Search Results (762)

Additive manufacturing methods can constitute a valuable alternative to conventional production techniques for components used in the heavy industry, particularly in foundry applications. This innovative manufacturing approach enables an expanded product portfolio as well as higher precision and geometrical complexity of ceramic components. One additive technology applicable to ceramic processing is robocasting, classified within the direct ink writing (DIW) family. In this method, a semi-fluid ceramic paste is extruded to build the part layer by layer; the shaped green body is subsequently fired (sintered) to attain its final functional properties. This study presents the results of materials characterization of printed ceramic filters, encompassing phase composition analysis, density measurements, three-point bending strength testing, hardness, and microstructural examination. The investigations demonstrated that the oxide ceramic Al₂O₃ processed by the modern robocasting method exhibits mechanical performance at a comparably high level relative to classical manufacturing routes (slip casting, ceramic injection molding, dry pressing). Moreover, the porosity results indicate that 3D printing technology enables lower post-sintering porosity. Full article

A common drawback of denoising methods of images is that all pixels are filtered regardless of the amount of noise affecting them individually. Since the essence of denoising is lowpass filtering, subjecting clean pixels to denoising results in blurring. In this paper, a filtering framework is introduced where a fitness function is incorporated in a Kalman filter (KF) to assess the suitability of accepting the value recommended by KF or retaining the existing value of a pixel. Furthermore, a limit on the number of iterations is imposed to avoid over filtering that leads to shrinkage of pixel value ranges of the channels and loss of spectral signatures. In post processing, the means of the filtered channels are shifted to their original values prior to filtering, to spread the pixel value ranges and regain important spectral signatures. The experiments involve the implementation of KF, extended Kalman filter (EKF), Kalman smoother (KS), extended Kalman smoother (EKS) and moving average filter (MAF) in filtering noisy channels of oil palm hyperspectral data under the same framework. Their performances are compared in terms of execution time, SNR gain, NIQE and SSIM metrics. In the second set of experiments, the performance of the improved KF with a fitness function and mean restoration is compared to those of KF and MAF. The results show that the improved KF outperforms the other two filters in the spectral signature characteristics and pixel value ranges of the denoised channels. Full article

Monitoring reclaimed wellsites in boreal forest environments requires accurate, scalable, and repeatable methods for assessing vegetation recovery. This study evaluates the use of uncrewed aerial vehicle (UAV)-based light detection and ranging (LiDAR) and multispectral (MS) imagery for individual tree detection, crown delineation, and classification across five reclaimed wellsites in Alberta, Canada. A deep learning workflow using 3D convolutional neural networks was applied to LiDAR and MS data collected in spring, summer, and autumn. Results show that LiDAR alone provided high accuracy for tree segmentation and height estimation, with a mean intersection over union (mIoU) of 0.94 for vegetation filtering and an F1-score of 0.82 for treetop detection. Incorporating MS data improved deciduous/coniferous classification, with the highest accuracy (mIoU = 0.88) achieved using all five spectral bands. Coniferous species were classified more accurately than deciduous species, and classification performance declined for trees shorter than 2 m. Spring conditions yielded the highest classification accuracy (mIoU = 0.93). Comparisons with ground measurements confirmed a strong correlation for tree height estimation (R² = 0.95; root mean square error = 0.40 m). Limitations of this technique included lower performance for short, multi-stemmed trees and deciduous species, particularly willow. This study demonstrates the value of integrating 3D structural and spectral data for monitoring forest recovery and supports the use of UAV remote sensing for scalable post-disturbance vegetation assessment. The trained models used in this study are publicly available through the TreeAIBox plugin to support further research and operational applications. Full article

