Citrus unshiu during fruit development. Here, we analyzed the total phenolic content (TPC), total carotenoid content (TCC), antioxidant activity, and metabolite profiles in C. unshiu fruit flesh during different stages of fruit development and evaluated their correlations. The TPC and antioxidant activity significantly decreased during fruit development, whereas the TCC increased. The metabolite profiles, including sugars, acidic compounds, amino acids, flavonoids, limonoids, carotenoids, and volatile compounds (mono- and sesquiterpenes), in C. unshiu fruit flesh also changed significantly, and a citrus metabolomic pathway related to fruit development was proposed. Based on the data, C. unshiu fruit development was classified into three groups: Group 1 (Aug. 1), Group 2 (Aug. 31 and Sep. 14), and Group 3 (Oct. 15 and Nov. 16). Although citrus peel was not analyzed and the sensory and functional qualities during fruit development were not investigated, the results of this study will help in our understanding of the changes in chemical profile during citrus fruit development. This can provide vital information for various applications in the C. unshiu industry.Citrus fruits undergo significant metabolic profile changes during their development process. However, limited information is available on the changes in the metabolites of  Citrus fruits are consumed globally and are one of the most important non-climacteric fruit crops in the fruit industry. This is because of their unique favorable taste and a large number of nutritional benefits ,2. Many Among these factors, fruit development involves significant changes in primary and secondary metabolites and can be divided into three stages: (I) fruit set and cell division, (II) growth via cell expansion, and (III) maturation ,9. FruitIn recent years, transcriptomic, proteomic, and metabolomic technologies have been used to demonstrate the regulation of fruit metabolism during citrus fruit development ,8,10. AmC. unshiu, commonly known as Satsuma mandarin, is an important easy-peeling mandarin and is widely cultivated throughout the main citrus production zones, including East Asia, Argentina, Australia, Peru, Spain, South Africa, and Uruguay [C. unshiu—which is bud burst in middle of Mar., blooms in May, and is harvested after the middle of Nov. in East Asia [C. unshiu have shown the changes in the phytochemical profiles of C. unshiu according to growing regions [C. unshiu during fruit development have not yet been analyzed.Among these varieties,  Uruguay . In partast Asia —is mostlast Asia ,17,18. A regions , post-ha regions , and the regions , the chaC. unshiu fruits harvested at five different developmental stages were analyzed using ultra-performance liquid chromatography–quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF MS), gas chromatography–mass spectrometry (GC-MS), and high-performance liquid chromatography (HPLC) to provide chemical information for various applications in the C. unshiu industry. Furthermore, based on the metabolites, a metabolic pathway associated with citrus fruit development was proposed, and the correlation between these metabolite profiles and antioxidant activities was analyzed.Therefore, in this study, the primary and secondary metabolites of C. unshiu fruit were analyzed according to fruit development (C. unshiu increased from 31.37 ± 3.79 g (Aug. 1) to 111.45 ± 11.52 g (Nov. 16), while the width and length were increased from 3.47 ± 1.92 and 4.16 ± 1.68 cm (Aug. 1) to 6.44 ± 2.71 and 5.20 ± 2.24 cm (Nov. 16), respectively. The color of C. unshiu changed from dark green (Aug. 1) to partially orange (Oct. 15) to full orange (Nov. 16). In addition, the citrus color index (CCI), a standard parameter used to determine the color of citrus [C. unshiu used in this study was divided into two stages: a size increase stage (Aug. 1–Sep. 14) and a color change stage (Oct. 15–Nov. 16). In general, fruit development occurs in three stages  [C. unshiu fruit  corresponded to cell expansion (the second stage), and the last two fruit (Oct. 15 and Nov. 16) corresponded to fruit maturation (the third stage).Weight, size, and color of elopment . The fruf citrus , showed f citrus . Based o g Nov. 1, while tC. unshiu, the total phenolic content (TPC) and antioxidant activity decreased, whereas the total carotenoid content (TCC) increased /g dry sample (Aug. 1) to 2.23 mg GAE/g dry sample (Nov. 16). By contrast, the TCC of C. unshiu fruit showed the opposite result. The highest content was observed on Nov. 16 , and the content was 4.3 times higher than that on Aug. 1 (6.93 mg βCE/100 g fresh sample). The decrease in TPC during citrus fruit development could be correlated with the increased activity of polyphenol oxidase (PPO), owing to an increase in fruit pH [With the advancement in fruit development of ncreased . The TPCfruit pH . By contfruit pH ,26.C. unshiu fruit during development was measured using DPPH and ABTS radicals (50) value of the C. unshiu flesh gradually increased during fruit development. The highest value was observed in harvested fruit on Nov. 16 (8192.77 mg/mL), and the value was four times higher than that on Aug. 1 (2047.27 mg/mL)  to Aug. 31 (1001.12 mg/mL) and then slightly increased on Nov. 16 (1181.56 mg/mL)  C. Additi6 mg/mL) C. The de. 1 2047. mg/mL , and the cross-validation determined by the permutation test (n = 200) of the PLS-DA model indicated that the model was statistically acceptable. The sample groups were clearly separated according to the fruit development stage. In particular, the Aug. 1 and Nov. 16 samples were clearly separated from each other by t(1), and both samples were discriminated from other samples by t(1) or t(2). Other samples were also clearly separated from each other.The results of metabolite analysis obtained by GC-MS, UPLC-Q-TOF MS, and HPLC  were comore plot . The gooC. unshiu samples in the PLS-DA score plot, the variable importance in the projection (VIP) and p-values of metabolites were analyzed based on normalized chromatogram intensities , acidic compounds , amino acids and peptides , and lipids  , and lysophosphatidylcholines (LPCs) ) were identified as the primary metabolites. In addition, flavonoids , volatile compounds , limonoids , and carotenoids (β-carotene and β-cryptoxanthin) were identified as the secondary metabolites. Similar metabolite profiles have also been observed during fruit development in other citrus varieties, including Navel orange [To identify the metabolites contributing to the discrimination among ensities . A totall orange , Persianl orange , and Poml orange .To compare the normalized chromatogram intensities of the identified metabolites during fruit development, the changes in metabolite profiles were visualized using heat maps based on the relative abundance of each metabolite , and theVaccinium corymbosum (Vc)MIPS2, VcIMP, and VcMIOX [Most sugars, including fructose, glucose, and mannose, accumulate during fruit development. In particular, the relative contents of glucose and fructose on Nov. 16 were approximately 1.7 and 2.0 times, respectively, more than those on Aug. 1. However, there was no significant change in the content of sucrose, the major sugar. A similar result was reported for the later maturation stage of Ponkan , in whicd VcMIOX .C. unshiu fruits during fruit development, the total sugar/total acid ratio was found to increase by 2.6 times during fruit development from Aug. 1 to Nov. 16. Among the amino acids, phenylalanine, suberyl glycine, threonine, alanine, proline, and arginine accumulated during fruit development. In particular, the contents of arginine and proline in the Nov. 16 samples were approximately 40 and 22 times higher than those in the Aug. 1 samples, respectively, whereas the contents of tryptophan, glutamic acid, serine, and stachydrine decreased, and tryptophan and serine were not observed in the Nov. 16 samples. In contrast, asparagine, aspartic acid, and glutamine accumulated on Sep. 14 or Oct. 15 and then decreased. Changes in the amino acid profile of citrus are metabolically related to the tricarboxylic acid cycle, citrate decline, and GABA shunt during fruit development [Unlike sugars, all the acidic compounds decreased during fruit development . Among telopment . These aelopment .C. unshiu , and cross-validation determined by the permutation test (n = 200) of the model indicated that the model was statistically acceptable. The PLS-biplot showed the fruit development of C. unshiu and was classified into three groups: Group 1 (Aug. 1), Group 2 (Aug. 31 and Sep. 14), and Group 3 (Oct. 15 and Nov. 16). In Group 1, there was good correlation with TPC, antioxidant activities, flavonoids, monoterpenes, chlorophyll (pheophorbide A), acidic compounds, and some amino acids (serine and tryptophan). In Group 2 there was a good correlation with sesquiterpenes, sucrose, limonin, and some amino acids (glutamine and aspartic acid). In Group 3, glucose, fructose, carotenoids, some amino acids , limonene, and linalool were grouped. These results showed a rich variation in the metabolite profile expressed in citrus, including antioxidant activity, color, and taste at different stages of C. unshiu fruit development [The correlation between antioxidant activity, metabolites, TPC, and TCC at different fruit developmental stages was visualized using a PLS-biplot . The gooelopment ,10,24,33C. unshiu fruits were harvested on five different dates  from C. unshiu trees grown in the fields of the Citrus Research Institute  depending on the degree of fruit development  of C. unshiu during fruit development were measured using a colorimeter , and CCI was calculated using the following equation [The appearance color values (equation :CCI = (1v/v) containing 0.2% formic acid at flow rate of 1.0 mL/min with isocratic elution. All analyses were performed in triplicate [For carotenoid extraction, fresh sample was crushed using a blender , and 1 g of fresh sample was mixed with 60 mL of methanol for 1 h. After removing the supernatant, 700 mL of a mixed solvent (hexane:acetone = 1:1) was added to the remaining residue to extract the pigment. The extraction solvent was removed using a rotary vacuum evaporator . After concentration, the dried residue was dissolved in 30 mL hexane and washed three times with distilled water. The residual moisture was removed by adding anhydrous sodium sulfate. TCC was determined using a spectrophotometer  at 448 nm. β-Carotene  was used as the standard, and TCC was expressed as mg βCE/g fresh sample . The indiplicate .C. unshiu flesh, 100 g of freeze-dried flesh samples was homogenized with 3 L of 70% aqueous ethanol at room temperature for 120 min under sonication.To measure the TPC and antioxidant activity in 2CO3 was added. The sample mixture was incubated at 25 °C for 60 min and then its absorbance was measured at 735 nm using a SpectraMax® M3 Multi-Mode Microplate Reader . Gallic acid was used as a standard and TPC was expressed as mg GAE/g dry extract.TPC was determined using the Folin-Denis method with some modifications . The dilv/v) for 15 h at room temperature in the dark. The sample extract (20 µL) was mixed with 180 μL of the ABTS solution, and after incubation at 25 °C for 15 min, absorbance was measured at 734 nm. The scavenging activities of DPPH and ABTS radicals were expressed as IC50 values (μg/mL).The DPPH free radical scavenging activity was determined as follows : 20 µL oN,O-bis-(trimethylsilyl)-trifluoroacetamide with 1% trimethylchlorosilane  was added for the derivatization of metabolites and reacted at 70 °C for 30 min. The derivatized samples were analyzed using Agilent Technologies 7890A GC System  equipped with a DB-5MS capillary column . The derivatized samples (1 μL) were injected at a split ratio of 1:50. Helium was used as a carrier gas at a flow rate of 1 mL/min, and the oven temperature program for the separation of nonvolatile metabolites was maintained at 70 °C for 2 min, then increased at a rate of 5 °C/min to 150 °C, 3 °C/min to 210 °C, 8 °C/min to 320 °C, and held for 8 min.GC-MS analysis was carried out according to our previous method with some modifications . To extrVolatile metabolites were analyzed by solid-phase microextraction (SPME)-GC-MS . Fresh sThe eluent non-volatile and volatile metabolites were detected using a 5975C Inactive XL MSD  system (Agilent Technologies) with electron ionization at 70 eV. The ion source and interface temperature were 230 °C and 280 °C, respectively. The data was obtained in the full-scan mode with a mass range of 45–550 m/z. Quality control (QC) samples were prepared by mixing all samples and were analyzed once for each sample set.UPLC-Q-TOF MS analysis was carried out according to our previous method with some modifications . To extrwww.metlin.scripps.edu (accessed on 8 January 2022)).The chromatographic intensities obtained by GC-MS were aligned based on retention time, normalized to the IS, and identified using mass spectral libraries (Wiley 9 and NIST 11). The MS data analyzed by UPLC-Q-TOF MS were collected, aligned, and normalized using the MarkerLynx software (Waters). The metabolites were tentatively identified using the online database connected to the UNIFI software (Waters) and the METLIN database  and visualized using partial least squares discriminant analysis (PLS-DA). Statistical differences between the obtained data, including TPC, TCC, antioxidant activities, and relative abundances of metabolite variables, were analyzed by one-way analysis of variance (ANOVA) with Duncan’s test using SPSS software , and their correlation was analyzed and visualized using SIMCA-P+ v.14.0.1.C. unshiu were observed during fruit development, and their correlation was analyzed. Additionally, the citrus metabolomic pathway related to fruit development has been proposed. Although citrus peel was not analyzed, and changes in the sensory and functional qualities during fruit development were not investigated in this study, these results indicated that metabolites associated with sensory quality various health benefits were changed during fruit development. In particular, these results will help to understand the changes in chemical profile during citrus fruit development and can provide valuable information for various applications of C. unshiu fruits in the citrus industry.In this study, significant changes in TPC, TCC, antioxidant activities, and metabolite profiles of


Bruguiera gymnorrhiza L.) fruit as a mangrove species has not been widely developed. However, the combination of lindur fruit functional food with soybean flour has high carbohydrates and protein potential to serve as an additional food product in emergency conditions. Therefore, this study aims to evaluate the nutritional content, dietary fiber, organoleptic quality, and sagon shelf-life estimation of lindur and soybean flour, in accordance with emergency food quality requirements. The shelf life was determined using the Accelerated Shelf-life Testing (ASLT) method with the Arrhenius equation, where the sample was stored in an incubator at a temperature of 27, 37, as well as 47°C for 30 days, and the water content, peroxide number, and free fatty acid content were tested every 5 days. In addition, formulation P1 comprising 45% lindur flour and 35% soybean flour was discovered to be the best formulation, with a composition of 251.5 kcal/bar of energy, 6.3 g of fat, 4.4 g of protein, 30 g of carbohydrates, 15.79% dietary fiber, 1.84% ash content, and 4.03% water content. Therefore, the nutritional content of sagon substitution for lindur flour is in accordance with the emergency food quality requirements, except for the protein content. Also, the P2 sagon sample’s shelf life was estimated to be 37 days in polypropylene plastic packaging at a room temperature of 27°C.Lindur ( Indonesia’s territory lies between the three tectonic plates and has many volcanoes, making the country vulnerable to natural disasters . Accordisagon, traditional Indonesian food with a sweet taste, dry texture, low water content, and consequently, long shelf life  is a mangrove fruit rich in carbohydrates and is often used by coastal communities as an alternative local food choice during the lean season, to prevent food insecurity . Soybean flour has a high protein content of 34.8% and a fiber content of 3.2% per 100 g , organoleptic quality, and shelf life of lindur fruit (B. gymnorrhiza L.) from Mangunharjo Village, Mangkang Kulon Village, Tugu District, Semarang has a green-brown color with brown spots (15–25 cm). Soybean flour, glutinous rice flour, grated coconut, sugar, and salt are among the other ingredients.Ripe lindur flour, soybean flour, and grated coconut for 10 min at 70°C. In a mixing bowl, combine the sugar, salt, grated coconut, lindur flour, soybean flour, and roasted glutinous rice flour. Stir until evenly combined. The dough is then formed into a sagon shape using a sagon mold. Sagon in the oven for 20 min at 150°C. The ripe sagon is removed from the mold and allowed to cool to room temperature (25°C) before being vacuum sealed in plastic. After vacuuming, the sagon is stored in a storage container for 2 days before being analyzed the following day.Roast the glutinous rice flour, sagon using lindur and soybean flour. A total of four formulations (lindur: soybean flour): P1 (45: 35%), P2 (40: 40%), P3 (35: 45%), P4 (30: 50%), were created in triplicates. This study formulated sagon samples’ analysis of water content was determined by the dry cup method .sagon. The samples given were P1 (45: 35%), P2 (40: 40%), P3 (35: 45%), and P4 (30: 50%). Each sagon sample is only repeated one time. The hedonic test analysis includes taste, color, aroma, and texture on a scale of 1–5  method with the Arrhenius equation, and the sample used was P2. P2 was chosen because, when compared to the other three treatments, it has the best organoleptic test findings. For this estimation, the sample was packed in a vacuum polypropylene plastic and stored in an incubator at 27, 37, as well as 47°C for 30 days, and the water content, peroxide number, as well as free fatty acid content were tested every 5 days. The product is kept at an accelerated temperature, with a minimum of three temperatures  that can hasten product quality decline.sagon flour.The moisture content was determined by using the oven method . The frek), intercept (constant), and correlation coefficient (R2) of a graph of reaction kinetics for order zero or order one was plotted from the storage research to forecast deteriorating behavior. Do this for all the key elements you have chosen, such as quality parameters. For each storage temperature, the graph depicts the link between storage time (x-axis) and the value of quality parameters (y-axis). Next, compare all the specified factors to obtain the value of k for each storage temperature. As the temperature rises, the value of k (slope) will rise as well. Then, using an Arrhenius equation, show the relationship between 1/T in Kelvin (x-axis) and Ln k (y-axis) (for three observed temperatures). The value of k is calculated at the specified storage temperature or distribution. The rate of degradation per day at that temperature is the value of k in this equation. The following formula is used to compute the value of k:The slope  is a value indicates the decrease of product quality; t, expired time; Ao, the initial concentration of the chemical of interest; At, the initial concentration of the chemical of interest at particular time “t.”where the sagon was determined through statistical analysis using one-way ANOVA followed by the Tukey and the Kruskal–Wallis tests and then by the Mann–Whitney test. In addition, the best formulation was determined using the Effectiveness Index (De Garmo) based on the nutritional content and organoleptic quality . The high content of amylose and amylopectin in starch has the capacity to facilitate the gelatinization process, leading to the hydrolyzation of  content , 17.sagon samples were oven-dried at 150°C for 20 min, to minimize protein and amino acids loss.Furthermore, the use of temperatures above 115°C tends to facilitate Maillard reaction while the use of 230°C for 30 min has the capacity to cause a loss of 15% of amino acids . In thissagon fat contents were discovered to range from 25.31 to 35.28%. In comparison with the emergency food standard provided by Zoumas et al. , which is 3 g/day   .sagon color is influenced by the substitution of lindur and soybean flour. The score of the organoleptic quality test of sagon color tends to increase as the concentration of lindur flour added decreases. lindur flour has a natural brown color that is influenced by processing as well as the presence of pigments such as chlorophyll, carotenoids, anthocyanins, anthoxanthins, and tannins  of all variables was found in formulation P1 comprising 45% B. gymnorrhiza L.), which is widely available in underdeveloped coastal areas. Lindur flour contains high energy, carbohydrates, and fiber, but the protein content of lindur is low, therefore, to increase the protein value, it can be increased by adding soybeans. The addition of soy flour as much as 35% have been through consideration correct, if you add too much soy flour, the fat content will increase so that the fat content exceeds the emergency food quality requirements. According to Efraim’s (?) research, adding 25% soybean flour to banana flour biscuits can boost fat content by 15.29%  that indicate that the type of packaging, storage time, and storage conditions can affect the water content of Amaranth Sorghum grains based Complementary Food (ASCF) products. High water content has the capacity to cause enzymatic hydrolyzation of fat . R2 is zero-order > first order, therefore, the shelf life was estimated using the regression of zero order. Furthermore, the parameter with the least activation energy value was used because the activation energy A reduction in activation energy implies a faster reaction time, and consequently, a faster reduction in product quality (The product is stored at room temperature of 27°C, therefore, the estimated shelf life is 37 days. According to the results, all parameters have a zero-order reaction, because the value of  quality . In this quality . For the quality .sagon storage.In this study, samples were stored at 27, 37, and 47°C. Every 10°C increase in temperature can hasten the deterioration of food quality during storage . Becauselindur fruit flour added, the higher the carbohydrate, energy, and water content. Protein, fat, and ash content, on the other hand, will decrease. The lower the protein and ash content, the less soy flour is used. Formulation P1 comprising 45% lindur flour and 35% soybean flour was concluded to be the best formulation. This formulation’s energy content, carbohydrates, fat, water content, ash content, and dietary fiber are in accordance with the emergency food quality requirements, however, the protein content is deficient. In addition, the shelf life of P2 sagon formulated with lindur and soybean flour and stored in polypropylene packaging was estimated to be 37 days at room temperature of 27°C, using the ASLT method.The energy, carbohydrate, fat, water, ash, and dietary fiber content of P1 are all within the emergency food quality requirements, but the protein content is still below the emergency food quality requirements. The more The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.The studies involving human participants were reviewed and approved by the Health Research Ethics Committee, Faculty of Medicine, Diponegoro University with ethical clearance No. 119/EC/KEPK/FKUNDIP/VI/2021. The patients/participants provided their written informed consent to participate in this study.DA, FA, NN, DS, YN, and TS wrote the manuscript. DA, FA, NN, and DS conceptualized and designed the study, collected the data, analyzed and interpreted the data, and performed the manuscript drafting and critical revision. YN and TS carried out the research, data collection and data analysis and interpretation. DA revised the manuscript critically. All authors contributed to the revision of the manuscript and read and approved the final version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Pseudomonas sp. FB15 phytase increases low-temperature activity and catalytic efficiency. In this study, the 3D structure of the N-terminal domain was predicted and substitutions for the amino acid residues of the region assumed to be the active site were made. The activity of mutants, in which alanine (A) was substituted for the original residue, was investigated at various temperatures and pH values. Significant differences in enzymatic activity were observed only in mutant E263A, suggesting that the amino acid residue at position 263 of the N-terminal domain is important in enzyme activity.The N-terminal domain of the  It is a polyanionic molecule with six phosphate groups that combines with important minerals such as cobalt, copper, iron, manganese, zinc, and calcium to form insoluble phytate salts , 2. Phytand fish . These aPhytase is divided into four types according to their catalytic and structural characteristics: beta-propeller phytase (BPP), purple acid phytase, histidine acid phytase, and cysteine phytase . Among tBacillus species, phytase of Pseudomonas sp. FB15 (PSphy) has an additional N-terminal domain. Our previous studies have reported that PSphy maintains high activity even at low temperatures [Bacillus-derived BPP increased the catalytic efficiency and enzyme activity at low temperatures [Unlike BPP produced by eratures . Removaleratures , whereaseratures . These rThis study was performed to identify amino acid residues that affect enzyme activity in the N-terminal domain of PSphy. Four amino acid residues , found in the region predicted as the active site, were chosen for testing. Selected amino acid residues were substituted using site-directed mutations and investigated for altered enzyme activity.https://swissmodel.expasy.org/) to select amino acid residues in the PT region of the N- terminal domain as substitution residues. The PT region of the N-terminal domain consists of beta sheets, where the polar residue E140, 217, 263 and the hydrophilic residue Q172 are located  region . The 3D  located . The ami located , and furEscherichia coli DH5α and BL21(DE3) were used as hosts for gene cloning and protein overexpression. Recombinant E. coli was incubated at 37°C and overexpressed at 25°C in Luria broth medium supplemented with ampicillin at 100 μg/ml. pET-22b(+) was used as the vector for cloning.E. coli DH5α was transformed and cultured in Luria broth agar medium containing ampicillin. Gene mutation in transformed colonies was confirmed through DNA sequencing.Site-directed mutagenesis was performed using the overlap extension method as described in a previous study . BrieflyOverexpression and purification of recombinant protein was performed as described in a previous study , 7. The PSphy activity was measured by the molybdate-blue method with some modification . Next, 5The relative activity of PSphy was measured at various temperatures and pH values. In all experiments, the substrate concentration was 1 mM and CaCl2 concentration was 4 mM.Significant differences in enzyme activity at various temperatures (25–50°C) were observed only at E263A, in which alanine was substituted for the original residue at position 263 . At the Until now, little research has been carried out to determine the properties of residues in the N-terminal domain of PSphy. Altering amino acid residues affects the optimum pH and activity of the enzyme . The speIn conclusion, this study investigated altered enzymatic activity by substituting polar amino acid residues based on the structural prediction of the PSphy N-terminal domain. Enzyme activity was increased by replacing amino acid residue E263 with alanine, indicating that position 263 of the N-terminal domain plays an important role in enzyme activity. Further studies are needed to explain additional functions, active sites, and mechanisms of action of the N-terminal domain.


