Search Results (626)

This study develops an interval-valued q-spherical fuzzy rough set TOPSIS framework (IVq-SFRS-TOPSIS) for multi-criteria group decision-making when expert judgments contain interval uncertainty, neutrality, and granular indiscernibility. The revised framework clarifies the relationship between interval-valued q-spherical and interval-valued T-spherical fuzzy models, defines admissible approximation operators over compatible domains, and introduces a radial projection step that guarantees closure under the IVq-SFN constraint whenever component-wise extrema would otherwise violate it. The proposed framework provides a mathematically balanced representation of interval-valued q-spherical fuzzy information, reflecting the concept of symmetry and supporting reliable group decision-making under uncertainty. The TOPSIS procedure is then formulated through expert aggregation, benefit–cost normalization, entropy-based criteria weighting, ideal-solution distance calculation, and closeness-coefficient ranking. The method is illustrated through a sustainable smart city development case using four AI-based alternatives and six criteria. Rather than claiming unconditional superiority, the revised comparative and sensitivity analyses examine how the ranking changes under alternative fuzzy decision models, different q values, perturbations to criteria weights, and perturbations to the decision matrix. The results indicate that the proposed framework provides an interpretable rough-boundary representation and a reproducible ranking mechanism for complex MCDM problems under interval-valued q-spherical uncertainty. Full article

Using low-Earth orbit (LEO) mobile communication constellations to transmit remote sensing satellite data represents an emerging paradigm for overcoming the bottleneck in downloading massive amounts of Earth observation data. However, dense concurrent access across multiple satellites triggers intense resource competition, severe visible-window fragmentation, and strict resource-exclusivity constraints. To address the complex scheduling challenges caused by high laser link establishment overhead and the high-dynamic motion between remote sensing satellites and LEO communication nodes, this paper proposes an Adaptive Multi-Objective Reconstruction Evolutionary Algorithm (AMOREA). The algorithm incorporates a hybrid initialization strategy to improve the quality of the initial solution set and designs a mission-level topology reconstruction mechanism that uses four complementary decomposition operators and a multi-strategy reconstruction pool to achieve effective resource aggregation. Furthermore, an adaptive weight feedback mechanism is introduced to dynamically adjust search priorities and balance global exploration with local exploitation. Simulation results show that, under the simulation settings of this study, AMOREA reaches a 100.0% completion rate for urgent high-priority tasks and an overall average task completion rate of 89.2%. In terms of multi-objective optimization performance, AMOREA obtains the highest mean hypervolume (HV) value among the compared algorithms, improving the mean HV by approximately 19.1% over NSGA-II, 17.6% over MOEA/D, and 67.6% over the Greedy baseline. These results indicate that AMOREA can generate higher-quality Pareto solution sets and improve the efficiency of high-dynamic inter-satellite transmission scheduling under the tested simulation settings. Full article

Implantable medical device (IMD) and Internet of Medical Things (IoMT) environments need intrusion detection systems that learn across distributed hospitals without centralizing sensitive data, while controlling admission, protecting shared model artifacts, filtering unreliable contributors, and supporting post-run auditability. However, many secure federated learning designs treat identity, privacy, robustness, and evidence verification as separate layers, leaving a gap between privacy-preserving execution and public accountability. This paper presents an implemented zero-trust hierarchical federated learning-based intrusion detection system (FL-IDS) framework for IMD/IoMT security analytics. Hospital clients train eXtreme Gradient Boosting (XGBoost) detectors; self-sovereign identity gates participation; contribution-level differential privacy (DP) perturbs exported booster leaf weights; country aggregators apply adaptive Krum-inspired selection; and the global server performs trust-weighted prediction-level fusion. The evidence layer binds artifacts using Module-Lattice-Based Digital Signature Algorithm signatures, canonical hashes, Merkle roots, decentralized publication, Ethereum Sepolia anchoring, and standalone auditor verification. The framework is evaluated on WUSTL-EHMS-2020, ECU-IoHT, and CICIoMT2024 under paired DP-disabled and DP-enabled modes. Under DP-enabled execution, CICIoMT2024 achieved an F1-score of 0.998789 and area under the receiver operating characteristic curve (AUROC) of 0.999814, ECU-IoHT achieved an AUROC of 0.999337, and WUSTL-EHMS-2020 remained DP-sensitive with an F1-score of 0.422880 and AUROC of 0.776685. All paired evidence runs passed standalone auditor verification, demonstrating that privacy-preserving learning and public accountability can be integrated within a single experimental FL-IDS pipeline. Full article

