Low Complexity Research Articles

Microwave planar resonator-based sensor for pharmaceutical tablet-moisture characterization based on complementary symmetric meander line-shaped structure

ABSTRACT This paper presents a microwave planar resonator-based sensor for moisture sensing application. The resonator sensor is designed utilising the Complementary Symmetric Meander Line-Shaped Resonator topology and operated in the S-band with the frequency range of 2.6–3.3 GHz. The main design criteria of this sensor are based on low manufacturing complexity and cost, ease of fabrication and integration with existing equipment, despite its high sensitivity and accuracy. When this sensor is loaded/unloaded with four different Material Under Tests with relative permittivity values varying from 2.3 to 4.3, the electromagnetic simulations were validated against experimental measurements according to the resonant notch frequency of transmission coefficient for the sensor’s material-characterisation performance. The simulated and experimental results show good agreements on the resonant frequencies and demonstrate the sensor’s high performance with a normalised sensitivity of 6%. Resonant frequencies are well correlated with material permittivity using a quadratic-polynomial regression with R 2 = 0.999. Moreover, moisture-content percentages of both dry and moist paracetamol tablets, measured by a loss-on-drying moisture analyser between 1.74% and 17.72%, have good correlation with the sensor’s resonant-frequency shift from on-line measurements. This resonator sensor has exhibited the potential for detecting moisture contents in small-sized materials in various industries.

Enhanced pediatric thyroid ultrasound image segmentation using DC-Contrast U-Net

Early screening methods for the thyroid gland include palpation and imaging. Although palpation is relatively simple, its effectiveness in detecting early clinical signs of the thyroid gland may be limited, especially in children, due to the shorter thyroid growth time. Therefore, this constitutes a crucial foundational work. However, accurately determining the location and size of the thyroid gland in children is a challenging task. Accuracy depends on the experience of the ultrasound operator in current clinical practice, leading to subjective results. Even among experts, there is poor agreement on thyroid identification. In addition, the effective use of ultrasound machines also relies on the experience of the ultrasound operator in current clinical practice. In order to extract sufficient texture information from pediatric thyroid ultrasound images while reducing the computational complexity and number of parameters, this paper designs a novel U-Net-based network called DC-Contrast U-Net, which aims to achieve better segmentation performance with lower complexity in medical image segmentation. The results show that compared with other U-Net-related segmentation models, the proposed DC-Contrast U-Net model achieves higher segmentation accuracy while improving the inference speed, making it a promising candidate for deployment in medical edge devices in clinical applications in the future.

Selective Enrichment via TiO2 Magnetic Nanoparticles Enables Deep Profiling of Circulating Neutral Glycosphingolipids.

Circulating neutral glycosphingolipids (neutral GSLs (nGSLs)) are a unique subset of nGSLs that detach from organs or cell membranes and enter the bloodstream. Altered molecular distribution of circulating nGSL is increasingly associated with diseases. However, profiling of circulating nGSLs presents a lasting challenge due to their low abundances and structural complexity. Although TiO2 magnetic nanoparticles (TiO2 MNPs) were effective for the enrichment of nGSLs in brain tissue, the protocol showed limited selectivity for circulating nGSLs because their abundances were 100-times lower in human plasma than in brain tissue. In this work, we optimized the key parameters of selective enrichment by TiO2 MNPs and achieved 1:10,000 selectivity for nGSLs over interfering phospholipids, while maintaining ∼70% recovery for different subclasses of nGSLs. By integrating TiO2 MNP-based selective enrichment with reversed-phase liquid chromatography mass spectrometry and charge-tagging Paternò-Büchi derivatization, we achieved deep profiling of over 300 structures of nGSLs and sulfatides across 5 orders of magnitude in relative abundances, a significant leap regarding lipid coverage. We also depicted the structural atlas of nGSLs with defined headgroup, long-chain base, N-acyl chain, the location of desaturation, and 2-hydroxylation. Such information provides a valuable resource for lipidomic studies concerning the roles of circulating nGSLs in health and diseases.

