Low Complexity Research Articles

Integration of acoustic, optical, and electrical methods in picoliter droplet microfluidics for rare particles enrichment

Rare particle enrichment plays a pivotal role in advancing numerous scientific research areas and industrial processes. Traditional enrichment methods encounter obstacles such as low efficiency, high cost, and complexity. Acoustic focusing, optical fiber detection, and electrical manipulation have shown potential in microfluidics for particle manipulation and analysis. This study pioneers the integration of the acoustic, optical, and electrical units to overcome the traditional limitations. The cooperative dynamics of acoustic and flow focusing are explored. The optical fibers with an enhanced detection algorithm greatly boost optical detection sensitivity. Furthermore, the droplet charging to enhance the tip charging phenomenon is complemented and validated. The detection and sorting accuracy of enriching large-size H22 cells reached 99.8% and 99.3%, respectively, with the target cell concentration increased by nearly 86-fold. Our work significantly enhances detection sensitivity and particle manipulation accuracy, ultimately offering a robust and reliable solution for generating droplets to enrich rare particles.

Scoping review of deep learning research illuminates artificial intelligence chasm in otolaryngology-head and neck surgery

Clinical validation studies are important to translate artificial intelligence (AI) technology in healthcare but may be underperformed in Otolaryngology - Head & Neck Surgery (OHNS). This scoping review examined deep learning publications in OHNS between 1996 and 2023. Searches on MEDLINE, EMBASE, and Web of Science databases identified 3236 articles of which 444 met inclusion criteria. Publications increased exponentially from 2012–2022 across 48 countries and were most concentrated in otology and neurotology (28%), most targeted extending health care provider capabilities (56%), and most used image input data (55%) and convolutional neural network models (63%). Strikingly, nearly all studies (99.3%) were in silico, proof of concept early-stage studies. Three (0.7%) studies conducted offline validation and zero (0%) clinical validation, illuminating the “AI chasm” in OHNS. Recommendations to cross this chasm include focusing on low complexity and low risk tasks, adhering to reporting guidelines, and prioritizing clinical translation studies.

An Efficient Implementation of Montgomery Modular Multiplication Using a Minimally Redundant Residue Number System

This paper presents an implementation of modular multiplication based on Montgomery’s scheme within the Residue Number System (RNS). The key innovation of the proposed approach lies in utilizing minimally redundant residue arithmetic, where the rank of a number serves as the primary positional characteristic of the residue code. Additionally, integer numbers are represented in rank form during base extension operations. Due to the low computational complexity of rank calculation in minimally redundant RNS and the specific constraints imposed on the RNS moduli sets, the proposed modular multiplication method achieves up to a 1.5 times performance improvement over non-redundant RNS counterparts. This approach is particularly suited for applications in public key cryptosystems.

On the Design of Kalman Filter with Low Complexity for 6G-Based ISAC: Alpha and Alpha–Beta Filter Perspectives

This study addresses the performance degradation of α and α-β filters in 6G Integrated Sensing and Communications (ISAC) scenarios, attributed to violations of linearity and steady-state assumptions. These filters are originally designed with time-invariant gains derived under such assumptions to ensure low computational complexity. However, deviations from ideal conditions—such as non-white, biased, or non-Gaussian process noise—necessitate corrective mechanisms. We propose a weighted process noise approach that accounts for increased uncertainty due to assumption violations while preserving the filters’ closed-form structure and computational efficiency. By integrating uncertainty into the conventional gain formulation, the proposed method achieves performance closer to the optimal filter. Numerical results demonstrate superior accuracy over conventional filters across various noise variances and scenarios, without requiring parameter tuning. Notably, performance improvements become more pronounced as the measurement interval decreases.

EU-Net and ACFS: An effective method for segmenting ore images collected on-site

EU-Net and ACFS: An effective method for segmenting ore images collected on-site

Fully memristive spiking neural network for energy-efficient graph learning.

