Search Results (4,010)

Air pollution poses significant risks to public health, ecosystems, and regional economies, particularly in rapidly developing regions. Despite its importance, the Middle East remains relatively understudied in regional air quality, with limited evaluations of pollutant transport and model performance. This study applies the WRF (Weather Research and Forecasting) model coupled with the CAMx (Comprehensive Air Quality Model with Extensions) model to simulate meteorology and air quality over West Asia, with a focus on the United Arab Emirates (UAE). Six representative months are analyzed, including three winter periods (January 2018, 2020, 2022) and three summer periods (June 2017, 2019, 2021). WRF shows good agreement with observations, reproducing near-surface temperature with an index of agreement (IOA) between 0.90 and 1.00 and generally low wind speed (MB < ±0.5 m s⁻¹) and wind direction biases (MB < ±0.5), although cloud-radiative forcing is underestimated during winter. CAMx reproduces PM_2.5 concentrations with moderate-to-high correlations (r = 0.44–0.65) and low bias, while AOD and O₃ column concentration show larger uncertainties. Satellite-based evaluation indicates good performance for NO₂ and CO column abundances but larger discrepancies for HCHO and SO₂, particularly during summer. Overall, the results demonstrate that the WRF-CAMx modeling system provides a reliable framework for regional air quality simulations over West Asia, while highlighting uncertainties associated with emissions, atmospheric chemistry, and satellite retrieval products. Full article

Glioblastoma (GBM) is one of the most aggressive human cancers, with therapeutic failure driven by pronounced intratumoral heterogeneity, microenvironmental plasticity, immune suppression, blood–brain barrier (BBB)-related pharmacological constraints, and adaptive resistance mechanisms. A major limitation in GBM research is the lack of a human-relevant experimental system able to reproduce these dynamic features while generating interpretable, multimodal datasets. In this context, we propose a testable organ-on-chip (OoC)-extracellular vesicle (EV)-deep learning (DL) framework in which patient-derived GBM cells, endothelial cells, astrocytes, pericytes, stromal cells, and immune components are organized within perfused microphysiological systems. EVs are selectively and temporally harvested from defined compartments, and imaging, barrier-function, sensor, and EV-cargo data are integrated through modality-specific and multimodal DL architectures. This framework is intended not as an immediately validated clinical tool but as an experimental roadmap for linking EV-mediated communication to measurable phenotypes such as BBB disruption, invasion, immune reprogramming, and drug response. We critically discuss the technical requirements of BBB-on-chip systems, EV source attribution, immune-component integration, DL model selection, data scarcity, overfitting, batch effects, domain shift, regulatory barriers, cost, throughput, and reproducibility. By repositioning OoC-EV-DL integration as a staged translational strategy rather than a clinically established solution, this work aims to define a realistic and biologically grounded route for advancing precision oncology in GBM. Full article

Internal combustion engines are a well-established, efficient and dispatchable solution for distributed power generation and they are widely used in various sectors including grid balancing, data centers and combined heat and power systems. Current research efforts focus on further increasing efficiency, enabling decarbonization through renewable fuels and improving responsiveness to electricity demand in the presence of variable renewable energy sources. In this context, the free-piston linear generator (FPLG) stands out as a highly promising technology, as it directly converts piston motion into electricity, offering high efficiency, reduced mechanical complexity and seamless grid integration. Initially explored for its high-efficiency potential with homogeneous charge compression ignition combustion at extreme compression ratios, opposed-piston FPLGs are now commercially available for distributed power generation, delivering global efficiencies exceeding 45%, near-zero emissions and multi-fuel capability. Building on the detailed studies conducted by Svrcek and co-authors, this work investigates the power-generation potential of low-temperature homogeneous combustion using CFD simulations with detailed chemical kinetics. First, rapid compression machine (RCM) experiments with methane were reproduced in simulations to validate the proposed methodology and to consolidate experimental findings on the maximum achievable efficiency. Subsequently, an extensive RCM simulation campaign supported the identification of optimal operating conditions in terms of air–fuel ratio using methane as fuel. The RCM results enabled the definition of a preliminary methane-fueled opposed-piston FPLG configuration. Full-cycle simulations including gas exchange, mixing and combustion demonstrated an indicated efficiency of 58% at an equivalence ratio $\varphi =0.5$ and a compression ratio of 50. The key novelties of this study are the development of a novel RCM-2 configuration that more closely reproduces the dynamic behavior of an opposed-piston FPLG including air-spring effects and the introduction of a divided intake port strategy to simultaneously reduce fuel slip and mitigate knocking behaviour through charge stratification. The simulation results for the proposed configuration confirm the potential of opposed-piston FPLGs for high-efficiency power generation and highlight key parameters affecting performance and emissions formation. Full article

