Search Results (2,591)

High denitration efficiency and strong adaptability to flue gas temperature fluctuations are the core properties of the NH₃-SCR catalyst. In this study, Fe₂O₃ modification is used as a means to explore the mechanism of adding Fe₂O₃ to broaden the temperature range of the 6CeO₂-40MnO₂/TiO₂ catalyst during the preparation process. The results show that the 6Fe₂O₃-6CeO₂-40MnO₂/TiO₂ catalyst exhibits excellent denitration performance, with a denitration efficiency higher than 90%. The temperature range is from 129 to 390 °C. N₂ selectivity and resistance to SO₂ and H₂O are good, and the denitration performance is significantly improved. When the Fe₂O₃ content is 6%, it promotes lattice shrinkage of TiO₂, improves its dispersion, refines the grain size, and increases the specific surface area of the catalyst. At the same time, Fe₂O₃ enhances the chemical adsorption of oxygen on the catalyst surface and increases the proportion of low-cost metal ions, thereby promoting electron transfer between active elements, generating more surface reactive oxygen species, increasing the oxygen vacancy content and adsorption sites for NO_x and NH₃, and significantly improving the redox performance of the catalyst. This effect is particularly conducive to the formation of strong acid sites on the catalyst surface. The NH₃-SCR reaction on the surface of the 6Fe₂O₃-6CeO₂-40MnO₂/TiO₂ catalyst follows both the L-H and E-R mechanisms, with the L-H mechanism being dominant. Full article

Radio astronomy requires precise target localization and tracking to ensure accurate observations. Conventional regulation methodologies, encompassing PID controllers, frequently encounter difficulties due to orientation inaccuracies precipitated by mechanical limitations, environmental fluctuations, and electromagnetic interferences. To tackle these obstacles, this investigation presents a reinforcement learning (RL)-oriented framework for high-accuracy monitoring in radio telescopes. The suggested system amalgamates a localization control module, a receiver, and an RL tracking agent that functions in scanning and tracking stages. The agent optimizes its policy by maximizing the signal-to-noise ratio (SNR), a critical factor in astronomical measurements. The framework employs a reconditioned 12-m radio telescope at King Mongkut’s Institute of Technology Ladkrabang (KMITL), originally constructed as a satellite earth station antenna for telecommunications and was subsequently refurbished and adapted for radio astronomy research. It incorporates dual-axis servo regulation and high-definition encoders. Real-time SNR data and streaming are supported by a HamGeek ZedBoard with an AD9361 software-defined radio (SDR). The RL agent leverages the Proximal Policy Optimization (PPO) algorithm with a self-attention actor–critic model, while hyperparameters are tuned via Optuna. Experimental results indicate strong performance, successfully maintaining stable tracking of randomly moving, non-patterned targets for over 4 continuous hours without any external tracking assistance, while achieving an SNR improvement of up to 23.5% compared with programmed TLE-based tracking during live satellite experiments with Thaicom-4. The simplicity of the framework, combined with its adaptability and ability to learn directly from environmental feedback, highlights its suitability for next-generation astronomical techniques in radio telescope surveys, radio line observations, and time-domain astronomy. These findings underscore RL’s potential to enhance telescope tracking accuracy and scalability while reducing control system complexity for dynamic astronomical applications. Full article

