Search Results (26,939)

The growing application of nanotechnology in wood modification has led to significant improvements in the durability, fire resistance, and biological stability of wood-based building materials, such as glued laminated timber (GLT), as well as related chemical products, including fire retardants and anticorrosion preservatives. While numerous review papers have focused on material performance and functionalisation strategies, a comprehensive analysis of the research methodologies employed in this field remains limited. This review addresses this gap by systematically examining the experimental and analytical methods used in studies on nanomaterial-modified wood surface treatments. Scientific articles published and indexed in the Web of Science and Scopus databases within the last ten years were selected using keywords related to wood, nanotechnology, and surface applications simulating industrial timber treatment processes applied in factories and construction sites. Publications were screened according to predefined inclusion and exclusion criteria. The study selection process was conducted according to the PRISMA methodology, and 74 studies meeting the inclusion criteria were selected for the final analysis. Extracted methodological features were coded and analysed using frequency-based descriptive statistics. Considerable methodological heterogeneity was observed among the analysed studies. Softwood species, TiO₂- and ZnO-based nanomaterials, and brushing or immersion treatments represented the most frequently investigated research configurations. Scanning electron microscopy (SEM), often combined with EDS and XRD analyses, occupied a central role within the analytical framework of nanomodified wood research. In contrast, long-term durability assessments, biological resistance testing, and fire-performance evaluations were comparatively underrepresented. The review also revealed substantial variability in the use of testing standards and statistical methods. By linking research methodologies to normative requirements for construction materials, this work provides a methodological framework supporting future research, standardisation, certification, and commercial implementation of nanomaterial-based wood protection systems. Full article

Accurate thermal management is crucial for ensuring the safety, longevity, and performance of lithium-ion batteries, especially in compact embedded systems like USB chargers, power banks, and IoT nodes. Despite extensive research on predictive thermal models and intelligent control frameworks, their implementation in resource-constrained microcontroller-class devices has been limited. Existing strategies in the literature, such as threshold-based or PID logic, cloud-enabled analytics, machine learning models, and observer-based estimators, are often reactive, computationally intensive, or dependent on external infrastructure, making them unsuitable for low-power, standalone applications. This study introduces a novel Scalable Embedded Thermal Intelligence architecture designed for real-time battery thermal regulation in locally executable, without cloud dependency, low-cost platforms. Unlike conventional methods, the proposed system operates entirely on-device using closed-form models implemented on an ESP32 microcontroller. It combines two synergistic algorithms: a static preemptive model that calculates a safe C-rate at startup based solely on ambient and initial battery temperature, and a dynamic disturbance-aware model that monitors temperature rise per SOC step and adjusts airflow or current adaptively without requiring high memory, floating-point units, or supervisory control. The architecture achieves sub-second response times, <7% RAM, and <25% Flash usage, and does not need cloud connectivity, simulation backend, or complex thermal-management infrastructures such as liquid cooling circuits, phase-change systems, or cloud-supervised architectures. The significant contribution of this work is not the introduction of a new electrochemical–thermal formulation, but the effective integration and application of previously validated closed-form thermal predictors on low-cost microcontroller-class hardware, designed for anticipatory battery thermal regulation while adhering to strict computational limitations. Compared to traditional battery thermal management systems using PCM, liquid-cooling circuits, or cloud-based predictive estimators, the proposed approach eliminates the need for complex thermal hardware, fluidic systems, external computing infrastructure and resource-efficient edge operation. This makes the system suitable for deployment in real-world embedded applications like USB-C smart charging cables, compact IoT power banks, and portable medical devices, where form factors, energy efficiency, and cost are critical. The proposed SETI framework offers a firmware-integrated architecture and a firmware-integrated solution that provides a lightweight embedded alternative for predictive thermal regulation for distributed energy systems and miniaturized electronics. Full article

Aluminium is one of the most carbon-intensive structural materials, making the direct reuse of aluminium members a highly effective strategy for reducing environmental impacts by avoiding primary production. Despite this potential, the reuse of aluminium structural members has received far less attention than steel reuse. This study addresses that gap through two complementary contributions. First, it develops a reuse pathway for aluminium structural members based on existing steel reuse frameworks while addressing aluminium-specific technical challenges. Second, it evaluates the environmental implications of this approach through a life cycle assessment of an aluminium portal frame tent structure in accordance with EN 15804+A2 and the EF 3.1 method, covering Modules A1–A5, C1–C4, and D. Three end-of-life scenarios are considered: a cut-off baseline, a recycling scenario, and a reuse scenario. Aluminium production accounts for 37.6% of the cradle-to-gate impact while representing only about 3.3% of the mass. Direct reuse lowers the net global warming potential by about 22% relative to recycling and is the lowest-impact option across all 16 impact categories. The results identify direct reuse as the environmentally preferable end-of-life route, although wider implementation depends on design for disassembly and a dedicated technical framework for reclaimed aluminium. Full article

