Autonomous Domains Research Articles

From Charles V to Philip IV of Spain: the concepts of Monarchia Universalis and Catholic Monarchy

ABSTRACT This text discusses the European system in the modern age, describing the concept of ‘state’ as an object bounded by property rights and its owner’s jurisdiction. In order to maintain the state, it was necessary to keep the inhabitants in a state of submission, through either persuasion or force. State policy consisted in preserving the possessions of the state, improving and increasing it, combining statecraft with the subjects and concert with other state-holders. States were not autonomous units, but domains, and state affairs concerned ways of increasing or maintaining their ownership. When two princes married, they united their states and created a new political and institutional framework, with each spouse taking on the conflicts and alliances of his or her partner, creating entirely new situations. Each peace treaty or alliance was solidly sealed with a dynastic union, as can be seen in the example of the rise of the House of Habsburg, which became the first European power after integrating the inheritances of three great lineages: the Austrian Habsburgs, who contributed fiefdoms and states in Central and Eastern Europe, the House of Burgundy, who contributed states in France and the Low Countries, and the House of Trastámara, who contributed the kingdoms of Castile and Aragon.

Autonomous operation domain generation and reduction method supporting the seamless power supply of important loads

Autonomous operation domain generation and reduction method supporting the seamless power supply of important loads

Isolated REM Sleep without Atonia Is Not Equivalent to REM Sleep Behavior Disorder in Early-Stage Parkinson's Disease.

In early-stage Parkinson's disease (PD), rapid eye movement (REM) sleep behavior disorder (RBD) predicts poor cognitive and motor outcome. However, the baseline significance and disease evolution associated with isolated REM sleep without atonia (iRWA, ie, enhanced muscle tone during 8.7% of REM sleep, but no violent behavior) are not well understood. The objective is to determine whether iRWA was a mild form of RBD and progressed similarly over time. Participants with early PD (<4 years from medical diagnosis) were included from 2014 to 2021 in a longitudinal study. They underwent interviews and examinations in the motor, cognitive, autonomous, psychiatric, sensory, and sleep domains every year for 4 years along with a video polysomnography and magnetic resonance imaging examination of the locus coeruleus/subcoeruleus complex (LC/LsC) at baseline. The clinical characteristics were compared between groups with normal REM sleep, with iRWA and with RBD, at baseline and for 4 years. Among 159 PD participants, 25% had RBD, 25% had iRWA, and 50% had normal REM sleep. At baseline, the non-motor symptoms were less prevalent and the LC/LsC signal intensity was more intense in participants with iRWA than with RBD. Over 4 years, participants with normal REM sleep and with iRWA had a similar cognitive and motor trajectory, whereas participants with RBD had greater cognitive and motor decline. We demonstrated that iRWA is frequent in early PD, but is not a milder form of RBD. Both groups have distinct baseline characteristics and clinical trajectories. They should be distinguished in clinical routine and research protocols. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

A data and knowledge driven autonomous intelligent manufacturing system for intelligent factories

The non-ferrous metal industry is encountering several challenges, including production efficiency, manufacturing information fragmentation, and human health problems, which highlights the importance of implementing autonomous intelligent manufacturing systems (AIMS). Recently, the foundation model like GPT-4, has garnered attentions due to its exceptional capabilities and proficiency in diverse domains and tasks, facilitating the realization of AIMS. However, the existing foundation models can only address basic general-purpose tasks and are difficult to use for industrial applications. In this paper, we propose a data and knowledge driven AIMS with industrial-generative pretrained Transformer (Industrial-GPT) for intelligent factories. The paradigms and architecture of autonomous intelligent factories are firstly defined. Then, we explore the mechanism with knowledge graph, digital twin, and Industrial-GPT, including multi-level autonomous perception, cross layer and domain cognition, and event-driven collaborative decision-making. Finally, the detailed case study is based on the cooperation with a zinc smelting intelligent factory to achieve networked collaborative manufacturing, and explores the theory and realization mechanism of AIMS on a small scale. We explore the experimental analyses, evaluation mechanisms and platform applications of AIMS at the workshop level. We believe this will help to realize larger scale AIMS in the future.

Assessing the role of biomarker feedback in a 12-week community weight management programme among overweight men: A pilot study.

