Continuous Risk Assessment Research Articles

Optimizing procurement efficiency: Frameworks for data-driven cost reduction and strategic vendor management

This paper explores data-driven frameworks for optimizing procurement efficiency, focusing on cost reduction strategies and strategic vendor management. Procurement is critical in enhancing operational resilience and supporting organizational objectives in an era of increasing financial pressures and complex supply chains. The study examines data analytics approaches, such as spend analysis, predictive cost modeling, and demand forecasting, which enable organizations to uncover cost-saving opportunities and streamline procurement processes. Additionally, it highlights how data-driven vendor management, through continuous evaluation, risk assessment, and performance tracking, contributes to sustainable supply chain efficiency. Key recommendations include establishing data governance frameworks, investing in advanced analytics tools, enhancing data literacy among procurement teams, and fostering transparent supplier relationships. By integrating analytics into procurement strategies, organizations can achieve long-term cost efficiency, build stronger supplier partnerships, and support sustainable growth.

Continuous Risk Assessment of Late and Term Preeclampsia Throughout Pregnancy: A Retrospective Cohort Study

Continuous Risk Assessment of Late and Term Preeclampsia Throughout Pregnancy: A Retrospective Cohort Study

A quantitative analysis of biosafety and biosecurity using attack trees in low‐to‐moderate risk scenarios: Evidence from iGEM

AbstractAs synthetic biology is extensively applied in numerous frontier disciplines, the biosafety and biosecurity concerns with designing and constructing novel biological parts, devices, and systems have inevitably come to the forefront due to potential misuse, abuse, and environmental risks from unintended exposure or potential ecological impacts. The International Genetically Engineered Machine (iGEM) competition often serves as the inception of many synthetic biologists’ research careers and plays a pivotal role in the secure progression of the entire synthetic biology field. Even with iGEM's emphasis on biosafety and biosecurity, continuous risk assessment is crucial due to the potential for unforeseen consequences and the relative inexperience of many participants. In this study, possible risk points for the iGEM projects in 2022 were extracted. An attack tree that captures potential risks and threats from experimental procedures, ethical issues, and hardware safety for each iGEM‐based attack scenario is constructed. It is found that most of the attack scenarios are related to experimental procedures. The relative likelihood of each scenario is then determined by using an established assessment framework. This research expands the traditionally qualitative analysis of risk society theory, reveals the risk formation in the synthetic biology team, and provides practical implications.

Continuous glucose data construction and risk assessment application of diabetic retinopathy complications for patients with type 2 diabetes mellitus

Managing diabetes mellitus (DM) includes achieving acceptable blood glucose levels and minimizing the risk of complications from DM. The appropriate glucose sensing method is continuous glucose monitoring (CGM). Effective evaluation metrics that reflect glucose fluctuations can be realized. However, compared with self-monitoring of blood glucose (SMBG), CGM data are not easy to obtain. Therefore, this article studies a fusion model to achieve this objective, including Gaussian process regression (GPR) and long short-term memory (LSTM). Compared with the three commonly used LSTM, GPR, and support vector machine, the proposed model can construct accurate results. By using the constructed CGM data, the conventional metrics, such as the mean amplitude of glycemic excursion (MAGE), mean blood glucose (MBG), standard deviation (SD), and time in range (TIR), are calculated. These metrics and other variables are input into statistical methods to realize diabetic retinopathy risk assessment. In this way, the relationship between the glycemic variability of the constructed CGM data by the mathematical model and DR could be achieved. The utilized statistical methods include single-factor analysis and binary multivariate logistic regression analysis. Results show that fasting blood glucose, disease course, history of hypertension, MAGE and TIR are independent risk factors for DR.

