Data-driven predictive maintenance scheduling policies for railways

Inspection and maintenance activities are essential to preserving safety and cost-effectiveness in railways. However, the stochastic nature of railway defect occurrence is usually ignored in literature; instead, defect stochasticity is considered independently of maintenance scheduling. This study presents a new approach to predict rail and geometry defects that relies on easy-to-obtain data and integrates prediction with inspection and maintenance scheduling activities. In the proposed approach, a novel use of risk-averse and hybrid prediction methodology controls the underestimation of defects. Then, a discounted Markov decision process model utilizes these predictions to determine optimal inspection and maintenance scheduling policies. Furthermore, in the presence of capacity constraints, Whittle indices via the multi-armed restless bandit formulation dynamically provide the optimal policies using the updated transition kernels. Results indicate a high accuracy rate in prediction and effective long-term scheduling policies that are adaptable to changing conditions

Mannitol polymorphs as carrier in dpis formulations: Isolation characterization and performance

The search for best performing carriers for dry powder inhalers is getting a great deal of interest to overcome the limitations posed by lactose. The aerosolization of adhesive mixtures between a carrier and a micronized drug is strongly influenced by the carrier solid-state properties. This work aimed at crystallizing kinetically stable D-mannitol polymorphs and at investigating their aerosolization performance when used in adhesive mixtures with two model drugs (salbutamol sulphate, SS, and budesonide, BUD) using a median and median/high resistance inhaler. A further goal was to assess in vitro the cytocompatibility of the produced polymer-doped mannitol polymorphs toward two lung epithelial cell lines. Kinetically stable (up to 12 months under accelerate conditions) α, and δ mannitol forms were crystallized in the presence of 2% w/w PVA and 1% w/w PVP respectively. These solid phases were compared with the β form and lactose as references. The solid-state properties of crystallized mannitol significantly affected aerosolization behavior, with the δ form affording the worst fine particle fraction with both the hydrophilic (9.3 and 6.5%) and the lipophilic (19.6 and 32%) model drugs, while α and β forms behaved in the same manner (11–13% for SS; 53–58% for BUD) and better than lactose (8 and 13% for SS; 26 and 39% for BUD). Recrystallized mannitol, but also PVA and PVP, proved to be safe excipients toward lung cell lines. We concluded that, also for mannitol, the physicochemical properties stemming from different crystal structures represent a tool for modulating carrier-drug interaction and, in turn, aerosolization performance

A Two-Phase Pattern Generation and Production Planning Procedure for the Stochastic Skiving Process

The stochastic skiving stock problem (SSP), a relatively new combinatorial optimization problem, is considered in this paper. The conventional SSP seeks to determine the optimum structure that skives small pieces of different sizes side by side to form as many large items (products) as possible that meet a desired width. This study studies a multiproduct case for the SSP under uncertain demand and waste rate, including products of different widths. This stochastic version of the SSP considers a random demand for each product and a random waste rate during production. A two-stage stochastic programming approach with a recourse action is implemented to study this stochastic NP-hard problem on a large scale. Furthermore, the problem is solved in two phases. In the first phase, the dragonfly algorithm constructs minimal patterns that serve as an input for the next phase. The second phase performs sample-average approximation, solving the stochastic production problem. Results indicate that the two-phase heuristic approach is highly efficient regarding computational run time and provides robust solutions with an optimality gap of 0.3% for the worst-case scenario. In addition, we also compare the performance of the dragonfly algorithm (DA) to the particle swarm optimization (PSO) for pattern generation. Benchmarks indicate that the DA produces more robust minimal pattern sets as the tightness of the problem increases

From the printer to the lungs: Inkjet-printed aerogel particles for pulmonary delivery

Inkjet printing is as an emerging technique in the biomedical field offering cost-effective solutions for flexible production and the engineering of personalized medicine solutions. Thermal inkjet printing technology in the “drop on demand” mode allows the design of fully automated deposition patterns with high spatial resolution for applications ranging from microparticles in drug formulations to cell deposition in regenerative medicine. In particular, novel formulations in the form of porous particles are sought for the treatment of respiratory disorders and the systemic administration of bioactive compounds using the pulmonary route. Aerogel particles, i.e. highly porous and light-weight nanoporous powders, are particularly promising as carriers for the pulmonary route. In this work, the preparation of aerogel microspheres by thermal inkjet printing followed by supercritical drying is presented for the first time to overcome the current processing limitations. Alginate aerogel particles were loaded with salbutamol sulphate, a bronchodilator used for the treatment of asthma attacks and chronic obstructive pulmonary disease, as a model drug for sustained pulmonary delivery. The optimized processing method allowed the preparation of reproducible nanostructured microparticles with modified salbutamol sulphate release profile and aerodynamic performance of relevance for oral inhalation purposes

Investigation of Circulating miRNA-133, miRNA-26, and miRNA-378 as Candidate Biomarkers for Left Ventricular Hypertrophy

Background/Aim: Left ventricular hypertrophy (LVH) involves increased muscular mass of the left ventricle due to increased cardiomyocyte size and is caused by cardiomyopathies. Several microRNAs (miRNAs) have been implicated in processes that contribute to heart disease. This study aimed to examine miRNA-133, miRNA-26 and miRNA-378 as candidate biomarkers to define prognosis in patients with LVH. Patients and Methods: The study group consisted of 70 patients who were diagnosed with LVH and 16 unaffected individuals who served as the control group. Real-time polymerase chain reaction (RT-PCR) was used to analyze serum miRNA-133, miRNA-26, and miRNA-378 expression levels in LVH patients and the control group. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic capability of miRNA-378. Results: When crossing threshold (CT) values were compared between patient and control samples, we found that there were no statistically significant differences in miRNA-133 and miRNA-26 CT values, while the miRNA-378 expression was significantly increased in LVH patients. ROC analysis demonstrated that the expression levels of miRNA-378 (AUC=0.484, p=0.0013) were significantly different between groups. Conclusion: We observed a statistically significant relationship between miRNA-378 expression levels and LVH, suggesting that circulating miRNA-378 may be used as a novel biomarker to distinguish patients who have LVH from those who do not