University of Maryland Global Campus

Biotechnology has almost unlimited potential to change our lives in very exciting ways. Many of the chemical reactions that produce these products can be fully optimized by performing them at extremes of temperature, pressure, salinity, and pH for efficient and cost-effective outcomes. Fortunately, there are many organisms (extremophiles) that thrive in extreme environments found in nature and offer an excellent source of replacement enzymes in lieu of mesophilic ones currently used in these processes. In this review, I discuss the current uses and some potential new applications of extremophiles and their products, including enzymes, in biotechnology.

Although it is increasingly recognised that higher education institutions have to play a critical role in the progression towards a sustainable development, the question of what fields and issues universities should attend to in their attempt to become more sustainable remains subject to debate. In recent years, sustainability assessment tools have begun to play a prominent role in strategies to reorient higher education institutions systematically and holistically towards sustainability. In the course of their further advancement, sustainability assessment tools have not only become instrumental facilitators of change processes towards sustainability, but also established implicit normative standards by framing the overall understanding of what fields and issues a sustainable university should engage with. So far, researchers in the field have paid little attention to the understandings of a sustainable university that are underpinning and informing sustainability assessment tools. This paper addresses this gap. Based on a comparative analysis of indicators and criteria, as well as introductory passages in supporting documents of twelve sustainability assessment tools, the authors sketch the dominance and marginalisation of different fields and issues. In doing so, the paper contributes to building the capacity for a more sophisticated and reflexive engagement with different approaches to assess and evaluate sustainability in higher education institutions.

This study explores the comparative effectiveness of AI-driven user behavior analysis and traditional security measures in cloud computing environments. It specifically examines their accuracy, speed, and predictive capabilities in detecting and responding to cyber threats. As reliance on cloud-based solutions intensifies, the integration of Artificial Intelligence (AI) and machine learning into cloud security has become increasingly vital. The research focuses on how AI-driven security systems, with their advanced pattern recognition and anomaly detection, compare to traditional methods in identifying deviations from standard user behaviors in cloud settings. Employing a quantitative approach, the study utilizes a detailed survey strategy, targeting cybersecurity professionals across multiple industries, including finance, healthcare, information technology, retail, and government sectors. The survey, comprising both closed-ended and Likert-scale questions, is designed to elicit nuanced responses on the perceptions and experiences of these professionals regarding AI-driven versus traditional security methods in cloud environments. The data, collected from a purposive sample of 243 cybersecurity personnel, is analyzed using multiple regression analysis. This analysis facilitates an understanding of the impact of different security systems on the efficacy of threat detection and response in cloud contexts. The results indicate that while both AI-driven and traditional methods significantly improve threat detection accuracy, traditional methods show a slight edge. Conversely, AI-driven systems demonstrate notably superior predictive capabilities and overall enhanced security performance. These findings suggest the necessity of a hybrid security strategy in cloud computing. Such an approach would combine the advanced capabilities of AI, particularly in predictive analytics and adaptability, with the rapid and reliable responses of traditional methods. This integrated strategy is proposed to effectively address the unique challenges posed by the dynamic and complex nature of cloud-based cyber threats. This study provides valuable insights for both businesses and IT professionals on the effective integration of AI-driven security measures in cloud environments. It highlights the evolving role of AI in cloud security and the importance of maintaining a balance between innovative AI approaches and established traditional methods to create a robust, comprehensive cloud security framework.

This study examines the ethical challenges and regulatory dynamics of Artificial Intelligence (AI) in relation to data integrity and its influence on social dynamics. Employing a cross-sectional survey approach, primary data was collected from 650 AI practitioners across various sectors, encompassing developers, data scientists, ethicists, and policymakers. The study investigated the correlations between regulatory compliance, ethical awareness, professional training, and experience in AI practice with the effectiveness of AI implementation and data integrity. The findings revealed a strong positive correlation between higher levels of regulatory compliance and perceived effectiveness in AI implementation, as well as between AI ethics awareness and data integrity assurance. Moreover, a significant relationship was observed between professional training in AI and its positive impact on social dynamics. However, experience in the AI field, while positively correlated, showed a weaker link to data integrity, indicating that experience alone is insufficient for ensuring effective AI practices. The study highlights the importance of ethical considerations, regulatory frameworks, and professional training in shaping AI development and its societal implications. The need for dynamic, adaptable, and inclusive regulatory frameworks that can align AI practices with societal values and ethical norms is emphasized. Future research directions include exploring AI ethics and regulation in diverse cultural contexts and the impact of emerging technologies like quantum computing on AI ethics.

