Search Results (565)

This paper introduces a secure simultaneous wireless information and power transfer (SWIPT) system using simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs). To enhance secrecy and energy harvesting, we propose a Modified Differentiated Creative Search (MDCS) technique to optimize the transmit beamforming, artificial noise, and STAR-RIS transmission/reflection coefficients simultaneously. The proposed paradigm considers imperfect cascaded channel state information (CSI) and integrates a viable non-linear energy harvesting model with limited CSI error models. The simulation results show that the proposed MDCS-based strategy exceeds existing benchmark techniques in terms of secrecy performance, robustness, and energy harvesting efficiency. The findings demonstrate that the proposed MDCS algorithm achieves a mean objective value of 0.1167, outperforming Differentiated Creative Search (DCS), Draco Lizard Optimizer (DLO), mean Differential Evolution algorithm (meanDE), Octopus, and Nutcracker Optimization Algorithm (NOA) by approximately 75.1%, 86.4%, 45.0%, 85.0%, and 74.7%, respectively. Furthermore, MDCS achieves faster convergence and improved solution stability, demonstrating its efficiency for robust beamforming optimization in STAR-RIS-assisted secure SWIPT systems with suboptimal CSI conditions. Full article

Business Process Analytics (BPA) has become increasingly important for improving organizational processes and supporting data-driven decision-making. However, existing Business Process Management and Data Analytics methodologies provide limited support for stakeholder participation, User-Centered Design (UCD), and collaborative implementation, creating barriers for organizations with limited analytical maturity. This study presents MIDA5, an initial methodological framework that integrates BPA, Data Analytics, Business Process Management, UCD, and gamification into a participative implementation methodology. Developed following a Design Science Research approach, MIDA5 comprises five phases, 14 stages, 33 activities, and 61 methodological artifacts. The framework was evaluated through a 3-month-and-12-day organizational case study conducted at a public university involving four core organizational participants, seven organizational stakeholders, and complementary organizational applications. The implementation formalized an undocumented process, integrated heterogeneous data sources, developed an analytical database and ETL workflow, and produced three interactive Power BI dashboards. The analytical solution achieved a System Usability Scale score of 84.2 and a SERVQUAL score of 4.35/5, providing initial evidence of usability, stakeholder acceptance, and organizational applicability. MIDA5 contributes an initial, collaborative, user-centered methodological framework that operationalizes BPA through structured stakeholder participation, standardized artifacts, and iterative organizational validation, while reducing methodological and technological barriers. Full article

Point clouds are widely used to represent 3D visual content in immersive media, digital twins, and autonomous systems, but acquisition, compression, transmission, and rendering can introduce visible geometry and attribute distortions. Full-reference point cloud quality assessment (FR-PCQA) aims to predict the perceptual quality of a distorted point cloud by comparing it with a reference. A reliable FR-PCQA model should consider both the perception of 3D content by the human visual system via projected views and the manifestation of quality degradation in the point cloud geometry, color, and spatial structure. In this paper, we propose a multi-view projection and 3D feature fusion model for FR-PCQA. The proposed model integrates two complementary branches. In the projection branch, DISTS is applied to multi-view renderings aligned with the reference to capture perceptual similarity, and an additional six groups of geometric and photometric fidelity features (e.g., occupancy, depth fidelity and gradient domain fidelity) are developed to describe explicit geometric and photometric differences in the projected observations. In the 3D Feature Fusion branch, PCQM measures local geometry and color degradation, while a global structural descriptor with eight groups covering point count, position, scale, spatial distribution, and density is constructed to characterize the global properties of point clouds. Finally, a gradient boosting regression tree (GBRT) regressor is employed to predict the final quality score. Extensive experimental results show that the Spearman rank order correlation coefficient (SROCC) values are 0.91537, 0.9101, and 0.9778 on the SJTU-PCQA, WPC, and ICIP2020 datasets, respectively, outperforming the existing PCQA methods. These results indicate that the proposed multi-view projection and 3D feature fusion model provides an accurate and interpretable solution for FR-PCQA. Full article

