Предварительный выбор сигнала для мониторинга и прогнозирования старения компонентов силовой передачи автомобиля

Predictive maintenance has become important for avoiding unplanned downtime of modern vehicles. With increasing functionality the exchanged data between Electronic Control Units (ECU) grows simultaneously rapidly. A large number of in-vehicle signals are provided for monitoring an aging process. Various components of a vehicle age due to their usage. This component aging is only visible in a certain number of in-vehicle signals. In this work, we present a signal selection method for in-vehicle signals in order to determine relevant signals to monitor and predict powertrain component aging of vehicles. Our application considers the aging of powertrain components with respect to clogging of structural components. We measure the component aging process in certain time intervals. Owing to this, unevenly spaced time series data is preprocessed to generate comparable in-vehicle data. First, we aggregate the data in certain intervals. Thus, the dynamic in-vehicle database is reduced which enables us to analyze the signals more efficiently. Secondly, we implement machine learning algorithms to generate a digital model of the measured aging process. With the help of Local Interpretable Model-Agnostic Explanations (LIME) the model gets interpretable. This allows us to extract the most relevant signals and to reduce the amount of processed data. Our results show that a certain number of in-vehicle signals are sufficient for predicting the aging process of the considered structural component. Consequently, our approach allows to reduce data transmission of in-vehicle signals with the goal of predictive maintenance.Прогнозное техническое обслуживание является важным для предотвращения незапланированных простоев современных транспортных средств. С расширением функциональности одновременно происходит быстрый рост обмена данными между электронными блоками управления. Большое количество бортовых сигналов позволяет осуществлять мониторинг процесса старения. Старение компонентов автомобиля зависит от того, как они используются. Элементы старения выявляются благодаря наличию ряда бортовых сигналов. В данной статье предложен метод выбора бортовых сигналов с целью определения соответствующих для проведения мониторинга и прогнозирования старения компонентов силовой передачи транспортных средств. Процесс старения рассматривается на основе степени засорения конструктивных элементов. Измерение процесса старения компонентов осуществляется в определенные промежутки времени. Благодаря такому подходу данные, полученные в неравномерно распределенные промежутки времени, предварительно обрабатываются для формирования сравниваемых бортовых показателей. На первом этапе агрегируем данные в определенные интервалы. Тем самым динамическая база бортовых данных уменьшается, что позволяет более эффективно анализировать сигналы. Также используем алгоритм машинного обучения с целью создания цифровой модели для измерения процесса старения. С помощью методологии локальных интерпретируемых модельно-агностических объяснений модель становится интерпретируемой. Это позволяет извлекать наиболее релевантные сигналы и тем самым сокращать объем обрабатываемых данных. Полученные результаты показывают, что для прогнозирования процесса старения рассматриваемого структурного компонента достаточно определенного количества бортовых сигналов. Таким образом, предлагаемый подход позволяет сократить передачу данных бортовых сигналов для проведения профилактического обслуживания

The Chemical Composition and Health-Promoting Effects of the Grewia Species—A Systematic Review and Meta-Analysis

Globally grown and organoleptically appreciated Grewia species are known as sources of bioactive compounds that avert the risk of communicable and non-communicable diseases. Therefore, in recent years, the genus Grewia has attracted increasing scientific attention. This is the first systematic review which focusses primarily on the nutritional composition, phytochemical profile, pharmacological properties, and disease preventative role of Grewia species. The literature published from 1975 to 2021 was searched to retrieve relevant articles from databases such as Google Scholar, Scopus, PubMed, and Web of Science. Two independent reviewers carried out the screening, selection of articles, and data extraction. Of 815 references, 56 met our inclusion criteria. G. asiatica and G. optiva were the most frequently studied species. We found 167 chemical compounds from 12 Grewia species, allocated to 21 categories. Flavonoids represented 41.31% of the reported bioactive compounds, followed by protein and amino acids (10.7%), fats and fatty acids (9.58%), ash and minerals (6.58%), and non-flavonoid polyphenols (5.96%). Crude extracts, enriched with bioactive compounds, and isolated compounds from the Grewia species show antioxidant, anticancer, anti-inflammatory, antidiabetic, hepatoprotective/radioprotective, immunomodulatory, and sedative hypnotic potential. Moreover, antimicrobial properties, improvement in learning and memory deficits, and effectiveness against neurodegenerative ailments are also described within the reviewed article. Nowadays, the side effects of some synthetic drugs and therapies, and bottlenecks in the drug development pathway have directed the attention of researchers and pharmaceutical industries towards the development of new products that are safe, cost-effective, and readily available. However, the application of the Grewia species in pharmaceutical industries is still limited

Phytochemical Profile, Biological Properties, and Food Applications of the Medicinal Plant Syzygium cumini

Syzygium cumini, locally known as Jamun in Asia, is a fruit-bearing crop belonging to the Myrtaceae family. This study aims to summarize the most recent literature related to botany, traditional applications, phytochemical ingredients, pharmacological activities, nutrition, and potential food applications of S. cumini. Traditionally, S. cumini has been utilized to combat diabetes and dysentery, and it is given to females with a history of abortions. Anatomical parts of S. cumini exhibit therapeutic potentials including antioxidant, anti-inflammatory, analgesic, antipyretic, antimalarial, anticancer, and antidiabetic activities attributed to the presence of various primary and secondary metabolites such as carbohydrates, proteins, amino acids, alkaloids, flavonoids (i.e., quercetin, myricetin, kaempferol), phenolic acids (gallic acid, caffeic acid, ellagic acid) and anthocyanins (delphinidin-3,5-O-diglucoside, petunidin-3,5-O-diglucoside, malvidin-3,5-O-diglucoside). Different fruit parts of S. cumini have been employed to enhance the nutritional and overall quality of jams, jellies, wines, and fermented products. Today, S. cumini is also used in edible films. So, we believe that S. cumini’s anatomical parts, extracts, and isolated compounds can be used in the food industry with applications in food packaging and as food additives. Future research should focus on the isolation and purification of compounds from S. cumini to treat various disorders. More importantly, clinical trials are required to develop low-cost medications with a low therapeutic inde