The broad objective of this study is to evaluate the vulnerabilities of an organization's information technology infrastructure, which include hardware and software systems, transmission media, local area networks, wide area networks, enterprise networks, intranets, and its use of the internet to cyber intrusions. To achieve this objective, the chapter explains the importance of social engineering in network intrusions and cyber-theft and the reasons for the rapid expansion of cybercrime. The chapter also includes a complete description and definition of social engineering, the role it plays in network intrusion and cyber identity theft, a discussion of the reasons for the rise in cybercrimes, and their impact on organizations. In closing the authors recommend some preventive measures and possible solutions to the threats and vulnerabilities of social engineering. The chapter concludes that while technology has a role to play in reducing the impact of social engineering attacks, the vulnerability resides with human behavior, human impulses, and psychological predispositions.

Facility operations and maintenance are defined as the functions, duties, and labor required daily to operate and preserve a facility asset to ensure its original function is available for its primary use and its functions are maintained throughout the facility's life. Organizations, facility management professionals, and their stakeholders expend billions of dollars annually to perform this function in the United States. Much of the cost is on inadequate facility operations that may be avoided. Utilizing the theoretical lens of the adaptive structuration theory, this rapid evidence assessment shall review the current body of scholarly literature to identify how artificial intelligence can be used with predictive maintenance to reduce a facility operations program's operations and maintenance costs. Through an organized systematic review process, this research shall utilize peer-reviewed scholarly articles published within the last 5 years to perform a rapid evidence assessment of predictive maintenance and artificial intelligence in facility operations. Through this rapid evidence assessment, the research finds three common themes that respond to the research question. The most significant theme is artificial intelligence, once implemented in the process, provides unbiased investment and repair recommendations from the analyzed data. An unanticipated discovery of interest is that the current body of literature identifies insufficient data as the number one barrier to the full implementation of artificial intelligence within a facility operations program.

Despite the typical human notion that the Earth is a habitable planet, over three quarters of our planet is uninhabitable by us without assistance. The organisms that live and thrive in these "inhospitable" environments are known by the name extremophiles and are found in all Domains of Life. Despite our general lack of knowledge about them, they have already assisted humans in many ways and still have much more to give. In this review, I describe how they have adapted to live/thrive/survive in their niches, helped scientists unlock major scientific discoveries, advance the field of biotechnology, and inform us about the boundaries of Life and where we might find it in the Universe.

The rapid advancement of artificial intelligence (AI) technologies, epitomized by tools like ChatGPT, Claude, Bard, Copilot, and Copy AI, has significantly reshaped various professional landscapes. This study aimed to assess the impact of these AI tools on professional performance, job dynamics, and societal perceptions. Amidst their benefits in enhancing efficiency and introducing novel capabilities, these tools also pose challenges concerning job displacement, ethical implications, and societal balance. Data from 1623 professionals across diverse industries were analyzed to assess AI tool utilization, functionality, user satisfaction, and perceived impacts. The results indicate that AI tools substantially enhance professional efficiency and are vital in diverse tasks including data analysis and decision-making. However, they also significantly affect traditional job roles, underscoring the urgency for workforce adaptation and skill development. Notably, the study unveils a generational gap in AI adoption, with younger users showing higher engagement compared to older cohorts, suggesting a digital divide. The study’s novelty lies in its comprehensive analysis of AI tool impacts across multiple professions, highlighting ethical and societal challenges. Concerns about AI-induced job displacement, privacy, and ethical use were evident, calling for responsible AI integration. The study advocate for targeted reskilling programs to equip the workforce for an AI-driven future and ethical guidelines to ensure AI tools' responsible development and use. This research contributes to the understanding of AI’s role in modern professional settings and offers strategic insights for policymakers, educators, and industry leaders. Emphasizing a balanced approach, the study urges for AI deployment that maximizes benefits while addressing potential risks and societal concerns.