Freeze-thaw strength increases microbial stability to enhance diversity-soil multifunctionality relationship

Insights into the impacts of freeze-thaw processes on soil microorganisms and their related functions in permafrost regions are crucial for assessing ecological consequences imposed by the shifts in freeze-thaw patterns. Through in-situ investigations on seasonal freeze-thaw processes in the active layer of permafrost in the Qinghai-Tibet Plateau, we found that microbial richness was higher and positively correlated with soil multifunctionality during the freeze-thaw stage (freezing and thawing periods) compared to the non-freeze-thaw stage (completely frozen and thawed periods). This relationship resulted from the higher microbial stability, which was highly consistent with the lower complexity, more keystone taxa, and greater robustness of networks. Although freeze-thaw strength exacerbated the greenhouse effect on climate, it was alleviated by the enhancement of diversity-soil multifunctionality relationship. These findings have substantial implications for exploring the responses of microbial-mediated soil multifunctionality and greenhouse effect in alpine permafrost to more drastic variations of freeze-thaw processes under future warming.

Secure short-packet communications in power beacon-assisted IoT networks over Nakagami-m fading channels

In this work, we investigate short-packet communications in power beacon (PB)-assisted Internet-of-Things (IoT) networks, where an energy-constrained actuator first harvests energy from a dedicated PB, and then transmits confidential signals to a desired controller in the presence of an eavesdropper. We derive a closed-form lower bound approximation expression for the average achievable effective secrecy rate (AESR) over Nakagami-m fading channels. To gain more insights, we also present the asymptotic average AESR in the high signal-to-noise ratio (SNR) regime. Specifically, analytical results indicate that an average AESR floor appears with the increase of SNR. Moreover, a low complexity one-dimensional search method is employed to maximize the average AESR by optimizing the energy harvesting length. Monte-Carlo simulations are provided to corroborate our analysis.

Gridless DOA Estimation Method for Arbitrary Array Geometries Based on Complex-Valued Deep Neural Networks

Gridless direction of arrival (DOA) estimation methods have garnered significant attention due to their ability to avoid grid mismatch errors, which can adversely affect the performance of high-resolution DOA estimation algorithms. However, most existing gridless methods are primarily restricted to applications involving uniform linear arrays or sparse linear arrays. In this paper, we derive the relationship between the element-domain covariance matrix and the angular-domain covariance matrix for arbitrary array geometries by expanding the steering vector using a Fourier series. Then, a deep neural network is designed to reconstruct the angular-domain covariance matrix from the sample covariance matrix and the gridless DOA estimation can be obtained by Root-MUSIC. Simulation results on arbitrary array geometries demonstrate that the proposed method outperforms existing methods like MUSIC, SPICE, and SBL in terms of resolution probability and DOA estimation accuracy, especially when the angular separation between targets is small. Additionally, the proposed method does not require any hyperparameter tuning, is robust to varying snapshot numbers, and has a lower computational complexity. Finally, real hydrophone data from the SWellEx-96 ocean experiment validates the effectiveness of the proposed method in practical underwater acoustic environments.

A Survey of Phase Shifters for Microwave Phased Array Systems

ABSTRACTPhased Array Systems (PASs) are extensively used in radar and telecommunication systems for diverse applications, including military surveillance and wireless broadband communication. Besides, they are becoming increasingly indispensable in modern applications such as multi‐function phased array radars for weather monitoring and aircraft tracking or high‐throughput satellite systems, which aim to provide fast broadband connectivity in remote areas. Despite the numerous advantages of phased array systems with RF phase shift, including high signal‐to‐noise and signal‐to‐interference ratios, as well as low complexity, they present a few limitations, including high cost and large chip areas. The cost and size of PASs are mainly dependent on the RF phase shifters, which are essential to their operation. This article describes the different types of phase shifters (mechanical, ferromagnetic/magnetic, electromechanical, and electronic). It compares their performance metrics, highlighting that electronic phase shifters are the most common type used in modern PASs. In this regard, a comparative study of different subcategories of electronic phase shifters, such as the switched‐type, reflective‐type, loaded‐transmission line, and vector‐sum phase shifters, are explored along with their advantages, drawbacks and state‐of‐art techniques used to address their limitations. Therefore, this article may serve as a reference for research milestones on RF phase shifters.

