Low Complexity Research Articles

Design of Digit Serial FIR Filter

Abstract- Today filter is the most important component in digital signal processing system from last few decade, on low complexity bit-parallel multiple constant multiplication (MCM) more efficient algorithm and architecture has been developed which are used in DSP system but it offer more complexity as compared to digit serial MCM filter due to which we are interested in design of digit serial MCM filter it has low cost and offers more delay. In this paper we address more the problem of optimizing the area required for to design filter and also we introduce high level synthesis algorithm and overall design architecture. In that experimental result of digit serial FIR MCM filter will be given in term of the area, delay and power efficiency for different algorithm and architecture. Keywords: –integer linear programming (ILP), digit serial arithmetic, finite impulse response ((FIR) filters, gate level area optimization, multiple constant multiplication.

Computational Exploration of the Inhibitory Mechanism of mRNA against the Phase Separation of hnRNPA2 Low Complexity Domains.

hnRNPA2, an RNA-binding protein involved in RNA metabolism and regulation, can undergo liquid-liquid phase separation (LLPS) to form dynamic biomolecular condensates. Previous experiments have reported that RNA molecules can inhibit the LLPS of the hnRNPA2 low complexity domain (LCD). However, the atomistic mechanisms underlying this inhibitory effect and RNA-LCD interactions remain largely elusive. Herein, the influence of mRNA A2RE11 on the single-chain conformational ensemble and transient interactions between LCD chains are investigated through all-atom-enhanced sampling molecular dynamics (MD) simulations. Our simulations reveal that aromatic residues are essential to intrachain interactions of single-chain hnRNPA2 LCDs as well as interchain interactions of LCD dimers. Through binding to aromatic and positively charged residues of the hnRNPA2 LCD, A2RE11 undermines the degree of collapse of the single-chain LCD and disrupts the aromatic stacking, hydrogen bonding, and cation-π interchain interactions. Our coarse-grained phase coexistence MD simulations further underscore the preeminence of interchain aromatic and cation-π interactions in regulating the phase behavior of hnRNPA2 LCD and the RNA binding affinity for the RGG and Y/FG(G) motifs. These findings from multiscale simulations lead to a greater appreciation of the complex interaction network underlying the phase separation and RNA-protein interaction of the hnRNPA2 LCD.

Continuous signal sparse encoding using analog neuromorphic variability

Abstract Achieving fast and reliable temporal signal encoding is crucial for low-power, always-on systems. While current spike-based encoding algorithms rely on complex networks or precise timing references, simple and robust encoding models can be obtained by leveraging the intrinsic properties of analog hardware substrates. We propose an encoding framework inspired by biological principles that leverages intrinsic neuronal variability to robustly encode continuous stimuli into spatio-temporal patterns, using at most one spike per neuron. The encoder has low model complexity, relying on a shallow network of heterogeneous neurons. It relies on an internal time reference, allowing for continuous processing. Moreover, stimulus parameters can be linearly decoded from the spiking patterns, granting fast information retrieval. Our approach, validated on both analog neuromorphic hardware and simulation for stimulus parameter regression and signal classification, demonstrates high robustness to noise, spike jitter, and reduced heterogeneity. Consistently with biological observations, we observed the spontaneous emergence of patterns with stereotyped spiking order. The proposed encoding scheme facilitates fast, robust and continuous information processing, making it well-suited for low-power, low-latency processing of temporal data on analog neuromorphic substrates.

YOLOv8n-RF: A Dynamic Remote Control Finger Recognition Method for Suppressing False Detection.

Gesture interaction is one of the novel human-computer interaction methods for smart TVs. Addressing the issues of false detection and high detection costs in gesture recognition algorithms for gesture interaction, this paper proposes the YOLOv8n-Remote Finger (YOLOv8n-RF) algorithm for dynamic remote control finger detection. This algorithm utilizes the CRVB-DSConvEMA module in the feature extraction network, adopts the SPPF-DSConvEMA module in the downsampling process, and introduces BiFPN in the Neck layer. Experiments conducted on the self-made Remote Finger dataset and the public HaGRID dataset demonstrated that, compared to the YOLOv8n algorithm, the proposed YOLOv8n-RF algorithm achieved an improvement in mean Average Precision (mAP) by 1.23% and 0.84%, respectively. Additionally, the model size was reduced by 2.49 M, the GFLOPs were decreased by 1.7, and the false detection rate was lowered by 22%. The YOLOv8n-RF algorithm meets the requirements of low cost and low complexity, which contributes to reducing false control operations on smart TVs.

