Operational Context Research Articles

Defining and characterizing the operational context for human systems integration

AbstractMany efforts from multiple academic and industrial disciplines have studied the notion of context. The systems engineering and human systems integration fields, however, lack a generalized definition and characterization of context, in particular the operational context of complex sociotechnical systems. This paper reviews context definitions and builds a generalized definition of the operational context of complex sociotechnical systems. The objective is then to identify and extract context properties which are often implicit in the literature and do not necessarily appear in the definitions. However, eliciting them is helpful for understanding what context is made of, how it relates to complex sociotechnical systems comprising human and non‐human agents, and how an early understanding of the operational context can prove valuable to designing efficient and robust systems. Our analysis is followed by an example of how our definition and derived properties apply to the case study of the design of a remote and virtual air traffic control center.

Quantifying the vulnerability of road networks to flood-induced closures using traffic simulation

Disruptions in road networks can significantly impact traffic flow, necessitating the identification and mitigation of vulnerable components. This study presents a methodology to assess the vulnerability and robustness of road networks, addressing the research gap in comprehensive approaches that effectively evaluate both controlled intersections and uncontrolled/free-flow highway segments. The proposed framework employs traffic simulation tools to model road closures within designated study areas, focusing on networks in Qatar. Performance measures such as average control delay and volume to capacity ratios are utilized to calculate vulnerability indices for network components. The two-pronged approach quantifies the impact of simulated closures at both the component and network levels, revealing critical intersections and road segments that markedly affect network performance during disruptions. The findings contribute to the field by providing transportation engineers with an effective quantitative tool to detect vulnerabilities in their road network, guiding design plans and actions to mitigate the potential harmful impacts of disruptive events on transportation infrastructure. The methodology's scalability ensures its applicability to various operational contexts, offering significant insights for transportation planning and infrastructure resilience. By establishing a comprehensive vulnerability analysis framework, this study enhances the understanding and management of road network vulnerabilities in complex urban environments. Further research on integrating dynamic traffic assignment and real-time data is recommended to further enhance the predictive accuracy and applicability of the proposed framework, ultimately improving transportation infrastructure resilience.

The Evolution of Dismounted Soldier Systems: A Comprehensive Review of GOSSRA’s Innovations

The Generic Open Soldier System Reference Architecture (GOSSRA) project represents a significant step forward in the evolution of Dismounted Soldier Systems (DSS). This article provides an in-depth analysis of the project, drawing on direct author participation, extensive project documentation, and a wide range of academic literature. It synthesizes key insights into the technological advancements, operational challenges, ethical considerations, and strategic implications of DSS. The research highlights the potential of advanced technologies to substantially enhance soldier capabilities but also underscores the complexities of integrating these technologies into cohesive and functional systems. The analysis delves into the operational effectiveness and flexibility of DSS, illustrating their ability to improve situational awareness, decision-making, and mobility in diverse operational contexts. However, it also points out the need for systems to adapt to varying scenarios and environments. The ethical and strategic dimensions of DSS deployment are explored, emphasizing the need for robust ethical frameworks and strategic planning to ensure that advancements in military capabilities align with ethical norms and contribute positively to global security. Lessons learned from past deployments and case studies provide invaluable insights into the real-world performance, user acceptance, and integration challenges of DSS. The article concludes with a forward-looking perspective, anticipating future developments in DSS and outlining strategies for their successful implementation. The conclusions and recommendations presented provide a roadmap for the development and integration of DSS, ensuring they evolve in alignment with the imperatives of modern and future warfare.