This research aimed to analyze the proteomic profile of the low-molecular mass fraction of salivary pools from patients with glioblastoma IDH wild type (GBM) to disclose the small protein and peptide components, including protein fragments, cryptides, and tumor-associated peptides, still lacking specific information in the literature, to the best of our knowledge. This fraction, corresponding to the unretained proteome fraction, was obtained by pretreating the acid-soluble fraction of saliva through Filter-Aided Sample Preparation devices with a filter molecular cutoff of 10 kDa. The fraction was analyzed by LC-MS in its entire form, without trypsin pre-digestion, following a top–down approach. Data from the analysis of pre- and post-operative salivary pools from patients with newly diagnosed and recurrent GBM were compared and discussed with data obtained in our previous study on the complementary salivary proteome fraction > 10 kDa analyzed by a bottom–up approach and data from the literature. The results highlighted a panel of GBM-associated peptide fragments from different protein precursors, namely, ANXA1, CFL1, GLUL, PFN1, H2AC12, ACTB, and HBB, which are suggested for further exploration as potential diagnostic and prognostic biomarkers and clinical applications. These findings, although providing only preliminary results on a small scale, offer new insights into the molecular characteristics of GBM tumor and lay the groundwork for further investigations on a large scale using saliva liquid biopsy for biomarker discovery and validation. The aim is to advance precision medicine and improve clinical outcomes in GBM, one of the most aggressive brain tumors with a poor prognosis, for which early diagnosis and monitoring of treatment response remain significant challenges. Full article

The adrenal glands are central to the stress response in cetaceans, yet their morphological and molecular changes under chronic stress remain poorly described. We investigated adrenal histology and protein composition in stranded common dolphins (Delphinus delphis) to assess whether post-mortem material can provide insights into stress physiology. Adrenal glands from 58 dolphins recovered along the Irish coast during a period of reported nutritional stress in the species were analyzed for adrenal mass, cortex-to-medulla (C:M) ratios, and cortical cell density. Additionally, two archival formalin-fixed, paraffin-embedded (FFPE) tissues were included in a pilot trial to assess the feasibility of protein extraction and mass spectrometry analysis. While adrenal mass did not differ significantly between stress types, chronically stressed dolphins exhibited significantly higher C:M ratios and cortical mass, consistent with cortical hypertrophy. Protein extraction from FFPE tissues was feasible, with the in-gel digestion method yielding more proteins (136) than the filter-aided sample preparation method (22). These findings demonstrate that histological and proteomic approaches can detect stress-related signatures in dolphins and highlight the potential of archival tissues for retrospective biomarker discovery. Full article

Background: Personalizing adjuvant chemotherapy (ACT) after curative resection in early-stage NSCLC remains unmet because prior ACT-biomarker findings rarely reproduce across studies. Key barriers are platform and preprocessing heterogeneity, dominant batch effects, and incomplete ACT annotations. As a result, many signatures that perform well in a single cohort fail during external validation. We created an open, harmonized meta-database linking gene expression with curated ACT exposure and survival to enable fair benchmarking and modeling. Methods: A PRISMA-guided search of 999 GEO studies (through January 2025) used LLM-assisted triage of titles, clinical tables, and free text to identify datasets with explicit ACT status and patient-level survival. Eight Affymetrix microarray cohorts (GPL570/GPL96) met eligibility. Raw CEL files underwent robust multi-array average; probes were re-annotated to Entrez IDs and collapsed by median. Covariate-preserving ComBat adjusted platform/study while retaining several clinical factors. Batch structure was quantified by principal-component analysis (PCA) variance, silhouette width, and UMAP. Two quality-control (QC) filters, median M-score deviation and PCA leverage, flagged and removed technical outliers. Results: The final meta-database comprises 1340 patients (223 (16.6%) ACT; 1117 (83.4%) observation), 13,039 intersecting genes, and 594 overall-survival events. Batch-associated variance (PC1 + PC2) decreased from 63.1% to 20.1%, and mean silhouette width shifted from 0.82 to −0.19 post-correction. Seven arrays (0.5%) were excluded by QC. Event depth supports high-dimensional survival and heterogeneity-of-treatment modeling, and the multi-cohort design enables internal–external validation. Conclusions: This first open, rigorously harmonized NSCLC transcriptomic database provides the sample size, demographic diversity, and technical consistency required to benchmark ACT-benefit markers. By making these data openly available, it will accelerate equitable precision-oncology research and enable data-driven treatment decisions in early-stage NSCLC. Full article

Since GPS (Global Positioning System) cannot meet accuracy requirements indoors, indoor Location-Based Services (LBSs) have become increasingly important. BLE (Bluetooth Low Energy) offers cost and accuracy advantages. Typically, the position fingerprinting method is used for indoor positioning. However, due to irregular reflection and absorption, the indoor environment introduces various offsets in Bluetooth RSSI (Received Signal Strength Indicator). This study analyzed the RSSI space and proposed a pre-processing workflow to improve position estimation accuracy by correcting offsets in RSSI space for BLE fingerprinting methods using machine learning. Experiments performed using different position estimation methods showed that the corrected data achieved a 6% improvement over the filter-only result. This study also evaluated the effects of different pre-processing and post-processing filters on positioning accuracy. Experiments were also conducted using a published dataset and showed similar results. Full article