Carboxylated carbon nanotubes (CCNT) as –COOH carriers were doped into a Nafion matrix. By adjusting the doping content from 0 wt% to 10 wt%, an IPMC with constant steady-state deformation has been achieved at a critical CCNT content of 2 wt%. Moreover, the increasing rate of the slow anode deformation with the CCNT content is tunable, which is found to be 2.26 mm s In this study, we propose to neutralize the relaxation deformation of Nafion-ionic polymer metal composite (IPMC) by slow anode deformation of Flemion-IPMC caused by carboxyl groups (–COOH). In contrast to the Nafion-IPMC, Flemion (perfluorocarboxylic acid polymer)-IPMC still slowly bends toward the anode side after a fast initial deformation and slight relaxation.17 Both of the deformations are too complex to predict or to control. The currently available IPMCs cannot meet the requirements of precise control fields, such as position control, shape control and constant force drive. Hence, a new kind of IPMC performing a steady and controllable deformation is urgently required for practical engineering applications.However, to our knowledge, IPMC has not been widely used in practice, which is mainly due to its highly unstable deformation properties. When subjected to a DC voltage under saturated conditions, an IPMC often shows a complex unstable deformation. Actuation of Nafion (perfluorosulfonic acid polymer)-IPMC is often prone to a relaxation deformation following a fast anode deformation. Various relaxation deformations have been reported. All of Pt-, Pd-, Au-, Cu-, and Ni-Nafion IPMCs show obvious relaxation deformation,Based on the deformation characteristics of Nafion- and Flemion-IPMC, if the relaxation deformation of Nafion-IPMC can be neutralized adequately by the slow anode deformation of Flemion-IPMC, a constant steady-state deformation without relaxation can be obtained approximately (as shown in 3H) bound to the side chains. Fast anode deformation is induced by the swelling effect due to the aggregation of cations together with bonded water molecules on the cathode side, while relaxation deformation is caused by the back migration of free water to the anode side.17–20 In contrast, Flemion is a perfluorocarboxylic acid ionomer with weak acid groups (–COOH) bound to the side chains. For Flemion-IPMC, there is always a considerable amount of undissociated –COOH groups in the matrix after cation exchange.21 The slow anode deformation of Flemion-IPMC is caused by the migration of the secondary dissociated H+ together with bonded water molecules to the cathode side.21 The difference in deformation properties is strongly influenced by the quantity and direction of the movable species,22–25 especially the secondary dissociated H+. Therefore, we propose an alternative method to produce a non-relaxation IPMC by utilizing a Nafion matrix doped with –COOH groups, which can generate an amount of secondary dissociated H+.Further solution was found from the essence of relaxation and slow anode deformation. To our knowledge, Nafion is a perfluorosulfonic acid ionomer with strong acid groups  were doped into a Nafion matrix as –COOH carriers to confirm the above assumption. The critical CCNT content is also evaluated at which the relaxation deformation and the slow anode deformation are eliminated. Here, Pd-electrode IPMCs are employed as specimens and fabricated by assembling CCNT-doped Nafion membranes and Pd electrode layers via an electroless plating method.29 The preparation process is described in detail in the ESI.†Carboxylated multi-walled carbon nanotubes (CCNT), with –COOH groups covalently bonded to the surface of MWCNT and very weak van der Waals force among the bundles, are water-soluble and have numerous ion insertion sites.SEM micrographs of all the prepared Nafion membranes were obtained using a field emission scanning electron microscope  and are shown in Fig. 2 in the ESI,In testing, a specimen  μm in thickness) was clamped by a gold clamp on one end with a free length of 30 mm after wiping off the surface water with filter paper. Then a 2 V DC voltage was applied to the specimen by an arbitrary power supply (HM8143) using Labview software. The deformation was measured and recorded for 50 seconds with a laser displacement sensor (Keyence LK-G80) at the measuring point which was 20 mm from the fixed end and at ambient temperature and humidity. The test platform is shown in †The time–displacement curves of the Nafion-IPMCs with various CCNT contents are shown in 3H groups. Identical to Flemion-IPMCs, there exist two kinds of driving cations: Na+ and H+ in the matrix. The dissociation equations are as follows:The experimental results verified our hypothesis well. In general, the CCNT-doped Nafion-IPMCs show a coupling deformation behavior of pure Nafion- and Flemion-IPMC. With CCNT being doped into the Nafion matrix, –COOH groups are brought into the hybrid IPMCs (except for 0 wt% CCNT content), existing with –SO21 after initial fast anode deformation, the free water molecules are driven back to the anode side because of the water concentration gradient and the pressure gradient causes the relaxation deformation, while the migration of the H+ cations and associated water molecules to the cathode side contributes to the slow anode deformation. Thus the CCNT/Nafion-IPMC exhibits a coupling behavior of the pure Nafion- and the Flemion-IPMC. The deformation property is highly correlated with the content of the CCNT in the hybrid membranes. The deformation varies from a large negative one to a positive increasing one when the content of CCNT changes from 0 wt% to 10 wt%. For the 0 wt% CCNT content IPMC, the fast anode deformation is induced by the aggregation of cations together with bound water molecules on the cathode side while the relaxation deformation is caused by the reversed diffusion of free water, which has been extensively studied and explained.16–22 The 1 wt% CCNT content IPMC still shows a negative relaxation deformation, but the amplitude of which decreases dramatically with a tendency reverting to the initial state when compared to that of the 0 wt% CCNT content IPMC. This is due to the migration of the secondary dissociated H+ and the carried water molecules to the cathode side , the deformation of the corresponding IPMCs, very similar to that of the Flemion-IPMC, increases infinitely. This is just because of the dramatically increased amount of the secondary dissociated H+ and carried water molecules  with CCNT content, which indicates a direct corresponding relationship between the growth rate and CCNT content. Once the CCNT content increases to 1 wt%, the growth rate will increase by 2.26 mm s−1. Such results fit well with the experimental results and reveal the essence of the deformation process of the CCNT-doped IPMCs in detail, which can be used to design IPMC devices with tunable actuation behaviors for precise control fields.As can be seen from −1 %−1. Therefore, such results are of great significance for the design of IPMCs with tunable actuation behaviors to meet the requirements of precise control fields.In summary, based on the nature of Nafion and Flemion matrices, and the deformation properties of the corresponding IPMCs, we dope CCNT as –COOH carriers into Nafion matrix to develop a new kind of IPMC performing a constant steady-state deformation. It is found that the deformation properties of the CCNT/Nafion-IPMCs are highly correlated with the content of the CCNT in the hybrid membranes. As the content of the CCNT increases from 0 wt% to 10 wt%, the deformation changes from a large negative one to a slow positive increasing one. The 2 wt% CCNT-doped IPMC exhibits a steady anode deformation without any relaxation deformation or slow anode deformation. Using a multi-diffusion equation to fit the deformations caused by the doping with CCNT, the fitting results fit well with the experimental results and can reveal the essence of the deformation process of the CCNT-doped IPMCs in detail. The increasing rate of the slow anode deformation with the CCNT content is about 2.26 mm sThere are no conflicts to declare.RA-008-C7RA11498B-s001


Ractopamine, as a feed additive, has attracted much attention due to its excessive use, leading to the damage of the human nervous system and physiological function. Therefore, it is of great practical significance to establish a rapid and effective method for the detection of ractopamine in food. Electrochemical sensors served as a promising technique for efficiently sensing food contaminants due to their low cost, sensitive response and simple operation. In this study, an electrochemical sensor for ractopamine detection based on Au nanoparticles functionalized covalent organic frameworks (AuNPs@COFs) was constructed. The AuNPs@COF nanocomposite was synthesized by in situ reduction and was characterized by FTIR spectroscopy, transmission electron microscope and electrochemical methods. The electrochemical sensing performance of AuNPs@COF-modified glassy carbon electrode for ractopamine was investigated using the electrochemical method. The proposed sensor exhibited excellent sensing abilities towards ractopamine and was used for the detection of ractopamine in meat samples. The results showed that this method has high sensitivity and good reliability for the detection of ractopamine. The linear range was 1.2–1600 μmol/L, and the limit of detection (LOD) was 0.12 μmol/L. It is expected that the proposed AuNPs@COF nanocomposites hold great promise for food safety sensing and should be extended for application in other related fields. In recent years, food safety has become a global concern due to the popularity of healthy diets. Due to the diversity of food types, the unknown source of pollution and the accuracy and timeliness of test results, there are also some regulatory blind spots in the field of food supervision. Therefore, the development of rapid, efficient and multiple food pollutant detection technologies has always been a difficult and hot spot in food safety research. In order to improve the taste of the product, some additives are added to food and livestock products. Among them, as an important neurotransmitter in the human central nervous system, ractopamine ,2 is add2+/Cu+ composite as an electrochemical sensing interface for detecting ractopamine, which was beneficial to the accumulation and electrochemical sensing of targets. The good linear relationship was in the range of 0.1–1.75 μM, and the detection limit was 0.041 μM − [The functional groups formed during the preparation of the material were analyzed by FTIR . Figure [AuCl4]− . Thus, t6]3−/4− solution were studied by cyclic voltammetry (CV). 