Search Results (2,966)

In this paper, we present a unique method to determine the level of bot contamination of web-based user agents. It is common practice for bots and robotic agents to masquerade as human-like to avoid content and performance limitations. This paper continues our previous work, using over 600 million web log entries collected from over 4000 domains to derive and generalize how the prominence of specific web browser versions progresses over time, assuming genuine human agency. Here, we introduce a parametric model capable of reproducing this progression in a tunable way. This simulation allows us to tag human-generated traffic in our data accurately. Along with the highest confidence self-tagged bot traffic, we train a Transformer-based classifier that can determine the bot contamination—a botness metric of user-agents without prior labels. Unlike traditional syntactic or rule-based filters, our model learns temporal patterns of raw and heuristic-derived features, capturing nuanced shifts in request volume, response ratios, content targeting, and entropy-based indicators over time. This rolling window-based pre-classification of traffic allows content providers to bin streams according to their bot infusion levels and direct them to several specifically tuned filtering pipelines, given the current load levels and available free resources. We also show that aggregated traffic data from multiple sources can enhance our model’s accuracy and can be further tailored to regional characteristics using localized metadata from standard web server logs. Our ability to adjust the heuristics to geographical or use case specifics makes our method robust and flexible. Our evaluation highlights that 65% of unclassified traffic is bot-based, underscoring the urgency of robust detection systems. We also propose practical methods for independent or third-party verification and further classification by abusiveness. Full article

Between 2020 and 2024, museums underwent accelerated digital transformation driven by the global health crisis and technological advances, exposing deep inequalities in access to technology and communication capabilities. Museums with limited resources had to rethink their digital strategies to sustain audience engagement and cultural relevance. This article presents a comparative study of museums in Argentina and Spain with restricted budgets, analyzing how they responded to challenges of uncertainty and scarcity. Using a mixed methodology—surveys of 22 professionals, interviews with directors of four representative museums, and qualitative case studies—this study examines the implemented solutions and their impacts. The findings highlight innovative practices grounded in creativity, strategic alliances, and intensive use of social media. Argentine museums excelled in tactical adaptation amid economic instability, while Spanish institutions showed stronger strategic planning. Private museums proved more flexible than their public counterparts, which faced greater bureaucratic constraints. This work contributes to debates on institutional resilience and offers a framework for sustainable digital communication in resource-limited contexts. Full article

Grid-based distribution networks represent an advanced form of smart grids that enable modular, region-specific optimization of power resource allocation. This paper presents a novel planning framework aimed at the coordinated deployment of distributed generation, electrical loads, and energy storage systems, including both dispatchable and non-dispatchable electric vehicles. A three-dimensional objective system is constructed, incorporating investment cost, reliability metrics, and network loss indicators, forming a comprehensive multi-objective optimization model. To solve this complex planning problem, an improved version of the NSGA-II is employed, integrating hybrid encoding, feasibility constraints, and fuzzy decision-making for enhanced solution quality. The proposed method is applied to the IEEE 33-bus distribution system to validate its practicality. Simulation results demonstrate that the framework effectively addresses key challenges in modern distribution networks, including renewable intermittency, dynamic load variation, resource coordination, and computational tractability. It significantly enhances system operational efficiency and electric vehicles charging flexibility under varying conditions. In the IEEE 33-bus test, the coordinated optimization (Scheme 4) reduced the expected load loss from 100 × 10⁻⁴ yuan to 51 × 10⁻⁴ yuan. Network losses also dropped from 2.7 × 10⁻⁴ yuan to 2.5 × 10⁻⁴ yuan. The findings highlight the model’s capability to balance economic investment and reliability, offering a robust solution for future intelligent distribution network planning and integrated energy resource management. Full article

Robotics can help address the growing worker shortage challenge of the manufacturing industry. As such, machine tending is a task collaborative robots can tackle that can also greatly boost productivity. Nevertheless, existing robotics systems deployed in that sector rely on a fixed single-arm setup, whereas mobile robots can provide more flexibility and scalability. We introduce a multi-agent multi-machine-tending learning framework using mobile robots based on multi-agent reinforcement learning (MARL) techniques, with the design of a suitable observation and reward. Moreover, we integrate an attention-based encoding mechanism into the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm to boost its performance for machine-tending scenarios. Our model (AB-MAPPO) outperforms MAPPO in this new challenging scenario in terms of task success, safety, and resource utilization. Furthermore, we provided an extensive ablation study to support our design decisions. Full article

