Search Results (24)

Sports activities often carry a high risk of injury, varying in severity, making the use of protective equipment, such as helmets and kneecaps, essential in many cases. Among all potential injuries, head injuries are the most crucial due to their severity. Hence, in the last decades, the scientific interest has been focused on establishing head injury criteria and improving the helmet design with the ultimate goal of the reduction in injury probability and increasing the athlete’s performance. In this context, the current study aims to develop a lightweight sports helmet with increased safety performance, utilizing topology optimization processes and advanced architected materials. In detail, the design of a conventional helmet was developed and modified applying in specific regions advanced architected materials, such as triply periodic minimal surfaces (TPMS) and hybrid structures, with functionally graded configurations to produce sandwich-like structures capable of absorbing mechanical energy from impacts. The developed helmet’s designs were numerically evaluated through dynamic finite element analyses (FEA), simulating the helmet’s impact on a wall with a specific velocity. Through these analyses, the plastic deformation of the designed helmets was observed, coupled with the stress concentration contours. Furthermore, the results of FEAs were utilized in order to calculate the values of the head injury criterion (HIC). Finally, the developed topologically optimized helmet design incorporating the hybrid lattice structure revealed increased energy absorption, reaching a HIC of 1618, improved by around 14% compared to the conventional design configuration. Full article

Enhancing the combustion efficiency and flame stability in conventional systems is essential for reducing carbon emissions and advancing sustainable energy solutions. In this context, electrohydrodynamic plasma actuators offer a promising active control method for modifying and regulating flame characteristics. This study presents a numerical investigation into the effects of a ring-type plasma actuator positioned on the co-flow air side of a non-premixed turbulent methane/air combustion system—an approach not previously reported in the literature. The ring-type plasma actuator was designed by placing electrodes along the perimeter of the small diameter wall of the air duct. The impact of the plasma actuator on the reacting flow field within the burner was analyzed, with a focus on its influence on the flow dynamics and flame structure. The results, visualized through velocity and temperature contours, as well as flow streamlines, provide insight into the actuator’s effect on flame behavior. Two operating modes of the plasma actuators were evaluated: co-flow mode, where the aerodynamic effect of the plasma actuators was directed downstream; and counter-flow mode, where the effects were directed upstream. The findings indicate that the co-flow actuation positively reduces the flame height and enhances the flame anchoring at the root, whereas counter-flow actuation slightly weakens the flame root. Numerical simulations further revealed that co-flow actuation marginally increases the energy release by approximately 0.13%, while counter-flow actuation reduces the energy release by around 7.8%. Full article

Image segmentation is a crucial task in artificial intelligence fields such as computer vision and medical imaging. While convolutional neural networks (CNNs) have achieved notable success by learning representative features from large datasets, they often lack geometric priors and global object information, limiting their accuracy in complex scenarios. Variational methods like active contours provide geometric priors and theoretical interpretability but require manual initialization and are sensitive to hyper-parameters. To overcome these challenges, we propose a novel segmentation approach, named PolarVoting, which combines the minimal path encoding rich geometric features and CNNs which can provide efficient initialization. The introduced model involves two main steps: firstly, we leverage the PolarMask model to extract multiple source points for initialization, and secondly, we construct a voting score map which implicitly contains the segmentation mask via a modified circular geometric voting (CGV) scheme. This map embeds global geometric information for finding accurate segmentation. By integrating neural network representation with geometric priors, the PolarVoting model enhances segmentation accuracy and robustness. Extensive experiments on various datasets demonstrate that the proposed PolarVoting method outperforms both PolarMask and traditional single-source CGV models. It excels in challenging imaging scenarios characterized by intensity inhomogeneity, noise, and complex backgrounds, accurately delineating object boundaries and advancing the state of image segmentation. Full article