Parallel and energy-efficient searching of the shortest paths on a large graph is challenging. Conventional methods commonly used are sequential and computing intensive, rendering them inadequate for addressing large-scale and real-time situations. Here, we propose a highly parallel, computation- and energy-efficient approach to shortest path-based graph learning based on an emerging memristor spiking neural network via algorithm-device codesign. The shortest path is obtained parallelly in nature using simultaneous spike traveling instead of arithmetic calculation, achieving extremely low time and space complexity. A nonlinear weight mapping approach is proposed to counterbalance the neuron intrinsic nonlinearity to guarantee accuracy to support large-scale graphs. The memristor hardware capability is experimentally demonstrated in unsupervised and supervised classification tasks. The estimated energy efficiency of 517.82 giga-traversal edges per second per watt outperforms field programmable gate arrays by three to four orders of magnitude, providing a pathway toward highly energy-efficient graph computing hardware.

Forest Fire Detection Algorithm Based on Improved YOLOv11n

To address issues in traditional forest fire detection models, such as large parameter sizes, slow detection speed, and unsuitability for resource-constrained devices, this paper proposes a forest fire detection method, FEDS-YOLOv11n, based on an improved YOLOv11n model. First, the C3k2 module was redesigned using the FasterBlock module, replacing C3k2 with C3k2-Faster in both the Backbone network and Neck section to achieve a lightweight model design. Second, an EMA attention mechanism was introduced into the C3k2-Faster module in the Backbone, replacing C3k2-Faster with C3k2-Faster-EMA to compensate for the accuracy loss in small-object detection caused by the lightweight design. Third, the original upsampling module in the Neck was replaced with the lightweight dynamic upsampling operator DySample. Finally, the detection head was improved using the SEAM attention module, replacing the original Detect head with SEAMHead, which enables better handling of occluded objects. The experimental results show that compared to YOLOv11n, the proposed FEDS-YOLOv11n achieves improvements of 0.9% in precision (P), 1.9% in recall (R), 2.1% in mean precision at IoU 0.5 (mAP@0.5), and 2.3% in mean precision at IoU 0.5–0.95 (mAP@0.5–0.95). Additionally, the number of parameters is reduced by 21.32%, GFLOPs are reduced by 26.98%, and FPS increases from 48.2 to 71.8. The FEDS-YOLOv11n model ensures high accuracy while maintaining lower computational complexity and faster inference speed, making it suitable for real-time forest fire detection applications.

EXPLORING THE ROLE OF GUT BACTERIA IN DIGESTIVE SYSTEM OF COW

The digestive system of cow, specifically rumen has complex and diverse ecosystem that has a significant impact on the breakdown of plant-based compounds, fermentation and absorption of nutrients. The purpose of this study was to analyze the structure, variety and functioning of rumen bacteria involved in the digestion of cow by using 16srRNA sequencing. This study provides the deep insight of bacteria involvement in digestion of cow, by identifying the composition of microbial community and their metabolic role. Genomic DNA was isolated using a fecal sample and amplified via PCR to target the 16S rRNA region of gene. After library construction and quantification sequencing was performed on the DNBSEQ platform. The raw information was filtered by eliminating poor-quality reads, adapters, sequence with low ambiguous bases and low complexity areas. 97.83% of original sequence was obtained as high-quality reads representing the excellent data integrity. Paired end reads overlapped with each other to create consensus tags for further analysis. Tags were clustered to OTU (operational taxonomic unit) with 97% similarity, producing 326 distinct OTUs in the sample. By using the Ribosomal Database project (RDP) and green-genes database taxonomic classification was performed indicated the presence of diverse microbial bacterial groups that are commonly found in ruminants. The key taxa are Oscillospiraceae, Lachnospiraceae, Bacteroidaceae, and Erysipelotrichaceae. These bacteria are recognized for their significant contributions to the production of volatile fatty acid (VFAs), fermentation of fiber, and degradation of cellulose.

Biomolecular Condensate-Based Artificial Organelle for Driving Compartmentalized Flux Control.

