Principles Of Processing Research Articles

Distinct retinal ganglion cell types in strictly subterranean, naturally microphthalmic mammals.

African mole-rats (Bathyergidae, Rodentia) are subterranean rodents that live in extensive dark underground tunnel systems and rarely emerge aboveground. They can discriminate between light and dark but show no overt visually driven behaviours except for light-avoidance responses. Their eyes and central visual system are strongly reduced but not degenerated. Here, we focus on retinal ganglion cells (RGCs). Sighted mammals have numerous RGC types with distinct morphological and functional properties that encode different aspects of a visual scene. We analysed the morphological diversity of 216 intracellularly dye-injected RGCs in the giant mole-rat (Fukomys mechowii) and 48 RGCs in Ansell's mole-rat (Fukomys anselli). Using a hierarchical cluster analysis on 11 morphological parameters, we show that both species possess at least five RGC types with distinct dendritic field sizes and branching patterns. These resemble some RGC types of the mouse and rat, but mole-rat RGCs feature overall sparser and more asymmetric branching patterns. The dendritic trees of most RGCs in all clusters are monostratified in the inner plexiform layer, but bistratified and multistratified/diffuse cells also exist. Thus, although RGC morphologies have become disorganized, the basic retinal organization principle of parallel information processing by distinct RGC types is retained.

Underwater SSP Measurement and Estimation: A Survey

Real-time and accurate construction of regional sound speed profiles (SSPs) is important for building underwater positioning, navigation, and timing (PNT) systems as it greatly affects signal propagation modes. In this paper, we summarize and analyze the current research status in the field of underwater SSP construction, where the mainstream methods include direct SSP measurement and SSP inversion. For the direct measurement method, we compare the performance of popular international and commercial brands of temperature, conductivity, and depth profilers (CTDs). For the inversion methods, the framework and basic principles of matched field processing (MFP), compressive sensing (CS), and deep learning (DL) are introduced, and their advantages and disadvantages are compared. Presently, SSP inversion relies on sonar observation data, limiting its applicability to areas that can only be reached by underwater observation systems. Furthermore, these methods are unable to predict the distribution of sound velocity in future time. Therefore, the mainstream trend in future research on SSP construction will involve comprehensive utilization of multi-source data to offer elastic sound velocity distribution estimation services for underwater users without the need for sonar observation data.

Grape Pomace as a Source of Phenolics for the Inhibition of Starch Digestion Enzymes: A Comparative Study and Standardization of the Efficacy.

The increase in the incidence of hyperglycemia and diabetes poses the challenge of finding cost-effective natural inhibitors of starch digestion enzymes. Among natural compounds, phenolics have been considered as promising candidates. The aims of this study were as follows: (a) to investigate the effectiveness of the inhibition of different winemaking byproducts towards intestinal brush border α-glucosidase and pancreatic α-amylase in vitro; (b) to calculate an efficacy index relative to the standard acarbose for the phenolic pool of winemaking byproducts, as well as for isolated phenolic compounds and for the phenolic pools of different plants studied in the literature, in order to rank winemaking byproducts with respect to the reference drug and other natural alternatives. Among winemaking byproducts, red grape skins showed the highest inhibitory activities towards both α-glucosidase and α-amylase, which were, on average, 4.9 and 2.6 µg acarbose equivalents/µg total phenolics (µg Ac eq/µg GAE), respectively. A correlation was observed between the total phenolic contents of red grape skins and their inhibitory effectiveness, which is useful for standardizing the efficacy of phenolic extracts obtained from different winemaking processes. In general, the inhibitory activity of the phenolic pool of grape skins was higher than those of isolated phenolic compounds, namely anthocyanins and monomeric and polymeric flavanols and flavonols, probably due to synergistic effects among compounds. Hence, bioactive phenolic fractions could be produced with the focus on functionality rather than purity, in line with the principles of sustainable processing. Based on the efficacy index developed to compare different phenolic compounds and phenolic-rich plants studied in the literature as starch digestion enzyme inhibitors, red grape skins proved to be cost-effective candidates.