Side scan sonar images have great application prospects in underwater surveys, target detection, and engineering activities. However, the acquired sonar images exhibit low illumination, scattered noise, distorted outlines, and unclear edge textures due to the complicated undersea environment and intrinsic device flaws. Hence, this paper proposes a multi-scale fusion strategy for side scan sonar (SSS) image correction to improve the low contrast and noise interference. Initially, an SSS image was decomposed into low and high frequency sub-bands via the non-subsampled shearlet transform (NSST). Then, modified multi-scale retinex (MMSR) was employed to enhance the contrast of the low frequency sub-band. Next, sparse dictionary learning (SDL) was utilized to eliminate high frequency noise. Finally, the process of NSST reconstruction was completed by fusing the emerging low and high frequency sub-band images to generate a new sonar image. The experimental results demonstrate that the target features, underwater terrain, and edge contours could be clearly displayed in the image corrected by the multi-scale fusion strategy when compared to eight correction techniques: BPDHE, MSRCR, NPE, ALTM, LIME, FE, WT, and TVRLRA. Effective control was achieved over the speckle noise of the sonar image. Furthermore, the AG, STD, and E values illustrated the delicacy and contrast of the corrected images processed by the proposed strategy. The PSNR value revealed that the proposed strategy outperformed the advanced TVRLRA technology in terms of filtering performance by at least 8.8%. It can provide sonar imagery that is appropriate for various circumstances. Full article

Dietary intake is known to impact athletic performance. The factors that influence dietary intake have been investigated widely, but their collective effect has not been examined. The primary aim of this cross-sectional study was to assess the relationship between dietary intake and nutrition knowledge, body image, weight control, macronutrient balance, and hedonic hunger. Forty-two student athletes or active individuals were recruited through contact with sporting organisations and course coordinators, advertising via twitter, and flyers posted within university buildings. Nutrition knowledge, body image, weight control, macronutrient balance, and hedonic hunger were measured using the Abridged Nutrition for Sport Knowledge Questionnaire, Body Image Disturbance Questionnaire and Contour Drawing Rating Scale, a Weight Fluctuation Measure, Australian Eating Score, and Power of Food Scale, respectively. Hierarchical regression analysis, correlation testing, and mean difference testing were applied. Carbohydrate intake, body image disturbance scores, weight fluctuation, and hedonic hunger for food tasted had a significant relationship (R² = 64.6%, Adj R² = 0.608%, p < 0.001) with dietary energy intake. Student athletes’ dietary intakes are influenced by multiple potentially modifiable factors. Future studies should use larger sample sizes, with interventions focusing on individual modifiable factors to determine how dietary intake can be most significantly impacted. Full article

High-resolution coastline detection and monitoring are challenging on a global scale, especially in flat areas where natural events, sea level rise, and anthropic activities constantly modify the coastal environment. While the coastline related to the 0-level contour line can be extracted from accurate Digital Terrain Models (DTMs), the detection of the real-time, instantaneous coastline, especially at low tide, is a challenge that warrants further study and evaluation. In order to investigate an efficient combination of methods that allows to contribute to the knowledge in this field, this work uses topographic total station measurements, Global Navigation Satellite System Real-Time Kinematic (GNSS RTK) technique, and the Structure from Motion (SfM) approach (using a low-cost drone equipped with optical and thermal cameras). All the data were acquired at the beginning of 2022 and refer to the areas of Boccasette and Barricata, in the Po River Delta (Northeastern of Italy). The real-time coastline obtained from the GNSS data was validated using the topographic total station measurements; the correspondent polylines obtained from the photogrammetric data (using both automatic extraction and manual restitutions by visual inspection of orhophotos) were compared with the GNSS data to evaluate the performances of the different techniques. The results provided good agreement between the real-time coastlines obtained from different approaches. However, using the optical images, the accuracy was strictly connected with the radiometric changes in the photos and using thermal images, both manual and automatic polylines provided differences in the order of 1–2 m. Multi-temporal comparison of the 0-level coastline with those obtained from a LiDAR survey performed in 2018 provided the detection of the erosion and accretion areas in the period 2018–2022. The investigation on the two case studies showed a better accuracy of the GNSS RTK method in the real-time coastline detection. It can be considered as reliable ground-truth reference for the evaluation of the photogrammetric coastlines. While GNSS RTK proved to be more productive and efficient, optical and thermal SfM provided better results in terms of morphological completeness of the data. Full article