This research aims to understand Chinese international students’ perceptions of the academic support provided by Spanish universities. It explores students’ feedback on their participation in such support, its perceived effectiveness, and their expectations and needs. Drawing from a hybrid sociocultural framework, this study employed a qualitative research design, conducting semi-structured interviews to collect data from 14 Chinese postgraduate students at the Autonomous University of Barcelona. Data were analysed using thematic analysis to uncover key themes related to the students’ experiences with the academic support system. The findings reveal that, although UAB offers various forms of academic support, both participation in, and the perceived effectiveness of, these provisions remain limited. Chinese students encounter challenges such as language barriers, unfamiliarity with the academic support system, and varying attitudes from faculty. The findings highlight a need for more practical and systematic academic writing and speaking courses, as well as culturally sensitive and internationalised support mechanisms. As a practical implication, the study suggests that universities should prioritise “situational” oral communication training that prepares students for active classroom participation and implement proactive outreach strategies, such as engaging departmental coordinators to directly promote available library and digital resources, thereby overcoming the current lack of student awareness. This study contributes to addressing a gap in the literature by providing empirical insights into the learning experiences of Chinese postgraduate students regarding academic support in Spain. It offers recommendations for UAB and other Spanish institutions to enhance their academic support systems, promoting a more inclusive and international environment for international students. Full article

Porcine group A rotavirus (PoRVA) is a significant cause of viral diarrhea in piglets, necessitating urgent global implementation of effective control strategies. This review assesses advancements in PoRVA in vitro cultivation and amplification, crucial for PoRVA vaccine development. Traditional PoRVA cultivation commonly employs primary porcine kidney cells or finite cell lines like MA-104, posing well-documented challenges in scalability, production cost, and their ability to recapitulate the natural intestinal microenvironment. Consequently, research has increasingly focused on adapting PoRVA to alternative systems, particularly immortalized porcine cell lines or physiologically relevant porcine intestinal organoids. This adaptation process, involving serial passaging, can induce genomic alterations and virulence attenuation in piglets, essential for generating live attenuated vaccine (LAV) candidates. Modern biotechnological tools, such as reverse genetics and synthetic genomics, have expedited the creation of recombinant PoRVA strains with defined antigenic profiles and enhanced in vitro growth characteristics. However, a significant concern regarding LAV candidates derived from cell culture adaptation is the risk of virulence reversion upon pig back-passage, necessitating thorough safety and genetic stability evaluations. Nevertheless, utilizing stable cell lines or organoid platforms presents a feasible and cost-effective approach for large-scale PoRVA vaccine production. Future research should focus on identifying vaccine candidates that provide broad protection and exceptional safety, with an emphasis on cross-protection against divergent epidemic genotypes, while ensuring the economic feasibility of innovative manufacturing approaches. Full article

Rapid urbanization, uncoordinated land-use growth, and insufficient integration of public transport have led to severe traffic congestion and declining mobility in Peshawar, Pakistan, even after the implementation of a Bus Rapid Transit (BRT) system. The core research problem addressed in this study is the mismatch between growing travel demand and the limited capacity, coverage, and operational efficiency of the existing urban transport network. This research aims to evaluate the current performance of Peshawar’s transport system and to identify integrated, evidence-based strategies to alleviate congestion and enhance urban mobility. Specifically, the objectives are to assess roadway level of service on major corridors, examine public transport user satisfaction with the BRT system, and propose targeted infrastructure and operational improvements. A mixed-methods approach was employed, combining traffic volume and level-of-service (LOS) analysis, public transport user surveys, and field observations at critical intersections. The findings indicate that several key arterial roads operate at LOS E–F during peak hours, and future traffic projections indicate widespread capacity failures under existing road geometries. Survey results reveal significant dissatisfaction with the BRT system, particularly due to limited spatial coverage, inadequate feeder routes, overcrowding, and excessive travel times. Based on these results, the study proposes integrated interventions, including road widening and auxiliary lanes, geometric and signalized junction improvements, expansion of BRT feeder services, development of new arterial and ring roads, and enhanced pedestrian and parking infrastructure. This study links quantitative traffic performance measures with user-perceived service deficiencies. It provides practical, data-driven guidance for policymakers and planners to support a more efficient, accessible, and sustainable urban transport system in Peshawar. Full article