Peri-urban regions around Southeast Asian megacities connect agriculture, markets, food-service facilities, households, and municipal waste systems, yet comparable data for individual waste streams are often unavailable. This study presents a screening framework for selecting the first organic waste streams and node types to measure in Tangerang Raya, Indonesia, before treatment performance data are available. The framework complements, rather than replaces, city scale circularity monitoring, life cycle assessment, and technology selection tools. Public and institutional data were screened by evidence class and temporal and spatial compatibility. The core Peri-Urban Organic Waste Circularity Readiness Index (PU-OCRI) evaluates five intrinsic criteria: feedstock concentration, source separation readiness, treatment pre-screening compatibility, institutional readiness, and the safety/quality gate. Scores represent collective author judgments linked to a criterion level evidence trail; they have not been independently rated by local stakeholders or empirically calibrated. Korea linked support is assessed separately and cannot affect the index. Available evidence included 3248.1 t of large chili production in Kabupaten Tangerang in 2024, 798,406 t yr⁻¹ of reported potential municipal-waste generation in Kota Tangerang in 2024, and a planning-based estimate that 52.89% of non-residential waste in Kota Tangerang Selatan was biogenic organic material. Under equal weights, market-linked organics scored 76/100 and garden and landscape organics 72; production-side residues and household food waste each scored 56, and mixed residual waste scored 32. In 100,000 weight only simulations, market linked organics ranked first in 65.9% of runs and garden and landscape organics in 31.2%. When each score was allowed to vary by one point and sampled together with the weights, the corresponding first-rank frequencies were 50.7% and 40.7%. These results define a provisional paired audit hypothesis, not evidence of superior circular-economy performance. A required 8–12-week comparison of market/food-service and garden/landscape nodes will apply predefined criteria for mass stability, contamination, safety, treatment feasibility, cost, and operator and stakeholder participation before scores are updated or any treatment or scale-up decision is made. Korea-linked cooperation is limited to digital logging, training, QA/QC, and pilot-operation protocols. Data provenance is explicit: the 798,406 t yr⁻¹ value is the issuing agency’s population × per-capita estimate, whereas 52.89% is an author calculated category sum (kitchen + garden + wood) used only as a screening proxy, not as a direct stream level measurement or the plan’s official aggregate organic fraction. Full article

Automatic modulation classification (AMC) is a critical enabler of cognitive radio (CR) systems, allowing secondary users to identify primary user modulation schemes and adapt transmission parameters in real time. Traditional AMC approaches, based on likelihood functions or hand-crafted features, suffer from degraded performance under low signal-to-noise ratio (SNR) conditions and realistic channel impairments. In this paper, we propose MS-SENet (Multi-Scale Squeeze–Excitation Network), a novel deep-learning architecture that integrates multi-scale convolutional feature extraction, squeeze-and-excitation channel attention, residual learning, bidirectional long short-term memory (BiLSTM) temporal modelling, and global attention pooling into a unified framework for robust AMC. The multi-scale convolution module employs parallel branches with kernel sizes of 3, 5, and 7 to capture both fine-grained phase transitions and coarse envelope patterns from raw in-phase/quadrature (I/Q) signal samples. Squeeze–excitation residual blocks perform channel-wise feature recalibration, enabling the network to emphasize informative feature maps while suppressing less relevant ones. A bidirectional LSTM layer models temporal dependencies across the signal sequence, and a global attention pooling mechanism performs weighted temporal aggregation prior to classification. We present a comprehensive taxonomy of deep-learning architectures for AMC organised along five axes—input representation, feature extraction, temporal modelling, regularization strategy, and architectural complexity—and conduct a rigorous comparative evaluation against ten baseline architectures on a RadioML-style synthetic dataset (110,000 samples, 11 modulation classes, and 20 SNR levels from −20 to +18 dB). The experimental results demonstrate that MS-SENet achieves a mean classification accuracy of 87.9% at SNR ≥ 0 dB (the average of the medium and high SNR regime averages: 86.06% for 0 ≤ SNR < 10 dB and 89.68% for SNR ≥ 10 dB) while maintaining a compact footprint of approximately 406 K parameters, making it suitable for deployment on resource-constrained edge devices. We further analyze the robustness of the proposed architecture to multipath fading, carrier frequency offset, and sample rate offset, confirming its resilience under practical operating conditions. MS-SENet is an architecture designed for automatic modulation classification of I/Q signals and is not related to the homonymous architecture for speech emotion recognition. Full article