This paper introduces the GOLEM ontology, a novel framework designed to provide a structured and computationally tractable representation of narrative and fictional elements. Addressing limitations in existing ontologies regarding the integration of fictional entities and diverse narrative theories, our model extends CIDOC CRM and LRMoo and leverages DOLCE’s cognitive foundations to provide a flexible and interoperable framework. The ontology captures complexities of narrative structure, character dynamics, and fictional worlds while supporting provenance tracking and pluralistic interpretations. The modular structure facilitates alignment with various literary and narrative theories and integration of external resources. Future work will focus on expanding domain-specific extensions, validating the model through larger-scale case studies, and developing a reader response module to systematically model the reception of narratives. By fostering interoperability between literary theory, fan cultures, and computational analysis, this ontology lays a foundation for interoperable comparative research on narrative and fiction. Full article

Crops nitrogen supply through the in situ mineralization of soil organic matter is a critical process for plant nutrition. However, accurately estimating the contribution of mineralization remains challenging. The complexity of biological, chemical, and physical processes in the soil, influenced by environmental conditions, makes it difficult to precisely quantify the amount of nitrogen available for crops. In this study, we created a database by collecting results from 121 mineralization monitoring experiments carried out between 2015 and 2021 on different experimental plots across Wallonia, Southern Belgium, and assessed the efficiency of predictive mineralization methods. The most impactful analytical parameters on in situ mineralization (ISM), determined using LIXIM program, appeared to be potentially mineralizable nitrogen (PMN) (r = 0.79). PMN, estimated by anaerobic soil incubation, also allowed the effective consideration of the after-effects of grassland termination and manure inputs. A multiple linear regression (MLR) combining PMN, POxC, pH, TOC:N, and TOC:clay significantly improved the prediction of soil nitrogen mineralization available for crops, achieving r = 0.87 (vs. r = 0.58 for the current method), while reducing dispersion by 41% (RMSE 56.35 → 33.13 kg N·ha⁻¹). The use of a more flexible Bootstrap Forest model (BFM) further enhanced performance, reaching r = 0.92 and a 50.8% reduction in dispersion compared to the current method (RMSE 56.35 → 27.76 kg N·ha⁻¹), i.e., about 16% lower RMSE than the MLR. Those models provided practical and efficient tools to better manage nitrogen resources in temperate agricultural systems. Full article

With the high penetration of renewable energy integrated into the power grid, the system exhibits strong randomness and volatility. To balance these uncertainties, a large amount of flexible regulating resources is required. This paper proposes an optimization method based on a Seq2Seq Transformer model, which takes stochastic renewable energy and load data as inputs and outputs the allocation ratios of various regulating resources. The method considers renewable energy stochasticity, power flow constraints, and adjustment characteristics of different regulating resources, while constructing a multi-objective loss function that integrates ramping response matching and cost minimization for comprehensive optimization. Furthermore, a multi-feature perception attention mechanism for stochastic renewable energy is introduced, enabling better coordination among resources and improved ramping speed adaptation during both model training and result generation. A multi-solution optimization framework with Pareto-optimal filtering is designed, where the Decoder outputs multiple sets of diverse and balanced allocation ratio combinations. Simulation studies based on a regional power grid demonstrate that the proposed method effectively addresses the problem of regulating resource capacity optimization in new-type power systems. Full article

Demand response (DR) is increasingly recognized as a critical flexibility resource for modernizing power systems, enabling the large-scale integration of renewable energy and enhancing grid stability. While the field of general electricity load forecasting is supported by numerous systematic reviews, the specific subfield of DR potential forecasting has received comparatively less synthesized attention. This gap leaves a fragmented understanding of modeling techniques, practical implementation challenges, and future research problems for a function that is essential for market participation. To address this, this paper presents a PRISMA-2020-compliant systematic review of 172 studies to comprehensively analyze the state-of-the-art in DR potential estimation. We categorize and evaluate the evolution of forecasting methodologies, from foundational statistical models to advanced AI architectures. Furthermore, the study identifies key technological enablers and systematically maps the persistent technical, regulatory, and behavioral barriers that impede widespread DR deployment. Our analysis demonstrates a clear trend towards hybrid and ensemble models, which outperform standalone approaches by integrating the strengths of diverse techniques to capture complex, nonlinear consumer dynamics. The findings underscore that while technologies like Advanced Metering Infrastructure (AMI) and the Internet of Things (IoT) are critical enablers, the gap between theoretical potential and realized flexibility is primarily dictated by non-technical factors, including inaccurate baseline methodologies, restrictive market designs, and low consumer engagement. This synthesis brings much-needed structure to a fragmented research area, evaluating the current state of forecasting methods and identifying the critical research directions required to improve the operational effectiveness of DR programs. Full article

