Appl. Syst. Innov., Volume 9, Issue 8 (August 2026) – 13 articles

To address the problems of complex process knowledge sources, heterogeneous representations, dispersed semantic associations, and limited reusability in the domain of machining distortion of thin-walled parts, this study proposes a knowledge graph construction method for the workpiece machining distortion domain, together with an intelligent decision-making framework driven by the collaboration of knowledge graphs and large language models. First, a domain ontology model is established around core concepts, including workpiece objects, deformation-driving factors, analytical resources, analytical methods, and optimization knowledge, thereby providing a unified semantic foundation for domain knowledge organization. Second, considering the characteristics of domain texts, such as dense technical terminology, ambiguous entity boundaries, and complex relation expressions, a dual-channel knowledge extraction method integrating BERT-BiLSTM-CRF and Universal Information Extraction (UIE) is developed to achieve high-precision extraction of entities and relations from unstructured texts. Knowledge fusion is further carried out through cross-validation, entity disambiguation, coreference resolution, and semantic alignment, and the extracted knowledge is ultimately stored and organized in Neo4j. Furthermore, an intelligent decision-making framework based on the collaboration of knowledge graphs and large language models is constructed. In this framework, a LoRA-tuned Qwen model is employed for user intent recognition and key information extraction, RapidFuzz WRatio is adopted for similar-node retrieval, and local subgraph construction, Label Propagation-based community detection, Betweenness Centrality-based key-node analysis, and evidence fusion are integrated to support process recommendation and intelligent question answering. Based on the proposed framework, an intelligent decision-making system is further developed for process recommendation and intelligent question answering in machining distortion scenarios. Experimental results show that the proposed dual-channel knowledge extraction model achieves an F1-score of 0.88, demonstrating its effectiveness in knowledge acquisition for the machining distortion domain. The constructed knowledge graph contains 4639 entities and 5822 relations, enabling a systematic representation of machining distortion knowledge. Case studies further demonstrate that the proposed method can generate interpretable recommendation results under complex process constraints in real industrial query scenarios. Overall, the proposed approach provides a feasible pathway for the structured organization, intelligent retrieval, and decision support of workpiece machining distortion knowledge. Full article

Tourism plays an important role in the global economy, contributing massively to the gross domestic product (GDP) and creating numerous jobs. The introduction of emerging technologies can accelerate the sector’s growth through personalization, improved user experience and better operational efficiency. The present work investigates the impact of visible light communications (VLC) in the tourism and hospitality industry based on the analysis of the recent literature published in the last decade, with the scope of improving tourist experience, operational efficiency and sustainability. Additionally, this work aims to critically evaluate the advantages and disadvantages of implementing VLC technology in the tourism and hospitality industry. For these purposes, this study presents a narrative review of the recent academic literature. The findings indicate that VLC technology can be used in a wide range of tourism-related applications, including contactless hotel services, indoor positioning and navigation, secure communications, accessibility solutions for visually impaired individuals and energy-efficient lighting infrastructure. In addition, this review demonstrates VLC’s potential to support the development of smart and sustainable tourism destinations through the integration of user-centered communication and illumination infrastructure. Finally, this work also identifies several challenges that affect large-scale deployment, including implementation costs, line-of-sight dependency and ongoing standardization issues. Full article

Product design constitutes an iterative process centered on user requirements. In this process, user requirements are collected, interpreted by designers and transformed into design objectives, which are then refined through solution generation and evaluation to yield the final product; this is typically an iterative human–computer interaction process. The intent understanding process converts vague user requirements into standardized expressions for designers. For complex products, existing intent understanding methods are hindered by excessive reliance on experience, lack of iterative mechanisms, and insufficient identification of critical performance attributes. To address these limitations, this paper proposes a computational methodology for element-based intent understanding, positioned as a foundational layer for future human–computer collaborative design systems. The concept of “elements” is introduced at three levels: function, performance, and structure—to aid in the standardization of design objectives. Relationships among elements are defined and used to construct design objectives, establishing a transformation model from elements to objectives. An iterative reconstruction method for element-objective transformation under design iteration is further proposed. The feasibility of this method is empirically validated via a reactor fuel-handling case study. Full article

