Search Results (3,193)

This article analyzes the effectiveness of Poland’s central government administration in implementing the 2030 Agenda for Sustainable Development, addressing the context of high-level strategic declarations versus actual policy outcomes. The study employs a qualitative critical document analysis, conducted as comprehensive desk research. This method involves a comparative analysis of official strategic and policy documents (e.g., “Strategy for Responsible Development”) against the empirical findings of external audits from the Supreme Audit Office (NIK), supplemented by national (GUS) and international statistical data. The analysis reveals a fundamental “implementation gap.” While Poland has successfully created a robust strategic and institutional framework, reflected in high international SDG rankings, this success masks deep deficits and stagnation in key areas, particularly in the environmental dimension. Audits consistently confirm systemic problems with inter-ministerial coordination, ensuring adequate financing, and the lack of reliable evaluation for key programs, such as “Clean Air” or the circular economy roadmap. Considering these findings, the study concludes that operational effectiveness does not match strategic declarations. The analysis identifies systemic weaknesses and recommends urgent, targeted strategic actions to bridge the gap between policy and practice, particularly by strengthening coordination and evaluation mechanisms. Full article

Traditionally, sea fog detection technologies have relied primarily on in situ observations. However, point-based observations suffer from limitations in extensive monitoring in marine environments due to the scarcity of observation stations and the limited nature of measurement data. Satellites effectively address these issues by covering vast areas and operating across multiple spectral channels, enabling precise detection and monitoring of sea fog. Despite the increasing adoption of deep learning in this field, achieving further improvements in accuracy and reliability necessitates the simultaneous use of multiple satellite datasets rather than relying on a single source. Therefore, this study aims to achieve higher accuracy and reliability in sea fog detection by employing a deep learning-based advanced co-registration technique for multi-satellite image fusion and autotuning-based optimization of State-of-the-Art (SOTA) semantic segmentation models. We utilized data from the Advanced Meteorological Imager (AMI) sensor on the Geostationary Korea Multi-Purpose Satellite 2A (GK2A) and the GOCI-II sensor on the Geostationary Korea Multi-Purpose Satellite 2B (GK2B). Swin Transformer, Mask2Former, and SegNeXt all demonstrated balanced and excellent performance across overall metrics such as IoU and F1-score. Specifically, Swin Transformer achieved an IoU of 77.24 and an F1-score of 87.16. Notably, multi-satellite fusion significantly improved the Recall score compared to the single AMI product, increasing from 88.78 to 92.01, thereby effectively mitigating the omission of disaster information. Ultimately, comparisons with the officially operational GK2A AMI Fog and GK2B GOCI-II Marine Fog (MF) products revealed that our deep learning approach was superior to both existing operational products. Full article

To address the issue of decreased localization accuracy and robustness in existing visual SLAM systems caused by imprecise identification of dynamic regions in complex dynamic scenes—leading to dynamic interference or reduction in valid static feature points, this paper proposes a dynamic visual SLAM method integrating instance-level motion classification, temporally adaptive super-pixel segmentation, and optical flow propagation. The system first employs an instance-level motion classifier combining residual flow estimation and a YOLOv8-seg instance segmentation model to distinguish moving objects. Then, temporally adaptive super-pixel segmentation algorithm SLIC (TA-SLIC) is applied to achieve fine-grained dynamic region partitioning. Subsequently, a proposed dynamic region missed-detection correction mechanism based on optical flow propagation (OFP) is used to refine the missed-detection mask, enabling accurate identification and capture of motion regions containing non-rigid local object movements, undefined moving objects, and low-dynamic objects. Finally, dynamic feature points are removed, and valid static features are utilized for pose estimation. The localization accuracy of the visual SLAM system is validated using two widely adopted datasets, TUM and BONN. Experimental results demonstrate that the proposed method effectively suppresses interference from dynamic objects (particularly non-rigid local motions) and significantly enhances both localization accuracy and system robustness in dynamic environments. Full article

