2Memristor-1Capacitor Integrated Temporal Kernel for High-Dimensional Data Mapping.

Compact but precise feature-extracting ability is core to processing complex computational tasks in neuromorphic hardware. Physical reservoir computing (RC) offers a robust framework to map temporal data into a high-dimensional space using the time dynamics of a material system, such as a volatile memristor. However, conventional physical RC systems have limited dynamics for the given material properties, restricting the methods to increase their dimensionality. This study proposes an integrated temporal kernel composed of a 2-memristor and 1-capacitor (2M1C) using a W/HfO2/TiN memristor and TiN/ZrO2/Al2O3/ZrO2/TiN capacitor to achieve higher dimensionality and tunable dynamics. The kernel elements are carefully designed and fabricated into an integrated array, of which performances are evaluated under diverse conditions. By optimizing the time dynamics of the 2M1C kernel, each memristor simultaneously extracts complementary information from input signals. The MNIST benchmark digit classification task achieves a high accuracy of 94.3% with a (196×10) single-layer network. Analog input mapping ability is tested with a Mackey-Glass time series prediction, and the system records a normalized root mean square error of 0.04 with a 20×1 readout network, the smallest readout network ever used for Mackey-Glass prediction in RC. These performances demonstrate its high potential for efficient temporal data analysis.

Spatial variation of pharmaceuticals in the unit processes of full-scale municipal wastewater treatment plants in Korea.

Several reports have elucidated the removal of pharmaceutical residues in municipal wastewater treatment plants (WWTPs). However, there remains a need to determine the spatial distribution of pharmaceuticals in the unit processes of full-scale municipal WWTPs. Herein, spatial variations of fifteen pharmaceuticals in the unit processes of four full-scale municipal WWTPs were assessed by analyzing both solid and liquid samples. Furthermore, different pathways of each pharmaceutical such as biodegradation, adsorption, deconjugation, and electrostatic interaction were investigated. Pharmaceutical mass loading were measured at various points for the different unit process and evaluated using liquid chromatography-tandem mass spectrometry. The average mass loading of acetaminophen and caffeine decreased tremendously in the first biological treatment process regardless of the process configuration. In contrast, a temporary increase was observed in the mass loading of ibuprofen in the anaerobic and/or anoxic processes, which was presumably caused by deconjugation. Additionally, the adverse effect of coagulation on ibuprofen removal was validated. The major removal mechanism for the selected antibiotics, except for sulfamethoxazole, was the adsorption by biosolids due to electrostatic interaction. Subsequently, a drastic decrease was observed in their mass loadings in the solid-liquid separation process of the WWTPs. The membrane bioreactor (MBR) shows excellent capability for mitigation of pharmaceuticals in municipal wastewater because it comprises a high concentration of biosolids that act as adsorbents. The evaluation of the spatial variations of the selected pharmaceuticals in different unit processes provides valuable information on their behavior and removal mechanisms.

Development of water quality criteria for arsenic to protect aquatic life based on species sensitivity distribution.

Arsenic is a hazardous environmental pollutant widely distributed globally. Arsenic toxicity is well known and it is regulated by many countries in terms of managing water quality and protecting aquatic organisms. Unfortunately, water quality criterion (WQC) to protect aquatic organisms has not been introduced in Korea yet. Thus, it is of great importance and necessity to introduce WQC to protect aquatic organisms from arsenic, as WQC play a significant role in protecting aquatic ecosystems from pollutants. Therefore, the purpose of this study is to derive arsenic water quality criterion for aquatic life in Korea. Arsenic acute toxicity tests were performed with 10 Korean native aquatic species, which belong to 7 different taxonomic groups. Based on the results of acute toxicity test and additional toxicity data from literature, the species sensitivity distribution (SSD) method was used in ecological risk assessment. The arsenic concentration of 95% protection level for aquatic life was 0.229 mg L-1 in this study. An assessment factor 3 and a background concentration 0.0004 mg L-1 were applied to the concentration value in consideration of the uncertainty of the data and the amount of arsenic natural generation. Consequently, the WQC value derived for arsenic was found to be 0.077 mg L-1. These results will serve as reference values to establish water quality criterion for the protection of aquatic life in Korea.

Spatial distribution of heavy metal accumulation in the sediments after dam construction.

