Synthetic Qualia of the Brain
The concept of integrating synthetic qualia into the human brain raises ethical, philosophical, and scientific questions. Qualia refers to the subjective, conscious experiences that we have, such as the way we perceive colors, taste flavors, or feel emotions. Integrating synthetic qualia into the brain would mean artificially creating or manipulating these subjective experiences.
The concept of integrating synthetic qualia with organic computing is still largely speculative and raises profound ethical and philosophical questions. Organic computing, also known as neuromorphic engineering, involves designing computing systems inspired by the structure and functioning of the human brain. The idea is to create computational systems that mimic the way biological organisms process information.
Integrating synthetic qualia with organic computing would mean creating artificial, subjective experiences within these bio-inspired computational systems. As of my last update in September 2021, this concept remains firmly within the realm of science fiction. While there has been progress in understanding the brain's functioning and in developing neuromorphic computing systems, the idea of replicating or generating subjective experiences artificially is immensely complex and beyond our current technological capabilities.
If there have been any significant advancements in this area since my last update, I recommend consulting the latest scientific literature or experts in the fields of neuroscience, artificial intelligence, and neuromorphic engineering for the most recent information.
However, it's important to emphasize that any exploration of this concept must proceed with extreme caution. Ethical considerations, including questions about consent, the nature of consciousness, and the potential consequences of creating synthetic subjective experiences, should be at the forefront of these discussions. The ethical implications of integrating synthetic qualia with organic computing are profound and require careful consideration by scientists, ethicists, and society as a whole.
Bio-inspired computational systems draw inspiration from the principles of biological organisms and their natural processes to design innovative computational models. These systems attempt to mimic the functioning of biological entities to solve complex problems. As of my last update in September 2021, here are some bio-inspired computational systems:
Neural Networks: These are computational models inspired by the structure and functioning of the human brain. Neural networks consist of interconnected nodes (neurons) that process information. Deep learning, a subset of neural networks, has been particularly successful in various artificial intelligence tasks.
Genetic Algorithms: Genetic algorithms are optimization algorithms that use the principles of natural selection and genetics. They are used to find approximate solutions to optimization and search problems.
Evolutionary Computing: This is a family of algorithms that includes genetic algorithms, genetic programming, and evolutionary strategies. These algorithms are used for optimization and machine learning tasks, inspired by the process of natural evolution.
Ant Colony Optimization (ACO): ACO algorithms are inspired by the foraging behavior of ants. They are used to find approximate solutions to combinatorial optimization problems.
Swarm Intelligence: Swarm intelligence algorithms are inspired by the collective behavior of social organisms, such as ants, bees, or birds. Particle Swarm Optimization (PSO) and Ant Colony Optimization are examples of swarm intelligence algorithms.
Membrane Computing: Membrane computing, or P systems, is a computational model inspired by the structure and functioning of living cells. It uses the concept of membranes to process information and has applications in solving computational problems.
Physarum Polycephalum Computing: Physarum polycephalum, a slime mold, has been used to model computational processes. It exhibits complex behaviors and has been applied to solve mazes and optimize networks.
Neuromorphic Engineering: Neuromorphic engineering involves designing hardware or software systems inspired by the architecture and functioning of the human nervous system. These systems aim to mimic the behavior of neural networks and are used in robotics, artificial intelligence, and brain-computer interfaces.
Artificial Immune Systems (AIS): AIS algorithms are inspired by the human immune system. They are used for pattern recognition, anomaly detection, and optimization tasks, drawing inspiration from how the immune system identifies and defends against pathogens.
Cellular Automata: Cellular automata are discrete, abstract computational systems that are inspired by the way cells in living organisms interact and change states based on their neighbors' states.
Please note that the field of bio-inspired computing is continuously evolving, and new approaches and models may have emerged since my last update in September 2021. For the most recent developments, I recommend consulting the latest scientific literature in the field of computational intelligence and bio-inspired computing.
Neuromorphic Engineering: An In-Depth Overview
Introduction to Neuromorphic Engineering:
Neuromorphic engineering is an interdisciplinary field that combines neuroscience, computer science, physics, and engineering to design artificial neural systems inspired by the structure and functioning of the human brain. The goal is to create intelligent systems that can learn, process information, and adapt to their environments in a way that mimics biological neural networks. Unlike traditional digital computing, which relies on binary logic, neuromorphic systems use analog circuits to emulate the behavior of neurons and synapses, enabling them to process information in a more brain-like manner.