Serious adverse events following immunization with COVID-19 vaccines in Lebanon: a retrospective analysis of the National Pharmacovigilance Database

Continuous surveillance and risk assessment of inactivated Coronavirus Disease 2019 (COVID-19)) vaccines provide an understanding of their safety profiles, guide vaccination strategy and public health policy. This study aims to analyze the characteristics and prevalence of officially reported serious adverse events following immunization (AEFIs) with inactivated COVID-19 vaccines by System Organ Class (SOC), age, and sex.To achieve this aim, a retrospective observational study was conducted between February 14th, 2021, and June 30th, 2022. Reported AEFIs were evaluated for data completeness. Causality assessment adhered to the World Health Organization guidelines.Findings revealed that the AEFIs occurrence did not significantly differ between vaccines used (ChAdOx1 vs. BNT162b2), sex, or SOC. The most prevalent AEFIs were vascular disorders (37%), followed by cardiac (25%) and nervous system disorders (14%). The adverse events were predominantly reported post-vaccination with the BNT162b2 vaccine, mainly after the first dose. The mean age was highest for miscellaneous disorders (70 ± 21.7 years) and the lowest for nervous system (46 ± 22 years) and immune system disorders (45 ± 19 years). Age differences were statistically different for vascular disorders (p = 0.003) and immune system disorders (p = 0.012).In conclusion, ongoing surveillance and risk assessment of the vaccine’s safety profile is crucial for detecting potential safety signals. Active surveillance of the reported serious AEFIs is highly needed to support evidence-based vaccination strategies and maintain public confidence in immunization programs,

Spatial and Bioaccumulation of Heavy Metals in a Sheep-Based Food System: Implications for Human Health.

Heavy metal contamination in agricultural soils presents serious environmental and health risks. This study assessed the bioaccumulation and spatial distribution of nickel, cadmium, zinc, lead, and copper within a sheep-based food chain in the Baia Mare region, Romania, which includes soil, green grass, sheep serum, and dairy products. Using inductively coupled plasma mass spectrometry (ICP-MS), we analyzed the concentrations of these metals and calculated bioconcentration factors (BCFs) to evaluate their transfer through trophic levels. Spatial analysis revealed that copper (up to 2528.20 mg/kg) and zinc (up to 1821.40 mg/kg) exceeded permissible limits, particularly near former mining sites. Elevated lead (807.59 mg/kg) and cadmium (2.94 mg/kg) were observed in industrial areas, while nickel and cobalt showed lower concentrations, but with localized peaks. Zinc was the most abundant metal in grass, while cadmium transferred efficiently to milk and cheese, raising potential health concerns. The results underscore the complex interplay between soil properties, contamination sources, and biological processes in heavy metal accumulation. These findings highlight the importance of continuous monitoring, risk assessment, and mitigation strategies to protect public health from potential exposure through contaminated dairy products.

Risk assessment of continued airworthiness event of transport aircraft rudder pedal strut damage

Abstract To ensure the safe operation of aircraft after delivery, a set of continued airworthiness risk assessment processes and methods for transport aircraft is studied and proposed, including unsafe condition identification, consequence severity determination, occurrence possibility assessment, risk level determination, and reaction time calculation. The continued airworthiness risk matrix is established. Based on the established process and method, the risk assessment is carried out for the continued airworthiness event of transport aircraft rudder pedal strut damage. The relationship between the risk level of the fleet aircraft and the flight duration is determined, and the reaction time of the unsafe condition of the aircraft in each flight duration is calculated. The results show that the process and method are feasible.

Predicting high blood pressure using machine learning models in low- and middle-income countries

Responding to the rising global prevalence of noncommunicable diseases (NCDs) requires improvements in the management of high blood pressure. Therefore, this study aims to develop an explainable machine learning model for predicting high blood pressure, a key NCD risk factor, using data from the STEPwise approach to NCD risk factor surveillance (STEPS) surveys. Nationally representative samples of adults aged 18-69 years were acquired from 57 countries spanning six World Health Organization (WHO) regions. Data harmonization and processing were performed to standardize the selected predictors and synchronize features across countries, yielding 41 variables, including demographic, behavioural, physical, and biochemical factors. Five machine learning models - logistic regression, k-nearest neighbours, random forest, XGBoost, and a fully connected neural network - were trained and evaluated at global, regional, and country-specific levels using an 80/20 train-test split. The models’ performance was assessed using accuracy, precision, recall, and F1 score. Feature importance analysis identified age, weight, heart rate, waist circumference, and height as key predictors of blood pressure. Across the 57 countries studied, model performances varied considerably, with accuracy ranging from as low as 58.96% in some models for specific countries to as high as 81.41% in others, underscoring the need for region and country-specific adaptations in modelling approaches. The explainable model offers an opportunity for population-level screening and continuous risk assessment in resource-limited settings.