The sedimentary environment has been modified in the Geum River where an estuary dam and midstream dams were constructed. Furthermore, the Geum River tributaries deliver contaminants from the wastewater of an industrial complex. However, the influence of tributaries and dams on sedimentary metal deposition has not been extensively studied. The objectives of this study are to assess metal accumulation and to investigate the source of the metals. Sediments were collected in the main channel and two tributaries on October 2013. Abnormal accumulations of fine sediments were not observed above the midstream dams. Chromium, Ni, and Zn showed higher concentrations in above the midstream dam, but their concentrations were not related to grain size. Cadmium, Cu, Pb, and Hg were much higher upstream from the first midstream dam and came from one of the major tributaries. Arsenic was the only element found at higher concentrations downstream from the last midstream dam and was likely sourced from abandoned mines and/or agricultural activity. The pollution indexes indicated deposition of all metals, except Cr and Ni, may have been affected by anthropogenic activity. With respect to long-term accumulation of the metals, accumulation of Pb, Zn, and Cu by anthropogenic input largely increased, implying accumulation of these metals has continued due to anthropogenic activity since the estuary dam was constructed. Our results suggest that changes in river flow caused by the estuary dam and anthropogenic input from tributaries sources increased the accumulation of heavy metals (e.g., Pb, Zn, Cu, and As).

Ultra-trace level determination of diquat and paraquat residues in surface and drinking water using ion-pair liquid chromatography with tandem mass spectrometry: a comparison of direct injection and solid-phase extraction methods.

Direct injection and solid-phase extraction methods for the determination of diquat and paraquat in surface and drinking water were developed using liquid chromatography with tandem mass spectrometry. The signal intensities of analytes based on six ion-pairing reagents were compared with each other, and 12.5 mM nonafluoropentanoic acid was selected as the best suited amongst them. A clean-up method was developed using Oasis hydrophilic-lipophilic balance; this was compared to the direct injection method, with respect to limits of detection, interference, precision, and accuracy. Limits of quantification of diquat and paraquat were 0.03 and 0.01 µg/L using the direct injection method, and 0.002 and 0.001 µg/L using the hydrophilic-lipophilic balance method. When the hydrophilic-lipophilic balance method was used to analyze target compounds in 114 surface water and 30 drinking water samples, paraquat and diquat were detected within a concentration range of 0.001-0.12 and 0.002-0.038 µg/L in surface water, respectively. When the direct injection method was used to analyze target compounds in the same samples, the detected concentrations of paraquat and diquat were within 25% in samples being >0.015 µg/L using the hydrophilic-lipophilic balance method. The liquid chromatography with tandem mass spectrometry method using direct injection can thus be used for routine monitoring of paraquat and diquat in surface and drinking water.

Comprehensive Assessment of Multidrug-Resistant and Extraintestinal Pathogenic Escherichia coli in Wastewater Treatment Plant Effluents.

Multidrug-resistant (MDR) Escherichia coli poses a significant threat to public health, contributing to elevated rates of morbidity, mortality, and economic burden. This study focused on investigating the antibiotic resistance profiles, resistance and virulence gene distributions, biofilm formation capabilities, and sequence types of E. coli strains resistant to six or more antibiotic classes. Among 918 strains isolated from 33 wastewater treatment plants (WWTPs), 53.6% (492/918) demonstrated resistance, 32.5% (298/918) were MDR, and over 8% (74/918) were resistant to six or more antibiotic classes, exhibiting complete resistance to ampicillin and over 90% to sulfisoxazole, nalidixic acid, and tetracycline. Key resistance genes identified included sul2, blaTEM, tetA, strA, strB, and fimH as the predominant virulence genes linked to cell adhesion but limited biofilm formation; 69% showed no biofilm formation, and approximately 3% were strong producers. Antibiotic residue analysis detected ciprofloxacin, sulfamethoxazole, and trimethoprim in all 33 WWTPs. Multilocus sequence typing analysis identified 29 genotypes, predominantly ST131, ST1193, ST38, and ST69, as high-risk clones of extraintestinal pathogenic E. coli. This study provided a comprehensive analysis of antibiotic resistance in MDR E. coli isolated from WWTPs, emphasizing the need for ongoing surveillance and research to effectively manage antibiotic resistance.