The primary objective of this study was to examine the feasibility of recruitment, retention, and delivery of biomarker feedback among men partaking in Shape Up, a physical activity for health programme. Secondarily, it explored the potential effects of biomarker feedback on participants' adherence and motivation levels. In this two-arm non-randomised pilot study, 46 men (mean±SD age 46.0±8.6 years) participating in the 12-week Shape Up programme were assigned to a control group or biomarker feedback group. Biomarker feedback consisted of 3 blood test panels: at baseline, 12 weeks, and 24 weeks (follow-up), each with a doctor's report. Both groups completed questionnaires (Short Active Lives Survey [SALS], Treatment Self-Regulation Questionnaire [TSRQ], and EQ-5D-5L) to gauge levels of motivation and engagement. The mean difference was calculated between baseline and programme end. Recruitment, retention, and attendance rates were determined. Mean recruitment (approach-to-consent), retention, and attendance rates were 96.3%, 92.3%, and 83.7% in the control group, and 85.2%, 86.2%, and 81.5% in the biomarker feedback group, respectively. Of biomarker feedback group participants, 86.4% attended their 12-week blood test and 45.5% attended their follow-up blood test. The mean 12-week differences (95% CI) for the control group and biomarker feedback group were 138.1 (2.7, 273.5) and 467.3 (249.4, 685.2) for active minutes per week, 0.2 (-0.8, 1.2) and 0.4 (0.0, 0.8) for autonomous TSRQ domains, 0.2 (-0.3, 0.8) and 0.8 (0.1, 1.4) for controlled TSRQ domains, and 19 (12.7, 26.5) and 27.2 (19.8, 34.6) for EQ-5D-5L scores, respectively. Biomarker feedback was well-received by participants and deemed feasible, with high recruitment and retention rates for the duration of the 12-week programme. Biomarker feedback may affect aspects of motivation but did not appear to influence adherence to the programme. Biomarker data may provide additional evidence of programme efficacy. Important design considerations are provided for definitive larger-scale trials.

RAMPART: Reinforcing Autonomous Multi-agent Protection through Adversarial Resistance in Transportation

In the field of multi-agent autonomous transportation, such as automated payload delivery or highway on-ramp merging, agents routinely exchange knowledge to optimize their shared objective and adapt to environmental novelties through Cooperative Multi-Agent Reinforcement Learning (CMARL) algorithms. This knowledge exchange between agents allows these systems to operate efficiently and adapt to dynamic environments. However, this cooperative learning process is susceptible to adversarial poisoning attacks, as highlighted by contemporary research. Particularly, the poisoning attacks where malicious agents inject deceptive information camouflaged within the differential noise, a pivotal element for differential privacy (DP)-based CMARL algorithms, pose formidable challenges to identify and overcome. The consequences of not addressing this issue are far-reaching, potentially jeopardizing safety-critical operations and the integrity of data privacy in these applications. Existing research has strived to develop anomaly detection-based defense models to counteract conventional poisoning methods. Nonetheless, the recurring necessity for model offloading and retraining with labeled anomalous data undermines their practicality, considering the inherently dynamic nature of the safety-critical autonomous transportation applications. Further, it is imperative to maintain data privacy, ensure high performance, and adapt to environmental changes. Motivated by these challenges, this paper introduces a novel defense mechanism against stealthy adversarial poisoning attacks in the autonomous transportation domain, termed Reinforcing Autonomous Multi-agent Protection through Adversarial Resistance in Transportation (RAMPART). Leveraging a GAN model at each local node, RAMPART effectively filters out malicious advice in an unsupervised manner, whilst generating synthetic samples for each state-action pair to accommodate environmental uncertainties and eliminate the need for labeled training data. Our extensive experimental analysis, conducted in a Private Payload Delivery Network (PPDN) —a common application in the autonomous multi-agent transportation domain—demonstrates that RAMPART successfully defends against a DP-exploited poisoning attack with a \(30\% \) attack ratio, achieving an F1 score of 0.852 and accuracy of \(96.3\% \) in heavy-traffic environments .

Coordinated inositide lipid-phosphatase activities of synaptojanin modulates actin cytoskeleton organization

