2025 Publications

  Betul Ozgen and Munevver Coskuner                                                                                                                                              Abstract: Modern neuroscience has found that the human brain is dynamic and prone to...

Mechatronics has revolutionized mechanical engineering by fusing computer science, electronics, and mechanics, presently with artificial intelligence as another powerful driving force. The key purpose of this paper is to explore how AI is redefining mechatronic systems to be more intelligent, flexible, and efficient. From smart factories and driverless cars to surgical robots and advanced prosthetic limbs, AI-driven mechatronics has introduced increased precision, decision-making, and reliability. Based on existing studies and applications, this paper demonstrates how AI is being utilized in sensor fusion, adaptive control, and predictive
maintenance, propelling mechatronics to become a key element and foundational technology for Industry 5.0’s future

The increasing global demand for mental health services has highlighted many shortcomings in access, affordability, and timeliness of care. In response, new applications powered by artificial intelligence (AI) in the form of chatbots have been developed to provide increased, scalable access to emotional support, cognitive behavioral techniques, and self-help resources. In this paper, we review the potential for the use of AI chatbots in mental health support and interventions through a focused analysis of applications, clinical evaluations, and user impressions. Moreover, key examples of applications, for example, Woebot, Wysa, and Youper, yielded some promising results for individuals who experience symptoms of anxiety and/or depression, especially individuals seeking low-barrier, less stigmatizing access to support. Several studies also found benefits in mood charting, facilitating emotional expression, and self-reflection. However, operational usability of AI applications often depends on design efficacy, adherence to evidence-informed therapeutic models, and active user participation. Indeed, while chatbots are cost-effective and scalable, there are some limitations. Examples of limitations include limited efficacy for some emotional crises that more complex, personalized interventions may deem necessary, insufficient personalization, and lack of true empathy. Additionally, clinical implications cannot progress without further addressing ethical concerns
regarding data privacy, informed consent from users, and algorithmic bias in AI responses. While AI chatbots are not substitutes for licensed mental health professionals, they represent a growing complement in the continuum of care. This paper concludes by emphasizing the need for hybrid human-AI models and stronger regulatory oversight to ensure responsible, safe, and effective deployment.

Mental disorders are significant sources of global disease but are diagnosed inadequately using techniques often grounded on subjective judgment and lacking biological validation. Prevention tools such as cognitive-behavioral therapy, school programs, and mobile health apps are promising entry points for early intervention but are limited by constraints of scalability, long- term effectiveness, and access, particularly in low- and middle-income countries. Recently developed breakthroughs using artificial intelligence (AI) and physiological monitoring offer a promising complement. Electroencephalography (EEG) and Galvanic Skin Response (GSR), when supplemented with AI, can discern complex physiology patterns associated with stress, anxiety, and depression. While EEG is afflicted by issues of noise, lack of spatial resolution, and variability when used individually, multimodal combinations of EEG and other biosignals and GSR have shown rich promise of diagnosis. Reports detail machine learning models founded on these signals that achieve 79–95% accuracy rates for distinguishing between and predicting affective states and clinical populations, and predictive models permit pre-warning of risks of mental health issues before their onset. These findings describe the possibility of AI-driven multimodal physiological monitoring overcoming the problems of conventional diagnosis with scalable, objective tools with worldwide promise.

New advancements in medical technology are making it possible to treat conditions that were previously difficult or even impossible to manage. Among the most promising of these technologies are nano-drugs specifically engineered to enter the body and target disease at the cellular level. What made nano-drugs so promising was that they often come with minimal side effects. However, because each individual has unique genetic and biological characteristics, a one-size-fits-all approach to nano-drugs is no longer ideal. This calls for a way to tailor these technologies to each and every person who relies on them. Personalized medicine has become
more and more popular over the years, ultimately transforming healthcare. By creating treatment plans using individual patients’ genetic, biological, and lifestyle data, the healthcare system can drastically improve treatment efficiency. This paper explores the potential benefits that come with integrating personalized medicine with nano-drug technologies. Additionally, our focus is not just on the healthcare benefits, as we dive into how this works from a business standpoint. Using
research from recent literature in biomedical engineering and healthcare marketing, we examine just how nano-drugs work to treat patients while also discussing the potential economic and ethical implications. This paper also focuses on the challenges of implementing this sort of system into healthcare, such as cost, accessibility, and data/privacy concerns. Overall, this paper dives into the possibilities of personalized nano-drug treatment for both patients and the healthcare system, while tracking the risks that come along with it.