Interval estimation for discrete‐time linear systems based on unknown input observer and reachability analysis

AbstractThis paper presents an interval estimation method for discrete‐time linear systems with disturbance and measurement noise. First, comparing the existing results, an unknown input observer (UIO) with fewer conservative conditions is designed to estimate the system state. In order to improve the estimation accuracy, a decoupling approach, which can be used to decouple the disturbance matrix, is proposed to overcome the effect of disturbances in the error system. Then, technology is introduced to enhance the robustness of the error system. Subsequently, based on the UIO and the decoupling approach, interval estimation of the system state can be attained through reachability analysis. Moreover, the proposed method exhibits a higher precision and lower complexity in comparison to the existing approaches. Eventually, the simulation results are presented to verify the efficiency of the proposed method.

State-of-Health Estimation for Lithium-Ion Batteries Based on Lightweight DimConv-GFNet

The accurate estimation of the state of health (SOH) is crucial for effective battery management systems. This paper proposes a deep learning model dimension-wise convolutions-globalfilter networks (DimConv-GFNet) for lithium-ion battery SOH estimation. Particularly, the DimConv-GFNet comprises the dimension-wise convolutions (DimConv), which collect the multi-scale local features from different sensor signals, and lightweight global filter networks (GFNet) to capture long-range dependencies in the Fourier frequency domain. Unlike Transformer attention architectures, GFNet utilizes spectral properties to facilitate global information exchange with a lower computational complexity. Experiments on two datasets with a total of 167 commercial LFP/graphite cells validate the effectiveness of DimConv-GFNet. Although the model shows slightly lower accuracy compared to the DimConv-Transformer baseline, it delivers competitive performance with a root mean squared error (RMSE) of 0.335%, mean absolute error (MAE) of 0.233% and a mean absolute percentage error (MAPE) of 0.230%. Remarkably, the DimConv-GFNet substantially reduces computational demands, requiring fewer than one-third of the Floating Point Operations (FLOPs) and parameters of DimConv-Transformer. These results demonstrate DimConv-GFNet strikes a good balance between accuracy and efficiency, positioning it as a promising solution for efficient and accurate SOH estimation in battery management applications.

VideoMamba Enhanced with Attention and Learnable Fourier Transform for Superheat Identification

Superheat degree (SD) is an important indicator for identifying the status of aluminum electrolytic cells. The fire hole video of the aluminum electrolytic cell captured by an industrial camera is an important basis for identifying SD. This article proposes a novel method that VideoMamba enhances with attention and learnable Fourier transform (CFVM) for SD identification. With a lower computational complexity and feature extraction capabilities comparable to transformers, VideoMamba offers the CFVM model a stronger feature extraction basis. The channel attention mechanism (CAM) block can achieve information exchange between channels. Through matrix eigenvalue manipulation, the learnable nonlinear Fourier transform (LNFT) block may guarantee stable convergence of the model. Furthermore, the LNFT block can efficiently use mixed frequency domain channels to capture global dependency information. The model is trained using the aluminum electrolysis fire hole dataset. Compared with recent fire hole identification models that primarily rely on neural networks, the method proposed in this paper is based on the concept of state space modeling, offering lower model complexity and enhanced feature extraction capability. Experimental results demonstrate that the proposed model achieves competitive performance in fire hole video identification tasks, reaching an identification accuracy of 85.7% on the test set.

Assessing the impact of climate change on the potential distribution of Keteleeria evelyniana Mast. in southwest China: a Maxent modeling approach.

Keteleeria evelyniana Mast., which is widespread in southwestern China, is valuable for studying under different future climate scenarios to assess potential distribution shifts in response to climate warming. Understanding these changes can provide theoretical support for species conservation, rational utilization, ecological restoration, and management of K. evelyniana habitats. The Maxent model was optimized using the package of ENMeval to adjust the Regularization Multiplier (RM) and Feature Class Combinations (FC) parameters. Utilizing 221 effective distribution points and 33 environmental variables, the potential distribution of K. evelyniana in current and future climate scenarios was predicted, with the key environmental variables analyzed. The model with FC = LQ and RM = 0.5, demonstrated low complexity, minimal overfitting, and high accuracy, achieving an AUC value of 0.946 with a standard deviation of 0.011. Under the current climate conditions, 68% of the suitable areas for K. evelyniana were focused on Yunnan Province, with additional areas in western and southwestern Guizhou, southwestern Sichuan, and the southeastern Xizang Autonomous Region. In various future climate scenarios, the suitable areas for K. evelyniana gradually decreased, with a maximum reduction of 33%. Simultaneously, the centroids of these areas are expected to migrate northward by up to 33 km. Temperature was the dominant factor affecting its distribution (77.8%), whereas the effects of soil variables and altitude were significant. This study clarified the current distribution of K. evelyniana, projected the potential shifts under different future climate scenarios, and identified the main environmental factors affecting the distribution. These findings offer valuable theoretical support for the conservation, ecological restoration, and sustainable use of K. evelyniana.

