Digital Instrumentation Research Articles

Self-Healing Control of Nuclear Power Plants Under False Data Injection Attacks

The transition from analog to digital instrumentation and control (I&C) systems introduces new threats caused by cyberattacks in the nuclear industry. This paper proposes a self-healing strategy to respond to a false data injection attack that targets digital I&C systems, which is a type of cyberattack commonly targeting nuclear power plants with the potential to cause serious physical impacts. This resilience strategy for self-healing control contains three components: (1) an anomaly detection model that can detect false data injection attacks, (2) a device-level control that utilizes inferred values to perform control under a detected false data injection, and (3) a system-level control that leverages another controller that is not under attack to lead the system back to a safe operation state when the device-level control is unavailable. Anomaly detection and device-level control use an autoencoder while system-level control utilizes reinforcement learning. The proposed self-healing resilience strategy is demonstrated with a generic pressurized water reactor (GPWR) simulator under false data injections, targeting the steam generator water level. The results show that the proposed strategy effectively leads the system back to a normal operation state under various false data injection cases.

Modification Of Impact Testing Tools For Research Of Aluminum Alloys Energy Absorption Profile

One of the most popular materials used in industry is aluminum and its alloys. The aluminum manufacturing process is likely to undergo a welding process. Aluminum can be welded by gas or arc welding, but arc welding is more satisfactory. The welding process on aluminum alloys has the potential to present a situation similar to Age hardening. This research was conducted to dig deeper into the impact of welding on the second phase strengthening mechanism in several series of aluminum alloys using impact test equipment with the addition of a modified digital instrumentation device. Modifications were made to the GOTECH impact test equipment model Charpy impact test 0027 by changing the pendulum and adding a plate to the holder to conform to the ASTM E23-02a standard and adding digital instrumentation tools, including load cells, amplifiers, data acquisition, and power supply. The specimens used were aluminum 5052 and 6061 with variations of base metal and TIG welded V 60°. The results of the modified impact test equipment can display a graph of the impact energy absorption of each specimen. Comparison between the manual calculation of absorbed impact energy and digital calculation of 5052 base metal aluminum specimens has an average deviation of 11,716 J, 5052 welded specimens have an average deviation of 1.341 J, 6061 base metal specimens have an average deviation of 0.729 J, and 6061 welded specimens has a mean of 0.845 J.

EMG Instrumentation Modeling and Feature Processing Based On Discrete Wavelet Transform

Electromyography (EMG) instrumentation is essential in generating electrical signals from skeletal muscles. EMG sensors are helpful in various cases requiring the detection of human muscle contractions, neuromuscular disorders, and rehabilitation. EMG instrumentation is divided into two parts, namely, the analogue part and the digital part. The EMG instrumentation design comprises a digital-to-analog converter (DAC), instrumentation amplifier, filter, and analog-to-digital converter (ADC). Meanwhile, in digital signal processing adopting the Discrete Wavelet Transform (DWT) method, frequency analysis using DWT has proven superior. It is used in various research and has exceptionally detailed coefficient features for classifying neuromuscular disease signals. Therefore, this research aims to design analogue and digital EMG instrumentation and identify features in the form of detailed coefficients. The data used are two Physionet signals from the anterior tibialis body with myopathy and neuropathy disorders. The results obtained for EMG analogue instrumentation provide the expected results until they reach the filter component stage. The resulting signal forms a block in the filter component, different from the initial EMG signal. Meanwhile, the DWT decomposition results are of the Daubechies4 wavelet type with the highest level 17, which has a high detail coefficient at low frequencies, high dilation and the result of a mixture of neuropathy and myopathy EMG signals, or in other words, at low energies, this result is by the DWT theorem. Determining the efficiency of the DWT detailed coefficient feature requires further study with the same signal subject. The DWT features obtained can then be developed for various needs in EMG signal recognition.