BACKGROUND: Aging and rural-urban disparities are two major social problems in today's ever-developing China. Much of the existing literature has supported a negative association between adverse community setting with the cognitive functioning of seniors, but very few studies have empirically investigated the impact of rural-urban community settings on cognitive decline in the late life course of the population in developing countries. METHODS: Data of seniors aged 65 or above (n = 1709) within CHARLS (The China Health and Retirement Longitudinal Study, a sister study of HRS), a nationally representative longitudinal cohort (2011-2015) in China, were analyzed using a multilevel modeling (MLM) of time within individuals, and individual within communities. Cognitive impairment was assessed with an adapted Chinese version of Mini-Mental State Examination. RESULTS: Urban community setting showed a significant protective effect (β = - 1.978, p < .000) on cognitive impairment in simple linear regression, and the MLM results showed it also had a significant lower cognitive impairment baseline (β = - 2.278, p < .000). However, the curvature rate of cognitive decline was faster in urban community setting indicated by a positive interaction between the quadratic time term and urban community setting on cognitive impairment (β = 0.320, p < .05). A full model adjusting other individual SES factors was built after model fitness comparison, and the education factor accounted for most of the within and between community setting variance. CONCLUSIONS: The findings suggest that urban community setting in one's late-life course has a better initial cognitive status but a potentially faster decline rate in China, and this particular pattern of senior cognitive decline emphasize the importance of more specific preventive measures. Meanwhile, a more holistic perspective should be adopted while construct a risk factor model of community environment on cognitive function, and the influence at society level needs to be further explored in future research.

The US government imposed two travel restriction policies to prevent the spread of the COVID-19 but may have funneled asymptomatic air travelers to selected major airports and transportation hubs. Using the most recent JHU COVID-19 database, American Community Survey, Airport and Amtrak data form Bureau of Transportation Statistics from 3132 US counties we ran negative binomial regressions and Cox regression models to explore the associations between COVID-19 cases and death rates and proximity to airports, train stations, and public transportation. Counties within 25 miles of an airport had 1.392 times the rate of COVID-19 cases and 1.545 times the rate of COVID-19 deaths in comparison to counties that are more than 50 miles from an airport. More effective policies to detect and isolate infected travelers are needed. Policymakers and officials in transportation and public health should collaborate to promulgate policies and procedures to protect travelers and transportation workers from COVID-19.

IMPORTANCE: Depression is one of the leading causes of disability in the United States. Depression prevalence varies by income and sex, but more evidence is needed on the role income inequality may play in these associations. OBJECTIVE: To examine the association between the Poverty to Income Ratio (PIR)-as a proxy for income-and depressive symptoms in adults ages 20 years and older, and to test how depression was concentrated among PIR. DESIGN: Using the 2005-2016 National Health and Nutrition Examination Survey (NHANES), we employed Negative Binomial Regression (NBRG) in a sample of 24,166 adults. We used a 9-item PHQ (Public Health Questionnaire, PHQ-9) to measure the presence of depressive symptoms as an outcome variable. Additionally, we plotted a concentration curve to explain how depression is distributed among PIR. RESULTS: In comparison with high-income, the low-income population in the study suffered more from greater than or equal to ten on the PHQ-9 by 4.5 and 3.5 times, respectively. The results of NBRG have shown that people with low-PIR (IRR: 1.30, 95% CI: 1.23-1.37) and medium-PIR (IRR: 1.55, 95% CI: 1.46-1.65) have experienced a higher relative risk ratio of having depressive symptoms. Women have a higher IRR (IRR: 1.29, 95% CI: 1.24-1.34) than men. We observed that depression was concentrated among low-PIR men and women, with a higher concentration among women. CONCLUSION AND RELEVANCE: Addressing depression should target low-income populations and populations with higher income inequality.

Generation Z is the youngest and newest entrants into the workforce. However, confusion about their characteristics, work values, and reward preferences hinders effort to attract, recruit, and retain this generational cohort into public sector organizations. Accordingly, this study investigates effective reward strategies for recruiting and retaining Generation Z into public sector organizations. I used an evidence-based research approach and an aggregative systematic review as the study methodology. The evidence curated from 32 studies reveals how the background and life experiences of Generation Z influence the importance they assign their work values, reward preferences, and how they prioritize rewards in terms of their employment decisions. Additionally, gender also influenced the importance Gen Z assigned to specific rewards. Overall, Gen Z’s strong attractiveness to specific extrinsic and intrinsic rewards makes public sector organizations a likely employer of choice and offers managers a viable strategy for attracting, recruiting, and retaining the youngest generational workforce.