6]3−/4− redox processes. The redox current of COFs/GCE(c) electrode was lower than that of the bare GCE(b) electrode, which may be due to the poor conductivity of COFs. The redox current of COFs/GCE(c) electrode was much lower than that of the AuNPs@COFs/GCE(a) electrode. The enhancement of redox current of the AuNPs@COFs/GCE(a) electrode may be due to the direct band gap and high electron transfer ability of AuNPs, which brought the AuNPs@COFs excellent conductivity, strong catalytic activity and a large specific surface area. The results show that AuNPs@COFs exhibit the strongest peak current response, indicating that the synergistic effect of the nanocomposites improves the electron transfer rate and catalytic activity. The heterogeneous electron transfer rate (k0) was also calculated using the method developed by Klingler and Kochi [0 = 2.74 × 10−3 cm s−1, which was faster than that of COFs/GCE (k0 = 1.08 × 10−3 cm s−1). The results show that AuNPs@COFs have good levels of conductivity and are suitable as electrode modification materials for electrochemical sensors.The electrochemical properties of different modified electrodes in [Fe(CN)nd Kochi . The resS component, which is the ohmic resistance from the solution, in series with the combination of constant phase element (GCE) combined in parallel with the charge transfer resistance (RCT) of the Faradaic process and the Wargburg impedance (ZW) component arising from the mass diffusion towards the electrode surface. Herein, the GCE component is incorporated instead of the true double layer capacitance (Cdl) due to heterogeneity in the electrode surface that contributes to the imperfect capacitance. The Rct value of COFs (c) was about 170 Ω and the conductivity was low. The Rct value of the bare GCE (b) was about 130 Ω, whereas the Rct value of AuNPs@COFs (a) decreased to about 90 Ω, which is due to the good conductivity of AuNPs and almost no agglomeration on the surface of COFs. It shows that different modified components have obvious synergistic effects. This result is consistent with the above CV curve. In summary, AuNPs@COF nanocomposites exhibit good charge transfer kinetics and a fast electron transfer.In order to study the electrochemical properties of different materials modified electrodes, the interface properties of different electrodes were evaluated by electrochemical impedance spectroscopy (EIS) in the frequency range of 0.01–100 kHz. The response of different modified electrodes to ractopamine was studied by cyclic voltammetry (CV). As shown in 1/2). The regression equation is as follows:In order to study the electrode reaction kinetics, the effect of scanning rate on ractopamine current and potential was studied using the CV method. In 1/2 in the Randles–Sevcik equation, indicating that the electron loss process on the surface of the AuNPs@COFs composites is controlled by diffusion [The oxidation peak current and reduction peak current are proportional to the square root of the scan rate, which is consistent with the quantitative relationship between Ip and υiffusion . It showIn order to further investigate the electrochemical catalytic activity of AuNPs@COFs nanocomposites for ractopamine, CV analysis was performed at a fixed scanning rate (100 mV/s) and a certain ractopamine concentration range  C. The reIt is necessary to optimize the working potential before quantitative analysis of ractopamine. The working potential affects the sensitivity of the sensor. 2 is 0.96 and 0.98, respectively. When the ractopamine concentration was high (>3.1 mmol/L), the current was nonlinear, which may be ascribed to the saturation of the catalytic sites on the working electrode surface. The calculated LOD [t test. The t values of the two linear ranges were 12.81 and 14.53, and the F values were 1918.98 and 932.27, p < 0.01, respectively, which shows that the method has a significant linear relationship, and good accuracy and precision. Compared with other reported ractopamine sensors , ascorbic acid (AA), glucose (Glu), uric acid (UA), dopamine, isoxsuprine, ritodrine, and fenoterol were detected individually using amperometric assay. As shown in 2O2, 3 mmol/L UA, and 3 mmol/L AA were added to the system in turn, the response current value remained basically unchanged. However, significant responses were obtained after adding ractopamine. These indicate that AuNPs@COFs/GCE has good anti-interference ability with ractopamine. In addition, six AuNPs@COFs/GCE electrodes were prepared using the same method to test the reproducibility. The relative standard deviation (RSD) was 0.51%, and the maximum increase of the current response was 5.31%, indicating that AuNPs@COFs/GCE have good reproducibility. It can be seen from Anti-interference ability is one of the most important indexes to test the practicability of electrode. AuNPs@COFs nanocomposites were used to detect some electroactive substances coexisting with glucose in real samples. Potential interferences such as hydrogen peroxide  was 0.12 μmol/L. Compared with the same type of ractopamine sensor, the sensor based on AuNPs@COF nanocomposites has a wider linear range, lower detection limit, higher stability and better selectivity. The electrochemical sensing platform has been successfully applied to the determination of ractopamine in meat extracts with good accuracy and reliability. Food safety detection methods based on functional nanomaterials will usher in new development opportunities in future.


The temporal nature of static and dynamic deformation of fibre metal laminates is discussed here. The aim of the study is to verify the proposed innovate model using layered composites. The modified relaxation model is based on the earlier formulated plasticity relaxation model for homogeneous materials. The proposed relaxation model makes it possible to describe the deformation of the layered composites from elastic to irreversible deformation, finalised by the failure moment. The developed approach allows us to consider the effects of the transition from static to dynamic loading. This means that the model-calculated dynamic limiting characteristics of the metal and the strength of brittle materials will have a determining character, depending on the loading history. The verification of the model using a glass fibre reinforced aluminium composite, glass fibre reinforced titanium composite, carbon fibre reinforced aluminium composite, and Kevlar fibre reinforced aluminium composite with different thickness ratios between metal and polymer layers is given. It is shown that the theoretical deformation curves of the metal composites at the various strain rates, finalised by brittle fracture of the polymer layers or continued irreversible deformation of remaining unbroken metal layers with destroyed polymer (fibre/epoxy) layers, are predicted. Based on the same structural−temporal parameters for five (Ti/GFRP (0/90)/Ti/GFRP(90/0)/Ti) and three (Ti/GFRP(0/90/90/0)/Ti) layers glass fibre reinforced titanium composites and the polymer layers, one-stage and two-stage stress drops during the irreversible deformation of the composite under static and dynamic loading are simulated. The change of the multi-stage fracture of the composite from static to dynamic loading and the fracture characteristic times of the polymer  and the metal (8.4 ms) are correlated. Continued plastic deformation of the composite after fracture of the polymer layers is related with different values of the characteristic relaxation times of the polymer (fibre/epoxy) and the metal layers. Two types of theories are used to numerically simulate the nonlinear initial deformation of the layered metal composite structures under static loads. The first type ,2 considThe multi-stage process of the fracture of the multilayer metal composites leads to the dynamic deformation dependencies of the metal composites differing from the typical deformation dependencies of homogeneous materials ,16. The To formulate the deformation model of the layered metal composites subjected to dynamic loads, the classical Johnson−Cook model for plastic deformation of metals, the Johnson−Cook damage model, and the delamination model are used ,25. The The studies listed above show the impossibility of using some of the unified approaches for predicting the deformation of multilayer composites. When studying composite materials under dynamic loads, new difficulties are added related to the dependence of the material response on the applied loading rate and generally on the loading history. For homogeneous materials, there is also no unified approach for calculating the deformation of a material under a dynamic load, as there are many different constitutional models. The main difficulty in using dynamic models for homogeneous materials is the binding of one material to a model and the applicability of the model in a certain range of strain rates. Undoubtedly, the integral approach of Tuler and Butcher shows a good result , but theIn this paper, the development of our idea, proposed earlier in ,28, by aΣ(t). These parameters allow for comparing the dynamic characteristics of the material obtained from experiments with different shapes of the loading pulse [τ is the incubation time of fracture associated with the dynamics of the micro-cracking relaxation processes preceding the macro-fracture event, Σ(t) is the time dependence of the local tensile stress in the specimen, and fσ is the static strength of the brittle fibres. The time of fracture ft is defined as the moment at which the equality sign in Equation (1) is attained. The parameter α characterizes the sensitivity of the material to the level (amplitude) of force field causing the fracture . The