Nuclear fusion represents a transformative solution for global energy systems, offering a carbon-free, inherently safe, and virtually inexhaustible power source. As the field transitions from experimental reactors like ITER to demonstration power plants (DEMO) capable of delivering net electricity to the grid (300–500 MW), the computational demands for plasma control have escalated. Modern fusion diagnostics, particularly soft X-ray (SXR) systems, generate massive data volumes that require high-throughput processing to ensure plasma stability and optimize energy gain. Recent breakthroughs in record-breaking plasma durations have further exposed the critical latency bottlenecks in traditional analytical workflows. This work addresses these challenges by introducing advanced computational strategies optimized towards next-generation reactors. Firstly, we present new data-processing algorithms in C++ and CUDA, achieving significant reductions in computation time. This allowed for more efficient analysis of collected experimental data for plasma confinement studies. Secondly, we discuss hardware architectures that will allow, in the future, up-scaling and parallel runtime processing of data with a feedback signal to the reactor control systems. We present a detailed analysis of the computational workflows underlying soft X-ray diagnostics, followed by a presentation of the proposed optimized algorithms. Their impact on prospective hardware system designs is then evaluated in terms of scalability, latency, and throughput. Performance evaluations demonstrated substantial speedups of both the sequential CPU-based and the parallel GPU-based algorithms, highlighting the potential of these methods for future real-time plasma control for energetically stable and efficient fusion power generation. The sequential and parallel algorithms were 18.8 and 89.1 times faster, respectively, versus the baseline implementation. The processing rate was increased from 31.8 MiB/s to 4.32 GiB/s. The results show the effectiveness of massively parallel computation for plasma diagnostics and pave the way towards further research to produce a cluster-based distributed system. The demand for such high-performance, real-time data processing methodologies extends beyond the plasma confinement domain and is expected to grow across energy systems as they become increasingly complex and data-driven. Full article

This study examines how faculty and staff experienced the WHOLE Experience Framework (WEF) and its perceived effectiveness in facilitating Belonging, Access, and Inclusive Excellence (BAIE) professional development workshops at a Hispanic Serving Institution. Grounded in andragogical theory and Universal Design for Learning, the WEF comprises five core components: Welcoming, Holistic, Open, Liberating, and Empowering. Using the workshop research methodology, this study engaged 164 faculty and staff across five workshops, assessing participants’ experiences with the framework and their confidence in applying BAIE principles in their professional practice. The survey results (n = 51) revealed high levels of participant satisfaction and reported increases in BAIE knowledge and confidence. Facilitators modeled active learning strategies to implement WEF components. The findings suggest that participants experienced the WEF as an effective framework for facilitating meaningful professional development experiences that prepare participants to integrate culturally responsive and access-centered practices into their work environments. Full article

The paper presents an automated, remotely controlled installation for the continuous-flow disinfection of treated wastewater. The proposed solution ensures the inactivation of microorganisms without heating the fluid and without the use of chemical disinfectants, thus reducing the environmental impact and resource consumption associated with conventional disinfection methods. The destruction of microorganisms is achieved by applying high-intensity electrical pulses, which cause irreversible permeabilization of cell membranes through the phenomenon of electroporation. The installation is fully automated and based on a closed-loop control system, in which a programmable logic controller (PLC) acquires data from specialized sensors and automatically regulates the process variables according to the measured operating conditions. The system implements a closed-loop control strategy, optimizing the amplitude, duration and frequency of the electrical pulses depending on the characteristics of the treated fluid and the working flow rate. By eliminating chemical reagents and limiting thermal effects, the proposed technology contributes to reducing energy consumption and increasing the sustainability of the disinfection process. The integration of electroporation with modern automation and monitoring solutions supports the implementation of circular economy principles and the development of sustainable strategies for the management and reuse of treated wastewater. The proposed PLC-SCADA architecture enables adaptive real-time control of the disinfection process by continuously adjusting pulse amplitude, duration, and repetition frequency according to wastewater characteristics and flow conditions. Compared with conventional chemical disinfection methods, the system eliminates the need for chemical reagents and minimizes the formation of secondary pollutants. In addition, the continuous-flow configuration facilitates integration into existing wastewater treatment infrastructures while supporting sustainable and energy-efficient operation. Full article