This study presents a comparative analysis of the energy and thermal behavior of a Peltier module operating in hot environments (28 °C to 40 °C) using Python and MATLAB/Simulink. A theoretical block model was developed to define governing equations, while an empirical Python-based framework was implemented to capture real-world non-linearities. Results demonstrate that heat absorption is fundamentally dependent on efficient heat dissipation; a maximum coefficient of performance (COP) of 3.1 was achieved at 1 A. However, operation in hot environments necessitates increased current to maintain low absorption temperatures, leading to a critical “thermal runaway” threshold beyond 5 A where internal Joule heating (scaling quadratically) outweighs the Peltier cooling effect (scaling linearly). While both platforms effectively evaluate heat transfer, the Python-based empirical model provided a more realistic description of cold-side absorption with prediction errors as low as 0.14%. These findings offer a robust pathway for optimizing Peltier cooling with potential industrial applications. Full article

Flying Ad Hoc Networks (FANETs) composed of highly mobile Unmanned Aerial Vehicles (UAVs) are increasingly employed in mission-critical applications. However, the open wireless medium, high mobility, and resource constraints of UAVs expose FANETs to severe security and privacy threats, particularly at the Medium Access Control (MAC) layer. Existing authentication and key agreement schemes often suffer from high computational overhead and insufficient protection against advanced attacks. To address these challenges, this paper proposes a Lightweight Key Agreement Scheme (LWKAS) based on signcryption using Hyper Elliptic Curve Cryptography (HECC) and one-way cryptographic hash functions. The proposed scheme integrates MAC-layer security while simultaneously ensuring secure authentication, confidentiality, and location privacy through the Chinese Remainder Theorem (CRT). The security analysis demonstrates that LWKAS resists multiple attacks, including Man-In-The-Middle (MITM), replay, impersonation, Ephemeral Secret Leakage (ESL), cloning, desynchronization, and DoS attacks. The proposed scheme is implemented in NS-3 and evaluated against existing state-of-the-art methods in terms of authentication delay, communication overhead, and computational cost. The results demonstrate that LWKAS achieves significantly lower computational complexity and communication overhead while providing stronger security guarantees suitable for resource-constrained FANET environments. Full article

Extra-articular distal humerus fractures present surgical challenges, often requiring technically demanding posterior approaches with high radial nerve injury risks or off-label use of anatomically mismatched proximal plates. This study aimed to computationally design and optimize a novel anterior osteosynthesis plate for these fractures. A three-dimensional fracture model was developed, utilizing a parametric design exploration and finite element analysis to evaluate fourteen plate geometries. Evaluated variables included length, thickness, screw configuration, and locking mechanisms under physiological axial compression, bending, and varus loads. The analysis revealed that plate thickness primarily determines construct stability, with a four-millimeter profile optimally balancing rigidity and a low anatomical footprint. Lengths exceeding 40 mm yielded diminishing stability returns, while a dense distal locking screw configuration proved essential for maintaining fracture reduction. Compared to conventional clinical systems, the optimized plate substantially reduced the severe axial instability observed in extra-articular distal humerus plates and mitigated the critical bending stress concentrations inherent to proximal humeral internal locking systems. By achieving balanced, multi-planar stability with a minimized footprint, this novel design provides a favorable mechanical environment for secondary bone healing while facilitating a less invasive surgical approach. Full article

Optimal sizing and design of renewable microgrids and hybrid renewable energy systems require balancing renewable resource variability, demand uncertainty, storage operation, reliability, and techno-economic constraints. Artificial intelligence and metaheuristic optimization strategies have been increasingly used to address these challenges; however, the evidence remains methodologically heterogeneous. This scoping review maps the literature on artificial intelligence, learning-based, metaheuristic, heuristic, and optimization-based strategies for renewable microgrid sizing and design. The review followed PRISMA-ScR guidelines. Searches were conducted in Scopus and the Web of Science Core Collection for research articles published between 2009 and March 2026. A total of 69 studies were included. Metaheuristics dominated the field, appearing in 63 studies, with particle swarm optimization and genetic algorithm-based strategies as the most frequent methodological families. Artificial intelligence and learning-based strategies were mainly used to support forecasting, surrogate modeling, uncertainty handling, and energy management. The most recurrent configurations involved photovoltaic, wind, and battery storage systems, often with diesel backup in stand-alone or off-grid contexts. The literature is strongly oriented toward metaheuristic sizing of PV–wind–battery microgrids, with emerging integration of AI-assisted prediction and decision-support strategies. Future studies should address reproducibility, uncertainty modeling, real-world validation, degradation assessment, explainability, and scalability. Full article