Evaluating the effectiveness of sonification in science education using Edukoi

AbstractScience, Technology, Engineering, and Mathematics classes are mainly taught using visual supports. However, the advancement of technology and the increasing efforts to equip schools with digital instrumentation have opened up the possibility of exploring new teaching avenues, such as sonification. We explored the efficacy of sonification in education using a novel interactive tool, Edukoi, in the context of astronomy, which is predominantly disseminated through spectacular images, animations, and visuals. Edukoi is a motion-sensing sonification tool that converts images to sound in real-time for educational applications. Our study, conducted with nearly 150 middle-school students, included a preliminary questionnaire investigating the perception, engagement, and motivation of students towards science; two sessions dedicated to testing Edukoi and assessing the potentiality of the software for the recognition of the colour and the shape of real and sketchy images; and a final second administration of the questionnaire to capture a possible beneficial effect of the use of the tool in the engagement towards science. Results showed the effectiveness of Edukoi in colour recognition and reasonable efficacy in shape identification. Although the questionnaire did not reveal an increment in science engagement over the time of the study, oral feedback from the students was positive. Edukoi presents a possible alternative teaching aid, potentially benefiting diverse learners, including the visually impaired. Further developments of the software are needed to enhance its effectiveness in conveying more complex features such as composite colours or shapes.

Cyber threat assessment of machine learning driven autonomous control systems of nuclear power plants

Advanced cyber-attacks against critical infrastructure and the energy sector are becoming more common. With the invention of autonomous control systems (ACS) within advanced nuclear reactor designs, system designers, reactor operators, and regulators must consider cybersecurity during the design and operational phases. This article provides a cyber threat assessment of machine learning (ML)-based digital twinning (DT) technologies in the context of advanced reactor ACS. A cyber–physical testbed was created to emulate nuclear reactor digital instrumentation and controls (I&C) and act as a basis for the ACS. The ACS was designed as two plant-level DTs predicting reactor malfunctions and determining control actions and two component-level DTs responsible for classifying component states and forecasting component inputs and outputs (I/O). Two duplicate ACS designs– one using a traditional ML framework and one using an automated ML (AutoML) framework– were created and tested against cyber-attacks on training data, real-time process data, and ML model architectures to determine their respective qualitative cyber-risk in terms of likelihood and impact. Both frameworks showed similar cyber-resilience against training, real-time, and ML architecture attacks, proving that neither is inherently more secure. Recommended safeguard and security measures are posed to system designers, reactor operators, and regulators to maintain the cybersecurity of ML-based DT technologies such as ACS, prompting a holistic view of shared responsibility for maintaining cyber-secure ML-based systems.

Experimental investigation of the shear response of large-scale fibre-reinforced concrete panels

Although fibre-reinforced concrete has been the subject of extensive research for decades, its mechanical behaviour in shear is still not well understood. Furthermore, hardly any large-scale shear tests on elements with moderate fibre contents have been conducted to date. The present paper addresses this knowledge gap by presenting and discussing the results of an experimental campaign on large-scale fibre-reinforced concrete shear panels. Six panels with dimensions of 2.00 m × 2.00 m × 0.27 m were tested in the Large Universal Shell Element Tester at ETH Zurich by applying homogeneous shear. In four of these experiments, the in-plane shear was combined with homogeneous uniaxial compression by simulating the longitudinal restraint provided by the chords in a girder. Five tests focused on concrete of normal strength with moderate fibre contents and one test studied the behaviour of ultra high-performance fibre-reinforced concrete. The combined application of digital image correlation instrumentation and distributed fibre optical sensing allowed gaining a deeper insight into the structural behaviour of the panels. Overall, the test results showed the high efficiency of the fibres as shear reinforcement in large-scale web elements. However, failure of the panels without any steel reinforcing bars was rather brittle, with only few cracks forming over the element. The provision of a moderate amount of distributed longitudinal bar reinforcement has been found to be highly beneficial to the crack behaviour and thus significantly increase the ductility.

Application of a Multi-Stage Testing and Evaluation Approach for a Safety-Related Digital Upgrade at a Nuclear Power Plant

There is an imminent need for existing United States nuclear power plants to reduce their operating and maintenance costs to remain economically viable. Digital technology provides significant opportunity for the existing nuclear power plant fleet to transform that way in which work is accomplished to reduce costs and allow the fleet to remain economically competitive. However, a careful understanding of the human-technology integration is needed to ensure the continued safe and reliable operation of these existing plants with new digital capabilities. This work presents interim findings in applying human factors engineering to a safety-significant digital upgrade for a United States nuclear power plant, following the new Alternative Review Process in the recently revised Digital Instrumentation and Control Interim Staff Guidance Licensing Process, Revision 2. The interim results described in this work provides an industry perspective, based on ongoing work, to support recent work published from Vazquez, Green, and Desaulniers (2022).