Towards dynamic decision-making in government as service organization: insights from systems thinking

Purpose Service organizations operate in an increasingly complex and uncertain context that makes decision-making challenging. Despite well-recognized changes in the operational context of government as service organization, service literature has given surprisingly limited attention to what these changes imply for organizational decision-making. This study aims to face with the lack of fit of decision-making theorizing with the reality, within which most service practitioners operate, in order to foster the relevance of decision-making in service research and properly approach the false assumptions and misguided instructions for action. Design/methodology/approach To rectify the situation, the purpose of this paper is to advance a more holistic understanding of decision-making in government as service organization. The authors do so by reviewing the sparse, though insightful, prior literature on decision-making in service research and identifying four foundational assumptions of decision-making in the service context, that radically differ from the traditional assumptions of decision-making within the wider management literature. Findings The authors contribute to service research by further advancing the emerging dynamic understanding of decision-making by developing eight systems thinking-informed research propositions and a connected research agenda. In doing so, the paper offers the essential ground work that can revitalize the field of service management and equip it for facing the challenges that government as service organization is encountering in the 21st century. Originality/value The formulated eight research propositions demonstrate that decision-making in a government as service organization occurs within complex adaptive systems composed of multiple subsystems and is characterized by a high degree of unpredictability. It is a process influenced by multiple actors part of the system and subsystems, through multiple feedback loops, where the implications of prior decisions inform the future decisions.

Understanding the Interaction between Delivery Robots and Other Road and Sidewalk Users: A Study of User-generated Online Videos

The deployment of autonomous delivery robots in urban environments presents unique challenges in navigating complex traffic conditions and interacting with diverse road and sidewalk users. Effective communication between robots and road and sidewalk users is crucial to address these challenges. This study investigates real-world encounter scenarios where delivery robots and road and sidewalk users interact, seeking to understand the essential role of communication in ensuring seamless encounters. Following an online ethnography approach, we collected 117 user-generated videos from TikTok and their associated 2067 comments. Our systematic analysis revealed several design opportunities to augment communication between delivery robots and road and sidewalk users, which include facilitating multi-party path negotiation, managing unexpected robot behaviour via transparency information, and expressing robot limitations to request human assistance. Moreover, the triangulation of video and comments analysis provides a set of design considerations to realise these opportunities. The findings contribute to understanding the operational context of delivery robots and offer insights for designing interactions with road and sidewalk users, facilitating their integration into urban spaces.

Regulatory compliance in the age of data privacy: A comparative study of the Nigerian and U.S. legal landscapes

This comparative study delves into the intricate regulatory compliance mechanisms governing data privacy in Nigeria and the United States. Employing a qualitative methodology, this research synthesizes existing literature and case studies to highlight the pivotal role of data protection regulations in safeguarding personal information in an increasingly digital world. The analysis begins with an exploration of the conceptual framework of data privacy, emphasizing key principles such as transparency, accountability, and data minimization. It then meticulously examines the Nigerian Data Protection Regulation (NDPR) and the diverse U.S. legal landscape, including the California Consumer Privacy Act (CCPA) and the Health Insurance Portability and Accountability Act (HIPAA). The main findings reveal significant differences in regulatory approaches, with Nigeria's centralized enforcement by the National Information Technology Development Agency (NITDA) contrasting sharply with the U.S.'s fragmented state and federal oversight. Case studies underscore the challenges faced by both jurisdictions in ensuring compliance, ranging from resource constraints in Nigeria to the complexities of navigating multiple regulatory requirements in the U.S. The study concludes that while both countries emphasize the importance of data privacy, their divergent legal landscapes reflect unique cultural, economic, and operational contexts. The research recommends harmonizing data protection standards, enhancing enforcement mechanisms, and fostering greater public awareness to address the evolving challenges of data privacy. This study contributes to the global discourse on data privacy by offering insights into the strengths and weaknesses of different regulatory frameworks, thereby providing a roadmap for policymakers, businesses, and other stakeholders to navigate the complexities of data privacy compliance. Keywords: Data Privacy, Regulatory Compliance, Nigeria, United States, Data Protection, Legal Frameworks.

Evaluating the cost and operational context for national human papillomavirus (HPV) vaccine delivery in three regions of Ethiopia.