Phase separation of NELFE modulates chromatin accessibility to promote dichotomous signaling pathways in hepatocellular carcinoma.

Biomolecular condensates partition various cellular processes including transcription, DNA repair, and RNA metabolism. We report NELFE, a member of the Negative Elongation Factor complex required for Polymerase II (Pol II) pausing, forms distinct foci mediated by two low complexity sequences. We show NELFE is oncogenic in hepatocellular carcinoma (HCC) by undergoing liquid-liquid phase separation (LLPS) with SMARCB1 to modulate chromatin accessibility to downregulate pro-apoptotic genes through Pol II pausing while activating pro-growth signals to promote HCC progression. Our work highlights the importance of NELFE LLPS as a mechanism of chromatin accessibility to regulate both paused and non-paused genes to drive tumorigenesis in hepatocellular carcinoma.

Securing Intelligent Reflecting Surface (IRS)–Aided NOMA Networks in Covert Wireless Communication Using Jammer

ABSTRACTIn this paper, an intelligent reflecting surface (IRS)–assisted non‐orthogonal multiple access (NOMA) network is proposed in a multi‐user scenario where the transmitter communicates with its receiver covertly by helping a friendly jammer. In this network, an adversary detects the communication existence of the users in the frequency band while the jammer sends the jamming signals to the adversary to degrade its performance. In this case, the analytical expressions for the secrecy outage probability (SOP), false alarm probability, and the missed detection probability at adversary are obtained. Rayleigh fading channel is assumed as the channel model while the covert communication performance is improved. For this purpose, the total effective rates are maximized by optimization of the transmission power, power allocation to multiple users, IRS reflection matrix, and also transmission probability adjustment with constraints on the detection performance and SOP. The problem is non‐convex; therefore, we present the genetic algorithm (GA) method to find the suboptimal solution for the problem with lower complexity. Numerical results show the performance improvement of the proposed algorithm in comparison to the benchmark algorithms.

Research and Optimization of White Blood Cell Classification Methods Based on Deep Learning and Fourier Ptychographic Microscopy.

White blood cell (WBC) classification plays a crucial role in hematopathology and clinical diagnostics. However, traditional methods are constrained by limited receptive fields and insufficient utilization of contextual information, which hinders classification performance. To address these limitations, this paper proposes an enhanced WBC classification algorithm, CCE-YOLOv7, which is built upon the YOLOv7 framework. The proposed method introduces four key innovations to enhance detection accuracy and model efficiency: (1) A novel Conv2Former (Convolutional Transformer) backbone was designed to combine the local pattern extraction capability of convolutional neural networks (CNNs) with the global contextual reasoning of transformers, thereby improving the expressiveness of feature representation. (2) The CARAFE (Content-Aware ReAssembly of Features) upsampling operator was adopted to replace conventional interpolation methods, thereby enhancing the spatial resolution and semantic richness of feature maps. (3) An Efficient Multi-scale Attention (EMA) module was introduced to refine multi-scale feature fusion, enabling the model to better focus on spatially relevant features critical for WBC classification. (4) Soft-NMS (Soft Non-Maximum Suppression) was used instead of traditional NMS to better preserve true positives in densely packed or overlapping cell scenarios, thereby reducing false positives and false negatives. Experimental validation was conducted on a WBC image dataset acquired using the Fourier ptychographic microscopy (FPM) system. The proposed CCE-YOLOv7 achieved a detection accuracy of 89.3%, showing a 7.8% improvement over the baseline YOLOv7. Furthermore, CCE-YOLOv7 reduced the number of parameters by 2 million and lowered computational complexity by 5.7 GFLOPs, offering an efficient and lightweight model suitable for real-time clinical applications. To further evaluate model effectiveness, comparative experiments were conducted with YOLOv8 and YOLOv11. CCE-YOLOv7 achieved a 4.1% higher detection accuracy than YOLOv8 while reducing computational cost by 2.4 GFLOPs. Compared with the more advanced YOLOv11, CCE-YOLOv7 maintained competitive accuracy (only 0.6% lower) while using significantly fewer parameters and 4.3 GFLOPs less in computation, highlighting its superior trade-off between accuracy and efficiency. These results demonstrate that CCE-YOLOv7 provides a robust, accurate, and computationally efficient solution for automated WBC classification, with significant clinical applicability.