Microbial cell factories have emerged as versatile bioreactors capable of orchestrating complex metabolic networks to convert renewable feedstocks into high-value biochemicals. Nevertheless, the diffusion-mediated dispersion of metabolic intermediates often compromises biosynthesis efficiency, primarily attributable to the absence of artificial subcellular compartments for spatiotemporal organization of catalytic enzymes. Herein, we established a synthetic biology platform leveraging engineered biomolecular condensates to achieve precise flux control via a modular pathway compartmentalization. First, the fused sarcoma low complexity domain (FUSLCD) was designed to combine the GCN4 to rationally integrate with GCN4 scaffold proteins to create programmable artificial organelles. Second, the protein recruitment and assembly functions of artificial organelles were identified by a short peptide pair or directly fusing with the FUSLCD protein in a spatial organization way. Third, using the 2'-fucosyllactose (2'-FL) de novo biosynthesis pathway as a model system, we demonstrated enhanced pathway efficiency by colocalizing critical enzymes within artificial organelles in engineered E. coli, yielding a significant improvement in 2'-FL titer through flux compartmentalization. This study not only overcome diffusion-limited reactions via engineered spatial organization but also offer a versatile toolkit for optimizing compartmentalized biosynthesis pathways.

Method for Calculating Field Convolution Based on the Decomposition of a Multi-Valued Extended Galois Field

Relevance. One of the unsolved problems of the theory of error-correcting coding is the problem of constructing decoders of long codes with low computational complexity. From the point of view of algebraic coding theory, the cornerstone for this is the operation of multiplying two polynomials a and b over the field GF(qk) modulo the third polynomial g. As q and k increase, the use of methods for calculating the field convolution based on the logarithm and antilogarithm operations becomes ineffective due to the use of a large amount of memory for constructing tables. Simplified implementations of the field convolution using the asymmetry of the accompanying matrix and analytical (non-tabular) methods of logarithm and antilogarithm using Zhegalkin polynomials have been developed only for q = 2. Multipliers based on shift registers have a significantly lower speed for large q and k.The aim of the study is to find options for reducing the computational complexity of the field convolution operation in multivalued extended Galois fields during its synthesis in the logical basis "AND"‒"OR"‒"NOT". Methods. An analysis of single-cycle methods for multiplying elements of a multivalued extended Galois field specified in vector or polynomial form for various power bases is carried out. Examples of calculating field convolutions in multivalued Galois fields by various methods are given. The structure of the considered type of fields is studied. Results. It is shown that addition and multiplication operations in the field GF(q) synthesized on the elements of the logical basis "AND"‒"OR"‒"NOT" make the main contribution to the complexity of the resulting logical circuit. It is revealed that using the property of decomposition of the field GF(qk) into subsets by the power of the primitive element of the field GF(q) allows to reduce the number of multiplication operations. A field convolution method based on the matrix method and the Hankel ‒ Toeplitz transform is proposed, taking into account the field structure, which allows to reduce the total number of logical elements and increase the performance of the designed circuit solution, namely, to reduce the Quine price and the circuit rank. A comparative assessment of the developed method is given. Scientific novelty. For the first time, a method of field convolution of two vectors in the field GF(qk) is proposed, one of which is presented in the indicator form. Theoretical / Practical significance. A new method for calculating the field convolution based on the decomposition of a multivalued extended Galois field is proposed. Reduction of the total number of logical operations is proved. The proposed solution can be used in the synthesis of encoding and decoding devices for multi-valued (symbolic) codes on binary logic elements.

On One Special Problem of Blades Identifi cation in Monitoring the Technical State of Gas Turbine Engine Air-Gas Channel

