Large-scale Control Research Articles

In situ mixer calibration for superconducting quantum circuits

Mixers play a crucial role in superconducting quantum computing, primarily by facilitating frequency conversion of signals to enable precise control and readout of quantum states. However, imperfections, particularly local oscillator leakage and unwanted sideband signal, can significantly compromise control fidelity. To mitigate these defects, regular and precise mixer calibrations are indispensable, yet they pose a formidable challenge in large-scale quantum control. Here, we introduce an in situ and scalable mixer calibration scheme using superconducting qubits. Our method leverages the qubit's response to imperfect signals, allowing for calibration without modifying the wiring configuration. We experimentally validate the efficacy of this technique by benchmarking single-qubit gate error and qubit coherence time.

Clinical trials of intratesticular administration of nanostructured lipid carriers encapsulated alpha-mangostin: Safety and efficacy on feline reproductive health

Surgical castration is a primary method for controlling male fertility, but it is impractical for large-scale population control of stray animals. Developing nanoparticle-mediated sterilants that induce cell apoptosis rather than necrosis is a complex and promising area of research. This study aimed to investigate the impact of intratesticular administration of alpha-mangostin encapsulated in nanostructured lipid carriers (AM-NLC) on testicular changes and any associated adverse effects over a 168-day observation period. Thirty-two healthy mature tomcats were enrolled. None of the cats treated with either AM-NLC (n = 28) or blank NLC (n = 4) exhibited noticeable complications related to pain or stress throughout the study, as assessed by clinical examination, blood profiles, and serum amyloid A levels. Histopathological analysis of AM-NLC treated cats revealed seminiferous epithelium degeneration, leading to defective tubules. Key findings included germ cell depletion, disorganized spermatogenic cells without spermatids in certain areas, apoptotic bodies, and intracytoplasmic vacuolization. The intertubular compartment showed no signs of inflammation, hyalinization, fibrosis, or necrosis. Despite widespread degeneration, some normal tubules were present in focal areas. The severity score of seminiferous tubule degeneration significantly increased from day 56 onwards (P < 0.05), suggesting a gradual and progressive compromise of the seminiferous epithelium. In contrast, testes from the blank-NLC group exhibited normal spermatogenesis. Overall, there were no significant changes in the volume of dissected testes, serum testosterone levels, or apoptotic index in AM-NLC-treated cats (P > 0.05). In conclusion, this study represents the first in vivo investigation of apoptotic-inducing agents as a novel nanomedicine-based antifertility compound for non-surgical castration in male animals. While the AM-NLC formulation proved safe for intratesticular administration, it failed to induce infertility in cats, as epididymal spermatozoa persisted throughout the study. Further research into alternative apoptosis-inducing nanomedicine sterilants remains both essential and challenging.

Enhanced COVID-19 Detection from X-ray Images with Convolutional Neural Network and Transfer Learning.

The global spread of Coronavirus (COVID-19) has prompted imperative research into scalable and effective detection methods to curb its outbreak. The early diagnosis of COVID-19 patients has emerged as a pivotal strategy in mitigating the spread of the disease. Automated COVID-19 detection using Chest X-ray (CXR) imaging has significant potential for facilitating large-scale screening and epidemic control efforts. This paper introduces a novel approach that employs state-of-the-art Convolutional Neural Network models (CNNs) for accurate COVID-19 detection. The employed datasets each comprised 15,000 X-ray images. We addressed both binary (Normal vs. Abnormal) and multi-class (Normal, COVID-19, Pneumonia) classification tasks. Comprehensive evaluations were performed by utilizing six distinct CNN-based models (Xception, Inception-V3, ResNet50, VGG19, DenseNet201, and InceptionResNet-V2) for both tasks. As a result, the Xception model demonstrated exceptional performance, achieving 98.13% accuracy, 98.14% precision, 97.65% recall, and a 97.89% F1-score in binary classification, while in multi-classification it yielded 87.73% accuracy, 90.20% precision, 87.73% recall, and an 87.49% F1-score. Moreover, the other utilized models, such as ResNet50, demonstrated competitive performance compared with many recent works.