The rapid advancement of high-resolution remote sensing technology has significantly expanded observational capabilities in the oil and gas sector, enabling more precise identification of petroleum infrastructure. Remote sensing now plays a critical role in providing real-time, continuous monitoring. Manual interpretation remains the predominant approach, yet is plagued by multiple limitations. To overcome the limitations of manual interpretation in large-scale monitoring of upstream petroleum assets, this study develops an end-to-end, deep learning-driven framework for intelligent extraction of key oilfield targets from high-resolution remote sensing imagery. Specific aims are as follows: (1) To leverage temporal diversity in imagery to construct a representative training dataset. (2) To automate multi-class detection of well sites, production discharge pools, and storage facilities with high precision. This study proposes an intelligent monitoring framework based on deep learning for the automatic extraction of petroleum-related features from high-resolution remote sensing imagery. Leveraging the temporal richness of multi-temporal satellite data, a geolocation-based sampling strategy was adopted to construct a dedicated petroleum remote sensing dataset. The dataset comprises over 8000 images and more than 30,000 annotated targets across three key classes: well pads, production ponds, and storage facilities. Four state-of-the-art object detection models were evaluated—two-stage frameworks (Faster R-CNN, Mask R-CNN) and single-stage algorithms (YOLOv3, YOLOv4)—with the integration of transfer learning to improve accuracy, generalization, and robustness. Experimental results demonstrate that two-stage detectors significantly outperform their single-stage counterparts in terms of mean Average Precision (mAP). Specifically, the Mask R-CNN model, enhanced through transfer learning, achieved an mAP of 89.2% across all classes, exceeding the best-performing single-stage model (YOLOv4) by 11 percentage points. This performance gap highlights the trade-off between speed and accuracy inherent in single-shot detection models, which prioritize real-time inference at the expense of precision. Additionally, comparative analysis among similar architectures confirmed that newer versions (e.g., YOLOv4 over YOLOv3) and the incorporation of transfer learning consistently yield accuracy improvements of 2–4%, underscoring its effectiveness in remote sensing applications. Three oilfield areas were selected for practical application. The results indicate that the constructed model can automatically extract multiple target categories simultaneously, with average detection accuracies of 84% for well sites and 77% for production ponds. For multi-class targets over 100 square kilometers, manual detection previously required one day but now takes only one hour. Full article

Unmanned aerial vehicle (UAV) tracking faces significant challenges due to small targets and background interference. Traditional anchor-based tracking algorithms require designing numerous proposals to capture such tiny targets, which entails unacceptable computational overhead. On the other hand, anchor-free tracking methods struggle to adapt to target scale variations, resulting in suboptimal tracking accuracy in anti-UAV tracking scenarios. To address these limitations, we pioneer the integration of diffusion models into visual tracking, proposing SiamDiff—a scale-adaptive anti-UAV framework. We reformulate the tracking task as a bounding box prediction problem, where a diffusion model is leveraged to generate scale-adaptive proposals. Furthermore, we propose a Learnable Mask Module (LMM) and a Frequency Channel Fusion Module (FCFM) to enhance discriminative feature extraction for small targets. Additionally, we design a Scale-Aware Diffusion Strategy (SADA) to boost robustness to scale variations. Experimental results on the Anti-UAV and Anti-UAV410 benchmarks demonstrate the effectiveness of our approach, achieving a State Accuracy (SA) of 71.90% and 67.03%, respectively, outperforming the baseline and other trackers. Moreover, our method shows superior adaptability to scale variations, confirming its robustness in complex anti-UAV tracking scenarios. Full article

Magnetic flux leakage (MFL) signals in steel wire rope defect detection are often corrupted by structural noise and environmental interference, leading to reduced defect recognition accuracy. This study proposes a denoising approach combining synchrosqueezing wavelet transform (SST) with dynamic time–frequency masking to enhance signal quality. The method first employs SST to redistribute time–frequency coefficients, improving resolution and highlighting defect-related energy concentrations. A dynamic masking strategy is then introduced to adaptively suppress noise by leveraging local energy statistics. Experimental results on a constructed dataset show that the proposed method achieves a signal-to-noise ratio (SNR) improvement compared to traditional wavelet denoising. This approach provides an effective solution for real-time monitoring of wire rope defects in industrial applications. Full article