This article is devoted to the problem of "targeted" fixing of the measurement results of spatial position of the blades’ tips of compressor or turbine of gas turbine engine (GTE) to a specific struct ural element of the power plant (blade) in monitoring the state of engine’s gas-air channel under limitations on use of diagnostic equipment and, in particular, the impossibility of installing the shaft’s angular position sensor, that ensures the synchronization of primary converters polling with the rotation period of the turbomachine rotor. The diagnosis of disruptive and surging phenomena in GTE compressor, detection of foreign objects entering the gas-air path of the engine, determination of fatigue failure of the blades are the typical examples of the tasks that require binding of the measurement results to each individual blade on a rotor wheel. The proposed solution is based on the analysis of a unique set of radial clearance values for the complete ensemble of rotor blades with the comparison of the current and previously obtained "reference" images of the turbo compressor’s bladed wheel. Two algorithms for blades’ number determining are considered. The algorithms do not require a separate channel for synchronization and use the minimum Euclidean distance and the maximum value of the cross-correlation function between the elements of the "reference" and current arrays of codes as criteria for blades’ identifying. The results of the performance test of both algorithms on a laboratory bench with a real compressor wheel are presented. It is shown that the criterion based on cross-correlation function calculation has higher noise immunity, and therefore it is the most preferable for practical implementation in actual practice measurement systems. Low computational complexity of the criterion allows to use it at the microprocessor level and to reduce the measuring system hardware.

A Computationally Efficient Learning-Based Control of a Three-Phase AC/DC Converter for DC Microgrids

This paper presents a novel learning-based control algorithm for three-phase AC/DC converters, which are key components in DC microgrids, for reliable power conversion. In contrast with conventional model-based nonlinear controllers that rely on detailed system modeling and manual gain tuning, the proposed method is model-free and eliminates such dependencies. By integrating a recurrent equilibrium network (REN), the controller achieves an enhanced dynamic response and robust steady-state performance, while maintaining a low computational complexity. Moreover, its closed-loop stability can be rigorously verified based on contraction theory and incremental quadratic constraints. To facilitate practical implementation, a design guideline is provided. Experimental results confirm that the proposed method outperforms conventional PI and model predictive controllers in terms of response speed, harmonic suppression, and robustness under parameter variations. Additionally, the algorithm is lightweight enough for real-time execution on embedded platforms, such as a TI DSP.

Artificial intelligence-driven natural language processing for identifying linguistic patterns in Alzheimer's disease and mild cognitive impairment: A study of lexical, syntactic, and cohesive features of speech through picture description tasks.

BackgroundLanguage deficits often occur early in the neurodegenerative process, yet traditional methods frequently fail to detect subtle changes. Natural language processing (NLP) offers a novel approach to identifying linguistic patterns associated with cognitive impairment.ObjectiveWe aimed to analyze linguistic features that differentiate cognitively unimpaired (CU), mild cognitive impairment (MCI), and Alzheimer's disease (AD) groups.MethodsData was extracted from picture description tasks performed by 336 participants in the DementiaBank datasets. 53 linguistic features aggregated into 4 categories: lexical, structural, syntactic, and discourse domains, were identified using NLP toolkits. With normal diagnostic cutoffs, cognitive function was evaluated with the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA).ResultsWith age and education as covariates, ANOVA and post-hoc Tukey's HSD tests revealed that linguistic features such as pronoun usage, syntactic complexity, and lexical sophistication showed significant differences between CU, MCI, and AD groups (p < 0.05). Notably, past tense and personal references were higher in AD than both CU and MCI (p < 0.001), while pronoun usage differed between AD and CU (p < 0.0001). Correlations indicated that higher pronoun rates and lower syntactic complexity were associated with lower MMSE scores and although some features like conjunctions and determiners approached significance, they lacked consistent differentiation.ConclusionsWith the growing adoption of artificial intelligence (AI)-based scribing, these results emphasize the potential of targeted linguistic analysis as a digital biomarker to enable continuous screening for cognitive impairment.

Joint phase-frequency modulation with binary amplitudes for optical camera communication

Constrained by the imaging mechanisms of cameras, the data transmission rate in optical camera communication (OCC) is considerably lower than that in traditional visible light communication (VLC). This Letter introduces a new, to the best of our knowledge, binary modulation scheme designed to increase OCC transmission rates, with the transmitter performing high-order modulation across frequency and phase dimensions and the receiver employing a frequency-domain demodulation approach. This proposed scheme exhibits better scalability and lower implementation complexity than existing schemes. Additionally, the influence of exposure time and readout time parameters of rolling shutter cameras on communication performance has been dissected and confirmed through numerical simulations and experimental validation, laying a theoretical foundation for the design and optimization of the OCC system.