Deep learning on chest X-ray and computed tomography scans for detection of COVID-19 as a part of a network-centric digital health stack for future pandemics

Developing a reliable rapid screening protocol for highly infectious diseases like COVID-19 is of paramount interest since it facilitates the isolation of infected patients from the rest of the population. Reverse-transcription polymerase chain reaction (RT-PCR) test is presently the most widely accepted gold-standard test to detect COVID-19. In this method, the RNA of the virus is duplicated by a process called reverse transcription to form DNA for facilitating the copying process. Fluorescent dye is attached to the viral genetic material and copied billions of times through the process called polymerase chain reaction. Enhanced fluorescence is used to identify the presence of genetic material of the virus. These tests are time-consuming and have significant false negatives, i.e., a person with COVID-19 might be categorized as not having the virus. Large-scale RT-PCR testing has its own share of problems such as logistics, availability and affordability in underdeveloped nations, and reliability of the test results. Machine learning algorithms can act as a cheaper supplementary/alternative diagnostic tool for the testing process. In the current study, using publicly available chest X-ray image datasets, different convolutional neural network (CNN)-based models were developed for efficient identification of COVID-19 infected patients, and their efficacies were compared. Key innovations in training the CNNs are discussed. Our results indicate that EfficientNet, SeResNext, and ResNet are best at classifying normal, pneumonia and COVID-19 cases, respectively. The ResNet architecture with transfer learning performed best at detecting COVID-19 with an accuracy of 94%, a rate far superior to that in the RT-PCR test, which is typically in the range of 70 &amp;ndash; 80%. This is particularly attractive as an additional noninvasive protocol since such technology-augmented detection is likely to help in reducing the psychological refractory period due to COVID-19 infections. Toward the healthy lung initiative in the post-COVID-19 era, we propose close coupling of the present diagnostic protocols with digital approaches to ensure more reliable personal care within the ambit of large-scale pandemic control mechanisms. Such integration with emerging technological tools can create a benchmark for the first line of defense against future global pandemics.

Design and Analysis of High Precision Flow Proportional Regulating Valve Based on Piezoelectric Drive

Abstract With the development of micro-satellite propulsion system towards high precision, light and miniaturization, higher requirements are put forward for precise adjustment of propulsion system. Based on the principle of piezoelectric micro-displacement drive, this paper designs a light and small proportional flow control valve, adopts compliant amplification mechanism, develops the throttling design of small flow in the form of valve needle, analyzes the flow characteristics in the proportional adjustment process under different parameters, and realizes the micro-flow control ability with a flow control range of 0∼2mg/s and a flow control accuracy better than 3%. This design and analysis method can meet the needs of large-scale and high-precision flow control, and provide technical support for subsequent engineering applications.

Molecular Characterization of Carbapenem and Colistin Resistance in Klebsiella pneumoniae Isolates Obtained from Clinical Samples at a University Hospital Center in Algeria.

The current study aimed to determine the molecular mechanisms of carbapenem and colistin resistance among the clinical isolates of Klebsiella pneumoniae from hospitalized patients admitted to a university hospital in Eastern Algeria. In total, 124 non-duplicate isolates of K. pneumoniae were collected from September 2018 to April 2019. Bacterial identification was performed using MALDI-TOF MS. The presence of extended spectrum β-lactamase (ESBL) genes, carbapenemase genes, chromosomal mutation and mcr genes in colistin-resistant K. pneumoniae were evaluated by PCR. ESBLs represented a rate of 49.1% and harbored blaCTX-M, blaTEM and blaSHV genes. Concerning carbapenems, 12 strains (9.6%) were resistant to ertapenem (MIC: 1-32 μg/mL), of which one strain (0.8%) was also resistant to imipenem (MIC: 32 μg/mL). Among these strains, nine (75%) harbored blaOXA-48 gene. Seven strains (5.6%) expressed resistance to colistin (MIC: 2-32 μg/mL), of which two harbored mcr-8 and mgrB genes simultaneously. The existence of a double resistance to colistin in the same strain is new in Algeria, and this could raise concerns about the increase in levels of resistance to this antibiotic (MIC: 32 μg/mL). The mgrB gene alone was observed in five isolates (71.4%), including two strains harboring blaOXA-48. This is the first report revealing the presence of K. pneumoniae strains carrying the blaOXA-48 gene as well as a mutation in the mgrB gene. Large-scale surveillance and effective infection control measures are also urgently needed to prevent the outbreak of various carbapenem- and colistin-resistant isolates.