To address the issues of unclear risk-coupling mechanisms and the subjective-objective imbalance in evaluation weights for underground gas storage, this paper proposes an assessment method integrating network analysis with game-theory-based fusion weighting. A comprehensive geology–wellbore–surface–auxiliary whole-system risk inventory is first established. Meanwhile, the cross-system risk conduction network is analyzed based on the identification of material, energy, and information flows among subsystems. Subsequently, fault tree analysis (FTA) and expert risk scoring (ERS) are integrated to form a coupling network-guided game theory-based weighting model (CN-GT). This mechanism introduces a game-theoretic deviation-minimization model to reconcile conflicts between subjective and objective information sources and explicitly incorporates risk conduction paths into the weight-aggregation process to quantitatively correct cross-system coupling effects. A case study is conducted at the Xiangguosi gas storage facility. Results from ablation experiments and benchmark method comparisons demonstrate that the cross-system coupling effect is significant; the weight of the risk factor “systemic risk caused by improper compressor operation” ranks first after integration, a contribution severely underestimated by traditional methods. Furthermore, the risk prioritization clearly identifies wellbore integrity and critical equipment reliability as the primary control points. This study provides a quantifiable and decision-support tool for the systematic risk management and control of gas storage. Full article

Malware classification increasingly relies on machine learning models that combine static and dynamic evidence, yet their practical use is often limited by dataset inconsistency, high-dimensional feature spaces, and insufficient transparency. This paper presents an explainable hybrid malware-classification pipeline built on an aligned public dataset in which static and dynamic features are matched at sample level and share the same class space. The framework combines a Random Forest static branch, a calibrated XGBoost dynamic branch, and a weighted late-fusion stage whose branch weights are derived from inner-validation weighted-F1 rather than from test performance. On the corrected no-leak benchmark, static reduction compresses the static space from 771 to 258 features, while sparse-aggressive reduction compresses the dynamic space from 21,918 to 374 features. An early-fusion XGBoost baseline achieves the best multiclass aggregate scores, whereas the validation-weighted calibrated hybrid provides the strongest false-negative-first Benign vs. Malware profile, reaching malware recall 0.9998, benign recall 0.8053, and one false negative on the test set. The study shows that, once leakage is removed and fusion is validation-driven, the preferred hybrid architecture depends on the operational objective rather than on a single aggregate metric. Full article

Intrusion detection systems (IDSs) are commonly evaluated through aggregate classification metrics, although operational workflows require detected flows to be interpreted, prioritized, and transformed into actionable evidence. This study proposes a detection-to-triage framework for multiclass intrusion detection using a CSE-CIC-IDS2018-derived experimental subset containing 213,463 records across one benign class and fourteen attack classes. The framework combines supervised multiclass classification, SHAP-style post hoc explanation, class-specific false positive analysis, and a Suspicious Flow Priority Score (SFPS) for analyst-oriented suspicious flow ranking. The practical role of SFPS is to reorder suspicious flows by combining model confidence, explanation strength, predefined attack severity, and validation-based false positive control, thereby producing a transparent triage list rather than a probability-only alert queue. Three detection backbones were evaluated under a shared preprocessing protocol: Random Forest, XGBoost, and a lightweight multilayer perceptron baseline. To assess stability, experiments were repeated across five random seeds. XGBoost achieved the strongest mean performance across most aggregate indicators, with an accuracy of 0.9494 ± 0.0011, a macro F1-score of 0.8366 ± 0.0193, a weighted F1-score of 0.9494 ± 0.0011, and a Matthews Correlation Coefficient of 0.9429 ± 0.0012. Random Forest produced closely comparable results, while the lightweight MLP remained lower on aggregate and macro-level indicators. False positive analysis showed that the alert burden was concentrated in selected classes and differed across models, confirming that aggregate performance alone is insufficient for assessing IDS usefulness. SHAP-style analysis identified stable flow-level contributors to XGBoost discrimination, while SFPS substantially changed the post-detection ordering of suspicious flows compared with probability-only ranking. The study does not claim universal state-of-the-art superiority, causal explanation, or deployment validation; instead, it demonstrates how multiclass IDS outputs can be extended into explainable, false positive-aware, and triage-oriented rankings for analyst review. Full article