Juvenile delinquency is a pressing problem in the United States; the literature emphasizes the importance of early interventions and the role of the family in preventing juvenile delinquency. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, PudMed, and Scopus, we included 28 peer-reviewed articles in English between January 2012 and October 2022. We evaluated the existing literature regarding the risk factors, protective factors, and interventions related to juvenile delinquency. We searched articles that discussed reducing juvenile delinquency and recidivism in the U.S. and coded them into four overarching themes: ‘family conflict and dysfunction’, ‘neglect and maltreatment’, ‘individual and family mitigating factors’, and ‘family- and community-based interventions. We found that family conflict and dysfunction and neglect and maltreatment were two primary predictors of juvenile delinquency. Notably, higher academic achievement and strong and positive parental relationships were factors that protected against delinquency amongst at-risk youth. Interventions that yielded optimal efficacy in curbing recidivism included family-based interventions, specifically family therapy, and community-based interventions. Considering multi-dimensional factors that affect delinquent behaviors, interventions should consider the influence of family, peers, neighborhood, schools, and the larger community.

In today's digital age, businesses use cloud services to boost their operations exponentially. While this trend presents excellent potential, it is marred by significant concerns over the safety of data stored in the cloud. As the demand for cloud computing increases, businesses face issues ranging from data breaches and provider security to service availability and regulatory compliance. It becomes crucial for organizations to enact stringent security protocols not only to protect user information but also to smoothen operational processes, enforce regulatory compliance, and delineate user behavior norms. To fortify cloud assets, businesses must adhere to foundational security guidelines covering confidentiality, risk analysis, e-discovery, business resilience, and third-party contract obligations. Therefore, while the cloud's advantages are manifold, they have intricate challenges. Businesses must prioritize impenetrable security, craft meticulous risk strategies, and fortify against potential threats to harness the full potential of cloud services while preserving their assets and reputation. These results show that most respondents have a confidence level of 7, indicating a relatively high level of trust in the current cloud security measures. Organizations must continuously assess and adapt their cloud security strategies to keep up with the dynamic digital environment and evolving threats for long-term security and operational efficacy.

This comprehensive study explores the integration and effectiveness of Artificial Intelligence (AI) in Identity and Access Management (IAM) within cloud environments. It primarily focuses on how AI can enhance user authentication, authorization, and access control, addressing the challenges and possibilities in cloud computing. The study adopts a mixed-methods approach, employing both quantitative and qualitative analyses. A survey involving 582 cybersecurity experts provides insights into the current state and potential of AI in IAM, while multiple regression analysis examines the impact of various factors on system effectiveness. Four hypotheses are explored: the impact of hardware and software configurations on system accuracy (H1), the influence of computational environments on reliability (H2), the role of demographic factors in user acceptance (H3), and the effect of technological enhancements on system performance and acceptance (H4). Findings indicate significant correlations between these factors and the effectiveness of AI in IAM. Notably, hardware configurations and security concerns influence system accuracy; computational environment variations affect system reliability; demographic factors impact user acceptance; and enhancements such as user feedback, advancements in AI technology, continuous learning algorithms, and system transparency improve performance and acceptance. These insights underscore the need for advanced hardware, standardized software, user-centric design, and continuous improvement in AI technologies for effective IAM in cloud environments. The study provides actionable recommendations for cloud service providers and developers, emphasizing the importance of involving users in development processes, ensuring transparency, and adopting adaptive algorithms. Future research directions include longitudinal studies on the impact of technological advancements and exploring demographic-specific responses to AI-integrated IAM solutions.

The brain is arguably the most powerful computation system known. It is extremely efficient in processing large amounts of information and can discern signals from noise, adapt, and filter faulty information all while running on only 20 watts of power. The human brain's processing efficiency, progressive learning, and plasticity are unmatched by any computer system. Recent advances in stem cell technology have elevated the field of cell culture to higher levels of complexity, such as the development of three-dimensional (3D) brain organoids that recapitulate human brain functionality better than traditional monolayer cell systems. Organoid Intelligence (OI) aims to harness the innate biological capabilities of brain organoids for biocomputing and synthetic intelligence by interfacing them with computer technology. With the latest strides in stem cell technology, bioengineering, and machine learning, we can explore the ability of brain organoids to compute, and store given information (input), execute a task (output), and study how this affects the structural and functional connections in the organoids themselves. Furthermore, understanding how learning generates and changes patterns of connectivity in organoids can shed light on the early stages of cognition in the human brain. Investigating and understanding these concepts is an enormous, multidisciplinary endeavor that necessitates the engagement of both the scientific community and the public. Thus, on Feb 22-24 of 2022, the Johns Hopkins University held the first Organoid Intelligence Workshop to form an OI Community and to lay out the groundwork for the establishment of OI as a new scientific discipline. The potential of OI to revolutionize computing, neurological research, and drug development was discussed, along with a vision and roadmap for its development over the coming decade.

