Low Complexity Research Articles (Page 1)

Bioprocesses for stem cell-based therapeutics are resource- and time-intensive, hindering the generation of sufficient data for machine learning-informed development. We describe a framework combining data augmentation, multivariate regression, and feature importance analysis to investigate the relationship between metabolites and critical quality attributes (CQAs) of cultured stem cells. A first-principles model (FP), a hybrid model using neural ordinary differential equations (ODEs) with stoichiometric constraints (HD), and a purely statistical neural ODE model (NODEAM) were considered. Probing these models through data augmentation, we generated synthetic data and amplified the biological information encoded in each modality, directly linking architectural choices to their ability to capture relevant dynamics. For all models, the validation error remained relatively constant, and the convergence of the feature importance tensor followed a power law with the number of augmented runs. Temporal feature importance and functional data analysis of variance revealed key time windows during which glucose and lactate showed strong correlation with CQAs. The HD model provided the best fidelity and accuracy, underscoring the value of combining mechanistic and statistical modeling for improved interpretability at lower complexity. Overall, this framework can yield insights into the physiology of cultivated cells and can be adapted for various culture-based biomanufacturing systems.

To reduce the pollutant emissions of water ecological POI logistics, the water ecological POI logistics route-planning method based on the improved water network space AGNES clustering model and the symmetrical simulated Huffman spatial searching tree (SHSST) algorithm is innovatively established. The improved AGNES algorithm is established for water ecological POI clustering, and then the logistics distribution center location model based on water ecological POI clustering is constructed. On the basis of an optimal distribution center, combining the symmetrical feature of vehicle moving paths and distances in logistics sub-intervals and logistics intervals, the sub-interval optimal route-searching algorithm based on the symmetrical SHSST is constructed to determine the optimal path for each logistics sub-interval, and then the global logistics route-planning algorithm based on undirected complete graph spatial search is constructed to search for the global optimal logistics route. Experiments prove that the proposed algorithm can accurately cluster water ecological POIs and output the logistics route with the lowest costs and pollutant emissions. Compared to the traditional AGNES and other clustering algorithms, the improved AGNES algorithm has lower time complexity. Compared to the traditional logistics route algorithms, SHSST has lower algorithm complexity, route costs, and pollutant emissions, and strong stability. The minimum and maximum optimization rates for the same route are 10.06% and 17.58%, while the minimum and maximum optimization rates for the optimal route are 11.41% and 14.29%; it could effectively reduce the negative impact of pollutants on the water ecological environment and POIs.

Abstract Conventional link prediction methods mainly aim to estimate pairwise relationships between nodes in graph structures, typically addressing single-type interactions. However, real-world complex systems often exhibit high-order group relationships that extend beyond binary interactions. For instance, a research paper is often co-authored by multiple researchers. To address the loss of high-order structural information in traditional graph models for representing multivariate interactions, we propose HP2PH, a novel hyperlink prediction method based on 2-head preferential hypergraph weighted random walk with restart. Firstly, considering the uncertainty of hyperedge cardinalities in non-uniform hypergraphs, we introduce a preferential hypergraph weighted random walk with restart strategy, called P-HWRWR. This strategy fully exploits the high-order topological properties of hypergraphs, and jointly optimizes the random walking sub-paths from node to hyperedge and from hyperedge to node by assigning weights to both the hyperedges and nodes encountered by the random walker. Subsequently, an unsupervised hyperlink prediction method based on the two-head preferential hypergraph weighted random walk with restart is proposed. This approach searches for potential member nodes within new hyperedges from different directions, ensuring efficient predicting multivariate interactions with relatively low time complexity. Finally, through extensive experimental verification and analysis on 10 non-uniform hypergraph datasets and 5 uniform hypergraph datasets, it is demonstrated that HP2PH achieves improvements of 0.8% to 137.2% in the AFS metric and 0.8% to 412.1% in the HPA metric on non-uniform datasets and achieves improvement of 94.4% to 202.5% in the AFS metric on uniform hypergraph datasets compared baselines. These experiments substantiate the superiority and operational viability of the developed method when predicting hyperlinks.