European legal standards of personal data protection

The article considers the main European legal acts that ensure the protection of personal data and confidential information about a person. After all, taking into account the European integration direction of Ukraine’s development, the experience of legal regulation of the institution of personal data on the European continent is important for our country. It was emphasized that the rapid technological development and globalization lead to the emergence of new difficulties for the protection of personal data: the scale of collection and sharing of personal data has increased significantly; technologies allow both private companies and public bodies to use personal data on an unprecedented scale in order to implement their activities and individuals are increasingly providing access to personal information to the public and on a global scale. It is emphasized that the protection of personal data as one of the fundamental human rights is currently guaranteed by an effective combination of various EU legal instruments, in particular the founding treaties of the EU and the EU Charter, which are acts of primary EU law, as well as acts of secondary EU law. The Council of Europe Convention «On the Protection of Individuals in Relation to Automated Processing of Personal Data» dated January 28, 1981 No. 108 was analyzed as the first international legally binding document that deals exclusively with issues of personal data protection, which outlines the key principles of personal data processing , the rights of a person in connection with the processing of his personal data, basic norms regarding cross-border data transfer. The author emphasizes the importance of Ukraine’s international legal obligations arising from the AA and the EU candidate status, which has once again raised the issue of the need to update and implement the latest EU standards in the field of personal data protection into national legislation. It is noted that rapid technological development and globalization lead to new challenges for personal data protection. The scale of collection and sharing of personal data has increased significantly. Technologies allow both private companies and public authorities to use personal data on an unprecedented scale to carry out their activities. Individuals are increasingly providing access to personal information to the public and on a global scale. The author emphasized the importance of Ukraine’s international legal obligations arising from the AA and the acquisition of the EU candidate status, which once again raised the issue of the need to update and implement the latest EU standards in the field of personal data protection into national legislation. The result of the development of a joint pan­European regulatory act in the field of regulating the processing and protection of personal data was the adoption of the General Regulation on the Protection of Personal Data with the aim of adapting the rules of personal data protection to the era of digital technologies, the main substantive novelties of this legal act were determined. The main tasks of the European Council for the Protection of Personal Data have been clarified.

Body Surface Potential Mapping: A Perspective on High-Density Cutaneous Electrophysiology.

The electrophysiological signals recorded by cutaneous electrodes, known as body surface potentials (BSPs), are widely employed biomarkers in medical diagnosis. Despite their widespread application and success in detecting various conditions, the poor spatial resolution of traditional BSP measurements poses a limit to their diagnostic potential. Advancements in the field of bioelectronics have facilitated the creation of compact, high-quality, high-density recording arrays for cutaneous electrophysiology, allowing detailed spatial information acquisition as BSP maps (BSPMs). Currently, the design of electrode arrays for BSP mapping lacks a standardized framework, leading to customizations for each clinical study, limiting comparability, reproducibility, and transferability. This perspective proposes preliminary design guidelines, drawn from existing literature, rooted solely in the physical properties of electrophysiological signals and mathematical principles of signal processing. These guidelines aim to simplify and generalize the optimization process for electrode array design, fostering more effective and applicable clinical research. Moreover, the increased spatial information obtained from BSPMs introduces interpretation challenges. To mitigate this, two strategies are outlined: observational transformations that reconstruct signal sources for intuitive comprehension, and machine learning-driven diagnostics. BSP mapping offers significant advantages in cutaneous electrophysiology with respect to classic electrophysiological recordings and is expected to expand into broader clinical domains in the future.