Ambient fine particulate matter (${\mathrm{PM}}_{2.5}$) poses a significant health risk in Europe, where many cities are exposed to levels exceeding WHO and EU guidelines. Reducing population exposure, therefore, calls for targeted and effective mitigation strategies. To support the implementation of optimal ${\mathrm{PM}}_{2.5}$ reduction policies, high-resolution air quality modeling is necessary. In this context, source allocation studies aim to link the pollution at a specific location to different emitters, typically expressing the contribution of each in terms of concentration differences. An alternative approach is the use of relative potentials, defined as the share of ${\mathrm{PM}}_{2.5}$ concentration reduced at a given receptor resulting from the reduction in the emissions from a given source. To calculate relative potentials, Source-Receptor Relationships (SRRs) can be used to mimic Chemical Transport Models, saving significant computation time when simulating emission reduction scenarios. However, while the relative potential indicator is increasingly used to guide source allocation analyses, its robustness with respect to meteorological variability has not been systematically evaluated. Given that meteorology can be a major driver of ${\mathrm{PM}}_{2.5}$ inter-annual variability, assessing this robustness is a prerequisite for the optimal use of SRRs in air quality planning. To address this gap, we use the SRR model SHERPA, based on the Chemical Transport Model EMEP, to evaluate the robustness of relative potentials of 150 European cities across four contrasting meteorological years (2015, 2017, 2019, and 2021). The contributions of four spatial reduction scales, six emission sectors and five emission precursors are analyzed. Our results show that relative potentials vary little with meteorology for most cities, with low inter-annual ranges for most spatial scales, precursors and sectors. These trends are consistent with EMEP simulations. They establish the robustness of the relative potential indicator and of SRR-based source allocations with respect to meteorological variability, supporting their use in guiding targeted air quality policies in Europe. Full article

This article examines the challenges and solutions related to frequency deviation (FD) in Continental Europe’s electric power systems, focusing on the effectiveness of Combined Cycle Power Blocks (CCPBs) in managing these deviations. Since 2023, persistent and significant frequency deviations (FDs) have been a concern, leading to the establishment of a working group by the ENTSO-E System Operation Committee to develop a robust action plan. The study highlights the necessity of employing advanced control strategies in CCPB to enhance frequency quality and ensure stable system operation. It delves into the mechanisms of Automatic Generation Control (AGC) to maintain power balance and frequency stability through primary and secondary controls. These controls are critical in adapting to real-time changes in load and generation, thereby securing the power system’s reliability and efficiency. The paper also discusses the implementation of Frequency Containment Reserves (FCRs) and their role in stabilizing system frequency following disturbances by automatically adjusting power outputs. Additionally, the research explores the integration of the Baltic States into the European Union (EU) energy market, aiming for enhanced system security and reliability. After all, the Baltic States achieved full synchronization with the Continental Europe Synchronous Area (CESA). The outcomes confirm the suitability of the investigated CCPB for participation in AGC and FCR services under interconnected system operation. Full article

This paper introduces an IIoT-based hybrid predictive maintenance system for industrial steam boilers, which responds to the increased demands for making intelligent and accurate decisions by leveraging data-driven analytics in complex industrial environments. The proposed approach presents comparative hybrid predictive monitoring frameworks based on Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models integrated with Statistical Process Control (SPC) and Cumulative Sum (CUSUM) monitoring techniques for industrial boiler monitoring; it allows accurate system behavior prediction coupled with enhanced anomaly detection across interconnected subsystems. To ensure practicability, the framework is implemented in an integrated operation technology and information technology (OT–IT) architecture with one year of real operation data from an industrial steam boiler in an oil refinery. A two-phase validation strategy is employed to overcome the gap between offline model development and application. During the initial phase, predictive models are developed and tested based on multivariate time-series data to model both the time dependence of the processes and the mechanical variables. The second phase involves the online deployment of the predictive monitoring framework through a Hardware-in-the-Loop (HiL) implementation with Programmable Logic Controller (PLC)-based and Open Platform Communications Unified Architecture (OPC UA) communication to enhance realistic system validation under emulated boiler process conditions without disrupting live plant operations. The experimental results indicate that the GRU model outperforms the LSTM, achieving good R² (0.8956) and mean absolute percentage error (MAPE, 0.6345%), demonstrating strong predictive accuracy across key operational variables. In addition, SPC is used to set up adaptive operational thresholds based on normal industrial process behavior, and then CUSUM is applied to the prediction residuals to improve the detection of the gradual degradation of the system. Real-time validation ensures system stability, low latency, and bidirectional data transfer between the OT and IT layers, enabling continuous monitoring and real-time decision-making. The proposed solution provides a practical and scalable predictive maintenance framework in an industrial context, particularly in oil and gas operations, that helps to transition to Industry 4.0 and intelligent asset management. Full article

