IF 4.5

Array Pub Date : 2026-03-01 Epub Date: 2026-02-11 DOI: 10.1016/j.array.2026.100714

Shumaila Javaid , Nasir Saeed

{"title":"The post-electromagnetic era: A vision for wireless communication beyond 6G","authors":"Shumaila Javaid , Nasir Saeed","doi":"10.1016/j.array.2026.100714","DOIUrl":"10.1016/j.array.2026.100714","url":null,"abstract":"<div><div>Electromagnetic (EM) communication is approaching fundamental physical and thermodynamic limits, where further performance gains through spectrum expansion and waveform optimization alone are increasingly unsustainable. The purpose of this paper is to explore how wireless communication may evolve beyond the EM paradigm by reframing information transfer as controlled manipulation of physical, biological, and cognitive states rather than radiative signal propagation. The main contribution of this work is a state-centric conceptual framework for post-6G communication. The paper identifies and categorizes ten foundational paradigms, including quantum-state transfer, atomic and lattice-level signaling, biological communication, cognitive telepresence, and spacetime-based coordination, defining potential non-EM and hybrid communication mechanisms. In addition, a research roadmap is outlined to place these paradigms within plausible future network generations beyond 6G. The key findings of this study are conceptual. The analysis shows that diverse communication mechanisms across physical, biological, and cognitive domains can be unified using common principles such as state transduction, coherence preservation, entropy management, and energy-aware conversion. These findings indicate that future communication systems may evolve from spectrum-bound infrastructures into adaptive and self-organizing networks that integrate information transfer with sensing, computation, and actuation. This work establishes a conceptual reference framework for future theoretical and interdisciplinary research on communication beyond conventional EM-based systems.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100714"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146184705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging blockchain and LLMs for patient–clinical trial matching 利用区块链和llm进行患者-临床试验匹配

IF 4.5

Array Pub Date : 2026-03-01 Epub Date: 2025-12-27 DOI: 10.1016/j.array.2025.100657

Diana Hawashin , Khaled Salah , Raja Jayaraman , Samer Ellahham , Ibrar Yaqoob

{"title":"Leveraging blockchain and LLMs for patient–clinical trial matching","authors":"Diana Hawashin , Khaled Salah , Raja Jayaraman , Samer Ellahham , Ibrar Yaqoob","doi":"10.1016/j.array.2025.100657","DOIUrl":"10.1016/j.array.2025.100657","url":null,"abstract":"<div><div>Efficiently matching patients to clinical trials is essential for advancing medical research and ensuring reliable outcomes. However, current matching methods face several challenges. These include data integrity issues from tampered records, privacy risks caused by weak anonymization, and manual processes that delay recruitment. In addition, centralized systems lack transparency, expose sensitive patient data to security vulnerabilities, and suffer from single points of failure that reduce resilience and trust. In this paper, we propose a blockchain and Large Language Models (LLMs)-driven solution for secure, trustworthy, traceable, decentralized, and transparent patient–clinical trial matching. Blockchain ensures data integrity, security, and transparency by eliminating single points of failure and enabling tamper-proof records. LLMs enhance patient–trial matching by automating the interpretation of complex eligibility criteria, improving accuracy, and significantly reducing the time required for manual review. Our approach uses Ethereum-based smart contracts to automate workflows such as trial registration, eligibility assessment, and consent tracking. We fine-tune GPT-4, T5, and Gemini on synthetic data derived from real clinical trial records and employ majority voting to ensure consistent and unbiased eligibility decisions. A prototype Gradio interface was developed as a minimum viable product (MVP) to demonstrate seamless interaction between LLMs and smart contracts. Performance evaluation based on accuracy (0.800), precision (0.733), recall (1.000), and F1-score (0.846) demonstrates reliable eligibility prediction. Cost analysis confirms affordability, and security evaluation verifies resilience against known threats. Comparison with existing solutions highlights the framework’s advantages in transparency, trust, and automation. The smart contract code is publicly available on GitHub.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100657"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bridging the skills gap through Agile methodologies in Vocational Software Development Education 通过敏捷方法在职业软件开发教育中弥合技能差距