Bearing fault detection in adjustable speed drives via self-organized operational neural networks

AbstractAdjustable speed drives (ASDs) are widely used in industry for controlling electric motors in applications such as rolling mills, compressors, fans, and pumps. Condition monitoring of ASD-fed induction machines is very critical for preventing failures. Motor current signature analysis offers a non-invasive approach to assess motor condition. Application of conventional convolutional neural networks provides good results in detecting and classifying fault types for utility line-fed motors, but the accuracy drops considerably in the case of ASD-fed motors. This work introduces the use of self-organized operational neural networks to enhance the accuracy of detecting and classifying bearing faults in ASD-fed induction machines. Our approach leverages the nonlinear neurons and self-organizing capabilities of self-organized operational neural networks to better handle the non-stationary nature of ASD operations, providing more reliable fault detection and classification with minimal preprocessing and low complexity, using raw motor current data.

Online Plain Language Tool and Health Information Quality

Complex and ineffective health communication is a critical source of health inequity and occurs despite repeated policy directives to provide health information that is easy to understand and applies health literacy principles. To evaluate the effectiveness of the Sydney Health Literacy Lab Health Literacy Editor, an easy-to-use online plain language tool that supports health information providers to apply health literacy guidelines to written health information. This randomized clinical trial, conducted online in Australia from May 2023 to February 2024, included a convenience sample of health information providers with no previous experience using the Health Literacy Editor. Analysts were blinded to study group. Participants were randomized 1:1 to the intervention or control group. Participants in the intervention group were provided access to the Health Literacy Editor and a 30-minute online training program prior to editing 3 prespecified health texts. The Health Literacy Editor gives objective, real-time feedback on words and sentences. Control participants revised the texts using their own standard health information development processes. The preregistered primary outcome was the text school grade reading score (using a validated instrument, the Simple Measure of Gobbledygook). Secondary outcomes were text complexity (percentage of text using complex language), use of passive voice (number of instances), and subjective expert ratings (5-point Likert scale corresponding to items on the Patient Education Materials Assessment Tool). A total of 211 participants were randomized, with 105 in the intervention group and 106 in the control group. Of 181 participants in the intention-to-treat analysis (mean [SD] age, 41.0 [11.6] years; 154 women [85.1%]), 86 were in the intervention group and 95 in the control group. Texts revised in the intervention group had significantly improved grade reading scores (mean difference [MD], 2.48 grades; 95% CI, 1.84-3.12 grades; P < .001; Cohen d, 0.99), lower text complexity scores (MD, 6.86; 95% CI, 4.99-8.74; P < .001; Cohen d, 0.95), and less use of passive voice (MD, 0.95 instances; 95% CI, 0.44-1.47 instances; P < .001; Cohen d, 0.53) compared with texts revised in the control group in intention-to-treat analyses. Experts rated texts in the intervention group more favorably for word choice and style than those in the control group (MD, 0.44; 95% CI, 0.25-0.63; P < .001; Cohen d, 0.63), with no loss of meaning or content. In this randomized clinical trial, the Health Literacy Editor helped users simplify health information and apply health literacy guidelines to written text. The findings suggest the tool has high potential to improve development of health information for people who have low health literacy. As an online tool, the Health Literacy Editor is also easy to access and implement at scale. ANZCTR Identifier: ACTRN12623000386639.