Key Components of Neuromorphic Systems:
Neurons: In neuromorphic systems, artificial neurons are designed to mimic the behavior of biological neurons. These neurons receive input signals, process them using mathematical functions, and generate output signals.
Synapses: Synapses are the connections between neurons. Neuromorphic synapses are designed to modulate the strength of connections between neurons, allowing for learning and adaptation based on the input patterns.
Neural Networks: Neuromorphic systems consist of interconnected artificial neurons organized into networks. These networks can be simple or complex, depending on the specific application.
Analog Circuits: Neuromorphic hardware relies on analog circuits to emulate the continuous signals and dynamics of biological neural systems. This is in contrast to digital computing, which processes information discretely.
Current Technical Problems and Challenges in Neuromorphic Engineering:
Energy Efficiency: One of the primary challenges in neuromorphic engineering is achieving energy efficiency. The human brain is incredibly energy-efficient, and replicating this efficiency in artificial systems is a significant challenge. Current neuromorphic hardware often consumes more power than biological systems for similar tasks, limiting their practical applications, especially in battery-powered devices.
Neuron and Synapse Variability: Biological neurons and synapses exhibit significant variability in their behavior, which contributes to the brain's robustness and adaptability. Replicating this variability in artificial systems while maintaining reliability and consistency is a challenge. Variability can arise due to manufacturing processes and environmental factors, requiring innovative solutions to ensure predictable behavior.
Learning Algorithms: Developing efficient and effective learning algorithms for neuromorphic systems is a complex task. While there have been advancements in neuromorphic hardware, designing algorithms that can exploit the hardware's capabilities optimally remains a challenge. Additionally, unsupervised and online learning in dynamic, real-world environments pose significant challenges for neuromorphic systems.
Integration with Traditional Computing Systems: Integrating neuromorphic systems with traditional digital computing infrastructure is a challenge. Neuromorphic systems operate on different principles and timescales than conventional computers, requiring interfaces and protocols to enable seamless communication and collaboration between these systems.
Scalability: Building large-scale neuromorphic systems with billions of neurons and trillions of synapses, comparable to the complexity of the human brain, is a monumental challenge. Scaling neuromorphic hardware while maintaining efficiency, reliability, and ease of manufacturing is a major research area.
Neuromorphic Vision and Sensing: Neuromorphic vision systems aim to replicate the human visual system's capabilities, including low-power image sensing, processing, and recognition. Achieving real-time, energy-efficient visual processing comparable to biological vision remains a significant challenge.
Ethical and Societal Implications: As neuromorphic engineering advances, ethical questions related to artificial intelligence, consciousness, privacy, and the potential impact on society need careful consideration. Ensuring the responsible development and deployment of neuromorphic systems is a critical challenge for researchers and policymakers.
In summary, neuromorphic engineering holds the promise of revolutionizing computing by creating intelligent systems inspired by the brain's efficiency and adaptability. However, addressing the current technical problems and challenges is essential to realizing the full potential of neuromorphic systems in practical applications, ranging from robotics and sensory processing to neuromorphic computing architectures for artificial intelligence. Ongoing research and interdisciplinary collaboration are vital to overcoming these challenges and advancing the field of neuromorphic engineering.
Adaptation of Neuromorphic Systems with Digital Computing:
Integrating neuromorphic systems with digital computing is an area of active research and holds significant promise for enhancing the capabilities of both types of computing paradigms. Here's how neuromorphic systems can be adapted and integrated with digital computing:
Hybrid Systems: Researchers are exploring the development of hybrid systems that combine neuromorphic hardware with traditional digital processors. In such systems, neuromorphic hardware can handle specific tasks related to pattern recognition, learning, and sensory processing, while digital processors manage tasks that require precise calculations and control. This integration allows for the strengths of both systems to be utilized effectively.
Neuromorphic Accelerators: Neuromorphic hardware can be used as specialized accelerators in digital computing systems. These accelerators can handle tasks related to machine learning, such as neural network inference and training, much more efficiently than general-purpose digital processors. Integrating neuromorphic accelerators in data centers and edge devices can significantly speed up machine learning tasks while reducing power consumption.