Extraction of cocoa proanthocyanidins and their fractionation by sequential centrifugal partition chromatography and gel permeation chromatography

Erworben im Rahmen der Schweizer Nationallizenzen (http://www.nationallizenzen.ch)Cocoa beans contain secondary metabolites ranging from simple alkaloids to complex polyphenols with most of them believed to possess significant health benefits. The increasing interest in these health effects has prompted the need to develop techniques for their extraction, fractionation, separation, and analysis. This work provides an update on analytical procedures with a focus on establishing a gentle extraction technique. Cocoa beans were finely ground to an average particle size of <100 μm, defatted at 20°C using n-hexane, and extracted three times with 50 % aqueous acetone at 50°C. Determination of the total phenolic content was done using the Folin-Ciocalteu assay, the concentration of individual polyphenols was analyzed by electrospray ionization high performance liquid chromatography-mass spectrometry (ESI-HPLC/MS). Fractions of bioactive compounds were separated by combining sequential centrifugal partition chromatography (SCPC) and gel permeation column chromatography using Sephadex LH-20. For SCPC, a two-phase solvent system consisting of ethyl acetate/n-butanol/water (4:1:5, v/v/v) was successfully applied for the separation of theobromine, caffeine, and representatives of the two main phenolic compound classes flavan-3-ols and flavonols. Gel permeation chromatography on Sephadex LH-20 using a stepwise elution sequence with aqueous acetone has been shown for effectively separating individual flavan-3-ols. Separation was obtained for (-)-epicatechin, proanthocyanidin dimer B2, trimer C1, and tetramer cinnamtannin A2. The purity of alkaloids and phenolic compounds was determined by HPLC analysis and their chemical identity was confirmed by mass spectrometry

Assessment of the anthelmintic activity of medicinal plant extracts and purified condensed tannins against free-living and parasitic stages of Oesophagostomum dentatum

Background: Plant-derived condensed tannins (CT) show promise as a complementary option to treat gastrointestinal helminth infections, thus reducing reliance on synthetic anthelmintic drugs. Most studies on the anthelmintic effects of CT have been conducted on parasites of ruminant livestock. Oesophagostomum dentatum is an economically important parasite of pigs, as well as serving as a useful laboratory model of helminth parasites due to the ability to culture it in vitro for long periods through several life-cycle stages. Here, we investigated the anthelmintic effects of CT on multiple life-cycles stages of O. dentatum. Methods: Extracts and purified fractions were prepared from five plants containing CT and analysed by HPLC-MS. Anthelmintic activity was assessed at five different stages of the O. dentatum life cycle; the development of eggs to infective third-stage larvae (L3), the parasitic L3 stage, the moult from L3 to fourth-stage larvae (L4), the L4 stage and the adult stage. Results: Free-living larvae of O. dentatum were highly susceptible to all five plant extracts. In contrast, only two of the five extracts had activity against L3, as evidenced by migration inhibition assays, whilst three of the five extracts inhibited the moulting of L3 to L4. All five extracts reduced the motility of L4, and the motility of adult worms exposed to a CT-rich extract derived from hazelnut skins was strongly inhibited, with electron microscopy demonstrating direct damage to the worm cuticle and hypodermis. Purified CT fractions retained anthelmintic activity, and depletion of CT from extracts by pre-incubation in polyvinylpolypyrrolidone removed anthelmintic effects, strongly suggesting CT as the active molecules. Conclusions: These results suggest that CT may have promise as an alternative parasite control option for O. dentatum in pigs, particularly against adult stages. Moreover, our results demonstrate a varied susceptibility of different life-cycle stages of the same parasite to CT, which may offer an insight into the anthelmintic mechanisms of these commonly found plant compounds

Signal Pre-Selection for Monitoring and Prediction of Vehicle Powertrain Component Aging

Predictive maintenance has become important for avoiding unplanned downtime of modern vehicles. With increasing functionality the exchanged data between Electronic Control Units (ECU) grows simultaneously rapidly. A large number of in-vehicle signals are provided for monitoring an aging process. Various components of a vehicle age due to their usage. This component aging is only visible in a certain number of in-vehicle signals. In this work, we present a signal selection method for in-vehicle signals in order to determine relevant signals to monitor and predict powertrain component aging of vehicles. Our application considers the aging of powertrain components with respect to clogging of structural components. We measure the component aging process in certain time intervals. Owing to this, unevenly spaced time series data is preprocessed to generate comparable in-vehicle data. First, we aggregate the data in certain intervals. Thus, the dynamic in-vehicle database is reduced which enables us to analyze the signals more efficiently. Secondly, we implement machine learning algorithms to generate a digital model of the measured aging process. With the help of Local Interpretable Model-Agnostic Explanations (LIME) the model gets interpretable. This allows us to extract the most relevant signals and to reduce the amount of processed data. Our results show that a certain number of in-vehicle signals are sufficient for predicting the aging process of the considered structural component. Consequently, our approach allows to reduce data transmission of in-vehicle signals with the goal of predictive maintenance