Digital transformation has encouraged companies to optimize their digital platform capabilities and big data analytics as strategic resources in creating innovation excellence. This study aims to examine the influence of Digital Platform Capability (DPC) and Big Data Analytics Capability (BDAC) on Innovation Performance (IP) in the telecommunications industry in Indonesia. Data was collected from 331 managerial respondents through a survey and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The results show that both DPC (β = 0.316; T = 5.282; P < 0.001) and BDAC (β = 0.484; T = 8.033; P < 0.001) have a significant positive effect on IP. These findings emphasize the importance of companies' ability to manage digital platform integration and utilize big data analytics to strengthen innovation performance. Theoretically, this study expands on the Resource-Based View (RBV) and Dynamic Capability View (DCV) by emphasizing the role of DPC and BDAC as dynamic resources that support innovation. The practical implications suggest that telecommunications companies need to develop integrated digital strategies, strengthen their analytical infrastructure, and foster a data-driven culture to enhance their competitiveness.

Adaptive dynamic resource allocation can cause tragedy of the commons in plants with nutrient competition.

Ethical, data security, and resource allocation considerations in AI integration for healthcare during Hajj: task force insights and future directions.

The exponential growth of Internet of Things (IoT) devices integrated with fifth-generation (5G) wireless networks has created unprecedented opportunities for ultra-low-latency applications while introducing complex security vulnerabilities and computational challenges. This paper presents a comprehensive framework for deploying adaptive lightweight Convolutional Neural Networks (CNNs) in 5G-enabled IoT environments to address intrusion detection, intelligent traffic classification, and dynamic resource optimization. We propose a novel multi-objective optimization approach that integrates Adaptive Depthwise Separable Convolutions (ADSC), Dynamic Quantization-Aware Training (DQAT), and Real time Pruning Strategy (RPS) specifically designed for 5G network slicing architectures. Our methodology incorporates federated learning principles, edge-cloud collaboration, and context-aware adaptation mechanisms. Comprehensive evaluation on multiple datasets, including NF-ToN-IoT-v2, NSL-KDD, and CICIDS-2017, demonstrates superior performance with 97.8% accuracy in multi-class attack detection, 76% reduction in computational overhead, 71% decrease in energy consumption, and 42% improvement in network throughput. The framework achieves inference times under 8.5ms on edge devices while maintaining robust security postures across heterogeneous IoT deployments. Statistical significance testing and large-scale ablation studies verify the effectiveness of each of the suggested elements.

Dynamic Resource Scheduling for Deterministic Communication, Computation, and Control Integration in Industrial Cyber–Physical Systems

The petroglyphs of Central Asia represent a distinctive form of symbolic and visual heritage that is gaining relevance in scholarly debates on cultural tourism, regional identity, and sustainable development. Although their archaeological importance has long been recognized, their integration into contemporary tourism planning remains conceptually fragmented and underexplored. This study provides a systematic and meta-analytic synthesis of scientific literature concerning petroglyphs in Kazakhstan, Uzbekistan, Kyrgyzstan, and Tajikistan. Through bibliometric analysis, it identifies dominant research themes, leading institutions, and international collaboration networks. The results confirm increasing interest in the educational, symbolic, and experiential tourism dimensions of petroglyphs. However, the review also highlights thematic and geographical imbalances, particularly the underrepresentation of certain regions and a scarcity of robust empirical studies. The meta-analytic component shows a moderate positive effect of petroglyph-based heritage on cultural tourism development. Kazakhstan emerges as the leading academic node in this domain. The study does not attempt to establish causality but seeks to clarify how petroglyphs have been conceptualized in scientific discourse over the past two decades. Bibliometric mapping shows strong correlations between publication volume and heritage-centered national strategies. Research also indicates that digital tools, such as 3D modeling and GIS, are increasingly employed to preserve and interpret petroglyph sites. Despite these innovations, community-based approaches remain limited, signaling the need for greater local engagement. By linking rock art research with tourism studies, digital humanities, and heritage policy, this article outlines potential directions for more inclusive, interdisciplinary, and empirically grounded future research. Ultimately, the findings support the notion that petroglyphs are not only remnants of the past, but dynamic resources for interpreting identity, enhancing tourism sustainability, and fostering cultural continuity within the evolving framework of regional development in Central Asia. These insights may guide policymakers, researchers, and cultural institutions in shaping integrative strategies. Further efforts should aim to connect heritage preservation with inclusive tourism, education, and long-term regional resilience. Strengthening regional cooperation and cross-border heritage initiatives may also amplify the role of petroglyphs in sustainable cultural development.