set of material constants τ and α determines the fracture process on a given scale level.To predict the strength of brittle materials in a wide range of strain rates, the structural−temporal approach ,33 is cong pulse . The fraThe relaxation model of plasticity ,30,31 foγ(t) (0 < γ(t) ≤ 1) is given by the following:The dimensionless relaxation function t is time, Σ(t) is the stress function of time, yσ is the static yield stress, and α is a dimensionless parameter of the material’s amplitude sensitivity. Inequality γ(t) = 1 in Equation (2) corresponds to the case of accumulating elastic deformation before the onset of the macroscopic yield at t*. A decrease in the relaxation function in the range of 0 < γ(t) < 1 corresponds to the transition of the material to the plastic deformation stage. During the plastic deformation stage, where t ≥ t*, the following condition is satisfied for γ(t):Here, yielding ,42, equat = *t  < 1). True stresses, trueσ), are determined by the following form:ψ, modE, yσ, β), is the coefficient related to the behaviour of stresses and β is the dimensionless scalar parameter (0 ≤ β < 1), which describes the degree of hardening of the material. Where β = 0, this corresponds to the absence of hardening. Considering the stages of elastic and plastic deformations separately, the general stress-strain relationship from Equation (4) is written as follows:Equality in Equation (3) is retained because the state is fixed at the yield moment when given in ), and thThe set of fixed parameters used in drawing the deformation curve is independent of the strain rate and is only related to the changes in the material’s structure. The method for determining the parameters is considered in Selyutina and Petrov 2022 .The multistage irreversible deformation of the multilayer composites under tension is studied based on the model outlined by Selyutina ,28. Utilσ(t) is the temporal dependence of the stress of the fibre metal laminate, 0E is the initial Young’s modulus of the fibre metal laminate, ε(t) is the temporal dependence of the strain of fibre metal laminate, yefft is the yielding time of the metal layers, γ(t) is the relaxation function, *t1, *t2 … *nt are the fracture times of the brittle fibre layers of the composite, E1, E2, … n−E1 are the Young’s modulus of fibre metal laminate upon fracture of brittle 1… n − 1 fibrous layers, n is the number of fibrous layers, β1, β2, … βn−1 are the degree of hardening of fibre metal laminate upon fracture of brittle 1… n − 1 fibrous layers, and Σ(t) is the temporal dependence of the load function in the specimen of the fibre metal laminate. Based on this function we built a model  written for the metal layers, as follows:effα and eff, iβ have constant values at any strain rate and different values for structurally different materials. orE to finE upon fracture of the brittle fibre layers and the change in the cross-sectional area of FML from orS to finS, as follows:Here,  a model  that preg papers ,30,34,43p element in p body, and models elastic−plastic deformation with hardening. The yielding point p body in two stages, The process of deformation of a multilayer composite can be represented in the form of a rheological model, as schematically shown in To establish the relationship between the parameters of the relaxation model of plasticity and the rheological model of the composite in Delamination between aluminium layers and fibres leads to a sharp drop in stress, corresponding to the removal of Hooke’s elastic elements, Σ(t). A united set of characteristic plasticity and fracture characteristic times also makes it possible to effectively predict important competitive phenomena in materials with a complex structural structure.Using the proposed model, it is possible to calculate the elastic, inelastic, and irreversible stages of composite deformation until the composite is completely destroyed. The proposed relaxation model for layered composites makes it possible to effectively describe the deformation response of the composite from elastic deformation to irreversible deformation, and to investigate further until the moment of failure. Moreover, this approach allows us to consider the effects of transition from static to dynamic loading, in which the dynamic limiting characteristics of the metal yield strength and the strength of brittle materials will have a determining character, depending on the loading history In this section, the structural−temporal parameters of the composite and its components are determined for metal composites with different structures. −1 (−1 and −1 (The pre-fracture stage may consist of the fracture of the polymer epoxy/glass fibre layers (including delamination process), which is different depending on the thickness of the composite layers . As the  386 s−1 a, and co 500 s−1 b.The theoretical static and dynamic deformation curves, marked by solid lines in The two-stage stress drop for statics in b differsΣ(t) allow for the application of the proposed model for various combinations of metal and polymer layers. As shown in Invariant structural−temporal characteristics of the loading history Typical dynamic dependences of layered composites with carbon  and Kevl−1) and dynamic loads (−1). The proposed relaxation model for the layered composites makes it possible to consider the effects of the transition from static to dynamic loading, in which the dynamic limiting characteristics of the metal yield strength and the strength of brittle materials will have a determining character, depending on the loading history.The modified plasticity relaxation model for heterogeneous materials was applied and verified for metal layered composites with different metallic and polymeric (fibre/epoxy) materials, tested under static /Ti/GFRP(90/0)/Ti) and three-layer (Ti/GFRP(0/90/90/0)/Ti) glass fibre reinforced titanium composites and polymer layers, one-stage and two-stage stress drops during the irreversible deformation of the composite under static and dynamic loading were simulated. The one-stage stress drop on the dynamic curve of the composite and the two-stage stress drop on the static curve of the composite were compared according to the competition principle of the temporal preparatory processes of the layers fracture stress of the polymer and the metal.The various deformation responses of metal composites in a wide range of strain rates, characterised by brittle, ductile, or ductile−brittle fractures, were predicted by the relaxation model of plasticity for heterogeneous materials. To model the important competitive phenomena on composite materials, a united set of characteristic times for plasticity and fracture was used.Thus, the use of the plasticity relaxation model is not limited to homogeneous materials, but can be very effective for layered composites. Using examples of the composites with different thickness layers and calculated characteristic relaxation times for polymer (fibre/epoxy) and metal layers, the presence of elastic−plastic deformation after the fracture of the polymer (fibre/epoxy) layers of the composite is interpreted.We believe that the basic ideas regarding the incubation time approach and the relaxation plasticity can serve as an effective tool in developing a new experimental standard and corresponding numerical schemes accounting for the unstable behaviour of deformation diagrams in the composite materials and their different components.


The study was aimed at obtaining a vermicelli formulation on a mixture of corn and rice flour, adding carrageenan and its economic analysis. The experiment applied a 2-factorial randomized block design, where factor 1 was a mixture of corn and rice flour (5 levels) and factor 2 was carrageenan concentration (5 levels), repeated three times. The data were analyzed using ANOVA provided in SPSS. When there were significant differences, the analysis proceeded with DMRT at a level of 5% to see differences among treatments. The results show that the higher the corn flour and carrageenan concentration, the higher the vermicelli's ash and fat content. The formulation produces wet vermicelli with a good appearance. The production of vermicelli uses an extruder method. The selected vermicelli formulation was a mixture of 25% corn flour with 75% rice flour and the addition of 0.6% carrageenan. The characteristics of the wet vermicelli are moisture content of 42.84%, ash content of 0.21% on a wet basis (wb), and fat content of 0.43% wb. The organoleptic test of vermicelli was color 3.9 (liked), aroma 3.6 (liked), texture 2.6 (quite soft), taste 3.7 (liked), and general appearance 3.5 (liked). Economically, making vermicelli made of corn and rice flour is profitable because the R/C ratio value is greater than one, which is 2.27. The resulting wet vermicelli resembles wet noodles, large in size and yellow in color, so it can be recommended as gluten-free noodles, suitable for consumption by people with gluten allergies. Agriculture and food sectors currently contribute to the economies all over the world. The contribution is more significant in agrarian countries, including Indonesia, than its counterparts since the agricultural products become raw material in the value chains. In Indonesia, the agriculture and food system industry contributes half of the total employment and one-third of the country's national income .Rice flour is one of the raw materials for making vermicelli, an important food type. However, using rice as a raw material might affect the national rice self-sufficiency program. Thus, vermicelli production needs alternative materials to substitute rice, so the production does not influence the program. One of the potential substitutes is corn flour.However, the vermicelli made of corn has an unexpected texture, which might be unfavorable to the consumers. In order to get a firm and soft texture of corn vermicelli, a texture-forming, namely, carrageenan material, is added to the production , 3. CarrVermicelli is consumed as a source of carbohydrates as a staple food to either complement or substitute rice. Vermicelli is a type of noodle made of rice flour. In order to produce vermicelli with good quality, raw materials with