Applied System Innovation

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

As generative AI technologies become increasingly embedded in educational cloud platforms, understanding their impact on teacher professional development is essential. Grounded in the stimulus–organism–response (S–O–R) framework, this study investigates how AI-driven stimuli—hedonicity, interactivity, and immersion—influence teachers’ self-regulation and resilience. Using structural equation modeling, data were collected from in-service teachers in Taiwan who actively utilize educational cloud platforms. The results reveal that all three AI-driven stimuli significantly enhance teachers’ self-regulation and resilience, which in turn are significantly associated with perceived value co-creation intentions. Specifically, self-regulation enables teachers to manage goals effectively, while resilience supports their recovery from technical setbacks. The findings indicate that self-regulation positively influences resilience, and both appear to mediate the relationship between perceived AI-driven stimuli and teachers’ value co-creation intentions. This study highlights the potential role of teachers’ psychological adaptability in AI-enhanced environments. Practical implications suggest that platform developers and administrators should prioritize AI features that foster self-directed learning and emotional engagement to promote collaborative willingness and professional alignment within modern educational ecosystems rather than implying proven macro-level transformation. Full article

To address the challenges of data security governance under new conditions and align with technological trends in data security, this paper proposes a graph algorithm-based method for identifying key personnel with critical permissions in the practical applications of large state-owned enterprises. This method can uncover potential permission influence factors within the system and evaluate the weight of influence from different perspectives, providing highly interpretable identification results. To tackle the issue of detecting anomalous user and entity behaviors in data security governance, a user and entity behavior anomaly detection method based on Generative Adversarial Networks (GAN) is introduced. Experimental results show that the proposed method achieves higher precision, recall, and F1-score averages compared to baseline models; specifically, an average F1-score of 0.75 versus 0.72 for LSTM-based TadGAN, 0.62 for ARIMA, and 0.65 for a commercial UEBA baseline across the three evaluation datasets. A data security platform was designed and developed, which plays a significant role in reducing data security risks, assisting enterprise compliance, and promoting data development and utilization. This platform has been applied in various centralized data management projects and meets the big data processing requirements in secure environments, demonstrating strong application and promotional value. Full article

Facility Layout Planning (FLP) remains a complex task for manufacturers seeking to improve productivity, reduce daily operating costs, and stay competitive in fast-changing markets. Traditional methods such as Systematic Layout Planning (SLP) offer useful guidelines for designing department layouts but still rely heavily on judgment and experience. At the same time, modern optimization and simulation techniques provide valuable quantitative insights. These techniques are often used separately rather than as part of an integrated process. In this work, a hybrid layout-planning approach that combines these techniques is developed and validated through an industrial case study, providing a practical decision-support process for facility layout planning. The process starts with SLP, which develops an initial layout using activity relationship charts, material-flow analysis, and handling-cost estimates. A simulation model then evaluates throughput, machine utilization, and work-in-progress, providing early indications of the layout’s real-world performance. A Genetic Algorithm (GA) is used to find improved configurations that reduce distances and costs. The optimized layouts are further tested through simulation. To demonstrate practical use, the framework was applied at a transformer manufacturing plant. It resulted in an approximately 35% reduction in material-handling costs. The results show that the optimized layout reduced material-handling costs from 7062.5 to approximately 4560 L.E. per transformer while increasing monthly throughput by 2.46% (approximately 11 transformers per month). Additionally, a what-if analysis was performed to identify opportunities for improvement, such as increasing production by using an automatic laser-cutting machine. The findings support data-driven decisions in facility layout design and long-term operational planning. Full article

This article develops a structural optimization algorithm for designing intelligent and adaptive radio communication systems. In modern tactical crisis operations characterized by high environmental dynamics and intense electromagnetic countermeasures, as well as in emergency response networks, static communication planning approaches prove increasingly ineffective. The paper introduces a stable normalization method for performance estimates and a mechanism for rapid algorithmic adaptation to shifting operational priorities. A key feature of the proposed approach is an adaptive objective function based on the weighted sum of normalized criteria for survivability, noise immunity, and reliability. The use of weighting coefficients enables rapid adjustment of optimization priorities in response to extreme conditions, whilst the application of a normalization method based on threshold values ensures the stability and comparability of evaluations. It is practical that the proposed algorithm will enable the synthesis of functionally stable and robust structures, thereby enhancing the reliability of command and control of forces and assets in both mission-critical operations and civil crisis situations. Full article