Study on the difference of composition between traditional and modern lime used in the restoration of Qufu San Kong Ancient Architectural

In recent years, a decline in the binding strength and frost resistance of lime binder used in the restoration of the Qufu San Kong ancient architectural complex in Shandong Province has been observed, which has significantly affected the quality of restoration. In this study, lime binder samples were collected from the Kuiwen Pavilion roof, limestone samples in the vicinity of the Confucius Temple, and modern industrial quicklime from various provinces in China. Analytical methods including X-ray diffraction (XRD) and Fourier-transform infrared (FTIR) spectroscopy were employed. Results indicate that the local limestone in Qufu contains impurities such as Si or Mg. XRD patterns reveal that the main phase of the Kuiwen Pavilion roof lime binder samples is calcite. Using FTIR and Scanning Electron Microscope- Energy Dispersive Spectrometer (SEM-EDS), the presence of hydrated calcium silicate (C-S-H) gel is identified in the samples. This suggests that the traditional quicklime-production process in Qufu yields a multiphase composite system. Modern industrial quicklime, aged for two months, showed no presence of C-S-H gel in the FTIR spectra and SEM-EDS results, indicating that it is a comparatively pure system primarily comprising CaO. The study suggests that traditional quicklime-production processes typically involve burning limestone with higher Si content, leading to the generation of a significant amount of dicalcium silicate. Additionally, the use of high-ash coal as fuel introduces reactive silicon dioxide into the quicklime product. These factors contribute to the formation of C-S-H gel in hydrated lime. The presence of C-S-H gel enhances lime binder properties such as binding strength and frost resistance. Modern quicklime production, which selects limestone with lower Si content and employs low-ash coal as fuel, aims to minimize or avoid the formation of C-S-H gel in hydrated lime. High-purity CaO quicklime products align with the principles of modern fine processing; however, they compromise the binding strength and durability of lime binder required for traditional architectural restoration.

Finite Element Simulation and Piezoelectric Sensor Array-Driven Two-Stage Impact Location on Composite Structures

Impact monitoring is an effective approach to ensuring the safety of composite structures. The accuracy of current algorithms mostly depends on the number of physical sensors, which is not an economical way for large-area composite structures. In order to combine the advantages of sparse and dense arrays, a two-stage collaborative approach is proposed to locate the general areas and precise positions of impacts on composite structures. In Stage I, the steering vector information of the possible position is simulated according to the principle of array sensor signal processing, and a virtual array sparse feature map is constructed. When an actual impact arrives, a similarity algorithm is then used to find the suspected area in the map, which narrows down the search area to a large extent. In Stage II, a compensated two-dimensional multiple signal classification (2D-MUSIC) algorithm-based imaging method is applied to estimate the precise position of the impact in the suspected area. Finally, the accuracy and effectiveness of the proposed method are validated by numerical simulation and experiments on a carbon fiber composite panel. Both numerical and experimental results verify that the two-stage impact location method can effectively monitor composite structures with sufficient accuracy and efficiency.

Melodic contour supersedes short-term statistical learning in expressive accentuation.

Sensory systems are permanently bombarded with complex stimuli. Cognitive processing of such complex stimuli may be facilitated by accentuation of important elements. In the case of music listening, alteration of some surface features -such as volume and duration- may facilitate the cognitive processing of otherwise high-level information, such as melody and harmony. Hence, musical accents are often aligned with intrinsically salient elements in the stimuli, such as highly unexpected notes. We developed a novel listening paradigm based on an artificial Markov-chain melodic grammar to probe the hypothesis that listeners prefer structurally salient events to be consistent with salient surface properties such as musical accents. We manipulated two types of structural saliency: one driven by Gestalt principles (a note at the peak of a melodic contour) and one driven by statistical learning (a note with high surprisal, or information content [IC], as defined by the artificial melodic grammar). Results suggest that for all listeners, the aesthetic preferences in terms of surface properties are well predicted by Gestalt principles of melodic shape. In contrast, despite demonstrating good knowledge of novel statistical properties of the melodies, participants did not demonstrate a preference for accentuation of high-IC notes. This work is a first step in elucidating the interplay between intrinsic, Gestalt-like and acquired, statistical properties of melodies in the development of expressive musical properties, with a focus on the appreciation of dynamic accents (i.e. a transient increase in volume). Our results shed light on the implementation of domain-general and domain-specific principles of information processing during music listening.

