Search Results (39,285)

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

Feature selection (FS) is a pivotal preprocessing task in data mining aimed at identifying optimal feature subsets to improve model generalization and reduce computational overhead. However, its NP-hard nature poses significant challenges for traditional optimizers in terms of search efficiency and solution quality. The Harris Hawks Optimization (HHO) algorithm is a state-of-the-art population-based metaheuristic method that demonstrates powerful capabilities in various optimization challenges. Despite its advantages, HHO encounters problems such as early stagnation and reduced accuracy. To mitigate these problems, we introduce an advanced algorithm called the Hybrid Strategy Harris Hawks Optimization (HSHHO). The HSHHO combines three key enhancements to support global search diversity and local refinement: (1) an exploration mechanism that utilizes the Self-Parameterized Map (SPM) alongside a dynamic logarithmic spiral to expand search breadth; (2) a nonlinear adjustment to the escape energy parameter for improved phase equilibrium; and (3) an elite perturbation approach that uses Cauchy–Gaussian mutation to strengthen local optimization and solution quality. We assessed HSHHO against eight well-known algorithms on 30 benchmark functions, where it exhibited superior results in the majority of cases. Finally, HSHHO is applied to address 18 feature selection tasks. The results demonstrated that HSHHO achieved highly competitive outcomes in terms of objective values, feature subset size, and classification performance in most datasets, reaching an average accuracy of 94.47%. Full article

Traumatic spinal cord injury (SCI) is a severe medical condition, often resulting in permanent disability, with significant impacts on patients’ quality of life and burden on healthcare systems. Current therapeutic approaches for SCI are insufficient, advocating for the development of more effective treatments. As changes in transcriptome post-SCI can provide clues for novel treatment strategies and targets, substantial efforts have been made recently to characterize such transcriptional changes and their spatiotemporal features. This narrative review focuses on how transcriptomics, alone or in combination with other omics data, can contribute to understanding SCI pathobiology and the mechanisms of post-SCI regeneration and guide the development of novel SCI therapies. It covers an arsenal of tools for transcriptomics studies and provides a concise summary of findings from the latest relevant studies (predominantly from 2020 to 2025), representing the major directions in the field. 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

Achieving the co-combustion of biomass and coal in utility boilers while reducing carbon dioxide emissions poses significant challenges owing to the divergent physicochemical properties of the fuels. These differences can induce high-temperature corrosion and erosion of heating surfaces, threatening boiler safety. Despite this, integrated CFD-based assessments of sulfidic corrosion and particle erosion risks remain insufficiently addressed under realistic biomass–coal cofiring conditions. In this study, an integrated CFD-based risk assessment framework was established for biomass–coal cofiring boilers. The main novelty lies in the combined evaluation of high-temperature sulfidic corrosion and particle erosion risks under different biomass injection strategies. Specifically, user-defined functions were developed to classify high-temperature sulfidic corrosion risks based on local O₂, CO, and H₂S concentrations; the effects of biomass injection layers were quantitatively compared; the Oka erosion model was coupled with CFD particle tracking to predict wall wear; and an entropy-weighted multi-indicator method was used to rank the overall safety of different cofiring strategies. This study found that sufficiently high near-wall H₂S concentrations in the main combustion zone indicate an increased risk of sulfidic corrosion under reducing-atmosphere conditions. Compared with pure coal combustion, biomass injection through layer A exacerbates wall corrosion, whereas biomass injection through layer AB mitigates it. Erosion is primarily localized near burner nozzles. Notably, biomass cofiring reduces the average erosion rate by 7.9–30.2% but increases the local maximum erosion rate by 7.1–25.1%. The comprehensive evaluation indicates that the condition with 30% RS injected from layer AB, mixed with coal, yields the best overall performance. The corrosion assessment is limited to sulfidic corrosion risks associated with reducing atmospheres and does not explicitly model alkali- or chlorine-induced corrosion. This study provides a theoretical foundation for biomass cofiring optimization and offers practical guidance for boiler operational safety and maintenance. Full article