A high-dimensional in-sensor reservoir computing system with optoelectronic memristors for high-performance neuromorphic machine vision.

In-sensor reservoir computing (RC) is a promising technology to reduce power consumption and training costs of machine vision systems by processing optical signals temporally. This study demonstrates a high-dimensional in-sensor RC system with optoelectronic memristors to enhance the performance of the in-sensor RC system. Because optoelectronic memristors can respond to both optical and electrical stimuli, optical and electrical masks are proposed to improve the dimensionality and performance of the in-sensor RC system. An optical mask is employed to regulate the wavelength of light, while an electrical mask is used to control the initial conductance of zinc oxide optoelectronic memristors. The distinct characteristics of these two masks contribute to the representation of various distinguishable reservoir states, making it possible to implement diverse reservoir configurations with minimal correlation and to increase the dimensionality of the in-sensor RC system. Using the high-dimensional in-sensor RC system, handwritten digits are successfully classified with an accuracy of 94.1%. Furthermore, human action pattern recognition is achieved with a high accuracy of 99.4%. These high accuracies are achieved with the use of a single-layer readout network, which can significantly reduce the network size and training costs.

Spatiotemporal Data Processing with Memristor Crossbar-Array-Based Graph Reservoir.

Memristor-based physical reservoir computing (RC) is a robust framework for processing complex spatiotemporal data parallelly. However, conventional memristor-based reservoirs cannot capture the spatial relationship between the time-varying inputs due to the specific mapping scheme assigning one input signal to one memristor conductance. Here, a physical "graph reservoir" is introduced using a metal cell at the diagonal-crossbar array (mCBA) with dynamic self-rectifying memristors. Input and inverted input signals are applied to the word and bit lines of the mCBA, respectively, storing the correlation information between input signals in the memristors. In this way, the mCBA graph reservoirs can map the spatiotemporal correlation of the input data in a high-dimensional feature space. The high-dimensional mapping characteristics of the graph reservoir achieve notable results, including a normalized root-mean-square error of 0.09 in Mackey-Glass time series prediction, a 97.21% accuracy in MNIST recognition, and an 80.0% diagnostic accuracy in human connectome classification.

Cross-Wired Memristive Crossbar Array for Effective Graph Data Analysis.

Graphs adequately represent the enormous interconnections among numerous entities in big data, incurring high computational costs in analyzing them with conventional hardware. Physical graph representation (PGR) is an approach that replicates the graph within a physical system, allowing for efficient analysis. This study introduces a cross-wired crossbar array (cwCBA), uniquely connecting diagonal and non-diagonal components in a CBA by a cross-wiring process. The cross-wired diagonal cells enable cwCBA to achieve precise PGR and dynamic node state control. For this purpose, a cwCBA is fabricated using Pt/Ta2O5/HfO2/TiN (PTHT) memristor with high on/off and self-rectifying characteristics. The structural and device benefits of PTHT cwCBA for enhanced PGR precision are highlighted, and the practical efficacy is demonstrated for two applications. First, it executes a dynamic path-finding algorithm, identifying the shortest paths in a dynamic graph. PTHT cwCBA shows a more accurate inferred distance and ≈1/3800 lower processing complexity than the conventional method. Second, it analyzes the protein-protein interaction (PPI) networks containing self-interacting proteins, which possess intricate characteristics compared to typical graphs. The PPI prediction results exhibit an average of 30.5% and 21.3% improvement in area under the curve and F1-score, respectively, compared to existing algorithms.

Thresholding Computing with Heterogeneous Integration of Memristive Kernel with Metal-Oxide-Semiconductor Capacitor for Temporal Data Analysis.

Precise event detection within time-series data is increasingly critical, particularly in noisy environments. Reservoir computing, a robust computing method widely utilized with memristive devices, is efficient in processing temporal signals. However, it typically lacks intrinsic thresholding mechanisms essential for precise event detection. This study introduces a new approach by integrating two Pt/HfO2/TiN (PHT) memristors and one Ni/HfO2/n-Si (NHS) metal-oxide-semiconductor capacitor (2M1MOS) to implement a tunable thresholding function. The current-voltage nonlinearity of memristors combined with the capacitance-voltage nonlinearity of the capacitor forms the basis of the 2M1MOS kernel system. The proposed kernel hardware effectively records feature-specified information of the input signal onto the memristors through capacitive thresholding. In electrocardiogram analysis, the memristive response exhibited a more than ten-fold difference between arrhythmia and normal beats. In isolated spoken digit classification, the kernel achieved an error rate of only 0.7% by tuning thresholds for various time-specific conditions. The kernel is also applied to biometric authentication by extracting personal features using various threshold times, presenting more complex and multifaceted uses of heartbeats and voice data as bio-indicators. These demonstrations highlight the potential of thresholding computing in a memristive framework with heterogeneous integration.