Identification of movie encoding neurons enables movie recognition AI

Natural visual scenes are dominated by spatiotemporal image dynamics, but how the visual system integrates "movie" information over time is unclear. We characterized optic tectal neuronal receptive fields using sparse noise stimuli and reverse correlation analysis. Neurons recognized movies of ~200-600 ms durations with defined start and stop stimuli. Movie durations from start to stop responses were tuned by sensory experience though a hierarchical algorithm. Neurons encoded families of image sequences following trigonometric functions. Spike sequence and information flow suggest that repetitive circuit motifs underlie movie detection. Principles of frog topographic retinotectal plasticity and cortical simple cells are employed in machine learning networks for static image recognition, suggesting that discoveries of principles of movie encoding in the brain, such as how image sequences and duration are encoded, may benefit movie recognition technology. We built and trained a machine learning network that mimicked neural principles of visual system movie encoders. The network, named MovieNet, outperformed current machine learning image recognition networks in classifying natural movie scenes, while reducing data size and steps to complete the classification task. This study reveals how movie sequences and time are encoded in the brain and demonstrates that brain-based movie processing principles enable efficient machine learning.

Prediction of provincial Digital Economy Development Index based on grey combination forecasting model

PurposeIn many instances, the data exhibits periodic and trend characteristics. However, indices like the Digital Economy Development Index (DEDI), which pertains to science, technology, policy and economy, may occasionally display erratic behaviors due to external influences. Thus, to address the unique attributes of the digital economy, this study integrates the principle of information prioritization with nonlinear processing techniques to accurately forecast rapid and anomalous data.Design/methodology/approachThe proposed method utilizes the new information priority GM(1,1) model alongside an optimized BP neural network model achieved through the gradient descent technique (GD-BP). Initially, the provincial Digital Economic Development Index (DEDI) is derived using the entropy weight approach. Subsequently, the original GM(1,1) time response equation undergoes alteration of the initial value, and the time parameter is fine-tuned using Particle Swarm Optimization (PSO). Next, the GD-BP model addresses the residual error. Ultimately, the prediction outcome of the grey combination forecasting model (GCFM) is derived by merging the findings from both the NIPGM(1,1) model and the GD-BP approach.FindingsUsing the DEDI of Jiangsu Province as a case study, researchers demonstrate the effectiveness of the grey combination forecasting model. This model achieves a mean absolute percentage error of 0.33%, outperforming other forecasting methods.Research limitations/implicationsFirst of all, due to the limited data access, it is impossible to obtain a more comprehensive dataset related to the DEDI of Jiangsu Province. Secondly, according to the test results of the GCFM from 2011 to 2020 and the forecasting results from 2021 to 2023, it can be seen that the results of the GCFM are consistent with the actual development situation, but it cannot guarantee the correctness of the long-term forecasting, so the combination forecasting model is only suitable for short-term forecasting.Originality/valueThis article proposes a grey combination prediction model based on the principles of new information priority and nonlinear processing.

Electrochemical cutting with flexible electrode of controlled online deformation

Abstract Improvements in aero-engine performance have made the structures of the aero-engine components increasingly complex. To better adapt to the processing requirements of narrow twisted channels such as an integral shrouded blisk, this study proposes an innovative method of electrochemical cutting in which a flexible tube electrode is controlled by online deformation during processing. In this study, the processing principle of electrochemical cutting with a flexible electrode for controlled online deformation (FECC) was revealed for the first time. The online deformation process of flexible electrodes and the machining process of profiles were analysed in depth, and the corresponding theoretical models were established. Conventional electrochemical machining (ECM) is a multi-physical field-coupled process involving electric and flow fields. In FECC, classical mechanics are introduced into the tool cathode, which must be loaded at all times during the machining process. Therefore, in this study, before and after the deformation of the flexible electrode, a corresponding simulation study was conducted to understand the influence of the online deformation of the flexible electrode on the flow and electric fields. The feasibility of flexible electrodes for online deformation and the validity of the theoretical model were verified by deformation measurements and in situ observation experiments. Finally, the method was successfully applied to the machining of nickel-based high-temperature alloys, and different specifications of flexible electrodes were used to complete the machining of the corresponding complex profiles, thereby verifying the feasibility and versatility of the method. The method proposed in this study breaks the tradition of using a non-deformable cathode for ECM and adopts a flexible electrode that can be deformed during the machining process as the tool cathode, which improves machining flexibility and provides a valuable reference to promote the ECM of complex profiles.

