Advanced Electromagnetics

From industrial equipment to communication interface: a study on the visual communicability and urban cultural IP generation mechanism of the No.1 blast furnace in Hanggang Park, Grand Canal

H. Xue, J. H. Chen — Tue, 08 Sep 2026 00:00:00 +0200

In the context of postindustrial cities, large-scale heavy industrial structures are gradually undergoing a fundamental transformation in their functional paradigm. This article takes Hanggang No.1 blast furnace as a typical research case, constructs a three-dimensional quantitative evaluation system for the visual communicability of large-scale industrial equipment, and extracts a fourth-order iterative generation coupling model for urban cultural IP under the guidance of industrial heritage. Firstly, clarify the core connotation, dimensional composition, and quantitative measurement indicators of visual communicability of industrial equipment, and deconstruct the three-dimensional visual symbol system of No.1 blast furnace; Secondly, a vertical review of the evolution of the entire lifecycle of blast furnaces is conducted, elucidating visual communicability as the core driving factor. Empirical analysis shows that: (1) the inherent visual communicability of industrial equipment is a prerequisite core constraint for the cultivation and formation of urban cultural IP; (2) The super tall vertical scale, corroded metal native texture, and complete continuous production flow line of the No.1 blast furnace have built an irreplaceable differentiated visual competitive barrier; (3) A multi-level composite communication interface built on physical space, short video social media, cultural and creative derivative products, and fashion and art exhibitions can promote the sustained diffusion and dissemination of industrial cultural symbols across scenes and age groups. This study expands the research and analysis dimensions of postindustrial heritage semiotics, providing a quantifiable indicator framework for evaluating the visual communication effectiveness of giant industrial structures.

Application of Intelligent Vision Technology in Dance Teaching Action Correction and Standardized Training

D. H. Wang, Y. Xiong, F. Li, L. Li — Tue, 08 Sep 2026 00:00:00 +0200

Under the dual background of digital transformation in art education and deep implementation of artificial intelligence technology, traditional dance teaching relies on subjective judgment by humans, difficulty in unifying movement standards, lagging error correction feedback, and difficulty in large-scale standardized training, which are increasingly prominent pain points. Intelligent vision technology relies on core technologies such as computer vision, deep learning, human pose estimation, and spatiotemporal feature extraction to achieve real-time capture of dance human key points, quantitative analysis of motion poses, construction of standard motion libraries, automatic recognition of deviations, and intelligent error correction feedback, providing a new technological paradigm for dance teaching motion correction and standardized training. This article takes the integration of intelligent visual technology and dance teaching as the research core, and adopts literature research method, system analysis method, technical architecture disassembly method, and application empirical analysis method to conduct systematic research from seven dimensions: theoretical basis, technical system, system construction, error correction mechanism, application scenarios, existing problems and optimization paths, and summary and prospect.

Clinical Efficacy Analysis of Emergency Surgical Trauma Repair —— Based on a Multicenter Prospective Cohort Study

S. L. Huang, X. L. Yue, G. Q. Li, M. X. Guan — Tue, 08 Sep 2026 00:00:00 +0200

Objective: To analyze the clinical efficacy of different trauma repair methods using a multicenter prospective cohort study design. Methods: This study enrolled 750 trauma patients from the emergency surgery departments of multiple tertiary hospitals in Guangxi region. Among them, 600 patients with open injuries were divided into three groups according to treatment methods for efficacy comparison: the traditional suture group, the new biological material dressing group, and the negative pressure wound therapy group. Results: The new biological material dressing adjuvant therapy and negative pressure wound therapy were superior to the traditional suture method in terms of wound healing time, scar formation, and infection rate. Different trauma types and treatment methods were closely associated with the occurrence of complications, and patient satisfaction was negatively correlated with wound healing time, scar size, and infection rate. Conclusion: This study provides a scientific basis for the clinical practice of emergency surgical trauma repair. It is recommended to select appropriate treatment methods according to wound conditions, and strengthen condition assessment, complication prevention, and postoperative care.

