Different dissection planes used in brow lift surgery—namely, subcutaneous, subgaleal, and subperiosteal—present distinct biomechanical and histological characteristics. This study aimed to compare these three dissection approaches in terms of tissue healing and mechanical resistance. In this experimental model using New Zealand white rabbits, three groups of eight animals each underwent subcutaneous, subgaleal, or subperiosteal dissection. After a 12-week healing period, tissue samples were harvested for either histological or biomechanical analysis. Epidermal, dermal, superficial musculoaponeurotic system (SMAS), and periosteal thicknesses were quantitatively measured using ImageJ software. Biomechanical parameters assessed included maximum load, stiffness, and yield load. Histological evaluation revealed preserved tissue integrity in all groups, with no evidence of fibrosis or disrupted collagen organization. A statistically significant difference in the epidermis/dermis ratio was observed between the control group and both the subgaleal and subperiosteal groups (p < 0.05). In biomechanical testing, the subperiosteal group demonstrated significantly superior values for maximum load, stiffness, and yield load compared to the other groups (p < 0.05). These findings suggest that while the subperiosteal plane offers greater long-term mechanical stability, the subcutaneous approach may support a more physiological healing pattern. This is supported by its closer resemblance to the control group in epidermis-to-dermis ratios and the absence of fibrotic remodeling. The preservation of native tissue architecture in this group highlights its potential as a biologically favorable plane in selected patients. Dissection plane selection should be individualized based on the patient’s specific tissue characteristics and surgical goals. This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes review articles, book reviews, and manuscripts that concern basic science, animal studies, cadaver studies, and experimental studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
This paper investigates how near-field beamfocusing can be achieved using a modular linear array (MLA), composed of multiple widely spaced uniform linear arrays (ULAs). The MLA architecture extends the aperture length of a standard ULA without adding additional antennas, thereby enabling near-field beamfocusing without increasing processing complexity. Unlike conventional far-field beamforming, near-field beamfocusing enables simultaneous data transmission to multiple users at different distances in the same angular interval, offering significant multiplexing gains. We present a detailed mathematical analysis of the beamwidth and beamdepth achievable with the MLA and show that by appropriately selecting the number of antennas in each constituent ULA, ideal near-field beamfocusing can be realized. In addition, we propose a computationally efficient localization method that fuses estimates from each ULA, enabling efficient parametric channel estimation. Simulation results confirm the accuracy of the analytical expressions and that MLAs achieve near-field beamfocusing with a limited number of antennas, making them a promising solution for next-generation wireless systems.
Wireless communication systems exhibit structural and functional similarities to neural networks: signals propagate through cascaded elements, interact with the environment, and undergo transformations. Building upon this perspective, we introduce a unified paradigm, termed wireless physical neural networks (WPNNs), in which components of a wireless network, such as transceivers, relays, backscatter, and intelligent surfaces, are interpreted as computational layers within a learning architecture. By treating the wireless propagation environment and network elements as differentiable operators, new opportunities arise for joint communication-computation designs, where system optimization can be achieved through learning-based methods applied directly to the physical network. This approach may operate independently of, or in conjunction with, conventional digital neural layers, enabling hybrid communication learning pipelines. In the article, we outline representative architectures that embody this viewpoint and discuss the algorithmic and training considerations required to leverage the wireless medium as a computational resource. Through numerical examples, we highlight the potential performance gains in processing, adaptability, efficiency, and end-to-end optimization, demonstrating the promise of reconfiguring wireless systems as learning networks in next-generation communication frameworks.
Answering complex, real-world queries often requires synthesizing facts scattered across vast document corpora. In these settings, standard retrieval-augmented generation (RAG) pipelines suffer from incomplete evidence coverage, while long-context large language models (LLMs) struggle to reason reliably over massive inputs. We introduce SPD-RAG, a hierarchical multi-agent framework for exhaustive cross-document question answering that decomposes the problem along the document axis. Each document is processed by a dedicated document-level agent operating only on its own content, enabling focused retrieval, while a coordinator dispatches tasks to relevant agents and aggregates their partial answers. Agent outputs are synthesized by merging partial answers through a token-bounded synthesis layer (which supports recursive map-reduce for massive corpora). This document-level specialization with centralized fusion improves scalability and answer quality in heterogeneous multidocument settings while yielding a modular, extensible retrieval pipeline. On the LOONG benchmark (EMNLP 2024) for long-context multi-document QA, SPD-RAG achieves an Avg Score of 58.1 (GPT-5 evaluation), outperforming Normal RAG (33.0) and Agentic RAG (32.8) while using only 38
