Exploring equipment electrocardiogram mechanism for performance degradation monitoring in smart manufacturing

Similar to the use of electrocardiogram (ECG) for monitoring heartbeat, this article proposes an equipment electrocardiogram (EECG) mechanism based on fine-grained collection of data during the entire operating duration of the manufacturing equipment, with the purpose of the EECG to reveal the equipment performance degradation in smart manufacturing. First, the system architecture of EECG in smart manufacturing is constructed, and the EECG mechanism is explored, including the granular division of the duration of the production process, the matching strategy for process sequences, and several important working characteristics (e.g., baseline, tolerance, and hotspot). Next, the automatic production line EECG (APL-EECG) is deployed, to optimize the cycle time of the production process and to monitor the performance decay of the equipment online. Finally, the performance of the APL-EECG was validated using a laboratory production line. The experimental results have shown that the APL-EECG can monitor the performance degradation of the equipment in real-time and can improve the production efficiency of the production line. Compared with a previous factory information system, the APL-EECG has shown more accurate and more comprehensive understanding in terms of data for the production process. The EECG mechanism contributes to both equipment fault tracking and optimization of production process. In the long run, APL-EECG can identify potential failures and provide assistance in for preventive maintenance of the equipment

Table1_The application of artificial intelligence in glaucoma diagnosis and prediction.DOCX

Artificial intelligence is a multidisciplinary and collaborative science, the ability of deep learning for image feature extraction and processing gives it a unique advantage in dealing with problems in ophthalmology. The deep learning system can assist ophthalmologists in diagnosing characteristic fundus lesions in glaucoma, such as retinal nerve fiber layer defects, optic nerve head damage, optic disc hemorrhage, etc. Early detection of these lesions can help delay structural damage, protect visual function, and reduce visual field damage. The development of deep learning led to the emergence of deep convolutional neural networks, which are pushing the integration of artificial intelligence with testing devices such as visual field meters, fundus imaging and optical coherence tomography to drive more rapid advances in clinical glaucoma diagnosis and prediction techniques. This article details advances in artificial intelligence combined with visual field, fundus photography, and optical coherence tomography in the field of glaucoma diagnosis and prediction, some of which are familiar and some not widely known. Then it further explores the challenges at this stage and the prospects for future clinical applications. In the future, the deep cooperation between artificial intelligence and medical technology will make the datasets and clinical application rules more standardized, and glaucoma diagnosis and prediction tools will be simplified in a single direction, which will benefit multiple ethnic groups.</p

Predicting Rate Constants of Alkane Cracking Reactions Using Machine Learning

Calculating the thermal rate constants of elementary combustion reactions is of great importance in theoretical chemistry. Machine learning has become a powerful, data-driven method for predicting rate constants nowadays. Recently, the molecular similarity combined with the topological indices were proposed to represent the hydrogen abstraction reactions of alkane [J. Chem. Inf. Model. 2023, 63, 5097–5106], which are, however, not applicable to alkane cracking reactions, another important class of combustion reactions, due to the cleavage of the C–C bond. In this work, a new feature selection scheme is proposed to describe both bimolecular and unimolecular cracking reactions. Molecular descriptors are elaborately selected individually for both reactants and products from those generated by the open-source software RDKit. Machine learning models combined with these molecular descriptors are proven to have the ability to accurately predict rate constants of both the hydrogen abstraction reactions of alkanes by CH3 and the alkane cracking reactions. The average deviation of the XGB-FNN model for prediction is around 60% for the hydrogen abstraction reactions of alkanes and 100% for the alkane cracking reactions. It is expected that the descriptors proposed in this work can be applied to build machine learning models for other reactions

Toward Alleviating Voltage Decay by Sodium Substitution in Lithium-Rich Manganese-Based Oxide Cathodes

