Applied System Innovation

This study investigates the realization and verification of safety-related functions of the KZ-MPC-MA national modular microprocessor-based railway interlocking system for Kazakhstan. The work focuses on the realization stage of the IEC 61508 safety lifecycle and establishes traceability between station-specific safety requirements, interlocking algorithms, software implementation, hardware–software integration, and functional-safety assessment. The implemented interlocking logic incorporates 29 traffic safety conditions defined for the considered station configuration. A fail-safe control architecture based on central and distributed controller modules was implemented using certified safety-related industrial controllers and the SILworX development environment. A laboratory prototype was developed to verify route-setting functions, switch and signal control, and fail-safe system response under specified operational and failure conditions. Quantitative functional-safety assessment was performed using the probability of failure on demand ($PF{D}_{\mathrm{avg}}$) as a supplementary measure and the probability of dangerous failure per hour ($PFH$) as the governing criterion for continuous/high-demand operation within the defined assessment boundary and assumptions. The obtained $PFH$ demonstrates that the investigated safety-related controller subsystem satisfies the specified SIL4 quantitative criterion. The study demonstrates the feasibility of realizing safety-related interlocking functions on the selected hardware–software platform and provides a basis for further system-level verification and validation. Full article

The use of satellite imagery to monitor the Sustainable Development Goals is increasingly common, driven by advances in remote sensing and deep learning. Introducing remote sensing into academic graduate programs at maritime institutions not only follows the current state of the research field but also raises awareness of the opportunities this technique offers for sustainable development and ecological research. For instance, remote sensing enables monitoring of ships discharging oil. A multidisciplinary approach is necessary for the highest-level research results, so to apply scientific methods effectively, an expert in the legal aspects of artificial intelligence also participated in this research. In this paper, we introduce remote sensing into seafarers’ education at the graduate level, which is now incorporated as a new course. The results indicate a high level of student engagement and demonstrate that integrating remote sensing technologies into maritime education could enhance students’ technical competencies and environmental awareness. The findings of this study highlight the potential of satellite remote sensing as an educational tool for supporting sustainability-oriented maritime education and provide practical insights for integrating emerging technologies into higher education curricula, providing practical insights for integrating emerging technologies into higher education curricula while establishing a transferable blueprint for bridging advanced data sciences with compliance-driven vocational training. Full article

Millimeter-wave (mm-Wave) massive MIMO systems enable high data rates but face significant challenges due to the limited number of radio-frequency (RF) chains and imperfect channel state information (CSI). This paper proposes a deep learning-based beamforming (DLBF) framework that directly learns analog beamforming vectors under strict hardware constraints. Unlike conventional optimization methods, the proposed approach employs an unsupervised learning strategy to maximize spectral efficiency while satisfying constant modulus constraints. The network model is explicitly designed to be robust against imperfect CSI, hardware phase noise, and varying channel conditions. Simulation results demonstrate that the proposed DLBF framework significantly outperforms traditional hybrid beamforming methods in terms of spectral efficiency, particularly in low-quality channel estimation and low signal-to-noise ratio (SNR) scenarios. Full article

Navigating large, unfamiliar campuses can be challenging for visitors, even with campus maps available. To address this issue, this study proposes an intelligent, autonomous campus navigation system to help users reach their destinations efficiently. The proposed system integrates computer vision, LiDAR, speech recognition, global positioning, and path-planning technologies to provide accurate, user-friendly guidance in complex environments. A YOLO-based neural network performs real-time building recognition, while a speech recognition module interprets users’ spoken destination requests and commands. GPS data are mapped to campus map coordinates to improve localization accuracy, and Dijkstra’s algorithm computes optimal navigation paths. All components are integrated into a graphical user interface that provides real-time visual feedback, including recognized building names and current location. Experimental results demonstrate that the proposed system achieves reliable building recognition, accurate speech understanding, and effective route planning, significantly reducing navigation time for users unfamiliar with the campus. The proposed framework not only enhances smart campus navigation but also shows strong potential for extension to other large-scale environments such as hospitals and shopping malls. Full article