The Retrieval-Augmented Generation (RAG) framework shows great potential in terms of improving the reasoning and knowledge utilization capabilities of language models. However, most existing RAG systems heavily rely on large language models (LLMs) and suffer severe performance degradation when using small language models (SLMs), which limits their efficiency and deployment in resource-constrained environments. To address this challenge, we propose Retrieval-Adaptive-Planning RAG (RAP-RAG), a lightweight and high-efficiency RAG framework with adaptive retrieval task planning that is compatible with both SLMs and LLMs simultaneously. RAP-RAG is built on three key components: (1) a heterogeneous weighted graph index that integrates semantic similarity and structural connectivity; (2) a set of retrieval methods that balance efficiency and reasoning power; and (3) an adaptive planner that dynamically selects appropriate strategies based on query features. Experiments on the LiHua-World, MultiHop-RAG, and Hybrid-SQuAD datasets show that RAP-RAG consistently outperforms representative baseline models such as GraphRAG, LightRAG, and MiniRAG. Compared to lightweight baselines, RAP-RAG achieves 3–5% accuracy improvement while maintaining high efficiency and maintains comparable efficiency in both small and large model settings. In addition, our proposed framework reduces storage size by 15% compared to mainstream frameworks. Component analysis further confirms the necessity of weighted graphs and adaptive programming for robust retrieval under multi-hop reasoning and heterogeneous query conditions. These results demonstrate that RAP-RAG is a practical and efficient framework for retrieval-enhanced generation, suitable for large-scale and resource-constrained scenarios. Full article

By combining social, ecological, and communal elements, urban parks significantly improve the quality of urban life. This paper explores the revitalization proposal for Areos Park in Tripoli, Greece, viewing it as an urban palimpsest reflecting layers of history, culture, and nature. While historically evolving from exclusive enclaves to vital public spaces fostering social equity and well-being, many urban parks, including those in Greece, suffer from neglect and underfunding, diminishing their landscape value and necessitating revitalization. Areos Park exemplifies these challenges, making it an ideal case study for exploring effective urban park revitalization strategies and demonstrating how urban areas can host critical landscape functions. Utilizing a design-based research (DBR) methodology, a design plan is proposed. The architectural concept focuses on revamping key areas, restoring historical features, adding small constructions, and repurposing existing buildings for community and educational uses. Concurrently, the landscape concept emphasizes biodiversity enrichment and ecological restoration through permeable surfaces and native Mediterranean vegetation, contributing to urban resilience to climate change. The overall design prioritizes accessibility and spatial connectedness to create an inclusive, resilient, and adaptable urban park addressing contemporary sustainability challenges and biodiversity loss. The project aims to establish Areos Park as a model for sustainable urban park regeneration in small cities, blending ecological enhancement with historical preservation. Full article

In today’s fast-paced world, secure and efficient access control is crucial for places like schools, gated communities, and corporate campuses. The system must overcome the issues of manual checking and record maintenance of traditional methods like RFID cards or license plate recognition. Our work introduces a budget-friendly, automated solution. A prototype was developed for a vehicle sticker recognition system to control and monitor gate access at NFC IET University as a case study. The automated system design will replace manual checking by detecting the car stickers issued to each vehicle by the university administration. An optimized lightweight YOLOv8 model is trained to identify three categories: IET stickers (authorized for access), non-IET stickers (unauthorized), and no sticker (denied access). A webcam connected to the Raspberry Pi 4 scans approaching vehicles. Authorized vehicles are allowed when the relevant class is detected, which signals a servo motor to open the gate. Otherwise, access to the gate is denied, and infrared (IR) sensors close the gates. A second set of IR sensors and a servo motor was also added to manage the exit side, preventing unauthorized tailgating. The system’s modular design makes it adaptable for different environments, and its use of affordable hardware and open-source tools keeps costs low, which is ideal for smaller institutions or communities. The prototype model is tested and trained on self-collected datasets comprising 506 images. Full article

Understanding the impact of climate change and altered hydrologic cycles on regional water storage trends is crucial for predicting changes in recharge and streamflow and informing decisions regarding drought resilience and flood mitigation. While many regions have become drier under global climate change, the northeast United States has experienced an increased precipitation intensity, driving groundwater rise. This study integrates terrestrial water storage data from NASA’s Gravity Recovery and Climate Experiment (GRACE) satellites and soil moisture data from Soil Moisture Active Passive (SMAP), as well as long-term instrumental groundwater records from USGS groundwater monitoring wells, to understand the nature of storage trends. The results show that while aquifer-wide groundwater storage anomalies have stabilized in recent years, shallow groundwater and certain surface water bodies have accumulated about 0.6 cm of water annually, adding over 10 cm to the landscape, since 2005. These findings indicate that excess water from heavy rainfall is mainly stored in the shallow subsurface as perched aquifers and temporary wetlands rather than deep (5–30 m) aquifers. Understanding this change in storage is crucial for improving water resource management and adapting more effectively to a changing climate in the region. Full article