Surveillance of avian influenza viruses in Hebei Province of China from 2021 to 2023: Identification of a novel reassortant H3N3

Avian influenza remains a global public health concern for its well-known point mutation and genomic segment reassortment, through which plenty of serum serotypes are generated to escape existing immune protection in animal and human populations. Some occasional cases of human infection of avian influenza viruses (AIVs) since 2020 posed a potential pandemic risk through human-to-human transmission. Both east-west and north-south migratory birds fly through and linger in the Hebei Province of China as a stopover habitat, providing an opportunity for imported AIVs to infect the local poultry and for viral gene reassortment to generate novel stains. In this study, we collected more than 6000 environmental samples (mostly feces) in Hebei Province from 2021 to 2023. Samples were screened using real-time RT-PCR, and virus isolation was performed using the chick embryo culture method. We identified 10 AIV isolates, including a novel reassortant H3N3 isolate. Sequencing analysis revealed these AIVs are highly homologous to those isolated in the Yellow River Basin. Our findings supported that AIVs keep evolving to generate new isolates, necessitating a continuous risk assessment of local avian influenza in wild waterfowl in Hebei, China.

Resilient cloud cluster with DevSecOps security model, automates a data analysis, vulnerability search and risk calculation

Automated, secure software development is an important digitalization task, solved with the DevSecOps approach. An important part of the DevSecOps approach is continuous risk assessment, which is necessary to identify and evaluate risk factors. Combining the development cycle with continuous risk assessment creates software development and operation synergies and minimizes vulnerabilities. The article presents the main methods of deploying web applications and ways to increase information security at all stages of product development. It also compares different types of infrastructures and cloud computing providers and analyzes modern tools used to automate processes. The cloud cluster was deployed using Terraform and the Jenkins pipeline, written in the Groovy programming language, which checks program code for vulnerabilities and allows you to fix violations at the earliest stages of developing secure web applications. The developed cluster implements the proposed algorithm for automated risk assessment based on the calculation (modeling) of cloud infrastructure threats and vulnerabilities, which operates in real-time, periodically collecting all information and adjusting the system according to the risk and applied controls. The algorithm for calculating risk and losses is based on statistical data and FAIR information risk assessment methodology. The risk value obtained using the proposed method is quantitative, which allows more efficient forecasting of information security costs in software development.

The Role and Impact of Artificial Intelligence on Supply Chain Management: Efficiency, Challenges, and Strategic Implementation

This research paper delves into the transformative impact of artificial intelligence (AI) on supply chain management, focusing on enhancing demand forecasting, operational efficiency, and customer satisfaction, while also managing costs and streamlining logistics operations. The adoption of AI in supply chains offers significant opportunities to improve service delivery and operational capabilities, which are crucial for maintaining competitiveness in the rapidly evolving business landscape. The study underscores the importance of AI technologies in reshaping supply chain dynamics by providing a comprehensive analysis of both the benefits and challenges associated with its implementation. Key benefits highlighted include the optimization of inventory management, enhanced accuracy of demand forecasting, reduced operational costs, and improved customer service. These enhancements are pivotal in achieving a competitive edge and adapting to changing market demands.However, the integration of AI into supply chains is not devoid of challenges. The paper identifies critical obstacles such as the need for significant cultural shifts within organizations, data security concerns, and the complexities of navigating legal and regulatory frameworks. These challenges require strategic management to ensure successful AI adoption and to mitigate associated risks.The research includes case studies of Arab companies that have integrated AI into their supply chains, offering practical insights into the real-world application of these technologies. These examples demonstrate both the potential rewards and the difficulties encountered, providing a balanced perspective on the practicalities of AI deployment in supply chain settings.In conclusion, while AI presents substantial opportunities for advancing supply chain management, it also necessitates careful consideration of various implementation challenges. The paper provides strategic recommendations for Arab companies aiming to leverage AI technologies effectively. These guidelines emphasize the need for thorough planning, continuous risk assessment, and fostering an adaptive organizational culture. By addressing these key areas, companies can harness the full potential of AI to enhance their supply chain operations and achieve sustainable growth.

