Prostate Cancer and Prostatic Diseases, covering all aspects of prostatic diseases, in particular prostate cancer. The journal is of interest to surgeons, oncologists, clinicians, and researchers involved in disease of the prostate.

Auto-regressive (AR) time series (TS) models are useful for structural damage detection in vibration-based structural health monitoring (SHM). However, certain limitations, e.g., non-stationarity and subjective feature selection, have reduced its wide-spread use. With increasing trends in machine learning (ML) technologies, automated structural damage recognition is becoming popular and attracting many researchers. In this paper, we combined TS modeling and ML classification to automatically extract damage features and overcome the limitation of non-stationarity. We propose a two-stage framework, namely auto-regressive integrated moving-average machine learning (ARIMA-ML) with modules for pre-processing, model parameter determination, feature extraction, and classification. Based on shaking table tests of a space steel frame, floor acceleration data were collected and labeled according to experimental observations and records. Subsequently, we designed three damage classification tasks for: (1) global damage detection, (2) local damage detection, and (3) local damage pattern recognition. The results from these three tasks indicated the robustness and accuracy of the proposed framework where 97%, 98%, and 80% average segment accuracy were achieved, respectively. The confusion matrix results showed the unbiased model performance even under an imbalanced-class distribution. In summary, the presented study revealed the high potential of the proposed ARIMA-ML framework in vibration-based SHM.

Aspergillus is a mould that is ubiquitous in nature and may lead to a variety of infectious and allergic diseases depending on the host's immune status or pulmonary structure. Invasive pulmonaryaspergillosisoccurs primarily in patients with severe immunodeficiency. The significance of this infection has dramatically increased with growing numbers of patients with impaired immune state associated with the management of malignancy, organ transplantation, autoimmune and inflammatory conditions; critically ill patients appear to be at an increased risk as well. The introduction of new noninvasive tests, combined with more effective and better-tolerated antifungal agents, has resulted in lower mortality rates associated with this infection. Chronic pulmonary aspergillosis is a locally invasive disease described in patients with chronic lung disease or mild immunodeficiency. Recently, the European Society for Clinical Microbiology and Infectious Diseases provided a more robust sub-classification of this entity that allows for a straightforward approach to diagnosis and management. Allergic bronchopulmonaryaspergillosis, a hypersensitivity reaction to Aspergillus antigens, is generally seen in patients with atopy, asthma or cystic fibrosis. This review provides an update on the evolving epidemiology and risk factors of the major manifestations of Aspergillus lung disease and the clinical manifestations that should prompt the clinician to consider these conditions. It also details the role of noninvasive tests in the diagnosis of Aspergillus related lung diseases and advances in the management of these disorders.

Two graphene-based nanofillers of different sizes were melt mixed with polylactide acid (PLA). Composite films based on graphite nanoplatelets (GNPs) with the largest lateral size showed superior electrical conductivity and a lower percolation threshold after melt-compounding and hot-pressing at room temperature. However, upon annealing in the melt, composites based on GNPs with the smallest lateral size displayed significantly improved electrical conductivity and a decrease in percolation threshold as a result of dynamic percolation while composites based on large GNPs showed hardly any change in conductivity and percolation threshold. Hybrid filler systems based on 5 wt% GNP with variable small/large GNP filler ratios displayed synergistic effects in the formation of a conductive network during thermal annealing, and an optimum filler ratio of 50/50 was found to achieve the highest conductivity after annealing. This annealing-induced increase in particle connectivity for composites based on 5 wt.% hybrid small/large GNPs was also reflected in rheological measurements by the manifestation of a plateau in the storage modulus at low frequencies after annealing while such phenomenon was not observed for nanocomposites solely based on either small or large GNPs at similar loadings.