Passive System Research Articles

Experimental Study of Transient Behavior of a Passive Air-Cooled Containment System Relevant to Advanced Nuclear Reactor

In advanced water-cooled nuclear reactors, the passive safety systems are preferably used to achieve enhanced safety during accidental conditions. The passive containment air cooling system (PCACS) is one of the safety systems used to remove heat from the containment during accidental conditions like a loss-of-coolant accident, station blackout, etc. in advanced nuclear reactors and small modular reactors with steel containment. The PCACS uses the buoyancy-driven flow of air to remove heat from the steel containment to avoid the over pressurization of the steel containment shell. It is extremely important to understand the natural convection around the containment shell so as to evaluate the performance of the PCACS. This paper presents an experimental investigation of the passive containment air cooling of an experimental test setup having a geometry very similar to that of an actual reactor. The air-side transient natural convection characteristics around the experimental containment system are studied in detail based on the temperature readings. The measured average heat transfer coefficient is compared with that predicted using well-known correlations available in literature. This study provides a better understanding of the natural convection flow around the containment and will help in further numerical investigations for actual-scale containments.

A PDMS/MWCNTs RFID flexible tag with advanced resonator design for read range enhancement in IoT monitoring systems

Low-cost and passive identification systems are necessary for real time IoT (internet of things) monitoring. The paper presents a novel designed chipless RFID tag towards read range enhancement of (2.70 mm). A novel approach of array-based resonators towards real time read range enhancement is proposed in this research work with efficient performance. A mould-based solution casting technique is used for chipless tag fabrication. Multi-Walled Carbon Nanotubes (MWCNTs) are used as electrodes with flexible Polydimethylsiloxane (PDMS) substrate. The developed battery-free tag can encode 3-bits data while staying in compact dimensions of 20 × 20mm2. The single unit tag is extended to 3 × 3-array structure. The novel approach leads the tag towards RCS increment of 25.17dBsm. The tag has been extended to 5 × 5-array for Poly-ethylene terephthalate (PET) substrate with silver nanoparticle-based ink radiator leading to RCS increment of 25.16dBsm. The tags read range performance is very highly reliable and stable as measured from multiple samples and is suitable for deployment in a smart sensing and IoT identification network.

Artificial intelligence and corporate ideation systems

AbstractMany companies leverage the creativity of their employees to gather ideas for innovations. These ideas are collected, saved, and evaluated via platforms known as corporate ideation systems. Moderated ideation systems (ideation 2.0) emerged as a solution to address the limitations of traditional, rather passive ideation systems (ideation 1.0). In this study, we apply a qualitative mixed‐method approach (literature review, company case studies, expert interviews, and focus group workshops) to examine how artificial intelligence (AI) technology may relieve the remaining pains of stakeholders in collaborative, moderated ideation systems. This leads to a new framework of corporate ideation systems, termed AI‐based ideation systems (ideation 3.0). We identify five major pains suffered by stakeholders in today's moderated ideation systems: creativity pain, content formulation pain, search pain, analytical pain, and administration pain. We find that AI agents act as pain relievers when serving five supporting functions: inspirer, stylist, matchmaker, analyst, and organizer. The interconnected nature of pains means that employing AI agents in certain functions within corporate ideation systems can create positive externalities across the entire system. Practical insights into AI agent implementation and application in corporate ideation systems are provided by six mini‐case studies, which lead to the proposition of two organizational principles: the contextualization of AI usage and the generalization of AI implementation as the requirements for successful ideation 3.0.

THE IMPACT OF ARTIFICIAL INTELLIGENCE ON LEGAL SYSTEMS

Artificial Intelligence (AI) is transforming sectors of the world, and the legal sector is no different. The emerging use of AI technologies into the judiciary has gigantic potential and issues. However, AI holds the potential to enhance the efficacy of legal processes by automating routine tasks like document searching, legal analysis, and contract analysis,reducing the cost of legal services, and making legal services more accessible. Artificial intelligence technologies such as predictive analytics also possess the ability to facilitate better decision-making by having the ability to distinguish between case law trends and predict outcomes. However, the universal application of AI also presents firm ethical, legal, and privacy concerns. Of these is the possibility of algorithmic bias, which can lead to biased or discriminatory judicial decisions. Another problem is a lack of transparency in AI decision-making, and therefore it can be difficult to explain how algorithms make decisions. Also, AI poses problems for traditional legal systems, as they were designed to take into account passive systems, not active ones. This paper discusses the advantages as well as challenges of AI legal systems, having a detailed look at their implications for lawyers, clients, and lawmakers. By this analysis, the paper emphasizes the need for a balanced regulatory environment for ensuring ethical use of AI while protecting individuals' rights and upholding justice in the legal system.

