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Threshold optimization for lesion size in lutetium-177 single-photon emission computed tomography imaging: a phantom-based evaluation
No full textBackground: Accurate lesion volume estimation is essential for reliable voxel-based dosimetry in Lu-177 radionuclide therapy. Conventional fixed-threshold segmentation-particularly the commonly used 40% threshold-can markedly underestimate small lesions due to partial volume effects, leading to substantial errors in quantitative SPECT-based dosimetry.
Purpose: This study systematically evaluated the relationship between lesion size and optimal threshold values in Lu-177 SPECT/CT imaging, quantified deviations introduced by the fixed 40% threshold, and established size-specific adaptive thresholds to improve segmentation and activity recovery accuracy.
Methods: A NEMA IEC body phantom with six spherical inserts (0.52-26.5 cm³) was filled with 20 mCi (740 MBq) Lu-177 at an 8 : 1 lesion-to-background ratio. SPECT/CT data were acquired using 60-90 projections with 10-20 s per frame. Images were reconstructed under 180 parameter combinations varying iterations, subsets, and filters. For each sphere, segmentation was performed using the fixed 40% threshold (40%ThS) and an adaptive, volume-matched threshold (AV%ThS) that reproduced the true physical volume.
Results: Optimal thresholds showed a strong inverse correlation with lesion size, decreasing from ~83% (1.15 cm³) to ~42% (26.5 cm³). The fixed 40% threshold substantially underestimated volumes less than 25 cm³, with quantitative deviations reaching 45% compared to AV%ThS. Best quantitative recovery was achieved with 90 projections × 20 s and OSEM 10 × 10 iterations/subsets with Butterworth filtering (0.45 cycles/cm, order 10).
Conclusion: A single fixed threshold is insufficient for accurate Lu-177 SPECT/CT dosimetry across diverse lesion sizes. Size-adaptive thresholding combined with optimized reconstruction parameters improves lesion delineation, enhances quantitative accuracy, and reduces dosimetric uncertainty in clinical practice
Enhancement of Mechanical and Dynamic Properties of Elastomeric Nanocomposites for Structural Vibration Isolation: A Review of Materials, Mechanisms, and Applications
No full textElastomeric materials, particularly natural rubber (NR), exhibit exceptional mechanical resilience, energy absorption, and damping characteristics, making them vital in various engineering applications involving dynamic loading and vibration control. However, inherent limitations such as poor ozone resistance, thermal instability, and insufficient mechanical stiffness necessitate material modification to meet the demands of advanced structural systems. This review presents a comprehensive examination of recent advancements in the reinforcement of elastomeric matrices with nanostructured fillers, focusing primarily on zinc oxide (ZnO), multi-walled carbon nanotubes (MWCNTs), and sodium bicarbonate (NaHCO3). The effects of these additives on the tensile strength, elongation at break, tear resistance, fatigue life, dynamic stiffness, and damping capacity of rubber-based composites are critically analyzed. Additionally, the study explores the integration of optimized rubber formulations into viscoelastic foundations for structural elements such as plates subjected to free and transient vibration loading. Emphasis is placed on the interplay between molecular chain structure, cross-linking mechanisms, filler dispersion, and their collective impact on the macroscopic behavior of the composite system. Numerical simulation approaches, including finite element modeling using SOLIDWORKS, are also reviewed to highlight their role in predicting the structural response of plate-foundation systems. The article consolidates experimental findings and theoretical models to guide future research and development of high-performance elastomeric systems in vibration mitigation and structural damping applications
Machine learning-assisted classification of lung cancer: the role of sarcopenia, inflammatory biomarkers, and PET/CT anatomical-metabolic parameters
No full textFunding Agency : Scientific and Technological Research Council of Turkey (TUBITAK)
Grant number : 123S718Accurate differentiation between non-cancerous, benign, and malignant lung cancer remains a diagnostic challenge due to overlapping clinical and imaging characteristics. This study proposes a multimodal machine learning (ML) framework integrating positron emission tomography/computed tomography (PET/CT) anatomic-metabolic parameters, sarcopenia markers, and inflammatory biomarkers to enhance classification performance in lung cancer. A retrospective dataset of 222 patients was analyzed, including demographic variables, functional and morphometric sarcopenia indices, hematological inflammation markers, and PET/CT derived parameters such as maximum and mean standardized uptake value (SUVmax, SUVmean), metabolic tumor volume (MTV), total lesion glycolysis (TLG). Five ML algorithms—Logistic Regression, Multi-Layer Perceptron, Support Vector Machine, Extreme Gradient Boosting, and Random Forest—were evaluated using standardized performance metrics. Synthetic Minority Oversampling Technique was applied to balance class distributions. Feature