Artificial intelligence has emerged as a promising paradigm for advancing the screening, diagnosis support, and monitoring of autism spectrum disorder (ASD) through scalable and data-driven clinical augmentation. Recent advances in machine learning, multimodal sensing, and digital phenotyping have enabled AI systems to analyze behavioral, neurophysiological, speech, and clinical data to identify early markers of ASD. Despite encouraging experimental results, major barriers to clinical translation remain, including limited generalizability, fragmented datasets, insufficient evaluation rigor, lack of semantic interoperability, and unresolved ethical and regulatory concerns. This systematic review provides a comprehensive technical review of AI for ASD, covering data modalities, feature engineering, learning paradigms, evaluation protocols, deployment architectures, and knowledge representation frameworks. Particular emphasis is placed on system-level and translational considerations, including cloud–edge infrastructures, explainable clinical decision-support systems, privacy-aware deployment, and ontology-driven reasoning. Beyond summarizing existing work, this paper critically analyzes challenges related to reproducibility, dataset bias, interpretability, and clinical integration and derives design requirements for next-generation trustworthy ASD AI systems. We argue that meaningful clinical impact will require the integration of multimodal learning, semantic knowledge representation, explainable reasoning, and human-in-the-loop decision processes to support safe, interpretable, and clinically deployable AI systems in pediatric healthcare environments. Full article

Industrial digital twins integrate physical objects, dynamic models, control systems, data analysis tools, 2D and 3D visualization, and existing software systems. Much research has focused on the functional composition of the digital twin, modeling, and application scenarios, while the organization of the digital twin as an evolving software system composed of technologically heterogeneous and autonomous subsystems remains insufficiently formalized. The goal of this study is to develop a conceptual composite architecture for an industrial digital twin, in which complex subsystems are viewed as highly interconnected and loosely coupled service components of a higher-order system. The research method is based on analogy, transfer, and adaptation of proven principles of distributed and microservice systems to the constraints of industrial digital twins. An architectural model is proposed that includes a physical object, a process model, SCADA subsystems, a unified data exchange subsystem, 2D and 3D representations, VR/AR components, and automated model building modules. The practical feasibility of the approach is demonstrated using a proof-of-concept digital twin of a methanol–ammonia co-production plant, integrating Honeywell UniSim Design R460.1, web-based SCADA, Unity, and specialized 2D and 3D representation generation modules. This demonstration confirms the feasibility of integrating independently developed subsystems and the technological heterogeneity of the solution, but does not constitute a production test of performance, scalability, or cost effectiveness. Requirements for contract stability, a consistent interaction environment, assigned data responsibility, version compatibility, and complete documentation are defined. Research limitations and areas for subsequent quantitative architecture validation are identified. Full article

Forests cover nearly one-third of the Earth’s land surface and are subject to increasing anthropogenic pressure, including unauthorised construction, infrastructure expansion, and habitat fragmentation. Detecting buildings concealed beneath forest canopy is essential for environmental monitoring and territorial surveillance, yet optical satellite imagery fails under persistent cloud cover and dense vegetation, and no existing approach provides reliable detection across contrasting forest ecosystems. We address this gap by proposing a hybrid SAR-based detection framework that combines twelve physically interpretable Scattering-Informed SAR Features (SISF)—derived from electromagnetic scattering theory across amplitude, polarimetric, temporal, and texture dimensions—with a universal Convolutional Neural Network, integrated through probability-level fusion with isotonic regional calibration. Rather than relying on individual feature thresholds, the framework identifies buildings through their characteristic multidimensional scattering signature—a combination that remains discriminative across biomes where any single SAR descriptor would fail. The framework was evaluated on a novel 7721-object multi-biome benchmark spanning forest-steppe (Kazakhstan), boreal forest (Komi Republic, Russia), and tropical rainforest (Brazil, Pará). The final Fusion + Regional Calibration model achieved an overall F1-score of 0.803 on the held-out test set (N = 1545), outperforming single-model baselines by up to 17 percentage points. Regional F1-scores ranged from 0.727 (Kazakhstan) to 0.944 (Komi Republic), with detection performance remaining robust under partial canopy occlusion (F1 = 0.911, versus 0.903 for unobscured buildings). An empirical inverse relationship between hard negative proportion and detection F1-score was identified within each biome, with the 28–35% proportions present in our sampled regions reported as a preliminary observation rather than a generally optimal range—a dataset design finding not previously reported in the literature. The proposed framework provides a physically interpretable solution for SAR-based building detection under forest canopy, demonstrating consistent performance across three contrasting forest biomes, with direct applications to environmental monitoring and territorial surveillance in forested regions. Full article