A new approach for neural decoding by inspiring of hyperdimensional computing for implantable intra-cortical BMIs

In the field of Brain Machine Interface (BMI), the process of translating motor intention into a machine command is denoted as decoding. However, despite recent advancements, decoding remains a formidable challenge within BMI. The utilization of current decoding algorithms in the field of BMI often involves computational complexity and requires the use of computers. This is primarily due to the reliance on mathematical models to address the decoding issue and perform subsequent output calculations. Unfortunately, computers are not feasible for implantable BMI systems due to their size and power consumption. To address this predicament, this study proposes a pioneering approach inspired by hyperdimensional computing. This approach first involves identifying the pattern of each stimulus by considering the normal firing rate distribution of each neuron. Subsequently, the newly observed firing pattern for each input is compared with the patterns detected at each moment for each neuron. The algorithm, which shares similarities with hyperdimensional computing, identifies the most similar pattern as the final output. This approach reduces the dependence on mathematical models. The efficacy of this method is assessed through the utilization of an authentic dataset acquired from the Frontal Eye Field (FEF) of two male rhesus monkeys. The output space encompasses eight possible angles. The results demonstrate an accuracy rate of 51.5% while exhibiting significantly low computational complexity, involving a mere 2050 adder operators. Furthermore, the proposed algorithm is implemented on a field-programmable gate array (FPGA) and as an ASIC designe in a standard CMOS 180 nm technology, underscoring its suitability for real-time implantable BMI applications. The implementation required only 2.3 Kbytes of RAM, occupied an area of 2.2 mm2, and consumed 9.32 µW at a 1.8 V power supply. Consequently, the proposed solution represents an accurate, low computational complexity, hardware-friendly, and real-time approach.

Data mining of PubChem bioassay records reveals diverse OXPHOS inhibitory chemotypes as potential therapeutic agents against ovarian cancer

Focused screening on target-prioritized compound sets can be an efficient alternative to high throughput screening (HTS). For most biomolecular targets, compound prioritization models depend on prior screening data or a target structure. For phenotypic or multi-protein pathway targets, it may not be clear which public assay records provide relevant data. The question also arises as to whether data collected from disparate assays might be usefully consolidated. Here, we report on the development and application of a data mining pipeline to examine these issues. To illustrate, we focus on identifying inhibitors of oxidative phosphorylation, a druggable metabolic process in epithelial ovarian tumors. The pipeline compiled 8415 available OXPHOS-related bioassays in the PubChem data repository involving 312,093 unique compound records. Application of PubChem assay activity annotations, PAINS (Pan Assay Interference Compounds), and Lipinski-like bioavailability filters yields 1852 putative OXPHOS-active compounds that fall into 464 clusters. These chemotypes are diverse but have relatively high hydrophobicity and molecular weight but lower complexity and drug-likeness. These chemotypes show a high abundance of bicyclic ring systems and oxygen containing functional groups including ketones, allylic oxides (alpha/beta unsaturated carbonyls), hydroxyl groups, and ethers. In contrast, amide and primary amine functional groups have a notably lower than random prevalence. UMAP representation of the chemical space shows strong divergence in the regions occupied by OXPHOS-inactive and -active compounds. Of the six compounds selected for biological testing, 4 showed statistically significant inhibition of electron transport in bioenergetics assays. Two of these four compounds, lacidipine and esbiothrin, increased in intracellular oxygen radicals (a major hallmark of most OXPHOS inhibitors) and decreased the viability of two ovarian cancer cell lines, ID8 and OVCAR5. Finally, data from the pipeline were used to train random forest and support vector classifiers that effectively prioritized OXPHOS inhibitory compounds within a held-out test set (ROCAUC 0.962 and 0.927, respectively) and on another set containing 44 documented OXPHOS inhibitors outside of the training set (ROCAUC 0.900 and 0.823). This prototype pipeline is extensible and could be adapted for focus screening on other phenotypic targets for which sufficient public data are available.Scientific contributionHere, we describe and apply an assay data mining pipeline to compile, process, filter, and mine public bioassay data. We believe the procedure may be more broadly applied to guide compound selection in early-stage hit finding on novel multi-protein mechanistic or phenotypic targets. To demonstrate the utility of our approach, we apply a data mining strategy on a large set of public assay data to find drug-like molecules that inhibit oxidative phosphorylation (OXPHOS) as candidates for ovarian cancer therapies.Graphical

Molecular Interactions between Tau Protein and TIA1: Distinguishing Physiological Condensates from Pathological Fibrils.