specific starch characteristics are needed. The ideal starch for vermicelli raw materials is starch that has a small granule size, high amylose content, degree of swelling, and limited solubility and the characteristics of type C paste  .starch noodles or instant starch noodles.Corn flour has properties similar to the special rice flour used as vermicelli ingredients; it has a high amylose content and is flavorful, making it suitable for making vermicelli. According to , local vCorn flour can be used as an alternative material in the manufacture of vermicelli because it has several advantages. According to , 12, corCorn has a low glycemic index (GI) of 46, and the index is lower than rice, which accounts for 72. It is considered a high GI since the value is above 70, moderate if it is between 56 to 69, and low below 55 . Foods wFarmers have developed varieties of corn and rice in the market, so it is necessary to characterize the seeds to produce flour as raw material for vermicelli from various varieties, especially those that produce high yields. The government continues to promote food diversification programs considering the high demand for rice and other imported raw materials. The law mandates the government to build solid food security by focusing on increasing national food production capacity for five strategic food commodities: rice, corn, soybean, sugar cane, and beef. Corn is one of the local commodities to support Indonesian food security. Corn contains nutritious values and has been considered a staple food in some areas of Indonesia since it fulfills national food needs of 26.0%  . From anThe research was conducted at the Postharvest Laboratory of Assessment Institute for Agricultural Technology (AIAT) East Java in Malang and the Food Laboratory of the Center for Agricultural Postharvest Research and Development in Bogor, Indonesia. The main ingredients were corn seeds and rice. Corn varieties are DK 88, Bisi 18, DK 979, DK 99, and Pertiwi, and corn is obtained from farmers' plantations in Kediri Regency, East Java. Rice varieties are Ciherang, Inpari 19, Inpari 4, Inpari 30, and IR 64, obtained from farmers' plantations in Malang Regency, East Java. Type and supporting materials were tapioca flour, carrageenan, and chemicals for analysis.w/w [The treatment of the vermicelli formulation was a mixture of corn flour with rice flour and carrageenan . There were 5 levels of corn flour: rice flour formulations, namely, (1) 0 : 100 (control); (2) 25 : 75; (3) 50 : 50; (4) 75 : 25; and (5) 100 : 0. There were 5 levels of added carrageenan, namely, 0, 0.3, 0.6, 0.9, and 1.2% The yield of flour is calculated based on the percentage of flour weight (yield) divided by the weight of seeds  used as flour and then multiplied by one hundred percent . The cheFinancial analysis is an essential part of product innovation since the analysis will determine the feasibility of the innovation. The community will adopt the innovation when the production is profitable . This anΠ) is the difference between total revenue and total cost, with the following equation: Π = TR–TC. Then, the R/C ratio compares revenue and total cost, formulated as R/C ratio = TR/TC.The cost is formulated as TC = TVC + TFC, where TC is defined as the total cost, TVC is the total variable cost, and TFC is the total fixed cost. The costs include the monetary value of inputs spent to finance the vermicelli products. Revenue or total revenue (TR) is the total money received from products that are successfully sold or the multiplication of the number of products produced (Q) with the selling price per unit product (P), which is formulated as TR = P × Q. Profit .The criteria used to assess the R/C ratio are as follows:Corn seeds were coarsely ground into corn kernels. Corn kernels are cleaned from the husk and organs. After cleaning, the corn kernels are soaked for about 3 hours and then drained. In moist conditions, corn kernels were ground into flour. The corn flour was dried in an oven at a temperature of 60°C for 16 minutes until the moisture content was 12% and then manually sieved with a particle size of flour that passed a 100 mesh sieve.The rice is washed and soaked for about 3 hours and then drained. In moist conditions, the rice was ground into flour. The flour was dried in an oven at 60°C for 16 minutes until the moisture content was 12% and then manually sieved with a particle size of flour that passed a 100 mesh sieve.After the treatment, mix rice flour, corn flour, and carrageenan. According to , the binThe equipment used was a milling tool to form kernels of corn seeds and a flouring tool to produce corn flour and rice flour. The sifter is used to sieve corn flour and rice flour to obtain a flour size of 100 mesh. Extruder is used for printing vermicelli. Soaking container is a tool used to soak corn and rice seeds. Stoves and pans were used to steam the dough until cooked. Equipment is used to analyze raw materials and vermicelli's physical and chemical properties—complementary equipment for organoleptic tests.p < 0.01). Bisi 18, DK 99, and Pertiwi varieties of corn had the same seed length, while DK 88 and DK 979 were shorter than the others. The varieties of corn include Bisi 18 (11.43 mm), DK 99 (11.07 mm), and Pertiwi (11.72 mm). They had similar length, width, and weight characteristics as 100 seeds. Likewise, DK 88 (9.95 mm) and DK 979 (9.90 mm) had a length, width, and weight lower than 100 seeds. The thickness of the seeds has a different pattern, and DK 979 variety was the thickest (0.464 cm), while DK 88 (0.385 cm) was the thinnest. Research results by [The physical characteristics of corn seed and rice grain of five varieties are presented in sults by  showed tFor the physical characteristics of the rice grain of five varieties, it appears that the longest rice grain was IR 64 (7.1 mm), while Inpari 19 was the shortest (6.4 mm). Rice grain width almost did not differ between varieties, the lowest being Inpari 4 (1.9 mm). The length variation of the five rice grain varieties was 6.4-7.1 mm, and the width was 1.9-2.2 mm. There was no correlation between the length and width of rice grain and weight. The research results by  showed tThe shape grain of rice can be determined based on the ratio between the length and width of the rice grain, namely, bold (<2.1), medium (>2.1-3), and slender (>3) . The meap < 0.01) (Varieties of corn and rice affect the yield of flour produced ( < 0.01) . The yieLactobacillus casei resulted in the highest yield (55.2%) at 60 hours of immersion. The presence of lactic acid bacteria (L. bulgaricus and L. casei) was able to degrade corn cell walls so that starch granules came out of the cells, which facilitated the milling process so that the yield increased.The yield of flour calculation is based on the dry weight of the material. The yield of flour states the efficiency value of the processing process so that it can be seen as the amount of flour produced from the initial raw material. The yield of corn flour obtained by research by  was 36% R2 = 0.8947) with the quadratic function equation y = 927.52x2 − 3645.6x + 3628.2, compared to the yield of corn flour (R2 = 0.6973) with the quadratic function equation y = 0.2945x2 − 20.139x + 387.91. This shows the difference between rice and corn. The heavier the rice, the higher the flour yield, while the corn flour is different.The yield of wet vermicelli is presented in p < 0.01), namely, flour composition and carrageenan concentration [Chemical analysis of vermicelli was carried out to determine the moisture, ash, and fat content. The results of the chemical analysis of vermicelli are presented in ntration , 30. TheThe moisture content of a food material significantly affects its shelf life because microbes are increasingly inhibited by the lower moisture content . The higThe lowest ash content of vermicelli was in the combination of 50% corn flour+50% rice flour mixed with the addition of carrageenan at a concentration of 0.3%, namely, 0.065% wet basis (wb), while the highest ash content was in the combination treatment of 75% corn flour+25% rice flour with the addition of carrageenan at a concentration of 1.2%, namely, 0.725%wb. The ash content in vermicelli was strongly influenced by the mineral content . CarrageThe lowest fat content of vermicelli was from the combination treatment of rice flour (100%) with the addition of carrageenan at a concentration of 0.9%, which was 0.325%wb, while the highest fat content was from the combination treatment of a mixture of 50% corn flour+50% rice flour with the addition of carrageenan at a concentration of 1.2%, which was 0.450%wb. The addition of corn flour increases the fat content of the vermicelli because the raw material corn had a higher fat content than rice. According to , the fatp < 0.01), namely, the mixed flour formulation and carrageenan concentration. The results of the organoleptic test of vermicelli are presented in The organoleptic test of vermicelli was influenced by the interaction between two factors . The aroma assessment range was between 2.45 and 3.55, with the criteria of dislike to moderate . The criteria for assessing the texture of the vermicelli were classified as very soft to very hard. The highest texture rating was from a mixed formulation of 100% corn flour, with 0.6% carrageenan, a score of 3.4 (moderate), while the vermicelli texture with the softest assessment was from a mixture formulation of 25% corn flour and 75% flour. Rice without adding carrageenan (0%) gets scored 2.1 (soft). It appears that the combination of a mixture of corn flour with the addition of carrageenan can increase the hardness of the vermicelli texture.The most preferred vermicelli taste was a mixture formulation of 25% corn flour and 75% rice flour, with the addition of 0.6% carrageenan, a score of 3.7 (like). The lowest rating is with a score of 2.7 (dislike), in several formulations. The most favorable general acceptance is the formulation of a mixture of 25% corn flour and 75% rice flour, without the addition of carrageenan (0%), but not different from 100% corn flour, 0.6% carrageenan addition, with a score of 3.6 (like). The lowest rating has a score