Cervical cancer is the second leading cause of cancer deaths among women in Ethiopia. Human papillomavirus (HPV) vaccination is a primary prevention method for cervical cancer and was introduced in Ethiopia in 2018. We conducted a cross-sectional, mixed-methods study to understand the operational context for the HPV vaccination program and the associated costs in three regions of Ethiopia. Operations research provided insights on the frequency and intensity with which HPV vaccination program activities were done, focusing on activities conducted to vaccinate the cohort receiving its first dose in October 2019 and second dose in January 2021. Microcosting was used to estimate the costs to the health system for these activities. Data collection using structured questionnaires was done at 60 health facilities, 17 woredas, and 9 zones/sub-cities that were randomly selected from three purposively selected regions and the national level. Financial costs (monetary outlays) and economic costs (financial costs plus opportunity costs of resource use) were estimated in 2019 US$. Health facilities delivered an average of 411 HPV vaccine doses during the reference period, ranging from a mean of 86 to 606 across the three regions. Aggregated across all levels of the health system, the estimated financial cost per dose across the pooled sample was $2.23, and the economic cost per dose was $7.19, excluding the cost of vaccines and supplies. There were regional variations in these estimates, with mean financial cost per dose ranging from $1.17 to $7.18 and mean economic cost per dose ranging from $5.80 to $18.13 across the three regions. Regional variations exist in the service volume, frequency, and intensity of conducting HPV vaccination activities, as reflected in the estimated costs of delivery. Data generated from this study can be used to inform program planning and budgeting, taking into account regional variations, for effective utilization of resources.

A corpus-based multi-dimensional analysis of the linguistic features of Aviation English

Aviation English (AE), a specialized register of English, prioritizes precision, brevity and clarity to maximize aviation safety. While there has been a growing focus within the linguistics community on AE training and assessment since the release of a set of standards and recommended practices, its linguistic properties remain comparatively underexplored. Drawing upon Biber's (1988) multi-dimensional (MD) analysis framework, the present study conducted a corpus-based comparative MD analysis to investigate the multi-dimensional linguistic profile of AE vs. casual conversational English (CE) and exploratory factor analysis (EFA) to extract and interpret the co-occurring linguistic features of routine AE and non-routine AE. The comparative MD analysis shows that AE exhibits more information condensation, less authorial stance and technicality, and fewer features of online information elaboration compared to CE. The EFA shows variations in the linguistic and textual composition of the two sub-registers of AE across two dimensions. Non-routine AE demonstrates a present-focused, viewpoint/intention-oriented approach, involving higher levels of integrative information flow compared to routine AE. Routine AE is characterized by a higher degree of information condensation and is marked by a planned, procedural, and intensive use of standard phraseology. Some pedagogical implications are then proposed for enhancing AE training to cultivate pilots' and air traffic controllers' language competence for precise, unambiguous communication tailored to both routine and non-routine operational contexts.

Integrating Lean Manufacturing with Industry 4.0: The Impact on Indian MSMEs

This research delves into the integration of Lean Manufacturing principles with Industry 4.0 technologies, exploring the emergence of "Lean 4.0" within the distinct operational context of Indian Micro, Small & Medium Enterprises (MSMEs). Recognizing the unique challenges and opportunities faced by businesses, the study adopts a robust mixed-methods approach. We combine rich primary data collected from 150 Indian MSMEs through in-depth interviews, insightful site visits, and comprehensive questionnaires. These firsthand accounts are further contextualized within a broader framework of existing literature and relevant case studies on Lean 4.0, providing a multifaceted perspective. Through rigorous data analysis conducted using R, the study illuminates the current landscape of Lean 4.0 adoption within Indian MSMEs. We go beyond simply documenting adoption rates, aiming to uncover the specific barriers hindering wider implementation. Crucially, the research critically assesses the tangible impact of Lean 4.0 integration on organizational efficiency, providing valuable insights for both practitioners seeking to optimize their operations and policymakers aiming to foster a more competitive manufacturing sector within India's diverse economic landscape.

