With the help of a PepsiCo grant, Keene State's Center for Research & Writing collaborated with Bricowlage to host "This Picture is Worth 100 Words" contest. The Center's tutors worked with the KSC Archives staff to choose 20 images representing life at Keene State over the years. These pictures were distributed around campus with the hope of inspiring student writers. Students were encouraged to write stories or poems of 100 words or fewer based on one of the images. A judging panel of administrators, faculty, staff, and students selected ten finalists, with the top five winners receiving a prize and the opportunity to have their pieces published at AEC and in the 2027 edition of Bricowlage. Rowan Adkins — "Morning remembers" Lee Gleason — "We rested" Bella Moran — "Now what?" Dyland Yardley — "Ovens hummed" Amie Lehane -- "Window (widō) noun" Austin Bartnicki — "Who can say"
This case study examines the interconnected public health challenges of homelessness, substance use disorder, and overdose in the Monadnock Region. Conducted in collaboration with the Center for Population Health at Cheshire Medical Center , the project applied the frameworks of Healthy People 2030 and Healthy Monadnock to design a three-pronged community health strategy. The approach focused on policy engagement, community-based harm reduction, and accessible education. Key objectives included increasing local business participation in community support initiatives by at least 5% through policy engagement, implementing harm reduction efforts to decrease improperly discarded needles and improve perceptions of safety and belonging, and providing educational programming on addiction, overdose prevention, and available support resources. This study highlights how collaborative, community-informed strategies can address complex social and health needs while strengthening local awareness, engagement, and access to support individuals experiencing substance use and homelessness.
Artificial intelligence (AI) is rapidly transforming work by automating and redesigning many routine cognitive tasks, including summarization, drafting, coding, scheduling, and basic analysis. These tasks are increasingly converted into low-cost software actions that benefit firms and reduce human workload. As most companies adopt AI across their services, this paper seeks to predict which occupations will be most affected by AI by 2036 using a task-based mathematical model grounded in linear algebra. Jobs are modeled as vectors of task time shares, while AI progress is captured through time-dependent capability and adoption functions. We introduce an AI Exposure Index and a Replacement Pressure Index to distinguish between task augmentation and job substitution. Sensitivity analyses examine outcomes under varying adoption speeds, capability growth rates, and substitution assumptions.
Art generated by Artificial Intelligence is causing real problems for actual, talented, human artists. While artists often spend years developing technical skills, personal style, and creative voice, AI systems produce images in seconds by drawing on large public datasets of existing artwork. This leads to fewer people hiring human artists. Beyond this economic impact, AI-generated art raises deeper questions about creativity, authorship, and artistic meaning. Real art comes from genuine human experiences and emotions. AI, however, is basically just a fancy copy machine. AI can create so much content so fast, it’s flooding the internet, making it harder for genuine artists to get noticed. In this research, we examine the ethical implications of AI-generated art through lenses of artistic labor, originality, and cultural value. By analyzing real-world cases, we argue that without clear regulation, transparency, and attribution standards, AI-generated art threatens to undermine the recognition and sustainability of human creativity.
Enzymes are specialized proteins that serve as catalysts for numerous chemical reactions necessary for survival. Enzymes contain active sites which are portions of the protein responsible for the overall chemical reactivity. Certain fields of chemistry focus on the synthesis of small molecules that resemble this active site and maintain its natural chemical reactivity. A group of chemicals referred to as vanadium haloperoxidases contain a vanadium metal center and are known to be effective at such enzyme mimicry. Our goal is to synthesize and study a vanadium haloperoxidase mimic and analyze its physical and chemical properties.
Cepheid variables are stars that undergo periodic pulsations caused by internal physical processes, producing predictable changes in brightness over time. Astronomers measure these variations using photometric observations; however, continuous data collection is often limited by seasonal visibility, weather, and telescope access. As a result, observational data frequently contain gaps and scatter that obscure underlying patterns. In this project, a physical and computational model simulating Cepheid behavior is developed, and statistical methods are applied to address incomplete and noisy data. These techniques are first tested on model-generated light curves (brightness versus time graphs) and then used to analyze observational data from the American Association of Variable Star Observers (AAVSO) database. From this analysis, key physical properties, including pulsation period and brightness range, are determined.