Benefiting from the merit of independent calibration without external reference, the shadow feature is investigated to retrieve wave steepness and wave height from X-band marine radar images. However, the wave period is currently required. Although the wave period could be achieved using the fundamental spectrum analysis technology from a radar image sequence, an external measuring device, like a wave buoy, is essential for the calibration of the wave spectrum. To solve this problem, an improved method for deriving significant wave height (SWH) is proposed by fusing the wave steepness extracted from the shadow feature of the radar image and the wavelength derived through rotated empirical orthogonal function analysis technology. Considering the attenuation relation of received echo in the distance direction, echo intensity calibration is adopted before extracting principal components. The collected X-band marine radar images are applied to verify the performance of the established SWH retrieval approach, with the observation of the wave buoy serving as the ground truth. Compared with the fundamental shadow statistical method, the correlation coefficient of the proposed approach rises by 0.08, while the root mean square error drops by 0.05 m. Full article

Higher education is increasingly digitalized, resulting in increased reliance on e-learning systems, and thus, e-learning system success should be sufficiently evaluated. However, students worldwide have shown unproductive e-learning use. Hence, the e-learning systems success model after the outbreak needs revision. To validate the e-learning systems success, the role of monitoring quality was examined in this study using current e-learning and information systems success models. Data from 600 students were analyzed with structural equation modelling (SEM) run by SMARTPLS 4. Results showed that user satisfaction was positively impacted by information quality, system quality and service quality but not by monitoring quality. Results further showed positive impact of user system use on student satisfaction, subsequently impacting student loyalty. Also, results showed user satisfaction significantly impacting learning effectiveness. This study enhances the information systems literature through the inclusion of monitoring quality into the DeLone and McLean information systems success model and through examining its impact on user satisfaction, loyalty intention, and learning effectiveness in e-learning environments. Full article

Multi-expert large language model systems need to dynamically distribute user requests among multiple candidate experts to improve inference accuracy and system efficiency in complex task scenarios. To address the issues of existing routing methods relying on manual rules, unified discriminant boundaries, or large-scale annotated data, as well as high maintenance costs during expert expansion, this paper proposes an expert routing method, BGMM-LSE, based on positive sample semantic distribution modeling. The method constructs a positive sample set using only the requests historically successfully processed by each candidate expert, and maps the requests into dense semantic vectors through a pre-trained text feature extraction model; subsequently, a Bayesian Gaussian mixture model (BGMM) is independently trained for each expert to characterize the capability distribution of its successful requests in the semantic space. During online inference, the system encodes the target request into a feature vector, calculates the log-likelihood score combining the retained Gaussian component parameters and the smoothed covariance matrix of each expert, and uses Log-Sum-Exp to aggregate the probability contributions of different components. Finally, the request is routed to the expert model(s) with the highest score. Experiments were evaluated based on seven tasks: Math, GSM-Symbolic, HumanEval, Mbpp, MMLU, AIME1983–2025, and HellaSwag. The results show that this method achieves an average routing accuracy of 86.25%. The experimental results demonstrate that BGMM-LSE can provide stable request distribution capabilities for multi-expert large language model systems while maintaining interpretability and scalability. Full article