Federated learning faces a fundamental privacy-utility-communication trilemma, and existing static defense mechanisms suffer from rigid adaptation and poor multidimensional coordination, leaving a critical gap in dynamic trade-off balancing. To address this, we propose DualMask, a cooperative optimization framework that integrates a client-side Adaptive Orthogonal Noise Canceler (AONC) with server-side Distributed Dueling Double Deep Q-Network (D3QN) scheduling and Particle Swarm Optimization (PSO)-based aggregation. The AONC module implements a triple-defense mechanism via orthogonal subspace projection: (1) layer-wise adaptive EMA-quantile clipping to mitigate threshold imbalance, (2) progress-aware noise decay that balances early-stage privacy with late-stage efficiency, and (3) directional tuning that dynamically adjusts parallel-to-orthogonal gradient ratios. On the server side, D3QN enables dynamic resource allocation across heterogeneous devices, while PSO fusion corrects non-IID aggregation bias through particle-swarm-based weight optimization. Experiments on CIFAR-10/100 and Shakespeare datasets demonstrate that DualMask achieves 5.2% higher accuracy (84.1% vs 79.4% in non-IID settings) and 34.4% faster convergence (210 vs 320 rounds) compared to FedAvg. Additionally, DualMask reduces the privacy budget from 4.5 to 2.8 and communication cost by 37.2% (45 MB vs 65 MB). This constitutes a significant Pareto improvement, substantially expanding the trilemma frontier. The code and data are available at https://github.com/zhou-weib/DualMask.git.

Internet TV business is facing pain points such as scattered resources and lagging decision-making in the process of digital transformation. This article constructs a data-driven business management and control model, which integrates business analysis, project accounting, and dynamic resource allocation mechanisms to improve business operation efficiency. The research proposed a closed-loop framework of "data analysis decision optimization", combined with Internet TV user behavior data, content resource data and financial data, to verify the effectiveness of the model in cost control and revenue growth, and provide a replicable management paradigm for the industry.

Context. Ensuring the consistency and adaptability of requirements in systems operating under dynamic conditions and limited resources is a pressing issue in modern requirements engineering, especially in intelligent diagnostic and decision-making environments. These systems must process conflicting, outdated, or ambiguous requirements while operating in environments characterized by high uncertainty and dynamic conditions.Objective. This work introduces a formalized methodology for analyzing and managing the compatibility of system requirements. The proposed approach integrates logical consistency, functional interaction, resource feasibility, and priority alignment to support system stability and responsiveness.Method. The methodology is implemented as a multi-level framework that incorporates formal representations of functional,non-functional, and data-related requirements. It employs scenario-based modeling, a set of compatibility assessment models, and a dynamic algorithm for integrating new requirements. The integration process includes compatibility checks, adaptive refinement, expert-based weighting, and real-time feedback. The methodology’s applicability is demonstrated through a hypothetical intelligent medical diagnostic system.Results. The proposed methodology enables systematic identification and resolution of requirement conflicts, ensuring consistent execution and effective prioritization under resource constraints. Scenario-driven modeling and the formalization of core requirements establish a foundation for adaptive system behavior and real-time decision-making.Conclusions. The developed methodology, which includes models and algorithms, enhances the reliability of intelligent systems operating in critical contexts. Future work will focus on extending the framework by incorporating fuzzy logic, machine learning techniques, and developing software tools for automated compatibility analysis and adaptive requirements management.