The use of modern digital devices for fixing the parameters of movement of agricultural units

Despite a significant range of methods for determining mechanical failures known in modern industries, the issue of large-scale use and expansion of the scope of modern digital devices and instrumentation systems, which previously had rather limited specifics in the form of individual elements of control systems, remains relevant as before. Taking into account that in the modern state of technology, high-tech digital solutions are most in demand, capable of carrying out in-place research and fixing the operating parameters of complex systems and units used in the agro-industrial complex at a qualitative level and with a minimum time spent on diagnostic work or testing, it seems to be the most promising the use of digital inclinometers, with their low cost, manufacturability and availability, providing significant opportunities for measuring and fixing the design and technological parameters of a complex, machine or unit. The article substantiates the use of a high-precision inclinometric complex both for carrying out diagnostic work and for measuring the necessary parameters of a technological complex in motion and presents the results of experimental studies.

Smart Green Box Trash Design Based on HC-SR04 Sensor Arduino Uno Integrated

This research has realized the design of a smart green trash box based on the HC-SR04 sensor, which is integrated with Arduino Uno. This research aims to design and build a digital instrumentation system based on Arduino Uno, applying Ultrasonic Sensors to the Green Box Trash System. In addition, this system is equipped with an information display via LCD and speakers so that we can find out the contents of the green trash box. Data retrieval by detecting objects with a distance of less than 10 cm to open the trash and measure the volume in the green trash box. The research results are displayed as a prototype Smart Green Trash Box design. Based on the Hc-Sr04 Sensor Integrated by Arduino Uno. The ultrasonic sensor in the Green Box Trash system works well by measuring the volume and can detect when an object is approaching with a distance of less than 10 cm. Assembling the green box using the HC-SR04 sensor, which is integrated with Arduino Uno functions according to the program created if, in the green box, there is HC-Sr04 1 sensor trash, it measure the volume of trash and then display it through the LCD, and if there is an object approaching the HC-Sr04 sensor 2, then the servo moves so that the green trash box can be opened. The Green Trash box can display trash volume and time on the 16x2 character LCD in real-time with the program uploaded to Arduino as monitoring.

Proposal of quantification method of dynamic system reliability model of digital RPS using Markov state-transition model

ABSTRACT In safety systems of the latest nuclear power plants such as the Advanced Boiling Water Reactor (ABWR) and the latest Pressurized Water Reactor (PWR), digital circuits are used for start-up signals of a reactor protection system (RPS) and emergency core cooling systems (ECCS). And several studies have been conducted to develop the method to build a system reliability model of RPS to be combined with probabilistic risk assessment (PRA) so far. However, these studies have not been sufficiently progressed in terms of the quantitative evaluation and coupling with the conventional PRA method. Despite of the efforts of Task group of Digital system reliability failure mode taxonomy (DIGREL, later becoming Working Group on Digital Instrumentation and Control [WGDIC]) of Organization for Economic Co-operation and Development (OECD)/Nuclear Energy Agency (NEA) in this field, the situation has not changed sufficiently. From these backgrounds, this paper presents a rational quantitative evaluation method of the dynamic system reliability model of the digital RPS which can represent time-dependency and is consistent with the conventional PRA method.

Quantitative evaluation of common cause failures in high safety-significant safety-related digital instrumentation and control systems in nuclear power plants

• A comprehensive quantitative evaluation of common cause failures is performed for safety-critical digital control systems; • Both hardware and software common cause failures are tracked and identified; • Failure probabilities of digital control systems are estimated via a multiscale reliability analysis approach. • The impact of common cause failures to plant safety is evaluated. Digital instrumentation and control (DI&C) systems at nuclear power plants (NPPs) have many advantages over analog systems. They are proven to be more reliable, cheaper, and easier to maintain given obsolescence of analog components. However, they also pose new engineering and technical challenges, such as possibility of common cause failures (CCFs) unique to digital systems. This paper proposes a Platform for Risk Assessment of DI&C (PRADIC) that is developed by Idaho National Laboratory (INL). A methodology for evaluation of software CCFs in high safety-significant safety-related DI&C systems of NPPs was developed as part of the framework. The framework integrates three stages of a typical risk assessment—qualitative hazard analysis and quantitative reliability and consequence analyses. The quantified risks compared with respective acceptance criteria provide valuable insights for system architecture alternatives allowing design optimization in terms of risk reduction and cost savings. A comprehensive case study performed to demonstrate the framework's capabilities is documented in this paper. Results show that the PRADIC is a powerful tool capable to identify potential digital-based CCFs, estimate their probabilities, and evaluate their impacts on system and plant safety.