Indian mythological narratives contain long event chains, recurring characters, moral conflicts, interactions between human and divine agents, and source-specific narrative styles. General-purpose large language models can generate fluent text while losing character continuity, thematic relations, or source-supported events. This study presents MythoBiLLM, a parameter-efficient framework for summarization and continuation generation from Indian mythological texts. The framework combines a frozen Llama 3.2 3B-Instruct backbone, LoRA-based adaptation, and a gated BiLSTM narrative-memory adapter. A corpus of public-domain English translations from the Ramayana, Mahabharata, Bhagavad-Gita, Vishnupuranam, Harivamsha, Hindu Tales, and Indian Myth and Legend contains 3,684,838 word-level tokens and 6057 segmented passages. Evaluation covers language modeling, summarization, continuation generation, entity consistency, theme retention, component ablation, robustness, human assessment, and statistical testing. Relative to LLM+LoRA, the complete framework reduces average perplexity from 23.4 to 19.8. In controlled comparisons, the BiLSTM adapter achieves an MCS of 0.713 on both tasks, compared with 0.699 for the parameter-matched MLP adapter, 0.704 for independently trained long-context LoRA, and 0.708 for retrieval augmentation. Full MythoBiLLM reaches MCS values of 0.762 for summarization and 0.744 for continuation generation. After entity consistency and style alignment are excluded from MCS, the complete configuration retains the highest scores of 0.751 and 0.731. These findings support the complete framework on the evaluated corpus, while the controlled comparisons indicate a modest complementary contribution from the BiLSTM and do not identify it as the sole source of the performance gains. Full article

Dynamic aeronautical information is still widely disseminated through textual Notice to Air Missions (NOTAMs), while message-oriented storage cannot directly maintain the evolving states of affected objects. The objective of this study is to determine whether NOTAM-derived object events can be organized into traceable bitemporal states that support accurate and efficient current and historical access. To this end, this paper proposes BiTE, a bitemporal event-centered database framework that connects object-level event evidence, historical state versions, and materialized current-state projections. By integrating business and system time with NOTAM-specific lifecycle rules, BiTE supports state maintenance, historical reconstruction, and source traceability. A MongoDB-based prototype was evaluated using 44,591 NOTAMs from five major U.S. aerodromes. Independent manual validation showed 96.14–100% agreement across object identification and lifecycle-maintenance tasks. Across 1500 manually verified queries, BiTE achieved F1 scores of 99.43% and 98.66% for current-state and airport-overview retrieval, respectively, and a historical hit rate of 95.80%, outperforming representative message-oriented, relational-bitemporal, and RDF-based implementations. Mean query latency remained below 3.7 ms, while functionally equivalent ablations confirmed the performance contribution of the layered architecture. These results demonstrate that BiTE enables accurate, traceable, and efficient object-state management for dynamic aeronautical information. Full article

Machine learning is increasingly used in entrepreneurship analytics to predict startup outcomes, frequently reporting accuracy above 0.90, yet whether such performance reflects a genuine ex-ante signal or methodological artifact remains unclear and consequential for investors, accelerators, and innovation-policy agencies. This study evaluates startup-outcome classification under a leakage-controlled, calibration-first protocol using two Crunchbase-derived datasets (66,368 firms; a 923-firm engineered-feature set), three success constructs, and three model families under five-fold stratified cross-validation. Removing outcome-correlated, survivorship-accumulating features lowers the area under the receiver operating characteristic curve by 0.05 to 0.09 on the large dataset, with every paired 95% confidence interval excluding zero, and by 0.19 on the engineered dataset; an independent study on the same 923-firm data without leakage control reports 88.1% accuracy. The leakage-controlled performance level is modest (0.66 to 0.77). Calibration rankings diverge from discrimination rankings: gradient boosting is well calibrated (expected calibration error of 0.006 to 0.024), whereas logistic regression shows large calibration error on imbalanced constructs (largely an artifact of class weighting rather than an intrinsic model property); post hoc isotonic recalibration then removes most of the error. The contribution is a reusable evaluation protocol for entrepreneurship analytics. Findings are associational and specific to the analyzed samples. Full article