The Evolution of Monitoring: From Reactive Alerts to Predictive Insights

Although there has been a change in the digital library landscape regarding content, demand, and even the lack of computing and storage bandwidth, all of this suggests a high requirement for the allocation algorithms. This study will focus on optimizing the demand for accessing digital libraries using predictive analytics. Machine learning algorithms can help analyze the access demand, peak usage, and operational prediction of content demand by relating to the history of the demand. Predictive analytics could be used to help in optimal dynamic resource allocation which is more resourceful in bandwidth use, less server workload and enhance efficiencies of content caching. The methodology of the algorithms applied in the current research will be time series forecasting, regression and classification algorithms as the most effective and accurate method or the best dynamic forecasting. In one case study of a university digital library, say, the effect of predictive analytics influenced the latency, user satisfaction and cost of operation. These effects are the possible forecasting analytics can do to enhance decision and resource designation in economics in the digital ecosystem. This research adds new knowledge to how digital libraries function and allocate information technology resources, combining intelligent allocation functions through predictive expectancies in creating a networked digital library. Future work will develop algorithms to allocate resources in real-time adaptive methods that are more efficient, responsive, and provide quality services to users.

A warp-level optimized GPU correlator with dynamic resource allocation for real-time GNSS software-defined radio receivers

This paper presents a dynamic joint resource allocation framework for downlink Integrated Sensing and Communication (ISAC) systems. The proposed approach simultaneously optimizes power and frequency assignment using an iterative optimization algorithm that adapts to real-time channel state information and sensing requirements. A composite utility function is introduced to balance sensing accuracy and communication throughput, enabling flexible trade-offs for different application scenarios. Comparative analysis against benchmark schemes demonstrates that the proposed method achieves notable improvements in both sensing accuracy and communication rate, while also providing insights into power distribution across users and sensing tasks. These results highlight the potential of the framework as a practical and adaptive solution for ISAC-enabled 6G networks.

Mobile web applications support important services like banking, e-commerce, healthcare as well as communication in the current digital era. However due to their widespread usage they are now more vulnerable to complex cyberattacks. The dynamic and resource-constrained characteristic of mobile settings is frequently too much for conventional safety protections to handle. In order to effectively identify vulnerabilities and intrusions in mobile online systems, this study suggests a hybrid cybersecurity framework that combines Ant Colony Optimisation (ACO) with Randomised Decision Tree Classifier (RDTC). Unlike existing optimisation-classifier hybrids the proposed ACO-RDTC introduces an adaptive feature-subset refinement mechanism specifically tailored for mobile web environments enabling efficient handling of dynamic traffic patterns and resource limitations. The approach employs comprehensive data preprocessing using Singular Value Decomposition (SVD) for dimensionality reduction as well as SelectKBest for appropriate feature selection. This combination was chosen because RDTC effectively manages heterogeneous decision boundaries, ACO offers excellent global optimisation capabilities as well as SVD with SelectKBest aids in noise reduction while preserving crucial discriminative features. detection and cyber threats. ACO further improves RDTC performance by dynamically optimizing hyperparameters and selecting the most discriminative feature subsets. Using the CSE-CIC-IDS 2018 dataset, the proposed ACO-RDTC model achieved 99.08% accuracy, 98.50% precision, 99.08% recall and 98.74% F1-score, outperforming existing tactics as well as considerably reducing false positives. Furthermore an ablation study validated the contribution of each module (SVD, SelectKBest and ACO), while cross-dataset testing on UNSW-NB15 recognised the model's generalizability with 97.82% accuracy, emphasising its robustness as well as scalability for securing modern mobile web environments.