A simple method for mapping the location of cross-β-forming regions within protein domains of low sequence complexity

Protein domains of low sequence complexity are unable to fold into stable, three-dimensional structures. In test tube studies, these unusual polypeptide regions can self-associate in a manner causing phase separation from aqueous solution. This form of protein:protein interaction has been implicated in numerous examples of dynamic morphological organization within eukaryotic cells. In several cases, the basis for low complexity domain (LCD) self-association and phase separation has been traced to the formation of labile cross-β structures. The primary energetic force favoring formation of these transient and reversible structures is enabled by polypeptide backbone interactions. Short, contiguous networks of peptide backbone amino groups and carbonyl oxygens are zippered together intermolecularly by hydrogen bonding as described by Linus Pauling seven decades ago. Here, we describe a simple, molecular biological method useful for the identification of localized, self-associating regions within larger protein domains of low sequence complexity.

Dynamic interchange of local residue–residue interactions in the largely extended single alpha-helix in Drebrin

Single alpha-helices (SAHs) are protein regions with unique mechanical properties, forming long, stable, monomeric helical structures in solution. To date, only a few naturally occurring SAH regions have been extensively characterized, primarily from myosins, leaving the structural and dynamic variability of SAH regions largely unexplored. Drebrin (developmentally regulated brain protein) contains a predicted SAH segment with unique sequence characteristics, including aromatic residues within the SAH region and a preference for arginine over lysine in its C-terminal half. Using circular dichroism (CD) and NMR spectroscopy, combined with small-angle X-ray scattering (SAXS) measurements, we demonstrate that the Drebrin-SAH is helical and monomeric in solution. NMR resonance assignment required specific 4D techniques to resolve severe signal overlap resulting from the low complexity and largely helical conformation of the sequence. To further characterize its structure, we generated a structural ensemble consistent with Cα, Cβ chemical shifts and SAXS data, revealing a primarily extended structure with non-uniform helicity. Our results suggest that dynamic rearrangement of salt bridges and potential transient cation-π interactions contribute to the formation and stabilization of both helical and non-helical local conformational states.

Efficient Variants of Wasserstein Distance in Hyperbolic Space via Space-Filling Curve Projection.

Hyperbolic spaces have been considered pervasively for embedding hierarchically structured data in the recent decade. However, there is a lack of studies focusing on efficient distance metrics for comparing probability distributions in hyperbolic spaces. To bridge the gap, we propose a novel metric called the hyperbolic space-filling curve projection Wasserstein (SFW) distance. The idea is to first project two probability distributions onto a space-filling curve to obtain a closed-form coupling between them and then calculate the transport distance between these two distributions in the hyperbolic space accordingly. Theoretically, we show the SFW distance is a proper metric and is well-defined for probability measures with bounded supports. Statistical convergence rates for the proposed estimator are provided as well. Moreover, we propose two variants of the SFW distance based on geodesic and horospherical projections, respectively, to combat the curse-of-dimensionality. Empirical results on synthetic and real-world data indicate that the SFW distance can effectively serve as a surrogate of the popular Wasserstein distance with low complexity.

Designing a 2D chaotic map with two discrete memristors: boosting chaos and encryption scheme

Abstract Due to excellent pseudo-random performance, chaotic systems are often applied in the fields of cybernetics, cryptology. However, many chaotic systems have poor chaotic property eg, narrow output range and lower complexity. For a pseudo-random number generator (PRNG) which can output pseudo-random number signal (PRNS), all these have negative effects on its practical application. To better solve these problem, this research designs a novel two-dimensional memristive boosting chaotic map (2D-MBCM). Along with several 2D discrete systems, 2D-MBCM has advantages in both generation time with sequence and system performance, it is of great significance for reducing the computing costs. Finally, by using field-programmable gate array (FPGA) platform, the boosting signals of 2D-MBCM are realized. The experiment result reflects the viability of its engineering application.

Determinants of Implementing an Adapted Version of STEADI for Fall Prevention of Older Adults Attending Outpatient Rehabilitation in a Large Health Care System.