Revealing scale effects and the mechanisms underlying the relationships between plant species and functional diversity is crucial for understanding the stability of desert ecosystems and formulating multiscale conservation strategies. In this study, the spatial patterns of plant species and functional diversity in the Gurbantunggut Desert were analysed via multiscale grid sampling. The results indicated that (1) both species diversity and functional diversity indices exhibited high spatial heterogeneity. At the small scale (10 m × 10 m), the values of the Shannon–Wiener and Pielou indices for fixed dunes were higher in the south than in the north. At the medium and large scales (20 m × 20 m and 50 m × 50 m, respectively), the index values were highest in the southwest, with generally greater values in the south than in the north. For semifixed and mobile dunes, the Shannon–Wiener and Pielou index values exhibited an east-high–west-low pattern at the 10 m × 10 m scale. This differentiation decreased with increasing scale, with the highest values observed in the northeast and southwest at the 50 m × 50 m scale. The spatial differentiation in functional diversity indices (Rao’s second-order entropy index and functional evenness index) exhibited distinct characteristics across the different dune types. (2) The spatial variation in all the diversity indices monotonically decreased with increasing scale, with the variance in the species diversity indices indicating the following order: Shannon–Wiener index > Pielou index > Simpson index. (3) The relationships between species richness and diversity indices exhibited significant scale dependence. At the small and medium scales, species richness was significantly positively correlated with the Shannon–Wiener index, Simpson index, and Rao’s quadratic entropy index and significantly negatively correlated with the Pielou evenness index and functional evenness index. However, at the large scale, none of these correlations were significant. (4) The species diversity indices and Rao’s quadratic entropy index were significantly positively correlated at the small and medium scales (p < 0.01), whereas a significant positive correlation with the functional evenness index was observed only at the 10 m × 10 m scale (p < 0.01). At the larger scale, these correlations became insignificant. In fixed dunes, areas of high Simpson index values exhibited a spatially complementary distribution with areas of high Shannon–Wiener index and Pielou index values, providing evidence for the combined effect of local processes such as competitive exclusion and dispersal limitation. Through comprehensive multiscale analysis, this study revealed the mechanisms underlying the scale-dependent relationships between plant species and functional diversity, thereby providing a theoretical basis for protecting and restoring desert biodiversity. Full article

Selective separation of quartz and feldspar is vital for high-purity silicate raw materials but is challenging due to similar surface chemistries. Conventional flotation typically requires high reagent dosages and hazardous chemicals, raising environmental and economic issues. This study proposes a sustainable flotation strategy using green, bio-derived reagents to improve quartz–feldspar separation by eco-friendly bio-derived reagents. Sodium oleate, a fatty acid collector, was used with low-toxicity modifiers to create synergistic systems. Flotation performance was tested by reagent dosage and pH, with mineral characteristics analyzed via X-ray Fluorescence (XRF) and Particle Size Distribution (PSD). Results showed that the investigated reagent systems improved the differential flotation response between quartz and K-feldspar. Under the optimized flotation conditions (pH 9.24), quartz recovery reached 84.01%, demonstrating that environmentally friendly reagent combinations can achieve favorable flotation performance while reducing chemical consumption. Response Surface Methodology (RSM) was used to optimize flotation variables like pH and reagent dosage, developing a model to predict conditions for favorable flotation response, enabling systematic process improvement. These findings highlight reagent-system optimization as an eco-friendly method for mineral beneficiation, aligning with green chemistry and sustainable practices. Full article

Back-ground: In 2022, in Niger, undernutrition was highly prevalent in 6–23-month-old infants and their diet was poorly diversified. Methods: This cluster randomized controlled trial was conducted in the Zinder region of Niger to monitor food and nutritional intakes from two food solutions, fortified infant flours (FIF) and ten nutritionally improved traditional recipes (NITR), in breastfed 6–11-month-old infants divided into four groups: control, responsive feeding (RF) awareness raising, RF + FIF, and RF + NITR. Data were collected at T0 (n = 322 infants) and 3 months later (T3, n = 300 infants). Results: At T0, 29% and 52% of infants had stunting and anemia, respectively, and 24% of them achieved minimum dietary diversity (MDD) in all groups. At T3, the MDD rates significantly increased, particularly in the RF + FIF and RF + NITR groups (71% and 81%, respectively). Food intake remained low in all groups, below the gastric capacity of children. Nevertheless, at T3, food intake was significantly higher in the RF + NITR group than in the other groups (p = 0.0209). Although porridges made with FIF were consumed in smaller quantities, thanks to their high energy density, the mean energy intake was higher in the RF + FIF group than in the control and RF groups. The energy intake of the RF + NITR group was even higher. This can be attributed to the fact that NITR-based meals were more varied, and colorful and offered different tastes and textures, thus appearing more appetizing and stimulating. Conclusions: A strategy that combines FIF and NITR appears relevant for improving nutritional intake in these contexts. 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

