Operational Context Research Articles

Artificial Intelligence in the U.S. Military Health System: Forging a New Frontier for Clinical Care and Efficiency

ABSTRACT The Military Health System (MHS) has historically been at the forefront of innovation in medicine and science, but it has also historically struggled to implement battlefield innovations or civilian technologies for wider domestic use. Artificial intelligence (AI) has emerged as a transformative force in health care with civilian health systems and institutions at the forefront of these innovations. While these tools have the potential to support resolution of military health’s most pressing issues, the MHS is behind its civilian counterparts in advancing AI. Adoption of AI could benefit the MHS in such areas as service member and beneficiary access to care; more precise allocation of medical personnel and resources; improved operations of military treatment facilities; early detection of emerging threats to health; and force multiplication of existing telehealth capabilities. This evolving and highly visible technology also presents challenges in the military context above those in the civilian context, such as additional levels of privacy and security, integration with purpose-built secure systems, and additional regulatory obligations. To address these, the MHS should engage in three lines of effort to advance AI: establishing governance, education and training of medical personnel, and engaging in research, development, testing, and piloting of AI applications. This will require dedicated personnel and resources for a substantial initial outlay to be recouped later through more effective administration and care. By leveraging lessons learned from civilian systems, the MHS can design, adopt, and implement AI solutions to improve care for service members in both domestic and operational contexts, and for their beneficiaries.

A Comprehensive examination of utilizing neutrosophic parameters in manufacturing industry through queueing approach

Queueing theory plays a pivotal role in analyzing the dynamics of manufacturing systems subject to random arrivals and service times. The M/G/1 queueing model, which represents a single-server queue with general distributions for inter-arrival and service times, is fundamental in this regard. However, traditional queueing models often fail to account for the inherent uncertainties encountered in real-world manufacturing scenarios. Neutrosophic theory provides a robust framework for modeling indeterminacy, ambiguity, and inconsistency in queueing parameters, thereby offering a more adaptable and precise depiction of system behavior. This research investigates the utilization of Neutrosophic sets in defining arrival rates, service times, and queue length distributions within the M/G/1 framework tailored to manufacturing settings. By employing numerical examples and comparative analyses, the study explores the effects of Neutrosophic parameters on key performance indicators such as mean waiting time, queue length distribution, and server utilization, considering distributions like Exponential, Erlang, and Deterministic. Additionally, it delves into the implications of integrating Neutrosophic parameters into queueing theory, providing valuable insights into improved decision-making and system optimization across various manufacturing operational contexts.

Models and P4R asset description for digital twin-based advanced planning and scheduling using cyber-physical integration for resilient production operation

Advanced planning and scheduling (APS) addresses the complex and uncertain nature of production control. A digital twin (DT), which incorporates simulations through cyber-physical integration, provides an advanced functionality for APS. To facilitate efficient design and implementation, a DT-based APS must satisfy three requirements: technical functionalities for resilience, robust models for diverse operational constraints, and efficient interoperability through cyber-physical integration. Although several studies have proposed the use of DT as a primary technology for APS, proposals that address the process, functionality, integration, and information models are lacking. Additionally, the existing asset descriptions cannot adequately capture the sophisticated characteristics of DT and necessary informational elements for APS. Thus, this study designed a process model, functionalities, and integration models for the DT-based APS and asset descriptions for snapshot synchronization. Crucial service-compositions and functionalities were defined using work-center-level lifecycles. Consequently, a process model was developed, which focused on core activities for resilience. Moreover, horizontal integration between DT and control functionalities and vertical integration between DT and standards, were proposed to enhance the DT-based APS. The proposed method effectively managed the product, process, plan, plant, and resource classes by ensuring adherence to asset administration shell principles. To validate the effectiveness of the proposed methods, two work centers with distinctly different characteristics were employed and demonstrated dominant preventive measures compared to static functionality-based methods. The primary contributions encompass the facilitation of integration and interoperability within a DT-based APS. The proposed methods support the advanced characteristics of DT, ensuring robustness and neutrality across heterogeneous operational contexts.