IF 4.5

Array Pub Date : 2026-03-01 Epub Date: 2025-12-11 DOI: 10.1016/j.array.2025.100614

Umi Sa'adah , Umi Laili Yuhana , Siti Rochimah , Maulidan Bagus Afridian Rasyid

{"title":"Bridging the skills gap through Agile methodologies in Vocational Software Development Education","authors":"Umi Sa'adah , Umi Laili Yuhana , Siti Rochimah , Maulidan Bagus Afridian Rasyid","doi":"10.1016/j.array.2025.100614","DOIUrl":"10.1016/j.array.2025.100614","url":null,"abstract":"<div><div>This study introduces a multi-semester framework for Vocational Software Development Education (VSDE) that incorporates Agile practices—specifically Scrum, Lean Startup, and Extreme Programming—into a Project-Based Learning (PBL) curriculum. Conducted at an Indonesian polytechnic with 51 students across six courses, the framework was evaluated using a mixed-methods case study that combined qualitative data (such as interviews and reflections) with quantitative indicators (including Net Promoter Score [NPS], Minimum Viable Product (MVP) delivery, and sprint completion rates). The findings demonstrated a systematic improvement, with the median NPS increasing from 20.0 in Sprint 1 to 56.9 in Sprint 5, and six out of seven teams showing progress (Friedman χ<sup>2</sup>(4) = 11.38, p = 0.023). Survey results indicated gains of 1.5–2.0 points in Agile competencies, while student reflections highlighted a greater sense of ownership and more adaptive delivery processes. Overall, the results indicate that the iterative adoption of Agile practices can enhance both product quality and professional readiness. This study presents a replicable model and provides practical guidance for curriculum design, stakeholder engagement, and scaling Agile pedagogy across diverse institutional contexts.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100614"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HLF-FSL: A decentralized federated split learning solution for IoT on hyperledger fabric HLF-FSL：一种基于超级账本结构的物联网分散联邦分裂学习解决方案

IF 4.5

Array Pub Date : 2026-03-01 Epub Date: 2026-01-23 DOI: 10.1016/j.array.2026.100685

Carlos Beis-Penedo , Rebeca P. Díaz-Redondo , Ana Fernández-Vilas , Manuel Fernández-Veiga , Francisco Troncoso-Pastoriza

{"title":"HLF-FSL: A decentralized federated split learning solution for IoT on hyperledger fabric","authors":"Carlos Beis-Penedo , Rebeca P. Díaz-Redondo , Ana Fernández-Vilas , Manuel Fernández-Veiga , Francisco Troncoso-Pastoriza","doi":"10.1016/j.array.2026.100685","DOIUrl":"10.1016/j.array.2026.100685","url":null,"abstract":"<div><div>Collaborative machine learning in sensitive domains demands scalable, <em>privacy-aware and access-controlled</em> solutions for enterprise-grade deployment. Conventional federated learning (FL) relies on a central server, introducing single points of failure and privacy risks, while split learning (SL) partitions models for privacy but scales poorly because of sequential training. We present HLF-FSL, a decentralized architecture that combines federated split learning (FSL) with the permissioned blockchain Hyperledger Fabric (HLF). Chaincode orchestrates split-model execution and peer-to-peer aggregation without a central coordinator, leveraging HLF’s transient fields and Private Data Collections (PDCs) to keep raw data and model activations off-chain and access-controlled. On CIFAR-10, MNIST and ImageNet-Mini, HLF-FSL matches the accuracy of a standard server-coordinated FSL baseline while reducing per-epoch training time versus Ethereum-based baselines. Performance and scalability tests quantify the Fabric coordination overhead via a component-level breakdown of SDK-facing latencies and communication volumes; empirically, this overhead increases wall-clock epoch time while preserving the same accuracy-vs-epoch behavior as a FedSplit Learning baseline.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100685"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146073734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evolution of physics-informed neural networks: Recent architectural variants and optimization strategies 物理信息神经网络的进化：最近的架构变体和优化策略

