Capitol Technology University

Background: The deployment of large language models (LLMs) in mental health therapy presents a compelling yet deeply fraught opportunity to address widespread disparities in access to psychological care. Recent empirical evidence reveals that these AI systems exhibit substantial shortcomings when confronted with complex clinical contexts. Methods: This paper synthesizes key findings from a critical analysis of LLMs operating in therapeutic roles and argues for the urgent establishment of comprehensive risk management frameworks, policy interventions, and ethical protocols governing their use. Results: LLMs tested in simulated therapeutic settings frequently exhibited stigmatizing attitudes toward mental health conditions and responded inappropriately to acute clinical symptoms such as suicidal ideation, psychosis, and delusions. Real-world evaluations reinforce these concerns. Some studies found that therapy and companion bots endorsed unsafe or harmful suggestions in adolescent crisis vignettes, while others reported inadequate chatbot responses to self-harm and sexual assault queries, prompting concern from clinicians, disappointment from patients, and calls for stronger oversight from policymakers. These failures contravene fundamental principles of safe clinical practice, including non-maleficence, therapeutic alliance, and evidence-based care. Moreover, LLMs lack the emotional intelligence, contextual grounding, and ethical accountability that underpin the professional responsibilities of human therapists. Their propensity for sycophantic or non-directive responses, driven by alignment objectives rather than clinical efficacy, further undermines their therapeutic utility. Conclusions: This analysis highlights barriers to the replacement of human therapists with autonomous AI systems. It also calls attention to the regulatory vacuum surrounding LLM-based wellness and therapy applications, many of which are widely accessible and unvetted. Recommendations include professional standards, transparency in training and deployment, robust privacy protections, and clinician oversight. The findings underscore the need to redefine AI as supportive, not substitutive.

While it is now clear that cyber threats are becoming more advanced as well as more frequent, traditional approaches to information security appear inadequate. In this chapter, more developed defense measures are discussed which can be considered as enhanced methods to counter threats inherent in contemporary cybersecurity. We discuss techniques like machine learning (ML)-natural language processing (NLP)-based anomaly detection, zero trust security model, and behavior-based threat intelligence, where we focus on their capacities to detect and mitigate modern sophisticated threats. Furthermore, the chapter looks at artificial intelligence (AI)-driven planning and implementation of automation and adaptive security that provide organizations with the ability to increase preparedness and response. As such, these more sophisticated strategies are effective because they overcome fixed or passive defenses and incorporate multiple defensive mechanisms compatible with modern constantly evolving threats.

The rapid evolution of artificial intelligence (AI) and autonomous systems is transforming the landscape of digital forensics and cybercrime investigation. This chapter explores the emerging paradigm of autonomous forensic agents and AI-augmented investigators, emphasizing their potential to revolutionize evidence collection, data analysis, and decision-making processes. It delves into the convergence of machine learning, natural language processing, robotics, and cognitive computing to create intelligent agents capable of independently navigating digital environments, identifying anomalies, and assisting human investigators with high precision and efficiency. The chapter also addresses the ethical, legal, and technical challenges that accompany the deployment of such systems, including concerns about bias, accountability, transparency, and interpretability. Drawing from real-world applications, research trends, and interdisciplinary innovations, this forward-looking analysis envisions a future where human expertise is seamlessly integrated with intelligent automation

As Massachusetts focuses on expanding world language programs and ad- dresses teacher shortages, the need for comprehensive, evidence-based information about effective recruitment practices has become increasingly urgent. This study offers timely insights as educational leaders national-wide seek data-driven strategies to attract and sustain diverse, high-quality language educators in the state's increasingly multilingual classrooms. This descriptive qualitative study, utilizing survey data from 64 educators, explored existing recruitment strategies and their effectiveness. Findings indicated reliance on online job postings, particularly SchoolSpring, alongside networking and district websites, yet highlighted challenges in successful hiring with extended recruitment timelines. While demand for Spanish teachers has grown over the past five years, districts often lack targeted incentives, and pay concerns are prevalent. This study highlights the need for innovative recruitment strategies and greater support for Spanish language educators to achieve state educational goals.

Artificial intelligence (AI) trends vary significantly across global regions, shaping the trajectory of innovation, regulation, and societal impact. This variation influences how different regions approach AI development, balancing technological progress with ethical and regulatory considerations. This study conducts a comparative analysis of AI trends in the United States (US), the European Union (EU), and Asia, focusing on three key dimensions: generative AI, ethical oversight, and industrial applications. The US prioritizes market-driven innovation with minimal regulatory constraints, the EU enforces a precautionary risk-based framework emphasizing ethical safeguards, and Asia employs state-guided AI strategies that balance rapid deployment with regulatory oversight. Although these approaches reflect different economic models and policy priorities, their divergence poses challenges to international collaboration, regulatory harmonization, and the development of global AI standards. To address these challenges, this paper synthesizes regional strengths to propose an adaptive AI governance framework that integrates risk-tiered oversight, innovation accelerators, and strategic alignment mechanisms. By bridging governance gaps, this study offers actionable insights for fostering responsible AI development while ensuring a balance between technological progress, ethical imperatives, and regulatory coherence.