Background/Objectives: The delayed effects of organophosphate poisoning may manifest years after exposure, often masked by age-related diseases. The aim of this retrospective cohort study was to identify the biochemical “trace” that could remain in patients decades after poisoning. We determined a wide range of biochemical parameters, along with the spectrum of esterified and non-esterified fatty acids (EFAs and NEFAs, respectively), in the blood plasma of a cohort of elderly patients diagnosed with occupational pathology (OP) due to (sub)chronic exposure to organophosphates in the 1980s. Methods: Elderly patients with and without a history of exposure to organophosphates were retrospectively divided into two groups: controls (n = 59, aged 73 ± 4, men 29% and women 71%) and those with OP (n = 84, aged 74 ± 4, men 29% and women 71%). The period of neurological examination and blood sampling for subsequent analysis was from mid-2022 to the end of 2023. Determination of the content of biomarkers of metabolic syndrome, NEFAs, and EFAs in blood plasma was performed by HPLC-MS/MS and GC-MS. Results: The medical histories of the examined elderly individuals with OP and the aged control group included common age-related diseases. However, patients with OP more often had hepatitis, gastrointestinal diseases, polyneuropathy, and an increased BMI. Analysis of metabolic biomarkers revealed, in the OP group, a decrease in the concentrations of 3-hydroxybutyrate (p < 0.05), 2-hydroxybutyrate (p < 0.0001), and acetyl-L-carnitine (p < 0.001) and the activity of butyrylcholinesterase (BChE) (p < 0.05), but an increase in the esterase activity of albumin (p < 0.05). Correlation analysis revealed significant relationships between albumin esterase activity and arachidonic acid concentrations in the OP group (0.64, p < 0.0001). A study of a wide range of fatty acids in patients with OP revealed reciprocal relationships between EFAs and NEFAs. A statistically significant decrease in concentration was shown for esters of margaric, stearic, eicosadienoic, eicosatrienoic, arachidonic, eicosapentaenoic, and docosahexaenoic fatty acids. A statistically significant increase in concentration was shown for non-esterified heptadecenoic, eicosapentaenoic, eicosatrienoic, docosahexaenoic, γ-linolenic, myristic, eicosenoic, arachidonic, eicosadienoic, oleic, linoleic, palmitic, linoelaidic, stearic, palmitoleic, pentadecanoic, and margaric acids. Decreases in the ratios of omega-3 to other unsaturated fatty acids were observed only for the esterified forms. Conclusions: The data obtained allow us to consider an increased level of NEFAs as one of the main cytotoxic factors for the vascular endothelium. Modification of albumin properties and decreased bioavailability of docosahexaenoic acid could be molecular links that cause specific manifestations of organophosphate-induced pathology at late stages after exposure. Full article

Accurately modelling surface solar radiation (SSR) is essential for environmental research but remains a significant challenge in topographically complex regions like Lake Baikal, where ground measurements are sparse. This study evaluates the performance of various open-access cloud cover products—from satellite sensors (AVHRR, MODIS) and ground-based observations—for modelling daily SSR totals, using a physical radiation model validated against in-situ measurements from 10 coastal stations. The results demonstrate that the choice of cloud data critically impacts model accuracy. The AVHRR satellite product yields the most reliable estimates (R² = 0.54, RMSE = 4.538 MJ/m²), significantly outperforming both ground-based cloudiness observations and the ERA5 reanalysis dataset. This finding underscores that spatially continuous satellite data provide a superior representation of cloud attenuation for regional modelling than point-based ground observations or reanalysis. Consequently, a physical model driven by high-quality satellite cloud masks is recommended as an effective methodology for generating reliable SSR fields. Full article

Image segmentation is one of the important applications of deep learning models, such as U-Net and Mask R-CNN, in medical imaging. The image segmentation process enables automated extraction of important information within images, including spine X-rays, saving medical practitioners hours of work. However, for X-ray images, low contrast and noise may affect the quality of the images and consequently reduce the effectiveness of the deep learning models in providing a robust segmentation. Image enhancement prior to feeding the images to segmentation models can help to overcome the issues caused by the low-quality images. This paper aims to evaluate the effects of three image enhancement methods, namely, the contrast-limited adaptive histogram equalization (CLAHE), histogram equalization (HE), and anisotropic diffusion (AD), for improving image segmentation performance of Mask R-CNN, non-transfer learning Mask R-CNN, and U-Net. The findings show image enhancement methods provide significant improvement to the U-Net, and, interestingly, no noticeable improvement of performance on Mask R-CNN is observed. The application of HE for transfer learning Mask R-CNN achieved the highest Dice score of 0.942 ± 0.001 for binary segmentation. The randomly initialized Mask R-CNN obtains the highest DSC of 0.941 ± 0.002 on the same task. On the other hand, for U-Net, despite the presence of statistically significant change by applying image enhancement methods, the model achieves a maximum Dice score of 0.916 ± 0.003, lower than Mask R-CNN with and without transfer learning. A study on image enhancement methods and recent deep learning algorithms is necessary to better understand the effect of image enhancement techniques using deep learning. Full article

