional counselors to proactively identify students for anxiety and depressive disorders before these conditions escalate. Application of machine learning in counseling research provides data‐driven insights that help enhance the understanding of mental health determinants, guide prevention and intervention strategies, and promote the well‐being of diverse student populations through counseling.

The thesis of this editorial is twofold. First, counselors should work with Artificial Intelligence (AI), and AI should work with counseling. Second, counselors should increase their involvement with, essentially, all things AI. Counseling should forge a path forward with artificial intelligence. This editorial is a bit premature for pragmatists. Where is artificial intelligence (AI) in mental health? The answer is seemingly a paradox, everywhere yet nowhere. Everywhere, through AI subfields like machine learning, natural language processing applications, chatbots, and the myriad ways that large language models find correlations in data hidden from the human eye. Also, AI is nowhere to be found in the human-to-human act of active listening and showing compassion. To begin, let's define AI. I prefer the straightforward definition of AI being the ability of non-human (read: synthetic, computer) entities to solve problems (Fulmer, 2019) or, perhaps we could say, complex problems (Tegmark, 2018) . Without getting into never-ending discussions about what intelligence is, this definition allows us to address AI more practically. Therefore, something (non-human) is artificially intelligent to the extent to which it can solve problems. Consider some of the many problems in mental healththe question of correct taxonomies, proper diagnosis, the best treatment plan, access to care, and you have an idea about how AI can be applied to our field.