Educational information systems have evolved into highly interconnected digital landscapes that support learning management platforms, student information systems, institutional repositories, and online assessment environments. As these systems increasingly operate across cloud infrastructures and mobile devices, ensuring the confidentiality, integrity, and availability (CIA Triad) of educational data is critical for safeguarding institutional operations and maintaining trust in digital education services. This paper investigates how next-generation security protocols, such as adaptive multi-factor authentication and advanced access control and data protection mechanisms, can reinforce ISO/IEC 27001:2022 requirements within contemporary educational information systems. The analysis maps emerging protocol capabilities to relevant new ISO/IEC 27001:2022 control domains, illustrating how they mitigate threats associated with unauthorized access, data manipulation, and service disruption. The proposed framework is further supported by an implementation-oriented mapping and an illustrative operational architecture that demonstrates the feasibility of translating prioritized security determinants into practical mechanisms. The FAHP analysis identifies access control mechanisms, backup and recovery, and data validation as the three highest-weighted determinants, with aggregate weights of 0.061, 0.059, and 0.057, respectively. These determinants are translated into a determinant-driven Security Operationalization Matrix that connects ISO/IEC 27001:2022 control domains, CIA dimensions, and technology recommendations, and is complemented by implementation feasibility considerations tailored to the budgetary, infrastructural, and resource constraints characteristic of educational institutions. Based on the prioritization results and conceptual operationalization, the proposed integrative approach provides a structured and progressively adoptable foundation for CIA-oriented security governance in digital educational environments. Full article

Consolidated bioprocessing (CBP) is difficult to monitor because enzyme production, lignocellulose degradation, sugar release, and fermentation occur simultaneously under sparse measurement, feedstock variability, and plant–model mismatch conditions. This study proposes a computational sensor-set design framework for digital-twin-assisted CBP monitoring. A five-state virtual plant, consisting of active biomass, cellulolytic enzyme activity, residual insoluble substrate, soluble sugar, and ethanol, was used to evaluate all 16 ethanol-mandatory measurement packages formed from ethanol, sugar, biomass, enzyme, and residual-substrate proxy channels. Candidate sensor sets were assessed using finite-difference output sensitivities, Fisher-information-based state-observability and parameter-identifiability analyses, eigenvalue and parameter-correlation diagnostics, and paired Monte Carlo unscented Kalman filter soft-sensing reconstruction. Within the tested five-state virtual-plant benchmark and with the specified excitation schedule, noise assumptions, burden indices, and scoring objective, ethanol-only sensing provided the weakest support for state-aware CBP digital-twin reconstruction. At a $6\mathrm{h}$ sampling interval, the state-observability log-pseudodeterminant increased from 4.18 with ethanol-only sensing to 8.56 after adding soluble sugar and to 16.42 with full-proxy monitoring. The ethanol–sugar–biomass–substrate package also gave strong reduced state-observability performance, with log-pseudodeterminants of 15.12, 13.76, and 12.51 at 6, 12, and $24\mathrm{h}$, respectively. Biomass and enzyme proxies contributed strongly to parameter learning, and the ethanol–sugar–biomass–enzyme package gave the strongest active parameter-identifiability performance, with log-pseudodeterminants of 10.82, 9.06, and 6.67 at 6, 12, and $24\mathrm{h}$, respectively. In the paired soft-sensing analysis, full-proxy monitoring reduced the mean latent-state RMSE from 1.1899 to 0.3756, followed by ethanol–biomass–enzyme–substrate with 0.3843 and ethanol–sugar–biomass–substrate with 0.4121. The primary aggregate ranking identified ethanol–sugar–biomass–substrate as the best overall package, with a sensor-value score of 0.8432 and a burden index of 7.0, followed by full-proxy monitoring with a score of 0.8173 and a burden index of 10.0. Robustness tests showed that ethanol–sugar–biomass–substrate remained top-ranked under uniform noise scaling, full UKF missingness, delay and bias stress test conditions, most scoring-weight scenarios, and all tested sensor-specific burden workflows. Full-proxy monitoring remained a close competitor under independent sensor-specific noise variation conditions and became top-ranked for some alternative operating trajectories. The proposed framework provides a simulation-based method for prioritizing informative measurement packages before implementing CBP digital twins in laboratory and pilot-plant settings. Full article