Background: The COVID-19 pandemic disrupted school life worldwide, heightening risks to students’ psychosocial well-being and mental health, and creating an urgent need for sustainable support strategies during crises. Drama-based interventions, as participatory arts-based approaches, are proposed as flexible interventions that can strengthen resilience, social interaction, and emotional expression in school communities. Objective: This study evaluated the impact of a large-scale, short-term, remote drama-based intervention on the psychosocial adjustment and well-being of primary school students during the pandemic. Methods: An embedded mixed methods design with a pre-post measurement was employed, involving 239 teachers and 719 students aged 9–13 years from schools across various regions of Greece. Psychosocial functioning was assessed using a standardized instrument measuring levels of social, school, and emotional competence, as well as behavioral difficulties. The intervention, totaling 700 min over seven weeks, followed a five-day weekly structure that combined health-focused and psychosocial activities. Results: Quantitative findings indicated improvements across several dimensions of psychosocial adaptation and well-being, while Reliable Change Index analysis revealed important individual-level changes. Qualitative data corroborated these results, highlighting enhanced peer collaboration, increased emotional expression, and stronger classroom cohesion, while also emphasizing the adaptability and scalability of the approach under restrictive conditions. Conclusions: The findings suggest that such artful interventions can make a meaningful contribution to promoting well-being and sustaining the educational and social life of school communities during public health emergencies, thereby adding to the applied psychology evidence based on effective school health interventions. Full article

Few publications have elaborated on the steady-state model, a ‘cybernetic model’ that should not be confused with its namesake in mathematical systems; it is rooted in preceding publications about general systems theories, something that is explored in this paper. First, the origins of homeostasis are briefly addressed as derived from biological concepts. After looking at its legacy the concepts of boundary zones from socio-economic theory, Shannon’s information theory, control mechanisms and engineering principles are added as a multidisciplinary amalgamation to modelling of primary, recurrent processes. The resulting cybernetic steady-state model offers a generic transdisciplinary framework for depicting regulatory and control processes within organisational and engineering systems as well as interaction between agents in networks. In the latter sense, it provides an explanatory concept for self-criticality in complex adaptive systems. Hence, it does not only have a rich heritage but also wide-ranging potential. For example, the steady-state model could be used to support action research and case studies in addition to serving as a reference model for other (business) process modelling techniques. Therefore, the steady-state model offers an enrichment of existing approaches to modelling recurrent processes —not only for operations management; it can also be seen as a more practical extension of Miller’s living systems and Beer’s viable system model making it suitable for a broad range of applications. Full article

The study evaluated the influence of dietary supplementation with nano-encapsulated cumin oil, B. subtilis, or a combination of both to mitigate the impacts of heat stress on the performance and health of growing rabbits. In the feeding trial, a total of eighty-four growing New Zealand White (35 days, 781.3 ± 1.8 g average body weight) were randomly distributed in a completely randomized design into four groups; each had 21 rabbits arranged in 7 replicates (3 rabbits each). The experiment lasted 42 days (35 days to 77 days). Growing rabbits received a basal diet (first group, CON) without additives, while the other groups were supplemented with nano-encapsulated cumin oil (NECO, 200 mg/kg), B. subtilis (BS, 500 mg/kg), or both (BSNO, 500 mg BS plus 200 mg/kg NECO). Adding BSNO significantly enhanced body weight gain, carcass weight, and feed conversion ratio and reduced mortality rate (p < 0.05). Additionally, the BSNO enhanced digestive system performance by increasing the secretion of trypsin enzymes, as well as nutrient digestibility, especially for protein and fiber (p < 0.05). Supplementing BSNO enhanced oxidative stability and immunity via higher levels of superoxide dismutase (SOD), IgA, IgG, triiodothyronine (T3), thyroxine (T4) and lower malondialdehyde (MDA) levels (p < 0.05), indicating a better ability to adapt to stress. During the examination of gut health, pathogenic bacteria counts decreased, as well as down-regulation of interleukin-6 (IL-6) gene expression and up-regulation of cationic amino acid transporter-1 (CAT-1), interleukin-10 (IL-10), and mucin-2 (MUC-2) gene expression (p < 0.05), supporting gut integrity. This study highlights the potential of mixing nano-encapsulated cumin oil and B. subtilis in growing rabbits’ diets as an effective strategy to counteract the negative effects of heat stress caused by high ambient temperatures. Full article