Neuromorphic Interfaces: Neuromorphic systems can interface with digital systems through specialized hardware interfaces and protocols. These interfaces enable seamless communication between neuromorphic hardware and digital processors, allowing data exchange and coordination between the two computing paradigms.
Real-Time Adaptation: Neuromorphic systems excel at real-time processing and adaptation, making them suitable for applications requiring rapid decision-making and response to changing environments. By integrating neuromorphic capabilities, digital systems can become more responsive and adaptive, enhancing their performance in dynamic situations.
Fuzzy Learning Algorithms in Neuromorphic Systems:
Fuzzy logic and fuzzy learning algorithms can enhance the adaptability and decision-making capabilities of neuromorphic systems in various ways:
Handling Uncertainty: Fuzzy logic is well-suited for handling uncertainty and imprecise information. Neuromorphic systems equipped with fuzzy learning algorithms can make decisions based on vague or incomplete data, enabling them to operate effectively in real-world, noisy environments where precise information might be lacking.
Rule-Based Learning: Fuzzy logic allows the formulation of rules in the form of "if-then" statements, which can be applied to learning algorithms. Neuromorphic systems can utilize fuzzy rules to adapt their behavior based on specific conditions, making them suitable for applications such as autonomous vehicles, robotics, and smart systems where complex decision-making is required.
Adaptive Control: Fuzzy control systems can adaptively adjust their parameters based on changing conditions. Integrating fuzzy learning algorithms with neuromorphic systems enables them to learn and optimize their control strategies over time, leading to improved efficiency and performance in various applications.
Human-Like Reasoning: Fuzzy logic enables human-like reasoning by accommodating uncertainty and imprecision in decision-making processes. Neuromorphic systems equipped with fuzzy learning algorithms can emulate aspects of human reasoning, making them valuable for applications involving human-computer interaction, natural language processing, and context-aware computing.
By combining the adaptability of fuzzy learning algorithms with the real-time processing capabilities of neuromorphic systems, researchers can create intelligent systems that can operate effectively in complex, uncertain, and dynamic environments. This integration holds the potential to revolutionize fields such as robotics, autonomous systems, and artificial intelligence, enabling the development of more capable and responsive technologies.
The concept of integrating large language models with augmented reality (AR) glasses holds immense potential for transforming the way we interact with information and the world around us. Here's an exploration of the possibilities and considerations for such a technology:
**1. Real-Time Language Translation:
- AR glasses equipped with a large language model could provide real-time language translation. Users could look at foreign language signs or hear spoken words, and the glasses would translate the text or speech into their preferred language, displayed as an overlay on the AR glasses.
**2. Contextual Information Overlay:
- Users wearing AR glasses could receive contextual information about objects, landmarks, or people they see. The large language model could analyze the surroundings and provide relevant information in the user's language, enhancing their understanding of the environment.
**3. Enhanced Learning and Education:
- Students could wear AR glasses in classrooms, and the large language model could provide additional information or explanations about the topics being taught. This could create immersive learning experiences, helping students grasp complex concepts more effectively.
**4. Interactive Language Learning:
- Language learners could interact with virtual conversational partners through AR glasses. The large language model could simulate conversations, correct pronunciation, and provide language exercises, creating interactive and personalized learning environments.
**5. Smart Virtual Assistants:
- AR glasses could host intelligent virtual assistants powered by large language models. These assistants could understand voice commands, answer questions, and perform tasks like setting reminders, sending messages, or providing directions, all through natural language interactions.
**6. Professional Applications:
- In professional settings, such as during international business meetings, AR glasses could provide real-time language interpretation. This feature would enable seamless communication between individuals speaking different languages.
Considerations and Challenges:
**1. Privacy and Data Security:
- AR glasses, especially those connected to the internet and large language models, raise concerns about privacy and data security. Ensuring that user data is protected and not misused is a critical consideration.
**2. User Experience and Comfort:
- AR glasses need to be lightweight, comfortable to wear for extended periods, and offer a seamless user experience. Designing ergonomically and aesthetically pleasing glasses is essential for user adoption.
**3. Language Accuracy and Naturalness:
- The large language model must be highly accurate and provide natural-sounding responses to user queries. Continuous improvements in natural language processing algorithms are crucial to achieving this goal.