importance analysis was conducted using the optimal model, and classification was repeated using the top 15 features. Among the models, Random Forest demonstrated superior predictive performance with a test accuracy of 96%, precision, recall, and F1-score of 0.96, and an average AUC of 0.99. Feature importance analysis revealed SUVmax, SUVmean, total lesion glycolysis, and skeletal muscle index as leading predictors. A secondary classification using only the top 15 features yielded even higher test accuracy (97%). These findings underscore the potential of integrating metabolic imaging, physical function, and biochemical inflammation markers in a non-invasive ML-based diagnostic pipeline. The proposed framework demonstrates high accuracy and generalizability and may serve as an effective clinical decision support tool in early lung cancer diagnosis and risk stratification
Rigicon inflatable penile prostheses have excellent measurable axial rigidity and very satisfied patients at 1-year follow-up as measured by validated study
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LSTM-AU: Dynamic Thresholding and Explainable Autoencoding for Cyber Defense
No full textIntrusion Detection Systems (IDS) are essential for securing networks today; nevertheless, many systems still exhibit issues such as redundancy of features, fixed thresholding, and a lack of interpretability. In this paper, we present a hybrid anomaly detection approach including Long Short-Term Memory Autoencoder (LSTM-AE), adaptive thresholding, and feature attribution. The LSTM-AE allows modelling of long-term temporal dependencies in network traffic while applying filtering to paradoxically include unnecessary traffic noise and redundancy for proper anomaly detection. The adaptive thresholding is capable of recalibrating to changes in traffic patterns that ultimately mitigate false alarms more accurately. Lastly, by incorporating the Shapley value-based attribution, the model's predictions can be explained by using the aspect of traffic that is most pertinent. he empirical exploration we present on the benchmark datasets demonstrates the effectiveness of the DeepShield model architecture: on CIC-IDS2017, the accuracy was 98.9%, with precision of 98.7%, recall of 98.5%, and F1-score of 98.6%, outperforming LSTM, CNN, and Random Forest baselines; on UNSW-NB15, the score was 95.6 accuracy, with precision of 95.3, recall of 95.0, and F1-score of 95.1, outperforming other competing measures. Based on these additional capabilities shown through the Shapley-based attribution, we can conclude that DeepShield achieves state-of-the-art detection effectiveness while translating the model into a space that is more interpretable, which makes it deployable in enterprise and industrial security that is highly reliant on the defendable integrity of networks
Liquefaction Behavior of Suction Bucket Foundations for Wind Turbines Under Soil Variability
No full textThis paper investigates the dynamic performance of suction bucket foundations supporting wind turbines in fully and partially saturated soils, a topic that has received limited attention in geotechnical research. Numerical analyses using PLAXIS2D were conducted to study the effects of relative density and degree of saturation on the dynamic behavior of suction bucket foundations installed in loose to dense sandy soils under fully and partially saturated conditions. The models were subjected to strong ground motion and cyclic wind loads, with responses such as excess pore pressure ratio, settlement, rotation, and spectral acceleration of the suction bucket foundation evaluated. The results demonstrate that partial saturation significantly reduces the settlement and rotation of suction bucket foundations compared to fully saturated sands, particularly in dense soils. However, partially saturated soils also transfer higher accelerations to the structure. These findings suggest that partial saturation can be a viable liquefaction mitigation technique for wind turbines, offering improved performance without disrupting turbine operation
Formulation and Evaluation of Orally Disintegrating Tablet Containing Aripiprazole in the Context of Quality by Design Approach
Full text linkAim/Background: The objective of this research is to develop and enhance the formulation of aripiprazole rapid disintegrating tablets utilizing the Quality by Design (QbD) methodology. Materials and Methods: A full factorial experimental design with three levels was employed to analyze the influence of key factors, namely the concentration of the filler (starch), ludipress concentration and disintegrant concentration, on important quality attributes such as disintegration time, friability and hardness. The formulation's drug-excipients interaction was examined using FTIR. Research was conducted to assess the stability of the product in accelerated conditions of 40ºC and 75% relative humidity. Results: FTIR analysis indicated that there was no notable chemical interaction seen in the solid form. The Aripiprazole fast disintegrating tablet formulations demonstrated satisfactory friability (0.77±0.16%), rapid disintegration time (66±0.58 sec) and appropriate hardness (48.35±3.22 N). The research revealed that the most favorable combination of independent components consisted of 15.8% filler (starch), 76% ludipress and 1.3% disintegrant. Conclusion: The accelerated stability experiments demonstrated that the hardness, friability, disintegration durations and drug release rate were within the permissible limits defined by the compendial standards. Implementing the Quality by Design (QbD) method may facilitate a comprehensive comprehension of how the Critical Material Attributes (CMAs) impact the Critical Quality Attributes (CQAs) of the final product of aripiprazole rapid disintegrating tablets