Stroke remains a leading cause of death and long-term disability worldwide, making prevention strategies a global health priority. Emerging technologies—including artificial intelligence (AI), wearable devices, digital health applications, and drone-assisted emergency systems—are increasingly being explored to improve stroke prevention and early management. In primary prevention, machine learning models can identify individuals at high risk of stroke using clinical and behavioral data with high reported predictive accuracy, although most models are derived from retrospective, single-center datasets and still require prospective external validation. Digital devices and wearable technologies enable continuous monitoring of cardiovascular risk factors and support behavioral interventions aimed at reducing vascular risk. In secondary prevention, AI-based tools are being developed to predict stroke recurrence, identify modifiable risk factors, and detect patients at risk of poor medication adherence. In the acute setting, AI-assisted neuroimaging platforms are already integrated into clinical and telestroke workflows, supporting rapid triage and treatment decisions. In parallel, drone-based emergency systems may contribute to improved outcomes by reducing prehospital delays and facilitating telemedicine-based triage in remote or resource-limited settings, although current evidence is derived largely from out-of-hospital cardiac arrest pathways rather than stroke-specific trials. Although advanced neurotechnological systems capable of real-time neurophysiological monitoring and closed-loop neuromodulation exist in other neurological disorders, their role in stroke prevention remains largely theoretical. Overall, these technologies offer promising opportunities to reshape the continuum of stroke prevention and care, but further validation, integration into clinical workflows, and evidence of real-world effectiveness are required before widespread implementation. Full article

As global power systems transition toward increasing penetration of renewable energy sources (RESs), such as solar and wind, maintaining frequency stability in converter-dominated low-inertia grids has become a critical challenge. This review examines the role of inertia in power system dynamics, emphasising the consequences of reduced mechanical inertia, the resulting increase in the rate of change of frequency (RoCoF), and the associated stability risks in grids with high inverter-based penetration. Inertial, primary, and secondary frequency response mechanisms are discussed alongside potential cascading failures, protection system triggering, and pathways toward fully renewable grids are assessed. Virtual inertia techniques, including synchronverters, swing-equation-based methods, virtual synchronous generators (VSGs), droop control, Virtual Oscillator Control (VOC), and matching control, are evaluated in terms of benefits, limitations, implementation complexity, and Technology Readiness Levels (TRLs). A key contribution is a multi-criteria evaluation framework that classifies these methods by control adaptability, scalability, and communication requirements, providing system operators with a structured basis for strategy selection. A comparative assessment of Phase-Locked Loop (PLL) synchronisation methods, including SRF-PLL, DDSRF-PLL, FLL-PLL, and Kalman filter-based approaches, is presented under weak-grid, unbalanced, and harmonic-distorted conditions. The integration of virtual inertia with energy storage technologies, such as batteries, supercapacitors, and flywheels, is also discussed, along with its role as an ancillary service within evolving electricity markets and grid codes. Collectively, this study provides a unified reference to advance intelligent, scalable, and deployment-ready frequency control in low-inertia renewable power systems, offering both theoretical insights and practical guidance for future high-RES grid architectures. Full article

Real-time semantic segmentation models offer an excellent balance between accuracy and inference speed. However, deploying these models in dynamic real-world environments often requires the ability to learn novel classes incrementally without retraining on the entire dataset. This capability is known as continual learning. In this regard, standard fine-tuning methods often suffer from catastrophic forgetting, where the model learns new information but loses accuracy on previously learned classes. The severity of this effect depends on the incremental setup, the available data, and the fine-tuning strategy. Contributing to this crucial domain, this paper proposes a novel continual learning framework tailored for PIDNet, which is a widely cited state-of-the-art real-time semantic segmentation model. Our method, PILOT (Parallel Incremental Learning Over Time), introduces a real-time and lightweight strategy by implementing a parallel Derivative branch (D-branch) designed to capture the high-frequency boundary information of novel classes while freezing the trained parameters of the original segmentation network. This novel setup allows the model to adapt to new semantic categories while preserving the knowledge of previously learned classes. By using only data associated with the new class, our model significantly reduces training overhead. Experimental results demonstrate that our approach successfully segments new classes while maintaining a high mean Intersection over Union (mIoU) on the original base classes, thereby outperforming prior continual learning approaches in this real-time segmentation setting. Overall, PILOT is shown to effectively mitigate catastrophic forgetting with minimal impact on inference latency, adding fewer than 5% additional parameters and reducing the frame rate by only about 9%, thus maintaining real-time performance. Full article