Small and medium enterprises routinely face a paradox in financial monitoring: their accounting documents exist, but the cost of converting heterogeneous PDFs into timely financial signals is prohibitive without dedicated analytical staff or specialized software. This paper presents a two-layer artifact, designed under Design Science Research, that bridges this gap using only public-web large language models (LLMs) and a parsimonious multi-criteria decision routine. Layer 1 implements a structured LLM-driven workflow that extracts account–value pairs from annual tax balance sheets without code, APIs, or fine-tuning. Layer 2 reconstructs auditable accounting aggregates and ranks yearly financial condition through TOPSIS with min–max normalization—a deliberate replacement for classical vector normalization, which fails when profitability indicators are negative, as routinely occurs in distress years. To avoid size effects and algebraic redundancy, the decision matrix uses only three criteria spanning liquidity, profitability, and solvency. The artifact is demonstrated in a four-year case study of an anonymized construction SME (2021–2024), with accountant-verified document-level match rates of 0.810, 0.998, 0.950, and 0.909. Equal weighting is the only weighting configuration used; a supplementary entropy-based dispersion diagnostic yields the same ordinal ranking—2024 > 2023 > 2021 > 2022—and 10,000 Monte Carlo replications, with uncertainty injected at the reconstructed-aggregate level, confirm that the extreme ranks are invariant across all runs. The contribution is methodological and practical: a transparent, low-infrastructure pipeline that brings first-pass financial screening within reach of SMEs operating under severe data and budget constraints. Full article

The pervasive integration of artificial intelligence across e-commerce ecosystems has fundamentally transformed the competitive landscape, rendering systematic and reproducible platform evaluation frameworks an operational necessity rather than an academic exercise. Conventional multi-criteria decision analysis approaches for e-commerce evaluation remain structurally constrained by their dependency on human expert panels, which introduce recruitment costs, cognitive biases, limited reproducibility, and the practical infeasibility of assembling genuinely multidisciplinary panels spanning e-commerce strategy, machine learning engineering, and financial technology simultaneously. This study proposes a novel decision support framework that integrates Large Language Model (LLM) multi-agent simulation with the Best–Worst Method (BWM) to derive reproducible priority weights for AI-driven e-commerce platform evaluation within a rigorous business intelligence architecture. Twelve domain-differentiated LLM agents—organized into three expertise groups representing e-commerce management, AI and machine learning technology, and digital payment systems—were instantiated with structured system prompts encoding professional domain knowledge and deployed across three independent simulation rounds to perform BWM pairwise comparisons across a comprehensive six-dimensional, 30-sub-criterion evaluation hierarchy. Inter-agent consensus was synthesized through geometric mean aggregation, with consistency verification conducted via BWM’s xi* indicator and inter-round stability assessed through coefficient of variation analysis. Results reveal that Transaction Security and Trust achieves the highest dimension-level weight (w = 0.248), followed by AI Recommendation Effectiveness (w = 0.213), with Personal Data Protection (G = 0.0750), Recommendation Accuracy (G = 0.0607), and Transaction Transparency (G = 0.0549) emerging as the three highest globally ranked sub-criteria. The aggregated consistency indicator xi* = 0.062 confirms logical coherence of the multi-agent judgment consensus, and all dimension weights exhibit CV values below 2.8%, demonstrating exceptional inter-round stability. Spearman rank correlations among the three domain-expertise groups exceed 0.92, confirming strong inter-group convergence. Sensitivity analysis under perturbations of ±10% and ±20% demonstrates that the top-five priority indicators are structurally stable. This study establishes LLM multi-agent BWM simulation as a methodologically rigorous, institutionally accessible, and computationally reproducible alternative to traditional expert elicitation for complex platform evaluation tasks. Full article