Social-emotional learning (SEL) has become a predominant tool throughout early educational
contexts to inform and prepare students to convey their own emotions, as well as interpret
others. This developed understanding is difficult to attain generally, but even more so for
students with intellectual and learning disabilities. Peers with ADHD and ASD have lower awareness and perceptiveness of emotions, especially when applying them within social environments, which can provide a barrier to lasting relationships and effective
communication. Throughout this paper, one will thoroughly discern the hindrances that
learning disabilities present in social forums and emotional discrepancies, as well as the great
promise that SEL provides in bridging the gap in not only communication but also
opportunity. This study has found that the usage of SEL within classrooms provides an
essential application for those with learning disabilities to orient themselves in the
social-cultural foundation. SEL allows an ease of communication that is often not afforded to
ADHD and ASD students, especially in presenting their emotions. This has resulted in
heightened participation in social activities and more comprehensive reciprocation of
common social practices. With “soft skills” being in increasing demand on the job market,
and academic spaces observing the necessity of these skills for healthy professional dialogue,
SEL proves a significant prospect for those with learning disabilities for increasing social
awareness and vocational development. With the growing prevalence of diagnoses every
year, establishing the presence of youth with ADHD and ASD further, SEL programs are
vital to the success of students with learning disabilities. This paints a hopeful abstract for the
future of these students. In particular, paired with the potential and efficiency of AI, SEL
programs can combat the everyday challenges that permanently impair these students’ ability
to prosper.

Artificial intelligence (AI) and machine learning (ML) are transforming the design, operation,
and sustainability of space systems. From autonomous navigation in rovers and spacecraft to predictive maintenance, structural optimization, and life-support management, AI enhances
performance and resilience in extreme environments. Integrating AI across engineering
disciplines—mechanical, electrical, computer, chemical, and environmental—enables real-time
decision-making, resource efficiency, and hazard mitigation, supporting long-duration missions,
planetary exploration, and in-situ resource utilization. This paper reviews current applications of AI in space robotics, power management, signal processing, propulsion, and habitat sustainability, highlighting the interdisciplinary nature of AI-driven innovation and outlining challenges and future directions for fully autonomous space operations.

Swarm robotics is an emerging field within robotic systems that focuses on the coordination
of multiple robots to perform collective tasks. This review paper explores the concept of
physicomimetics, a physics-inspired method used in swarm robotics to control groups of
robots through virtual forces. We begin by examining the foundational work of Spears et al., which introduced the concept of physicomimetics, and then explore key developments and improvements by other researchers over time, such as the creation of global lattices, the usage of physicomimetics in non-omnidirectionally free agents, and the generalization of forming polygonal shapes with physicomimetics. Additionally, the original limitations of physicomimetics are presented alongside various solutions proposed across different studies and applications. The fields of applications and how physicomimetics is implemented in each context are outlined. Applications in various types of autonomous vehicles—including ground, aerial, and marine robots—are also discussed. Lastly, we identified current limitations and open research questions in the continued development and practical
deployment of physicomimetics within swarm robotics.

This brief investigation examines social media algorithms to explore their impact on teenagers.
Teenagers use social media for a variety of reasons, including education, communication with others, and leisure. Over time, social media has developed into a vast network of individuals
from various backgrounds. While social media platforms play a significant role in informing and
engaging teenagers, their algorithmic systems often do more harm than good by creating
ideological divides and encouraging addictive behaviors. Using active research from case studies and articles, this paper analyzes the effects that social media algorithms have on teenagers and briefly explains each effect, its consequences, and potential solutions. It outlines how a typical algorithm on social media works and how it is controlled. Additionally, it highlights the psychological tactics used by algorithms that lead to addiction and negatively affect teenagers’ mental health. Algorithms can also raise unrealistic standards in the minds of teenagers, further impacting their mental health. Some platforms even employ AI-driven algorithms, where artificial intelligence (AI) scans and controls nearly everything users see and interact with. This paper also communicates the double-edged nature of algorithms, how they may seem effective on the surface, but often contain hidden risks that we often fail to notice. Since teenagers frequently use social media, this analysis reveals the hidden truths and dangers that these algorithms pose to them today.