Background/Objectives: Image-based finite element analysis (FEA) is increasingly used in dental biomechanics; however, its reliability is often limited by insufficient experimental benchmarking and a lack of standardized workflows. This study aimed to quantitatively benchmark a Cone beam computed tomography-based (CBCT) finite element pipeline using experimentally measured strain in restored human molars. Methods: Extracted human mandibular molars were restored using a total-etch adhesive system and bulk-fill composite resin. Specimen-specific finite element models were generated from CBCT data using a standardized segmentation and meshing workflow. Numerical simulations were compared with experimentally measured strain obtained during mechanical loading using Digital Image Correlation. Agreement between numerical and experimental data was assessed using regression analysis, Bland–Altman analysis, and equivalence testing. Results: A total of 304 spatially clustered paired measurements nested within 16 specimens were analyzed. FEM predictions showed strong correlation with experimental data (r = 0.91–0.97; R² up to 0.937) and low relative error (~5–6%). The model systematically overestimated deformation by approximately 10–15%. Equivalence was confirmed within ±15% for dentin and composite, and within ±20% for enamel. Bland–Altman analysis revealed proportional bias and heteroscedasticity, particularly in dentin. Conclusions: The proposed CBCT-based finite element workflow demonstrates strong benchmarking agreement with experimental measurements and provides reproducible estimates of mechanical behavior within defined tolerance limits under controlled experimental conditions. Despite systematic overestimation, the model exhibits stable and reproducible behavior under controlled conditions. These findings support the use of experimentally benchmarked, image-based FEA workflows in dental biomechanical research. Full article

Although interior-point methods (IPMs) have transformed mathematical programming since 1984, the original projective Karmarkar algorithm is rarely documented step by step on reproducible engineering examples that combine algorithmic transparency with real resource allocation constraints. This article therefore does not propose a new variant of Karmarkar’s algorithm; rather, its scientific contribution is the reproducible MATLAB implementation, canonical-form conversion, and comparative validation of the original projective method against the revised Simplex method and Barnes’ affine scaling variant in two engineering settings. The case studies are (i) the minimum-weight plastic design of a rigid frame with seven candidate plastic hinge locations and six collapse mechanisms and (ii) the optimal allocation of crop patterns in the Caplina Valley (Tacna, Southern Peru), an arid irrigated system with an irrigated command area of 1253 ha, monthly labor availability of 22,239 jornales, and water availability derived from Caplina River discharges at 75% persistence. For Case I, the algorithm reached F = 1.001 in the normalized dual space, which corresponds to F = 4.251 in the original structural objective after applying the scaling factor 17/4; relative to the analytical optimum F* = 4.25, this gives |4.251 − 4.25|/4.25 = 2.4 × 10⁻⁴ after 20 iterations. For Case II, the model yielded the maximum net production value of USD 703,135.92, allocating 948.47 ha among 12 crops while satisfying water, labor, market, and land constraints. The double validation confirms the algorithm’s strictly interior trajectory, polynomial-time rationale, and transparent internal parameters (α = 0.7968, ε = 10⁻⁸), making the implementation a reproducible benchmark for educational use and for future AI–operations research hybrid solvers in regions with limited access to commercial optimization software. Full article

Digital twin technology is becoming a core enabler for the intelligent transformation of civil engineering. This review adopts an integrated mixed-method design that combines a reproducible bibliometric protocol with structured content analysis to connect macro-level knowledge evolution with domain-specific engineering implementation. Based on the Web of Science Core Collection, the study analyzes publication trends, collaboration patterns, highly cited studies, keyword co-occurrence, network centrality, and citation bursts, and then reviews application status and technical pathways across five thematic areas: intelligent construction, bridge engineering, tunnel engineering, smart water conservancy, and other infrastructure. Key findings include: rapid growth in publication volume after 2021, three dominant keyword clusters (model/system construction, structural health monitoring and sensing, and AI-enabled optimization/decision-making), and an evolution of research frontiers from concept introduction to engineering scenario deepening and further to three-dimensional reconstruction, knowledge fusion, and intelligent decision-making. The content analysis shows differentiated technical pathways across sub-domains and identifies data heterogeneity/interoperability as the most urgent bottleneck because it constrains model updating, cross-platform integration, and engineering-scale deployment. Future directions should focus on data standardization, hybrid modeling, platform interoperability, artificial intelligence empowerment, and full-lifecycle cross-system coordination. This review provides a quantitatively supported panoramic reference for digital twin research in civil engineering. Full article

