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Necrotizing enterocolitis (NEC), with the main manifestations of bloody stool, abdominal distension, and vomiting, is one of the leading causes of death in neonates, and early identification and diagnosis are crucial for the prognosis of NEC. The emergence and development of machine learning has provided the potential for early, rapid, and accurate identification of this disease. This article summarizes the algorithms of machine learning recently used in NEC, analyzes the high-risk predictive factors revealed by these algorithms, evaluates the ability and characteristics of machine learning in the etiology, definition, and diagnosis of NEC, and discusses the challenges and prospects for the future application of machine learning in NEC. [ABSTRACT FROM AUTHOR]

Problem statement: Maintenance is an important factor in semiconductor factories, not only because of costs and the need for the uninterrupted operation of semiconductor equipment, but also the time and expense required for maintenance. If maintenance procedures are not performed properly, the equipment will have low efficiency or break down, production capacity will decrease and the company will incur extra costs. Therefore, the evaluation of maintenance performance has become a critical issue in semiconductor industries. Approach: This study evaluated maintenance performance by using the Analytical Hierarchy Process (AHP), Grey Relational Analysis (GRA) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The weight of maintenance indicators was derived by AHP method, which were input to the GRA and TOPSIS method for evaluate the performance of Condition-Based Maintenance (CBM) and Time-Based Maintenance (TBM) strategies. Results: Actual data was provided by a well-known semiconductor factory in Taiwan. This study evaluated and compared the performance of different maintenance strategies implemented in semiconductor companies. Empirical results indicated that the CBM strategy had better maintenance performance than the TBM strategy in semiconductor companies and the maintenance indicators which should be improved were also identified. Conclusion/Recommendations: The feasibility of the maintenance evaluation method was demonstrated through an actual scenario, which can help managers make decisions objectively and distinguish the advantages and disadvantages of the maintenance strategy. Key words: Maintenance, analytical hierarchy process, grey relational analysis, performance evaluation, semiconductor factories, INTRODUCTION Once an equipment or system has been build up, it should be maintained for efficiency and workability. Maintenance program can be regarded as a production sustainable function to prevent [...]

Comprehensive Summary (±)‐Atrachinenins A—C (1—3), three meroterpenoids possessing novel C27 skeletons, were isolated from the rhizomes of Atractylodes chinensis. (±)‐Atrachinenin A (1) possesses an unprecedented cage‐like skeleton with a fused 6/6/5/5 tetracyclic system (decahy‐dro‐1,5:4a,7‐dimethanobenzo[7]annulene), which may be formed by the intermolecular cyclization of (±)‐atrachinenin C (3). Compared to 1, (±)‐atrachinenin B (2) features an additional unique five‐membered peroxyl bridge ring to form a complex 6/6/5/5/5 cyclic ring system. Their structures and absolute configurations were elucidated by HRESIMS, NMR spectroscopic data, ECD calculation, and X‐ray diffraction. A plausible biogenetic pathway was proposed. Compound 1 exhibited potential TGF‐β inhibitory activity at 25 μmol/L. [ABSTRACT FROM AUTHOR]

Phellodendri Chinensis Cortex (PCC) and Atractylodis Rhizoma (AR) are frequently used as herb pair to treat eczema and gout owing to their synergistic effects. Alkaloids are the major ingredients from PCC and the effect of their combination on the in vivo processing of alkaloids remains unclear. In this study, a simple and reliable UPLC–MS/MS method for simultaneous determination of six alkaloids in rat plasma was developed. This method was applied to a comparative pharmacokinetic study between PCC and PCC–AR in rats. Effect of AR on absorption of alkaloids was investigated by a single‐pass intestinal perfusion study. The effect of AR on urinary excretion of alkaloids was studied. Pharmacokinetic studies showed that the values of rea under the concentration–time curve of phellodendrine, magnoflorine and palmatine were greater in the PCC–AR group than in the PCC group. The intestinal absorptive parameters absorption rate constant and effective permeability of phellodendrine and jatrorrhizine in PCC–AR groups were higher than those in the PCC group. Urinary excretion studies revealed that the excreted amount of alkaloids in the PCC–AR group was lower than that in the PCC group. The results revealed that the combination of PCC and AR improves intestinal absorption of alkaloids and reduces their urinary excretion, which enhances their systemic exposure. This study may explain the synergetic effects of PCC and AR in clinical applications. [ABSTRACT FROM AUTHOR]

Yield enhancement in semiconductor fabrication is important. Even though IC yield loss may be attributed to many problems, the existence of defects on the wafer is one of the main causes. When the defects on the wafer form spatial patterns, it is usually a clue for the identification of equipment problems or process variations. This research intends to develop an intelligent system, which will recognize defect spatial patterns to aid in the diagnosis of failure causes. The neural-network architecture named adaptive resonance theory network 1 (ART1) was adopted for this purpose. Actual data obtained from a semiconductor manufacturing company in Taiwan were used in experiments with the proposed system. Comparison between ART1 and another unsupervised neural network, self-organizing map (SOM), was also conducted. The results show that ART1 architecture can recognize the similar defect spatial patterns more easily and correctly. Index Terms--ART1, defects, semiconductor, SOM, spatial pattern recognition, yield.