Synaptojanin proteins are evolutionarily conserved regulators of vesicle transport and membrane homeostasis. Disruption of synaptojanin function has been implicated in a wide range of neurological disorders. Synaptojanins act as dual-functional lipid phosphatases capable of hydrolyzing a variety of phosphoinositides (PIPs) through autonomous SAC1-like PIP 4-phosphatase and PIP2 5-phosphatase domains. The rarity of an evolutionary configuration of tethering two distinct enzyme activities in a single protein prompted us to investigate their individual and combined roles in budding yeast. Both PIP and PIP2 phosphatase activities are encoded by multiple gene products and are independently essential for cell viability. In contrast, we observed that the synaptojanin proteins utilized both lipid-phosphatase activities to properly coordinate polarized distribution of actin during the cell cycle. Expression of each activity untethered (in trans) failed to properly reconstitute the basal actin regulatory activity; whereas tethering (in cis), even through synthetic linkers, was sufficient to complement these defects. Studies of chimeric proteins harboring PIP and PIP2 phosphatase domains from a variety of gene products indicate synaptojanin proteins have highly specialized activities and that the length of the linker between the lipid-phosphatase domains is critical for actin regulatory activity. Our data are consistent with synaptojanin possessing a strict requirement for both two-domain configuration for some but not all functions and provide mechanistic insights into a coordinated role of tethering distinct lipid-phosphatase activities.

Physics-informed models of domain wall dynamics as a route for autonomous domain wall design via reinforcement learning

Prompted by limited available data, we explore data-aggregation strategies for material datasets, aiming to boost machine learning performance. Our findings suggest that intuitive aggregation schemes are ineffective in enhancing predictive accuracy.

Privacy-preserving trust management method based on blockchain for cross-domain industrial IoT

The Industrial Internet of Things (IIOT) contains many devices from different autonomous domains (e.g., factories), which need to cooperate to complete complex manufacturing process. Devices from different domains collaborate with each other, which greatly raises trust concerns about device-to-device interactions. Existing trust management approaches may result in the risk of privacy leakage and low accuracy of trust evaluation. Thus, trust issues during interaction remain unsolved but imperative. In this paper, we propose a blockchain-based privacy-preserving trust management architecture PPTMA. Specifically, PPTMA adopts federated learning to train a task-specific trust model for different collaborative task. Our work is the first attempt to research the relationship between the weight calculation of trust metric and the change of context in the field of trust management. To preserve the privacy of devices, differential privacy (DP) is exploited during the trust evaluation process. In addition, a game theory-based incentive mechanism is proposed to encourage the IIOT device for actively and honestly submitting the trust data into the blockchain as so to promote the accuracy of trust computing. Finally, we also design a parallel consensus protocol (OPBFT) which realizes an assembly line to speed up the efficiency of the consensus process. The idea of consensus assembly line firstly proposed by us brings new opportunities for improving the consensus efficiency. Extensive experiments have been conducted to show the effectiveness and efficiency of the proposed method.

Modeling and analysing Cyber–Physical Systems in HOL-CSP

Modelling and Analysing Cyber–Physical Systems (CPS) is a challenge for Formal Methods and therefore a field of active research. It is characteristic of CPSs that models comprise aspects of Newtonian Physics appearing in system environments, the difficulties of their discretization, the problems of communication and interaction between actors in this environment as well as calculations respecting time-bounds. We present a novel framework to address these problems developed with industrial partners involved in the Autonomous Car domain. Based on HOL-CSP, we model time, physical evolution, “scenes” (global states) and “scenarios” (traces) as well as the interaction of “actors” (vehicles, pedestrians, traffic lights) inside this framework. In particular, discrete samplings are modelled by infinite internal choices.For several instances of the modelling framework, we give formal proofs of a particular safety property for Autonomous Cars: if each car follows the same driving strategy defined by the so-called Responsibility-Sensitive Safety (RSS), no collision will occur. The proofs give rise to a number of variants of RSS and optimizations as well as a test-case partitioning of abstract test cases and a test-strategy for integration tests.

Autonomous Cross Domain Adaptation Under Extreme Label Scarcity.

A cross domain multistream classification is a challenging problem calling for fast domain adaptations to handle different but related streams in never-ending and rapidly changing environments. Notwithstanding that existing multistream classifiers assume no labeled samples in the target stream, they still incur expensive labeling costs since they require fully labeled samples of the source stream. This article aims to attack the problem of extreme label shortage in the cross domain multistream classification problems where only very few labeled samples of the source stream are provided before process runs. Our solution, namely, Learning Streaming Process from Partial Ground Truth (LEOPARD), is built upon a flexible deep clustering network where its hidden nodes, layers, and clusters are added and removed dynamically with respect to varying data distributions. A deep clustering strategy is underpinned by a simultaneous feature learning and clustering technique leading to clustering-friendly latent spaces. A domain adaptation strategy relies on the adversarial domain adaptation technique where a feature extractor is trained to fool a domain classifier by classifying source and target streams. Our numerical study demonstrates the efficacy of LEOPARD where it delivers improved performances compared to prominent algorithms in 15 of 24 cases. Source codes of LEOPARD are shared in https://github.com/wengweng001/LEOPARD.git to enable further study.