Abstract The demand for high performance strain sensors with both high accuracy and efficient computation is escalating in fields such as aerospace, civil engineering, and machinery manufacturing. However, integrating high measurement precision and low computational complexity in a single sensor remains a challenge. Here, we propose a multi-fiber speckle strain sensor based on structure-data fusion. The sensor is innovatively designed by optimizing the spatial topological structure of multiple fibers, with a single-turn coaxial fiber bundle as the core sensing architecture, consisting of one central transmitting fiber and six surrounding receiving fibers. When coherent light from the transmitting fiber is reflected by the measured object, the receiving fibers capture speckle patterns with strong correlation. Through coding preprocessing of these speckles and analysis via a BP neural network system (including six Sub-BPNNs and one Gross-BPNN), strain measurement is achieved. As the sensor detects strain changes, the structured speckle field formed by the multi-fiber layout encodes abundant strain-feature information, reducing the complexity of subsequent signal processing. The fusion of spatial structure information and speckle data features effectively mitigates algorithmic uncertainty. Benefiting from this structure-data fusion strategy, the sensor achieves high measurement accuracy (MAE = 0.1133, RMSE = 0.1591) with low computational requirements. The proposed multi-fiber speckle sensing methodology shows significant potential for applications in complex engineering environments.

Abstract The increasing demand for high-performance 5G networks has driven the adoption of Filter Bank Multicarrier (FBMC) as a superior alternative to traditional OFDM due to its enhanced spectral efficiency and reduced out-of-band emissions. However, FBMC systems face challenges in channel estimation and interference cancellation caused by non-orthogonal subcarriers. This paper proposes a novel Recurrent Neural Network (RNN)-based Joint Channel Estimation and Interference Cancellation (JCEIC) method that leverages Long Short-Term Memory (LSTM) networks to exploit temporal correlations in doubly-selective channels, enabling accurate channel estimation and effective interference mitigation with low computational complexity. Our simulations demonstrate that the proposed approach significantly reduces the Bit Error Rate (BER), outperforming conventional methods—particularly at low SNRs, where FBMC achieves a BER below 0.1 at just 5 dB—while approaching ideal channel performance. By combining optimized pilot placement with deep learning-driven interference cancellation, this work provides a robust and scalable solution for 5G and beyond, bridging the gap between theoretical advancements and practical deployment in next-generation wireless systems.

Abstract Dynamic resource management enables supercomputing applications to change resource allocations at runtime. This capability promises significant improvements in application efficiency and overall supercomputer utilization. However, adoption is limited by insufficient support in resource managers and programming environments. Furthermore, developing resource-flexible applications introduces significantly higher programming complexity than their static counterparts. While MPI extensions have been proposed for resource flexibility, significant programmability challenges persist. The “Dynamic Processes with PSets (DPP)” design principles define programming model agnostic abstractions for dynamic resource control, and have been prototypically implemented by extending Open MPI and OpenPMIx (termed MPI-DPP ). MPI-DPP enables fine-grained process management but relies on low-level message-passing, complicating implementation of dynamic and irregular workloads. Asynchronous Many-Task (AMT) programming offers a compelling alternative. AMT splits computations into fine-grained tasks dynamically scheduled by the runtime system, enabling load balancing and responsiveness to resource changes. Although resource-flexible AMTs remain rare, GLB is a notable exception, offering automatic load balancing and dynamic resource capabilities. However, GLB is built on “APGAS for Java”, which is uncommon in HPC. We present DPP-GLB , a C++ AMT runtime that integrates GLB ’s high-level task abstraction and load balancing with the resource control capabilities of MPI-DPP . We evaluate DPP-GLB , GLB , and MPI-DPP on SuperMUC-NG, analyzing both programming complexity and runtime performance. Results show that GLB is easy to use, featuring built-in load balancing and resource flexibility. MPI-DPP offers superior performance for node changes, albeit at the cost of increased programming complexity. DPP-GLB achieves a balance of low programming complexity and efficient, scalable dynamic resource support.