Cutaneous infections and chronic inflammatory wounds remain difficult to treat because antimicrobial resistance, polymicrobial biofilms, excessive protease activity, oxidative stress, and impaired barrier repair collectively reduce the effectiveness of conventional topical therapies. Plant-derived antimicrobial peptides (AMPs) and peptide-associated bioactives offer antimicrobial, antibiofilm, immunomodulatory, and tissue reparative potential; however, their clinical translation is limited by proteolytic instability, poor stratum corneum penetration, short cutaneous residence time, formulation variability, cytotoxicity risks and limited human evidence. The key research gap is the lack of an integrated translational framework linking plant-derived peptide bioactivity with polymer engineering, advanced delivery systems, skin microenvironment biology, manufacturability, and regulatory feasibility. This review aims to critically evaluate the design principles, therapeutic mechanisms, delivery platforms, and translational barriers of plant-based peptide–polymer therapeutics for cutaneous infection and inflammation. We summarize major classes of plant-derived antimicrobial peptides, including defensins, cyclotides, thionins, hevein-like peptides, snakins, lipid transfer proteins, and knottin-type scaffolds, and examine engineering strategies such as self-assembly, aromatic N-capping, PEGylation, lipidation, dendritic architectures, and stimuli-responsive conjugation. We further discuss topical matrices, nanocarriers, liposomes, electrospun fibers, and surface-tethered biomaterials as delivery platforms for improving peptide stability, local retention, and controlled release. Finally, we identify key translational bottlenecks, including selectivity, toxicity, scalability, batch reproducibility, regulatory classification, and insufficient clinical validation. Mechanism-driven peptide optimization, quality-by-design manufacturing, standardized preclinical models, and controlled clinical trials will be essential for advancing these systems toward safe and effective dermatological therapies. Full article

Background: Sleep deprivation is one of the major public health and performance risk factors, with documented effects on vigilance, executive function, emotional regulation, and safety-critical behaviour. This review examines how event-related potentials (ERPs)—which provide millisecond-level resolution of cognitive processing stages—can clarify which neural processes are most affected by sleep loss, from early sensory encoding to later evaluative and control-related stages. Materials and Methods: This study was conducted as a systematic review of human studies on sleep deprivation and ERPs. Eligible studies included human participants, focused primarily on acute/total sleep deprivation, and reported ERP outcomes (e.g., amplitude, latency, topography, or related event-locked EEG measures). Searches were performed in major biomedical/psychology databases using sleep deprivation and ERP terms, with additional forward/backward citation searching. Data was extracted in a structured format (participant characteristics, deprivation protocol, ERP methods, behavioural outcomes, ERP findings, and recovery/countermeasure effects). Due to substantial heterogeneity in paradigms, protocols, and ERP methods, findings were synthesised narratively rather than meta-analysed. Risk of bias was assessed with RoB 2 and ROBINS-I. Results: The search identified 854 records, of which 82 studies were included following deduplication, screening, full-text review, and citation chasing. Samples were typically small, highly selected, and dominated by healthy young adults, with frequent attrition related to prolonged wakefulness and EEG data-quality constraints. Across studies, sleep deprivation produced stage-specific and task-dependent ERP effects rather than a single uniform pattern. The most consistent findings involved mid-to-late components. These components typically showed prolonged latency and reduced amplitude. In some cases, amplitude increases were observed and interpreted as compensatory recruitment. Early sensory/pre-attentive components (e.g., P1/N1/MMN/P50) were often relatively preserved, but showed selective vulnerability, including latency slowing, reduced filtering/gating, or decreased phase locking. A recurring observation was a behaviour–ERP dissociation, where ERP abnormalities were detectable even when behavioural impairment was modest, indicating covert neural inefficiency or compensation. Recovery sleep, naps, and countermeasures (e.g., modafinil, caffeine) produced partial, component-specific recovery, with amplitude and latency often recovering at different rates. Conclusions: The evidence indicates that sleep deprivation primarily disrupts higher-order, late-stage, and temporally coordinated neural processing, while earlier sensory processing is often preserved but becomes slower and less stable. Among ERP markers, the P300/P3 family is the most robust and informative signature of sleep loss effects and recovery. ERPs are therefore a sensitive tool for detecting neural dysfunction and compensation under sleep deprivation, including changes that may precede overt behavioural decline. Future research must improve the generalisability and reproducibility of ERP findings by employing larger, more diverse samples, alongside more standardised methodological, recording, and reporting practices. Full article