3D Printing MXene-Based Electrodes for Supercapacitors.

3D printing, as an advanced and promising strategy for processing electrode for energy storage devices, such as supercapacitors and batteries, has garnered considerable interest in recent decades. The interest in 3D printed electrodes stems from its exceptional performance and manufacturing features, including customized sizes and shapes and the layer-by-layer processing principle, etc., especially integrating with MXene which allows the manufacturing of electrodes from different raw materials and possessing desired electrochemical properties. Herculean challenges, such as material compatibility of the printing inks, nondurable interfacial or bulk mechanical strength of the printed electrodes, and sometimes the low capacitance, lead to inferior electrochemical performance and hinder the practical applications of this promising technology. In this review, we firstly summarize the representative 3D printing methods, then, review the MXene-based 3D printing electrodes made from different materials, and last, provide electrochemical performance of 3D printing MXene-based electrodes for supercapacitors. Furthermore, based on a summary on the recent progress, an outlook on these promising electrodes for sustainable energy devices is provided. We anticipate that this review could provide some insights into overcoming the challenges and achieving more remarkable electrochemical performance of 3D printing supercapacitor electrodes and offer perspectives in the future for emerging energy devices.

SUBSTANTIATION OF METHODOLOGICAL RECOMMENDATIONS FOR THE USE OF INFORMATION AND MEASURING SYSTEM BUILT ON THE LATEST HARDWARE DEVELOPMENTS DURING THE TESTING OF AME

The constant process of development of both hardware and software perfection inevitably leads to the improvement (creation) of existing (new) advanced information and measurement systems. At the same time, there is a need to develop methodological recommendations for the use of advanced information gathering and processing systems built on the basis of the latest hardware systems and complexes and software products for conducting heterogeneous tests. At the same time, there is a need to develop methodological recommendations for the use of a promising IMS for collecting and processing information built on the basis of the latest hardware systems and complexes and software products for conducting heterogeneous tests. Modern systems used for testing AME are measuring complexes that include modules for connecting sensors, modules for converting analogue sensor signals into digital form, modules for storing the received information in digital form and modules for communication with external computerised devices. Depending on the tasks of the test or experiment, the IMS can be supplemented (simplified) by the need to use certain measuring complexes and systems to measure parameters and obtain reliable information. The ideas, principles and algorithms that currently constitute the methodology of information processing have already made it possible to make a significant breakthrough in information processing technology. Processing methods and principles of their implementation for each field have their own specific features, which are primarily determined by the specific type of information carrier, encoding methods and ways of presenting the results of information processing. As a result, information processing arrangements for different areas often appear to be outwardly dissimilar. However, this external dissimilarity hides the same methodology and principles of building processing systems, which is crucial. The authors of the article study the capabilities of the latest hardware systems and complexes which should be included in advanced IMS and substantiate new methodological approaches, in accordance with regulatory and legal documents, to the use of such IMS during testing of AME.