Collaboration Practices for the Cybersecurity of Supply Chains to Critical Infrastructure

This work describes the collaboration practices of a community of interest in the UK that brings together cybersecurity professionals with a shared interest in improving supply chain cybersecurity for Operational Technology (OT) environments. This research emphasizes the need for collective responsibility between organizations and provides a set of principles for adopting a code of practice and partnership approach to supply chain cybersecurity. This work has enabled cybersecurity experience from several critical infrastructure sectors, including energy, rail, aviation, water, health, and food, to analyze the uptake and practical use of existing supply chain guidance, identifying gaps and challenges. The community has examined touch points with the supply chain and identified improvements related to the communication of cybersecurity requirements, technical and commercial engagement between customers and suppliers, and in the tailoring of implementations towards operational technology contexts. Communicating the context of securing cyber-physical systems is an essential perspective for this community. This work exemplifies a partnership framework and is translating experiences into useful guidance, particularly for OT systems, to improve cybersecurity levels across multiple contributors to critical infrastructure systems.

The Introduction of American Naval Ordnance in the Caribbean Sea in the Late Modern Period: Operational dimensions and American expansion

Since the 1970s, the Caribbean has been a hub for nautical archaeology, often focusing on European naval ordnance as evidence of early modern maritime occupation. The mid-nineteenth century ushered in a new military context with rapid American expansion, introducing contemporary innovations in American naval artillery. This study delves into the significance of American naval ordnance in the late nineteenth-century Caribbean, with a focus on its potential for revealing new imperialism and evolving regional dynamics in the archaeological record. It analyses the late nineteenth-century naval operational context in the Caribbean, concentrating on USS Kearsarge and its guns’ history from 1862–94 and its loss on Roncador Cay. Primary sources provide insight into their evolution associated with the wreck site, with emphasis placed on Dahlgren shell guns as a potential identifying feature.

Planning of multi-production line maintenance

Efficiency of production lines is significantly impacted by maintenance activities, which are often overshadowed by the focus on production planning. The symbiotic relationship between maintenance and production activities is a critical yet overlooked factor. We examine the importance of maintenance planning, specifically within the dynamic context of production line operations. In this maintenance planning setting, scarce resources, particularly technicians, are commonly disregarded. Our research addresses the challenge of real-time maintenance planning across multiple production lines by developing a state-of-the-art heuristic, which takes into account machine production states and resource availability and is augmented by re-enactment procedures. We validate the heuristic by employing Markov Decision Processes and digital twin Discrete Event Simulations, which leverage real-world production data. This modeling framework is new for this specific problem. In our experiments, we compare the heuristic against optimal policies and practitioner-based policies. Our heuristic matches the performance of optimal policies in the Markov Decision Processes and significantly outperforms FCFS and practitioner-based policies in the Discrete Event Simulations. Notably, our heuristic is not only effective against standard policies, realizing improvement of 1% to 3%, but it is also the first to be generally applicable to production lines involving multiple machines and resources in real-time.

National trends and outcomes of robotic emergency general surgery in the United States