Arsenic is a known carcinogen and environmental pollutant commonly found in ground water throughout New England. The reliance on private wells and lenient, water-testing laws in New Hampshire has increased health concerns across the state, where more than 10% of the population is estimated to be chronically exposed to high levels of Arsenic. Previous studies on Arsenic's effect on mammalian cells have identified multiple biological effects, such as an increase in reactive oxidative species and activation of cancer-causing genes (p53). However, most of these studies have focused on high Arsenic doses that are not representative of chronic concentrations. In this study, we use environmentally relevant, chronic Arsenic concentrations to study the stress responses in mammalian cells. To quantify our results, we use two celluar assays or techniques to measure stress responses in our cells when chronically exposed to Arsenic during a 24-h period.
Indoor air quality (IAQ) is a critical component of occupant comfort, cognitive performance, and overall environmental health in educational settings. This field-based evaluation examines changes in key IAQ parameters—carbon dioxide (CO₂), oxygen, temperature, and relative humidity—at the beginning and end of regularly scheduled class sessions using a GrayWolf Indoor Air Quality Monitor. CO₂ concentrations are assessed as a surrogate indicator of ventilation effectiveness under normal occupancy conditions. The objective of this project is to evaluate whether classroom environmental conditions remain within commonly accepted comfort and ventilation benchmarks over the duration of instruction. Observed trends will be interpreted in relation to general dilution ventilation performance and building air exchange adequacy. Findings will contribute to a better understanding of how occupancy dynamics influence indoor environmental quality in academic spaces.
This project assesses mold presence and indoor air quality conditions in selected off-campus student rental houses. The objective is to apply industrial hygiene sampling methods to evaluate environmental conditions and interpret laboratory findings using established guidance values. Students will collect airborne mold samples using calibrated sampling pumps and spore trap cassettes while also measuring oxygen concentration, carbon dioxide, temperature, and relative humidity. Samples will be submitted to an accredited laboratory for analysis. Laboratory results and environmental data will be compared to published evaluation criteria to identify potential moisture-related concerns and ventilation limitations. Based on the findings, students will assess indoor environmental risks and develop recommendations to improve housing conditions. This project emphasizes applied field sampling, laboratory coordination, and professional data interpretation in a real-world residential setting.
As artificial intelligence (AI) becomes increasingly integrated into architectural practice, questions have been raised about whether AI systems could one day replace human architects. While AI technologies can assist with tasks such as drafting, modeling, and data analysis, they lack human judgment, intuition, and originality that are essential to architectural design. Creativity, emotional awareness, and the ability to understand human needs allow architects to create meaningful and functional spaces, qualities that current AI systems cannot fully replicate. In this project, we will examine the limitations of artificial intelligence in architecture by analyzing the aspects of design that remain fundamentally human. By analyzing real-world cases, we will answer several key questions: Why can’t current AI perform the same tasks as a human architect? In what ways can AI support architects during the creative and technical design processes? If AI continues to develop, how might the future of the architectural profession change?
Green chemistry is the design of chemical processes that reduce or eliminate hazardous substances, minimize waste, and use safer, renewable materials. Guided by these principles, our work in Dr. Anderson’s lab focuses on developing more environmentally friendly conditions for forming an amide, a common functional group in organic chemistry. Traditionally, this reaction uses diethyl ether, a highly flammable solvent derived from petroleum. To improve safety and sustainability, we are replacing ether with methyl tetrahydrofuran (MeTHF), a solvent made from agricultural waste that is less hazardous and renewable, while also exploring alternative reagents to further reduce environmental impact. By systematically testing greener conditions, we aim to maintain reaction efficiency while making the overall process safer and more sustainable.
Professionalism and civility are frequently cited as essential components of effective workplace culture; however, definitions and behavioral expectations vary across disciplines and industries. This faculty-mentored foundational literature review examines how professionalism and civility are defined in contemporary workplace research and identifies the behaviors associated with those definitions. Drawing from peer-reviewed literature in organizational psychology, management, healthcare, and related fields, this project maps definitional trends and categorizes behaviors described as professional, civil, unprofessional, or uncivil. The objective of this review is to clearly define these concepts and identify the specific behaviors that represent professionalism and civility in modern organizations. Establishing definitional clarity provides a necessary foundation for future research examining broader organizational and safety implications. Such clarity is essential for evaluating whether breakdowns in professionalism and incivility function as contributors to recognized psychosocial hazards in occupational settings.