This paper investigates the settlement experiences of Hong Kong British National (Overseas) [BN(O)] migrants in the UK, with a particular focus on housing as a central mechanism shaping their wellbeing, security, and integration. Following the introduction of the BN(O) visa route in 2021, this study draws on qualitative interviews with migrants in the North of England to explore how housing mediates conditional settlement under a marketised migration regime. Findings reveal that housing functions as the primary infrastructure of settlement, influencing employment, education, and family life, while access is conditioned by migrants’ capacity to absorb market risks such as advance rent payments and landlord discretion. The study highlights significant intra-group stratification shaped by financial resources, family composition, and transnational support, with family responsibilities intensifying housing precarity and constraining choices. Moreover, a moralised ethos of self-reliance among migrants normalises hidden insecurity and limits formal support-seeking. This research contributes to migration and housing scholarship by demonstrating how ostensibly humanitarian migration pathways reproduce uneven security through housing systems, underscoring the need for policy interventions that address the cumulative effects of housing insecurity on settlement and wellbeing. Full article

The assessment of humanity’s ecological footprint has become increasingly critical in contemporary discourse due to growing environmental challenges. This study examines the economic evaluation of the ecological footprint with a particular focus on forest ecosystem services and food productivity. Using harmonized secondary data from FAOSTAT, EUROSTAT, the World Bank, and IPBES, the analysis covers selected developed and emerging economies, including the European Union, the United States, China, Brazil, and other representative countries. This study investigates the macroeconomic implications of natural capital degradation by applying a panel data econometric model to European Union countries over the period 2010–2023. Moving beyond descriptive approaches, the research formulates and tests three hypotheses linking biodiversity, environmental pressure, and green transition variables to economic performance. Using harmonized data from Eurostat and Statista, the study employs a fixed-effects regression framework to estimate the impact of biodiversity indicators, greenhouse gas emissions, renewable energy share, and environmental protection expenditures on GDP per capita. The results demonstrate that biodiversity preservation and resource efficiency are positively associated with economic performance, while environmental degradation—proxied by greenhouse gas emissions—exerts a statistically significant negative effect. Additionally, the findings confirm that investments in renewable energy and environmental protection contribute to long-term economic stability. By providing a transparent data structure, explicit variable operationalization, and reproducible econometric specification, the study offers an original empirical contribution to ecological economics and addresses the limitations of prior literature that relied primarily on descriptive synthesis. Full article

Understanding the spatial proteomic landscape of human tumors is essential for dissecting cellular heterogeneity and microenvironmental interactions in cancer biology. Traditional bulk proteomic approaches, however, obscure spatial information and average out signals from distinct cell populations. Here, we present a detailed and reproducible micro-quantitative protocol for spatially resolved proteomic analysis of specific cellular subpopulations isolated from immunohistochemistry (IHC)-labeled formalin-fixed paraffin-embedded (FFPE) tissue sections using laser microdissection (LMD). By combining IHC staining to visually define phenotypically distinct cells within preserved tissue architecture and precise LMD capture, approximately 6000 target cells can be isolated per sample for downstream proteomic quantification. Despite the ultra-low input, optimized lysis and digestion steps ensure consistent peptide recovery and highly reproducible label-free LC–MS/MS data across replicates. Integrating immunohistochemistry staining-guided spatial sampling with ultrasensitive quantitative proteomics, this workflow enables reliable cell-type-specific profiling directly within human tumor tissues. The protocol bridges histopathology and proteomics, offering a practical framework for translational research exploring spatial protein signatures and tumor microenvironmental heterogeneity. Full article

Landfalling typhoons are the dominant trigger of short-duration extreme rainfall along the Zhejiang coast. It is necessary to estimate the recurrence of future typhoon rainfall at the city scale under the global-warming scenarios. Using Super Typhoon Lekima (2019) as a representative high-impact event, this study develops an event-based assessment framework for Taizhou city by combining the Weather Research and Forecast (WRF) model simulation, pseudo-global-warming (PGW) perturbation experiments, and generalized extreme value analysis. The historical simulation is first evaluated against the China Meteorological Administration best track, storm intensity evolution, and station rainfall observations. Future counterparts of the same event are then generated using CMIP6-derived thermodynamic perturbations under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. Finally, scenario-dependent rainfall totals are projected onto a historical GEV curve to identify equivalent historical rainfall return periods. Results show that the WRF setup reproduces the main track, intensity tendency, and rainfall timing of Lekima with reasonable fidelity. The ensemble-mean cumulative rainfall over the Taizhou area increases from 204.75 mm in the historical simulation to 335.85, 366.72, 400.79, and 464.08 mm under the four SSPs, respectively. These increases translate into equivalent historical rainfall return periods of 47.40, 84.61, 164.28, and 604.05 years, compared with 5.24 years for the historical case. The results indicate that the moderate thermodynamic rainfall amplification produces a highly nonlinear escalation of event rarity based on historical frequency statistics. This implies that future typhoon rainfall should be interpreted using scenario-aware benchmarks within the historical reference framework. Full article