This paper presents a comparative empirical analysis of three modeling approaches applied to the forecasting of Morocco’s Real Effective Exchange Rate (REER): the ARIMA model, the Random Forest algorithm, and the Dynamic Factor Model (DFM). Utilizing a comprehensive macroeconomic quarterly dataset spanning from 1999Q4 to 2021Q3, the study assesses the out-of-sample predictive performance of these models over a structurally dynamic period, including the transition to a more flexible exchange rate regime in 2018 and the global shock induced by the COVID-19 pandemic. The findings reveal that the Random Forest model significantly outperforms both ARIMA and DFM in terms of accuracy and adaptability to structural breaks. Variable importance analysis highlights the dominant role of real economic fundamentals, particularly industrial value added, inflation, and exports in explaining REER movements. In contrast, the ARIMA model underreacts to exogenous shocks due to its univariate structure, while the DFM suffers from a loss of predictive power likely caused by excessive dimensionality reduction. Full article

Vehicular Ad-Hoc Networks (VANETs) play a critical role in Intelligent Transportation Systems (ITS), enabling communication between vehicles and roadside infrastructure. This paper proposes an Adaptive Context-Aware VANET Routing (ACAVR) protocol designed to handle the challenges of high mobility, dynamic topology, and variable vehicle density in urban environments. The proposed protocol integrates context-aware routing, dynamic clustering, and geographic forwarding to enhance performance under diverse traffic conditions. Simulation results demonstrate that ACAVR achieves higher throughput, improved packet delivery ratio, lower end-to-end delay, and reduced routing overhead compared to existing routing schemes. The proposed ACAVR outperforms benchmark protocols such as DyTE, RGoV, and CAEL, improving PDR by 12–18%, reducing delay by 10–15%, and increasing throughput by 15–22%. Full article

Thermodynamic fault diagnosis of marine steam turbines remains challenging due to non-stationary multivariate sensor data under stochastic loads and transient conditions. While conventional threshold-based methods lack the sophistication for such dynamics, existing data-driven Transformers struggle with inherent non-stationarity. To address this, we propose a hybrid DLinear–Transformer framework that synergistically integrates localized trend decomposition with global feature extraction. The model employs a dual-branch architecture with adaptive positional encoding and a gated fusion mechanism to enhance robustness. Extensive evaluations demonstrate the framework’s superiority: on public benchmarks (SMD, SWaT), it achieves statistically significant F1-score improvements of 2.7% and 0.3% over the state-of-the-art TranAD model under a controlled, reproducible setup. Most importantly, validation on a real-world marine steam turbine dataset confirms a leading fault detection accuracy of 94.6% under variable conditions. By providing a reliable foundation for identifying precursor anomalies, this work establishes a robust offline benchmark that paves the way for practical predictive maintenance in marine engineering. Full article