This paper presents a study on flow hydrodynamics for single-channel serpentine flow field (SCSFF) and cross-split serpentine flow field configurations (CSSFF) for different geometric dimensions of channel and rib width ratios with electrode intrusion over varying compression ratios (CRs) in an all-iron redox flow battery. Pressure drops $\left(\Delta p\right)$ measured experimentally across a cell active area of 131 cm² for different electrolyte flow rates were numerically validated. A computational fluid dynamics study was conducted for detailed flow analyses, velocity magnitude contours, flow distribution, and uniformity index for the intrusion effect of a graphite felt electrode bearing a thickness of 6 mm with a channel compressed to varying percentages of 50%, 60%, and 70%. Experimental pressure drops $\left(\Delta p\right)$ over the numerical value resulted in the maximum error approximated to 4%, showing good agreement. It was also reported that the modified version of the cross-split serpentine flow field, model D, had the lowest pressure drop, $\Delta p$, of 2223.4 pa, with a maximum uniformity index at the electrode midplane of 0.827 for CR 50%, across the active cell area. The pressure drop $\left(\Delta p\right)$ was predominantly higher for increased compression ratios, wherein intrusion phenomena led to changes in electrochemical activity; it was found that the velocity distribution was quite uniform for a volumetric uniformity index greater than 80% in the felt. Full article

Synthetic aperture radar (SAR), as an active microwave sensor, can inevitably receive radio frequency interference (RFI) generated by various electromagnetic equipment. When the SAR system receives RFI, it will affect SAR imaging and limit the application of SAR images. As a kind of RFI mitigation method, notch filtering method is a classical method with high efficiency and robust performance. However, the notch filtering methods pay no attention to the protection of useful signals. This paper proposed a modified 2-D notch filter based on image segmentation for RFI mitigation with signal-protected capability. (1) The adaptive gamma correction (AGC) approach was utilized to enhance the SAR image with RFI in the range-frequency and azimuth-time domain. (2) The modified selective binary and Gaussian filtering regularized level set (SBGFRLS) model was utilized to further process the image after AGC to accurately extract the contour of the useful signals with interference, which is more conducive to protecting the useful signals without interference. (3) The Generalized Singular Value Thresholding (GSVT) based low-rank sparse decomposition (LRSD) model was utilized to separate the RFI signals and the useful signals. Then, the useful signals were restored to the raw data. The simulation experiments and measured data experiments show that the proposed method can effectively mitigate RFI and protect the useful signals whether there are RFI with single source or multiple sources. Full article

In transarterial radioembolization (TARE) of hepatocellular carcinoma (HCC) with Yttrium-90 (Y-90) microspheres, recent studies correlate dosimetry from bremsstrahlung single photon emission tomography (SPECT/CT) with treatment outcomes; however, these studies focus on measures of central tendency rather than volumetric coverage metrics commonly used in radiation oncology. We hypothesized that three-dimensional (3D) isodose coverage of gross tumor volume (GTV) is the driving factor in HCC treatment response to TARE and is best assessed using advanced dosimetry techniques applied to nuclear imaging of actual Y-90 biodistribution. We reviewed 51 lobar TARE Y-90 treatments of 43 HCC patients. Dose prescriptions were 120 Gy for TheraSpheres and 85 Gy for SIR-Spheres. All patients underwent post-TARE Y-90 bremsstrahlung SPECT/CT imaging. Commercial software was used to contour gross tumor volume (GTV) and liver on post-TARE SPECT/CT. Y-90 dose distributions were calculated using the Local Deposition Model based on post-TARE SPECT/CT activity maps. Median gross tumor volume (GTV) dose; GTV receiving less than 100 Gy, 70 Gy and 50 Gy; minimum dose covering the hottest 70%, 95%, and 98% of the GTV (D70, D95, D98); mean dose to nontumorous liver, and disease burden (GTV/liver volume) were obtained. Clinical outcomes were collected for all patients by chart and imaging review. HCC treatment response was assessed according to the modified response criteria in solid tumors (mRECIST) guidelines. Kaplan-Meier (KM) survival estimates and multivariate regression analyses (MVA) were performed using STATA. Median survival was 22.5 months for patients achieving objective response (OR) in targeted lesions (complete response (CR) or partial response (PR) per mRECIST) vs. 7.6 months for non-responders (NR, stable disease or disease progression per mRECIST). On MVA, the volume of underdosed tumor (GTV receiving less than 100 Gy) was the only significant dosimetric predictor for CR (p = 0.0004) and overall survival (OS, p = 0.003). All targets with less than CR (n = 39) had more than 20 cc of underdosed tumor. D70 (p = 0.038) correlated with OR, with mean D70 of 95 Gy for responders and 60 Gy for non-responders (p = 0.042). On MVA, mean dose to nontumorous liver trended toward significant association with grade 3+ toxicity (p = 0.09) and correlated with delivered activity (p < 0.001) and burden of disease (p = 0.05). Dosimetric models supplied area under the curve estimates of > 0.80 predicting CR, OR, and ≥grade 3 acute toxicity. Dosimetric parameters derived from the retrospective analysis of post-TARE Y-90 bremsstrahlung SPECT/CT after lobar treatment of HCC suggest that volumetric coverage of GTV, not a high mean or median dose, is the driving factor in treatment response and that this is best assessed through the analysis of actual Y-90 biodistribution. Full article