Future needs of human reliability analysis: The interaction between new technology, crew roles and performance

Human Reliability Analysis (HRA) is an important part of probabilistic risk/safety assessment (PRA/PSA) for nuclear power plants (NPPs). Data has played a central part in HRA, to underpin nominal task probabilities, providing time reliability estimates, and as a basis for multipliers for performance shaping factors (PSFs). New NPPs apply digital instrumentation & control (I&C) systems to support monitoring and control. Will this require new or updated HRA methods and more data as compared to today? We have seen that the combination of the new I&C technology and how it is used by the crew is decisive for the performance of the joint system, comprising the crew and the I&C technology. This paper focuses on the way in which new technology may affect human performance as well as the relation between crew roles, teamwork, and performance. For HRA, two questions are important: What is the impact of teamwork and assigned crew roles on recovery from failure and upset plant conditions? And how should dependency be treated in modern control rooms? This paper discusses these matters based on empirical studies in the OECD Halden Reactor Project and underlines the importance of empirical studies on these topics, since the interactions between new technology, prescribed work processes and actual practices, and crew behavior, are often not obvious. This paper is an extension of a paper presented at the ESREL 2020 – PSAM15 conference.

Overview and Recommendations for Cyber Risk Assessment in Nuclear Power Plants

Digital instrumentation and control (I&C) systems are being deployed in nuclear power plants (NPPs) for both existing and advanced reactor designs. As I&C systems become more digitized to allow features like near autonomous control and remote operation, they introduce greater cyber risk to NPPs. Cyberattacks targeting industrial control systems (ICSs) are growing in both qualities and capabilities, which indicates that cybersecurity needs to be an integral part of risk assessment in the industry. Although there are some risk assessment methods in traditional information technology (IT) cybersecurity, the differences between IT and ICS cybersecurity make it infeasible to apply these risk assessment methods directly to ICSs. Some research has focused on risk assessment methods for ICSs, but few studies focus on applications to NPPs. Ideal risk frameworks for the nuclear industry are dynamic and account for system dependencies; this survey review focuses on such risk assessment methods both in and outside the nuclear field. The major challenges in cybersecurity risk assessment research are pointed out, and further research suggestions and considerations for cyber risk assessment in I&C systems are identified.

Algorithmic accountability: robodebt and the making of welfare cheats

PurposeThe paper aims to investigate how accounting techniques, when embedded within data-driven public-sector management systems, mask and intensify the neoliberal ideological commitments of powerful state and corporate actors. The authors explore the role of accounting in the operationalisation of “instrumentarian power” (Zuboff, 2019) – a new form of power that mobilises ubiquitous digital instrumentation to ensure that algorithmic architectures can tune, herd and modify behaviour.Design/methodology/approachThe authors employ a qualitative archival analysis of publicly available data related to the automation of welfare-policing systems to explore the role of accounting in advancing instrumentarian power.FindingsIn exploring the automation of Australia's welfare debt recovery system (Robodebt), this paper examines a new algorithmic accountability that has emerged at the interface of government, technology and accounting. The authors show that accounting supports both the rise of instrumentarian power and the intensification of neoliberal ideals when buried within algorithms. In focusing on Robodebt, the authors show how the algorithmic reconfiguration of accountability within the welfare system intensified the inequalities that welfare recipients experienced. Furthermore, the authors show that, despite its apparent failure, it worked to modify welfare recipients' behaviour to align with the neoliberal ideals of “self-management” and “individual responsibility”.Originality/valueThis paper addresses Agostino, Saliterer and Steccolini's (2021) call to investigate the relationship between accounting, digital innovations and the lived experience of vulnerable people. To anchor this, the authors show how algorithms work to mask the accounting assumptions that underpin them and assert that this, in turn, recasts accountability relationships. When accounting is embedded in algorithms, the ideological potency of calculations can be obscured, and when applied within technologies that affect vulnerable people, they can intensify already substantial inequalities.

