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We focus on reviewing state-of-the-art developments of dedicated PET scanners with irregular geometries and the potential of different aspects of multifunctional PET imaging. First, we discuss advances in non-conventional PET detector geometries. Then, we present innovative designs of organ-specific dedicated PET scanners for breast, brain, prostate, and cardiac imaging. We will also review challenges and possible artifacts by image reconstruction algorithms for PET scanners with irregular geometries, such as non-cylindrical and partial angular coverage geometries and how they can be addressed. Then, we attempt to address some open issues about cost/benefits analysis of dedicated PET scanners, how far are the theoretical conceptual designs from the market/clinic, and strategies to reduce fabrication cost without compromising performance.

In blocky and layered rock slopes, toppling failure is a conventional type of instability which may occur in mining and civil engineering. If this kind of slope failure happens as a consequence of another type of failure, it is mentioned to as secondary toppling failure. “slide-toe-toppling” is a kind of secondary toppling failures, where the toe part of the slope is toppled as a consequence of a semi or non-circular sliding failure at the upper of the slope. This failure has been studied using physical modelling and analytical method. In the present study, firstly a brief review of the toppling failure in rock slopes is summarized, and the mechanism of slide-toe-toppling failure is described. Then, this failure is examined through a series of numerical modelling. Distinct element method (UDEC software) and finite element method (Phase2 software) were used in this research. Three types of slide-toe-toppling failures, including flexural, blocky, and block-flexural, are simulated. Comparison between two numerical modellings (distinct element and finite element methods) with the result of pre-existing physical models and analytical solution, illustrates that UDEC software has a better agreement than Phase2 software. This accordance indicates that this numerical code is well capable to analyze the behavior of slide-toe-toppling failure.

Background This study aimed to investigate the value of clinical, radiomic features extracted from gross tumor volumes (GTVs) delineated on CT images, dose distributions (Dosiomics), and fusion of CT and dose distributions to predict outcomes in head and neck cancer (HNC) patients. Methods A cohort of 240 HNC patients from five different centers was obtained from The Cancer Imaging Archive. Seven strategies, including four non-fusion (Clinical, CT, Dose, DualCT-Dose), and three fusion algorithms (latent low-rank representation referred (LLRR),Wavelet, weighted least square (WLS)) were applied. The fusion algorithms were used to fuse the pre-treatment CT images and 3-dimensional dose maps. Overall, 215 radiomics and Dosiomics features were extracted from the GTVs, alongside with seven clinical features incorporated. Five feature selection (FS) methods in combination with six machine learning (ML) models were implemented. The performance of the models was quantified using the concordance index (CI) in one-center-leave-out 5-fold cross-validation for overall survival (OS) prediction considering the time-to-event. Results The mean CI and Kaplan-Meier curves were used for further comparisons. The CoxBoost ML model using the Minimal Depth (MD) FS method and the glmnet model using the Variable hunting (VH) FS method showed the best performance with CI = 0.73 ± 0.15 for features extracted from LLRR fused images. In addition, both glmnet-Cindex and Coxph-Cindex classifiers achieved a CI of 0.72 ± 0.14 by employing the dose images (+ incorporated clinical features) only. Conclusion Our results demonstrated that clinical features, Dosiomics and fusion of dose and CT images by specific ML-FS models could predict the overall survival of HNC patients with acceptable accuracy. Besides, the performance of ML methods among the three different strategies was almost comparable.