DynChain-Vul: Adaptive Blockchain Sharding and Secure Cross-Shard Vulnerability Data Sharing for Cloud-Based Vulnerability Lifecycle Governance

Z. C. Sun, J. Li, B. Wu — Tue, 08 Sep 2026 00:00:00 +0200

Cloud-based vulnerability lifecycle governance requires continuous sharing of vulnerability intelligence, security evidence, remediation records, exploit verification results, and risk assessment information among cloud service providers, security operation centers, software vendors, and regulatory agencies. These security data are highly distributed, dynamically generated, and privacy-sensitive, demanding scalable, trustworthy, and auditable cross-domain data sharing. Although blockchain provides decentralized trust, tamper resistance, and traceability, conventional blockchain architectures suffer from limited throughput, excessive storage redundancy, and inefficient cross-shard data exchange, making them difficult to support large-scale cloud vulnerability governance involving heterogeneous computing resources. This paper proposes DynChain-Vul, an adaptive blockchain sharding architecture for secure vulnerability data sharing in cloud-based vulnerability lifecycle governance. DynChain-Vul first profiles node computing capability, network bandwidth, communication latency, and storage resources, and then employs Gaussian mixture modeling together with dynamic weighted consistent hashing to allocate virtual nodes according to heterogeneous resource characteristics, thereby improving shard balance and overall system scalability. To accommodate continuously changing workloads, a dynamic shard mapping index supports adaptive shard splitting and merging while minimizing data migration overhead. For secure cross-shard vulnerability information exchange, DynChain-Vul integrates multi-path redundant indexing based on Kademlia routing with a threshold-signature verification mechanism, enabling reliable vulnerability data retrieval without relying on a centralized trusted coordinator. Experimental results on a blockchain emulation platform demonstrate that DynChain-Vul increases system throughput from 725.98 TPS to 1493.17 TPS as the number of shards increases from 2 to 8, reduces transaction confirmation latency from 39.23 ms to 5.92 ms, maintains shard-load covariance within 0.12– 0.15, and keeps aggregated cross-shard signature storage nearly constant. These results indicate that adaptive blockchain sharding combined with verifiable cross-shard vulnerability data sharing provides an efficient and trustworthy foundation for cloud-based vulnerability lifecycle governance, supporting scalable collaborative vulnerability intelligence sharing and secure security operations.

FedCIPHER: Causal-Invariant and Privacy-Hardened Prototype Federation for Robust Network Intrusion Detection under Non-IID Traffic

Q. M. Chen — Tue, 08 Sep 2026 00:00:00 +0200

Collaborative network intrusion detection requires security operation centers, cloud platforms, and industrial edge domains to learn from distributed traffic without centralizing packet traces, asset identifiers, or attack evidence. Federated learning limits direct data movement, yet conventional parameter averaging is fragile when attack classes are rare, traffic distributions are non-independent and identically distributed (non-IID), and model updates can be inspected or poisoned. This paper proposes FedCIPHER, a causal-invariant and privacy-hardened prototype federation framework for multi-class network threat detection. Each client learns a gated causal representation by exchanging nuisance components between traffic samples and enforcing counterfactual prediction consistency. Instead of uploading full model gradients, the client releases clipped, orthogonally sketched first- and second-order class prototypes protected by distributed Gaussian noise and secure summation. An evidential uncertainty score and a class-conditional deviation statistic jointly control robust server aggregation, while a private global prototype bank regularizes locally personalized detectors. We provide a Rényi differential privacy bound, a bounded-influence analysis for malicious clients, and a convergence characterization that explicitly separates client drift, sketch distortion, and privacy noise. Experiments on UNSW-NB15, CICIDS2017, ToN-IoT, and BoT-IoT with 20 clients show that FedCIPHER obtains average Macro-F1 of 88.70% under Dirichlet concentration α = 0.3, exceeding FedAvg, FedProto, FEDIIR, and PROTEAN by 6.68, 3.69, 3.61, and 2.43 percentage points, respectively. At ε = 4 and δ = 10−5, membership-inference AUC is reduced to 0.566 while Macro-F1 decreases by only 0.42 points relative to the non-private variant. FedCIPHER also retains 82.83% Macro-F1 with 30% malicious clients and reduces per-round upload by 99.59% compared with full-model federation. These results indicate that causal prototype federation offers an effective balance among detection utility, privacy, poisoning resilience, and communication efficiency for cross-domain network data security.