Development of the Combined Operations Method to Improve the Efficiency of Block Encryption

Real-time information protection requires the implementation of special methods aimed at providing reliable and fast encryption algorithms to protect personal and corporate information from unauthorized access. With the growth of data volumes and the speed of their processing, the importance of effective encryption methods increases significantly. One of the common, reliable and well-known encryption algorithms is AES (Advanced Encryption Standard), also known as Rijndael, which is a symmetric block cipher. AES has high efficiency and cryptoresistance and is suitable for processing large volumes of data. The reliability and speed of encryption and decryption using the AES algorithm depends on the size of the key and the data. This paper proposes to improve algorithm of symmetric block encryption AES to provide faster data processing. The possibility of combining mathematical operations that have a similar principle of processing elements is shown. This approach made it possible to reduce the processing time for data encryption and decryption compared to known implementations. A comparative analysis of the practical implementation of the standard and optimized AES cryptoalgorithms has been carried out. The general principles of the proposed method are to transform all two-dimensional arrays into one-dimensional arrays, add auxiliary tables for ShiftRows and MixColumns operations, and combine operations with similar element processing principles. The simulation results showed that the modified implementation of the AES algorithm demonstrates a reduction in processing time of up to 50% when encrypting and up to 75% when decrypting data compared to known results

Effective Scientific Presentations Across Formats: A Modern Guide for Science Trainees and Researchers

This paper presents a comprehensive tutorial on delivering effective scientific presentations in the modern era, addressing a landscape altered by the COVID-19 pandemic. The shift to virtual and hybrid formats has fundamentally changed the norms of how scientific findings are shared, creating new challenges and opportunities for presenters. Drawing from over a decade of experience teaching and refining presentation skills, we address a soft-skill gap in clinical and translational science training. The tutorial outlines four key topics: (1) identifying your scientific story, (2) using effective slide design, (3) presenting data, and (4) delivering your presentation. These lessons, developed through extensive seminars and individual coaching at various academic institutions, enable researchers to enhance communication and audience engagement across presentation formats. Our approach, informed by cognitive processing and data visualization principles, has adapted to the new reality of virtual and hybrid presentations. This work contributes practical insights for optimizing presentation skills in this changed communication landscape. By offering actionable recommendations derived from real-world experience, this tutorial provides valuable guidance for science trainees, junior faculty, and established researchers across disciplines. It aims to equip readers with the necessary tools to deliver compelling scientific presentations in any format, advancing effective academic discourse in an era where virtual and hybrid presentations have become the norm.

Fractal Biology — Evolution from Molecular to Cognitive, and Psychological Dimensions

Biological and artificial intelligence (BI and AI) share the fundamental principles of space-time information processing based on symmetry transformation. Therefore, cognitive-science-inspired AI represents a promising area of exploration. A convincing example are the fractal structure of human languages and protein assembly. Biological processes’ temporal and spatial plasticity links them to basic laws of physics. Continuous advances in fundamental physical theories allow understanding of all aspects of space-time symmetry (STS) natively intertwined with the principle of relativity and causality. Spatial aspects of symmetry represented by three sub-domains such as chirality, fractality, and topology, are widely studied in biology. The role of chirality in biology has been analyzed in several recent reviews. However, the fractals and topological states of biological structures is a relatively new and fast-developing branch of science. Here, we trace publications exploring the role of fractal symmetry in all hierarchical states of biological organization, including at the molecular, cellular, morphological, physiological, perceptual, cognitive, and psychological levels. The coverage of the above-listed areas in current studies is sharply unequal and unsystematic. A broad view of biological fractality opens a unique opportunity to discriminate between a healthy state and a wide range of disease conditions. Psychiatric, neurological, and immune disorders are associated with aberrant molecular assembly and morphological changes in neural circuits, suggesting that the chain of chirality/fractality transfer through all levels of physiological organization deserves persistent attention.

Radar-Based Fall Detection: A Survey.

Fall detection, particularly critical for high-risk demographics like the elderly, is a key public health concern where timely detection can greatly minimize harm. With the advancements in radio frequency technology, radar has emerged as a powerful tool for human detection and tracking. Traditional machine learning algorithms, such as Support Vector Machines (SVM) and k-Nearest Neighbors (kNN), have shown promising outcomes. However, deep learning approaches, notably Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), have outperformed in learning intricate features and managing large, unstructured datasets. This survey offers an in-depth analysis of radar-based fall detection, with emphasis on Micro-Doppler, Range-Doppler, and Range-Doppler-Angles techniques. We discuss the intricacies and challenges in fall detection and emphasize the necessity for a clear definition of falls and appropriate detection criteria, informed by diverse influencing factors. We present an overview of radar signal processing principles and the underlying technology of radar-based fall detection, providing an accessible insight into machine learning and deep learning algorithms. After examining 74 research articles on radar-based fall detection published since 2000, we aim to bridge current research gaps and underscore the potential future research strategies, emphasizing the real-world applications possibility and the unexplored potential of deep learning in improving radar-based fall detection.