Hyperplane tree-based data mining with a multi-functional memristive crossbar array.

This study explores the stochastic and binary switching behaviors of a Ta/HfO2/RuO2 memristor to implement a combined data mining approach for outlier detection and data clustering algorithms in a multi-functional memristive crossbar array. The memristor switches stochastically with high state dispersion in the stochastic mode and deterministically between two states with low dispersion in the binary mode, while they can be controlled by varying operating voltages. The stochastic mode facilitates the parallel generation of random hyperplanes in a tree structure, used to compress spatial information of the dataset in the Euclidian space into binary format, still retaining sufficient spatial features. The ensemble effect from multiple trees improved the classification performance. The binary mode facilitates parallel Hamming distance calculation of the binary codes containing spatial information, which measures similarity. These two modes enable efficient implementation of the newly proposed minority-based outlier detection method and modified K-means method on the same hardware. Array measurements and hardware simulations investigate various hyperparameters' impact and validate the proposed methods with practical datasets. The proposed methods show linear O(n) time complexity and high energy efficiency, consuming <1% of the energy compared to digital computing with conventional algorithms while demonstrating software-comparable performance in both tasks.

Memristive Crossbar Array-Based Probabilistic Graph Modeling.

Modern graph datasets with structural complexity and uncertainties due to incomplete information or data variability require advanced modeling techniques beyond conventional graph models. This study introduces a memristive crossbar array (CBA)-based probabilistic graph model (C-PGM) utilizing Cu0.3Te0.7/HfO2/Pt memristors, which exhibit probabilistic switching, self-rectifying, and memory characteristics. C-PGM addresses the complexities and uncertainties inherent in structural graph data across various domains, leveraging the probabilistic nature of memristors. C-PGM relies on the device-to-device variation across multiple memristive CBAs, overcoming the limitations of previous approaches that rely on sequential operations, which are slower and have a reliability concern due to repeated switching. This new approach enables the fast processing and massive implementation of probabilistic units at the expense of chip area. In this study, the hardware-based C-PGM feasibly expresses small-scale probabilistic graphs and shows minimal error in aggregate probability calculations. The probability calculation capabilities of C-PGM are applied to steady-state estimation and the PageRank algorithm, which is implemented on a simulated large-scale C-PGM. The C-PGM-based steady-state estimation and PageRank algorithm demonstrate comparable accuracy to conventional methods while significantly reducing computational costs.

Nationwide evaluation of microplastic properties in municipal wastewater treatment plants in South Korea.

Wastewater treatment plants (WWTPs) are considered a significant microplastic discharge source. To evaluate the amount and characteristics of microplastics discharged from WWTPs in South Korea, we selected 22 municipal WWTPs nationally and investigated microplastics at each treatment stage. The mean microplastic removal efficiency by WWTPs was >99%, and most of the microplastics were removed by sedimentation with the second clarifier during wastewater treatment. Consequently, the microplastic removal efficiency of WWTPs did not significantly differ from that of the adopted wastewater treatment technology because a second clarifier was applied in most WWTPs. However, for WWTPs operating a tertiary treatment process, the removal efficiency was enhanced compared with that of WWTPs discharging after a second clarifier. Although the microplastic removal efficiency was high by WWTP, the discharge contribution to the water environment could not be ignored because of the amount of treated wastewater, resulting in an increase of 5.8-270.9 items/m3 of microplastics in the receiving water. The characteristics of microplastics in WWTPs, including their components, shape, and size, were also evaluated. The most detected components included polytetrafluoroethylene and polyester. Most microplastics detected were categorized as fragments and fibers, while other types were hardly detected. The size of more than 70% of the microplastics detected in WWTPs was under 300 µm, implying that the size of microplastics required to control in WWTPs was much smaller than the defined size of microplastics. An evaluation of the correlation between other pollution factors and microplastic abundance did not reveal positive correlations, and microplastic occurrence was not affected by changing seasons, which may need to be evaluated with further studies. Research should also be performed on the effect of influent sources on the level of microplastic abundance and fate of ultrafine plastics in WWTPs.