Electrical induction generators are vulnerable to winding faults that can degrade operational reliability and lead to unplanned maintenance. This study proposes a multiple-convolutional neural network (Multi-CNN) extreme ensemble learning framework for intelligent fault diagnosis and maintenance decision support using three-phase current and voltage measurements. Experimental recordings representing healthy operation, inter-turn faults, and inter-winding faults were segmented into non-overlapping 200-sample windows. Hjorth activity, mobility, and complexity were calculated for the three-phase current signals and the three-phase voltage signals, producing 18 features for each of 900 instances. Four convolutional neural network architectures were trained, and their class-probability outputs were combined through an extreme learning machine. Stratified blocked five-fold cross-validation was used to evaluate the models while preserving the chronological structure of the data. The proposed ensemble achieved 98.111% accuracy, 98.146% precision, 98.111% recall, 98.108% F1-score, and 97.167% Matthews correlation coefficient, correctly classifying 883 of 900 out-of-fold instances. It also attained a macro-averaged area under the receiver operating characteristic curve of 0.995. These results demonstrate that Hjorth-based electrical-signal characterization and Multi-CNN ensemble fusion can provide accurate and computationally efficient support for fault identification and predictive maintenance decisions in electrical induction generators. Full article

Cancer incidence exhibits substantial spatial disparities associated with environmental, behavioral, built-environment, healthcare access, and socioeconomic conditions, yet the extent to which these county-level associations vary geographically and across spatial scales remains insufficiently understood. This study evaluates an explainable spatial epidemiology workflow that integrates Random Forest (RF), SHapley Additive exPlanations (SHAP), permutation importance, Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) to examine county-level incidence for all-site cancer as a composite benchmark, colorectal cancer, female breast cancer, and melanoma of the skin across Texas, USA. All outcomes were screened from a common leakage-safe pool of 44 predictors. RF-SHAP/permutation screening was conducted only in spatially separated discovery counties, and the retained predictors were subsequently evaluated using OLS, GWR, and MGWR in independent confirmation counties. The screening procedure retained 19–23 predictors across outcomes, reducing model dimensionality by approximately 48–57%. Although screening substantially reduced AICc, in-sample also decreased, indicating improved parsimony and complexity-adjusted fit rather than improved explanatory performance; same-cardinality random, correlation-based, and LASSO benchmarks further showed that the advantage of RF-based screening was outcome- and model-dependent. In independent confirmation analyses, GWR was preferred by AICc for all-site cancer (AICc = 442.79), whereas OLS was preferred for colorectal cancer (356.48), female breast cancer (367.50), and melanoma (234.56); MGWR was not AICc-preferred for any outcome. However, where estimation was feasible, MGWR provided complementary multiscale information by distinguishing fitted associations characterized by near-global versus more localized spatial bandwidths. Five-fold nested spatial-block cross-validation showed stronger geographic predictive performance for RF, with pooled values of 0.422, 0.235, 0.442, and 0.341 for all-site, colorectal, breast, and melanoma outcomes, respectively, whereas GWR produced negative held-out for all four outcomes. These findings support explainable-ML screening primarily as a transparent dimensionality reduction strategy and demonstrate complementary roles for spatial modeling: AICc evaluates whether additional spatial complexity is justified, MGWR characterizes predictor-specific spatial scales where feasible, and spatial-block validation evaluates geographic predictive generalization. The resulting associations and spatial scales are interpreted as ecological and descriptive rather than causal. Full article