Fundamentals of legislation in autonomous artificial intelligence systems

The paper proposes a method for defining a dedicated operational context as part of the development and deployment of autonomous corporate governance systems. The case study of autonomous board of directors systems is examined. A significant part of the operational context for the autonomous corporate governance systems consists of the regulatory and legal framework that regulates the company’s operations. A special operational context for autonomous artificial intelligence systems can be defined by simultaneously formulating local regulatory documents in two versions, i.e., to be used by people and by autonomous systems. In such a case, the artificial intelligence system receives a clearly defined operational context that allows such a system to perform its functions with a required operational quality. Local regulations that take into account the specificity of operations involving individuals and autonomous artificial intelligence systems can become the foundation of the relevant legislation that would regulate the development and deployment of autonomous systems.

A Review of Force Health Protection Aspects of Lyme Disease in the U.S. Military.

Lyme disease (LD) is an underrated threat to the military that negatively impacts mission readiness. Lyme disease has traditionally been thought to only be a risk in an operational context, where training or deployments are frequently conducted in heavily wooded environments. However, this view diminishes risks posed by many off-duty outdoor recreational activities. Furthermore, although the Army introduced a permethrin factory-treated Army Combat Uniform in 2012, permethrin retention and subsequent protection have been shown to decrease significantly after 3 months of wear. Thus, although LD is a known health risk that threatens unit readiness, beyond using treated uniforms there has been little progress at the unit level to address this threat. Focusing on a narrative review of LD and its impact on U.S. military force health protection, sources included DoD websites and policies, government press releases and information papers from sources like the CDC and FDA, and scholarly peer-reviewed journals with full-text access from the online databases EBSCOhost, MEDLINE, SCOUT, and Google Scholar. Searches included the following key words: LD and military, Army, etiology, epidemiology, incidence, treatments, post-treatment LD, and chronic LD. Articles were selected for review based on the relevance of their abstracts and titles. Although the incidence of LD appears to be increasing among service members, it is difficult to attribute this increase to military-related duties. Also, despite ongoing LD research specifically conducted and funded by the DoD, there are limited data on the mitigating effects of force education and permethrin-treated uniforms on the LD threat. Therefore, it is reasonable to conclude that LD negatively impacts military readiness and monetary costs diverted from other priorities. Lyme disease poses a genuine threat to the health and careers of service members and is an often-overlooked disruptor to military operations. Simple, feasible prevention strategies that are tailored to high-risk geographic regions can be emphasized by military units to reduce the incidence of on-duty and off-duty cases. Additionally, there remains a critical need for new preventative and diagnostic measures for LD.

Choice-Learn: Large-scale choice modeling for operational contexts through the lens of machine learning

Choice-Learn: Large-scale choice modeling for operational contexts through the lens of machine learning

Applied radiation tags for warhead dismantlement transparency

This study explores the feasibility of applying radiation tags as a Chain of Custody tool to confirm the dismantlement of individual warheads by investigating time dependent emissions from special nuclear material. Precedents of photon and neutron sources that have been used to irradiate warhead components are presented. Measurements of irradiated Highly Enriched Uranium (HEU) are used to benchmark simulations with the CINDER90 depletion code module within MCNP. Delayed gamma emissions are simulated as a function of time since irradiation of moderated HEU and Weapons Grade Plutonium with photon and neutron sources. Models of commercial 9 MV and 15 MV linacs were found to induce ∼109fissions/g and ∼1011fissions/g, respectively, creating 3 to 4 orders of magnitude more delayed gammas than neutron sources, modeled at 1010n/s. However, a DT neutron source was found to induce sufficient fissions, ∼107fissions/g, to create quantifiable signatures relative to intrinsic emissions. Applied radiation tagging is determined to be technically feasible, but its operational context and practical implementation requires significant development.