Abstract Accurate assessment of lithium-ion batteries (LIBs) state of health (SOH) is crucial for ensuring the safety of electrochemical energy storage systems and electric vehicles, as well as enhancing the reliability of battery management systems. However, reliance on single health feature extraction and suboptimal model selection increases computational burden and compromises estimation accuracy. To overcome these limitations, this study proposes a novel SOH estimation framework for LIBs that integrates comprehensive feature extraction with an optimized hybrid model – KPCA–ISSA–LSSVM to improve both prediction accuracy and efficiency. Sixteen HFs are extracted from charge–discharge segments of different cycles and categorized into four groups—current, voltage, temperature, and incremental capacity—to characterize electrochemical aging mechanisms underlying capacity degradation. After Gaussian filtering and anomaly removal, dual-correlation analysis with normalized curve assessment selects features with correlation coefficients above 0.8, enhancing feature quality. KPCA then reduces the dimensionality of these correlated features before input into the ISSA–LSSVM predictor, improving model training efficiency and accuracy while maintaining low complexity. Comparative experiments on three NASA battery datasets show that the proposed method achieves mean absolute errors below 0.6% and R² above 0.997 across different training ratios, significantly outperforming benchmark models in prediction accuracy and robustness.

Abstract Chaotic systems play an indispensable role in the fields of cryptography and information security. Sine-Transform-Based Chaotic System (STBCS) can address the shortcomings of low complexity and limited chaotic behaviour of classical chaos systems. In this paper, a compact hardware STBCS is proposed and developed on the FPGA device by using the Stochastic Computation (SC) technique. The traditional arithmetic operations are replaced by the SC and finite state machines design. The structure of STBCS is optimised, where the disturbance method is employed to improve the chaotic behaviours and also taking the SC method into account for implementation. The hardware performance of the proposed design is verified via various tests of the chaotic system and corresponding random number generator. Experimental results show that the utilisation of the hardware resources is reduced especially the DSP components compared to the traditional design methods. This provides an efficient design for the random generator of the alternative cryptosystems.

What we believe to be a novel multiplication-free multi-stage distribution-matching (MSDM) algorithm based on bit-level probabilistic shaping (PS) is proposed and experimentally validated. Unlike classical probabilistic shaping methods such as constant composition distribution matching (CCDM), which require bit-to-symbol class arithmetic coding, the proposed MSDM scheme achieves target probabilistic distributions by decomposing symbol probability into bit probabilities. By doing so, in contrast to conventional distribution matchers where extensive multiplication operations are needed, only simple bit-level logical operations are required, while excellent signal shaping performance is preserved. Experimental results indicate that, when compared to the uniform distribution signal under the same net bit rate, the MSDM-based PS-16QAM signal at 1.6 bits/dimension achieves an optical signal-to-noise ratio gain of 1.28 dB. Meanwhile, the proposed scheme exhibits significantly lower complexity than the conventional CCDM while maintaining comparable performance. This proposed method also alleviates the error propagation effect in the inverse DM process compared to CCDM.

This paper presents the design, implementation, and performance evaluation of an Adaptive Joint SCAMP Filter and Relay Weight Optimization Scheme for a wireless Amplify-and-Forward (AF) cooperative relay network operating over frequency-selective fading channels. Conventional AF systems suffer from compounded noise and Inter-Symbol Interference (ISI) due to cascaded multi-tap channel effects. To address these limitations, this work employs a Joint Adaptive Filtering approach that simultaneously optimizes the source pre-coding filter and the relay amplification weight to minimize the end-to-end Mean Squared Error (MSE) and enhance the achievable data rate. The joint optimization problem is solved using the Projected Subgradient Method (PSGM), which provides robustness against non-linear constraints such as sparsity while maintaining low computational complexity. The algorithm is implemented and tested in a MATLAB simulation environment under a time-varying Auto-Regressive (AR(1)) fading model. Key performance metrics such as MSE convergence, filter characteristics, achievable rate, and robustness to parameter variations are analyzed. Simulation results demonstrate that the proposed adaptive joint scheme achieves 25–33% higher achievable rate than the conventional Fixed AF Relay and nearly double the throughput of a Direct Link transmission. The results validate that adaptive joint filtering provides superior spectral efficiency, improved ISI mitigation, and stable convergence, making it a practical and scalable solution for next-generation cooperative communication systems.