This study aimed to investigate the diagnostic performance of machine learning-based radiomics analysis to diagnose coronary artery disease status and risk from rest/stress Myocardial Perfusion Imaging (MPI) single-photon emission computed tomography (SPECT). A total of 395 patients suspicious of coronary artery disease who underwent 2-day stress-rest protocol MPI SPECT were enrolled in this study. The left ventricle myocardium, excluding the cardiac cavity, was manually delineated on rest and stress images to define a volume of interest. Added to clinical features (age, sex, family history, diabetes status, smoking, and ejection fraction), a total of 118 radiomics features, were extracted from rest and stress MPI SPECT images to establish different feature sets, including Rest-, Stress-, Delta-, and Combined-radiomics (all together) feature sets. The data were randomly divided into 80% and 20% subsets for training and testing, respectively. The performance of classifiers built from combinations of three feature selections, and nine machine learning algorithms was evaluated for two different diagnostic tasks, including 1) normal/abnormal (no CAD vs. CAD) classification, and 2) low-risk/high-risk CAD classification. Different metrics, including the area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE), were reported for models’ evaluation. Overall, models built on the Stress feature set (compared to other feature sets), and models to diagnose the second task (compared to task 1 models) revealed better performance. The Stress-mRMR-KNN (feature set-feature selection-classifier) reached the highest performance for task 1 with AUC, ACC, SEN, and SPE equal to 0.61, 0.63, 0.64, and 0.6, respectively. The Stress-Boruta-GB model achieved the highest performance for task 2 with AUC, ACC, SEN, and SPE of 0.79, 0.76, 0.75, and 0.76, respectively. Diabetes status from the clinical feature family, and dependence count non-uniformity normalized, from the NGLDM family, which is representative of non-uniformity in the region of interest were the most frequently selected features from stress feature set for CAD risk classification. This study revealed promising results for CAD risk classification using machine learning models built on MPI SPECT radiomics. The proposed models are helpful to alleviate the labor-intensive MPI SPECT interpretation process regarding CAD status and can potentially expedite the diagnostic process.

The potential release of pollutants from a low-grade ore dump in the Miduk mine was evaluated based on geochemical and mineralogical characteristics. The main environmental risks associated with the dump were the potential for acid mine drainage (AMD) generation and the transport of acidic leachate with high contents of toxic elements (As, Pb, Cu and Zn) into the depths of Earth. Geochemical characteristics of rocks such as the content of sulphur species (Stotal, Spyrite and Ssulphate) were assessed. In addition, the elemental composition of the rock and leaching solution samples were determined. Based on both static and NAG tests, materials with a paste pH < 4 had the high potential of AMD generation. Principal component analysis (PCA) was employed to integrate the results of AMD prediction tests with mineralogical and geochemical compositions of the rocks. Based on PCA, eight principal components (PC1–PC8) were accounted for 93% of the total variance of data. Two components of PC2 and PC3 were determined as primary factors of AMD generation. The extracted principal components were interpolated within the dump by ordinary kriging method. Based on the environmental risk map of the PC3, the main source of AMD generation is the north-eastern part of the dump, which spatially varied from the dump surface up to a depth of 3 m. Heterogeneous distribution of preferential paths by providing different oxygen and moisture contents is responsible for non-uniform pyrite oxidation at different parts of the dump. The results of the present study will provide useful information for further rock dump management approaches.

Electrokinetic remediation is a cost-effective and efficient method that utilizes electrical current to transport ions within the subsurface. This process aims to remediate soil contamination caused by industrial activities, which poses threats to wildlife, water quality, and air quality. To assess the impact of the electrokinetic process on tank leaching efficiency, two electrode configurations were tested: vertical and horizontal arrays. These tests considered variable electrode spacing and different voltages in the soil residue. Additionally, the movement of copper cations from the anode to the cathode under this process was investigated. Results show that the horizontal electrode array is more effective in transporting soil moisture because of its broader contact with the soil. After 20 days of using the electrokinetic method with vertical electrodes, the soil moisture content decreased by 12.28%; with horizontal electrodes, it dropped by 38.4%. Also, the concentration of copper in the soil near the cathode electrode increased from 0.54 to 0.77% after 20 days. The estimated copper ion content in the cathode area after 20 days was between 150 and 350 mol/m3, aligning closely with the measured value of 192.5 mol/m3. These results indicate that the electrokinetic process can significantly enhance copper recovery efficiency in tank leaching processes and curtail environmental side effects. Overall, this study provides valuable insights into the benefits of using the electrokinetic process to remediate leaching residue and improve the efficiency of industrial processes.