Private gardens provide vital opportunities for people to interact with nature. The most popular form of interaction is through garden bird feeding. Understanding how landscape features and seasons determine patterns of movement of feeder-using songbirds is key to maximising the well-being benefits they provide. To determine these patterns we established three networks of automated data loggers along a gradient of greenspace fragmentation. Over a 12-month period we tracked 452 tagged blue tits Cyantistes caeruleus and great tits Parus major moving between feeder pairs 9,848 times, to address two questions: (i) Do urban features within different forms, and season, influence structural (presence-absence of connections between feeders by birds) and functional (frequency of these connections) connectivity? (ii) Are there general patterns of structural and functional connectivity across forms? Vegetation cover increased connectivity in all three networks, whereas the presence of road gaps negatively affected functional but not structural connectivity. Across networks structural connectivity was lowest in the summer when birds maintain breeding territories, however patterns of functional connectivity appeared to vary with habitat fragmentation. Using empirical data this study shows how key urban features and season influence movement of feeder-using songbirds, and we provide evidence that this is related to greenspace fragmentation.

Comparisons between the ribonucleotide sequences of 5S rRNAs of the Gram-negative eubacteria indicate that several families, namely Enterobacteriaceae, Vibrionaceae and Aeromonadaceae possess remarkably similar evolutionary histories. A study of the phylogenetic relationships among these groups, through cluster analysis and construction of evolutionary trees, suggests the existence of dissimilar rates of evolution along the several lineages. These dissimilarities are most evident in comparisons between the phylogenetic depths of the Enterobacteriaceae and Vibrionaceae. Detection of disparate rates of evolution, as well as their influence on the interpretation of the natural taxonomy of this group of bacteria, is discussed.

This study presents a comprehensive analysis of the organic skincare and cosmetics industry, focusing on the sourcing practices of ingredients and their implications for consumer health, ethical considerations, and environmental impact. The research employs a quantitative approach, utilizing data from 700 working-class women, analyzed through descriptive statistics, correlation, and regression methods. The findings highlight a critical need for enhanced transparency and ethical accountability in sourcing practices within the industry. A significant correlation was identified between the geographical proximity of organic skincare producers to consumers and an increased risk of unethical and unsafe skincare products, underscoring the importance of stringent quality control and ethical oversight. Additionally, the research explored the environmental aspects of sourcing practices and found that, while there is a relationship with ecological footprints, the impact is less substantial than initially presumed. This points towards the necessity for a broader and more comprehensive approach to sustainability in the organic skincare industry. Another key finding is the strong correlation between the cost of sourcing ingredients and the likelihood of small-scale producers compromising on product safety and ethical standards. This reveals a fundamental challenge in balancing economic viability with ethical and safety considerations. Based on these findings, the study recommends that industry regulators adopt a holistic approach to sustainability, focusing on sustainable farming practices and reducing carbon footprints, especially for small-scale producers. Future studies are suggested to further investigate the long-term health and environmental impacts of organic skincare ingredients.

This research paper explores the integration of Enterprise Risk Management (ERM), the ISO 27001 standard, and mobile forensics methodologies as a comprehensive framework for enhancing digital security measures within modern business ecosystems. Employing a quantitative research design, this paper utilized a survey methodology, gathering data from 372 professionals across various sectors including risk management, IT/security, and forensic analysis. The analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the research hypotheses and assess the impact of the integrated approach on organizational digital security capabilities. The findings reveal a significant positive effect of integrating ERM, ISO 27001, and mobile forensics on an organization’s ability to manage digital risks effectively. Specifically, the integrated approach was found to enhance strategic digital security management, improve the identification, assessment, and mitigation of digital risks, strengthen information security management practices, and elevate the effectiveness and efficiency of digital crime investigation processes. These outcomes underscore the value of a cohesive strategy that leverages the strengths of ERM, ISO 27001, and mobile forensics in addressing the complex and interconnected digital threat landscape. Based on the results, the study recommends adopting a holistic security framework, investing in continuous professional development, leveraging technological advancements for proactive security management, and fostering a culture of security and collaboration. Such measures are crucial for organizations aiming to enhance their resilience against cyber threats and protect their digital assets in the face of sophisticated cyber-attacks. This research contributes to the field of cybersecurity by providing empirical evidence on the benefits of an integrated approach to digital security, offering practical guidelines for organizations seeking to improve their digital security measures, and highlighting the need for continuous adaptation and collaboration in the fight against cyber threats.