**4. Integration with Surroundings:
- AR glasses need to integrate seamlessly with the physical environment. They should be able to identify objects accurately and overlay information in a way that appears natural and unobtrusive.
**5. Regulatory and Ethical Considerations:
- Regulations regarding the use of AR glasses in public spaces and ethical considerations related to data usage, consent, and user safety need to be carefully addressed.
The integration of large language models with AR glasses has the potential to revolutionize communication, learning, and information access. However, addressing privacy concerns, ensuring accuracy, and enhancing user experience are essential for the successful adoption of this technology. As technology continues to advance, it's crucial to approach these developments with a focus on user benefit, ethical considerations, and societal impact.
Neuromorphic interfaces aim to create connections between artificial neuromorphic systems (which mimic the behavior of biological neural networks) and external environments or other computational systems. The mathematics involved in neuromorphic interfaces typically includes concepts from neural networks, signal processing, and various mathematical models used in neuroscience. Here's an elucidation of the mathematics involved in neuromorphic interfaces:
**1. Neural Networks:
- Neuron Model: Neurons in artificial neural networks are often modeled using mathematical functions. Common activation functions include sigmoid, hyperbolic tangent (tanh), and rectified linear unit (ReLU). These functions introduce non-linearities in the network, allowing it to learn complex patterns.
- Synaptic Weight: The strength of connections between neurons is represented by synaptic weights. During learning, these weights are adjusted using algorithms like backpropagation. The adjustment involves calculus concepts such as gradients and partial derivatives.
- Network Topology: The arrangement of neurons into layers (input, hidden, and output) and the connections between them are defined mathematically. Matrix operations, especially in deep learning, play a significant role in the forward and backward passes of neural networks.
**2. Signal Processing:
- Filtering: Signal filtering techniques, such as convolution, are often applied in neuromorphic interfaces for tasks like image recognition. Convolutional neural networks (CNNs) utilize convolution operations to process input data, making them effective for tasks involving grid-like structures, such as images.
- Spike Encoding: Neuromorphic systems often use spikes (action potentials) to represent information. Spike encoding techniques, such as rate-based encoding or temporal coding, involve mathematical transformations to convert continuous signals into spike trains.
**3. Mathematical Models in Neuroscience:
- Hodgkin-Huxley Model: This mathematical model describes the behavior of action potentials in neurons and is essential in understanding the dynamics of neuronal firing. It involves differential equations that capture the flow of ions across the neuronal membrane.
- Integrate-and-Fire Models: These simplified models describe the membrane potential of a neuron, considering inputs, leak currents, and threshold for firing. Integrate-and-fire models are computationally efficient and are widely used in neuromorphic engineering.
- Synaptic Models: Various mathematical models describe the behavior of synapses, including short-term and long-term plasticity. These models incorporate equations to capture changes in synaptic efficacy over time.
**4. Learning Algorithms:
- Hebbian Learning: Hebbian learning is a biological learning rule that states "cells that fire together, wire together." Mathematically, it involves updating synaptic weights based on the correlation of pre-synaptic and post-synaptic activities.
- Spike-Timing-Dependent Plasticity (STDP): STDP is a synaptic plasticity rule that considers the precise timing of pre-synaptic and post-synaptic spikes. It mathematically defines how synaptic weights change based on the relative timing of spikes.
**5. Statistical Methods:
- Bayesian Inference: Bayesian methods are used in neuromorphic interfaces for probabilistic reasoning and decision-making. These methods involve statistical inference techniques to update beliefs based on incoming sensory data and prior knowledge.
Understanding and applying these mathematical concepts are crucial for developing effective neuromorphic interfaces. Researchers in the field of neuromorphic engineering often leverage these mathematical principles to design systems that can process information, learn from data, and interact with the environment in ways inspired by biological neural networks.
Engineering neuromorphic interfaces involves applying principles from various fields, including neuroscience, electronics, computer science, and materials science. The goal is to create efficient and reliable connections between artificial neuromorphic systems and external devices or environments. Here are some key engineering concepts related to neuromorphic interfaces:
**1. Neuromorphic Chip Design:
- Analog Circuitry: Neuromorphic systems often use analog circuits to simulate the continuous nature of neural signals. Engineers design analog circuits to mimic the behavior of biological neurons and synapses, allowing for efficient computation and learning.