The safety of a foundation can be viewed from two different perspectives, bearing capacity, and the settlement. There are many articles focused on the bearing capacity of a foundation built near the slope. However, investigating the settlement of the foundation rest near the slope is very limited. In order to increase the safety of the structure, besides the elastic settlement, the study of time-dependent behavior of footing is of great importance in geotechnical engineering. In this research, a semi-analytical solution has been proposed to obtain the viscoelastic settlement of a footing adjacent to a slope. Based on the developed Airy stress function, distributed stress within the slope due to foundation load was computed analytically and then displacement has been acquired by using the finite difference method. The outcome of the proposed method was compared with COMSOL finite element software and good agreement between those was observed.

Hepatitis B virus infection causes chronic disease such as cirrhosis and hepatocellular carcinoma. The metabolomics investigations have been demonstrated to be related to pathophysiologic mechanisms in many disorders such as hepatitis B infection. The aim of this study was to investigate the saliva metabolic profile of patients with chronic hepatitis B infection and to identify underlying mechanisms as well as potential biomarkers associated with the disease. Saliva from 16 healthy subjects and 20 patients with chronic hepatitis B virus were analyzed by nuclear magnetic resonance (NMR). Then, multivariate statistical analysis was performed to identify discriminative metabolites between two groups. A set of metabolites were detected, including propionic acid, putrescine, acetic acid, succinic acid, tyrosine, lactic acid, butyric acid, pyruvic acid, 4-pyridoxic acid and 4-hydroxybenzoic acid, which in combination with one another could accurately distinguish patients from healthy controls. Our results clearly demonstrated altered metabolites are involved in nine metabolic pathways. Metabolomics has the potential to be considered as a novel clinical tool for hepatitis B diagnosis while contributing to a comprehensive understanding of disease mechanisms.

The aim of this work is to investigate the applicability of radiomic features alone and in combination with clinical information for the prediction of renal cell carcinoma (RCC) patients’ overall survival after partial or radical nephrectomy. Clinical studies of 210 RCC patients from The Cancer Imaging Archive (TCIA) who underwent either partial or radical nephrectomy were included in this study. Regions of interest (ROIs) were manually defined on CT images. A total of 225 radiomic features were extracted and analyzed along with the 59 clinical features. An elastic net penalized Cox regression was used for feature selection. Accelerated failure time (AFT) with the shared frailty model was used to determine the effects of the selected features on the overall survival time. Eleven radiomic and twelve clinical features were selected based on their non-zero coefficients. Tumor grade, tumor malignancy, and pathology t-stage were the most significant predictors of overall survival (OS) among the clinical features (p < 0.002, < 0.02, and < 0.018, respectively). The most significant predictors of OS among the selected radiomic features were flatness, area density, and median (p < 0.02, < 0.02, and < 0.05, respectively). Along with important clinical features, such as tumor heterogeneity and tumor grade, imaging biomarkers such as tumor flatness, area density, and median are significantly correlated with OS of RCC patients.

Purpose – The purpose of this study is to understand whether being relocated to a society where remanufactured products are promoted can change consumers’ perception towards them. Also, the authors wish to identify major underlying factors for remanufactured product purchase intentions. There is strong evidence in the literature that factors such as societal norms, price, age, income, education level, and availability can have significant influence on the behavioral intentions of consumers. Design/methodology/approach – The nature of the study is to draw an embedded theory from data itself. To explore the research questions in this study, a grounded theory was used. The authors use a theoretical sampling technique and interviewed 45 India-born consumers residing in the USA for at least a period of more than six months. Findings – The results indicate that the level of environmental consciousness, individual values, post-use perceptions, nature of purchase and socio-cultural norms are the major drivers of consumer purchase intentions. Sub-categories of these five drivers are personal and contextual factors. Personal factors include personal attitudes and beliefs, individual personality and environmental consciousness. Contextual factors are societal norms, price, promotion/advertisement, service quality and brand image. Social implications – Use of remanufactured products is one of the ways to achieve sustainability. It is not only an environmentally friendly but also cost-effective approach. Given the major drivers identified through this study, firms can focus some on these drivers to improve their carbon footprint and bottom line. Originality/value – This study is first to consider the decision-making process of consumer purchase of remanufactured products. In this regard, our study offers some understanding of the entire process through an action diagram.