Robust decentralized controller design for highly Maneuverable Aircraft

Large-scale control systems often encounter challenges due to their intricate control structures, leading to delays and uncertainties when traditional centralized control methods are implemented in engineering applications. In response to these challenges and with the aim of enhancing control efficiency, the exploration of decentralized control approaches has become increasingly prominent. This paper presents two decentralized control strategies: fixed structure control theory, which is built upon the foundational principles of traditional robust controller design, and the weighted model approximation method, which integrates weighted model reduction with optimization strategies. The fixed structure control theory predetermines architecture of the controllers, significantly alleviating the computational burden associated with the development of robust controllers. Conversely, the weighted model approximation method proposes a more intuitive and succinct pathway for controller design and engineering application. The efficacy of these strategies is evaluated through their application to the Highly Maneuverable Aircraft (HIMAT) system. The results demonstrate that, the fixed structure control theory generally exhibits good performance. However, in certain engineering scenarios, the conclusions derived from the weighted model approximation method for decentralized controller design are easier to comprehend and facilitate rapid application.

Large-scale control in turbulent flows over surface riblets

The drag reduction efficacy of a large-scale flow control over a rough surface is studied via direct numerical simulations of turbulent channels (at friction Reynolds numbers Reτ=180) by combining together wall riblets and streamwise counter-rotating swirls. In particular, the height of triangular riblets is h+≈10 (+indicating wall units), while the number of riblets (NRib in the range 1–56) along the periodic spanwise direction is varied to find the optimum. The swirls are generated by the spanwise opposed wall-jet forcing (SOJF) in the Navier–Stokes equation, whose controlling parameters follow the optimal ones as for the smooth wall. In total, 12 cases of combined SOJF and riblets are performed to investigate the coupling effects between the two methods. We find a range of NRib=7–14 (with the spanwise width z+≈140−280) yields the largest drag reduction (up to 20%) for Reτ=180, much higher than riblets control only (about 3%). Compared to SOJF control only, riblets suppress the secondary swirls of SOJF hence decreasing drag, while the lateral and down washing motions of SOJF impinging on riblets would increase drag—the opposite two effects thus giving rise to an optimal. Through examinations on coherent structures, we elucidate that the attenuation of both large-scale coherent motions and small-scale random fluctuations leads to the net drag reduction. We conclude that large-scale control is a robust approach in the cases of rough surfaces, and the parameters can be selected for maximum drag reduction in each particular situation.

Efficient and scalable reinforcement learning for large-scale network control

The primary challenge in the development of large-scale artificial intelligence (AI) systems lies in achieving scalable decision-making—extending the AI models while maintaining sufficient performance. Existing research indicates that distributed AI can improve scalability by decomposing complex tasks and distributing them across collaborative nodes. However, previous technologies suffered from compromised real-world applicability and scalability due to the massive requirement of communication and sampled data. Here we develop a model-based decentralized policy optimization framework, which can be efficiently deployed in multi-agent systems. By leveraging local observation through the agent-level topological decoupling of global dynamics, we prove that this decentralized mechanism achieves accurate estimations of global information. Importantly, we further introduce model learning to reinforce the optimal policy for monotonic improvement with a limited amount of sampled data. Empirical results on diverse scenarios show the superior scalability of our approach, particularly in real-world systems with hundreds of agents, thereby paving the way for scaling up AI systems.

Exploring the use of fast electron backscattering for the determination of the C/O ratios: possible applications to graphene oxide and reduced graphene oxide

GO and rGO are carbon nanostructures with a wide range of applications. The carbon/oxygen ratio (C/O) is a basic parameter that determines many of its essential properties. The large-scale production of these materials requires the use of fast and representative methods to determine the C/O ratio. In the present work, the feasibility of using fast electron backscattering to determine this relationship was explored. The method is rapid and does not require previous sample treatment, its accuracy is approximately 11%–15% for C/O ∼ 10–35 and its precision is around 0.4%. It was estimated that the backscattered electrons being detected came from a sample mass of 0.04–0.4 g, depending on the synthesis method used. The accuracy of the backscatter method is approximately equal to that of EDX and XPS, but it surpasses them in precision and representativeness. Backscattering equipments are more economical when compared to the above-mentioned techniques; however, it can be affected by the presence of impurities. Therefore, we consider it an appropriate method for the large-scale control of samples of GO and rGO produced by a given technology and calibrated with a rigorous elemental analysis.