Sacredness as a psychological construct has not received much critical attention in the field of psychology due to a tradition of associating it with Christian spirituality by default. Recent endeavors have moved towards a more secular definition with limited success. In this study, a scale which quantitatively provides evidence towards a secular model of sacredness including but not limited to religion and spirituality was produced. 189 participants were asked what is most sacred to them, they then completed the novel scale, along with already-validated tests. This was done to understand if individuals were deriving their sacred perceptions from solely spiritual sources, or whether a more holistic model of sacredness is more accurate. Findings strongly indicate that sacredness as a concept can be conceptualized as being composed of at least four factors, those being social, emotional, spiritual, and practical sacredness.
This study examined whether gender and empathy predict guilty verdicts for a sex-trafficked survivor in a mock trial. Using data from 196 U.S. adults, results showed that women reported greater empathy for the survivor, and individuals with lower empathy were more likely to render a guilty verdict. Men were 181% more likely than women to find the survivor guilty. Findings highlight the importance of empathy in legal judgments involving trafficked survivors.
Arsenic is a widespread environmental toxin that commonly contaminates groundwater in New England and poses significant global public health risks. As a known carcinogen, arsenic exposure has been associated with long-term health consequences that may persist across generations. While many studies have examined the short-term effects of acute arsenic toxicity, far fewer have evaluated multigenerational responses at environmentally relevant concentrations. We investigated changes in life-history traits in the ecotoxicological model organism, Daphnia, exposed to chronic arsenic treatments. Our results demonstrate that life-history responses vary significantly across arsenic concentrations in a non-linear manner. Growth-related parameters were more strongly affected at higher concentrations, whereas reproductive traits showed comparatively lower sensitivity. Ongoing work will examine the effects of arsenic exposure across ten generations and assess associated patterns of somatic arsenic accumulation. Understanding both short- and long-term responses will provide deeper insight into the mechanisms underlying toxicity of common environmental contaminants such as arsenic.
Rural residents in New Hampshire’s Monadnock region experience significant healthcare barriers, including geographic isolation and transportation challenges. This has led to higher chronic disease and mortality rates in the area. Guided by the Social Ecological Model, this intervention proposes a mobile health clinic to provide free screenings, immunizations and health education. The effectiveness is evaluated by pre- and post- surveys, service utilization metrics, and the amount of primary care referrals. The program aims to increase health knowledge and preventive behaviors while reducing distance-related barriers. Success is defined by improved service accessibility and successful patient integration into primary care networks. This community-centered approach addresses the root causes of rural health disparities by bringing services directly to underserved populations. The mobile clinic model offers a scalable strategy to improve health equity and long-term outcomes in geographically isolated regions.
Ring-closing metathesis (RCM) reactions are organic chemistry transformations that have won the Nobel prize and are now widely used in the industrial production of pharmaceuticals and polymers. However, these reactions are commonly performed in hazardous solvents such as dichloromethane. This work explores the development of greener RCM conditions that align with the 12 Principles of Green Chemistry, as outlined by the American Chemical Society, particularly less hazardous chemical syntheses and the use of safer solvents and auxiliaries. The model reaction investigated is the ring-closing metathesis of diethyldiallylmalonate to form a cyclopentene product, a versatile intermediate in pharmaceutical syntheses. Alternative green solvents have shown promising results in this synthesis, specifically reactions run in dimethyl carbonate. Future work will pivot towards finding catalytic alternatives to the ruthenium-based system, and expansion of the optimized conditions to assess reaction scope beyond the model substrate.
This project originated as a student-led architectural concept that evolved into a professional partnership with the Softwood Lumber Board, the world’s premier softwood organization. Tasked with showcasing the versatility of softwood species, our team designed the HoneyPod: a modular, collapsible space designed to define intimate environments within larger architectural spaces. We addressed three primary constraints: ensuring the structure fits within standard shipping containers, improving user ergonomics, and utilizing component-based assembly. The resulting "flat-pack" system allows for efficient transport without sacrificing aesthetic or structural integrity. As a scalable proof-of-concept, the HoneyPod supports the Softwood Lumber Board’s mission to expand the boundaries of timber construction. What began as a classroom assignment now travels the country, demonstrating the capabilities of softwood while showcasing the innovative design work of three Keene State College students.