Safety-Related Instrumentation and Control Pilot Upgrade: Initial Scoping Phase Implementation and Lessons Learned

In May 2016, the U.S. Nuclear Regulatory Commission (NRC) staff provided a digital instrumentation and control (I&C) regulatory infrastructure integrated action plan to the NRC for approval. One of the objectives of that plan was to establish a clear regulatory structure with reduced regulatory uncertainty to enable the expanded safe use of digital I&C in commercial nuclear reactors while continuing to ensure safety and security. To achieve this end, the NRC, with collaboration from industry, developed a streamlined License Amendment Request Alternate Review (AR) process for safety-related (SR) digital I&C upgrades. In spite of this effort, the industry has remained reluctant to perform such I&C upgrades because of perceived regulatory and financial risks associated with being the first or an early adopter of the AR process for SR I&C upgrades. The U.S. Department of Energy Light Water Reactor Sustainability Program at the Idaho National Laboratory performed Initial Scoping Phase research to help break this impasse by supporting a SR I&C Pilot Upgrade, working with MPR Associates, ScottMadden Inc., and Exelon Generation. Exelon’s Limerick Generating Station (LGS) was selected as the target for this research. This paper summarizes the Initial Scoping Phase engineering and operations, licensing, and project management activities necessary to bound the scope, schedule, and estimated cost of the project sufficiently to enable utility management authorization of Conceptual Design Phase activities. These efforts and associated products are intended to provide a template to support larger industry efforts to perform similar upgrades as a foundation stone for a digital transformation that will improve plant safety, reliability, and operational performance while lowering plant total cost of ownership. As a result of the combined effort of Exelon Generation and research participants, Conceptual Design Phase activities for the subject upgrade at LGS were approved by Exelon. The U.S. Department of Energy also awarded a $50 million cost share award to Exelon in order to pave the way for SR I&C modernization and associated control room upgrades across the U.S. nuclear fleet. Additional research reports are planned for the Conceptual Design Phase, Detailed Design Phase, and the Implementation Phase of the LGS project to document the process followed and promulgate lessons learned to industry.

Big Data-Driven Urban Management: Potential for Urban Sustainability

With the world’s rapidly growing urbanization, urban sustainability is now expected for urban life. Due to this rapid growth, meeting the emerging challenges for urban management and sustainability worldwide is challenging. Big data-driven technologies can be an excellent solution to address these upcoming challenges. Therefore, this study explores the potential of big data technologies for ensuring sustainability in urban management. The study conducted a systematic literature review guided by PRISMA (preferred reporting items for systematic review and meta-analysis) on publications over the last 21 years. The study argues that urban management is an integrated function of public and private agencies to address the significant challenges of urban life and to develop the city as more competitive, habitable, and sustainable. Urban management can utilize big data analytics (BDA) for digital instrumentation, data-informed policy decisions, governance, real-time management, and evidence-based decisions. Urban sustainability can ensure the smooth operation of urban affairs through strategic planning under three major dimensions: social, economic, and environmental. Big data technologies can ensure smart transport, traffic, waste management, energy, environment, infrastructure, safety, healthcare, planning, and citizen participation in regular urban affairs to provide a better urban life. This study develops several indicators that will be helpful for concerned stakeholders in policy, planning, designing, and implementing sustainable urban development.

A cybersecurity platform for simulating transient responses of emulated programmable logic controllers in instrumentation and control systems for a pwr plant

ABSTRACT This research used the developed LOBO Nuclear CyberSecurity (LOBO NCS) platform at the University of New Mexico to investigate potential cyber vulnerabilities of digital Instrumentation and Control (I&C) systems for components in a representative Pressurized Water Reactor (PWR) plant during simulated operation transients. This platform links physics-based MATLAB Simulink models to emulated or physical Programmable Logic Controllers (PLCs) in I&C systems. An efficient data transfer interface and broker are developed to manage and coordinate communication between the emulated PLCs and the Simulink models. Presented transient results are for the steam generator and the feedwater PLCs following an increase in steam load demand. Related results are for the pressurizer and the pressure and water-level PLCs during simulated surge-in and surge-out transients. These results are for nominal transient operations and when the emulated PLCs are subject to simulated false data injection attacks (FDIAs). Obtained results of the pressurizer and the pressure PLC using the LOBO-NCS are in excellent agreement with those using the DOE SCEPTRE framework at Sandia National Laboratories.