An intelligent encryption decision method for autonomous domain of multilayer satellite network

Multi-layer satellite network (MLSN) is an important part of the Space-Air-Ground Integrated Network (SAGIN), it has obvious advantages in coverage density, business processing, service efficiency and other comprehensive capabilities compared with ordinary single-layer satellite networks. However, due to the fact that many space nodes, highly dynamic topology changes and wireless links scattered in free space, MLSNs are facing the dual challenges of inefficient organization and lack of security. In this case, this paper considers providing secure encryption decision support for the return of data obtained by low earth orbit (LEO) observation satellites relying on the local autonomous region of multi-layer satellite space. By quantifying the security strength of encryption algorithms, the work to be optimized is described as the problem of maximizing the security strength under the constraint of return delay of the data. This paper thus proposes a solution based on deep reinforcement learning (DRL), which relies on reasonably setting the key supporting elements such as state, action and reward closely related to the problem to achieve fast and reliable decision output, and the simulation results show that the proposed method has better performance than many comparison methods.

Multi-party anonymous authentication scheme in heterogeneous network slicing

Network slicing is a logical network function that supports communication service requirements for specific usage scenarios or business models. It has become an essential factor in solving future network architecture. In recent years, heterogeneous network slicing between multiple autonomous domains has emerged, with significant limitations compared to single-domain networks. Data sharing in heterogeneous network slices challenge the confidentiality of sensitive data significantly. Preventing sensitive data from leaking and protecting core data have become urgent problems to be solved. Therefore, we propose a multi-party (multi-sender to multi-receiver) anonymous authentication scheme on certificateless cryptography(CLC) and identity-based cryptosystem(IBC) institutional domain Internet of Vehicles(IoV) to smart healthcare. Our scheme realizes heterogeneous communication between multiple senders and multiple receivers, and uses different system parameters for heterogeneous cryptographic systems. In this work, the limitation of one-to-many or many-to-one communication in existing heterogeneous networks is broken through. The proposed work utilizes the pseudo-identity generated by the trusted authority to communicate in IoV slices, ensuring the anonymity of the sender; The authorization Center TA can uniquely determine the true identity of the vehicle through pseudo-random values for traceability. What is more, the Lagrange interpolation formula is employed to prevent the leakage of the identities of smart healthcare slices, ensuring the anonymity of the receiver. Under the random oracle model, the proposed scheme has proved to satisfy unforgeability, confidentiality, and anonymity under the computational Diffie–Hellman problem. Experimental results indicate that our proposal achieves better performance than existing schemes.

Understanding the antecedents of hedonic motivation in autonomous vehicle technology acceptance domain: A cross-country analysis

The literature on autonomous vehicle (AV) acceptance highlights the significance of hedonic motivation in AV adoption. Numerous studies empirically confirm hedonic motivation as either the most or one of the most influential factors in the acceptance of AV. This fact calls for a need to achieve a wider understanding of the potential AV users’ perceived enjoyment (i.e., hedonic motivation). To this end, this study investigates the antecedents of hedonic motivation in the AV technology acceptance domain. The partial least square structural equations modeling approach was applied to analyze the data collected from 1823 respondents from 11 countries via an online survey. The developed hypotheses are examined for the entire sample, as well as separately for the Global North (GN) countries' sample, Global South (GS) countries' sample, and each individual country through a cross-country analysis. The results for the entire sample indicate that social influence is the strongest predictor of hedonic motivation, consistent with the findings of the GN sample. However, in the GS sample, self-efficacy emerges as the strongest predictor of hedonic motivation. Perceived safety is the second strongest predictor of hedonic motivation for both the GN and GS samples, highlighting its importance in relation to the perceived enjoyment of PAV. Trust does not significantly contribute to hedonic motivation, while the enjoyment of driving conventional cars has a small negative impact on hedonic motivation in the GS sample. The cross-country analysis reveals general patterns in the findings of the GN and GS samples, while highlighting a few exceptions. The results of the multi-group moderation analysis highlight the significant impact of the respondents' geographical distribution (GN vs GS) on their perceived enjoyment of PAV. Additionally, the analysis indicates that female respondents who enjoy driving conventional cars are less likely to perceive PAV as enjoyable compared to male participants who enjoy driving conventional cars.