Mobile networks have advanced significantly, providing high-throughput voice, video, and integrated data access to support connectivity through various services to facilitate high user density. This traffic growth has also increased the complexity of outlier detection (OD) for fraudster identification, fault detection, and protecting network infrastructure and its users against cybersecurity threats. Autoencoder (AE) models are widely used for outlier detection (OD) on unlabeled and temporal data; however, they rely on fixed anomaly thresholds and anomaly-free training data, which are both difficult to obtain in practice. This paper introduces statistical masking in the encoder to enhance learning from nearly normal data by flagging potential outliers. It also proposes a quasidynamic threshold mechanism that adapts to reconstruction errors, improving detection by up to 3% median area under the receiver operating characteristic (AUROC) compared to the standard 95% threshold used in base AE models. Extensive experiments on the Milan Human Telecommunications Interaction (HTA) dataset validate the performance of the proposed methods. Combined, these two techniques yield a 31% improvement in AUROC and a 34% lower computational complexity when compared to baseline AE, long short-term memory AE (LSTM-AE), and seasonal auto-regressive integrated moving average (SARIMA), enabling efficient OD in modern cellular networks.

RNA molecules play central roles in the assembly and regulation of biomolecular condensates, often in concert with proteins containing low complexity RNA-binding domains. Recently, it has been shown that RNA can phase-separate in the absence of any proteins. Unlike protein-based condensates, RNAs condensates are strongly influenced by magnesium ions which play crucial roles in their dynamics and thermodynamics, giving rise to base-specific lower critical solution temperatures (LCSTs). The molecular basis and functional significance of sequence and ion-dependent LCST behavior RNA condensates have yet to be elucidated. Here, we use atomistic simulations to systematically dissect the driving forces underlying the sequence-, ion-, and temperature-dependent phase behaviors of RNA condensates. By choosing RNA tetranucleotides alongside their ssDNA counterpart and chemically modified analogs, we map equilibrium thermodynamic profiles and structural ensembles across various external conditions. Our results show that magnesium ions promote LCST behavior by inducing local disorder-order transitions within RNA structures. Additionally, the base chemistry and the 2'hydroxyl group of the ribose sugar further modulate this LCST response. In agreement with experiments, we find that the thermal stability of RNA condensates follows the order Gn > An > Cn > Un, governed by the balance of base stacking and hydrogen bonding interactions. Moreover, our simulations reveal that posttranslational nucleotide modifications can fine-tune the threshold of RNA self-assembly and the resulting condensate structures.

Object detection technology plays a vital role in monitoring the growth status of aquaculture organisms and serves as a key enabler for the automated robotic capture of target species. Existing models for underwater biological detection often suffer from low accuracy and high model complexity. To address these limitations, we propose AOD-YOLO—an enhanced model based on YOLOv11s. The improvements are fourfold: First, the SPFE (Sobel and Pooling Feature Enhancement) module incorporates Sobel operators and pooling operations to effectively extract target edge information and global structural features, thereby strengthening feature representation. Second, the RGL (RepConv and Ghost Lightweight) module reduces redundancy in intermediate feature mappings of the convolutional neural network, decreasing parameter size and computational cost while further enhancing feature extraction capability through RepConv. Third, the MDCS (Multiple Dilated Convolution Sharing Module) module replaces the SPPF structure by integrating parameter-shared dilated convolutions, improving multi-scale target recognition. Finally, we upgrade the C2PSA module to C2PSA-M (Cascade Pyramid Spatial Attention—Mona) by integrating the Mona mechanism. This upgraded module introduces multi-cognitive filters to enhance visual signal processing and employs a distribution adaptation layer to optimize input information distribution. Experiments conducted on the URPC2020 and RUOD datasets demonstrate that AOD-YOLO achieves an accuracy of 86.6% on URPC2020, representing a 2.6% improvement over YOLOv11s, and 88.1% on RUOD, a 2.4% increase. Moreover, the model maintains relatively low complexity with only 8.73 M parameters and 21.4 GFLOPs computational cost. Experimental results show that our model achieves high accuracy for aquaculture targets while maintaining low complexity. This demonstrates its strong potential for reliable use in intelligent aquaculture monitoring systems.