High-Dimensional Physical Reservoir with Back-End-of-Line-Compatible Tin Monoxide Thin-Film Transistor.

This work demonstrates a physical reservoir using a back-end-of-line compatible thin-film transistor (TFT) with tin monoxide (SnO) as the channel material for neuromorphic computing. The electron trapping and time-dependent detrapping at the channel interface induce the SnO·TFT to exhibit fading memory and nonlinearity characteristics, the critical assets for physical reservoir computing. The three-terminal configuration of the TFT allows the generation of higher-dimensional reservoir states by simultaneously adjusting the bias conditions of the gate and drain terminals, surpassing the performances of typical two-terminal-based reservoirs such as memristors. The high-dimensional SnO TFT reservoir performs exceptionally in two benchmark tests, achieving a 94.1% accuracy in Modified National Institute of Standards and Technology handwritten number recognition and a normalized root-mean-square error of 0.089 in Mackey-Glass time-series prediction. Furthermore, it is suitable for vertical integration because its fabrication temperature is <250 °C, providing the benefit of achieving a high integration density.

A stochastic photo-responsive memristive neuron for an in-sensor visual system based on a restricted Boltzmann machine.

In-sensor computing has gained attention as a solution to overcome the von Neumann computing bottlenecks inherent in conventional sensory systems. This attention is due to the ability of sensor elements to directly extract meaningful information from external signals, thereby simplifying complex data. The advantage of in-sensor computing can be maximized with the sampling principle of a restricted Boltzmann machine (RBM) to extract significant features. In this study, a stochastic photo-responsive neuron is developed using a TiN/In-Ga-Zn-O/TiN optoelectronic memristor and an Ag/HfO2/Pt threshold-switching memristor, which can be configured as an input neuron in an in-sensor RBM. It demonstrates a sigmoidal switching probability depending on light intensity. The stochastic properties allow for the simultaneous exploration of various neuron states within the network, making identifying optimal features in complex images easier. Based on semi-empirical simulations, high recognition accuracies of 90.9% and 95.5% are achieved using handwritten digit and face image datasets, respectively. In addition, the in-sensor RBM effectively reconstructs abnormal face images, indicating that integrating in-sensor computing with probabilistic neural networks can lead to reliable and efficient image recognition under unpredictable real-world conditions.

Heterogeneous density-based clustering with a dual-functional memristive array.

In the big data era, the requirement for data clustering methods that can handle massive and heterogeneous datasets with varying distributions increases. This study proposes a clustering algorithm for data sets with heterogeneous density using a dual-mode memristor crossbar array for data clustering. The array consists of a Ta/HfO2/RuO2 memristor operating in analog or digital modes, controlled by the reset voltage. The digital mode shows low dispersion and a high resistance ratio, and the analog mode enables precise conductance tuning. The local outlier factor is introduced to handle a heterogeneous density, and the required Euclidean and K-distances within the given dataset are calculated in the analog mode in parallel. In the digital mode, clustering is performed based on the connectivity among data points after excluding the detected outliers. The proposed algorithm boasts linear time complexity for the entire process. Extensive evaluations of synthetic datasets demonstrate significant improvement over representative density-based algorithms, and the datasets with heterogeneous density are clustered feasibly. Finally, the proposed algorithm is used to cluster the single-molecule localization microscopy data, demonstrating the feasibility of the suggested method for real-world problems.

In-Materia Annealing and Combinatorial Optimization Based on Vertical Memristive Array.

Due to its area and energy efficiency, a memristive crossbar array (CBA) has been extensively studied for various combinatorial optimization applications, from network problems to circuit design. However, conventional approaches include heavily burdening software fine-tuning for the annealing process. Instead, this study introduces the "in-materia annealing" method, where the inter-layer interference of vertically stacked memristive CBA is utilized as an annealing method. When mapping combinatorial optimization problems into the configuration layer of the CBA, exponentially decaying annealing profiles are generated in nearby noise layers. Moreover, in-materia annealing profiles can be controlled by changing compliance current, read voltage, and read pulse width. Therefore, the annealing profiles can be arbitrarily controlled and generated individually for each cell, providing rich noise sources to solve the problem efficiently. Consequently, the experimental and simulation of Max-Cut and weighted Max-Cut problems achieve notable results with the minimum software burden.