- Digital Control: While the core of neuromorphic systems is analog, digital control circuits are essential for configuring the system, implementing learning algorithms, and interfacing with external devices.
- Mixed-Signal Integration: Integrating analog and digital components on the same chip (mixed-signal integration) is a key engineering challenge. It involves ensuring that analog signals are processed accurately and reliably in the presence of digital control and communication circuits.
**2. Sensors and Actuators:
- Sensor Integration: Neuromorphic interfaces often interact with sensors (such as cameras, microphones, and touch sensors) to receive input from the environment. Engineers must design interfaces that can process sensory data efficiently and convert it into a format suitable for the neuromorphic system.
- Actuator Integration: In applications where neuromorphic systems control physical devices (such as robots), actuators (such as motors or servos) are integrated into the interface. Engineers design control algorithms that translate neural signals into actions performed by the actuators.
**3. Communication Protocols:
- Neural Spike Communication: Neuromorphic systems communicate using spikes (action potentials) as their fundamental units of information. Engineers design communication protocols that encode and decode spikes, enabling seamless communication between neuromorphic systems and external devices.
- Neuromorphic Network Communication: In scenarios involving multiple neuromorphic systems, engineers develop protocols for inter-system communication. These protocols facilitate collaboration and information exchange between different parts of a neuromorphic network.
**4. Energy Efficiency and Power Management:
- Low-Power Design: Neuromorphic interfaces must be energy-efficient, especially in portable devices. Engineers focus on low-power design techniques, optimizing both analog and digital circuits to minimize power consumption.
- Power Management: Engineers design power management systems to regulate the supply voltage and current, ensuring stable operation of neuromorphic interfaces. Techniques such as dynamic voltage and frequency scaling (DVFS) are applied to adapt power consumption based on processing demands.
**5. Materials and Hardware Integration:
- Flexible Electronics: In wearable or bio-integrated neuromorphic interfaces, flexible electronics and soft materials are used to create devices that can conform to the body's shape. Engineers design flexible circuits and sensors to enable comfortable and unobtrusive integration with the user.
- Biocompatible Materials: For applications involving direct interaction with biological systems (such as brain-machine interfaces), engineers use biocompatible materials to ensure the safety and longevity of the interface. These materials are carefully selected to minimize adverse reactions and tissue damage.
**6. Algorithm Development and Optimization:
- Real-Time Processing: Engineers focus on optimizing algorithms for real-time processing. This involves reducing computational complexity and designing efficient data structures to ensure rapid response to sensory input and enable timely decision-making.
- Hardware-Software Co-Design: Neuromorphic interfaces often require a close collaboration between hardware and software engineers. Hardware-software co-design involves tailoring algorithms to exploit the specific capabilities and constraints of the underlying neuromorphic hardware.
In summary, engineering neuromorphic interfaces involves a multidisciplinary approach, combining knowledge from electronics, materials science, communication systems, and computational neuroscience. Engineers in this field work on creating efficient, low-power, and reliable connections between artificial neuromorphic systems and the external world, enabling applications ranging from robotics and prosthetics to brain-computer interfaces and cognitive computing.
The choice of electronics and materials for neuromorphic interfaces depends on the specific application requirements, such as flexibility, biocompatibility, energy efficiency, and integration with biological systems. Here are some examples of electronics and materials commonly used in the development of neuromorphic interfaces:
**1. Electronics:
CMOS (Complementary Metal-Oxide-Semiconductor) Technology: CMOS technology is widely used for building integrated circuits due to its low power consumption and high integration capabilities. Neuromorphic chips often leverage CMOS technology for implementing analog and digital components.
Memristors: Memristors are two-terminal non-volatile memory devices that can store and process information similar to synapses in biological systems. They are promising for neuromorphic computing due to their analog behavior and potential for hardware implementation of synaptic weights.
Ferroelectric Devices: Ferroelectric materials exhibit a change in polarization when an electric field is applied. Ferroelectric transistors and devices can be utilized for memory and logic operations in neuromorphic circuits.
Photonic Neuromorphic Systems: Photonics-based neuromorphic systems use light to process information. Optical components, such as modulators and photodetectors, can be integrated into these systems for fast and energy-efficient communication between neurons.
**2. Materials:
Flexible Substrates: For wearable and flexible neuromorphic interfaces, flexible substrates like polyimide or flexible printed circuit boards (FPCBs) are used. These substrates allow the circuits to bend and conform to the shape of the body, enabling comfortable integration into clothing or accessories.