A reproducible design evaluation system is developed in this study by integrating the grey statistical method (GSM), analytic hierarchy process (AHP), and fuzzy comprehensive evaluation (FCE). Eight first-level and 40 second-level indicators were identified, covering product positioning, appearance features, function, materials, process structure, ergonomics, interactive experience, and economic efficiency. Through GSM screening, the indicator set was refined, and AHP weighting results confirmed logical consistency. Product positioning (weight = 0.190), appearance features (0.165), and interactive experience (0.151) were the most influential first-level indicators. At the second level, user-group positioning (global weight = 0.056), pattern and finish design (0.055), market positioning (0.052), door panel material (0.051), and door style design (0.051) were found to be important indicators of innovation. Interactive experience metrics such as operational convenience and labor-saving access highlighted the increasing importance of usability. FCE modeling results provided deterministic scores, reducing subjectivity and enabling reproducible evaluation. Beyond cabinetry, the GSM–AHP–FCE model can be used to evaluate sensor-integrated smart furniture, tactile interfaces, ergonomic layouts, or feedback mechanisms through various tests. Full article

Enterprise strategic decisions must reconcile conflicting stakeholder interests under time pressure, yet the consulting that traditionally supports them remains out of reach for most small and medium-sized enterprises. Large language models make parts of this work automatable, but a single model speaks with one voice, and its reasoning can be neither inspected nor contested. As frontier models continue to improve, whether structured multi-agent deliberation is worth its additional cost has therefore become an empirical question rather than a design assumption. This study designs, prototypes, and evaluates Servi.AI, a personality-aware multi-agent intelligent decision support system for enterprise strategy. The system grounds every recommendation in a traceable evidence chain retrieved over a knowledge graph. It stages a statement–discussion–consensus roundtable in which role-specialized agents argue from conflicting professional stances. It also simulates how synthetic stakeholders, calibrated against a public personality dataset of 874,434 respondents, will experience the candidate decision. The roundtable characterizes how a decision is argued, whereas the sandbox characterizes how it will be experienced. A questionnaire with 133 screened decision-makers confirms these requirement priorities. We evaluate the system across five experimental axes: 2800 controlled simulation runs and a twelve-case benchmark judged blind across three model families. A strong single model attains the highest holistic scores (8.22–8.56/10 across judges), while deliberation contributes auditable role-grounded reasoning, conflict surfacing, and an executable blueprint. Ablating retrieval loses all 71 valid pairwise comparisons, while a heterogeneous five-family agent pool significantly improves risk coverage (Cliff’s $\delta =+0.75$). Retrieval thus drives the evidence-side qualities, and the role structure drives the deliberation-side ones: deliberative value is decomposable along architectural components, a middle-range design proposition. These findings support selective rather than default deployment. The released benchmark, judging protocol, and raw results provide a reusable basis for deciding when multi-agent decision support is worth its cost. Full article

Intelligent monitoring systems in domains such as renewable energy, electrical grid management, and environmental sensing continuously generate high-dimensional multivariate time-series data characterized by non-stationarity and multi-scale temporal dependencies. Accurate long-term forecasting of system parameters is essential for proactive maintenance, operational scheduling, and cost reduction, yet many existing models rely solely on numerical sequences and lack mechanisms to incorporate higher-level contextual information. This study proposes a multimodal long-term forecasting framework that integrates frequency-aware signal decomposition with semantic-enhanced representation learning. The framework comprises four components: (1) a wavelet-based feature extraction module that captures multi-scale periodic patterns through energy-guided frequency selection; (2) a semantic feature extraction module that encodes statistical summaries of the input into language embeddings via a pretrained language model; (3) a cross-attention fusion module that dynamically aligns temporal and semantic representations; and (4) a multi-scale MLP ensemble for robust prediction. Experiments on three benchmark datasets—Solar Power, ETTh1, and Weather—show that the framework achieves competitive accuracy against strong baselines, including PatchTST and iTransformer, with its strongest results on data exhibiting complex multi-scale seasonal structure, where it attains the second-best mean squared error on the Weather dataset. A controlled ablation isolating the pretrained embedding from a direct numerical encoding of the same statistics indicates a small, dataset-specific benefit that is comparable in magnitude to seed-to-seed variation. Overall, the proposed framework provides a modular and interpretable architecture combining frequency-aware and semantic-aware processing for intelligent system management. Full article