An adapted version of the Centers for Disease Control and Prevention Stopping Elderly Accidents, Deaths, and Injuries (STEADI) fall prevention initiative was implemented for older adults≥65years of age attending 34 outpatient physical therapy clinics in a large state-wide health system. We explored physical therapists' (PTs) use and perceived determinants of implementation of an adapted version of STEADI using an explanatory mixed-methods approach (n=50 surveys, 13 interviews). We analyzed survey data using descriptive statistics and mapped interview data to Consolidated Framework for Implementation Research 2.0 constructs using rapid template analysis. Participants believed that falls were preventable, used STEADI>50% of the time, and agreed that STEADI was suitable and implementable. The STEADI components with low reported complexity aligned with high use and are included in the original STEADI (>50%; assessing and intervening in foot problems/footwear, home safety, balance, strength, endurance, gait, activity modifications, and caregiver training). Components with high reported complexity aligned with lower use, and the majority are not included in the original STEADI (<50%; assessing medication, vestibular function, cognition, and pelvic health interventions). Implementation facilitators included compatibility, embedding components of STEADI (eg, questionnaire and functional assessment) in the workflow and electronic health record (EHR), and relational connections. Implementation barriers included perceived lack of capability to conduct specific STEADI components (eg, medication assessment and specific interventions), lack of the EHR workflow of assessment and intervention components, and desire for more clinical decision support in the EHR, implementation support, and ongoing training. Physical therapists reported higher adoption rates and lower complexity to implement components original to STEADI or common in physical therapy practice compared to the adapted/additional components added by the health system. The study results can be used to develop and adapt strategies to support the implementation and dissemination of STEADI or adapted versions in other outpatient clinics and health systems.

Abstract 4035: Elucidating the DNA binding dynamics of EWSR1::FLI1 using biochemical methods

Abstract Importance: This work signifies a breakthrough in our ability to study Ewing sarcoma by developing a protocol to purify functional WT EWSR1::FLI1. This development changes the level of detail to which we can study EWSR1::FLI1 and potentially other difficult to purify fusion oncoproteins. Objective: Ewing sarcoma is a pediatric bone and soft tissue sarcoma that arises in adolescents and young adults. The mutation that causes &amp;gt;85% of Ewing sarcoma cases is caused by the fusion oncoprotein, EWSR1::FLI1. Efforts to purify recombinantly expressed EWSR1::FLI1 have proven challenging for decades due to aggregation caused by the low complexity domain (LCD) of EWSR1. Our goal was to purify recombinantly expressed and functional EWSR1::FLI1 to quantitatively understand the nature of binding dynamics to various DNA sequences. Methods: We successfully expressed and purified EWSR1::FLI1 using a newly developed protocol. We performed electrophoretic mobility shift assays (EMSAs) to test binding of EWSR1::FLI1 on naked- and nucleosome-wrapped DNA. Binding constants were determined with DNA containing either a canonical high affinity (HA) ETS DNA binding sequence or a 2X GGAA microsatellite repeat. Results: Our laboratory has successfully developed a protocol to consistently achieve highly pure and functional EWSR1::FLI1. EWSR1::FLI1 has approximately 7-fold tighter binding to a 2X GGAA repeat, than to HA ETS sequence. For comparison, the minimal FLI DNA binding domain proteins (ETS85 and ETS102) bind to HA ETS sequences and GGAA microsatellites at similar affinities. These experiments suggest that the EWSR1 LCD alters the binding dynamics of the ETS DBD domain and that EWSR1::FLI1 preferentially binds to GGAA microsatellites. We tested the pioneer function of EWSR1::FLI1 by performing EMSAs with nucleosome-wrapped DNA. EWSR1::FLI1 bound to nucleosome-wrapped DNA containing GGAA repeats with only 3-fold less affinity than naked DNA. For comparison, when ETS85 and ETS102 bound nucleosome-wrapped DNA, this affinity was ∼30-fold less, showing potentially weak pioneering function of the FLI DBD. Conclusions&amp; Relevance to Pediatric Care: These studies allow us to understand the physical nature of fusion oncoproteins such as EWSR1::FLI1 and will be the foundation for therapeutics that could be used to treat Ewing sarcoma patients. Citation Format: Jack Tokarsky, Blanche Chen, Michael G. Poirier, Emily Theisen. Elucidating the DNA binding dynamics of EWSR1::FLI1 using biochemical methods [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4035.