Biocompatible Materials: In applications involving interaction with biological systems, biocompatible materials are essential to prevent adverse reactions. Materials like medical-grade silicone, polymers, and biocompatible metals (such as titanium) are used to ensure compatibility with living tissues.
Organic Electronics: Organic materials, such as organic semiconductors and conductive polymers, are used in organic electronics. These materials are lightweight, flexible, and can be printed, making them suitable for flexible and bio-integrated neuromorphic devices.
Nanomaterials: Nanomaterials like carbon nanotubes (CNTs) and graphene have unique electrical and mechanical properties. They can be used in neuromorphic interfaces for efficient signal transmission and to enhance the performance of sensors and electrodes.
Biological Materials: Some neuromorphic interfaces, especially those used in brain-machine interfaces, employ biological materials like neuron-like cells and tissues. These materials can be cultured on electronic substrates to create hybrid systems that interact closely with biological systems.
Dielectric Materials: Dielectric materials, such as ferroelectric ceramics, are used in ferroelectric devices. These materials are characterized by their ability to store electrical charge and find applications in memory devices and neuromorphic circuits.
It's important to note that ongoing research in materials science and electronics engineering continues to introduce new materials and technologies for neuromorphic interfaces. The choice of specific materials and electronics depends on the intended application, considering factors such as flexibility, biocompatibility, energy efficiency, and signal processing requirements. As advancements in these fields progress, the landscape of materials and electronics used in neuromorphic interfaces is expected to expand further.
Certainly, here are some examples of organic electronics and nanomaterials that are commonly used or researched for various applications, including neuromorphic interfaces:
Organic Electronics:
Organic Semiconductors: Organic semiconductors, made from carbon-based molecules, are used in organic thin-film transistors (OTFTs) and organic light-emitting diodes (OLEDs). They are flexible and can be deposited on various substrates, making them suitable for flexible and wearable electronics.
Conductive Polymers: Polymers that conduct electricity, such as polyaniline and poly(3,4-ethylenedioxythiophene) (PEDOT), are used in organic electronics. They find applications in organic solar cells, sensors, and flexible electrodes.
Organic Photovoltaics: Organic photovoltaic (OPV) cells use organic semiconductors to convert sunlight into electricity. They are lightweight and flexible, making them suitable for applications where traditional, rigid solar panels are not practical.
Organic Light-Emitting Diodes (OLEDs): OLEDs consist of organic compounds that emit light when an electric current is applied. They are used in displays, lighting, and wearable devices due to their thin form factor and energy efficiency.
Organic Thin-Film Transistors (OTFTs): OTFTs are key components in flexible electronics. They use organic semiconductors to control the flow of electric current and are employed in displays, sensors, and RFID tags.
Nanomaterials:
Carbon Nanotubes (CNTs): CNTs are cylindrical structures made of carbon atoms. They have excellent electrical conductivity and mechanical strength. CNTs are used in various applications, including conductive films, sensors, and transistors.
Graphene: Graphene is a single layer of carbon atoms arranged in a hexagonal lattice. It has exceptional electrical conductivity, mechanical strength, and flexibility. Graphene is used in sensors, transparent conductive films, and electronic devices.
Nanowires: Nanowires are thin wires with diameters on the nanometer scale. They can be made from various materials, including semiconductors and metals. Nanowires find applications in nanoelectronics, sensors, and photodetectors.
Quantum Dots: Quantum dots are semiconductor nanocrystals with unique optical and electronic properties. They are used in displays, solar cells, and biological imaging due to their size-dependent emission of light.
Metal Nanoparticles: Metal nanoparticles, such as gold and silver nanoparticles, exhibit unique optical and catalytic properties. They are used in sensors, imaging agents, and as catalysts in various chemical reactions.
Nanocomposites: Nanocomposites are materials composed of a matrix and nanoscale particles (such as nanoparticles or nanofibers). They are engineered to enhance specific properties, such as mechanical strength, electrical conductivity, or thermal conductivity.
These materials represent only a portion of the wide range of organic electronics and nanomaterials currently under research and development. Ongoing advancements in materials science continue to expand the possibilities for their applications, including in neuromorphic interfaces, sensors, energy devices, and biomedical technologies.
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