Lithium-rich manganese-based oxides (LMROs), as one of the most promising high-capacity cathodes, suffer from serious capacity fading and discharge voltage decay during repeated cycles. Here we have successfully enhanced cycle stability and rate capability of LMRO cathode material through introducing a certain amount of Na into LMRO microspheres. In particular, the discharge voltage decay per cycle significantly decreases from 4.40 to 1.60 mV. These enhancements may be attributed to the Na in Li layers, which can promote the kinetics of lithium ion diffusion and facilitate the electronic and ionic conductivity. More remarkably, Na dopant can effectively suppress the transformation from layered to spinel structure by serving as the fixed pillars in Li layers to inhibit the formation of three adjacent vacancies and Mn migration. In addition, full-cell investigations further show the Na-doped LMRO materials have great commercial value. Therefore, our findings may boost understanding in designing high-capacity and good stability cathode materials for LIBs

Retracted article: Tranilast attenuates neuropathic pain during type-2 diabetes by inhibiting hypoxia-induced pro-inflammatory cytokines in Zucker diabetic fatty rat model

We, the Editors and Publisher of the journalArchives of Physiology and Biochemistry, have retracted the following article: Wei Zhang, Jun Ma, Shan Wang, Tao Huang & Min Xia (2020) Tranilast attenuates neuropathic pain during type-2 diabetes by inhibiting hypoxia-induced pro-inflammatory cytokines in Zucker diabetic fatty rat model, Archives of Physiology and Biochemistry, DOI: 10.1080/13813455.2020.1854309 Since publication, the authors informed the Publisher and Editor that the authors had not received ethics approval from the Institutional Animal Care and Use Committee (IACUC) at the time that their research was conducted. As the Editor and Publisher also have concerns about the integrity of the reported results which the authors have been unable to address, all parties have agreed to retract the article to ensure correction of the scholarly record. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as ‘Retracted’.</p

Intelligent fault diagnosis of rotor-bearing system under varying working conditions with modified transfer convolutional neural network and thermal images

The existing intelligent fault diagnosis methods of rotor-bearing system mainly focus on vibration analysis under steady operation, which has low adaptability to new scenes. In this article, a new framework for rotor-bearing system fault diagnosis under varying working conditions is proposed by using modified convolutional neural network (CNN) with transfer learning. First, infrared thermal images are collected and used to characterize the health condition of rotor-bearing system. Second, modified CNN is developed by introducing stochastic pooling and Leaky rectified linear unit to overcome the training problems in classical CNN. Finally, parameter transfer is used to enable the source modified CNN to adapt to the target domain, which solves the problem of limited available training data in the target domain. The proposed method is applied to analyze thermal images of rotor-bearing system collected under different working conditions. The results show that the proposed method outperforms other cutting edge methods in fault diagnosis of rotor-bearing system

Data_Sheet_1_Clinical Efficacy and Safety of Massage for the Treatment of Restless Leg Syndrome in Hemodialysis Patients: A Meta-Analysis of 5 Randomized Controlled Trials.pdf

AimWe conducted this meta-analysis to evaluate the clinical efficacy and safety of massage for the treatment of hemodialysis patients with restless leg syndrome (RLS).MethodsA comprehensive literature search was performed using the PubMed database, EMBASE database (via OVID), and the Cochrane Library in order to identify eligible randomized controlled trials (RCTs) published before August 31, 2021. After extracted essential data and assessed risk of bias of each eligible study, we calculated the pooled estimate of RLS score and safety after treatment. Statistical analysis was performed by using Review Manager 5.3.ResultsFive studies involving 369 hemodialysis patients with RLS were analyzed. The RLS score after treatment [mean difference (MD), −12.01; 95% confidence interval (CI), −14.91 to −9.11] and mean difference of RLS score at the beginning and end of treatment [mean difference (MD), −11.94; 95% confidence interval (CI), −15.45 to −8.43] in a massage group was significantly better than that in route care group. Subgroup analysis suggested that massage with lavender oil also significantly reduced the RLS score after treatment (MD, −14.22; 95% CI, −17.81 to −10.63) and mean difference of RLS score at the beginning and end of treatment (MD, −14.87; 95% CI, −18.29 to −11.45) compared with route care. Meanwhile, massage regime significantly relieved RLS severity compared with route care but did not increase adverse events.ConclusionMassage may be a preferred treatment modality for hemodialysis patients with RLS because it effectively reduces RLS symptoms, relieves RLS severity, and does not increase the risk of adverse events. However, future study with a larger sample size is warranted due to the fact that only limited number of eligible studies with small sample size are enrolled.</p