Quantitative Integrative Survival Prediction in Multiple Myeloma Patients Treated With Bortezomib-Based Induction, High-Dose Therapy and Autologous Stem Cell Transplantation.

Given the high heterogeneity in survival for patients with multiple myeloma, it would be clinically useful to quantitatively predict the individual survival instead of attributing patients to two to four risk groups as in current models, for example, revised International Staging System (R-ISS), R2-ISS, or Mayo-2022-score. Our aim was to develop a quantitative prediction tool for individual patient's 3-/5-year overall survival (OS) probability. We integrated established clinical and molecular risk factors into a comprehensive prognostic model and evaluated and validated its risk discrimination capabilities versus R-ISS, R2-ISS, and Mayo-2022-score. A nomogram for estimating OS probabilities was built on the basis of a Cox regression model. It allows one to translate the individual risk profile of a patient into 3-/5-year OS probabilities by attributing points to each prognostic factor and summing up all points. The nomogram was externally validated regarding discrimination and calibration. There was no obvious bias or overfitting of the prognostic index on the validation cohort. Resampling-based and external evaluation showed good calibration. The c-index of the model was similar on the training (0.76) and validation cohort (0.75) and significantly higher than for the R-ISS (P < .001) or R2-ISS (P < .01). In summary, we developed and validated individual quantitative nomogram-based OS prediction. Continuous risk assessment integrating molecular prognostic factors is superior to R-ISS, R2-ISS, or Mayo-2022-score alone.

Cyber risk assessment of cyber-enabled autonomous cargo vessel

The increasing interest in autonomous ships within the maritime industry is driven by the pursuit of revenue optimization, operational efficiency, safety improvement and going greener. However, the industry’s increasing reliance on emerging technologies for the development of autonomous ships extends the attack surface, leaving the underlying ship systems vulnerable to potential exploitation by malicious actors. In response to these emerging challenges, this research extends an existing cyber risk assessment approach called FMECA-ATT&CK based on failure modes, effects and criticality analysis (FMECA), and the MITRE ATT&CK framework. As a part of our work, we have expanded the FMECA-ATT&CK approach to assessing cyber risks related to systems with artificial intelligence components in cyber-enabled autonomous ships (e.g. autonomous engine monitoring and control). This new capability was developed using the information and semantics encoded in the MITRE ATLAS framework. FMECA-ATT&CK has been adopted due to its comprehensive and adaptable nature and its promising venue for supporting continuous cyber risk assessment. It helps evaluate the cyber risks associated with the complex and state-of-the-art operational technologies on board autonomous ships. The cyber risk assessment approach assists cybersecurity experts in aligning mitigation strategies for the cyber defence of autonomous ships. It also contributes towards advancing overall cybersecurity in the maritime industry and ensures the safe and secure sailing of autonomous ships. Our key findings after applying the proposed approach against a model of an autonomous cargo ship is the identification of the Navigation Situation Awareness System (NSAS) of the ship as being at the highest risk followed by the Autonomous Engine Monitoring and Control (AEMC) system. Additionally, we identified 3 high, 48 medium, and 5776 low risks across 29 components.

Retrospective, Observational Analysis on the Impact of SARS-CoV-2 Variant Omicron in Hospitalized Immunocompromised Patients in a German Hospital Network-The VISAGE Study.