We examined how familiarity with low or high complexity images influences complexity, liking and understanding judgments. Participants were first familiarized with either low or high complexity images. They then rated a set of intermediately complex images on perceived complexity, liking, and understanding. Data were analyzed using Bayesian mixed-effects models, controlling for declarative art knowledge, art interest, and visual art recognition. Participants familiarized with high complexity images rated intermediately complex images as less complex, indicating a contrast effect. Higher declarative art knowledge was linked to increased complexity ratings. Additionally, liking judgments were predicted by complexity ratings, especially among participants familiarized with simpler images, revealing that prior exposure shapes the relationship between complexity and liking. In contrast, understanding judgments were unaffected by complexity ratings or familiarization. These findings highlight the role of prior exposure in shaping visual complexity judgments, demonstrating how familiarity with certain complexity levels recalibrates perceptual baselines and influences subsequent evaluative judgments.

Background: The aim of this study was to separately assess return to work (RTW) and return to sports (RTS) rates and timelines following surgical and conservative treatment of tibial plateau fractures (TPF). A secondary objective was to identify factors associated with faster recovery. Methods: All patients with TPF treated at a single level I trauma center between 1 January 2008 and 31 December 2016 were retrospectively reviewed. Standardized questionnaires were used to evaluate pre- and postoperative work and sports activity. Subgroup and correlation analyses were performed to investigate the influence of demographic and treatment-related factors on RTW and RTS duration. Results: A total of 105 patients were included, of whom 85% (n = 89) received surgical treatment and 15% (n = 16) were treated conservatively. RTW was achieved by 100% of surgically treated and 93% of conservatively treated employed patients, with a mean duration of 11.3 ± 9.5 weeks and 6.5 ± 4.2 weeks, respectively. RTS was achieved by 85% of surgically treated and 86% of conservatively treated previously active patients, occurring after a mean of 22.1 ± 17.9 weeks and 12.2 ± 8.8 weeks, respectively. Male sex, lower fracture complexity, absence of external fixation, and shorter operative times were associated with faster recovery. A general shift toward low-impact and recreational sports and a reduction in sport types and weekly training sessions were observed. Conclusions: Independent of the treatment modality, high RTW and RTS rates are observed within six months following TPF. The identified factors may help guide patient counseling and improve individual rehabilitation planning.

The real-valued fast Fourier transform (RFFT) is well-suited for high-speed, low-power FFT processors, as it requires approximately half the arithmetic operations compared to the traditional complex-valued FFT (CFFT). While RFFT can be computed using CFFT hardware, a dedicated RFFT implementation offers advantages such as lower hardware complexity, reduced power consumption, and higher throughput. However, unlike CFFT, the irregular signal flow graph of RFFT presents challenges in designing efficient pipelined architectures. In our previous work, we have proposed a high-level programming approach using Open Computing Language (OpenCL) to implement the forward RFFT architectures on Field-Programmable Gate Arrays (FPGAs). In this article, we propose a high-level programming approach to implement the inverse RFFT architectures on FPGAs. By identifying regular computational patterns in the inverse RFFT flow graph, our method efficiently expresses the algorithm using a for loop, which is later fully unrolled using high-level synthesis tools to automatically generate a pipelined architecture. Experiments show that for a 4,096-point inverse RFFT, the proposed method achieves a 2.36x speedup and 2.92x better energy efficiency over CUDA FFT (CUFFT) on Graphics Processing Units (GPUs), and a 24.91x speedup and 18.98x better energy efficiency over Fastest Fourier Transform in the West (FFTW) on Central Processing Units (CPUs) respectively. Compared to Intel’s CFFT design on the same FPGA, the proposed one reduces 9% logic resources while achieving a 1.39x speedup. These results highlight the effectiveness of our approach in optimizing RFFT performance on FPGA platforms.