IF 4.5

Array Pub Date : 2026-03-01 Epub Date: 2026-01-22 DOI: 10.1016/j.array.2026.100688

Ahmad , Husna Zafar , Aneeqa Zafar , Muhammad Noveel Sadiq , A.K. Awasthi , Homan Emadifar , Karim K. Ahmed

{"title":"Evolution of physics-informed neural networks: Recent architectural variants and optimization strategies","authors":"Ahmad , Husna Zafar , Aneeqa Zafar , Muhammad Noveel Sadiq , A.K. Awasthi , Homan Emadifar , Karim K. Ahmed","doi":"10.1016/j.array.2026.100688","DOIUrl":"10.1016/j.array.2026.100688","url":null,"abstract":"<div><div>Physics-Informed Neural Networks (PINNs) are a machine learning technique that directly incorporates the governing physics of problems, such as partial differential equations (PDEs) and ordinary differential equations (ODEs), into the neural network architecture. The primary goal of PINNs is to approximate solutions while satisfying given constraints and minimizing the residuals of the differential equations. PINNs have been employed to solve various problems, including integro-differential equations, fractional differential equations, and stochastic PDEs. Over the past two years, significant advancements have addressed the challenges associated with PINNs, resulting in notable improvements in accuracy and performance. This article provides a comprehensive summary of the latest methodologies contributing to these advancements, focusing on innovations in hyperparameter optimization and novel PINN variants inspired by other neural networks. Examples include MultiInNet-PINN, Transformer-based PINNs such as Tr-PINN and PINNsFormer, as well as PINNs incorporating attention mechanisms and recurrent neural network (RNN) architectures (PIANN). Additionally, recent research on domain decomposition techniques in PINN architectures are highlighted. By consolidating recent architectural and algorithmic advances, this research identifies critical research opportunities for enhancing the reliability, efficiency, and broader applicability of PINNs in scientific computing.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100688"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146073944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent selection algorithm of vision guided feeding path for AGV car transporting cigarette accessories 卷烟配件运输AGV车视觉引导投料路径智能选择算法

IF 4.5

Array Pub Date : 2026-03-01 Epub Date: 2026-01-21 DOI: 10.1016/j.array.2026.100683

Zhiyuan Liang, Pengtao He, Xiaofei Huang, Xiaolei Zhao, Bin Wei, Weili Zeng

{"title":"Intelligent selection algorithm of vision guided feeding path for AGV car transporting cigarette accessories","authors":"Zhiyuan Liang, Pengtao He, Xiaofei Huang, Xiaolei Zhao, Bin Wei, Weili Zeng","doi":"10.1016/j.array.2026.100683","DOIUrl":"10.1016/j.array.2026.100683","url":null,"abstract":"<div><div>During the transportation of cigarette accessories, AGV trolleys often encounter challenges such as complex environments, variable paths, and dynamic obstacles. Traditional path planning methods - such as laser navigation or two-dimensional code navigation - often exhibit limitations including delayed response, poor adaptability, and difficulties in achieving global optimization when dealing with illumination variations, path deviations, and multi-task concurrency. To address these issues, this study investigates a vision-guided intelligent selection algorithm for the feeding path of AGVs in cigarette accessory transportation. The algorithm employs a vision-based AGV path deviation recognition method to identify deviations in the feeding path during cigarette accessory delivery. A multi-step grid approach is utilized to model the feeding environment as a grid map. Within this grid map, an intelligent feeding path selection model based on the pigeon-inspired optimizer (PIO) is applied. An objective function is designed to achieve the shortest path for safely reaching the feeding destination after path deviation. The PIO algorithm optimizes the path to enable vision-guided intelligent selection of the feeding path for AGVs transporting cigarette accessories. Experimental results demonstrate that the proposed algorithm can dynamically plan the optimal feeding path, reduce travel distance, and improve feeding efficiency in vision-guided feeding path selection for AGVs delivering cigarette accessories.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100683"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146073943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AgnoSVD: Dynamic resource allocation for serverless workloads using collaborative filtering AgnoSVD：使用协作过滤为无服务器工作负载进行动态资源分配