Acute pulmonary embolism (APE) remains a significant cause of mortality and morbidity despite increasing prophylaxis for deep venous thrombosis (DVT). The IVC filter is a temporary or permanent intravascular device that traps migrating thrombi from their origin in the pelvis or a lower limb into the pulmonary vasculature, thereby preventing significant APE. The current and longstanding indications for placing an IVC filter are in patients with documented lower extremity DVT and acute APE who also have absolute contraindications to anticoagulation or have experienced an acute, hemodynamically unstable APE requiring ventilatory and vasoactive support, with limited cardiovascular reserve. Updated guidelines have led to a significant rise in IVC filter placements for specific therapeutic indications of venous thromboembolism compared to prophylactic use. Meta-analyses show that IVC filter placement is associated with a lower risk of subsequent APE but an increased risk of DVT. However, there appears to be no significant reduction in APE-related mortality and no change in all-cause mortality. Early complications after IVC filter placement typically relate to procedural issues and include bleeding or infection at the venous access site, development of arteriovenous fistulas, accidental arterial puncture, and post-procedural access site hematoma or thrombosis. Additional early complications include IVC filter malposition, incomplete expansion, IVC penetration, or guidewire entrapment. Delayed complications may involve DVT below the filter, IVC occlusion due to the filter, IVC filter migration, fracture of one of the IVC filter components, IVC rupture, or IVC thrombosis. Retrieval of IVC filters by simple, advanced, or open techniques should be considered after weighing the risk-to-benefit for the individual patient. Deployment of the IVC filter remains an important component of interventional APE management within the narrow indications currently proposed. Current guidance recommends that an untethered temporary IVC filter should be placed and retrieved once the contraindication to anticoagulation is resolved. Full article

Filtration of submicron particles and nanoparticles is an important problem in nano-industry and in air conditioning and ventilation systems. The presence of submicron particles comprising fungal spores, bacteria, viruses, microplastic, and tobacco-smoke tar in ambient air is a severe problem in air conditioning systems. Many nanotechnology material processes used for catalyst, solar cells, gas sensors, energy storage devices, anti-corrosion and hydrophobic surface coating, optical glasses, ceramics, nanocomposite membranes, textiles, and cosmetics production also generate various types of nanoparticles, which can retain in a conveying gas released into the atmosphere. Particles in this size range are particularly difficult to remove from the air by conventional methods, e.g., electrostatic precipitators, conventional filters, or cyclones. For these reasons, nanofibrous filters produced by electrospinning were developed to remove fine particles from the post-processing gases. The physical basis of electrospinning used for nanofilters production is an employment of electrical forces to create a tangential stress on the surface of a viscous liquid jet, usually a polymer solution, flowing out from a capillary nozzle. The paper presents results for investigation of the filtration process of metal oxide nanoparticles: TiO₂, MgO, and Al₂O₃ by electrospun nanofibrous filter. The filter was produced from polyvinylidene fluoride (PVDF). The concentration of polymer dissolved in dimethylacetamide (DMAC) and acetone mixture was 15 wt.%. The flow rate of polymer solution was 1 mL/h. The nanoparticle aerosol was produced by the atomization of a suspension of these nanoparticles in a solvent (methanol) using an aerosol generator. The experimental results presented in this paper show that nanofilters made of PVDF with surface density of 13 g/m² have a high filtration efficiency for nano- and microparticles, larger than 90%. The gas flow rate through the channel was set to 960 and 670 l/min. The novelty of this paper was the investigation of air filtration from various types of nanoparticles produced by different nanotechnology processes by nanofibrous filters and studies of the morphology of nanoparticle deposited onto the nanofibers. Full article