Data Analytics methodologies provide structured approaches for transforming organizational data into actionable knowledge. However, many existing methodologies emphasize technical activities while offering limited support for stakeholder participation, user-centered validation, and organizational adoption. This study presents the construction of MIDA5, a Data Analytics methodology for Business Process Improvement developed using the Design Science Research paradigm. The research combined a comparative analysis of existing Data Analytics methodologies with an empirical experimentation process conducted in an organizational environment. The experimentation involved the execution and analytical deconstruction of a previously implemented Data Analytics methodology to identify operational limitations, stakeholder-related challenges, and methodological gaps. The findings were synthesized into design requirements, methodological components, and design needs that guided the construction of MIDA5. The resulting artifact incorporates principles of Business Process Analytics, User-Centered Design, and User Engagement and Gamification Dynamics through a five-phase structure supported by activities, artifacts, and stakeholder validation procedures. The study contributes a traceable Design Science-based development process that connects empirical findings with design decisions and provides a methodological foundation for the complete methodological specification and empirical evaluation of MIDA5. Accordingly, this study focuses on artifact construction rather than on demonstrating the effectiveness of the resulting methodology. Full article

Organizations are under constant strain to substitute the traditional ETL pipelines and monolithic warehouses for the dynamic cloud-native modular architectures. Despite rapid adoption, the Modern Data Stack (MDS) literature remains fragmented: lakehouse, mesh, and fabric paradigms are studied in isolation, and no prior review has linked component-level decisions to organizational maturity. This systematic review addresses that gap. Following PRISMA 2020 guidelines across six databases, 650 records were screened and 141 studies were retained for thematic synthesis (the corpus was peer-reviewed, with a small number of primary-source technical preprints screening was performed primarily by one reviewer; a 10% double-screened sample yielded Cohen’s κ = 0.81). Six functional layers (ingestion, storage, transformation, orchestration, analytics, and observability/governance) and four dominant architectural patterns (cloud warehouse, lakehouse, data mesh, and data fabric) were identified. Components were evaluated against five criteria—scalability, cost efficiency, vendor neutrality, learning curve, and business impact—across three organizational archetypes (startup, SME, and enterprise). A three-phase maturity model (Foundation, Extension, Consolidation), a five-stage iterative implementation cycle, and a RACI governance matrix constitute the resulting strategic framework. Governance emerged as simultaneously the least-adopted layer in the corpus and the most consequential for long-run adoption success. The framework is propositional; empirical validation through an expert Delphi study, multi-case longitudinal analysis, and an AHP-based practitioner survey are planned as future work. Full article

Limited by the computational performance limits of the CPU(Central Processing Unit), the traditional Spark architecture struggles to achieve high throughput and low latency under the dual pressure of a large data scale and real-time requirements in centralized control systems. This work uses a publicly available CNC(Computer Numerical Control) milling dataset as a functional validation proxy for time-series data processing, then extends validation to a large-scale synthetic power transmission grid dataset. Furthermore, Spark-GPU(Graphics Processing Unit) collaboration suffers from load balancing failure due to heterogeneous resource scheduling and communication overhead, thus failing to unleash its performance potential. This paper proposes a Spark-GPU fusion acceleration technology path. The path consists of three key components: first, it integrates the RAPIDS accelerator; second, it designs a GPU-aware partitioning and task co-scheduling strategy; and third, it optimizes the zero-copy data path. Together, these components realize an integrated collaboration of heterogeneous resources. Validation on real-world datasets yields the following results. In real-time aggregation scenarios, the proposed solution improves throughput by a factor of 3.7 over the pure CPU baseline and reduces end-to-end latency by 62%. Compared with the basic GPU solution, GPU utilization rises from 51.7% to 72.3%, representing a relative improvement of 39.8%. Furthermore, the solution meets industrial-grade high availability requirements. This research significantly improves the processing throughput and reduces end-to-end latency in typical centralized control scenarios, thus providing a feasible technical route for demanding concurrent centralized control scenarios such as electric power industry manufacturing with high real-time demands. Full article