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Throughput analysis plays an important role in the operations and management of automotive manufacturing. Predicting how the system throughput changes over time helps the plant managers to make timely operational decisions to meet the daily production requirement. In today's automotive production systems, the availability of sensing data reflecting process variables and machine status across the plant floor raises new opportunities for improving the throughput prediction accuracy. However, challenges exist in extracting the most important features from the high-dimensional data and capturing the complicated time-vary interdependency among different assets in the system. To overcome such challenges, in this paper we propose a hierarchical Recurrent Neural Network (RNN)-based framework that is composed of clustering, dimension reduction and feature selection, regression, and prediction pruning/adjustment. In addition to predicting end-of-line throughput, our framework then identifies the associations for low throughput to facilitate downtime prevention and maintenance decision-making. The proposed framework is applied to an automotive production system, and its effectiveness is demonstrated by comparison with conventional methods. [ABSTRACT FROM AUTHOR]

Dysfunctional autophagy induced by excessive reactive oxygen species (ROS) load and inflammation accelerates the development of Alzheimer's disease (AD). Recently, there has been an increasing interest in selenium-enriched ingredients (SEIs), such as selenoproteins, selenoamino acids and selenosugars, which could improve AD through antioxidant and anti-inflammation, as well as autophagy modulating effects. This review indicates that SEIs eliminate excessive ROS by activating the nuclear translocation of nuclear factor erythroid2-related factor 2 (Nrf2) and alleviate inflammation by inhibiting the mitogen-activated protein kinases (MAPKs)/nuclear factor kappa-B (NF-κB) pathway. Furthermore, they can activate the adenosine 5'-monophosphate-activated protein kinase (AMPK)/mammalian target of rapamycin (mTOR) pathway, and subsequently promote amyloid beta (Aβ) clearance and reduce memory impairments. SEIs are ubiquitous in many plants and microorganisms, such as Brassicaceae vegetables, yeast, and mushroom. Enzymatic hydrolysis, as well as physical processing, such as thermal, high pressure and microwave treatment, are the main techniques to modify the properties of dietary selenium. This work highlights the fact that SEIs can inhibit inflammation and oxidative stress and provides evidence that supports the potential use of these dietary materials to be a novel strategy for improving AD. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a progressive neurodegenerative disorder. Recently, sustained neuroinflammatory response in microglia and astrocytes has been found to cause the deposition of amyloid beta plaques and the hyperphosphorylation of tau protein, thereby accelerating AD progression. The lipoxin A4-transcription factor nuclear factor-kappa B and mitogen-activated protein kinase pathways have been shown to play important roles in the regulation of inflammatory processes. There is growing research-based evidence suggesting that dietary whole-plant foods, such as mushrooms and berries, may be used as inhibitors for anti-neuroinflammation. The beneficial effects of whole-plant foods were mainly attributed to their high contents of functional macromolecules including polysaccharides, polyphenols, and bioactive peptides. This review provides up-to-date information on important molecular signaling pathways of neuroinflammation and discusses the anti-neuroinflammatory effects of whole-plant foods. Further, a critical evaluation of plants' macromolecular components that have the potential to prevent and/or relieve AD is provided. This work will contribute to better understanding the pathogenetic mechanism of neuroinflammation in AD and provide new approaches for AD therapy. [ABSTRACT FROM AUTHOR]

Toward the treatment of waste solution containing heavy metals, direct precipitation of the metal ions from an acidic solution without alkaline neutralisation is still the greatest challenge. Based on the ligand properties of benzene tricarboxylic acid (BTC) to copper ions, a simple ball milling with 90 min at 400 rpm was used to activate BTC to enhance its capacity for copper removal from the pH of the original solution around 3–4. A set of analytical methods were used to characterise the activated BTC sample and BTC-Cu precipitate before and after copper precipitation. Compared with the raw BTC, the activated BTC could efficiently remove copper ions over 90% from an initial copper concentration of 100 mg/L in a shorter time from an acidic media with lower pH of around 2.60 and the maximum adsorption capacity can be stable at about 111.70 mg/g, resulting from probably the enhanced deprotonation effect for copper incorporation. Furthermore, at controlled dosage, the activated BTC demonstrated much high selectivity on precipitating copper ions from other heavy metals of Ni, Mn, Zn and Cd and provided a new approach for easy copper recycling from waste solution as secondary sources. This process may serve the purpose of recycling both metal and acidic solutions after the purification. [ABSTRACT FROM AUTHOR]