Optimizing Clinical Outcomes: A Nomogram for Predicting Gastric Cancer Survival in Elderly Patients Following Gastrectomy

J. Li, J. Zhu, X. Song, Y. H. Zhou, J. Zhang — Tue, 08 Sep 2026 00:00:00 +0200

Background: Owing to the aging global population, the prevalence of gastric cancer (GC) in the elderly is on the increase. The study aimed to investigate risk variables correlated with cancer-specific survival (CSS) and to construct and validate a nomogram to calculate the probability of CSS after gastrectomy in elderly patients with non-metastatic GC. Methods: Overall, 5462 postgastrectomy patients with geriatric non-metastatic GC who were diagnosed between 2006 and 2015, and 1078 patients diagnosed between 2004 and 2005 from the Surveillance, Epidemiology, and End Results (SEER) database were incorporated in this study. We randomly selected 3846 patients from the 5462 patients to constitute the training cohort to construct the nomogram, and the other 1616 patients were used for internal validation. Additionally, the other 1078 patients were used for external validation. CSS predictors were screened using univariate Cox proportional hazards regression analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, Random Survival Forest (RSF), and multivariate Cox proportional hazards regression analysis. A model for predicting CSS was developed using Cox proportional hazards regression analysis, and both static and dynamic web-based nomograms were provided. Results: A total of 10 variables, including gender, age, race, tumor size, primary site, grade, positive lymph nodes (PLNs), examined lymph nodes (ELNs), T stage, and months from diagnosis to treatment (MFDTT), were incorporated in the development of the nomogram. The areas under the curve (AUCs) for the training cohort at 3, 5, and 10 years were 0.780 (95% CI, 0.765–0.795), 0.792 (95% CI, 0.778– 0.807), and 0.803 (95% CI, 0.781–0.826). Following internal validation, these values were 0.809 (95% CI, 0.789–0.830), 0.822 (95% CI, 0.801–0.843), and 0.824 (95% CI, 0.793–0.854). The model-predicted CSS converges with the actual CSS according to the calibration curves. The nomogram showed higher clinical benefit than the TNM staging system, according to the decision curve analysis (DCA) for all three cohorts. The nomogram score was utilized as a basis for grouping high- and low-risk patients, and significant statistical differences (p<0.0001) were demonstrated between the two groups in the Kaplan–Meier (K-M) curves. Conclusions: Our novel nomogram can conveniently and accurately predict CSS in elderly patients with non-metastatic GC after gastrectomy, which can assist clinicians in adopting individualized treatment strategies to improve the prognosis of elderly patients with GC.

Construction of a Standardized Pain Management Program for Traumatic Orthopedic Patients and Its Impact on Postoperative Rehabilitation Quality

J. Y. Yao, L. J. Zhang, X. L. Yue, M. X. Guan, L. Y. Li — Tue, 08 Sep 2026 00:00:00 +0200

Objective: To construct a standardized pain management program for traumatic orthopedic patients and explore its impact on postoperative rehabilitation quality. Methods: The current status of pain management was reviewed through literature analysis. Based on evidence-based medicine and personalized patient needs, a program was constructed encompassing standardized pain assessment, treatment strategies, and a multidisciplinary collaboration model. A randomized controlled trial was conducted with 200 traumatic orthopedic patients, divided into an experimental group (standardized program) and a control group (routine management). Indicators including pain level, limb function recovery, psychological state, and quality of life were compared between the two groups. Results: The experimental group showed significantly greater improvement in all indicators compared to the control group (P < 0.05). Conclusion: The standardized pain management program can effectively improve the postoperative rehabilitation quality of traumatic orthopedic patients and holds important clinical application and promotion value.