Quantum-tunneling deep neural network for optical illusion recognition

The discovery of the quantum tunneling (QT) effect—the transmission of particles through a high potential barrier—was one of the most impressive achievements of quantum mechanics made in the 1920s. Responding to the contemporary challenges, I introduce a deep neural network (DNN) architecture that processes information using the effect of QT. I demonstrate the ability of QT-DNN to recognize optical illusions like a human. Tasking QT-DNN to simulate human perception of the Necker cube and Rubin’s vase, I provide arguments in favor of the superiority of QT-based activation functions over the activation functions optimized for modern applications in machine vision, also showing that, at the fundamental level, QT-DNN is closely related to biology-inspired DNNs and models based on the principles of quantum information processing.

Towards a better understanding of the surface smoothing effect in gas cluster ion beam processing with molecular dynamics simulation and experiment.

In recent years, the application of gas cluster ion beam (GCIB) technology has made great progress. Due to the similar essence of a monoatomic ion beam, the GCIB also shows flashes of brilliance in material processing. It has been reported that smoothness can be greatly improved after the rough surface is bombarded by the GCIB. This indicates that the GCIB processing has great potential in optical fabrication. Although the surface smoothing effect has been investigated, there is still a lack of dynamic micro-analysis for GCIB processing, which is limited for better understanding the mechanism of smoothing effect. In this paper, the surface smoothing effect in GCIB processing is explicitly investigated with molecular dynamics (MD) simulation and experiment. The principle of GCIB processing is compared with the traditional monoatomic ion-beam based processing, and details of the MD simulation procedure are introduced. Based on this, the dynamic micro-analysis of GCIB processing is conducted under different processing conditions. The simulations reveal the phenomena of atomic removal and migration in GCIB processing, which plays an important role in explaining the mechanism of surface smoothing effect. The experiment was performed on the silicon substrate with the in-house GCIB processing machine. The results indicate that the initial rough surface with dense protrusions can be greatly smoothed, and the root mean square (RMS) value is reduced from 0.586 nm to 0.191 nm. Both simulation and experiment can provide a better understanding of smoothing effect mechanism in GCIB processing.

Kombucha–Chlorella–Proteinoid Biosynthetic Classifiers of Audio Signals

ABSTRACTThis paper describes the development of a bioinspired composite material capable of audio classification applications. Hydrogel matrices produced by microorganisms combined with synthetic biology elements, allow for the development of adaptable bioelectronics that connect biology and technology in a customized way. In this study, a composite population of kombucha, chlorella, and proteinoids (thermal proteins) is utilized to respond to acoustic signals converted to electrical waveforms. The kombucha zoogleal mats, which are made and populated by over 60 species of yeasts and bacteria, offer a matrix at the micro level that is connected to the photosynthetic microalgae chlorella. Proteinoids formed through thermal condensation exhibit unique patterns of signaling kinetics. This living material has the ability to be electrically stimulated and can process signals in a way feasible for sensory applications. Using English alphabet audio inputs, a systematic analysis demonstrates the capability to differentiate audio waveforms based solely on biological composite responses. The use of spectral analysis allows for the identification of specific spike timing patterns that encode unique characteristics of individual letters. Moreover, network disturbances result in specific changes in output, so validating the ability to adjust waveform classification. The study demonstrates that kombucha–chlorella–proteinoid composites provide a durable and versatile bioelectronic platform for immediate auditory processing. The work represents progress toward the development of bioelectronic systems that can be customized based on the principles of biological sensory processing, cognition, and adaptation.