Social media provides a vital arena for rare disease (RD) communities, fostering support, advocacy, and knowledge sharing. Rare Disease Day generates a large-scale online conversation, yet previous research has relied mainly on static, cross-sectional snapshots. This study captures the longitudinal evolution of the Spanish-language Twitter debate around Rare Disease Day across a fixed yearly window (1 February to 15 March) from 2008 to 2023. After filtering for Spanish-language posts, a corpus of 308,823 tweets (72,740 originals) was analyzed. We combined hashtag frequency analysis to assess topic salience with social network analysis (SNA) of co-occurrence networks to identify central thematic clusters. Results show progression from early generic expressions to increasingly deliberate, action-oriented communication, reflecting a shift towards empowered activism. A headline finding is the structural centrality and persistence of the hashtag #investigación (#research), underscoring the community’s enduring call for scientific progress. SNA further revealed the difference between transient virality—often linked to political or celebrity-driven hashtags—and the stable, identity-related topics at the core of the debate. Longitudinal hashtag analysis, particularly using SNA, provides a powerful tool to identify stable priorities of online health communities beyond transient media noise. Full article

Environmental education plays a vital role in cultivating environmentally responsible citizens. Although teachers are central to environmental education, their pedagogical practices remain under-researched. Thus, this study targeted to systematically review empirical research on environmental education teaching practices to synthesize findings and identify gaps. 2273 papers between 2015 and 2024 from Teacher Reference Center, ERIC, and GreenFILE were filtered to cover studies focused on environmental education teaching practices by teachers under formal education. To interpret trends of discoveries, we propose an expanded Technological Pedagogical Content Knowledge framework—TPAC+E—by incorporating environmental knowledge alongside existing dimensions. Majority of 111 peer-reviewed articles employed case study designs and interviews, with an increasing emphasis on digital technologies in the post-COVID era. Common teaching practices identified include cross-disciplinary integration, outdoor learning, participatory approaches, and the promotion of critical thinking and empathy. However, widespread reliance on textbooks and teacher-centered instruction persists. The review also highlights significant research gaps in primary education and in underrepresented regions such as the Global South and East Asia. We advocate for more interdisciplinary and context-specific approaches, along with enhanced support for teacher training and curriculum development. This review offers both practical and conceptual insights to advance equitable and effective environmental education worldwide. Full article

Background: Severe acute pancreatitis (SAP) presents with Multiple Organ Dysfunction Syndrome (MODS) in ~15% of cases, accounting for ~35% of early deaths within 48 h. Major complications—shock, renal failure, and respiratory insufficiency—arise from an overwhelming systemic inflammatory response driven by markedly elevated pro-inflammatory cytokines. Massive release of IL-2, IL-6, and TNF-α underlies the systemic inflammatory response syndrome (SIRS). Continuous veno-venous hemofiltration (CVVH) with the oXiris filter, adsorbing endotoxins and cytokines, has been used in sepsis and applied early in SAP to reduce cytokine load and organ injury. Aims: To evaluate the efficacy and safety of early CVVH with the oXiris filter in modulating the systemic inflammatory response by removing toxic cytokines from the bloodstream in patients with SAP complicated by organ dysfunction and refractory sepsis. Methods: This single-centre, retrospective, observational study was conducted at a tertiary university hospital between 2000 and 2022. Forty-eight consecutive patients with SAP at onset, defined according to the 2012 Atlanta Classification, with an APACHE II score ≥ 19 and persistent organ dysfunction (>48 h), were included. All patients were unresponsive to initial intensive care within the first 24 h and underwent urgent laparotomy with extensive peritoneal lavage, pancreatic necrosectomy, and placement of multiple abdominal drains, followed by transfer to the intensive care unit. CVVH (Prismax system) with the oXiris filter was initiated within 12 h post-surgery. IL-6 and TNF-α were selected as inflammatory markers and measured in both serum and ultrafiltrate at baseline (0 h) and at 24, 48, 72, and 96 h. These measurements were correlated with clinical parameters and prognostic scores (APACHE II, SOFA). Results: Treatment was well tolerated in all patients. The 28-day survival rate was 97.9%. There was a significant time-dependent decrease in IL-6 (p = 0.019) and TNF-α (p = 0.008) concentrations in the ultrafiltrate, consistent with high early adsorption followed by a reduced cytokine burden, whereas serum levels showed a non-significant downward trend (IL-6 p = 0.08; TNF-α p = 0.310). The APACHE II score decreased from 23 postoperatively to 8 by the second week (−65.2%; p = 0.013), with a statistically significant correlation between cytokine reduction and clinical improvement. Adverse events were rare and manageable. Conclusions: Early CVVH with the oXiris filter in SAP, complicated by MODS and refractory sepsis, proved safe, well-tolerated, and potentially effective in reducing cytokine burden and improving prognostic indices. These findings support the hypothesis of a relevant immunomodulatory effect, warranting prospective controlled trials to confirm its true impact on survival and organ recovery. Full article