BackgroundRobot-assisted surgery has seen exponential adoption over the last decade. Although the safety and efficacy of robotic surgery in the elective setting have been demonstrated, data regarding robotic emergency general surgery remains sparse. MethodsAll adults undergoing non-elective appendectomy, cholecystectomy, small or large bowel resection, perforated ulcer repair, or lysis of adhesions were identified in the 2008 to 2020 National Inpatient Sample. Temporal trends were analyzed using a rank-based, non-parametric test developed by Cuzick (nptrend). Using laparoscopy as a reference, multivariable regressions were used to evaluate the association between robotic techniques and in-hospital mortality, major complications, and resource use for each emergency general surgery operation. ResultsOf an estimated 4,040,555 patients undergoing emergency general surgery, 65,853 (1.6%) were performed using robotic techniques. The robotic proportion of minimally invasive emergency general surgery increased significantly overall, with the largest growth seen in robot-assisted large bowel resections and perforated ulcer repairs. After adjustment for various patient and hospital-level factors, robot-assisted large bowel resection (adjusted odds ratio 0.73, 95% confidence interval 0.58–0.91) and cholecystectomy (adjusted odds ratio 0.66, 95% confidence interval 0.55–0.81) were associated with significantly reduced odds of perioperative blood transfusion compared to traditional laparoscopy. Although robotic techniques were associated with modest reductions in postoperative length of stay, costs were uniformly higher by increments of up to $4,900. ConclusionRobotic surgery appears to be a safe and effective adjunct to laparoscopy in minimally invasive emergency general surgery, although comparable cost-effectiveness has yet to be realized. Increasing use of robotic techniques in emergency general surgery may be attributable in part to reduced complications, including blood loss, in certain operative contexts.

Assessing racial bias in healthcare predictive models: Practical lessons from an empirical evaluation of 30-day hospital readmission models

ObjectiveDespite increased availability of methodologies to identify algorithmic bias, the operationalization of bias evaluation for healthcare predictive models is still limited. Therefore, this study proposes a process for bias evaluation through an empirical assessment of common hospital readmission models. The process includes selecting bias measures, interpretation, determining disparity impact and potential mitigations. MethodsThis retrospective analysis evaluated racial bias of four common models predicting 30-day unplanned readmission (i.e., LACE Index, HOSPITAL Score, and the CMS readmission measure applied as is and retrained). The models were assessed using 2.4 million adult inpatient discharges in Maryland from 2016 to 2019. Fairness metrics that are model-agnostic, easy to compute, and interpretable were implemented and apprised to select the most appropriate bias measures. The impact of changing model’s risk thresholds on these measures was further assessed to guide the selection of optimal thresholds to control and mitigate bias. ResultsFour bias measures were selected for the predictive task: zero-one-loss difference, false negative rate (FNR) parity, false positive rate (FPR) parity, and generalized entropy index. Based on these measures, the HOSPITAL score and the retrained CMS measure demonstrated the lowest racial bias. White patients showed a higher FNR while Black patients resulted in a higher FPR and zero-one-loss. As the models’ risk threshold changed, trade-offs between models’ fairness and overall performance were observed, and the assessment showed all models’ default thresholds were reasonable for balancing accuracy and bias. ConclusionsThis study proposes an Applied Framework to Assess Fairness of Predictive Models (AFAFPM) and demonstrates the process using 30-day hospital readmission model as the example. It suggests the feasibility of applying algorithmic bias assessment to determine optimized risk thresholds so that predictive models can be used more equitably and accurately. It is evident that a combination of qualitative and quantitative methods and a multidisciplinary team are necessary to identify, understand and respond to algorithm bias in real-world healthcare settings. Users should also apply multiple bias measures to ensure a more comprehensive, tailored, and balanced view. The results of bias measures, however, must be interpreted with caution and consider the larger operational, clinical, and policy context.

Pourquoi l’hyperthermie devient un danger quotidien en endourologie ?

Introduction: Laser lithotripsy is a key method for breaking down urinary stones, utilizing the heat from the laser. However, inappropriate management of this heat can cause significant thermal damage. This document explores these thermal dynamics and their consequences.Materials and Methods: After a review of the fundamental principles of laser lithotripsy, a critical examination of relevant experimental and clinical research is conducted. This approach allows for a synthesis of current knowledge on the impact of laser operational parameters on urinary tissues.Results: The roles of conduction and convection principles in the propagation and dissipation of heat are highlighted. The concept of thermal dose for assessing thermal risks, comparisons between different types of lasers (Ho:YAG and TFL), and the variations in temperature induced by laser lithotripsy in various experimental and operational contexts are discussed.Conclusion: Becoming aware of the risks and deepening the understanding of the interaction between lasers and urinary stones and surrounding tissues are essential first steps in preventing complications.