A comprehensive review of model compression techniques in machine learning

This paper critically examines model compression techniques within the machine learning (ML) domain, emphasizing their role in enhancing model efficiency for deployment in resource-constrained environments, such as mobile devices, edge computing, and Internet of Things (IoT) systems. By systematically exploring compression techniques and lightweight design architectures, it is provided a comprehensive understanding of their operational contexts and effectiveness. The synthesis of these strategies reveals a dynamic interplay between model performance and computational demand, highlighting the balance required for optimal application. As machine learning (ML) models grow increasingly complex and data-intensive, the demand for computational resources and memory has surged accordingly. This escalation presents significant challenges for the deployment of artificial intelligence (AI) systems in real-world applications, particularly where hardware capabilities are limited. Therefore, model compression techniques are not merely advantageous but essential for ensuring that these models can be utilized across various domains, maintaining high performance without prohibitive resource requirements. Furthermore, this review underscores the importance of model compression in sustainable artificial intelligence (AI) development. The introduction of hybrid methods, which combine multiple compression techniques, promises to deliver superior performance and efficiency. Additionally, the development of intelligent frameworks capable of selecting the most appropriate compression strategy based on specific application needs is crucial for advancing the field. The practical examples and engineering applications discussed demonstrate the real-world impact of these techniques. By optimizing the balance between model complexity and computational efficiency, model compression ensures that the advancements in AI technology remain sustainable and widely applicable. This comprehensive review thus contributes to the academic discourse and guides innovative solutions for efficient and responsible machine learning practices, paving the way for future advancements in the field.Graphical abstract

Advancing Hydrogen Gas Utilization in Industrial Boilers: Impacts on Critical Boiler Components, Mitigation Measures, and Future Perspectives

This review sets out to investigate the detrimental impacts of hydrogen gas (H2) on critical boiler components and provide appropriate state-of-the-art mitigation measures and future research directions to advance its use in industrial boiler operations. Specifically, the study focused on hydrogen embrittlement (HE) and high-temperature hydrogen attack (HTHA) and their effects on boiler components. The study provided a fundamental understanding of the evolution of these damage mechanisms in materials and their potential impact on critical boiler components in different operational contexts. Subsequently, the review highlighted general and specific mitigation measures, hydrogen-compatible materials (such as single-crystal PWA 1480E, Inconel 625, and Hastelloy X), and hydrogen barrier coatings (such as TiAlN) for mitigating potential hydrogen-induced damages in critical boiler components. This study also identified strategic material selection approaches and advanced approaches based on computational modeling (such as phase-field modeling) and data-driven machine learning models that could be leveraged to mitigate potential equipment failures due to HE and HTHA under elevated H2 conditions. Finally, future research directions were outlined to facilitate future implementation of mitigation measures, material selection studies, and advanced approaches to promote the extensive and sustainable use of H2 in industrial boiler operations.

Single track orbit determination analysis for low Earth orbit with approximated J2 dynamics

In the domain of Space Situational Awareness (SSA), the challenges related to orbit determination and catalog correlation are notably pronounced, exacerbated by data scarcity. This study introduces an initial orbit determination methodology that relies on data obtained from a single surveillance radar, with the need for fast algorithms within an operational context serving as the main design driver. The result is a linearized least-squares fitting procedure incorporating an analytically formulated approximation of the dynamics under the J2 perturbation, valid for short-term propagation. This algorithm utilizes all available observables, including range-rate, distinguishing it from other similar methods. A significant contribution of this paper is the enhancement of estimation quality by incorporating information about the object’s predicted orbital plane into the methodology, a method denoted as OPOD. The proposed methods are evaluated through a series of simulations against a classical range and angles fitting method (GTDS) to examine the effects of track length and measurement density on the quality of full state estimation, including the impact of using arcs that are too short. The OPOD methodology shows promising results throughout a wide range of scenarios.