The interaction of tau protein with other key proteins essential for stress granule formation determines their functional and pathological impact. In a biological framework, the synergy between Alzheimer's associated tau protein and the stress granule core protein TIA1 is widely recognized. However, the molecular details of this association remain unclear. In this study, we throw light on the importance of the state in which the TIA1 exists in mediating its association with the tau protein. Investigations were carried out on the three repeat constructs of tau (K19) and different structures formed by TIA1. Specifically, the condensate formed by TIA1 full-length (TIA1-FL) protein as well as fibril formed by low complexity domain of TIA1 (TIA1-LCD). The dynamics of K19 inside TIA1-FL condensates and the aggregation kinetics of K19 in the presence of TIA1-LCD fibrils were examined using various biophysical techniques. Relaxation-based solution NMR spectroscopic investigations suggest a weak interaction with TIA1 condensates and indicated a reduction in the dynamics of K19 within these TIA1 condensates. In contrast, a significant interaction was observed between K19, and TIA1-LCD fibrils primarily mediated through 321KCGS324 and 306VQIVYKPVDLSKV317. Our findings emphasize that the interaction between Tau and TIA1 varies depending on whether TIA1 is in its physiological condensate form or its pathological fibril state.

Retraction Note: A novel approach of low complexity distributed UA algorithm is used for traffic load balancing and interference in next generation networks

Retraction Note: A novel approach of low complexity distributed UA algorithm is used for traffic load balancing and interference in next generation networks

Long-term health outcomes of young patients with low complexity coronary disease: A weighted analysis according to revascularization strategy.

Age is an important factor weighing on revascularization outcomes decisions. We analysed long-term health outcomes of young patients with low complexity coronary anatomy suitable for both CABG and PCI, according to revascularization strategy. Patients 60 year-old or less, undergoing invasive coronary angiography between Jan/2007 and Dec/2015, presenting with proximal LAD involvement, left main or multivessel disease and a SYNTAX Score (SS) ≤22 were retrospectively selected. An inverse probability of treatment weight methodology generated a pseudopopulation with well-balanced characteristics, which was used to estimate the average treatment effect between PCI (n = 374) and CABG (n = 173). Mean age was 53 ± 7 yo, 27% had DM and 48% presented with an ACS. Mean SYNTAX Score was 13.6 ± 4.9 and 68% underwent PCI as index revascularization strategy. In the weighed population, the adjusted hazard of the primary end-point of all-cause death at total follow-up (median 9.3 years [IQR: 6.9; 11.7]) was 0.40 (95% CI 0.19-0.7) for CABG vs PCI (incidence-rate 5.8 vs 14.0 deaths/1000-person-years). Accounting for death as competing-risk, the cumulative hazard of new revascularization (sub-distribution HR 0.72; 95% CI 0.32-1.25) and any cause hospital readmission (sub-distribution HR 0.70; 95% CI 0.41-1.07) were lower in CABG patients, as opposed to death/stroke rates at 30-days which were higher with CABG (0.3% vs 1.7%; risk ratio = 5.84). In this quasi-experimental analysis of young patients with CAD and an equivalent indication for both PCI or CABG, improved long-term health outcomes were favourably associated with CABG, as compared to PCI. These observations support the need for dedicated randomized trials with longer follow-up in order to better inform lifetime treatment options.

Prediction of Flotation Deinking Performance: A Comparative Analysis of Machine Learning Techniques

Flotation deinking is one of the most widely used techniques for the separation of ink particles from cellulose fibers during the process of paper recycling. It is a complex process influenced by a variety of factors, and is difficult to represent and usually results in models that are inconvenient to implement and/or interpret. In this paper, a comprehensive study of several machine learning methods for the prediction of flotation deinking performance is carried out, including support vector regression, regression tree ensembles (random forests and boosting) and Gaussian process regression. The prediction relies on the development of a limited dataset that assumes representative data samples obtained under a variety of laboratory conditions, including different reagents, pH values and flotation residence times. The results obtained in this paper confirm that the machine learning methods enable the accurate prediction of flotation deinking performance even when the dataset used for training the model is limited, thus enabling the determination of optimal conditions for the paper recycling process, with only minimal costs and effort. Considering the low complexity of the Gaussian process regression compared to the aforementioned ensemble models, it should be emphasized that the Gaussian process regression gave the best performance in estimating fiber recovery (R2 = 97.77%) and a reasonable performance in estimating the toner recovery (R2 = 86.31%).