The task allocation decision-making architecture for Single Pilot Operation (SPO) represents a critical development direction in aviation to address soaring operational costs and the global pilot shortage. This paper systematically reviews the synergistic enabling mechanisms of Reinforcement Learning (RL) and Deep Learning (DL) within this architecture. DL serves as the perceptual foundation, processing visual information via CNNs and optimizing human-machine interaction through Transformers to achieve efficient multimodal data comprehension and situational awareness. RL functions as the decision core, leveraging methods such as multi-agent Proximal Policy Optimization (PPO) and Deep Q-Network (DQN) to model complex task allocation problems as Markov Decision Processes, enabling dynamic resource scheduling and multi-constraint optimization. Through deep integration in a “perception-decision-optimization” closed-loop, this dual approach significantly enhances the SPO system’s responsiveness, robustness, and safety in high-real-time, high-uncertainty environments. This collaborative mechanism provides critical theoretical foundations and technical pathways for developing next-generation intelligent aviation systems compliant with airworthiness standards and enabling efficient human-machine collaboration.

Integrating the Confucian perspective on error with the modern scientific “trial-and-error” approach, this study proposes the concept of “Error-sharing Teachers”. This framework encourages teachers to systematically identify domain-specific misconceptions and intentionally share their own pedagogical trial-and-error experiences, thereby transforming private failures into dynamic curricular resources. Consequently, a teacher’s professional authority no longer rests on infallibility, but on an expert understanding of the epistemic pathways of error and the demonstrated ability to guide learners through error-tolerant inquiry. The study redefines the pedagogical relationship, presenting education as a guided, collaborative trial-and-error process built on mutual empowerment, thereby facilitating a meaningful epistemic integration of traditional cultural insights with contemporary educational paradigms.

Engaging adolescents in public health decision-making is critical to ensuring their needs are met. However, a common barrier for adolescents to engage is a lack of known opportunities available to participate, such as through youth advisory groups (YAGs). We created a dynamic public health resource: a publicly available, interactive online map that synthesises over 400 YAGs across Australia, increasing the visibility of opportunities for adolescents to engage in decisions affecting their health, wellbeing and futures.

Correction: Estimation of dynamic geologic CO2 storage resources in the Illinois basin, including effects of brine extraction, anisotropy, and hydrogeologic heterogeneity

The Internet of Things (IoT) has advanced Smart City services through extensive device connectivity. LoRa, a leading LPWAN technology, provides long-range communication with low power consumption but suffers from scalability, latency, and energy-efficiency challenges in dense urban settings. To address these issues, this study introduces an integrated optimization framework that combines adaptive data rate (ADR) control, multi-channel communication, and dynamic resource allocation. The framework aims to reduce transmission delays, minimize packet collisions, and improve overall energy performance. It leverages multi-channel communication to distribute traffic, resource scheduling to prioritize critical data, and ADR to adjust transmission power and data rate based on real-time network conditions. Large-scale simulations conducted in OMNET++ demonstrate significant improvements over standard LoRa configurations, including baseline, ADR-only, and multi-channel setups. In a representative urban environment, the proposed framework achieved packet delivery rates of approximately 92.3% at 300 nodes and 85.7% at 900 nodes, while maintaining low latency and energy consumption. Overall, the integrated approach delivers robust performance across varying node densities, making it a strong candidate for future large-scale IoT deployments in Smart City architectures.

This article presents a framework for integrating advanced machine learning models within PostgreSQL to optimize query performance and manage workloads dynamically. The integration creates a paradigm shift from static, rule- based optimization to adaptive, data-driven approaches that respond to changing conditions. PostgreSQL's extensible architecture provides an ideal foundation for implementing ML-enhanced components without modifying core database code. The framework encompasses four key areas: query optimizer enhancement using gradient boosting and neural networks, adaptive indexing mechanisms that automatically adjust to workload patterns, dynamic resource allocation through workload classification and forecasting, and a comprehensive model training pipeline. Experimental evaluations across analytical, transactional, and hybrid workloads demonstrate significant improvements in cardinality estimation accuracy, execution plan quality, resource utilization, and administrative overhead reduction. The modular design enables incremental adoption in production environments while maintaining compatibility with existing applications, illustrating how traditional relational database systems can evolve to meet modern data challenges through machine learning integration.