Research on signal recognition method of acoustic beacon for underwater acoustic active and passive positioning dynamics system based on machine learning

The ocean is vital for human survival and development, serving as the birthplace of life and a source of food, minerals, and scientific research materials. It plays a crucial role in global trade, economic growth, climate regulation, and ecological balance. Underwater positioning technology is fundamental to marine engineering, with underwater acoustic passive positioning being essential for sonar source localization. Active and passive acoustic systems help measure underwater noise and determine target locations. Passive systems rely on signals emitted by targets, while active systems use interaction signals for positioning. This study applies machine learning to improve acoustic beacon signal recognition in underwater positioning. Results show that machine learning enhances recognition speed by 8% and detection accuracy by 9% compared to traditional methods. By optimizing underwater acoustic signal recognition, this approach enhances positioning accuracy, reduces costs, and advances intelligent marine technology, providing innovative solutions for complex marine environments.

Integrating Ambient In-Home Sensor Data and Electronic Health Record Data for the Prediction of Outcomes in Amyotrophic Lateral Sclerosis: Protocol for an Exploratory Feasibility Study.

Amyotrophic lateral sclerosis (ALS) leads to rapid physiological and functional decline before causing untimely death. Current best-practice approaches to interdisciplinary care are unable to provide adequate monitoring of patients' health. Passive in-home sensor systems enable 24×7 health monitoring. Combining sensor data with outcomes extracted from the electronic health record (EHR) through a supervised machine learning algorithm may enable health care providers to predict and ultimately slow decline among people living with ALS. This study aims to describe a federated approach to assimilating sensor and EHR data in a machine learning algorithm to predict decline among people living with ALS. Sensor systems have been continuously deployed in the homes of 4 participants for up to 330 days. Sensors include bed, gait, and motion sensors. Sensor data are subjected to a multidimensional streaming clustering algorithm to detect changes in health status. Specific health outcomes are identified in the EHR and extracted via the REDCap (Research Electronic Data Capture; Vanderbilt University) Fast Healthcare Interoperability Resource directly into a secure database. As of this writing (fall 2024), machine learning algorithms are currently in development to predict those health outcomes from sensor-detected changes in health status. This methodology paper presents preliminary results from one participant as a proof of concept. The participant experienced several notable changes in activity, fluctuations in heart rate and respiration rate, and reductions in gait speed. Data collection will continue through 2025 with a growing sample. The system described in this paper enables tracking the health status of people living with ALS at unprecedented levels of granularity. Combined with tightly integrated EHR data, we anticipate building predictive models that can identify opportunities for health care services before adverse events occur. We anticipate that this system will improve and extend the lives of people living with ALS. DERR1-10.2196/60437.

Performance Improvement in a Vehicle Suspension System with FLQG and LQG Control Methods

This study investigates the effect of active control on a quarter-vehicle suspension system. The car suspension system was modeled using the Lagrange–Euler method. The linear quadratic Gaussian (LQG) and fuzzy linear quadratic Gaussian (FLQG) control methods were designed and used for active control to increase vehicle handling and passenger comfort, with the aim of reducing or eliminating vibrations by performing active control of passive suspension systems using these methods. The optimum values of the coefficients of the points where the membership functions of the LQG and Fuzzy LQG methods touch were obtained using the grey wolf optimization (GWO) algorithm. The success of the control performance rate of the applied methods was compared based on the passive suspension system. In addition, the obtained results were compared with each other and with other studies using the integral time-weighted absolute error (ITAE) performance criterion. The proposed control method yielded significant improvements in vehicle parameters compared with the passive suspension system. Vehicle body movement, vehicle acceleration, suspension deflection, and tire deflection improved by approximately 88.2%, 91.5%, 88%, and 89.4%, respectively. Thus, vehicle driving comfort was significantly enhanced based on the proposed system.