This study examined how late-night eating influences stress, sleep, and cognitive performance in college-aged individuals. Participants consumed a final early (7–8 pm) or late (10 pm–12 am) meal and abstained from caffeine and alcohol. The next morning, they completed a stress-inducing task, mood scales, and physiological measures, such as blood pressure and skin conductance, while fasted. Preliminary data shows groups differ significantly in baseline systolic blood pressure, indicative of increased physiological stress, and a larger reduction of blood pressure across the task. Integrating physiological, cognitive, and self-reported data, this project highlights meal timing as a modifiable factor in stress resilience and performance, with potential implications for health and academic functioning.
STEM learning is most powerful when students see how science, technology, engineering, and mathematics (STEM) work together in the real world rather than as separate classroom units. In response, we created a multiweek integrated STEM unit for third-grade learners centered on sea turtles. The unit engages students in learning about six sea-turtle species while intentionally aligning activities with NGSS (Next Generation Science Standards) and Common Core ELA and Mathematics standards. The lessons themselves include a combination of writing, reading, sensory, and engineering-based activities. This comprehensive unit concludes with a multi-day, narrative, game-based summative, in which students will complete challenges and make choices using their knowledge to help their small groups of turtles survive throughout their entire life cycle. This presentation will showcase the process and final lesson plans created to explain to attendees.
Postpartum depression (PPD) is a critical health crisis stemming from inadequate access to resources. Our program targets New Hampshire women aged 16-25 through three strategic pillars: Education, Community, and Policy. We propose interactive educational websites and local Planned Parenthood classes to build literacy and support networks. Crucially, we advocate for extending maternity leave from 12 to 24 weeks, providing the necessary recovery time to reduce systemic stress. By implementing standardized pre- and post- programs screenings, we aim to achieve a 30% improvement in participants’ mental health status. This integrated approach ensures young mothers aren’t just surviving the “fourth trimester”. This proposed intervention was developed and shared with The Center for Population Health.
Combinatorial Threshold-Linear Networks (CTLNs) are a neural network model that is used to simulate the firing rates of neurons. This model is based on a system of differential equations that compute the firing rates of each neuron in the system. In particular, we are interested in a special family of CTLNs called core motifs. Identifying these core motifs is integral to extrapolating CTLN findings to larger networks like the brain, but checking if CTLNs are core is computationally complex and difficult to scale. In an effort to more easily identify core motifs, I formed two conjectures that rule out large numbers of CTLNs as not core using simpler computations. These two conjectures use determinant sign and out-degree uniformity respectively to drastically reduce the computations required to find core motifs, allowing for greater scalability and computability.
New Hampshire funds its K-12 education system primarily through local property taxes, with the state contributing only a small percentage of the total school budget. This makes New Hampshire unique among the New England states, which fund schools primarily through broad base taxes, and creates a wide disparity in school funding conditions. This case study aims to take a closer look at three districts; Grantham School District, Hampton School District, and Bethlehem School District. These three districts have the highest test scores in the state while having below average property values, family incomes, and disadvantaged student rates. This study attempts to identify the reasoning behind this, and potential gaps that districts can close to improve academic performance with limited financial resources. After the AEC study will be published by Dr. Carozza in the NH School Scoop, a weekly newsletter sent out on New Hampshire education news.
What if cybercrime could be stopped before it happens? Using data from the FBI Internet Crime Report this project adapts predictive policing, traditionally used for physical crimes, to analyze cybercrime data, uncovering patterns in who is targeted and how. Focusing on high-risk groups, especially adults aged 60 and older, reveals not only key vulnerabilities but also actionable prevention strategies. The goal is to help law enforcement strategies and public education to create a safer digital world. Early results show older adults face higher risks, but solutions exist. Let’s shift from reaction to prevention.