High-resolution mapping of tree species and associated uncertainty by combining aerial remote sensing data and convolutional neural networks ensemble

Mapping tree species diversity is essential for monitoring and managing forest ecosystems. Automating ecoforestry mapping using remote sensing images remains an important challenge due to the tremendous variability in forest covers and the conditions under which images used for classification are acquired. Deep learning algorithms have been increasingly used over the last few years to analyse remote sensing data for mapping tree species. However, most of these studies focus only on a small number of species or a limited area and avoid providing a spatially explicit representation of the uncertainty related to the predictions, rendering them unsuitable for operational use.In this study, we used an ensemble of convolutional neural networks to map forest species and land cover types across a 10,000 km2 area in Quebec, Canada, spanning mixed and boreal forests. We built a georeferenced label database to train and test nine models, which resulted from the combination of three training datasets and three-commonly used convolutional neural network architectures (VGG16, ResNet50v2, Densenet121). These models were trained on multiband aerial photographs and a canopy height model derived from airborne lidar point clouds and used to map the diversity and distribution of tree species and land cover types. The level of agreement among models was used to generate uncertainty maps. The performance of the super-ensemble using 1311 independent forest inventory plots and assessed the extent to which uncertainty maps could serve as a spatially explicit indicator of model performance.The super-ensemble achieved 90% global accuracy. Our results indicated that the performance of the super-ensemble surpassed that of all individual architectures while also showing a positive effect of the canopy height model on performance. The comparison of the super-ensemble map with the proportion of basal area measured in forest inventory plots confirmed the reliability of the model over the study area. Our results also indicated that mapping the inter-model agreement provides a reliable spatially explicit estimate of model performance. The robustness and reliability of the proposed approach support its use in an operational context while also providing a conceptual framework to evaluate the reliability of uncertainty maps.

A Multi-Criterion Evaluation Process for Determining Cost-Effective Harvesting Systems in Fragmented Boreal Forests

Nordic forests, like those found in Canada, used to consist of large and relatively homogeneous mature stands. Such a spatial pattern allows for harvest operations to be highly concentrated, minimizing procurement costs. However, the growing fragmentation of these forests makes planning difficult and increases the costs of road building and machinery relocation. While operational solutions have been developed in regions with small harvest areas, their transferability to different settings is unknown. Finding the most suitable combination of equipment for a given context is challenging considering the multitude of possibilities. The objective of this study is to identify, from all possible options, a subset of harvest systems expected to perform well in fragmented boreal forests. The results from this research are two-fold. First, a comprehensive review of forest machines and harvest systems is provided. Second, a multi-criteria decision analysis (MCDA) methodology is proposed to evaluate the alternatives. In a boreal forest context, the conventional harvester-forwarder system (CTL) was ranked among the best solutions, along with mild adaptations of the usual configurations. Several whole-tree (WT) system configurations were also highly ranked. While the results are specific to the case studied, the review and selection methodology can serve in different operational contexts.

Effects of a human amniotic membrane extract on ARPE-19 cells

BackgroundHuman Amniotic Membrane (hAM) is endowed with several biological activities and might be considered an optimal tool in surgical treatment for different ophthalmic pathologies. We pioneered the surgical use of hAM to treat retinal pathologies such as macular holes, tears, and retinal detachments, and to overcome photoreceptor damage in age-related macular degeneration. Although hAM contributed to improved outcomes, the mechanisms of its effects are not yet fully understood. The characterization and explanation of the effects of hAM would allow the adoption of this new natural product in different retinal pathologies, operative contexts, and hAM formulations. At this end, we studied the properties of a hAM extract (hAME) on the ARPE-19 cells.Methods and resultsA non-denaturing sonication-based technique was developed to obtain a suitable hAME. Viability, proliferation, apoptosis, oxidative stress, and epithelial-mesenchymal transition (EMT) were studied in hAME-treated ARPE-19 cells. The hAME was able to increase ARPE-19 cell viability even in the presence of oxidative stress (H2O2, TBHP). Moreover, hAME prevented the expression of EMT features, such as EMT-related proteins, fibrotic foci formation, and migration induced by different cytokines.ConclusionsOur results demonstrate that the hAME retains most of the properties observed in the whole tissue by others. The hAME, other than providing a manageable research tool, could represent a cost-effective and abundant drug to treat retinal pathologies in the future.