PAFWF-EEGC Net: parallel adaptive feature weight fusion based on EEG-dynamic characteristics using channels neural network for driver drowsiness detection
No full textDrowsy driving is considered one of the most dangerous causes of road accidents and deaths worldwide. Drivers’ concentration is directly affected by fatigue, which affects their reaction time, reducing their attention and decision-making ability on the road. This can often lead to dangerous situations. With the development of Human Computer Interface systems and the rise of intelligent transportation systems, examining the effects of driver fatigue has become more critical, and research aimed at reducing the risk of fatigue-related accidents has gained importance. For this purpose, this study proposes a Parallel Adaptive Feature Weight Fusion based on EEG-Dynamic Characteristics using Channels Neural Network (PAFWF-EEGC Net) to detect the driver drowsiness condition. Two signal processing techniques are used to extract EEG dynamic features: first, Continuous Wavelet Transform (CWT) to capture the spectral-temporal features by accurately estimating both time and frequency localizations, and second, Fast Fourier Transform (FFT)—Power Spectrum Density (PSD) to convert the signals from the time domain to the frequency domain and show the distribution of signal power over frequency. These extracted dynamic features are passed to Attention channels and Parallel Adaptive Feature Fusion to integrate the most relevant feature channels to detect mental state. Furthermore, three processing dataset scenarios and cross-validation techniques are used to validate the Net. The Net showed excellent performance through ninefold/3rd scenario by achieving 98% detection accuracy, and 84%, 88.75%, 93.8% average detection accuracy through 1st, 2nd, 3rd scenarios respectively
Antimicrobial Lock Therapy: Is it a Real Savior in Pediatric Hematopoetic Stem Cell Transplant (HSCT) Patients?
No full textObjective: Central line-associated bloodstream infection (CLABSI) is a significant cause of morbidity and mortality in patients undergoing hematopoietic stem cell transplantation (HSCT). Antimicrobial lock treatment (ALT), when utilized alongside systemic antibiotics, may be lifesaving when catheter removal (CR) is not feasible.
Materials and methods: This retrospective study analyzed the clinical, laboratory, and microbiologic characteristics of CLABSI episodes of pediatric patients who underwent HSCT and applied ALT.
Results: There were 137 cases of CLABSI (63.5 male) who were given ALT. The median age was 48 (3-204) months. The most common causative microorganism was Gram-negative bacteria, encountered in 85 patients (62%). Forty-six patients (33.6%) had Gram-positive bacterial growth, whereas 6 had (4.4%) fungal infection. ALT was successful in 77.4% of the patients (n=106). CR was required in 25 patients (18.2%). The CLABSI-related mortality rate was 12.4%. When the outcome of ALT was evaluated, post-transplantation cyclophosphamide (PTCy) use, fungal growth, persistent bacteremia/fungemia, re-HSCT, inappropriate empirical antibiotic use, hypotension, and pediatric intensive care unit admission were significantly more common in the "unsuccessful" ALT group. The patients in the unsuccessful group had higher C-reactive protein [110.2 (1.10-323.5) mg/L] levels when compared to the successful ALT group [58 (0.2-450.3) mg/L] (p=0.029). The presence of hypotension, HLA-mismatch transplantation, and persistent bacteremia/fungemia were independent risk factors for ALT failure.
Conclusion: ALT can be an effective catheter-saving strategy in HSCT pediatric patients. Nevertheless, patients should be monitored very closely during ALT, and the presence of certain risk factors should be taken into account
Performance Evaluation of Multiple Wind Turbines Integrated with Buildings
No full textWind power became easy to access, clean, safe and cost-competitive among all renewable energy sources. It became one of the fastest-growing renewable energy resources in electricity generation. The wind power Horizontal axis wind turbine (HAWT) is proportional to the swept area. A multi-rotor can increase the area of the wind turbine in an array or a large diameter of a single rotor. Rotor sizes are continuously expanding with mature technology. In this research, a study was carried out to describe the flow simulation of a two-rotor, three-blade, ducted horizontal-axis wind turbine to evaluate its performance. The coefficient of performance increased by converging channels with convergence angles of 20° and 12°, respectively, because convergent ducts cause an increase in wind speed. DMRWT simulation and numerical analysis by MATLAB and ANSYS FLUENT. Both approaches presented good results