During scenarios of housing damage after a natural disaster, decision-making occurs under pressure to rebuild. The expediency associated with disruptive events and the corresponding time constraints for damage evaluation are compounded by a historical lack of allocated resources that can limit recovery and lead to undesired impacts (e.g., gentrification, forced migration). The objective of this study is to present a novel evaluation method for evaluating recovery progress using modern image analysis techniques. The application of low-cost drone imagery provides wider accessibility to equipment due to the lower cost, while enabling rapid data collection with dynamic analysis (e.g., multiple and iterative reviews). This study investigates the potential of tracking disaster debris build-up and visual repairs as recovery cues through a case study of the 2020 tornado in Tennessee. The validation of this approach supports the multi-attribute “blue roof” and debris build-up metric implementation for recovery evaluation to assist government decision-makers.
This research project examines how artificial intelligence (AI) is affecting the way students learn and understand information. With the growing use of AI tools in schools, many students now rely on AI to help complete assignments and study for classes. While these tools can support learning, there is concern that overuse may reduce critical thinking, change study habits, and limit long-term knowledge retention. The main goal of this project is to explore how often students rely on AI and whether this reliance helps or hurts their learning. This study will use student surveys and classroom examples to examine patterns of AI use among students who regularly interact with these tools. By focusing on the current generation of learners, this research aims to better understand the balance between AI as a helpful educational resource and AI as a replacement for independent thinking. The findings aim to help students and educators make informed decisions about how AI should be used in education.
In this study, we developed a new shoulder-specific musculoskeletal disorders (MSD) assessment tool designed to address a critical gap in traditional ergonomic evaluations. It systematically captures both personal and physical risk factors associated with shoulder-related MSDs. The tool development was completed in the Fall 2025 semester. This Spring 2026, it will be tested in actual workplace settings from February to March. We partnered with safety professionals from manufacturing, pharmaceutical, and construction companies around New England. Following training, the tool will be applied during routine job evaluations, and data will be collected across multiple tasks, job roles, and work environments. Quantitative analyses will be performed to examine inter-rater reliability, margin of error, and potential sources of bias.
Photodecomposition involves the breakdown of chemical compounds due to the absorption of light energy. The rate at which a compound decomposes may vary due to several different factors, including what is present in the chemical’s environment and the wavelength (i.e., color) of light used. We have measured the photodecomposition rate of the compound tetrachloroaurate when exposed to ultraviolet light in the presence of several common alcohols. The rate is found to be highly dependent upon both the identity and concentration of alcohol, revealing detailed information about the photodecomposition mechanism. A mathematical model of our proposed mechanism exhibits excellent agreement with experimental data.
Arsenic contamination of drinking water is a major global health concern, including in parts of New Hampshire. Originating from both natural and anthropogenic sources, arsenic exposure is associated with severe health outcomes, although its precise mechanisms of toxicity remain incompletely understood. Tissue-specific vulnerability further complicates risk assessment, as certain organs may accumulate arsenic at higher levels than others. Environmentally relevant concentrations often do not cause immediate toxicity; however, arsenic readily crosses the placenta and has been linked to developmental defects in newborns. Despite these risks, few studies have quantified tissue-specific toxicity or somatic arsenic accumulation during long-term exposure. In this study, we employ laser ablation–inductively coupled plasma mass spectrometry (LA-ICP-MS) to quantify whole-body and tissue-specific arsenic accumulation in the model organism Daphnia under chronic exposure. Preliminary results indicate minimal short-term changes in whole-body accumulation, but elevated arsenic concentrations in tissues such as the brood pouch.
As advances in artificial intelligence and digital technology continue to reshape higher education, assistive technologies have become increasingly important in supporting students with disabilities. While assistive technologies have the potential to create more inclusive learning environments, access to these tools is often uneven, and many students remain unaware of the resources available to them. In this research, we will examine how institutions implement assistive technologies, how widely they are used, and whether they truly empower students or unintentionally introduce new challenges, such as over-reliance on technology or privacy concerns. By analyzing existing public datasets, this study evaluates whether AI-based assistive tools based on disability are contributing to greater equity in higher education for students with disabilities, or whether additional institutional policies and ethical considerations are necessary to ensure that these technologies truly support student success and still protect the privacy of students and professors as well.