ABSTRACT We propose an adaptive chaotic image encryption scheme that integrates spiral pixel shuffling, chaos‐driven dynamic S‐box substitution, and hybrid chaotic diffusion. The design employs a coupled logistic–tent map with golden‐ratio perturbation, expanding the key space to approximately , where the exponent denotes the effective size. This hybrid approach addresses limitations of existing schemes—such as restricted randomness, vulnerability to differential attacks, and impractical runtime for real‐time applications—while maintaining low computational complexity suitable for IoT and medical contexts. Comprehensive evaluations confirm strong security: NPCR , UACI , entropy , and near‐zero pixel correlation, demonstrating resistance to statistical, differential, and chosen‐plaintext attacks. Side‐channel leakage simulations further show stable per‐pixel timing in the microsecond range and consistent power traces, indicating robustness against timing and power analysis, with future hardware validation planned. The proposed scheme achieves an average encryption time of 43.97 s and decryption time of 32.26 s for color images, confirming suitability for real‐time image processing. These results position the proposed method as a lightweight, secure, and practical framework for protecting medical data, multimedia content, and IoT edge devices.

Line and section selection is a key link in the effective management of single-phase ground faults in distribution networks. To address the issues of insufficient identification capability and poor adaptability of existing steady-state quantity-based single-phase fault diagnosis methods, this paper proposes a new method based on the steady-state reference value of zero-sequence current. First, the zero-sequence current values of each switch on non-faulty feeders are collected after each fault occurs to form the reference values. After a fault occurs, the difference between the real-time zero-sequence current value and the reference value of each switch is calculated, which is used as the fault characteristic quantity (FCQ). During the fault line selection stage, the FCQ of each switch is compared, and the feeder whose switch has the highest FCQ is determined to be the faulty one. In the fault section selection stage, if the ratio of the FCQs between any two switches on the faulty feeder exceeds a preset empirical threshold, the fault is determined to lie between these two switches. Theoretical analysis and simulation verification show that the proposed method exhibits high accuracy and sensitivity under complex scenarios, such as resonant grounding systems, overhead-cable hybrid lines, and three-phase unbalanced loads. Further field case analysis confirms that the proposed method possesses the characteristics of fast identification speed, low computational complexity, and high reliability.

Inhibition of ELOVL6 activity impairs mitochondrial respiratory function and inhibits tumor progression in FGFR3-mutated bladder cancer cells.

Accurate lymph node (LN) segmentation is highly beneficial for diagnosing and treating head and neck diseases. However, because of the varying sizes and complex shapes of LNs from the head and neck, as well as their blurred boundaries with surrounding tissues in computed tomography (CT) images, it is difficult for physicians to manually identify the region of interest (ROI). Although existing 3D-volumetric-convolution-based methods play an important role in LN boundary extraction, they suffer from high computational complexity. To tackle these issues, we develop an efficient and lightweight volumetric convolutional neural network, named LNSNet, for the LN segmentation from the head and neck region. Our LNSNet presented a 3D Volume Block, which mainly combines Volumetric Partial Convolution (VPConv) with point-wise convolution to decrease computational complexity and parameter count. In addition, both a Lightweight Boundary Enhancement Module (LBEM) and a depthwise separable convolution are added to the bottom of LNSNet to improve the accuracy of LN segmentation. 678 3D LNs extracted from 123 patients with head and neck cancer were used for evaluation. We trained the model using 5-fold cross-validation and tested it on an independent test set. Our model had fewer parameters and lower computational complexity than some state-of-the-art models, with a Dice Similarity Coefficient (DSC) of up to 73.81% and the Average Surface Distance (ASD) and 95th percentile' Hausdorff Distance (HD95) are only 0.92 and 2.52mm, respectively. LNSNet improves computational efficiency and robustness by reducing parameter count and complexity, making it more attractive in practicalapplications.