Vertical Distribution Characteristics of Sound Field Spectrum Splitting for Moving Sound Source in SOFAR Channel

The frequency shift of multipath sound rays induced by the motion of a sound source in an ocean waveguide environment is a crucial factor affecting the detection capabilities of both active and passive sonar systems, as well as the quality of underwater communication. Therefore, investigating the sound field characteristics of a moving sound source in the SOFAR channel is of significant importance. By comparing the spectra of continuous-wave (CW) signals with pulse widths of 1 s and 15 s received by a vertical array in SOFAR channel, it was observed that the sound field of the moving source exhibits a stable spectral splitting characteristic. Two frequency shift bright lines in the vertical direction were identified, corresponding to two sets of sound ray paths. One set of sound ray paths corresponds to the direct sound and the first surface-reflected sound, and the other set of sound ray paths corresponds to the first seabed-reflected sound and the first surface- and seabed-reflected sound. This study revealed that the spectral splitting of the moving sound source’s sound field displays a distribution trend in a depth direction similar to that of the multipath delay structure. A multipath sound ray frequency shift calculation model, based on ray theory, was developed to explain and predict the vertical distribution pattern of spectral splitting in the sound field of a moving sound source. By combining the model with measured data, it was found that the spectral splitting arises from the frequency shift differences corresponding to multipath sound ray paths. Additionally, the frequency shifts for the D&S and B&SB ray paths are generally proportional to the cosine values of the initial grazing angles of the sound waves at the emission source and the cosine values of the horizontal azimuthal angle between the source motion direction and the receiver.

Wavelet deep unfolding network for iterative stripe noise removal in wideband microwave imaging system for EMS localization

The wideband microwave imaging system is a passive focal-plane imaging system which is used for large-scale, wideband electromagnetic interference source (EMS) imaging. The system is mainly composed of a parabolic reflecting surface and a multi-channel ultra-wideband signal acquisition system. However, due to the influence of manufacturing processes and the varied response characteristics of the sensors to different frequency radiation, the stripe noise exists in the obtained electromagnetic (EM) images, which severely affects the accuracy of localization. To solve this problem, an innovative wavelet deep unfolding network from the perspective of the transform domain is presented in this paper. The network fully considers the inherent characteristics of stripe noise and the complementary information between the coefficients of different wavelet sub-bands to accurately estimate stripe noise while minimizing computational cost. An iterative deep unfolding structure is employed to remove stripe noise by exploiting the correlation between adjacent row signals. It iteratively refines the noise estimation, using the output of each network iteration as input for the subsequent one. A bidirectional gated recurrent unit with a spatial attention mechanism is introduced to enhance the long-time correlation, thus separating the scene details from the stripe noise more thoroughly and restoring the details accurately. Furthermore, a novel stripe noise mathematical model and a wideband dataset are developed. These innovations enable the proposed algorithm to effectively handle dynamically varying noise in wideband. The extensive experiments on simulated and real data demonstrate that our proposed method outperforms several classical de-striping methods on both quantitative and qualitative assessments.

Feasibility and Acceptability of a Technology Mediated Fall Risk Prevention Intervention for Older Adults with Mild Cognitive Impairment.

Falls and fall-related injuries are significant public health issues for adults 65 years of age and older. The annual direct medical costs in the US as a result of falls are estimated to exceed $50 billion, and this estimate does not include the indirect costs of disability, dependence, and decreased quality of life. This project targets community dwelling older adults (OA) with mild cognitive impairment (MCI) who are socially vulnerable and thus at high risk for falling. We have developed an innovative technology-supported nursing-driven intervention called Sense4Safety to 1) identify escalating risk for falls real-time through in-home passive sensor monitoring (including depth sensors); 2) employ machine learning to inform individualized alerts for fall risk; and 3) link 'at risk' socially vulnerable older adults with a coach who guides them in implementing evidence-based individualized plans to reduce fall-risk. The purpose of this study was to assess the feasibility and acceptability of the Sense4Safety intervention through participant interviews. We recruited a cohort of 11 low-income OA with MCI who received the intervention for 3 months. Our study findings indicate overall feasibility of the intervention with most participants (n=9; 82%) having confidence in the passive monitoring system to effectively predict fall risk and generate actionable and tailored information that informs educational and exercise components. Passive sensing technologies can introduce acceptable platforms for fall prevention for community dwelling older adults with MCI.

Active and Passive Vector Surveillance Systems in Lyme Endemic Minnesota and Their Correlation to Human Disease.