The efficiency of data-driven research relies not only on high-quality data and sufficient computational resources but also depends sensitively on the personal knowledge management of the researcher. The multitude of digital artefacts created during a researcher’s daily workflow might comprise experimental results, simulation results, literate programming notebooks analysing experiments and simulations, statistical models, machine learning models, figures, tables, and conversations with generative Artificial Intelligence systems. In order to trace and track these interconnected research artefacts over several months of research or even extended research periods and different research projects, these artefacts need to be systematically named so that they can be referenced in note-keeping systems and research outputs. Therefore, the naming and referencing scheme for research artefacts needs to be flexible, consistent, efficient and support the linking of artefacts across different software frameworks and even classical laboratory notebooks. This article introduces a hierarchical naming scheme and the supporting open-source Python package contexere together with best practices for the personal knowledge management for postgraduate students and early career researchers, which provides a clear and linkable structure for data artefacts and thus supports effective personalised research workflows. Full article

Deep learning-based automated classification of dermatoscopic skin lesions has exhibited promising potential in diagnostics. However, two prominent issues need to be addressed before achieving high-quality diagnostic tools: inconsistent performance in the case of imbalanced classes and poor clinical interpretability of models. Even though some studies have attempted to leverage both deep and shallow learning by combining pretrained convolutional neural networks (CNNs)-based feature extraction with classical machine learning (ML) models, very few of them systematically explore several model combinations based on various clinically important metrics, such as F1-score, precision, recall, accuracy, etc., and utilize decision threshold calibration techniques. In this research, we present an evaluation of a systematic framework with threshold calibration for the comparison of several hybrid models on seven-class skin lesion classification (multi-class) on the HAM10000 dataset. In particular, we used deep features extracted from three pretrained CNN architectures, i.e., DenseNet201, InceptionV3 and EfficientNet-B4. These deep features were used as inputs for six different classical classifiers. As a result, we obtained 18 comparable hybrid models that were then systematically compared by multiple clinically relevant metrics: accuracy, macro-precision, macro-recall, macro-F1, ROC-AUC, Precision-Recall-AUC, and log loss. Also, fold-wise optimization of decision thresholds was performed, which was based on the maximization of the macro-F1 score. Finally, we found out that DenseNet201 with an SVM-RBF classifier yielded the highest performance among all 18 tested models, showing 90.88% accuracy, 90.7% macro-precision, and 0.921 ROC-AUC. To analyze the clinical plausibility, top-performing models were further explained with explainable artificial intelligence (XAI) techniques: Grad-CAM, LIME and Occlusion Sensitivity. Results show that the most successful models concentrated mostly on lesion-specific areas. Overall, this study contributes a reproducible hybrid-XAI model-selection framework rather than a single black-box classifier, supporting more transparent and clinically meaningful skin lesion diagnosis. Full article

Waste cooking oils (WCO) are large-scale residual streams from domestic and industrial food processing. Their improper disposal poses severe environmental risks, yet their integration into the oleochemical sector offers a strategic opportunity for the green transition by substituting fossil-based feedstocks. This systematic review provides a comprehensive assessment of WCO valorization as a sustainable precursor for high-value products, specifically biopolymers, bioplasticizers, and biolubricants. The study followed the PRISMA 2020 guidelines, searching PubMed, Scopus, and MDPI databases (up to September 2025). The search strategy utilized combinations of keywords present in the title. Inclusion criteria focused on peer-reviewed chemical and biotechnological conversion pathways published in English within the last decade. Studies addressing biofuel production, patents, and review were excluded. Screening, data extraction, and qualitative risk of bias assessment, centered on experimental reproducibility and reporting transparency, were performed independently by multiple reviewers. From an initial pool of 2637 records, 87 studies met the eligibility criteria. The analysis reveals that polyhydroxyalkanoates (PHAs) represent the most extensively researched pathway, followed by WCO-derived epoxides and innovative biolubricant formulations. While several studies report high conversion yields under optimized conditions, the transition from bench-scale to industrial implementation remains hindered by the heterogeneous composition of WCO and a lack of standardized pre-treatment protocols. WCO valorization shows transformative potential for the circular economy, offering a dual benefit of waste mitigation and sustainable material synthesis. However, future research must address scalability challenges and feedstock variability. This review identifies emerging trends and provides a roadmap for the industrial adoption of WCO-based processes in the framework of clean technologies. Full article