Bone marrow-derived mononuclear cells (BMMCs) have shown beneficial effects on tissue repair, largely attributed to the paracrine action of bioactive mediators such as lysophosphatidic acid (LPA). This study aimed to evaluate the effects of BMMC treatment in a rat model of renal ischemia/reperfusion (I/R) injury, focusing on LPA-related molecular pathways. Male Wistar rats were divided into three groups: control; I/R, subjected to bilateral renal artery clamping for 30 min followed by 24 h of reperfusion; and I/R + BMMC, which received 1 × 10⁶ BMMCs per kidney directly into the renal capsule post-ischemia. During reperfusion, the rats were placed in metabolic cages for urine collection, renal function and protein expression. BMMC treatment did not reverse the I/R-induced increase in urine volume or decrease in glomerular filtration rate, serum potassium, or filtered sodium load. However, it prevented proteinuria, increased blood urea nitrogen, and enhanced urinary potassium excretion. Mechanistically, BMMC treatment prevented I/R-induced upregulation of LPAR1, downregulated LPAR2 and LPAR3, restored plasma LPA levels, and reduced renal autotaxin content. These results suggest that BMMCs modulate harmful LPA-related signaling and may contribute to renal protection through paracrine mechanisms in the setting of acute I/R injury. Full article

Background and Objective: Cervical cancer remains one of the leading causes of death among women worldwide, particularly in regions with limited access to early screening. Pap smear screening is the primary tool for early detection, but manual interpretation is labor-intensive, subjective, and prone to inconsistency and misdiagnosis. Accurate segmentation of cervical cell nuclei is essential for automated analysis but is often hampered by overlapping cells, poor contrast, and staining variability. This research aims to develop an improved algorithm for accurate cervical nucleus segmentation to support automated Pap smear analysis. Method: The proposed method involves a combination of adaptive gamma correction for contrast enhancement, followed by Otsu thresholding for segmentation. Post-processing is performed using adaptive morphological operations to refine the results. The system is evaluated using standard image quality assessment metrics and validated against ground truth annotations. Result: The results show a significant improvement in segmentation performance over conventional methods. The proposed algorithm achieved a Precision of 0.9965, an F-measure of 97.29%, and an Accuracy of 98.39%. The PSNR value of 16.62 indicates enhanced image clarity after preprocessing. The method also improved sensitivity, leading to better identification of nuclei boundaries. Advanced preprocessing techniques, including edge-preserving filters and multi-Otsu thresholding, contributed to more accurate cell separation. The segmentation method proved effective across varying cell overlaps and staining conditions. Comparative evaluations with traditional clustering methods confirmed its superior performance. Conclusions: The proposed algorithm delivers robust and accurate segmentation of cervical cell nuclei, addressing common challenges in Pap smear image analysis. It provides a consistent framework for automated screening tools. This work enhances diagnostic reliability in cervical cancer screening and offers a foundation for broader applications in medical image analysis. Full article

As foundational infrastructure for spatiotemporal information, the Global Navigation Satellite System (GNSS) delivers high-precision positioning, navigation, and timing (PNT) services worldwide. However, satellite atomic clock drift causes satellite clock bias, degrading PNT service quality. Compared to post-processed clock bias products and real-time estimation, satellite clock bias prediction offers a key advantage: it provides high-precision real-time clock bias even in scenarios with limited real-time data or poor communication. Through analysis and summarization of error sources in prediction models, this paper proposed generalized modeling frameworks for both classical and AI-based approaches. We reviewed current research on classical mathematical models—including polynomial, grey, Kalman filter, and time series models—and AI-based models such as machine learning (ML), multilayer perceptron (MLP), recurrent neural networks (RNN), and Transformer architectures. Technical characteristics, applicability, and limitations of each model were discussed. While AI-based models demonstrate superior flexibility and adaptability in complex scenarios compared to classical approaches, they require extensive datasets and computational resources. In conclusion, we summarized the advantages, disadvantages, and future research directions, offering insights for developing next-generation real-time high-precision GNSS PNT services. Full article