Glomerular O₂.⁻ production in Cbs^+/+/Asm^+/+, Cbs^+/+/Asm^+/−, Cbs^+/+/Asm^−/−, Cbs^+/−/Asm^+/+, Cbs^+/−/Asm^+/− and Cbs^+/−/Asm^−/− mice.

<p>A: Representative ESR spectra traces for O₂.⁻ production in 6 different groups of mice. B: Values are arithmetic means ± SEM (n = 5 each group) of O₂.⁻ production in Cbs^+/+/Asm^+/+, Cbs^+/+/Asm^+/−, Cbs^+/+/Asm^−/−, Cbs^+/−/Asm^+/+, Cbs^+/−/Asm^+/− and Cbs^+/−/Asm^−/−. * Significant difference (<i>P</i><0.05) compared to the values from Cbs^+/+/Asm^+/+ mice; ^# Significant difference (<i>P</i><0.05) compared to the values from Cbs^+/−/Asm^+/+ mice.</p

Renal tissue ceramide production and Asm activity in Cbs^+/+/Asm^+/+, Cbs^+/+/Asm^+/−, Cbs^+/+/Asm^−/−, Cbs^+/−/Asm^+/+, Cbs^+/−/Asm^+/− and Cbs^+/−/Asm^−/− mice.

<p>Values are arithmetic means ± SE (n = 6 each group) of total ceramide concentrations (A), ceramide production (B) and Asm activity (C) in Cbs^+/+/Asm^+/+, Cbs^+/+/Asm^+/−, Cbs^+/+/Asm^−/−, Cbs^+/−/Asm^+/+, Cbs^+/−/Asm^+/− and Cbs^+/−/Asm^−/− mice. The AOI shows the higher magnification of boxed area in overlaid images in panel B. AOI: Area of interest. * Significant difference (<i>P</i><0.05) compared to the values from Cbs^+/+/Asm^+/+ mice; ^# Significant difference (<i>P</i><0.05) compared to the values from Cbs^+/−/Asm^+/+ mice.</p

Glomerular injury in Cbs^+/+/Asm^+/+, Cbs^+/+/Asm^+/−, Cbs^+/+/Asm^−/−, Cbs^+/−/Asm^+/+, Cbs^+/−/Asm^+/− and Cbs^+/−/Asm^−/− mice.

<p>A: Values are arithmetic means ± SEM (n = 6 each group) of urinary total protein excretion, B: Urinary albumin excretion in Cbs^+/+/Asm^+/+, Cbs^+/+/Asm^+/−, Cbs^+/+/Asm^−/−, Cbs^+/−/Asm^+/+, Cbs^+/−/Asm^+/− and Cbs^+/−/Asm^−/− mice. C: Photomicrographs show typical glomerular structure (original magnification, x400) in Cbs^+/+/Asm^+/+, Cbs^+/+/Asm^+/−, Cbs^+/+/Asm^−/−, Cbs^+/−/Asm^+/+, Cbs^+/−/Asm^+/− and Cbs^+/−/Asm^−/− mice. D: Summarized data of glomerular damage index (GDI) by semi-quantitation of scores in 6 different groups of mice (n = 6 each group). For each kidney section, 50 glomeruli were randomly chosen for the calculation of GDI. * Significant difference (<i>P</i><0.05) compared to the values from Cbs^+/+/Asm^+/+ mice; ^# Significant difference (<i>P</i><0.05) compared to the values from Cbs^+/−/Asm^+/+ mice.</p