Cross-attention mechanism-based spectrum sensing in generalized Gaussian noise

Spectrum sensing (SS) technology is essential for cognitive radio (CR) networks to effectively identify and utilize idle spectrum resources. Due to the influence of noise characteristics in the channel, providing accurate sensing results is challenging. In order to improve the performance of SS under non-Gaussian noise and overcome the limitations of existing methods that are mostly based on a single feature, we propose a novel time-frequency cross fusion network (TFCFN). Specifically, we utilize gated recurrent units (GRU) to capture long-term dependencies in the time domain on the original signals, meanwhile, we perform a fast Fourier transform (FFT) on the original signals to obtain the frequency domain information, and subsequently use convolutional neural networks (CNN) to extract the local spatial features in the frequency domain. Ultimately, these time-domain and frequency-domain features are dynamically fused through a cross-attention mechanism to construct more comprehensive and robust features for signal classification. We use generalized Gaussian distribution (GGD) as the noise model and reconstruct the RadioML2016.10a dataset to explore the performance under various noise conditions. The experimental results show that compared with the baseline methods, TFCFN exhibits better detection ability and maintains lower complexity in both Gaussian and non-Gaussian noise environments. Notably, when the shape parameter of GGD is set to 0.5 and the signal-to-noise ratio (SNR) of the received signal is -16dB, it can maintain the probability of false alarm (Pf\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P_f$$\\end{document}) of 10% while still ensuring the probability of detection (Pd\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P_d$$\\end{document}) of over 90%.

How Are Diagnosis-Related Groups and Staffing Allocation Systems Associated with the Complexity of Nursing Care? An Observational Study

Background: In Italy, Diagnosis-Related Groups (DRGs) have been adopted for hospital services reimbursement. In some Italian regions, nurse staffing allocation is purely volume-based, with different minutes/patient/day categories determined by the type of hospital ward. The Information System of Nursing Performance (SIPI) is a valid and reliable tool assessing nursing care complexity as an indicator of the actual nursing care demand. Evidence is lacking about the ability of current resource allocation methods to account for the nursing care demand. Objective: To evaluate the association between (1) DRG rates and nursing care complexity and (2) hospital ward categories of nurse staffing and nursing care complexity. Methods: All patients discharged from the medical department of an Italian hospital over a data collection period were eligible. To assess the association between nursing care complexity (SIPI) and DRGs, the distribution of the DRG rate (median and first–third quartile) was compared for cases with high or low complexity. To evaluate the association between nursing care complexity (SIPI) and nurse staffing, the frequency of high complexity within nurse staffing categories (120/180/240 min/patient/day) was compared. Because the sample was very large, methods of statistical inference were not applied, and only descriptive measures were reported. Results: 6872 hospitalizations were included. The median DRG rate for high and low complexity admissions were very similar (EUR 3536 and EUR 3285, respectively). The proportion of admissions with high complexity decreased for wards with higher staffing allocation rates. Conclusion: DRG reimbursement and the nurse staffing allocation systems were ineffective in accounting for nursing care complexity. The SIPI could help identify areas requiring more financial and staffing resources for nursing care.

Using bioinformatics to investigate functional diversity: a case study of MHC diversity in koalas.