Development of an integrated energy management system for off-grid solar applications with advanced solar forecasting, time-of-use tariffs, and direct load control

Effectively managing and maximizing the integration of renewable energy sources is essential for a sustainable power grid due to the stochastic and intermittent nature of renewable energy generation. This study develops a comprehensive Integrated Energy Management System incorporating supply-demand side management in the form of time-of-use credit, direct load control, and generator control to enhance photovoltaic utilization in off-grid applications. A novel three-step solar energy forecasting approach is proposed in this paper, utilizing low-level data fusion and regression models to predict next-day photovoltaic generation with improved accuracy, and a rule-based decision algorithm is developed to correct forecast errors and manage loads dynamically. A techno-economic analysis covering a 20-year duration is carried out for scenarios with and without the integrated energy management system; three configurations are investigated for supplying an off-grid residential home, including diesel generator, diesel generator/photovoltaic system, and diesel generator/photovoltaic system/integrated energy management system. Results reveal that the hybrid configuration with integrated energy management system achieved 44 % and 46 % reductions in costs and carbon dioxide emissions compared to the diesel generator alone, and 8 % and 9 % compared to the diesel generator/photovoltaic setup respectively. The Integrated Energy Management System further enhanced photovoltaic utilisation rate by over 113 % when compared to the diesel generator/photovoltaic system. Further evaluations include customer behaviour impacts, demonstrating that a fully automated system with 100 % compliance significantly outperforms systems with manual customer control, highlighting the detrimental effect of overrides on the efficiency of direct load control. The flexibility of the Integrated Energy Management System framework allows potential adaptation for on-grid applications, showcasing its utility in diverse operational contexts.

Economic Analysis of On-Street Parking with Urban Delivery

The surge in online shopping has dramatically increased the demand for short-term curb access for package pickups and deliveries, leading to heightened competition for limited curb space. This paper addresses the problem of how the unique parking demand of deliverers, particularly their parking duration for delivery attempts linked to parking space availability, affects the dynamics of urban curb parking systems. We develop continuum models of a curb parking system and perform analytical analyses to understand the dynamics and steady-state properties of the system under the influence of increased urban deliveries. We conduct comparative statics to examine how various curb management measures, such as pricing, parking duration caps, and dedicated delivery bays, influence the equilibrium conditions, followed by comparisons of the theoretical capacity of these measures. We further demonstrate the working mechanism of delivery bays and their role in forestalling specific failures within a hybrid system with both general parkers and deliverers. Finally, we investigate curb management strategies in nonstationary operational contexts and prescribe the optimal strategies therein. Our findings offer valuable insights into the unique properties that deliverers introduce to curb parking dynamics, highlighting the need for a strategic reevaluation of current management practices. We find that pricing strategies for metered parking to general parkers prove to be more efficient and flexible compared with other interventions. Notably, our analysis suggests that when curb parking pricing is optimally calibrated, the necessity for dedicated delivery bays diminishes. Furthermore, we reveal that optimal curb management strategies could diverge in response to surges in demand, depending on whether the increase sources from general parkers or deliverers. To be effective, the sizing of delivery bays must align with the underlying causes of parking scarcity. History: This paper has been accepted for the Transportation Science Special Section on ISTTT25 Conference. Funding: This work was supported by the George Washington University [University Facilitating Fund]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2024.0569 .