Trust evaluation in Industrial Internet of Things (IIoT) edge networks must account for both device behavior and the operational importance of industrial assets. Existing models often apply uniform scoring rules, which may limit their response to semantic attacks and whitewashing behavior while increasing the processing burden on edge devices. This paper presents an Asset-Aware Resilient Trust (ART) framework. ART separates dynamic behavioral credibility from physical asset criticality through a dual-plane architecture. Cross-layer evidence is collected from communication, identity, physical, and semantic interactions. A Fuzzy Triggered-Entropy Weight Method (Fuzzy T-EWM) recalculates evidence weights only when the observed fluctuation exceeds a preset threshold. Trust scores are updated using a Fast-Drop Slow-Rise rule, together with a tolerance margin for routine network jitter. The simulation results show that ART detects stealthy False Data Injection attacks, limits trust recovery after whitewashing behavior, and reduces accumulated computational overhead by 76.4% compared with the Standard EWM baseline. The credibility-weighted aggregation mechanism also limits collusive recommendation manipulation during cold-start evaluation. These results support differentiated trust regulation for IIoT edge networks. Full article

Food waste research often applies the Motivation–Opportunity–Ability (MOA) framework, yet conventional aggregate measures may obscure the distinct roles of physical context and cognition-related capacity. Using a macro-contextual, micro-primary dual-layer design, this study first uses World Bank data from 176 countries to provide structural context; this macro layer is not statistically linked to the student-level model. The main behavioral inference comes from matched plate-weighing and questionnaire data from 170 students across two purposively selected ordinary higher education institutions in northern and southern China. Within this exploratory and context-specific micro-level sample, the baseline three-dimensional MOA model explains only 4.1% of variance in log-transformed plate waste, whereas decomposing Opportunity into social and physical components and representing the Ability extension through behavioral ability and a two-item cognitive-capacity proxy improves model fit. The five-dimensional model explains 44.1% of variance ($F=26.2$, $p<0.001$). Johnson relative weight analysis indicates that Physical Opportunity (51.1%) and the two-item cognitive-capacity proxy (46.3%) account for most explained MOA variance in this sample. Item-level sensitivity checks further suggest that portion estimation and nutrition knowledge should be interpreted as distinct cognition-related indicators rather than as a validated latent scale. Robustness checks across raw, log-transformed, winsorized, logistic, and quantile specifications indicate consistent positive associations for Physical Opportunity and consistent negative associations for cognition-related indicators. Because the design is cross-sectional, these findings identify associations rather than causal effects; physical-environment redesign and cognitive-capacity support should therefore be treated as candidate directions for future intervention testing rather than as confirmed intervention effects. By linking objectively measured plate waste to institutional dining conditions, the study contributes to sustainability research on responsible consumption, resource efficiency, low-carbon campus operations, and practical pathways for reducing avoidable food-related environmental burdens in university settings. Full article

Early fault prediction of wind turbines is critical for ensuring wind farm safety and reducing operation and maintenance costs. However, the latent and progressive nature of incipient faults, together with concurrent failures across multiple subsystems, makes accurate root-cause identification challenging. In addition, severe class imbalance between normal and faulty samples further degrades prediction performance, particularly for minority fault types. To address these challenges, this paper proposes a novel fault prediction model, M²ANet, using SCADA data within a 30-min pre-fault window. The model combines a dual-memory module with progressive dilated convolutions to efficiently capture multi-scale temporal dependencies from high-dimensional operational variables. An entropy-bias penalty is further introduced into the loss function to adaptively regularize the predicted probability distribution, alleviating overconfidence under imbalanced data conditions and improving the recognition of minority faults. Experiments on a real-world wind farm dataset show that M²ANet achieves an overall accuracy of 90.73% and a weighted F1-score of 90.62% in multi-class fault prediction, outperforming 10 representative baseline models. In addition to these aggregate metrics, per-class evaluation confirms the model’s robustness under class imbalance. Notably, for yaw system faults, which account for only 1.9% of the samples, M²ANet achieves a recall of 95.92% with a 30-min-ahead warning. These results demonstrate its effectiveness and reliability for early fault prediction in practical wind turbine applications. Full article