This study introduces a novel oxygenating facial mask enriched with hemp seed extract, which uniquely combines advanced bubble-generating technology with botanically derived antioxidants for enhanced skin care. The innovative mask forms microbubbles that simulate targeted oxygen delivery, accelerating cell renewal and improving active ingredient absorption. In a randomized, controlled trial, forty participants used either the hemp seed extract mask (F1) or a placebo (F2) over eight weeks. Both formulations demonstrated excellent physical stability for 60 days, maintaining consistent pH, color, fragrance, viscosity, and foaming properties. Notably, F1 demonstrated superior foam persistence and product stability. Clinically, the hemp mask significantly increased skin hydration (up to 65.7%, p < 0.05), reduced sebum levels (32.9%), and lowered erythema (up to 46.9 AU or 12.9%, p < 0.01), without altering skin color or causing adverse effects. Consumer satisfaction with F1 exceeded F2 by 10.7%. The novelty of this work lies in the integration of oxygenating bubble technology and hemp seed extract—demonstrating synergistic effects on skin barrier function, hydration, sebum control, and erythema reduction. These findings highlight the mask’s potential as a next-generation cosmeceutical with meaningful clinical and commercial value. Full article

Pavement condition assessment using computer vision has emerged as an efficient alternative to traditional manual surveys, which are often labor-intensive and time-consuming. Leveraging deep learning, pavement distress such as cracks can be automatically detected, segmented, and quantified from high-resolution images captured by survey vehicles. Although numerous segmentation models have been proposed to generate crack masks, they typically require extensive pixel-level annotations, leading to high labeling costs. To overcome this limitation, this study integrates the Segmentation Anything Model (SAM), which produces accurate segmentation masks from simple bounding box prompts while leveraging its zero-shot capability to generalize to unseen images with minimal retraining. However, since SAM alone is not an end-to-end solution, we incorporate YOLOv8 for automated crack detection, eliminating the need for manual box annotation. Furthermore, the framework applies local refinement techniques to enhance mask precision and employs Optical Character Recognition (OCR) to automatically extract embedded GPS coordinates for geospatial mapping. The proposed framework is empirically validated using open-source pavement images from Yamanashi, demonstrating effective automated detection, classification, quantification, and geospatial mapping of pavement cracks. The results support automated pavement distress mapping onto real-world road networks, facilitating efficient maintenance planning for road agencies. Full article

In the era of the Internet of Things (IoT), video surveillance, as a vital component of smart cities and public security systems, faces the critical challenge of efficiently detecting abnormal behaviors within massive video streams. However, existing weakly supervised video anomaly detection methods are often limited by the scarcity of abnormal samples, the similarity between normal and abnormal segments, and the insufficient modeling of temporal dependencies. To address these challenges, this paper proposes a novel approach that integrates temporal structural attention with contrastive learning. On the one hand, causal masks and temporal decay weights are incorporated into the attention mechanism to explicitly constrain temporal relations and prevent future information leakage; on the other hand, positive/negative offsets and a contrastive learning strategy are employed to enhance the discriminability of abnormal segments in the latent space. Experiments conducted on multiple public video anomaly detection datasets validate the effectiveness of the proposed method, with results showing superior performance over existing mainstream models: the AUC increases to 98.1%, ACC reaches 96.1%, and the F1-score improves to 94.5%. These findings demonstrate that the proposed method can provide more intelligent, efficient, and reliable anomaly detection for IoT-based video surveillance, holding significant implications for public safety and intelligent monitoring. Full article

Purposes: Beam hardening artifacts caused by dental implants remain one of the most significant limitations of cone-beam computed tomography (CBCT), often compromising the evaluation of peri-implant bone and potentially masking critical diagnostic findings. Although metal artifact reduction (MAR) and noise-optimization filters such as the Adaptive Image Noise Optimizer (AINO) are widely available in commercial CBCT systems, their effectiveness varies depending on implant configuration and scanning parameters. A clearer understanding of how implant positioning influences artifact severity—together with how MAR and AINO perform under different conditions—is essential for improving diagnostic reliability. Materials and Methods: A fresh frozen cadaver head, with dental implants inserted using two configurations (C1 and C2), was scanned using different scan parameters, with and without metal artifact reduction and image optimization filters. The percentages of gray value alteration due to artifacts were evaluated, using registered pre-implant scans as a control. Regions of interest were defined by an experienced researcher. For the two implant conditions, ROIs were placed as follows: C1—lingual, buccal and mesial to the mesial implant; lingual, buccal and distal to the distal implant; and an additional ROI between the implants (n = 7); C2—lingual, buccal, mesial and distal to each implant (n = 8). For each ROI, the mean gray value was measured in five consecutive axial slices, and rescaled according to calibration points in air and soft tissue. Results: Significant differences were found in gray values across configurations and scan modes. In the C2 configuration, combined MAR and AINO restored gray values in certain ROIs from 1.227 (OFF) to 1.223 (MAR+AINO), closely matching the control (1.227). In contrast, C1 showed limited improvement; for example, buccal ROI gray values decreased from 3.978 (OFF) to 3.323 (AINO) compared to the control (3.273), with no significant benefit from additional MAR. Conclusion: Artifacts from implants can be significantly affected by their (relative) position and the use of MAR and AINO. Full article