Against the backdrop of increasing global uncertainty and the growing acceptance of sustainable development principles, enhancing supply chain resilience has become a core issue for enterprises in managing risks and ensuring operational security. Based on a review of the literature and theoretical analysis, this study constructs an evaluation system comprising 12 third-level indicators across three dimensions: proactive defense capability, green operational capability, and collaborative recovery capability. When determining whether there are interdependent relationships among the indicators, this study introduces an extended Bayesian fusion method based on trapezoidal fuzzy numbers to evaluate and confirm these relationships, thereby reducing biases arising from subjective judgments. By quantifying experts’ assessments of the relationship strength and confidence levels between indicators using trapezoidal fuzzy numbers, this method effectively integrates the opinions of multiple experts, reducing the randomness and subjectivity associated with individual judgments. During the ANP weight calculation stage, to overcome the ambiguity and uncertainty inherent in traditional pairwise expert comparisons, trapezoidal fuzzy numbers were similarly used to quantify the comparison results. These were then defuzzified using the mean area metric to construct a precise judgment matrix. Finally, using the publicly available annual reports and ESG disclosure data from three multinational corporations—one in the semiconductor manufacturing sector (Company T), one in industrial digital manufacturing (Company S), and one in the food and beverage industry (Company N)—as empirical samples, the cross-industry applicability and validity of the constructed evaluation system were verified. The results demonstrate that this method can systematically reflect the key factors influencing sustainable supply chain resilience and their weighting structure. Full article

Precision agriculture and sustainable farming practices require autonomous environmental monitoring systems capable of operating in remote areas with limited energy and connectivity. However, the high cost of existing professional technology remains a significant barrier to widespread adoption. This study presents the development of a solar-powered smart sensing node designed for autonomous operation that integrates TinyML and dual-mode wireless connectivity via LoRaWAN and Wi-Fi for intelligent monitoring. The system features a custom-designed cup anemometer and multispectral sensing capabilities integrated into a compact single-tower architecture. All structural components, including radiation shields and a modular PVC frame, were designed for low-cost manufacturing and mass production. A single hermetic housing protects the core control electronics and is designed to improve durability in harsh outdoor environments. A Multi-Layer Perceptron model was implemented on the edge to enable intelligent data fusion and compensation, while a dynamic sampling strategy optimized power consumption. Experimental results demonstrate the feasibility of the proposed architecture through adaptive spectral acquisition over a daily illumination cycle, embedded MLP-based sensor fusion, and telemetry-oriented data compression that substantially reduces the number of transmitted samples. The main contribution of this work is a system-level architecture that integrates sensing, embedded intelligence, solar-energy harvesting, hybrid wireless communication, and telemetry optimization into a compact, low-cost, and field-deployable prototype IoT platform for sustainable precision agriculture. Full article

The misalignment between workforce competences and the requirements of digitally evolving occupations is a critical barrier to SME competitiveness. This study’s primary contribution is theoretical and methodological: it reconceptualizes the workforce skill gap as a firm-level human-capital–technology complementarity constraint rendered observable and commensurable through the ESCO taxonomy, and abstracts four transferable design principles—commensurability, macro–micro integration, a transferable metric, and modular extraction. Drawing on human capital theory, the knowledge-based view, and skill-biased technical change, the framework maps anonymized employee CVs to ESCO occupational requirements through a deterministic natural language processing procedure and computes a Skill Gap Indicator as the complement of evidenced competence coverage. A prototype Intelligent Learning Management System, developed within the LUCE project, instantiates the framework as a proof of concept, translating identified gaps into targeted training recommendations. Applied to a convenience sample of publicly available professional profiles, the indicator has a mean of 0.956, interpreted as a conservative upper-bound estimate rather than a literal deficit. The empirical results are an exploratory demonstration that motivates, rather than confirms, the posited link between skill gaps and firm performance; a cross-sectional test found no significant association, which the design cannot adjudicate. Confirmatory testing would require sample expansion, employer-provided workforce records, and a longitudinal design, identified as priorities for future research. The study thus contributes a standardised, interoperable, and transferable approach to measuring and comparing workforce skill gaps in SMEs. Full article