Designing a time-to-digital converter using quantum-dot cellular automata nanotechnology

As a nanoscale computing paradigm, quantum-dot cellular automata (QCA) technology demonstrates significant advantages over conventional CMOS implementations, including improved device density, minimized power dissipation, and increased operational speed. As fundamental building blocks in QCA architectures, time-to-digital converters (TDCs) enable the operation of all-digital delay-locked loops, systems paramount to contemporary timing solutions. These digital loops particularly excel in applications requiring (1) multi-phase clock synthesis and (2) sub-clock-cycle temporal resolution. This work introduces an optimized QCA-based TDC with 228 cells (30% reduction) and 0.2668 μm2 area. The design achieve delay of 1 QCA clock cycle and 40% lower complexity than conventional approaches. Our architecture enables precise timing for digital PLLs/DLLs in nanoelectronics. The compact cell arrangement overcomes CMOS scaling limits through QCA’s nanowatt power and terahertz speeds. These advancements position QCA-TDCs as prime candidates for next-gen terahertz computing systems. Simulation outcomes validate the effectiveness of the developed architecture, marking a significant step forward in the practical realization of QCA-based timing circuits.

The Personal Narratives of Croatian-Speaking Children With Developmental Language Disorder: Investigating the Influence of Emotional Valence at Linguistic, Propositional, and Macrostructure Levels.

Although it is known that children with developmental language disorder (DLD) have difficulty composing personal narratives, previous studies have not employed a wide range of measures that reflect functioning at the different levels of discourse processing, as proposed in the Linguistic Underpinnings of Narrative in Aphasia (LUNA) framework. Because children evoke pleasant or unpleasant events through personal narratives, this study examines whether the emotional valence of the event influences the narrative production of Croatian-speaking children with DLD and those with typical language development (TLD) at the linguistic, propositional, and macrostructure levels. Fifty 10-year-old Croatian-speaking children with DLD and 50 gender-matched peers with TLD told personal narratives elicited through emotion-based prompts using the Global TALES protocol. Emotional valence was analyzed by coding each narrative event as either pleasant (positive) or unpleasant (negative). The narratives were evaluated using measures from the linguistic (lexical diversity, grammatical complexity, and accuracy), propositional (mazes, incomplete utterances, local coherence) and macrostructure (global coherence) levels. Our results show that children with DLD who speak Croatian have difficulty forming personal narratives at all three levels of discourse production, as evidenced by lower grammatical complexity and accuracy, more incomplete utterances, and lower local and global coherence. These difficulties are consistent in both positive and negative narratives. Results also showed that negative narratives are chronologically better ordered than positive narratives in both groups of children. These findings provide further evidence that children with DLD have considerable difficulty producing linguistically well-organized, complete, and coherent personal narratives across different levels of the LUNA framework, regardless of the emotional valence of the events they recount. https://doi.org/10.23641/asha.28819385.

A Coevolutionary Algorithm Based on Constraints Decomposition for Constrained Multi-objective Optimization Problems

Constrained multi-objective optimization problems (CMOPs) are challenging for evolutionary algorithms (EAs). Due to the interaction of multiple constraints, the constrained Pareto fronts (CPFs) exhibit various complex characteristics, e.g., degeneracy, discontinuity or irregularity. Most algorithms achieve poor convergence and diversity performance on these problems. Therefore, we proposed a coevolutionary framework based on constraints decomposition to solve complex CMOPs. Specifically, this framework decomposes the CMOP into multiple help subproblems with a single constraint, thereby decoupling the complex constraints. Then, multiple subpopulations optimize these subproblems to assist in solving the original problem. In addition, a two-stage strategy is used to fully utilize the auxiliary populations to search for feasible solutions. In addition, an evolutionary state detection strategy based on historical information is proposed, which is used to determine whether the evolution moves to the next stage. The framework can take the advantage of the low complexity of single-constraint problems to help algorithm search the complete feasible regions. Experiments on benchmark problems show that the proposed algorithm is competitive with eight other most representative constrained evolutionary algorithms in terms of convergence and diversity performance.