Vector surveillance is often used to predict tick-borne diseases in endemic regions. Active and passive vector surveillance systems offer differing benefits and limitations; understanding how the outputs of these systems differ and how they correlate to human disease is essential to public health decision-making. Active and passive vector surveillance systems in Minnesota between 2018 and 2023 present an opportunity for comparison between these surveillance methods. To (1) analyze, compare, and contrast the results of active vector surveillance with crowd-sourced approaches, and (2) explore how these sources predict risk of Lyme disease. In this ecological comparative analysis, descriptive statistics were performed to evaluate characteristics of each surveillance method to assess differences in seasonality, life stage, and species of ticks. Negative binomial regression was used to analyze correlation to Lyme disease. There are differences between data sources in tick life stage, species, and seasonality. Active surveillance using small mammal trapping had a majority larval (85%) and I. scapularis (76%) ticks. In contrast, passive surveillance had a majority of adult (96%) and D. variabilis (75%) ticks. Observations in both data sources were skewed to the early third of the tick season, although this was more exaggerated in the passive surveillance data. Observations of ticks from both data sources positively correlated with cases of Lyme disease. Observed differences in tick characteristics between the 2 data sources may represent real differences between tick populations and human encounters. Some differences may be explained by observation, reporting, and sampling biases. Increased observations of ticks at the beginning of the season indicate potential utility of enhanced human Lyme disease surveillance at that time. These One Health findings signal an opportunity for early identification of high tick-borne disease years through integrated active and passive tick surveillance that informs the conduct of human disease surveillance.

Decentralized Active Control of Distributed Damping Subs for Stick/Slip Reduction in Drilling

Summary Torsional stick/slip vibrations are a significant cause of drillstring damage and failure. Using damping subs distributed along the drillstring has been demonstrated in both simulations and laboratory experiments as a viable solution to reduce stick/slip in drilling. The effectiveness in mitigating stick/slip can be further improved through active control of the damping strength of one or several subs. This paper evaluates a control scheme, which relies only on local downhole measurements, for various operating conditions simulated with a drillstring mechanics model. Damping subs with sleeves supported on bearings and incorporated eddy current brakes are installed along the drillstring, adding viscous damping to the system to mitigate torsional vibrations. The damping amount is proportional to the relative rotational speed between the sleeve and the pipe and therefore maximized when the sleeves are nonrotating. However, if the braking force surpasses the static friction holding the sleeve in place, the sleeve slips and reduces its braking abilities. To counteract this, an on-off-based control scheme with proportional control (P-off controller) is implemented at the level of each sub to manipulate the value of its damping coefficient. The off condition is triggered when local measurements indicate slipping of the sleeve. Simulations of various drilling scenarios conducted with a coupled axial and torsional drillstring model show that the P-off controller can reduce stick/slip in the system in both off-bottom and on-bottom conditions. The results indicate that the average damping coefficient, and implicitly the torque added to the system, can be greatly reduced compared with a passive damping system, consisting of the same number and positioning of subs but with constant damping coefficients. Also, the settling time of the downhole rotational speed is significantly reduced compared with the passive system. The damping subs can also be used in combination with a surface stick/slip mitigation system. The control scheme is decentralized as it relies only on local knowledge, such as pipe rotational speed inferred from an inertial measurement unit, to estimate the topdrive revolutions per minute (RPM) setpoint. Therefore, communication with the surface or with neighboring subs is not a requirement for the control implementation. The controller also shows robustness against variations in the topdrive RPM and noisy downhole measurements, which is important for a real-world application. The presented approach differs from existing downhole torsional vibration damping solutions, as it does not rely on a single tool placed inside or near the bottomhole assembly, but on several subs distributed along the drillstring, with their damping strength adjusted directly downhole in a decentralized manner.

Passive thermal management systems with phase change material-based methods for lithium-ion batteries: A state-of-the-art review

Passive thermal management systems with phase change material-based methods for lithium-ion batteries: A state-of-the-art review

PSMA-targeted delivery of docetaxel in prostate cancer using small-sized PDA-based micellar nanovectors.

PSMA-targeted delivery of docetaxel in prostate cancer using small-sized PDA-based micellar nanovectors.

Experimental evaluation and modeling of passive thermodynamic vent system in a small liquid nitrogen tank

Experimental evaluation and modeling of passive thermodynamic vent system in a small liquid nitrogen tank

Numerical investigations on the performance of passive heat removal system of small modular reactor under accidental conditions

Numerical investigations on the performance of passive heat removal system of small modular reactor under accidental conditions

Experimental investigation of the pressure oscillation characteristics of condensation-induced water hammer in a low-height-difference passive heat removal system

Experimental investigation of the pressure oscillation characteristics of condensation-induced water hammer in a low-height-difference passive heat removal system

Gas-propelled anti-hair follicle aging microneedle patch for the treatment of androgenetic alopecia.

Gas-propelled anti-hair follicle aging microneedle patch for the treatment of androgenetic alopecia.

Innovative two-phase thermosyphon-based PRHR system for prolonged passive heat removal in light water reactors

Innovative two-phase thermosyphon-based PRHR system for prolonged passive heat removal in light water reactors

Experimental study on the application of a Passive Displacement Dual Coil Cooling System in a tropical climate

Experimental study on the application of a Passive Displacement Dual Coil Cooling System in a tropical climate