Endemic SARS-CoV-2 infections still burden the healthcare system and represent a considerable threat to vulnerable patient cohorts, in particular immunocompromised (IC) patients. This study aimed to analyze the in-hospital outcome of IC patients with severe SARS-CoV-2 infection in Germany. This retrospective, observational study, analyzed administrative data from inpatient cases (n = 146,324) in 84 German Helios hospitals between 1 January 2022 and 31 December 2022 with regard to in-hospital outcome and health care burden in IC patients during the first 12 months of Omicron dominance. As the primary objective, in-hospital outcomes of patients with COVID-19-related severe acute respiratory infection (SARI) were analyzed by comparing patients with (n = 2037) and without IC diagnoses (n = 14,772). Secondary analyses were conducted on IC patients with (n = 2037) and without COVID-19-related SARI (n = 129,515). A severe in-hospital outcome as a composite endpoint was defined per the WHO definition if one of the following criteria were met: intensive care unit (ICU) treatment, mechanical ventilation (MV), or in-hospital death. In total, 12% of COVID-related SARI cases were IC patients, accounting for 15% of ICU admissions, 15% of MV use, and 16% of deaths, resulting in a higher prevalence of severe in-hospital courses in IC patients developing COVID-19-related SARI compared to non-IC patients (Odds Ratio, OR = 1.4, p < 0.001), based on higher in-hospital mortality (OR = 1.4, p < 0.001), increased need for ICU treatment (OR = 1.3, p < 0.001) and mechanical ventilation (OR = 1.2, p < 0.001). Among IC patients, COVID-19-related SARI profoundly increased the risk for severe courses (OR = 4.0, p < 0.001). Our findings highlight the vulnerability of IC patients to severe COVID-19. The persistently high prevalence of severe outcomes in these patients in the Omicron era emphasizes the necessity for continuous in-hospital risk assessment and monitoring of IC patients.

Strategies for protecting IT supply chains against cybersecurity threats

This review paper explores the multifaceted realm of cybersecurity within IT supply chains, addressing the intricate challenges posed by digital vulnerabilities, high-profile cyber incidents, and emerging threats. It highlights the criticality of continuous risk assessment, the implementation of international security standards, and the necessity for enhanced management of third-party vendors. The paper also delves into advanced technological solutions like blockchain, AI, and machine learning for bolstering security, advocating for best practices including the zero-trust model, regular employee training, and secure software development. Emphasizing a proactive over a reactive approach, the paper underscores the evolving nature of cyber threats and the imperative for adaptive strategies. It calls for concerted efforts from businesses, policymakers, and IT professionals to prioritize and continuously refine cybersecurity measures in safeguarding IT supply chains against future threats. Keywords: IT Supply Chain, Cybersecurity, Risk Management, Blockchain, Zero-Trust Model, Artificial Intelligence.

Integration of Real-Time Monitoring and Data Analytics to Mitigate Sand Screenouts During Fracturing Operations

Summary Sand screenout, the most frequent incident during hydraulic fracturing, is one of the major threats to operational safety and efficiency. Screenout occurs when advancing hydraulic fractures are blocked by injected proppant-slurry, stall, and develop fluid overpressure. Because massive wells are still being hydraulically fractured every year, operational safety has become a critical and urgent issue that has always been overshadowed by the whether-or-not controversy. However, the suddenness and unheralded surprise of screenout make it extremely difficult to predict and handle. Previous efforts attempt to predict screenout as discrete events by interpreting injection pressure directly. We propose and then demonstrate a self-updating (via data and experience augmentation) and customizable (numerical models and algorithms) data-driven strategy of real-time monitoring and management for screenout based on records of shale gas fracturing. Two new indicators—proppant filling index (PFI) and safest fracturing pump rate (SFPR)—are improved and then integrated into the strategy. The PFI reveals the mismatch between injected proppant and hydraulic fractures and provides a continuous time-historical risk assessment of screenout. A pretrained ensemble learning model is applied to process the geological and hydraulic measurements in real time for the PFI evolution curve during fracturing operations. Integrated with the SFPR, a stepwise pump rate regulation strategy is deployed successfully to mitigate sand screenout for field applications. Four field trials are elaborated, which are representative cases exhibiting the data-driven approach to monitor and manage sand screenout during hydraulic fracturing.