Implementation of Bayesian networks and Bayesian inference using a Cu0.1Te0.9/HfO2/Pt threshold switching memristor.

Bayesian networks and Bayesian inference, which forecast uncertain causal relationships within a stochastic framework, are used in various artificial intelligence applications. However, implementing hardware circuits for the Bayesian inference has shortcomings regarding device performance and circuit complexity. This work proposed a Bayesian network and inference circuit using a Cu0.1Te0.9/HfO2/Pt volatile memristor, a probabilistic bit neuron that can control the probability of being 'true' or 'false.' Nodal probabilities within the network are feasibly sampled with low errors, even with the device's cycle-to-cycle variations. Furthermore, Bayesian inference of all conditional probabilities within the network is implemented with low power (<186 nW) and energy consumption (441.4 fJ), and a normalized mean squared error of ∼7.5 × 10-4 through division feedback logic with a variational learning rate to suppress the inherent variation of the memristor. The suggested memristor-based Bayesian network shows the potential to replace the conventional complementary metal oxide semiconductor-based Bayesian estimation method with power efficiency using a stochastic computing method.

Vertically Stackable Ovonic Threshold Switch Oscillator Using Atomic Layer Deposited Ge0.6Se0.4 Film for High-Density Artificial Neural Networks.

Nanodevice oscillators (nano-oscillators) have received considerable attention to implement in neuromorphic computing as hardware because they can significantly improve the device integration density and energy efficiency compared to complementary metal oxide semiconductor circuit-based oscillators. This work demonstrates vertically stackable nano-oscillators using an ovonic threshold switch (OTS) for high-density neuromorphic hardware. A vertically stackable Ge0.6Se0.4 OTS-oscillator (VOTS-OSC) is fabricated with a vertical crossbar array structure by growing Ge0.6Se0.4 film conformally on a contact hole structure using atomic layer deposition. The VOTS-OSC can be vertically integrated onto peripheral circuits without causing thermal damage because the fabrication temperature is <400 °C. The fabricated device exhibits oscillation characteristics, which can serve as leaky integrate-and-fire neurons in spiking neural networks (SNNs) and coupled oscillators in oscillatory neural networks (ONNs). For practical applications, pattern recognition and vertex coloring are demonstrated with SNNs and ONNs, respectively, using semiempirical simulations. This structure increases the oscillator integration density significantly, enabling complex tasks with a large number of oscillators. Moreover, it can enhance the computational speed of neural networks due to its rapid switching speed.

Emergency contraceptive provision in the emergency department and risk analysis for delayed utilization in South Korea.

In certain nations, the emergency department (ED) has been designated as the primary center to provide emergency contraception (EC). However, analyses of ED visits for EC are limited. Moreover, ED-based research that focuses on time is limited to only a few surveys. The aims of this study were to examine the characteristics of ED visitors for EC and the interval between the coitus and arrival at the ED, and to analyze the factors associated with delays in visiting the ED. This retrospective cohort study involved patients at 2 urban tertiary academic hospitals in South Korea. All patients who presented to the ED for EC between January 2019 and December 2021 were analyzed. The median age of the participants was 26 years. The most common variables were age of 20 to 29 years (42.0%), evening visits (34.9%), weekends or public holidays (62.6%), single status (89.2%), and visits after contraceptive failure (79.1%). The mean time interval was 7.49 hours, and 77.4% of all patients visited the ED within 12 hours. Patients who received public sex education presented earlier (P < .001). ED visits after nonconsensual sexual incidents represented significantly delayed presentations (P < .001). Regression analysis revealed that both the lack of public education and the occurrence of nonconsensual coitus were associated with incident-to-ED visit intervals of >12 hours. Most patients received emergency contraceptive pill (ECP) within the recommended timeframe. In particular, nationwide school-based public sex education positively affected early ECP access. In contrast, ECP provision was delayed for patients who experienced nonconsensual coitus. Strategies for timely ECP access should account for possible concerns about stigmatization and privacy.