This paper addresses the stabilization and drive–response synchronization of the four-dimensional hyperchaotic finance model using active backstepping (ABS) and active control (AC). The contribution is not the introduction of a new control paradigm but a unified implementation of AC and ABS for the Yu finance model, together with explicit Lyapunov convergence estimates, reproducible numerical benchmarking, and robustness-oriented performance assessment. For the ideal full-state-feedback setting, Lyapunov arguments establish exponential stabilization for the ABS-controlled system and exponential synchronization for both AC and ABS. The numerical protocol quantifies settling time, norm-relative overshoot, envelope-based decay rate, integrated control energy, CPU time, and actuator peak/RMS values. The results show that AC provides smooth and energy-efficient synchronization, whereas ABS gives fast convergence and a nonlinear Lyapunov-based stabilization framework but requires higher actuation effort. Robustness tests under parameter mismatch and additive measurement noise indicate bounded trajectories and decaying synchronization errors under the tested perturbation levels. The results also clarify the trade-off between convergence speed, control energy, implementation complexity, and actuator feasibility. Full article

Standalone use of EAHEs for room cooling is a passive and nature-based alternative to air conditioning technology that can be used to mitigate the increase in electricity and GWP-refrigerant consumption associated with cooling in buildings. EAHEs replacing air conditioning is documented in the technical and research literature. However, for office-room cooling, EAHEs are mostly employed as a support to air conditioning systems for precooling outdoor air. The larger cooling loads and the stricter design conditions commonly used in the sizing of office rooms prevent the most commonly investigated EAHE typologies from operating effectively in standalone cooling mode. To assess the feasibility of alternative typologies, such as large-diameter EAHEs, tools that are capable of modeling the complexity of the coupled heat and moisture transfer between air and soil are particularly valuable. For detailed assessments, researchers typically turn to advanced commercial tools; however, developments in free and open-source scientific programming languages that combine symbolic computation packages with efficient numerical solvers of partial differential equations allow analyses at reduced cost that are comparable to those from commercial tools. This paper shows how one such programming language can be used to study the coupled heat and moisture transfer problem in EAHEs. Starting from the symbolic form of the mathematical problem, the numerical implementation is described and validated with monitoring data from an existing large-diameter EAHE. Using the validated computational model, the paper proceeds to study the sensitivity of load removal in EAHEs operating in standalone and precooling cooling modes, highlighting fundamental differences between both operating modes, identifying the most relevant design parameters and providing guidance on the conditions under which an EAHE enables self-sufficient cooling of office buildings. The results show how, for a hot and dry climate, standalone EAHEs with large diameters (∼1 m), buried at depths larger than 3 m, allow the removal of up to 20 kWh/m² of room sensible cooling loads, a level that is consistent with the cooling demand of low-energy office buildings. Full article

Accurately identifying the functional characteristics of urban rail transit stations and classifying them accordingly helps uncover passenger flow patterns and optimize resource allocation, thereby enhancing the coordination efficiency of multimodal urban transportation systems. Existing studies on the delineation of station influence areas often exhibit overlapping zones, leading to insufficient characterization of regional heterogeneity. Additionally, classification methods predominantly rely on static single indicators and lack integration of multi-scale features. To address these limitations, this paper proposes a non-overlapping zoning algorithm for precisely defining station influence areas. By incorporating multidimensional indicators—including dynamic passenger flows, resident attributes, connection characteristics, and spatial distribution—a fine-grained station classification model is developed using an enhanced Partitioning Around Medoids (PAM) algorithm. Building on the classification outcomes, a dual-scenario framework (weekday vs. weekend) is established, and Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) models are applied to analyze the spatiotemporal patterns of passenger flows. A case study of Beijing rail transit stations demonstrates that the enhanced PAM algorithm significantly improves clustering performance. Four distinct station types are identified on weekdays: Peripheral Basic-Service Type, Core Commuting-Aggregation Type, Exurban Residential-Transit-Dependent Type, and Multifunctional-Complex Type. On weekends, stations are classified into three categories: Peripheral Living-Service Type, Core Leisure-Vitality Type, and Central Mixed-Use Type. Furthermore, the driving factors of passenger flows exhibit notable spatiotemporal heterogeneity: on weekdays, commuting demand dominates, with jobs–housing ratio, educational attainment ratio, and road network density serving as core positive factors; on weekends, leisure demand becomes prominent, showing strong synergistic effects among jobs–housing ratio, Points of Interest (POI) density, and road network connectivity. The research findings provide theoretical support for the functional classification and refined management of rail transit stations. Full article