The palladium catalyzed Suzuki coupling is a widely utilized method for carbon-carbon bond formation in pharmaceuticals and other industries. My research aims to evaluate the effect of applying green chemistry principles to the suzuki coupling, specifically focusing on alternative solvent systems, less hazardous materials, and energy efficient reaction routes. Green chemistry focuses on maximizing efficiency of chemical reactions, while attempting to reduce or eliminate hazardous waste. My project will attempt to find the line between efficiency and hazardous waste reduction for the suzuki coupling. These results will be compared to those of traditional materials and reaction conditions by analyzing reaction yield, conversion percentage, and spectroscopic data. The results of this research project will be used to determine if green chemistry principles can be applied to the Suzuki coupling while maintaining efficiency and product yield.
The purpose of the current study is to investigate how societal expectations of femininity relate to women’s mental health across sexual orientations. While existing research has examined the mental health outcomes associated with conformity to feminine norms among women, much of this work has focused primarily on heterosexual populations. As a result, the unique experiences of lesbian, bisexual, queer, and pansexual women who are often navigating both gendered expectations and sexual orientation-based stigma remain underrepresented in research. To address this gap, the study recruited female participants from Keene State College to participate in a survey containing the Depression Anxiety Stress scales (DASS-21) and the Conformity to Feminine Norms Inventory (CNFI45). Regression analyses were conducted to identify any relationships between the variables.
Throughout the history of aviation and warfare, aircraft and weapons systems have been primarily designed and operated by humans, with large-scale mechanization emerging only in the twentieth century. Recently, artificial intelligence (AI) has begun to influence both civilian aviation and military operations, particularly through the development of unmanned aerial vehicles. This study examines the growing role of AI in aircraft manufacture, operation, and drone-based warfare, focusing on whether AI should function as a supportive tool or enable greater autonomy. Using historical analysis, modern case studies, such as the Russo-Ukrainian War, and current AI research, the project evaluates the feasibility, limitations, risks, and ethical implications of increased automation. Special attention is given to determining which tasks may be fully automated and which require continued human oversight, given the high risks associated with aviation and combat operations.
Adverse Childhood Experiences (ACEs) are a prevalent challenge among adolescents ages 10-19. ACEs are associated with negative short and long-term health, behavioral and educational outcomes, with greater exposure resulting in the increased risk of chronic diseases, mental health disorders, and reduced educational and occupational opportunities. This case study envisions a program which would aim to reduce such wider reaching negative impacts. ThriveED is a proposed educational initiative with the envisioned focus on improving awareness and understanding among adults in both academic and social life in communities across New Hampshire. It would emphasize trauma-informed education for school staff, improved collaboration between families and teachers, and engagement of community partners through evidence-based programming to address the issue of ACEs. By providing the resources necessary for adults to recognize and respond to ACEs, this program seeks to foster resilience, improve academic performance, and reduce long term negative social and academic outcomes for adolescents.
In June of 2022, the United States Supreme Court ruled in Dobbs v. Jackson Women's Health Organization to eliminate the constitutional right to abortion, overturning the landmark Roe v. Wade decision and granting authority to regulate abortions to individual state legislatures. Abortion policy at the state level can take many different forms, including bans on the basis of gestational period, reason, and method, exceptions to those bans, and requirements to qualify for the exceptions. Although the full scope of consequences the court’s decision will have is yet to be experienced, certain patterns or correlations in natality data can be observed as early as 9 months post-Dobbs. This study examines the relationship between the relative severity of abortion restriction in 2022, abortion rates in 2022, and natality patterns in 2023 pertaining to birth order and age of mother.
Women in the United States currently face a gender pay gap of earning only 81 cents to the man’s dollar. This project focuses on the pay gap within STEM jobs. Is the pay gap potentially related to the low amounts of women we have seen in STEM jobs over the years? This is an important element to discuss and research as it provides clear evidence on if women are being treated fairly in the workplace and getting equal compensation. This project uses the Unites States Bureau of Labor Statistics findings on job and earning metrics to make careful calculations regarding the number of people in each line of STEM work as well as the average pay they receive each year. The findings of this project show that even though women are making major strides in involving themselves in the STEM field, their pay still falls short compared to men.