During the recruitment process, it is possible for CVs to appear well-organized. However, it is not always straightforward to compare them. The same competence may be denoted by different designations, and the levels of competence are not universally employed in the same manner. Natural Language Processing (NLP) methodologies can extract these data points; however, ensuring the consistency of this data across multiple CVs remains a challenge. In a multitude of cases, the comparability of two profiles remains ambiguous. In the present study, an ontological approach is adopted to solve this issue. The concept under discussion is that of the extraction of entities from CVs and their subsequent representation in a more structured form, utilizing RDF and an ontology aligned with ESCO—the multilingual classification of European Skills, Competences, and Occupations. Subsequently, the rules of SHACL are applied to verify the semantic coherence of the data; the validated data are transmitted to a model for classification. At this stage, the dataset becomes smaller, but semantically cleaner, more traceable, and enriched with validation indicators that can be used by the classification model. The proposed system is implemented as a set of microservices. A Spring Boot component coordinates the flow, whilst the Python services, implemented using Python 3.10.12 are responsible for the primary processing stages including extraction, validation and classification. A same-corpus ablation was conducted to separate ontology-guided profile selection from the contribution of the validation-derived quality features. On the same 35,770 filtered CV–job pairs, adding these features increased external benchmark accuracy from 0.794 to 0.809, recall from 0.760 to 0.865, F1-score from 0.749 to 0.786, and ROC-AUC from 0.881 to 0.887. A p-value of 0.00540 paired with a 1.54 effect ratio from McNemar’s test showed a statistically significant paired difference between the two configurations. However, precision decreased from 0.739 to 0.720 while Average Precision compressed from 0.851 down to 0.844. Rather than scaling performance uniformly across the entire evaluation suite, the ontology layer acts as a targeted traceability and semantic refinement filter that contributes information beyond filtered-profile selection alone and produces a metric-dependent change in classifier behaviour at the validation-selected threshold. Full article

This study presents an integrated design-to-operation framework for a PV–battery solar highway lighting system supported by IoT-based monitoring for highway-scale deployment. The proposed framework combines pole-level PV-battery sizing, annual energy-reliability simulation, road-level techno-economic optimization, and IoT-based digital monitoring. The Shoubra–Banha Freeway in Egypt, a 40 km corridor with a two-sided lighting arrangement, was selected as the case study. In the pole-level phase, dimming strategies, PV capacities, battery sizes, and battery technologies were evaluated using sequential parametric analysis under a reliability constraint of Loss of Load Probability (LLP) below 1%. The S2 aggressive dimming profile achieved the best operating performance, with an LLP of 0.003, energy reliability of 99.70%, and 2.89 kWh annual unmet load. The minimum feasible PV capacity was 0.8 kW, while the smallest acceptable storage capacity was 4.8 kWh nominal capacity, corresponding to approximately 3.84 kWh usable capacity under an 80% allowable depth of discharge. Among the tested battery technologies, LiFePO₄ achieved the best reliability performance, with an LLP of 0.007 and a 99.25% battery deficit coverage ratio. In the road-level phase, three deployment configurations were compared. Case B, using 12 m pole height and 36 m spacing, was selected as the best-balanced solution, requiring 2224 poles, achieving 99.30% energy reliability, an LLP of 0.007, annual PV generation of 3,178,341 kWh, and total CAPEX of approximately 2.88 × 10⁸ EGP, equivalent to about 5.76 million USD based on an assumed exchange rate of 1 USD = 50 EGP. Lastly, the development of an IoT-based monitoring system design utilizing sector gateways, telemetry variables, alarm conditions, MQTT protocols, and dashboard displays was carried out. The scenario for gateways at 5 km intervals was advised due to its better fault isolation, lower gateway workload, and scalability. This indicates that the suggested approach offers a viable, cost-efficient, and technologically enabled solution for automated solar-powered street lighting systems. Full article