The performance of modern high-performance computing systems is increasingly constrained not by computational power, but by the efficiency of memory subsystem interaction. Cache behavior optimization thus becomes a critical requirement for developers of computationally intensive applications, including numerical simulations, scientific computing, and machine learning kernels. However, existing analysis tools face fundamental limitations; they either provide only aggregated statistics, or exhibit a “semantic gap” by presenting data in machine addresses rather than source code constructs, which impedes targeted optimization of complex data-intensive programs. This paper introduces CATS (C Annotated Trace-based Cache Simulator), a novel hybrid method and toolset for detailed cache efficiency analysis, designed to overcome these limitations. CATS combines source-level static analysis with dynamic tracing at the intermediate representation (IR) level to generate semantically annotated memory traces, enabling precise identification of which source code data structures (arrays, structs, dynamically allocated objects) cause cache misses. The paper describes the methodology and architecture of CATS and presents a foundational validation of the approach through comparative accuracy analysis against reference simulators (gem5, Valgrind) on regular computational kernels (General Matrix Multiplication, GEMM). Systematic error analysis quantifies CATS’s accuracy bounds across different input sizes for the evaluated cache configuration (L1: 32 KB, L2: 256 KB). The current work explicitly focuses on single-threaded CPU-based C programs; extension to irregular kernels, multi-threaded workloads, hardware accelerators (GPU, FPGA), and specific AI applications are identified as future research. Early CATS application at the design stage enables identification of algorithmic cache bottlenecks before final implementation, complementing traditional compiler-level optimizations. Full article

To address the coupling among power supply topology, module selection, capacity allocation, and N-2 contingency assessment in flexible traction power supply systems, a deep-reinforcement-learning-guided branch-and-bound method is proposed for equipment siting and sizing. The Actor network selects preferred branching actions, whereas the Critic network ranks search nodes; exact bound-based pruning and N-2 verification remain governed by the underlying branch-and-bound procedure. A feasibility-preservation result shows that any reported solution satisfies the prescribed N-2 capacity-support condition. Case study results show that the proposed method reduces the power deficit to zero under typical N-2 contingencies by establishing a medium-voltage DC through-feeding section and flexible interconnection channels. Compared with the reference case, the main configuration cost increases by 12.5%, indicating that the proposed method can enhance the system’s support capability under severe contingencies at the expense of a moderate increase in configuration cost. Full article

In modern process industries, data-driven soft sensors have become indispensable for monitoring critical process variables that are inaccessible to direct measurement. Nevertheless, the accurate modeling of industrial processes remains challenging due to their intrinsic complexities, such as time-series behaviors, measurement outliers, redundant variables, and potential concept drift. Existing approaches can address subsets of these challenges but generally lack a unified mechanism that integrates robust offline modeling, variable-importance analysis, and efficient online adaptation. To address these issues, this study proposes an online-updating robust soft sensor framework based on bidirectional long short-term memory (BiLSTM) with integrated gradients (IG) and smoothed quantile loss (SQLoss). During offline modeling, a soft-sensing model is constructed using a BiLSTM, and the proposed SQLoss is introduced to reduce the influence of outliers; the IG method is then employed to evaluate the importance of input variables, enabling input variable selection. During online operation, model parameters associated with significant variables are selectively updated based on IG-derived variable importance, thereby addressing concept drift. Finally, experimental results on an industrial desulfurization process demonstrate that, compared with the best-performing competing basic learner, the proposed SQLoss-BiLSTM-IG reduces the average root mean squared error (RMSE) and mean absolute percentage error by 5.26% and 1.87%, respectively, while increasing the average correlation coefficient by 1.94%; in the online evaluation, the proposed updating strategy achieves a mean RMSE of 2.441, demonstrating its effectiveness in handling concept drift. Moreover, the analysis of key variable importance is consistent with field experience, offering valuable insights for optimizing the desulfurization control system. Full article

