International Journal of Advanced AI Applications

Research on the Impact of Artificial Intelligence on Human Resource Management in Listed Enterprises and Countermeasures

2026-03-24T07:04:07+00:00

The integration of artificial intelligence into human resource management has accelerated significantly, particularly within listed enterprises that face unique pressures from investors, regulators, and public scrutiny. Drawing upon the resource-based view, signaling theory, and sociotechnical systems theory, this study develops a comprehensive theoretical framework to examine the multifaceted impact of AI on HRM in listed enterprises. The analysis identifies three primary impact pathways: algorithmic decision-making in recruitment and selection, predictive analytics in performance management and retention, and automation in HR service delivery. The study further explores organizational implications, including structural changes within HR functions, evolving workforce skill requirements, and transformations in employee-employer relationships. In response to these challenges, countermeasures are proposed across four domains: strategic alignment of AI with HR objectives, ethical governance frameworks, workforce reskilling initiatives, and hybrid human-AI work design. The framework provides theoretical contributions to the AI-HRM literature and offers practical guidance for executives, HR leaders, and boards in listed enterprises seeking to leverage AI for competitive advantage while managing associated risks.

Graph Neural Networks for Surface-State Prediction in Topological Semimetals: Principles, Architectures, and Emerging Applications

2026-03-20T01:17:44+00:00

Topological semimetals—encompassing Weyl semimetals, Dirac semimetals, and nodal-line semimetals—host surface states whose existence is guaranteed by bulk topology rather than surface termination details. Predicting these surface states accurately and efficiently is essential for connecting theoretical classifications to experimental observables such as ARPES spectra, quantum oscillations, and anomalous transport coefficients. Classical first-principles approaches are accurate but computationally expensive; they struggle to scan parameter spaces, disorder effects, and heterostructure geometries at scale. Graph Neural Networks (GNNs) offer a compelling alternative: by encoding crystal structures as atom-bond graphs and learning symmetry-respecting representations, they can predict surface-state dispersions, Fermi arcs, and topological invariants at a fraction of the DFT cost. This review systematically examines the theoretical underpinnings of topological surface states, the design principles of GNN architectures suited to this task, benchmark comparisons with DFT, and case studies spanning Weyl semimetals, nodal-line systems, and magnetic topological semimetals relevant to spin transport. We identify open challenges—including disorder, strong correlations, and finite-temperature dynamics—and propose directions for next-generation models.

A Multi-Strategy Method for Spot Center Localization and Error Measurement in Engineering Applications

2026-02-09T14:49:50+00:00

To address the challenges of existing automatic spot-center positioning methods in engineering applications, namely the difficulty in simultaneously achieving real-time performance, positioning accuracy, and detection stability, this study proposes a multi-strategy approach for spot-center positioning and error measurement. This method integrates the center of mass approach, elliptical fitting, and two-dimensional Gaussian fitting. It selects strategies based on real-time and accuracy constraints for different detection scenarios, while introducing a unified error measurement model for the quantitative evaluation of positioning results. By establishing a pixel-to-physical coordinate mapping, a systematic evaluation of spot center localization and aiming error was achieved using metrics such as the root mean square error. Experiments demonstrated that under both static and dynamic conditions, this method exhibits excellent robustness and stability, meeting the comprehensive requirements for accuracy and real-time performance in engineering applications.

Clinical Study of Battlefield Acupuncture for Acute Pain and Edema Management in Distal Radius Fractures within an Emergency Setting

2026-03-20T16:37:36+00:00

Distal radius fractures (DRF) are highly prevalent traumatic injuries often complicated by severe acute pain and prolonged edema, which hinder early functional recovery and increase healthcare burdens. This prospective, randomized controlled trial evaluated the efficacy of Battlefield Acupuncture (BFA) as an adjunct therapy for 64 patients with DRF during the acute edema phase. Participants were randomly assigned to either a BFA group (n=32), receiving auricular press needles at five specific points combined with standard manual reduction and splinting, or a Control group (n=32) receiving standard care alone. Results demonstrated that the BFA group achieved a superior analgesic effect, with Visual Analogue Scale (VAS) scores decreasing by over 50% within 72 hours post-intervention. Additionally, the BFA group showed significantly faster edema resolution, with the swelling index decreasing by 35.0 ± 4.2% within 72 hours (P=0.003), accompanied by a marked increase in serum β-endorphin levels (+42.3 ± 5.8 pg/mL, P<0.01) and reduced Substance P. No severe adverse events were observed, and the BFA group reported higher patient satisfaction and optimized medical resource utilization. In conclusion, BFA is a safe, effective, and economically feasible intervention that enhances acute symptom control and the early recovery trajectory of patients with distal radius fractures.

Design of Intelligent Disinfection Doormat System Based on STM32

2026-03-10T14:11:58+00:00

At present, disinfection at the entrances and exits of public places relies heavily on manual operation. To improve the disinfection efficiency and intelligent level of areas with high-frequency personnel flow, this study adopts a modular design method and a centralized control strategy of the main control chip. Taking the STM32 chip as the system’s core control unit, it integrates a stress sensing module, an alcohol atomization disinfection module, an OLED status display module, a Bluetooth communication module and a buzzer prompt module. The system structure is optimized via modular division and function integration, with reasonable control logic and execution process designed to achieve coordinated and stable operation of each module. Finally, a physical prototype is fabricated and performance tests are conducted. Test results demonstrate that the system offers fast response, stable operation and high disinfection efficiency, enabling automatic induction disinfection. It is suitable for high-traffic public places and exhibits excellent practicality and promotional value.