Lysine-specific demethylase 1 (LSD1) is a histone-modifying enzyme, which is a significant target for anticancer drug research. In this work, 40 reported tetrahydroquinoline-derivative inhibitors targeting LSD1 were studied to establish the three-dimensional quantitative structure–activity relationship (3D-QSAR). The established models CoMFA (Comparative Molecular Field Analysis (q² = 0.778, ${\mathrm{R}}_{\mathrm{pred}}^2$ = 0.709)) and CoMSIA (Comparative Molecular Similarity Index Analysis (q² = 0.764, ${\mathrm{R}}_{\mathrm{pred}}^2$ = 0.713)) yielded good statistical and predictive properties. Based on the corresponding contour maps, seven novel tetrahydroquinoline derivatives were designed. For more information, three of the compounds (D1, D4, and Z17) and the template molecule 18x were explored with molecular dynamics simulations, binding free energy calculations by MM/PBSA method as well as the ADME (absorption, distribution, metabolism, and excretion) prediction. The results suggested that D1, D4, and Z17 performed better than template molecule 18x due to the introduction of the amino and hydrophobic groups, especially for the D1 and D4, which will provide guidance for the design of LSD1 inhibitors. Full article

The early diagnosis of cardiovascular diseases (CVDs) can effectively prevent them from worsening. The source of the disease can be effectively detected through analysis with cardiac magnetic resonance imaging (CMRI). The segmentation of the left ventricle (LV) in CMRI images plays an indispensable role in the diagnosis of CVDs. However, the automated segmentation of LV is a challenging task, as it is confused with neighboring regions in the cardiac MRI. Deep learning models are effective in performing such complex segmentation because of the high performing convolutional neural networks (CNN). However, since segmentation using CNN involves the pixel-level classification of the image, it lacks the contextual information that is highly desirable in analyzing medical images. In this research, we propose a modified U-Net model to accurately segment the LV using context-enabled segmentation. The proposed model achieves the automatic segmentation and quantitative assessment of LV. The proposed model achieves the state-of-the-art accuracy by effectively utilizing various hyperparameters, such as batch size, batch normalization, activation function, loss function and dropout. Our method demonstrated a statistical significance in the endo- and epicardial walls with a dice score of 0.96 and 0.93, respectively, an average perpendicular distance of 1.73 and percentage of good contours of 96.22 were achieved. Furthermore, a high positive correlation of 0.98 between the clinical parameters, such as ejection fraction, end diastolic volume (EDV), end systolic volume (ESV) and gold standard was obtained. Full article