Fault Injection for TensorFlow Applications

As machine learning (ML) has seen increasing adoption in safety-critical domains (e.g., autonomous vehicles), the reliability of ML systems has also grown in importance. While prior studies have proposed techniques to enable efficient error-resilience (e.g., selective instruction duplication), a fundamental requirement for realizing these techniques is a detailed understanding of the application's resilience. In this work, we present TensorFI 1 and TensorFI 2, high-level fault injection (FI) frameworks for TensorFlow-based applications. TensorFI 1 and 2 are able to inject both hardware and software faults in any general TensorFlow 1 and 2 program respectively. Both are configurable FI tools that are flexible, easy to use, and portable. They can be integrated into existing TensorFlow programs to assess their resilience for different fault types (e.g., bit-flips in particular operations or layers). We use the TensorFI 1 and TensorFI 2 to evaluate the resilience of 11 and 10 ML programs written in TensorFlow, including DNNs used in the autonomous vehicle domain. The results give us insights into why some of the models are more resilient. We also measure the performance overheads of the two injectors, and present 4 case studies, two for each tool, to demonstrate their utility.

Classifying and counting the Dalits in the late colonial period: The conundrum of the Jatavs

The Jatavs of the United Provinces were not legally recognised as a separate caste until 1942; and then only as a consequence of an exceptional revision of legislation. Yet, for some considerable time before, the provincial authorities had routinely treated Jatavs as if they had already been granted the status of an officially recognised distinct caste grouping. This was despite a ruling in 1933, endorsed jointly by the India Office, the Government of India, and the provincial government, that the Jatavs were not a separate caste. The case of the Jatavs is examined here in the context of the contradictions and confusion in the policies of the colonial authorities, first towards the Depressed Classes, and later in the construction of the category that eventually became the Scheduled Castes. In addition, it is argued that those contradictions also created interstices of ambiguity that many Dalit representatives explored, interrogated, and exploited as they generated the space in which to assert their agency. The history of the Jatavs is an important instance of subaltern politics participating in the procedures of the colonial regime rather than operating in some separate autonomous domain of activity. By engaging with the existing power structures and processes, Jatav leaders created the opportunity to expose and take advantage of the contradictions generated by the confusion in the exercises of classifying and counting conducted by the colonial state.

Республика Бурятия в панораме 100-летия: исторические вехи и достижения

Goals. The article aims to consider some key historical milestones and achievements in Buryatia’s nation-state building for over 100 years. The paper analyzes the conditions for the emergence of Buryat-Mongolian autonomy, and the factors that prompted reorganizations of the Republic’s political structure in the context of changes witnessed in the nationwide political agenda. Special attention is paid to the contribution of the Republic to the Great Victory, certain analytical emphases be laid on present-day challenges, achievements and development prospects. Materials and methods. The work examines unpublished documents stored at the State Archive of the Russian Federation, Russian State Archive of Sociopolitical History, and State Archive of Buryatia. The principles of historicism and consistency prove instrumental enough in reconstructing the process of nation-state building in Buryatia, identifying specific features, and tracing general trends of state development in the Republic’s political life. Results. On 30 May 1923, the Presidium of the All-Russia Central Executive Committee decided to unite Siberian and Far Eastern autonomous domains (oblasts) of Buryat-Mongols into one autonomous socialist Soviet republic centered around the city of Verkhneudinsk. It is then and there that the statehood of Buryat-Mongols — consolidated by a single territory, economic integrity, and historical ties of all resident peoples — was proclaimed. The shaping and rapid development of industries in the Republic, transformations in agriculture, education and health care converted the territory of Buryatia into a strong supporting point that would render every possible assistance to the nation during the Great Patriotic War. The post-war years up to the 1960s and early 1970s became a period of progressive development in all spheres of life. At the all-Union level, merits of the Republic were distinguished with the Order of Lenin (1958), Order of Friendship of Peoples (1972), and Order of the October Revolution (1973). Events of the late 1980s and the subsequent collapse of the USSR served as a test of state viability to the Republic. The economic depression led to an unprecedented deterioration of the social situation which could actually result in a severest interethnic conflict. However, the accumulated experiences of cross-cultural interaction, flexible administrative and managerial systems, high educational and scientific levels of the population, and a unified language environment proved instrumental in neutralizing negative trends and strengthening constructive ones — as part of interethnic dialogue within the new statehood of Russia. Despite the complicated processes of the 1980s–1990s, Buryatia confirmed its loyalty to the principles of federalism and integrity of Russia. Another important milestone was the 1994 adoption of the Constitution of the Republic of Buryatia. Over the past hundred years, the Republic has developed as a major political/legal entity of the Russian Federation — in cultural, economic, and other aspects. Nowadays, the peoples of Buryatia — regardless of faith and ethnic identity — join efforts to develop public economy and culture, shape a common destiny of the Republic and the country on the basis of friendship, harmony, and mutual understanding.