Cross-domain zero-shot learning for enhanced fault diagnosis in high-voltage circuit breakers

Ensuring the stability of high-voltage circuit breakers (HVCBs) is crucial for maintaining an uninterrupted supply of electricity. Existing fault diagnosis methods typically rely on extensive labeled datasets, which are challenging to obtain due to the unique operational contexts and complex mechanical structures of HVCBs. Additionally, these methods often cater to specific HVCB models and lack generalizability across different types, limiting their practical applicability. To address these challenges, we propose a novel cross-domain zero-shot learning (CDZSL) approach specifically designed for HVCB fault diagnosis. This approach incorporates an adaptive weighted fusion strategy that combines vibration and current signals. To bypass the constraints of manual fault semantics, we develop an automatic semantic construction method. Furthermore, a multi-channel residual convolutional neural network is engineered to distill deep, low-level features, ensuring robust cross-domain diagnostic capabilities. Our model is further enhanced with a local subspace embedding technique that effectively aligns semantic features within the embedding space. Comprehensive experimental evaluations demonstrate the superior performance of our CDZSL approach in diagnosing faults across various HVCB types.

Cognitive performance during exposure to moderate normobaric hypoxia after sleep restriction: Relationship to physiological and stress biomarkers

IntroductionExposure to moderate levels of simulated hypoxia has subtle cognitive effects relative to ground level, in healthy individuals. However, there are few data on the cognitive consequences of the combination of hypoxia and partial sleep deprivation, which is a classic military or civilian operational context. In this study, we tested the hypothesis that exposure to moderate hypoxia while sleep-restricted impairs several domains of cognition, and we also assessed physiological parameters and salivary concentrations of cortisol and alpha-amylase. MethodSeventeen healthy males completed two sessions of cognitive tests (sustained attention using the PVT psychomotor vigilance task and executive functions using the Go-NoGo inhibition task and N-Back working memory task) after 30 min (T + 30′) and 4 h (T + 240′) of exposure in a normobaric hypoxic tent (FIO2 = 13.6 %, ≃ 3,500 m) (HY). This was completed after one night of sleep restriction (3 a.m. to 6 a.m. bedtime, SRHY) and one night of habitual sleep (10 p.m. to 6 a.m. bedtime, HSHY) (with cross-over randomization). The two nights sleep architecture and physiological parameters (oxygen saturation (SpO2) and heart rate (HR) during T + 30′ and T + 240′sessions were analyzed. Salivary cortisol and alpha-amylase (sAA) concentrations were analyzed before hypoxia, after the T + 30′ and T + 240′ cognitive sessions, and after leaving the hypoxic tent. ResultsSustained attention (RT and number of lapses in the PVT) and executive functions (Go-NoGo and 1-Back and 2-Back parameters, as inhibition and working memory signatures) were impaired in the SRHY condition compared to HSHY. SpO2 and HR were higher after 4 h compared with 30 min of hypoxia in the HSHY condition, while only HR was statistically higher in the SRHY condition. In SRHY, salivary AA concentration was lower and cortisol was higher than in HSHY. A significant increase in sAA concentration is observed after the cognitive session at 4 h of hypoxia exposure compared to that at 30 min, only in the SRHY condition. There are significant positive correlations between reaction time and the corresponding heart rate (a non-invasive marker of physiological stress) for the executive tasks in the two sleep conditions. This was not observed for salivary levels of sAA and cortisol, respective reliable indicators of the sympathoadrenomedullary system and the hypothalamic-pituitary adrenocortical system. ConclusionExposure to moderate normobaric hypoxia (≃ 3500 m / ≃ 11,500 ft simulated) after a single night of 3-hour sleep impairs cognitive performance after 30 min and 4 h of exposure. The key determinants and/or mechanism(s) responsible for cognitive impairment when exposed to moderate hypoxia with sleep restriction, particularly on the executive function, have yet to be elucidated.