The increasing adoption of collaborative robotics is reshaping the relationship between technology, organization, and human work in industrial environments. Within the transition from Industry 4.0 to Industry 5.0, Humanized Robots (HuRs) are understood not as a distinct technological category, but as a human-centered interpretation of existing collaborative robotics and human–robot collaboration configurations, emphasizing proximity, adaptability, transparency, ergonomic support, and socio-technical integration. However, the literature on human–robot collaboration remains fragmented across technical-regulatory, human factors, and organizational perspectives, with limited attention to how risk assessment outputs are translated into operational practices and decision-making support over time. To address this gap, this paper proposes a preliminary conceptual socio-technical framework for the safe integration of HuRs in industrial human-centered systems. The framework is structured around five interdependent operational levels: hazard identification, risk measures, validation and testing, KPI-based monitoring, and operational control. At its core, the framework adopts and extends the concept of safety readiness, defining it as the organizational capability to translate, sustain, and update risk-related decisions across the system lifecycle while preserving alignment between assessed risk and managed risk under changing operational conditions. The framework, its KPI-based operationalization, and the scenario-based application are conceptual and illustrative in nature. Their operational validity remains to be established through empirical studies in real HuR/HRC settings. This paper’s core contribution lies in formalizing the integration layer through which risk assessment outputs are translated into validation, KPI-based monitoring, and operational-control decisions across the system lifecycle, with safety readiness serving as the bridging construct between assessed risk and managed risk. This paper contributes by reconceptualizing safety in HuR systems as a dynamic and lifecycle-based property, formalizing the integration layer through which risk assessment outputs are translated into validation, KPI-based monitoring, and operational-control decisions across the system lifecycle, with safety readiness serving as the bridging construct between assessed risk and managed risk. Full article

The rapid digitalization and decarbonization of electrical power systems have brought increased operational complexity and new occupational risk dynamics. This transition renders traditional compliance-based safety models inadequate for managing the emerging complexities of cyber–physical and socio-technical systems. This paper develops a conceptual socio-technical safety architecture for occupational risk management in electrical power systems, grounded in the concepts of systems innovation and socio-technical modeling. A structured narrative review of international standards, accident investigations, and emerging technologies is conducted to reinterpret hazards as interacting subsystems within a dynamic, adaptive framework. The proposed framework synthesizes technical safety controls, human reliability factors, and artificial intelligence-driven predictive maintenance within a single architecture, supported by dynamic feedback loops. The model addresses nonlinear risk propagation across smart grid applications, hydrogen systems, and battery energy storage systems. By transitioning from a reactive to a proactive, adaptive approach to safety governance, the architecture enhances the resilience of electrical power systems, reduces the potential for cascading failures, and aligns occupational safety with infrastructure modernization strategies for electrical power systems. The framework provides a conceptual basis for integrating technology innovation with occupational risk management across complex energy infrastructures undergoing digital transformation. Full article

The increasing influence of social media has amplified the risks associated with automated accounts that spread misinformation, manipulate public opinion and carry out malicious activities. To address this challenge, this study presents an explainable, feature-based approach for detecting social bots on $\mathbb{X}$ (formerly Twitter) using user-profile information derived from account metadata and content characteristics. We consolidate and extend existing research by bringing together one of the most comprehensive feature sets explored to date, combining raw attributes, features proposed in the literature, and newly introduced credibility and engagement indicators, together with a previously unexploited profile-personalisation signal. Through a feature engineering and selection process that integrates Mutual Information, Random Forest Importance, and SHAP values, we evaluate the contribution of each feature category and assess its generalisation capacity across three benchmark datasets. Our experiments demonstrate that classical machine learning models enriched with the selected features can match or surpass several state-of-the-art approaches while preserving interpretability. Furthermore, we propose and validate, on the more recent and challenging TwiBot-22 dataset, three categories of features (universal, common, and dataset-specific) that provide a transparent and adaptable basis for generalisable bot detection. Full article

Structural equation modeling (SEM) studies commonly derive constructs and paths from the manually reviewed literature. Expert judgment remains essential, but incomplete coverage and undocumented selection decisions can make this stage difficult to evaluate. We therefore develop a theory-guided, AI-assisted procedure for generating SEM candidate models from systematic literature evidence. PICOC defines the scope, and queries identify the primary records. Backward citation searching adds foundational studies, whereas forward searching adds recent applications. Sentence-BERT and BERTopic are then used to examine semantic structure. Topic terms, representative documents, concept evidence, and theoretical criteria inform the mapping from topics to candidate constructs. The retained constructs are assigned possible SEM roles and assembled into candidate paths. The result is a documented front-end method for candidate model development, not an empirically validated SEM model. Full article