A comparison of SWATH-MS methods for measurement of residual host cell proteins in adeno-associated virus preparations

IntroductionAnalysis of residual host cell proteins in adeno-associated virus (AAV) preparations is challenging due to low availability and high complexity of samples. One strategy to address these challenges is through development of improved liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods with greater sensitivity and reduced sample requirement.MethodsIn this work, we compare the performance of four sequential window acquisition of all theoretical fragment ion mass spectra (SWATH-MS) methods for identification and quantitation of residual HCPs in rAAV2, -5, -8, and -9 preparations produced with human embryonic kidney 293 (HEK293) cells and purified using immunoaffinity chromatography. Key SWATH-MS parameters including spectral library construction (data dependent vs. in silico), data processing software (DIA-NN vs. Skyline), and mass spectrometer instrument (Sciex TripleTOF 6600 vs. Sciex ZenoTOF 7600) were assessed. Method attributes including sample requirement and processing time, and method outputs including protein and precursor identifications, host cell protein quantitation comparisons across methods, and quantitation coefficients of variance (CV) were considered to help establish a SWATH-MS workflow well-suited for rAAV HCP analytics.ResultsA 78% increase in HCP identifications, 80% reduction in sample requirement, and 70% reduction in instrument runtime was achieved with an in silico spectral library, data processing in DIA-NN, and data collection with the Sciex ZenoTOF 7600 instrument (DIA-NN-7600 method) compared to a previously established method using a DDA-derived spectral library, data processing in Skyline, and data collection with the Sciex TripleTOF 6600 instrument (Skyline-DDA-6600 method). Additionally, the DIA-NN-7600 method shows median HCP quantitation CV below 10% for triplicate data acquisitions, and comparable quantitation to other methods for a panel of highly abundant residual HCPs previously identified in rAAV downstream processing.DiscussionThis work highlights a SWATH-MS method with data collection and processing specifically tailored for rAAV residual HCP analysis.

Investigation of Multiple Hybrid Deep Learning Models for Accurate and Optimized Network Slicing

In 5G wireless communication, network slicing is considered one of the key network elements, which aims to provide services with high availability, low latency, maximizing data throughput, and ultra-reliability and save network resources. Due to the exponential expansion of cellular networking in the number of users along with the new applications, delivering the desired Quality of Service (QoS) requires an accurate and fast network slicing mechanism. In this paper, hybrid deep learning (DL) approaches are investigated using convolutional neural networks (CNNs), Long Short-Term Memory (LSTM), recurrent neural networks (RNNs), and Gated Recurrent Units (GRUs) to provide an accurate network slicing model. The proposed hybrid approaches are CNN-LSTM, CNN-RNN, and CNN-GRU, where a CNN is initially used for effective feature extraction and then LSTM, an RNN, and GRUs are utilized to achieve an accurate network slice classification. To optimize the model performance in terms of accuracy and model complexity, the hyperparameters of each algorithm are selected using the Bayesian optimization algorithm. The obtained results illustrate that the optimized hybrid CNN-GRU algorithm provides the best performance in terms of slicing accuracy (99.31%) and low model complexity.

Collection of the digital data from the neurological examination

This review presents the status quo of how far the digitalization of elements of the neurological examination has progressed. Our focus was on studies that assessed the examination conducted in person, rather than through telemedicine platforms. Five hundred and twenty studies were included in this systematic review. The digital tools covered ten elements of the neurological examination: gait (173, 33%), motor system (149, 29%), eyes (85, 16%), cognitive functions (53, 10%), sensory system (47, 9%), balance (35, 7%), other movements (24, 5%), other cranial nerves (24, 5%), coordination (10, 2%), and autonomic nervous system (9, 2%). Most of the tools were portable (442, 85%), and in 215 studies (41%) the devices were wearable. The cost of the digital tools used was described and discussed in 167 (32%) studies. Most devices (61%) had a low complexity, and half required high additional analytic effort.