This special edition presents the results of the exploration of properties and sphere of applications of cutting-edge engineering materials, providing a comprehensive understanding of their role in the development of various engineering fields. The compilation will serve as a valuable resource for researchers and engineers seeking to stay at the forefront of developments in applied material science, and we hope that it will inspire their further exploration and innovation.

Abstract The rise in software development has intensified reliance on complex applications across sectors. However, this growth is paralleled by increased cybersecurity threats, revealing critical vulnerabilities within the Software Development Life Cycle (SDLC). Agile and DevOps methodologies, while offering speed and adaptability, often overlook vital security concerns, thus heightening exposure to cyber risks. This study addresses a novel research gap by examining how discrepancies in security integration across SDLC methodologies can lead to significant gaps in cybersecurity posture. Using a combination of case study scenarios, literature-informed analysis, and focus groups with subject matter experts, this research presents a comprehensive applied approach to cybersecurity in software development. Notably, the study engages participants with advanced expertise and real-world experience, whose insights enhance understanding of secure SDLC integration’s theoretical and practical aspects. This multi-method approach provides emerging cybersecurity professionals with actionable hands-on strategies to identify and mitigate vulnerabilities, aligning with current industry standards. The study’s findings underscore the urgency of standardized security practices within the SDLC, contributing to developing industry-wide best practices prioritizing security alongside speed and flexibility. By bridging theoretical frameworks with applied research, this study offers significant advancements for cybersecurity education and industry practice, preparing future professionals to address evolving cybersecurity challenges effectively.

This article explores the feasibility and potential advantages of employing magnetocaloric refrigeration systems in space exploration missions over traditional ammonia-based or vapor compression-based methods. Unique challenges posed by the space environment include extreme temperatures and the current toxic refrigerants. Magnetocaloric materials can address these challenges. The magnetocaloric refrigeration process is a known viable process and is being developed for earth-bound applications. Removing refrigerant from the heating and cooling process is one of the only sustainable pathways to reducing toxic, ozone-depleting, greenhouse-effecting, and energy-intense processes. Removing refrigerant-based heating and cooling systems in space will pave the way for earth-bound advancement as well. Additionally, the study will investigate the benefits of reduced reliance on traditional refrigeration methods for cooling critical spacecraft components and habitats, thereby, paving the way for safer, more reliable, and efficient space missions.

The digital landscape has witnessed a significant transformation since the introduction of cookies in the mid-1990s, evolving from simple user tracking mechanisms to complex tools integral to online user experiences and targeted advertising. This evolution, however, has not come without consequences; the proliferation of cookies has raised substantial concerns regarding user privacy and data security, prompting the development of regulatory frameworks such as the General Data Protection Regulation (GDPR)1 and the ePrivacy Directive.2 This paper undertakes a critical analysis of the intricate intersection between cookies, the ePrivacy Directive and the GDPR, with a particular focus on the IAB Belgium ruling.3 This landmark case has catalysed significant changes in consent practices, reshaping the digital advertising ecosystem and compelling businesses to reassess their data protection strategies. Notably, the ruling reinforces the primacy of consent under GDPR for cookie deployment, particularly in the context of personalised advertising. The decision also brings into stark relief the unresolved tension between consent-based models and the use of legitimate interest as an alternative legal basis for data processing. While the IAB Belgium ruling firmly aligns with the GDPR’s stringent consent requirements, the European Court of Justice’s (ECJ) subsequent rulings on legitimate interest introduce a potential divergence. For example, in the Koninklijke Nederlandse Lawn Tennisbond (KNLTB) case,4 the court recognised commercial legitimate interest as a lawful basis for processing data, yet this recognition did not extend to cookies, which are central to behavioural advertising and commercial profiling. The recent European Data Protection Board (EDPB) guidelines5 further complicate this regulatory landscape, as they emphasise the need for legitimate interest assessments but offer limited insight into how this legal basis should apply to cookies. This confluence of judicial and regulatory decisions underscores the ongoing challenges in harmonising legitimate interest with cookie-related data processing, calling for a more cohesive regulatory framework. As organisations navigate this complex regulatory environment, the insights provided in this paper aim to serve as a valuable resource for understanding the evolving dynamics of cookie compliance and the broader implications for data protection in the digital age. The paper ultimately seeks to inform stakeholders of the pressing need for accountability and user-centric approaches in the realm of digital privacy.