Abstract 5289: Mechanisms of metastasis revealed through single-cell spatial transcriptome analysis in patients with liver metastatic colorectal cancer

Abstract Background: The efficacy of treating colorectal cancer (CRC) liver metastases is hindered by significant tumor heterogeneity, both within and between tumors. This diversity stems from varied cell populations and complex interactions in the tumor microenvironment, necessitating a deeper understanding of spatial organization and cellular communication patterns. Methods: We examined 23 formalin-fixed paraffin-embedded tissue samples from 11 patients, comprising primary lesions and matched liver metastasis (6 with synchronous and 5 with metachronous metastases) using NanoString 6K CosMx Spatial Molecular Imaging (SMI). Pathologists selected optimal fields of view at tumor invasive fronts. Image processing, cell segmentation, and feature extraction utilized an in-house CosMx SMI pipeline with the Cellpose algorithm. Following quality control, the remaining cells underwent unsupervised clustering with differential gene expression analysis and cell-to-cell interaction analyses performed using the Seurat R package. Results: Analysis of 105, 624 high-quality cells revealed 23 distinct clusters, categorized as ten epithelial tumors, three cancer-associated fibroblast (CAF), two endothelial cells, five immune cells (T cell, Monocyte, Macrophage, SPP1+ Macrophage, and Plasma cell), alveolar cells, hepatocytes, and low complexity cell populations. We identified significant differences in gene expression between colon and liver CAFs, with liver CAFs displaying upregulation of 92 genes associated with epithelial-mesenchymal transition (EMT). Enhanced communication between CAFs, T cells, and macrophages was observed in liver metastases, with specific ligand-receptor pairs (SPP1-ITGA4/ITGB1, LGALS9-P4HB, LGALS9-CD44, CXCL12-CXCR4) enriched in the metastatic microenvironment. Conclusion: Our findings demonstrate distinct spatial heterogeneity and intercellular communication networks in CRC liver metastases. The identification of liver-specific CAF gene expression profiles and unique ligand-receptor interactions provides new insights into metastatic mechanisms and potential therapeutic targets. Citation Format: Jongwon Lee, Yeseul Kim, Hyo Seon Ryu, Jongmin Sim, Chungyeul Kim, Hyun Woo Kwon, Hyun Je Kim, Jong Min Park, Ah-Reum Lim, Jung Sun Kim, Hwa Jung Sung, Xingyi Guo, Jungmin Choi, Jungyoon Choi. Mechanisms of metastasis revealed through single-cell spatial transcriptome analysis in patients with liver metastatic colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5289.

Heat Transfer of Various Pore-Structure Polylactic Acid Plastic Through Fire Dynamic Simulation

Polylactic acid (PLA) is a biodegradable polymer served as thermal management material, and the heat transfer performance is closely related to its pore structure theoretically, but it is unclear how the structure affects its heat transfer. Therefore, a novel approach is proposed to address this issue by numerical simulations at low complexity and cost; three series of porous PLA are investigated with various structures in pore shape, size, and interval by fire dynamic simulation (FDS) and theoretical modeling, respectively. Meanwhile, the relationship between the hole structure and heat release rate (HRR) is developed by defining a new characteristic shape factor (Sc). It demonstrates that adjusting the pore structure of PLA significantly alters its heat transfer, evidenced by significant variations in HRR and smoke density. Additively, the approach for evaluating heat transfer is elucidated, encompassing the hole closure, vortex heat dissipation effect, and narrow variable speed effect. It provides some theoretical basis for designing porous thermal management materials.

Fleet formation identification and analyzing method based on disposition feature for remote sensing

Fleet formation identification in remote sensing is a significant focus in maritime surveillance. However, fleet may occur with different ship dense and noisy data due to the complex background and different satellite resolution, few studies have discussed formation identification considering the limits of sensing and application. This study introduces an effective fleet formation identification and analysis method based on disposition features for remote sensing. We fully consider and analyze detection performance in remote sensing applications, such as false alarms, missed detections, ship position errors and observed ship attitudes. A hierarchical density-based spatial clustering with noise method is introduced to cluster fleet regions. The robust disposition features are designed for various kinds of formations without using training data, enabling discrete remote sensing observations. Our method is robust to ship detection results and has low computational complexity, making it highly suitable for real applications. The advantages of our method were demonstrated through extensive experiments. The experimental results show that the proposed method achieves formation identification with an accuracy over 95% within less than 0.15 s when the ship detection performance changes over a large range, leading to a performance improvement of 5–27% compared with that of other comparative methods.