Smart city environmental monitoring depends on sparse air quality sensor networks and analytics services that remain reliable under node additions, outages, and missing streams. We propose an operational deep learning framework for citywide cross-location forecasting from a limited set of sensors, delivering low-latency, real-time concentration heatmaps at unsensed locations by combining temporal prediction with spatial regression. We formulate single-stage spatiotemporal forecasting and benchmark nine recurrent, convolutional, and multilayer architectures against classical baselines. The framework forecasts O${}_3$, NO${}_2$, PM${}_{2.5}$, and PM${}_{10}$ over horizons from 1 hour to 10 days. Using open monitoring data from Madrid (Spain) and Cali (Colombia), we evaluate generalization by holding out stations, reflecting deployment to new sensor nodes and sparse coverage regimes. We further compare missing data handling strategies and show that common imputation can substantially degrade accuracy, increasing RMSE by up to 74% in some settings. Beyond prediction, the framework provides a basis for guiding sensor network densification; confidence estimates can highlight locations where additional sensors may be most beneficial. These results provide actionable guidance for deploying AI-enabled sensing services with robust performance under realistic sensor reliability constraints while supporting real-time citywide mapping. Full article

The growing importance of embodied carbon (EC) in building decarbonisation requires transparent, context-specific Life Cycle Assessment (LCA) approaches. This study develops a practical framework for quantifying cradle-to-site EC (A1–A4), combining detailed post-construction material quantification with a structured data selection methodology. Carbon factors (CFs) are primarily sourced from geographically representative Environmental Product Declarations (EPDs) and evaluated through a reliability framework that incorporates material similarity, geographical proximity, and data completeness. An Analytic Hierarchy Process (AHP) is further applied to select representative values for key materials such as ready-mix concrete. The application of this framework highlights the critical influence of data representativeness on EC results and demonstrates a transparent and reproducible approach for reducing uncertainty in early-stage assessments. The case study yields a total EC of 228 kg CO₂e/m², with structural materials identified as the main carbon hotspots: ready-mix concrete accounts for approximately 40% of total impacts, reinforcing steel for around 11%, while masonry systems, infill, and levelling layers contribute a significant additional share. Together, these materials represent slightly more than 75% of total embodied emissions. Beyond the numerical results, the study shows that a limited number of material categories dominate the carbon footprint, enabling targeted decarbonisation strategies. The proposed framework is designed to be transferable to similar building contexts and supports more robust, data-driven decision-making in the Portuguese construction sector and beyond. It is particularly relevant in regions where locally representative environmental data are not necessarily sufficient, as it provides a structured approach for developing embodied carbon assessments under such condition. Full article

Background/Objectives: Heterogeneous imaging protocols, a lack of labeled data, and domain shifts continue to make training deep learning models to analyze medical images a challenge. This study presents ETDACVO (Enhanced Tasmanian Devil Anti-Conservative Variable Optimization), a hybrid evolutionary optimization system designed to improve convergence stability and cross-domain robustness in brain tumor MRI classification. Methods: ETDACVO combines Tasmanian Devil Optimization (TDO), Anti-Conservative Variable Optimization (ACVO), and Exponentially Weighted Moving Average (EWMA) smoothing to stabilize evolutionary parameter updates. Unlike existing approaches that optimize augmentation policies or optimizer dynamics separately, ETDACVO simultaneously evolves both components within a single evolutionary loop. The framework was evaluated on four MRI datasets (Nickparvar, Mendeley, BRISC, and Figshare), comprising 28,151 images. In addition, a convergence-aware explainability mechanism, CA-EA-GradCAM, was developed by integrating gradient saliency, transformer attention, and evolutionary convergence confidence to generate confidence-sensitive tumor localization maps. Results: Experimental results demonstrated that ETDACVO achieved a 2.3–2.5% improvement in classification accuracy and converged 19–22 epochs faster than baseline optimizers. The statistical significance of these improvements was confirmed using paired statistical tests (p < 1 × 10⁻⁵). Cross-dataset transfer experiments further showed strong domain-shift resilience, with performance retention reaching 92.8%. The proposed CA-EA-GradCAM mechanism provided interpretable and confidence-aware tumor localization maps. Conclusions: ETDACVO provides a robust and computationally efficient optimization framework for deep-learning-based medical image analysis. By jointly optimizing augmentation strategies and optimizer dynamics, the framework enhances convergence stability, cross-domain robustness, and interpretability, making it a promising approach for reliable brain tumor MRI classification under heterogeneous imaging conditions. Full article