Training IoT Development for Enhancing Search and Rescue Tracking and Educational Tools

The Internet of Things (IoT) Development Training at Universidade Oriental Timor Lorosa’e aims to address challenges in tracking search and rescue teams and enhance educational tools. This training is designed to provide an in-depth understanding of IoT technology and its applications in operational and educational contexts. The methods used include theoretical sessions, hands-on practice, and project evaluation. The evaluation results show a significant increase in participants' knowledge levels: understanding of IoT concepts increased from 40% to 85%, knowledge of IoT applications rose from 35% to 80%, the ability to use IoT devices improved from 30% to 75%, and skills in developing IoT systems advanced from 25% to 70%. The success of this training reflects the positive impact of IoT technology in enhancing participants' skills, potentially bringing long-term benefits to both fields discussed at Universidade Oriental Timor Lorosa’e.

Multi-objective optimization for economic load distribution and emission reduction with wind energy integration

In today’s power systems operation, the dual challenge of optimizing economic load distribution while minimizing power plant emissions is pivotal. This challenge is accentuated by the pressing environmental concerns and the finite nature of fossil fuel reserves. In this context, renewable energy sources, notably wind power, have emerged as indispensable alternatives due to their cost-effectiveness and environmental compatibility. However, the inherent variability of wind velocity introduces uncertainty into power output, necessitating innovative approaches to address this complexity. To tackle this issue, we propose a scenario-based probabilistic approach that dynamically considers the slope rate of power output. By leveraging the Blue Whale multi-objective algorithm and employing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) criterion, we identify significant solutions from the Pareto set across a spectrum of scenarios. Our method is rigorously evaluated across various systems and operational contexts, revealing its superiority over alternative algorithms. Specifically, our approach achieves lower objective function values, reduced standard deviation, and superior overall performance. These findings underscore the critical importance of efficient power system management in balancing environmental sustainability and economic viability. By embracing innovative methodologies, we can navigate the evolving energy landscape and contribute towards a more sustainable future.

Are contemporary facial recognition algorithms making human facial comparison performance worse?

Facial recognition plays a vital role in several security and law enforcement workflows, such as passport control and criminal investigations. The identification process typically involves a facial recognition system comparing an image against a large database of faces to return a list of probable matches, called a candidate list, for review. A human then looks at the returned images to determine whether there is a match. Most evaluations of these systems tend to examine the performance of the algorithm or human in isolation, not accounting for the interaction that occurs in operational contexts. To ensure optimal whole system performance, it is important to understand how the output produced by an algorithm can impact human performance. Anecdotal claims have been made by users of facial recognition systems that the images being returned by new algorithms in these systems have become more similar in appearance compared to old algorithms, making their job of determining the presence of a match more difficult. This paper explores whether these claims are true and whether the latest facial recognition algorithms decrease human performance compared to an old algorithm from the same company. We examined the performance of 40 novice participants on 120 face matching trials. Each trial required the participant to compare a face image against a candidate list containing eight possible matches returned by either a new or old algorithm (60 trials of each). Overall, participants were more likely to make errors when presented with a candidate list from a new algorithm. Specifically, they were more likely to misidentify an incorrect identity as a match. Participants were more accurate, confident, and faster on candidate lists from the older algorithm. These findings suggest that new algorithms are generating more plausible matches, making the task of determining a match harder for humans. We propose strategies to potentially improve performance and recommendations for future research.

Advancing Military Medical Planning in Large Scale Combat Operations: Insights From Computer Simulation and Experimentation in NATO's Vigorous Warrior Exercise 2024.