Railway logistics terminals are crucial nodes in the national logistics system. Prior to the market-oriented reform of railway logistics, the warehousing operations at these stations primarily focused on temporary storage services before and after shipment, making it difficult to provide customers with integrated warehousing and transportation logistics services. With the advancement of railway marketization reforms, railway logistics terminals have gradually begun to offer socialized warehousing services, acquiring the capability to provide integrated warehousing and transportation services. In response to market development needs and the requirements for green development in railway logistics, an optimization design model for obtaining green railway logistics solutions is established, considering factors such as carbon emission costs, integrated warehousing and transportation logistics service costs, fuzzy constraints on logistics network capability, transportation time windows and the customer’s risk tolerance level. The model takes minimizing carbon emission costs and railway logistics service costs as dual-objective functions and uses a standardized weighting method to convert the dual-objective functions into a single-objective function for solving using triangular fuzzy numbers to characterize the ability constraints of network nodes, making the model more realistic. This model aims to minimize these costs and assesses the impact of factors such as changes in delivery time limits, shipment quantity, shipment batches, the superposition of multiple goods batches and customer preferences on the railway logistics solution for different scenarios. Research indicates that reasonably designing delivery time limits and aligning shipment times with railway transportation time windows can effectively reduce carbon emissions and logistics costs, and the risk tolerance level has a significant impact on the reliability of railway logistics solutions. Full article

Driver fatigue is a major cause of road accidents, and in-cabin monitoring is increasingly embedded into the Internet-of-Things (IoT) ecosystem of modern vehicles. Deploying such monitoring directly on microcontroller-class devices is challenging: models must fit tight memory and compute budgets, provide reliable confidence estimates, and adapt online to new drivers and conditions. We propose KAN-CLUE, an uncertainty-aware continual TinyML framework for driver fatigue detection from near-infrared periocular images at the IoT edge. KAN-CLUE combines a compact convolutional backbone with a Kolmogorov–Arnold Network (KAN) classification head that outputs Dirichlet-distributed class probabilities and a principled predictive uncertainty measure. A lightweight activation-histogram mechanism provides an additional out-of-distribution (OOD) score, and both signals drive an on-device continual learning scheme that selectively updates a small subset of parameters under a KAN-specific EWC-style regularization. On the ULg DROZY drowsiness database, the quantized KAN-CLUE model uses roughly 167k parameters (about 165 kB in Flash), requires on the order of ${10}^6$ MACs, and achieves around 3.1 ms latency on a Cortex-M–class microcontroller, while reaching 97.7% test accuracy with improved calibration and OOD detection compared with softmax-based TinyML baselines. Full article

The rapid growth of digital transformation (Dx) initiatives across sectors has created an urgent need for structured, scalable, and accurate management of project knowledge. Without effective organization, valuable insights from Dx projects remain fragmented, limiting their reuse and hindering informed decision-making. This research addresses the gap by designing, developing, and validating Dx-Onto, a domain-specific core ontology implemented in OWL and purpose-built for representing and managing knowledge about Dx projects. Dx-Onto models entities, relationships, and attributes from diverse project documentation into a unified knowledge graph, enabling semantic search, cross-project analysis, and context-aware retrieval. To assess performance, a two-pronged evaluation strategy was adopted: (1) scalability experiments using synthetic datasets measured query execution times across volumes ranging from 10 to 1000 projects, and (2) a comparative benchmark against the Core Ontology of Organisational Transformation (COOT) was conducted using a heterogeneous real-world corpus of Thai digital transformation documents. The results confirm Dx-Onto’s capacity to scale and demonstrate a higher domain fit (85.2% vs. 77.3%) and superior analytical utility—including transformation-phase and strategic-dimension diagnostics that are structurally impossible under a general-purpose baseline. By positioning Dx-Onto as the core semantic layer for a future Hybrid LLM-Ontology framework, this work lays the groundwork for intelligent, scalable, and reliable knowledge management solutions in the digital transformation domain. Full article