Cybersecurity risks in online banking: A detailed review and preventive strategies applicatio

In an era where the digital transformation of the banking sector intersects with the escalating complexity of cyber threats, this paper endeavors to dissect the multifaceted realm of cybersecurity within the banking industry. With a backdrop of increasing online banking adoption and the concomitant rise in cybercrime, the study aims to illuminate the current cybersecurity landscape, evaluate the efficacy of existing frameworks and propose strategic enhancements to fortify digital defenses. Employing a methodological amalgam of literature review and analysis of recent cybersecurity incidents, this investigation delves into the intricacies of cyber threats, the financial repercussions of breaches and the robustness of current cybersecurity measures in banking. The scope of this paper encompasses a comprehensive examination of recent cyber incidents, an assessment of the financial impact of cyber-attacks, an evaluation of the effectiveness of existing cybersecurity frameworks and the formulation of strategic recommendations for bolstering cybersecurity measures. Through this scholarly inquiry, key findings emerge, highlighting the critical need for dynamic cybersecurity strategies that integrate advanced technologies, promote regulatory compliance and foster a culture of cybersecurity awareness. Conclusively, the study posits that the banking sector must embrace a holistic and adaptive approach to cybersecurity, underscored by strategic investments in technology, education, and collaboration. Recommendations advocate for the integration of Big Data analytics, artificial intelligence and continuous risk assessment methodologies to navigate the evolving cyber threat landscape effectively. This paper serves as a clarion call to banking institutions, urging a reinvigorated commitment to cybersecurity resilience in safeguarding financial assets and customer trust against the backdrop of digital transformation.

Municipal Compost Public Health, Waste Management, and Urban Agriculture: A Decadal Study of Fugitive Pb in City of Boston, Massachusetts, USA.

Compostable materials constitute roughly half of waste generated globally, but only 5% of waste is actually processed through composting, suggesting that expanding compost programs may be an effective way to process waste. Compostable waste, if properly collected and processed, has value-added end use options including: residential and park landscaping, remediation of brownfield sites, and as growing media in urban agriculture (UA). Since 2001, our lab has partnered with The Food Project, a non-profit focused on youth leadership development through urban farming. From 2006 to 2022 we collected compost materials that were delivered to the farm from a variety of local sources and analyzed a suite of biogeochemical properties including lead (Pb) concentrations, organic carbon, and grain size distribution. Pb concentrations of Boston's municipal compost always exceeded the current City of San Francisco soil and compost purchase standard (80μg/g). In 2012 Boston's composting program was halted when it exceeded the 400μg/g Environmental Protection Agency's Pb in soil benchmark. Urban Pb is geomobile and must be managed to minimize resuspension and transport of fines whose Pb concentration is often elevated compared to bulk compost. Consequently, urban farmers have to source lower Pb compost from suburban suppliers at significantly greater cost. Over a 15 year period and through several city vendor contracts, Pb concentrations in municipal compost remain at levels that warrant continued surveillance and risk assessment.

Profiling of Antibiotic Residues in Soil and Vegetables Irrigated Using Pharmaceutical-Contaminated Water in the Delhi Stretch of the Yamuna River, India