Essential oils have attracted considerable attention as natural antimicrobial agents for food preservation due to their broad-spectrum activity against foodborne microorganisms. Although numerous studies report strong antimicrobial effects under in vitro conditions, their effectiveness in real food systems is often substantially reduced. This review critically examines the discrepancy between in vitro antimicrobial activity and actual performance in food matrices. Particular attention is given to the influence of food matrix interactions, physicochemical instability, volatility, sensory limitations, and microbial adaptation on the efficacy of essential oils. A conceptual framework is presented to systematically summarize the major factors limiting antimicrobial performance in practical food applications. In addition, current strategies aimed at improving applicability, including encapsulation technologies, nanoemulsions, synergistic combinations, and active packaging systems, are discussed. Available evidence indicates that simplified experimental models frequently overestimate the practical efficacy of essential oils. More realistic and system-oriented evaluation approaches are therefore necessary to improve the translation of laboratory findings into food applications. Overall, essential oils remain promising candidates for natural food preservation, although their successful industrial application will depend on overcoming important technological and practical limitations. Full article

Glioblastoma remains the most aggressive primary brain malignancy, with poor survival despite maximal safe resection, radiotherapy, and temozolomide-based chemotherapy. A major obstacle to effective treatment is the spatially heterogeneous blood–brain barrier/blood–tumor barrier, which restricts drug penetration into infiltrative tumor regions and limits uniform intratumoral exposure. Albumin-based delivery is attractive in glioblastoma because it addresses several formulation-level barriers at once: poor aqueous solubility of hydrophobic payloads, short systemic exposure, and the need for a biocompatible carrier that can interact with albumin-handling pathways such as gp60/albondin, SPARC, FcRn, and caveolin-associated transport. This review examines albumin-based strategies explored for glioblastoma, with an emphasis on albumin-bound paclitaxel nanoparticles, engineered albumin nanoparticles, dual-payload systems, albumin-binding photosensitizers, macrophage-assisted delivery, and albumin-bound pathway-directed agents. Preclinical evidence suggests that these platforms can improve brain-tumor drug exposure, support rational combinations, and synergize with BBB/BTB-opening technologies. Early clinical studies combining low-intensity pulsed ultrasound with microbubbles and albumin-bound paclitaxel provide human proof of concept for regional pharmacokinetic enhancement in recurrent glioblastoma, although survival benefit remains unproven. The available evidence supports albumin-based delivery as a rational formulation strategy. Its clinical value in GBM will depend on three testable requirements: spatial pharmacokinetic confirmation, biomarker-guided patient selection, and reproducible BBB/BTB modulation. Full article

Aflatoxins and fumonisins are two of the most prevalent and toxicologically significant mycotoxins contaminating global food supplies, particularly maize and groundnuts. Although several regulated mycotoxins contribute to food safety concerns, this review focuses on aflatoxins and fumonisins because they frequently co-occur in maize and maize products. Their widespread prevalence, distinct toxicological mechanisms, and combined health effects necessitate an integrated exposure and risk assessment. This review critically evaluates the current state of exposure assessment and its implications for human health. We examine the evolution of sample preparation techniques, highlighting the transition from traditional liquid–liquid extraction to advanced approaches such as QuEChERS and green extraction technologies that can handle the divergent physicochemical properties of lipophilic aflatoxins and hydrophilic fumonisins. Analytical methods are compared, from the robust but limited HPLC-FLD to the multi-analyte capabilities of LC-MS/MS and the emerging potential of aptamer-based biosensors. Furthermore, the review addresses the critical challenge of modified mycotoxins that evade routine detection yet may contribute to total toxicity. By synthesizing data on biomarkers of exposure and the mechanisms of co-exposure, we discuss the complex interplay between these toxins in the etiology of hepatocellular carcinoma and neural tube defects. The review concludes that mitigating the public health burden of mycotoxins requires a holistic strategy that integrates HRMS for non-targeted analysis with human biomonitoring to capture the accurate individual-level exposure. Full article