Some clinical situations, such as the closure of pronounced diastemas, and the transformation of malformed, small, or peg-shaped teeth, require a rebalancing of dental proportions accompanied by a modification of the gingival contour. A traditional treatment plan can include surgical, prosthetic, and/or orthodontic treatments. In some cases, it can be considered too invasive, and not all patients are willing to undertake long therapies. To overcome these limitations, a possible solution could be the application of the Biologically Active Intrasulcular Restoration (BAIR) technique, which allows us to modify the natural emergence tooth profile using simple intrasulcular direct restorations. The aims of this paper are to investigate possible effects on gingival health, and to assess the patient satisfaction about the aesthetic intervention performed. Periodontal data were collected, and patient satisfaction was registered using the VAS questionnaire. All sites healed without complications, no adverse events were registered or reported by the patients, and no signs of periodontal morbidity were visible. The results show that the patients evaluated the final aesthetics in a positive way, and they perceived a good condition of periodontal health as well. The intervention is felt as almost painless, and patients do not report significant post-operative distress. In conclusion, the BAIR technique can provide a valid therapeutic alternative for patients for whom traditional treatments are not indicated. It is a minimally invasive intervention where both the operating times and the biologic and economic costs are reduced. Full article

Radiotherapy and chemotherapy are conventional cancer treatments. Around 60% of all patients who are diagnosed with cancer receive radio- or chemotherapy in combination with surgery during their disease. Only a few patients respond to the blockage of immune checkpoints alone, or in combination therapy, because their tumours might not be immunogenic. Under these circumstances, an increasing level of extracellular adenosine via the activation of ecto-5’-nucleotidase (CD73) and consequent adenosine receptor signalling is a typical mechanism that tumours use to evade immune surveillance. CD73 is responsible for the conversion of adenosine monophosphate to adenosine. CD73 is overexpressed in various tumour types. Hence, targetting CD73’s signalling is important for the reversal of adenosine-facilitated immune suppression. In this study, we selected a potent series of the non-nucleotide small molecule inhibitors of CD73. Molecular docking studies were performed in order to examine the binding mode of the inhibitors inside the active site of CD73 and 3D-QSAR was used to study the structure–activity relationship. The obtained CoMFA (q² = 0.844, ONC = 5, r² = 0.947) and CoMSIA (q² = 0.804, ONC = 4, r² = 0.954) models showed reasonable statistical values. The 3D-QSAR contour map analysis revealed useful structural characteristics that were needed to modify non-nucleotide small molecule inhibitors. We used the structural information from the overall docking and 3D-QSAR results to design new, potent CD73 non-nucleotide inhibitors. The newly designed CD73 inhibitors exhibited higher activity (predicted pIC₅₀) than the most active compound of all of the derivatives that were selected for this study. Further experimental studies are needed in order to validate the new CD73 inhibitors. Full article

This research concerns a design and construction of a bone mineral density (BMD) and bone mineral content (BMC) measurement system based on dual energy X-ray absorptiometry (DEXA). An indirect X-ray detector is designed by optical coupling CMOS sensor with image on the intensifying screen. A dedicated microcontroller X-ray apparatus is used as an X-ray source to capture two energy level X-ray of middle phalanges bone of middle finger. The captured image is processed based on modified Beer-Lambert law to compute bone mineral density. Bone mineral content is also computed by determining the area of the phalanges bone using active contour. The designed bone mineral density (BMD) and bone mineral content (BMC) measurement system is low-cost and hence can be distributed at district hospital for screening purposes of Osteoporosis of the elderly. Compared with BMD measured from commercial model, BMD measurement of our system acquires linear relation with R2 equals 0.969. The mean square error between the normalized BMD value and that of the commercial model is 0.0000981. Full article

To understand the dynamics of a living system, the analysis of particular and/or cellular dynamics has been performed based on shape-based center point detection. After collecting sequential time-lapse images of cellular dynamics, the trajectory of a moving object is determined from the set of center points of the cell analyzed from each image. The accuracy of trajectory is significant in understanding the stochastic nature of the dynamics of biological objects. In this study, to localize a cellular object in time-lapse images, three different localization methods, namely radial symmetry, circular Hough transform, and modified active contour, were considered. To analyze the accuracy of cellular dynamics, several statistical parameters such as mean square displacement and velocity autocorrelation function were employed, and localization error derived from these was reported for each localization method. In particular, through denoising using a Poisson noise filter, improved localization characteristics could be achieved. The modified active contour with denoising reduced localization error significantly, and thus allowed for accurate estimation of the statistical parameters of cellular dynamics. Full article