Conservation genomics can greatly improve conservation outcomes of threatened populations, including those impacted by disease. Understanding diversity within immune gene families, including the major histocompatibility complex (MHC) and toll-like receptors (TLR), is important due to the role they play in disease resilience and susceptibility. With recent advancements in sequencing technologies and bioinformatic tools, the cost of generating high-quality sequence data has significantly decreased and made it possible to investigate diversity across entire gene families in large numbers of individuals compared to investigating only a few genes or a few populations previously. Here, we use the koala as a case study for investigating functional diversity across populations. We utilised previous target enrichment data and 438 whole genomes to firstly, determine the level of sequencing depth required to investigate MHC diversity and, secondly, determine the current level of diversity in MHC genes in koala populations. We determined for low complexity, conserved genes such as TLR genes 10 × sequencing depth is sufficient to reliably genotype more than 90% of variants, whereas for complex genes such as the MHC greater than 20 × and preferably 30 × sequencing depth is required. We used whole genome data to identify 270 biallelic SNPs across 24 MHC genes as well as copy number variation (CNV) within class I and class II genes and conduct supertype analysis. Overall, we have provided a bioinformatic workflow for investigating variation in a complex immune gene family from whole genome sequencing data and determined current levels of diversity within koala MHC genes.

A reservoir computing approach in active noise control for vehicle applications

Reservoir Computing (RC) is an alternative approach to Recurrent Neural Networks (RNN) that can avoid some shortcomings of conventional gradient decent-training that are mainly related to high computational complexity and the reduced ability to "learn" long-range dependencies. In RC the input data are transformed into patterns in a high-dimensional space by an RNN called the "reservoir", which remains unchanged during training. The desired output signal is generated as a linear combination of the neuron's signals from the input-excited reservoir. The linear part of the RC architecture, which is called the "readout" can be trained with a simple learning algorithm such as linear regression. In this work an RC architecture is proposed for the attenuation of acoustic disturbances encountered in vehicle cabins such as aircrafts and yachts. At first, a dataset of acoustic pressure measurements from real-world cabins was used to generate the readout and reservoir layers. Subsequent computer simulations demonstrated that the proposed architecture can successfully attenuate such acoustic disturbances. Finally, the performance of RC in Active Noise Cancellation task was compared with both the linear FxLMS algorithm and a conventional RNN, showing that it has several advantages regarding acoustic pressure reduction combined with relatively low computational complexity.

Biogeographic shifts in the microbial co-occurrence network features of three domains across complex environmental gradients in subtropical coastal waters

BackgroundBacteria, Archaea, and Microeukaryotes comprise taxonomic domains that interact in mediating biogeochemical cycles in coastal waters. Many studies have revealed contrasting biogeographic patterns of community structure and assembly mechanisms in microbial communities from different domains in coastal ecosystems; however, knowledge of specific biogeographic patterns on microbial co-occurrence relationships across complex coastal environmental gradients remains limited. Using a dense sampling scheme at the regional scale, SSU rRNA gene amplicon sequencing, and network analysis, we investigated intra- and inter-domain co-occurrence relationships and network topology-based biogeographic patterns from three microbial domains in coastal waters that show environmental gradients across the inshore-nearshore-offshore continuum in the East China Sea.ResultsOverall, we found the highest complexity and connectivity in the bacterial network, the highest modularity in the archaeal network, and the lowest complexity, connectivity, and modularity in the microeukaryotic network. Although microbial co-occurrence networks from the three domains showed distinct topological features, they exhibited a consistent biogeographic pattern across the inshore-nearshore-offshore continuum. Specifically, the nearshore zones with intermediate levels of terrestrial impacts reflected by multiple environmental factors (including water temperature, salinity, pH, dissolved oxygen, and nutrient-related parameters) had a higher intensity of microbial co-occurrence for all three domains. In contrast, the intensity of microbial co-occurrence was weaker in both the inshore and the offshore zones at the two ends of the environmental gradients. Archaea occupied a central position in the microbial inter-domain co-occurrence network. In particular, members of the Thaumarchaeota Marine Group I (MGI, now placed within the Family Nitrosopumilaceae of the Phylum Thermoproteota) appeared to be the hubs in the biogeographic shift between inter-domain network modules across environmental gradients.ConclusionsOur work offers new insights into microbial biogeography by integrating network features into biogeographic patterns, towards a better understanding of the potential of microbial interactions in shaping biogeographic patterns of coastal marine microbiota.