The movement of emerging pollutants, particularly antibiotics, from surface water to crops through the process of plant uptake poses a significant public concern related to the agricultural utilization of untreated water and biosolids. It is essential to consider the potential risk of inadvertent human exposure and the accumulation of drug residues in fresh food crops, particularly in the case of various green vegetables. Nowadays, environmental research is mainly focusing on the bioaccumulation and threat posed by pharmaceutical residues, especially antibiotics and personal care products in the soil–plant ecosystem. In the present study, the concentration of pharmaceuticals in soil samples collected from agricultural fields irrigated with Yamuna water were evaluated using suitable extraction and sophisticated instrumentation techniques. Out of the five sampling locations, the highest concentration of ofloxacin was reported at Site-V (0.265 µg/mg) during the premonsoon period and the lowest concentration (0.014 µg/mg) during the postmonsoon at Site-II, in terms of the average concentration; this could be due to the combined effect of anthropogenic activities, surface water runoff, and the combined load of drains in the vicinity. For the quantification of the selected antibiotics in a green leafy vegetable (Spinach oleracea), the concentration of ofloxacin was found to be the highest at Okhla barrage Site-V (5.586 µg/mg) during the premonsoon period, with the lowest concentration observed at Site-I, from 1.382 µg/mg to 1.698 µg/mg, during the postmonsoon period. The higher concentration of ofloxacin in crops is because of its susceptibility to being absorbed at the soil’s surface. Plant absorption of antibiotics is influenced primarily by the biological characteristics of the plant, encompassing factors like the lipid and carbohydrate composition of the plant roots. Additionally, the physiochemical properties of the drugs, including molecular size, Kow, and pKa, play a significant role in this process. The antibiotics showed greater variation in their concentration during the premonsoon than in the postmonsoon period, which may be because of precipitation, dilution, and the leaching effect of the soil. For all three of the drugs studied, the concentrations followed the order of ofloxacin &gt; amoxicillin &gt; erythromycin. Thus, the effective management of contaminated soils and vegetables must consider continuous monitoring and risk assessment of high-priority antibiotics to prevent negative effects on the natural environment and human health.

Zoonotic transmission risk of Acinetobacter baumannii from Alaskan wildlife

Abstract Introduction/Objective Acinetobacter baumannii is a multi-drug resistant gram-negative bacterium often isolated in water and soil. A challenge in both human and veterinary nosocomial infections, Acinetobacter is adaptable, forming biofilms and carbapenem resistance (CRAB). As an opportunistic infection, Acinetobacter is an emerging pathogen especially in immunocompromised individuals which include marginalized housing insecure populations. Anchorage dedicates large areas for urban park environments providing high impact of human and moose wildlife interaction. Previous studies link zoonotic transmission of bacteria through direct fecal contact or water contamination. The significant number of vulnerable unsheltered individuals in Anchorage using public green belts presents a potential risk for Acinetobacter baumannii colonization. Methods/Case Report To assess environmental Acinetobacter exposure from urban wildlife, we ground collected 150 moose fecal samples between 2019-2022. Samples were placed into support medium, cultured onto MacConkey agar and incubated at 37C. Biochemical identification and antibiotic susceptibility testing were performed using MicroScan™ (Beckman/Coulter Brea, CA) conventional panels with antimicrobial interpretation along clinical laboratory standards (CLSI). Results (if a Case Study enter NA) Acinetobacter baumannii was isolated in moose feces, with proportion of positive samples increasing annually: 7.5% (2019), 12.5% (2020), and 14.2% (2021). Antimicrobial analysis found 100% of samples resistant to cefpodoxime, cefazolin, ceftriaxone, cefuroxime, ampicillin, and ampicillin-sulbactam, with 72% resistant to ertapenem and imipenem. Aztreonam resistance was 22%, cefepime 17%, with only 5% resistant to ciprofloxacin. Conclusion The increasing colonization of moose feces with multi-drug resistant Acinetobacter baumannii potentially poses a significant public health risk in Anchorage, Alaska. With moose inhabiting areas used by the vulnerable homeless and outdoor enthusiasts, there exists a high risk for exposure potential towards opportunistic infection, and colonization. The multi-drug resistance pattern on observed on several of A. baumannii isolates suggests the possibility of CRAB. As one of the five pathogens listed as an urgent threat by the CDC, it is imperative to study potential sources of carbapenem resistant A. baumannii, including environmental exposure. Finding moose fecal samples positive for A. baumannii indicates a need for continued surveillance and risk assessment for community acquired infection.