The ongoing conflict in Ukraine from Russian invasion presents a critical challenge to medical planning in the context of multi-domain battle against a peer adversary deploying conventional weapon systems. The potential escalation of preventable morbidity and mortality, reaching a scale unprecedented since World War II, underscores the paramount importance of effective phases of care from Point of Injury (PoI)/Point of Wounding (PoW) or Point of Exposure (PoE) to Role 1 (R1) and Role 2 (R2) echelons of care.The NATO Vigorous Warrior (VW) Live Exercise (LIVEX) serves as a strategic platform for NATO and its partners, providing an opportunity to challenge operational concepts, experiment, innovate life-saving systems, and foster best practices across the Alliance. This study delineates the strategic application of the VW LIVEX platform for the adaptation of the computational simulation software Simulation for the Assessment and Optimization of Medical Disaster Management (SIMEDIS) within the context of Large-Scale Combat Operations (LSCO). The SIMEDIS computer simulator plays a pivotal role by furnishing real-time insights into the evolving injury patterns of patients, employing an all-hazards approach. This simulator facilitates the examination of temporal shifts in medical timelines and the ramifications of resource scarcity against both morbidity and mortality outcomes. The VW LIVEX provides a unique opportunity for systematic validation to evaluate the results of the computer simulator in a realistic setting and identify gaps for future concepts of operations. We report the process and methodologies to be evaluated at the VW LIVEX in far forward and retrospective medical support operations. Using the SIMEDIS simulator, we can define battlefield scenarios for varied situations including artillery, drone strikes, and Chemical, Biological, Radiological, Nuclear, and explosive (CBRNe) attacks. Casualty health progressions versus time are dependent on each threat. Mortality is computed based on the concepts found in Tactical Combat Casualty Care (TCCC) of "self-aid"/"buddy-aid" factoring in the application or absence of definitive traumatic hemorrhage control and on the distribution policy of victims to medical treatment facilities through appropriate Command and Control (C2) ("Scoop and Run" versus "Stay and Play"). The number of medical supplies available along with the number of transport resources and personnel are set and are scalable, with their effect on both morbidity and mortality quantified.Concept of Medical Operations can be optimized and interoperability enhanced when shared data are provided to C2 for prospective medical planning with retrospective data. The SIMEDIS simulator determines best practices of medical management for a myriad of injury types and tactical/operational situations relevant to policy making and battlefield medical planning for LSCO. The VW LIVEX provides a Concept Development and Experimentation platform for SIMEDIS refinement and conclusive insights into medical planning to reduce preventable morbidity and mortality. Recommending further iterations of similar methodologies at other NATO LIVEXs for validation is crucial, as is information sharing across the Alliance and partners to ensure best practice standards are met.

Cover-source mismatch in steganalysis: systematic review

Operational steganalysis contends with a major problem referred to as the cover-source mismatch (CSM), which is essentially a difference in distribution caused by different parameters and settings over training and test data. Despite it being of fundamental importance in an operational context, the CSM problem is often overlooked in the literature. With the goal to increase the visibility of this problem and attract the interest of the community, the present paper proposes a systematic review of the literature. It summarizes gathered knowledge and major open questions over the last 20 years of active research on CSM: terminology, methods of measurement, known causes, and mitigation strategies. Over 100 papers exploring, mitigating, assessing, or discussing steganalysis under train-test mismatch were collected by sampling scholar databases, and tracing references, cited and generated. For image steganalysis, the literature provided enough evidence to quantify the impact of causes, and the effectiveness of mitigation strategies.

Operant ethanol self-administration behaviors do not predict sex differences in continuous access home cage drinking

Understanding sex differences in disease prevalence is critical to public health, particularly in the context of alcohol use disorder (AUD). The goal of this study was to understand sex differences in ethanol drinking behavior and define the precise conditions under which sex differences emerge. Consistent with prior work, C57BL/6J females drank more than males under continuous access two-bottle choice conditions. However, using ethanol self-administration - where an operant response results in access to an ethanol sipper for a fixed time period - we found no sex differences in operant response rates or ethanol consumption (volume per body weight consumed, as well as lick behavior). This remained true across a wide range of parameters including acquisition, when the ethanol sipper access period was manipulated, and when the concentration of the ethanol available was scaled. The only sex differences observed were in total ethanol consumption, which was explained by differences in body weight between males and females, rather than by sex differences in motivation to drink. Using dimensionality reduction approaches, we found that drinking behavior in the operant context did not cluster by sex, but rather clustered by high and low drinking phenotypes. Interestingly, these high and low drinking phenotypes in the operant context showed no correlation with those same categorizations in the home cage context within the same animals. These data underscore the complexity of sex differences in ethanol consumption, highlighting the important role that drinking conditions/context plays in the expression of these differences.