Background: Recent research suggests that damage to right hemisphere regions homotopic to the left hemisphere language network affects language abilities to a greater extent than previously thought. However, few studies have investigated acute disruption of language after lesion to the right hemisphere. Here, we examined lesion correlates of acute speech deficits following left and right hemisphere ischemic stroke to clarify the neural architecture underlying early language dysfunction. Methods: We retrospectively analyzed 410 patients (225 left, 185 right hemisphere lesions) from the Stroke Outcome Optimization Project dataset. Presence and severity of speech deficits was measured using the National Institute of Health Stroke Scale Best Language subscore within 48 h of onset. Manual lesion masks were derived from clinical MRI scans and normalized to MNI space. Lesion-symptom mapping was conducted using voxelwise and region-of-interest analyses with permutation correction (5000 iterations; p < 0.05), controlling for total lesion volume. Results: Speech deficits were observed in 53.7% of the cohort (58.2% left, 48.1% right hemisphere lesions). In the full sample, the presence of speech deficits was associated with bilateral subcortical and perisylvian damage, including the external and internal capsules, insula, putamen, and superior fronto-occipital fasciculus. Severity of speech deficits localized predominantly to left hemisphere structures, with peak associations in the external capsule (Z = 6.39), posterior insula (Z = 5.64), and inferior fronto-occipital fasciculus (Z = 5.43). In the right hemisphere cohort, the presence and severity of speech deficits were linked to homologous regions, including the posterior insula (Z = 3.70) and external capsule (Z = 3.63), although with smaller effect sizes relative to the left hemisphere cohort. Right hemisphere lesions resulted in milder deficits despite larger lesion volumes compared with left hemisphere lesions. Conclusions: Acute speech impairment following right hemisphere stroke is associated with damage to a homotopic network encompassing perisylvian cortical and subcortical regions analogous to the dominant left hemisphere language network. These findings demonstrate that damage to the right hemisphere consistently results in acute speech deficits, challenging the traditional left-centric view of post-stroke speech impairment. These results have important implications for models of bilateral language representation and the neuroplastic mechanisms supporting language recovery. Full article

Deep Sedation (DS) allows for shorter recovery times, reduced complication rates and increased cost-effectiveness compared to general anesthesia. In prolonged DS, High Flow Nasal Oxygen Therapy (HFNOT) ensures adequate oxygenation. Concerns remain regarding potential masking of inadequate ventilation and induction of hyperoxia. In this single-center observational study, we continuously monitored tcPCO₂ and mitoPO₂ in 30 patients using the SenTec Monitoring System and Cellular Oxygen METabolism (COMET^®, Photonics Healthcare, Utrecht, The Netherlands) device to observe the effect of prolonged DS with HFNOT on periprocedural ventilation and oxygenation. Measurements were taken at baseline and 30, 60, 90 and 120 min after starting DS. tcPCO₂ significantly increased after 30 (55.5 (34.5–61.9) mmHg, p < 0.001), 60 (54.8 (52.5–62.2) mmHg, p < 0.001), 90 (56.5 (53.1–69.3), p < 0.001) and 120 (55.8 (50.7–56.6) mmHg, p = 0.02) minutes of DS compared to baseline (37.3 (34.5–45.5) mmHg), surpassing the normal range (35–45 mmHg). mitoPO₂ increased non-significantly from baseline (69.6 (43.9–76.7) mmHg) compared to 30 (80.5 (65.7–98.9) mmHg, p = 0.19), 60 (78.6 (70.3–85.8) mmHg, p = 0.19), 90 (74.4 (52.7–86.3) mmHg, p = 0.38) and 120 (85.6 (82.5–98.0) mmHg, p = 0.38) minutes. We observed increased tcPCO₂ and a non-significant rise in mitoPO₂ over time, without adverse effects. These findings highlight the potential of continuous sensor-based monitoring to improve real-time detection of ventilation and oxygenation. Full article