This study examines digital hospitality as a socio-technical system in which technological adoption and human resource (HR) practices jointly shape guest experiences and workforce dynamics. The research is situated at CitizenM hotels in Paris, a brand recognized for its integration of mobile applications, automated check-in, and the ambassador model of flexible role design. A mixed-methods approach was applied, combining a guest survey (n = 517) with semi-structured interviews with managers. Exploratory and confirmatory factor analyses confirmed a five-factor structure of guest perceptions: Digital Efficiency, Smart Personalization, Service Satisfaction, Trusted Security, and Digital Loyalty. Structural equation modeling showed that efficiency significantly drives satisfaction, while personalization and security strongly predict loyalty. Managerial insights revealed that these outcomes rely on continuous investment in training, mentorship, and flexible role allocation. Overall, the findings suggest that digital transformation enhances value creation not by substituting but by reconfiguring human service, with technology alleviating routine tasks and enabling employees to focus on relational and creative aspects of hospitality. The study concludes that effective digital hospitality requires the alignment of technological innovation with supportive HR practices, ensuring both guest satisfaction and employee motivation. Full article

With the rapid development of artificial intelligence technology in the field of intelligent manufacturing, convolutional neural networks (CNNs) have shown excellent performance and generalization capabilities in industrial applications. However, the huge computational and resource requirements of CNNs have brought great obstacles to their deployment on low-end hardware platforms. To address this issue, this paper proposes a scalable CNN accelerator that can operate on low-performance Field-Programmable Gate Arrays (FPGAs), which is aimed at tackling the challenge of efficiently running complex neural network models on resource-constrained hardware platforms. This study specifically optimizes depthwise separable convolution and the squeeze-and-excite module to improve their computational efficiency. The proposed accelerator allows for the flexible adjustment of hardware resource consumption and computational speed through configurable parameters, making it adaptable to FPGAs with varying performance and different application requirements. By fully exploiting the characteristics of depthwise separable convolution, the accelerator optimizes the convolution computation process, enabling flexible and independent module stackings at different stages of computation. This results in an optimized balance between hardware resource consumption and computation time. Compared to ARM CPUs, the proposed approach yields at least a 1.47× performance improvement, and compared to other FPGA solutions, it saves over 90% of Digital Signal Processors (DSPs). Additionally, the optimized computational flow significantly reduces the accelerator’s reliance on internal caches, minimizing data latency and further improving overall processing efficiency. Full article

The transition to decarbonized and distributed energy systems has increased interest in flexibility markets as a key tool to manage variability and coordinate distributed energy resources. However, the fast growth and conceptual fragmentation of this field hinder the building of coherent models and scalable solutions. This paper presents a scoping review of 243 peer-reviewed articles published between 2013 and 2025, applying the TEAM Framework and Business Model Canvas. Through a structured data matrix of 35 variables, we analyze how flexibility is defined and modelled, the coordination mechanisms applied, and how business dimensions are integrated. The results reveal major inconsistencies in terminology, actor roles, price formation, and interoperability modelling. We identify critical gaps in cost modelling and business model integration, especially in low-TRL studies. This review provides a comprehensive and cross-cutting synthesis of existing approaches, offering a reference framework for future research, policy design, and market implementation of distributed flexibility mechanisms. Full article

Field-Programmable Gate Arrays (FPGAs) are increasingly shaping the future of network security, thanks to their flexibility, parallel processing capabilities, and energy efficiency. In this survey, we examine 50 peer-reviewed studies published between 2020 and 2025, selected from an initial pool of 210 articles based on relevance, hardware implementation, and the presence of empirical performance data. These studies encompass a broad range of topics, including cryptographic acceleration, intrusion detection and prevention systems (IDS/IPS), hardware firewalls, and emerging strategies that incorporate artificial intelligence (AI) and post-quantum cryptography (PQC). Our review focuses on five major application areas: cryptographic acceleration, intrusion detection and prevention systems (IDS/IPS), hardware firewalls, and emerging strategies involving artificial intelligence (AI) and post-quantum cryptography (PQC). We propose a structured taxonomy that organises the field by technical domain and challenge, and compare solutions in terms of scalability, resource usage, and real-world performance. Beyond summarising current advances, we explore ongoing limitations—such as hardware constraints, integration complexity, and the lack of standard benchmarking. We also outline future research directions, including low-power cryptographic designs, FPGA–AI collaboration for detecting zero-day attacks, and efficient PQC implementations. This survey aims to offer both a clear overview of recent progress and a valuable roadmap for researchers and engineers working toward secure, high-performance FPGA-based systems. Full article