Value of DNA mixture-to-mixture comparisons within an operational context

Since 1995, national forensic DNA databases have used a maximum number of contributors, and a minimum number of loci to reduce the risk of providing false leads. DNA profiles of biological traces that do not meet these criteria cannot be loaded into these databases. In 2023, about 10 % of more than 15,000 trace DNA profiles analyzed in western Switzerland were not compared at the national level, even though they were considered to be interpretable, mainly because they contained the DNA from more than two persons. In this situation, police services can request local comparisons with DNA profiles of known persons and/or with other traces, but this occurs in only a small proportion of cases, so that DNA mixtures are rarely used to help detect potential series. The development of probabilistic genotyping software and its associated tools have made possible the efficient performance of this type of comparison, which is based on likelihood ratios (LR) rather than on the number of shared alleles.To highlight potential common contributors for investigation and intelligence purposes, the present study used the mixture-to-mixture tool of the software STRmix v2.7 to compare 235 DNA profiles that cannot be searched the Swiss DNA database. These DNA profiles originated from traces collected by six different police services in 2021 and 2022. Traces were selected by the police based on information that indicated that they were from potential series. Associations between profiles were compared with expected investigative associations to define the value of this approach. Among the 27,495 pairwise comparisons of DNA profiles, 88 pairs (0.3 %) showed at least one potential common contributor when using a LR threshold of 1000. Of these 88 pairs, 60 (68.2 %) were qualified by the police services as “expected” (60/88), 22 (25.0 %) as “possible”, and six (6.8 %) as “unexpected”. Although it is important to consider the limits of this approach (e.g., adventitious or missed associations, cost/benefit evaluation, integration of DNA mixture comparison in the process), these findings indicate that non CODIS loadable DNA mixtures could provide police agencies with information concerning potential series at both the local and national level.

Energy recovery in automotive systems: A review of regenerative braking, flywheel, thermoelectric, rankin cycle and electric turbo compound

In the backdrop of escalating global vehicle proliferation and the consequent surge in carbon dioxide (CO2) emissions, there is a compelling urgency to innovate energy recovery methods during vehicular operations for heightened energy efficiency and substantial environmental emission reductions. This paper discussed a variety of vehicle technologies to address environmental emissions issues and critically evaluates contemporary energy recovery technologies across three core dimensions: automotive thermal energy conversion, electronic turbine systems (ETC), and kinetic energy recovery systems. By analyzing empirical data encompassing CO2 emission reduction, energy recovery efficiency enhancement, fuel economy improvement, and applicability diversity across vehicle models, the study aims to establish a scientific foundation for continuous vehicular energy efficiency enhancement and emission reduction. This entails a comprehensive investigation of internal combustion engine principles for potential heat energy recovery, juxtaposing conventional and advanced cycles like the Rankine cycle. Moreover, it involves assessing ETC effectiveness in utilizing exhaust energy while acknowledging system complexity and dynamic response challenges. Additionally, the performance disparities of kinetic energy recovery systems like regenerative braking and exhaust energy recovery in distinct operational contexts are examined. By meticulously comparing experimental data, the paper intends to provide a comprehensive assessment of each technologys merits and drawbacks in advancing energy efficiency and emission reduction, try to pick one or more combinations to minimise the impact, thereby guiding future research and application in automotive energy recovery, and contributing to sustainable automotive engineering technology advancement.