Background: Evaluation of LLM-based automated grading often relies on comparison with a single human score, which can obscure meaningful variability among raters of open-ended answers. This study introduces ConsensusGrade, a consensus-aware framework that treats the human reference as a scoring envelope rather than as a single point. Methods: We analyzed 1000 open-ended student answers from 100 students across 10 questions, each graded by four evaluators. Six previously generated and aligned automated grading configurations from GradeAgentOps were compared with the four-rater human reference. The score sets were generated using Llama 3.3 70B Instruct as the primary grader, with Qwen 2.5 14B Instruct for semantic repair. Results: Human evaluators showed meaningful agreement, with ICC(A,1) = 0.712, but exact four-rater agreement occurred in only 2.2% of records. Broad score dispersion occurred in 59.0%. All automated configurations showed negative bias relative to the human median. FULL achieved 68.5% inside-envelope positioning and a chance-adjusted score of 0.454; under the central-trimmed envelope, this rate decreased to 34.3%, while configuration ordering was preserved. Conclusions: ConsensusGrade provides a diagnostic framework for interpreting automated scores relative to observed human variability; inside-envelope rates should not be interpreted as stand-alone measures of grading accuracy. Full article

The design of regular microreliefs requires process and tool variables that satisfy surface-coverage, lubricant-retention, and residual-depth requirements. This theoretical and computational study formulates the task as a two-objective inverse problem for rigid-ball burnishing of external cylinders and compliant-roller burnishing of internal tubes. The analytical chain maps force or imposed displacement and relative curvature to indentation, elastic recovery, periodic cavity overlap, relative dimple area $F_n$, and specific oil capacity q. A full-factorial grid generates a discrete catalog of process settings and provides the initial seeds for continuous refinement by the non-dominated sorting genetic algorithm II (NSGA-II). Under a common budget of 648 forward evaluations, the two-phase method achieved $95.9\%$ of the median target-centered hypervolume of pure NSGA-II and produced a higher hypervolume than Latin hypercube sampling (LHS) in eight of ten paired runs. A comparison calibrated at the 200 N data point yielded a held-out axial-width error of $-6.5\%$ at 400 N, consistent with the predicted force–width trend under the stated comparison conditions. Morris screening identified imposed displacement as the most influential variable for both objectives, with crown-base thickness also strongly influencing q. The resulting candidates are illustrative model-based solutions that satisfy the stated constraints and lie within the reference machine-coordinate envelope; process-specific calibration and experimental assessment are required before quantitative implementation. Full article

Earthquakes remain a critical threat to global infrastructure. Recent catastrophic events, such as the 2023 Kahramanmaraş earthquakes in Türkiye and Syria, underscore the vital necessity of rapid, accurate post-disaster building damage evaluations. Structural collapse under seismic loading leads to substantial loss of life and severe economic disruption, particularly in regions dominated by aging building stocks that predate modern seismic design codes. To address the limitations of conventional manual inspections, this study introduces a comprehensive artificial intelligence (AI) framework designed to automate and enhance post-earthquake structural assessments. Leveraging a heterogeneous dataset from the 2021 Haiti earthquake, which includes both categorical building attributes and post-disaster imagery, the proposed approach employs rigorous data preprocessing and exploratory analysis to identify key vulnerability indicators and resolve data inconsistencies. Independent predictive pipelines were developed utilizing state-of-the-art machine learning algorithms for tabular data and deep learning architectures for image analysis. Subsequently, a novel hybrid meta-classifier was implemented to fuse these distinct modalities. By integrating spatial and structural context with direct visual evidence of damage, the hybrid model is successful in estimating structural damage severity. Among all evaluated approaches, this multimodal framework significantly improved predictive reliability. The hybrid model achieved a classification accuracy of 89%, consistently outperforming isolated tabular and image-based models. These findings highlight the efficacy of multimodal data fusion in disaster analytics and suggest that AI-driven hybrid architectures can serve as robust, scalable decision support tools for structural engineers and emergency response agencies. Full article

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