Large-Scale Solar-Powered UAV Attitude Control Using Deep Reinforcement Learning in Hardware-in-Loop Verification

Large-scale solar-powered unmanned aerial vehicles possess the capacity to perform long-term missions at different altitudes from near-ground to near-space, and the huge spatial span brings strict disciplines for its attitude control such as aerodynamic nonlinearity and environmental disturbances. The design efficiency and control performance are limited by the gain scheduling of linear methods in a way, which are widely used on such aircraft at present. So far, deep reinforcement learning has been demonstrated to be a promising approach for training attitude controllers for small unmanned aircraft. In this work, a low-level attitude control method based on deep reinforcement learning is proposed for solar-powered unmanned aerial vehicles, which is able to interact with high-fidelity nonlinear systems to discover optimal control laws and can receive and track the target attitude input with an arbitrary high-level control module. Considering the risks of field flight experiments, a hardware-in-loop simulation platform is established that connects the on-board avionics stack with the neural network controller trained in a digital environment. Through flight missions under different altitudes and parameter perturbation, the results show that the controller without re-training has comparable performance with the traditional PID controller, even despite physical delays and mechanical backlash.

A learning optimization for resilience enhancement of risk-informed traffic control system with hazardous materials transportation under uncertainty

A learning optimization is proposed to enhance overall resilience of interdependent traffic systems with hazardous materials (hazmat) transportation under uncertainty. To this end, three interconnected systems are proposed against traffic dynamics and congestion in a complex environment of regular traffic and hazmat carriers incurred with time-varying travel cost. In order to increase control resilience against traffic instability, a reinforcement learning hazmat network is proposed. In order to efficiently improve computation resilience against disruption of risk uncertainty, a resilience-aware learning optimization is proposed to obtain optimal solutions. In order to demonstrate computational performance of proposed approach, numerical experiments are performed at a real-world city under various kinds of traffic conditions. Computational comparisons are numerically made with stochastic and robust optimization at large-scale traffic control systems. As it reported, the proposed learning-based optimization can better greatly enhance overall traffic system resilience, control resilience and computation resilience compared to other models under mixed risk of hazmat transportation and uncertain demand.

Prediction-Based Control of Large-Scale Networked Control Systems

Prediction-Based Control of Large-Scale Networked Control Systems

The recombinant shingles vaccine is associated with lower risk of dementia

There is emerging evidence that the live herpes zoster (shingles) vaccine might protect against dementia. However, the existing data are limited and refer only to the live vaccine, which is now discontinued in the United States and many other countries in favor of a recombinant vaccine. Whether the recombinant shingles vaccine protects against dementia remains unknown. Here we used a natural experiment opportunity created by the rapid transition from the use of live to the use of recombinant vaccines to compare the risk of dementia between vaccine types. We show that the recombinant vaccine is associated with a significantly lower risk of dementia in the 6 years post-vaccination. Specifically, receiving the recombinant vaccine is associated with a 17% increase in diagnosis-free time, translating into 164 additional days lived without a diagnosis of dementia in those subsequently affected. The recombinant shingles vaccine was also associated with lower risks of dementia than were two other vaccines commonly used in older people: influenza and tetanus–diphtheria–pertussis vaccines. The effect was robust across multiple secondary analyses, and was present in both men and women but was greater in women. These findings should stimulate studies investigating the mechanisms underpinning the protection and could facilitate the design of a large-scale randomized control trial to confirm the possible additional benefit of the recombinant shingles vaccine.

Tunable quantum emitters on large-scale foundry silicon photonics

Controlling large-scale many-body quantum systems at the level of single photons and single atomic systems is a central goal in quantum information science and technology. Intensive research and development has propelled foundry-based silicon-on-insulator photonic integrated circuits to a leading platform for large-scale optical control with individual mode programmability. However, integrating atomic quantum systems with single-emitter tunability remains an open challenge. Here, we overcome this barrier through the hybrid integration of multiple InAs/InP microchiplets containing high-brightness infrared semiconductor quantum dot single photon emitters into advanced silicon-on-insulator photonic integrated circuits fabricated in a 300 mm foundry process. With this platform, we achieve single-photon emission via resonance fluorescence and scalable emission wavelength tunability. The combined control of photonic and quantum systems opens the door to programmable quantum information processors manufactured in leading semiconductor foundries.

Potential Association of The Pathogenic Kruppel-like Factor 14 (KLF14) and Adiponectin (ADIPOQ) SNVs with Susceptibility to T2DM.