Composite Design and Friction-Wear Characteristics of High-Efficiency Transmission Gear Materials

Z. Y. Li, X. Y. Mi — Fri, 21 Aug 2026 00:00:00 +0200

To meet the high-speed and heavy-load service requirements of high-efficiency transmission gears, Fe-2Ni iron-based powder metallurgy was used as the matrix, and a composite system reinforced by TiC hard phase and MoS2 solid lubricant was designed. The gear composites were prepared by hot pressing sintering. The effects of reinforcement ratio and sintering parameters on microstructure and mechanical properties were studied, and the friction and wear behaviors under different loads, sliding speeds and lubrication conditions were investigated. The results show that the optimal sintering process is 1150 ◦C, 60 min and 30 MPa, with a relative density of 98.6% and microhardness of HV 620. The composite with 10 wt% TiC and 3 wt% MoS2 exhibits the best comprehensive performance, with a friction coefficient as low as 0.08 and a wear rate reduced by 62% compared with the matrix. The wear mechanism transforms from abrasive wear to oxidative wear and slight delamination wear. The composite presents excellent friction reduction and wear resistance, which can provide a quantitative reference for the design of high-performance transmission gears.

Business Environment Optimization and Audit Quality in the STAR Market: Inverted U-Shaped Nonlinear Effect and ESG Risk Moderation Mechanism

Y. X. Li — Fri, 21 Aug 2026 00:00:00 +0200

The business environment constitutes a fundamental institutional arrangement shaping the behavioral incentives of capital market participants. However, whether its relationship with audit quality follows a monotonic linear pattern or exhibits more complex nonlinear dynamics remains an open empirical question. Using a sample of 4,502 firm-year observations from the STAR Market and ChiNext Board during 2020–2024, this paper employs the modified Jones model to measure audit quality and constructs a comprehensive provincial business environment index encompassing four dimensions — government efficiency, legal environment, financial development, and market maturity — using the entropy-weight method. Through individual fixed-effects panel models, we systematically examine the impact of business environment on audit quality. The findings reveal three key insights. First, a significant inverted U-shaped nonlinear relationship exists between business environment and audit quality, with an inflection point at approximately 0.544. Second, ESG disclosure serves as a risk-moderating barrier rather than a mediating channel. Third, significant heterogeneity is observed across regions, industries, and firm life-cycle stages. This paper breaks the conventional assumption of a linear institutional-audit quality relationship and provides new empirical evidence and policy implications for audit regulation under the registration-based IPO system.

Data-driven Personalized Family Music Therapy: a Reinforcement Learning Recommendation Model Based on Family Member Behavior and Feedback

Fri, 21 Aug 2026 00:00:00 +0200

This study proposes a data-driven personalized family music therapy recommendation framework to address the limitations of conventional recommendation systems in modeling emotional interactions among multiple family members. The framework employs a reinforcement learning approach tailored to family environments, where emotional states evolve through continuous interpersonal interactions and feedback. A relationship-aware family state representation is developed by integrating behavioral observations and physiological responses, enabling the modeling of both individual emotional states and emotional contagion among family members. To support adaptive intervention, the action space incorporates music selection, spatial delivery strategies, and intentional silence decisions, allowing the system to balance active intervention with natural emotional self-regulation. In addition, a multi-channel reward mechanism is designed by combining physiological recovery, behavioral relaxation, and environmental stability indicators, while a temporal credit assignment strategy addresses delayed therapeutic effects. Experimental evaluation on a multimodal dataset covering four representative family stress scenarios demonstrates that the proposed framework achieves 89.7% accuracy in family emotion representation, 86.3% consistency with expert intervention judgments, and an 82.3% positive emotion conversion rate after continuous intervention. These findings suggest that reinforcement learning can provide an effective foundation for transforming music recommendation systems from individual entertainment tools into proactive family emotional support systems.