IF 4.5

Array Pub Date : 2026-03-01 Epub Date: 2025-12-27 DOI: 10.1016/j.array.2025.100662

Md. Shariar Kabir, Muhammad Abdullah Adnan

{"title":"AgnoSVD: Dynamic resource allocation for serverless workloads using collaborative filtering","authors":"Md. Shariar Kabir, Muhammad Abdullah Adnan","doi":"10.1016/j.array.2025.100662","DOIUrl":"10.1016/j.array.2025.100662","url":null,"abstract":"<div><div>In serverless computing, determining the optimal resource configurations for workloads poses significant challenges, particularly due to the cloud provider’s limited visibility into workload specifics. This complexity is amplified when dealing with diverse workloads that vary in their characteristics. In this paper, we present AgnoSVD, an approach for predicting the optimum resource configuration for an incoming workload using Singular Value Decomposition (SVD). The proposed model uses collaborative filtering to extract the latent factors of the workloads and resource profiles. Therefore, the model remains agnostic to the specific details of the functions and the resource configurations. We tested our approach on well-known serverless systems like AWS lambda and Apache OpenWhisk and evaluated the system using 99 functional workloads. These workloads encompass both individual functions and chains of functions, addressing a range of computational and learning problems. To validate the system’s ability to adapt to changes, we also evaluated our system using functions with different input parameter sizes. Our evaluation shows that the model reaches convergence within 2 feedback iterations and results in a 32.41% decrease in average cost and a 5.18% average speedup, outperforming other state-of-the-art approaches.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100662"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

VEGAN: CCTV video quality enhancement with GAN-based foreground separation and super-resolution VEGAN：利用基于gan的前景分离和超分辨率增强CCTV视频质量

IF 4.5

Array Pub Date : 2026-03-01 Epub Date: 2026-01-03 DOI: 10.1016/j.array.2025.100673

Ali Asghar , Wareesa Sharif , Amna Shifa

{"title":"VEGAN: CCTV video quality enhancement with GAN-based foreground separation and super-resolution","authors":"Ali Asghar , Wareesa Sharif , Amna Shifa","doi":"10.1016/j.array.2025.100673","DOIUrl":"10.1016/j.array.2025.100673","url":null,"abstract":"<div><div>As countries rapidly transition toward smart cities, closed-circuit television (CCTV) surveillance systems are playing an increasingly vital role in ensuring public safety and enabling urban analytics. However, the visual quality of CCTV footage is often degraded by environmental factors (e.g., motion blur, low resolution, and poor illumination), which significantly impact the quality of service, as well as the reliability and effectiveness of these systems. To address these issues, this research proposes a generative adversarial network (GAN)-based framework, named VEGAN (Video Enhancement with Generative Adversarial Network), that combines reconstruction, adversarial, and facial component losses with adaptive balancing to optimise visual sharpness, temporal stability, and identity preservation. VEGAN integrates four key components: (1) a pixel counter, which identifies high and low quality frames; (2) Modified MoCoGAN, which separates foreground and background features to disentangle content from motion; (3) a Recurrent Neural Network, which captures complex temporal and motion patterns; and (4) a Super-Resolution module, which enhances low-quality pixels to recover fine spatial details. The enhanced foreground generated by these combined modules is seamlessly fused with a high-quality background frame, resulting in substantially improved overall video quality. Experimental evaluations demonstrate VEGAN’s effectiveness, achieving an average Learned Perceptual Image Patch Similarity (LPIPS) score of 0.041 and a Video Multimethod Assessment Fusion (VMAF) score of 56.13, indicating significant perceptual and quantitative improvements. These findings highlight VEGAN’s effectiveness in video-based analytics, supporting more accurate performance in tasks such as event detection and activity recognition.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100673"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Agentic artificial intelligence is the future of cancer detection and diagnosis 人工智能是癌症检测和诊断的未来