Reliability Analysis and Modelling of Containment Spray System Based on Petri Net

Reliability modelling for digital instrumentation and control systems in nuclear power plants is one of the new trends. The containment spray system in the digital I&C system of nuclear power plant is selected, and the generalized stochastic Petri net model is used to model the reliability of the containment spray system. A single-cycle simulation of the generalized stochastic Petri net model is carried out under consideration of a variety of dynamic events, and the probability of system start-up failure is obtained. The results show that the generalized stochastic Petri net model can well reflect the impact of various dynamic events on system security. Therefore, the generalized stochastic Petri net model is suitable for reliability modelling of I&C systems in nuclear power plants.

The Metaverse as a virtual form of data-driven smart urbanism: platformization and its underlying processes, institutional dimensions, and disruptive impacts

The emerging phenomenon of platformization has given rise to what has been termed "platform society,“ a digitally connected world where platforms have penetrated the heart of urban societies—transforming social practices, disrupting social interactions and market relations, and affecting democratic processes. One of the recent manifestations of platformization is the Metaverse, a global platform whose data infrastructures, governance models, and economic processes are predicted to penetrate different urban sectors and spheres of urban life. The Metaverse is an idea of a hypothetical set of “parallel virtual worlds” that incarnate ways of living in believably virtual cities as an alternative to future data-driven smart cities. However, this idea has already raised concerns over what constitutes the global architecture of computer mediation underlying the Metaverse with regard to different forms of social life as well as social order. This study analyzes the core emerging trends enabling and driving data-driven smart cities and uses the outcome to devise a novel framework for the digital and computing processes underlying the Metaverse as a virtual form of data-driven smart cities. Further, it examines and discusses the risks and impacts of the Metaverse, paying particular attention to: platformization; the COVID-19 crisis and the ensuing non-spontaneous "normality" of social order; corporate-led technocratic governance; governmentality; privacy, security, and trust; and data governance. A thematic analysis approach is adopted to cope with the vast body of literature of various disciplinarities. The analysis identifies five digital and computing processes related to data-driven smart cities: digital instrumentation, digital hyper-connectivity, datafication, algorithmization, and platformization. The novelty of the framework derived based on thematic analysis lies in its essential processual digital and computing components and the way in which these are structured and integrated given their clear synergies as to enabling the functioning of the Metaverse towards potentially virtual cities. This study highlights how and why the identified digital and computing processes—as intricately interwoven with the entirety of urban ways of living—arouse contentions and controversies pertaining to society’ public values. As such, it provides new insights into understanding the complex interplay between the Metaverse as a form of science and technology and the other dimensions of society. Accordingly, it contributes to the scholarly debates in the field of Science, Technology, and Society (STS) by highlighting the societal and ethical implications of the platformization of urban societies through the Metaverse.

Ultra-high speed holographic shape and displacement measurements in the hearing sciences

The auditory system of mammals enables the perception of sound from our surrounding world. Containing some of the smallest bones in the body, the ear transduces complex acoustic signals with high-temporal sensitivity to complex mechanical vibrations with magnitudes as small as tens of picometers. Measurements of the shape and acoustically induced motions of different components of the ear are essential if we are to expand our understanding of hearing mechanisms, and also provide quantitative information for the development of numerical ear models that can be used to improve hearing protection, clinical diagnosis, and repair of damaged or diseased ears.We are developing digital holographic methods and instrumentation using an ultra-high speed camera to measure shape and acoustically-induced motions in the middle ear. Specifically we study the eardrum, the first structure of the middle ear which initializes the acoustic-mechanical transduction of sound for hearing. Our measurement system is capable of performing holographic measurement at rates up to 2.1 M frames per second. Two shape measurement modalities had previously been implemented into our holographic systems: (1) a multi-wavelength method with a wavelength tunable laser; and (2) a multi-angle illumination method with a single wavelength laser. In this paper, we present a third method using a miniaturized fringe projection system with a microelectromechanical system (MEMS) mirror. Further, we optimize the processing of large data sets of holographic displacement measurements using a vectorized Pearson's correlation algorithm. We validate and compare the shape and displacement measurements of our methodologies using a National Institute of Standards and Technology (NIST) traceable gauge and sound-activated latex membranes and human eardrums.