Image-based geo-localization is a challenging task that aims to determine the geographic location of a ground-level query image captured by an Unmanned Ground Vehicle (UGV) by matching it to geo-tagged nadir-view (top-down) images from an Unmanned Aerial Vehicle (UAV) stored in a reference database. The challenge comes from the perspective inconsistency between matched objects. In this work, we propose a novel metric learning scheme for hard exemplar mining to improve the performance of cross-view geo-localization. Specifically, we introduce a Dynamic Online Cross-Batch (DOCB) hard exemplar mining scheme that solves the problem of the lack of hard exemplars in mini-batches in the middle and late stages of training, which leads to training stagnation. It mines cross-batch hard negative exemplars according to the current network state and reloads them into the network to make the gradient of negative exemplars participating in back-propagation. Since the feature representation of cross-batch negative examples adapts to the current network state, the triplet loss calculation becomes more accurate. Compared with methods only considering the gradient of anchors and positives, adding the gradient of negative exemplars helps us to obtain the correct gradient direction. Therefore, our DOCB scheme can better guide the network to learn valuable metric information. Moreover, we design a simple Siamese-like network called multi-scale feature aggregation (MSFA), which can generate multi-scale feature aggregation by learning and fusing multiple local spatial embeddings. The experimental results demonstrate that our DOCB scheme and MSFA network achieve an accuracy of 95.78% on the CVUSA dataset and 86.34% on the CVACT_val dataset, which outperforms those of other existing methods in the field. Full article

Secure authentication in vehicular ad hoc networks (VANETs) remains a fundamental challenge due to their dynamic topology, susceptibility to attacks, and scalability constraints in multi-hop communication. Existing approaches based on elliptic curve cryptography (ECC), blockchain, and fog computing have achieved partial success but suffer from latency, resource overhead, and limited adaptability, leaving a gap for lightweight and hardware-rooted trust models. To address this, we propose a multi-hop mutual authentication protocol leveraging Physical Unclonable Functions (PUFs), which provide tamper-evident, device-specific responses for cryptographic key generation. Our design introduces a structured sequence of phases, including pre-deployment, registration, login, authentication, key establishment, and session maintenance, with optional multi-hop extension through relay vehicles. Unlike prior schemes, our protocol integrates fuzzy extractors for error tolerance, employs both inductive and game-based proofs for security guarantees, and maps BAN-logic reasoning to specific attack resistances, ensuring robustness against replay, impersonation, and man-in-the-middle attacks. The protocol achieves mutual trust between vehicles and RSUs while preserving anonymity via temporary identifiers and achieving forward secrecy through non-reused CRPs. Conceptual comparison with state-of-the-art PUF-based and non-PUF schemes highlights the potential for reduced latency, lower communication overhead, and improved scalability via cloud-assisted CRP lifecycle management, while pointing to the need for future empirical validation through simulation and prototyping. This work not only provides a secure and efficient solution for VANET authentication but also advances the field by offering the first integrated taxonomy-driven evaluation of PUF-enabled V2X protocols in multi-hop Wi-Fi environments. Full article

Background: During the COVID-19 pandemic, transgender and gender-diverse (TGD) people and people with disabilities in India faced disproportionate barriers to accessing vaccination services. Building on previous studies, this study explored the experiences of COVID-19 vaccine access in these two marginalized communities, using the WHO Behavioral and Social Drivers (BeSD) framework. Methods: Keeping community-based participatory methods (CBPR) at heart, we conducted a survey adapted from the BeSD COVID-19 survey tool. The survey was adapted using insights from a prior study, a literature review, stakeholder consultations, and discussions with a community leadership group (CLG) and an advisory board (AdB). Participants were recruited through transgender, gender-diverse, and disability rights networks. Data were analyzed descriptively, using percent analysis, and psychometrically, using exploratory factor analysis on polychoric correlations. Results: The adapted BeSD survey tool showed a high 0.85 (p < 0.05) internal consistency and criterion validity. Moreover, it showed a high willingness to be vaccinated (for ease of access to other services and community responsibility); however, systemic barriers hindered vaccination access. TGD people and people with disabilities faced multiple barriers in being vaccinated. The TGD community reported documentation mismatches and mistrust in health systems. People with disabilities reported mobility challenges, escort dependence, financial challenges, and variable accessibility at vaccination sites. Both groups faced digital exclusion, received inadequate information that did not address their specific needs, and experienced inconsistent implementation of inclusive policies. Community-led facilitation led to more uptake. Conclusions: Vaccine willingness alone is insufficient to ensure that vaccines reach everyone. Addressing trust deficits, infrastructural barriers, and digital exclusions requires diligent attention and commitment from the government to mitigate broader challenges faced by TGD people and people with disabilities. Full article