IF 4.5

Array Pub Date : 2026-03-01 Epub Date: 2025-12-31 DOI: 10.1016/j.array.2025.100676

Sayedur Rahman , Md. Tanzib Hosain , Nafiz Fahad , Md. Kishor Morol , Md. Jakir Hossen

{"title":"Agentic artificial intelligence is the future of cancer detection and diagnosis","authors":"Sayedur Rahman , Md. Tanzib Hosain , Nafiz Fahad , Md. Kishor Morol , Md. Jakir Hossen","doi":"10.1016/j.array.2025.100676","DOIUrl":"10.1016/j.array.2025.100676","url":null,"abstract":"<div><div>Agentic artificial intelligence systems, particularly Large Language Models (LLMs) and Vision-Language Models (VLMs), are a big change in oncology because they can find and diagnose cancer in ways that have never been done before. In accordance with PRISMA 2020 criteria, we conducted a systematic search across nine databases from January 2023 to September 2025, reviewing 3986 records and incorporating 123 papers that assessed agentic AI in cancer detection and diagnosis. Research demonstrated swift expansion (91.9% published in 2024-2025) across various cancer kinds, with breast (22.0%) and lung cancer (13.8%) being the most extensively examined. GPT-4 versions showed performance similar to that of human experts: they found errors better than pathologists (89.5% vs. 88.5%), classified skin lesions as well as dermatologists (84.8% vs. 84.6%), and staged ovarian cancer with 97% accuracy compared to 88% by radiologists. Zero-shot LLMs consistently surpassed conventional supervised models. But there were big problems, like factual errors in 15%–41% of instances, algorithmic bias, and low agreement with tumor boards (50%–70%). Agentic AI has a lot of promise for finding cancer, especially in organized tasks. However, the research so far suggests that it should be used as an aid rather than an independent system. Concerns about reliability and bias in algorithms are two of the most important impediments. Future priorities encompass Retrieval-Augmented Generation(RAG) systems, domain-specific models, and forthcoming trials to ascertain clinical value.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100676"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Flatfoot disorder recognition based on the YOLO-ChA algorithm 基于YOLO-ChA算法的平足障碍识别

IF 4.5

Array Pub Date : 2026-03-01 Epub Date: 2026-02-07 DOI: 10.1016/j.array.2026.100699

Raheleh Khaleghizadeh , Sara Motamed , Elham Askari

{"title":"Flatfoot disorder recognition based on the YOLO-ChA algorithm","authors":"Raheleh Khaleghizadeh , Sara Motamed , Elham Askari","doi":"10.1016/j.array.2026.100699","DOIUrl":"10.1016/j.array.2026.100699","url":null,"abstract":"<div><div>We address automatic flatfoot recognition from plantar-pressure images by proposing YOLO-ChA, a task-aware integration of channel attention into YOLO. Unlike generic YOLO + attention variants, YOLO-ChA injects channel attention only in the PANet neck at the P3 and P4 scales (stride 8 and 16) before feature concatenation, in order to preserve arch-relevant low–mid spatial cues. We further introduce an anatomy-guided channel grouping (heel/midfoot-arch/forefoot) that biases feature fusion toward clinically meaningful arch loading patterns without requiring a separate spatial attention branch. This is implemented using a lightweight ECA-style cross-channel interaction that keeps parameters and FLOPs essentially unchanged. On a clinically acquired plantar-pressure dataset of 978 images from 489 subjects (flatfoot vs. normal), YOLO-ChA achieved an overall classification accuracy of 95.14% under subject-disjoint train/test splits and 10-fold cross-validation. Comprehensive ablation studies over attention placement (backbone/neck/head) and attention type (SE/CBAM/ECA-style) show that the proposed neck-only, stride-aware design provides the best trade-off between accuracy and computational cost. These results indicate that domain-informed attention placement can improve flatfoot screening performance in clinical plantar-pressure analysis while remaining lightweight enough for practical deployment.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100699"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146184898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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