To evaluate the associations of the pathogenic variants in Kruppel-like Factor 14 (KLF 14) and Adiponectin (ADIPOQ) with susceptibility to type 2 diabetes mellitus (T2DM). Type 2 diabetes mellitus (T2DM) is a pandemic metabolic disease characterized by increased blood sugar and caused by resistance to insulin in peripheral tissues and damage to pancreatic beta cells. Kruppel-like Factor 14 (KLF-14) is proposed to be a regulator of metabolic diseases, such as diabetes mellitus (DM) and obesity. Adiponectin (ADIPOQ) is an adipocytokine produced by the adipocytes and other tissues and was reported to be involved in T2DM. To study the possible association of the KLF-14 rs972283 and ADIPOQ-rs266729 with the risk of T2DM in the Saudi population. We have evaluated the association of KLF-14 rs972283 C>T and ADIPOQ-rs266729 C>G SNV with the risk to T2D in the Saudi population using the Amplification Refractory Mutation System PCR (ARMS-PCR), and blood biochemistry analysis. For the KLF-14 rs972283 C>T SNV we included 115 cases and 116 healthy controls, and ADIPOQ-rs266729 C>G SNV, 103 cases and 104 healthy controls were included. Results indicated that the KLF-14 rs972283 GA genotype and A allele were associated with T2D risk with OR=2.14, p-value= 0.014 and OR=1.99, p-value=0.0003, respectively. Results also ADIPOQ-rs266729 CG genotype and C allele were associated with an elevated T2D risk with an OR=2.53, p=0.003 and OR=1.66, p-value =0.012, respectively. We conclude that SNVs in KLF-14 and ADIPOQ are potential loci for T2D risk. Future large-scale studies to verify these findings are recommended. These results need further verifications in protein functional and large-scale case control studies before being introduced for genetic testing.

Large-Scale Traffic Signal Control Using Constrained Network Partition and Adaptive Deep Reinforcement Learning

Large-Scale Traffic Signal Control Using Constrained Network Partition and Adaptive Deep Reinforcement Learning

Intersec2vec-TSC: Intersection Representation Learning for Large-Scale Traffic Signal Control

Intersec2vec-TSC: Intersection Representation Learning for Large-Scale Traffic Signal Control

Opposition flow control for reducing skin-friction drag of a turbulent boundary layer

This work explores the dynamic response of a turbulent boundary layer to large-scale reactive opposition control, at a friction Reynolds number of Reτ≈2240. A surface-mounted hot-film is employed as the input sensor, capturing large-scale fluctuations in the wall-shear stress, and actuation is performed with a single on/off wall-normal blowing jet positioned 2.4δ downstream of the input sensor, operating with an exit velocity of vj=0.4U∞. Our study builds upon the work of Abbassi [] and includes a control-calibration experiment and a performance assessment using PIV- and PTV-based flow field analyses. With the control-off calibration-experiment conducted , a transfer kernel is identified so that the velocity fluctuations that are to-be-controlled can be estimated. The controller targets large-scale high-speed zones in an “opposing” mode and low-speed zones in a “reinforcing” mode. A desynchronized mode was tested for reference and consisted of a statistically similar control mode, but without synchronization to the incoming velocity fluctuations. An energy-attenuation of about 40 % is observed for the opposing control mode in the frequency band corresponding to the passage of large-scale motions. This proves the effectiveness of the control in targeting large-scale motions: an energy-intensification of roughly 45% occurs for the reinforcing control mode instead, while no change in energy, within the wall-normal range targeted, appears with the desynchronized control mode. Moreover, direct measures of the skin-friction drag are inferred from PTV data. Results indicate that the opposing control logic yields the lowest wall-shear stress (3% lower than the desynchronized control, and 10% lower than the uncontrolled flow). Finally, a FIK-decomposition of the skin-friction coefficient revealed that the off-the-wall turbulence follows a consistent trend with the PTV-based wall-shear stress measurements, although biased by an increased shear in the wake of the boundary layer given the formation of a plume due to the jet-in-crossflow actuation. Published by the American Physical Society 2024

Large-scale control over collective cell migration using light-controlled epidermal growth factor receptors.

Receptor tyrosine kinases (RTKs) are thought to play key roles in coordinating cell movement at single-cell and tissue scales. The recent development of optogenetic tools for controlling RTKs and their downstream signaling pathways suggested these responses may be amenable to engineering-based control for sculpting tissue shape and function. Here, we report that a light-controlled EGF receptor (OptoEGFR) can be deployed in epithelial cell lines for precise, programmable control of long-range tissue movements. We show that in OptoEGFR-expressing tissues, light can drive millimeter-scale cell rearrangements to densify interior regions or produce rapid outgrowth at tissue edges. Light-controlled tissue movements are driven primarily by PI 3-kinase signaling, rather than diffusible signals, tissue contractility, or ERK kinase signaling as seen in other RTK-driven migration contexts. Our study suggests that synthetic, light-controlled RTKs could serve as a powerful platform for controlling cell positions and densities for diverse applications including wound healing and tissue morphogenesis.