Sustainable agricultural production systems are a global consensus. Their life-cycle economic feasibility is essential for long-term sustainable goals. This study integrates life-cycle costing with building energy simulation to assess the cost performance of conventional and innovative greenhouse tomato production systems in China’s Hexi Corridor, using dynamic thermal load modeling to overcome empirical-data limitations in traditional life-cycle costing. Under the facility-lease farming model, construction companies incur life-cycle costs of CNY 10.53·m⁻²·yr⁻¹ for the conventional concrete-walled Gobi solar greenhouse and CNY 10.45·m⁻²·yr⁻¹ for the innovative flexible insulation-walled Gobi solar greenhouses. However, farmer greenhouse contractors achieve 10.5% lower life-cycle costs for tomato cultivation in the conventional structure (CNY 2.87·kg⁻¹·yr⁻¹) than in the innovative one (CNY 3.21·kg⁻¹·yr⁻¹) due to 52.6% heating energy savings from the integrated active solar thermal systems. Furthermore, life-cycle cash flow analysis confirms construction companies incur non-viable returns, while farmers achieve substantial profits, with 52.5% higher cumulative profits obtained in the conventional greenhouse than the innovative greenhouse. This profit allocation imbalance threatens sustainability. Our pioneering stakeholder-perspective assessment provides evidence-based strategies for government, investors, and farmers to optimize resource allocation and promote sustainable Gobi agriculture. Full article

Future wireless networks are expected to deliver enhanced spectral efficiency while being energy efficient. MIMO and other non-orthogonal transmission schemes, such as non-orthogonal multiple access (NOMA), offer substantial theoretical spectral efficiency gains. However, these gains have yet to translate into practical deployments, largely due to limitations in current signal processing methods. Linear transceiver processing, though widely adopted, fails to fully exploit non-orthogonal transmissions, forcing massive MIMO systems to use a disproportionately large number of RF chains for relatively few streams, increasing power consumption. Non-linear processing can unlock the full potential of non-orthogonal schemes but is hindered by high computational complexity and integration challenges. Moreover, existing message-passing receivers for NOMA depend on specially designed sparse signals, limiting resource allocation flexibility and efficiency. This work presents NL-COMM, an efficient non-linear processing framework that translates the theoretical gains of non-orthogonal transmissions into practical benefits for both the uplink and downlink. NL-COMM delivers over 200% spectral efficiency gains, enables 50% reductions in antennas and RF chains (and thus base station power consumption), and increases concurrently supported users by 450%. In distributed MIMO deployments, the antenna reduction halves fronthaul bandwidth requirements, mitigating a key system bottleneck. Furthermore, NL-COMM offers the flexibility to unlock new NOMA schemes. Finally, we present both hardware and software architectures for NL-COMM that support massively parallel execution, demonstrating how advanced non-linear processing can be realized in practice to meet the demands of next-generation networks. Full article

This work explores the integration of Proton Exchange Membrane (PEM) electrolysis waste heat with district heating networks (DHN), aiming to enhance the overall energy efficiency and economic viability of hydrogen production systems. PEM electrolysers generate substantial amounts of low-temperature waste heat during operation, which is often dissipated and left unutilised. By recovering such thermal energy and selling it to district heating systems, a synergistic energy pathway that supports both green hydrogen production and sustainable urban heating can be achieved. The study investigates how the electrolyser’s operating temperature, ranging between 50 and 80 °C, influences both hydrogen production and thermal energy availability, exploring trade-offs between electrical efficiency and heat recovery potential. Furthermore, the study evaluates the compatibility of the recovered heat with common heat emission systems such as radiators, fan coils, and radiant floors. Results indicate that valorising waste heat can enhance the overall system performance by reducing the electrolyser’s specific energy consumption and its levelized cost of hydrogen (LCOH) while supplying carbon-free thermal energy for the end users. This integrated approach contributes to the broader goal of sector coupling, offering a pathway toward more resilient, flexible, and resource-efficient energy systems. Full article