A Sufi conception of order: Ibn ʿArabī’s discourse on governance

Abstract This article outlines, through a reading of Muḥyī al-Dīn Ibn ʿArabī’s Tadbīrāt al-ilāhiyya fī iṣlāḥ al-mamlaka al-insāniyya (Divine governance in the improvement of the kingdom of the self), a Sufi conception of order that encompasses a discourse on political governance and cuts across the realms of the cosmos, the self, and the polity. It explores the isomorphic relation and mutual ‘folding’ linking the governance of the worldly realm with that of the soul. In contrast to Montesquieu’s scheme of the ‘countervailing passions’, governance at both levels is built on the Sufi notion of ‘struggle against the self (jihād al-nafs)’ and on a theory of the human being as a microcosm. Within Ibn ʿArabī’s system, the political order is not an autonomous domain with its own rationality as developed by early modern European theorists of reason of state. A single rationality guides the governance of the microcosm and the macrocosm.

Hierarchical Reinforcement Learning in Multi-Domain Elastic Optical Networks to Realize Joint RMSA

To improve the network scalability, a large elastic optical network is typically segmented into multiple autonomous domains, where each domain possesses high autonomy and privacy. This architecture is referred to as the multi-domain elastic optical network (MDEON). In the MDEON, the routing, modulation, and spectrum allocation (RMSA) for the inter-domain service requests are challenging. As a result, deep reinforcement learning (DRL) has been introduced recently where the RMSA policies are learned during the interaction of the DRL agents with the MDEON environment. Due to the autonomy of each domain in the MDEON, joint RMSA is essential to improve the overall performance. To realize the joint RMSA, we propose a hierarchical reinforcement learning (HRL) framework which consists of a high-level DRL module and multiple low-level DRL modules (one for each domain), with the collaboration of DRL modules. More specifically, for the inter-domain service requests, the high-level module obtains some abstracted information from the low-level DRL modules and generates the inter-domain RMSA decision for the low-level modules. Then the low-level DRL module gives the intra-domain RMSA decision and feeds back to the high-level module. The proposed HRL framework preserves the autonomy of each single domain while delivering effective overall network performance through the cooperation of the high-level and low-level DRL modules. Simulation results demonstrate that our proposed method outperforms previous approaches.

The C-Terminal Acid Phosphatase Module of the RNase HI Enzyme RnhC Controls Rifampin Sensitivity and Light-Dependent Colony Pigmentation of Mycobacterium smegmatis.

RNase H enzymes participate in various processes that require processing of RNA-DNA hybrids, including DNA replication, transcription, and ribonucleotide excision from DNA. Mycobacteria encode multiple RNase H enzymes, and prior data indicate that RNase HI activity is essential for mycobacterial viability. However, the additional roles of mycobacterial RNase Hs are unknown, including whether RNase HII (RnhB and RnhD) excises chromosomal ribonucleotides misincorporated during DNA replication and whether individual RNase HI enzymes (RnhA and RnhC) mediate additional phenotypes. We find that loss of RNase HII activity in Mycobacterium smegmatis (through combined deletion of rnhB/rnhD) or individual RNase HI enzymes does not affect growth, hydroxyurea sensitivity, or mutagenesis, whereas overexpression (OE) of either RNase HII severely compromises bacterial viability. We also show that deletion of rnhC, which encodes a protein with an N-terminal RNase HI domain and a C-terminal acid phosphatase domain, confers sensitivity to rifampin and oxidative stress as well as loss of light-induced carotenoid pigmentation. These phenotypes are due to loss of the activity of the C-terminal acid phosphatase domain rather than the RNase HI activity, suggesting that the acid phosphatase activity may confer rifampin resistance through the antioxidant properties of carotenoid pigment production. IMPORTANCE Mycobacteria encode multiple RNase H enzymes, with RNase HI being essential for viability. Here, we examine additional functions of RNase H enzymes in mycobacteria. We find that RNase HII is not involved in mutagenesis but is highly toxic when overexpressed. The RNase HI enzyme RnhC is required for tolerance to rifampin, but this role is surprisingly independent of its RNase H activity and is instead mediated by an autonomous C-terminal acid phosphatase domain. This study provides new insights into the functions of the multiple RNase H enzymes of mycobacteria.