of 2.9 (moderate).In addition, it was also influenced by the amylose and amylopectin levels of flour. The lower the amylose content in the material causes the gel structure to be weaker and causes the dissolved solids to be greater so that the vermicelli texture is low (soft). The texture of the vermicelli was also affected by the stickiness on the surface because the amylopectin molecules form amorphous or less compact regions, making it easier for water to penetrate . The amyEconomic analysis of vermicelli production is a way to control finances so that the level of business success achieved during vermicelli production takes place. Producers use this analysis to make calculations and determine steps to improve and increase profits in their business activities .Cost analysis was a tool used to explain the relationship between costs and the company's volume or the number of products. Cost analysis was formed based on two costs, namely, fixed costs and variable costs. Fixed costs were costs incurred in a fixed amount, not influenced by the volume to be produced, such as depreciation costs of tools and machines used. Variable costs were incurred according to the production volume, for example, input costs .The costs formed were in line with agroindustry research of similar products that use the same calculations, such as research on the financial feasibility analysis of corn-based noodles . RevenueProfit was the difference between total revenue and total costs incurred. Processing agricultural products can increase profits compared to fresh agricultural products that are directly sold . VermiceBusiness feasibility analysis needs to be carried out to determine the number of services and expenses made as well as the revenue obtained to analyze the economics of this business activity. Several assumptions are needed. Assumptions are set to assist in data processing, setting the production cost, and making cashflows. The assumptions set include the number of employees' working days, the product's selling price, the increase in expected production capacity, increase in raw material prices, and project life .In business analysis, what we need to know is business capital. Business capital is usually referred to as the initial costs incurred to start a business. According to , the cosFixed costs are production costs that arise due to fixed production factors, even though the number of products produced varies . MeanwhiCost calculations are carried out to include investment costs, semivariable variable costs, fixed costs, and other costs. Investment costs are the amount of capital or costs used to start a business or develop a business . The detThis is in line with the opinion of  that fixThe price per unit of product based on the total cost is Rp 1,362.00, while the price per unit of product based on variable costs is Rp 1323.00. It can be concluded that this value must be achieved in every sale of one product unit so that a business can be profitable. The cost of production based on the total cost is obtained from the division of the total cost by the final amount of production. At the same time, the price per unit based on variable costs is obtained from the division of variable costs by the final amount of production.For this business to benefit from each sale of one unit of product, the product per unit sale should be Rp 3,000.00. The selling price that must be achieved to get a profit for each unit of wet vermicelli packaging is Rp 3,000.00 per unit of product to get an income of Rp 4,320,000.00; the amount is obtained from the product of the product unit price multiplication with the number of products produced each time in the production process. So that the net profit can be obtained by selling products per unit is Rp. 3,000.00 is Rp 2,359,150.00. Profit is the difference between the total revenue and the total cost of production. The most affordable wet vermicelli formulation was a mixture of 25% corn flour, 75% rice flour, and 0.6% carrageenan. The yield of wet vermicelli is 144%. The resulting vermicelli has the following characteristics: yellow color, 42.835% moisture content, 0.210%wb ash content, and 0.425%wb fat content. In the organoleptic test, vermicelli that has the criteria of preferred colour (score 3.9), aroma (score 3.6) and soft texture (score 2.6) was moderate. There was a preferred taste (score 3.7) and general acceptance (score 3.5). Economically, making wet vermicelli is profitable with an R/C ratio greater than one, which is 2.27. The resulting wet vermicelli resembles wet noodles, large in size and yellow in color, so it can be recommended as gluten-free noodles, suitable for consumption by people with gluten allergies.


Platycodon grandiflorus (Jacq.) A.DC. root (PGR) flour is well known for its medical and edible values. In order to develop nutritionally fortified products, breads were prepared using wheat flour, partially replaced with PGR flour. The rheological properties and microstructure of dough and the physicochemical characterization of bread were investigated. Results showed that lower level of PGR addition (3 and 6 g/100 g) would improve the baking performance of breads, while the higher level of PGR addition (9 g/100 g) led to smaller specific volume (3.78 mL/g), increased hardness (7.5 ± 1.35 N), and unpalatable mouthfeel (21.8% of resilience and 92.6% of springiness) since its negative effect on the viscoelasticity and microstructure of dough. Moreover, sensory evaluation analysis also showed that the PGR3 and PGR6 breads exhibited a similar flavor to the control bread, but the 9 g/100 g addition of PGR provided bread with an unpleasant odor through its richer volatile components. As expected, the phenolic content and antioxidant capacity of bread increased significantly (p < 0.05) as PGR flour was added to the bread formulation. The total phenolic content (TPC) ranged from 14.23 to 22.36 g GAE/g; thus, DPPH• and ABTS•+ scavenging capacity increased from 10.44 and 10.06 μg Trolox/g to 14.69 and 15.12 μg Trolox/g, respectively. Therefore, our findings emphasized the feasibility of PGR flour partially replacing wheat flour in bread-making systems. Nowadays, health-conscious consumers increased the demand for innovative and functional food products with nutritious and healthy values. Bread, one of the most important staple foods worldwide, could be used as the vehicle for delivering the nutritional and functional compounds to the consumers . PreviouPlatycodon grandiflorus (Jacq.) A.DC. root (PGR), commonly applied in medicine and the human diet, is an exceptionally popular and widespread medical herb widely cultivated in Asia, including China, Japan, and Korea  withThe moisture content of the bread was measured using an HB43-S moisture analyzer . The specific volume of the bread was expressed by the ratio of volume (mL) and weight (g), and the volume of the bread was determined by the millet displacement method. E) was calculated as follows:L indicates lightness, a indicates the red/green value, and b indicates the blue/yellow value. L0, a0, and b0 are the color of PGR0 bread. Meanwhile, the color of the bread crumbs was determined by a spectrometer color analyzer . The total difference of color . TPA measurement of bread was performed with a pretest speed of 1.0 mm/s, test speed of 1.0 mm/s, post-test speed of 2 mm/s, and deformation level of up to 60%. There was 30 s for the dough balance between the two-time compression. Crumb characteristics of hardness, resilience, cohesion, and springiness were recorded . The electronic nose  was employed to determine the taste of bread samples, which could detect different odors by 14 metal oxide sensor arrays. Before the measurements, 3 g of bread samples were sealed in a headspace bottle and left at 35 °C for 10 h to equilibrate the volatile flavors inside. Then, the electronic nose tests were conducted under the following conditions: 300 s of cleaning time, 120 s of preparation time, 0.6 L/min of flow rate, 25 °C of detection temperature, 55 ± 2% of relative humidity, 60 s of detection time, 100 s of zero count, and 10 s of automatic adjustment zero time. Ultimately, the characteristic values of each sensor were extracted as the electronic nose results . R2 = 0.9940).The total polyphenol content (TPC) determination of breads was carried out by the Folin–Ciocalteu method according to Velioglu, Mazza, Gao, and Oomah (1998)  with sliDPPH• scavenging activity determination of bread samples was carried out. A total of 10 mg of DPPH was dissolved in 25 mL of 80% methanol to obtain a methanolic DPPH solution. Moreover, the blank was prepared by the mixture solution of 2 mL of methanolic DPPH solution and 2 mL of 80% methanol. A total of 2 mL of bread extract was mixed with 2 mL of methanolic DPPH solution. The mixed solution was shaken thoroughly and then placed at room temperature to incubate for 30 min, and the absorbance was recorded at 517 nm. The DPPH• scavenging activity of the sample was expressed as μg Trolox/g calculated from a calibration curve .ABTS•+ scavenging activity of bread was determined using the methods reported by Re et al. (1999)  ak (2021) , in whicIn order to develop a new functional product with improved baking performance and biochemical quality, PGR flour was added to the bread. Dough characteristics, including rheological properties and dough microstructure, were apparently affected by the addition of PGR flour. Consequently, the baking performance of obtained breads was varied. For the results, it is noteworthy that the addition of a high level of PGR flour (9 g/100 g) had a negative effect on the quality of bread, such as lower specific volume and inferior texture attributes. While the addition of 3 g/100 g and 6 g/100 g PGR flour maintained or improved the bread quality. Besides the baking performance, the biochemical quality of the bread was also evaluated. Results showed that PGR0 and PGR3 bread was closer in flavor properties. Nevertheless, the 9 g/100 g addition of PGR flour significantly changed the flavor of bread through richer volatile components. Notably, the antioxidant capacity of wheat bread was improved as the addition of PGR flour increased. Therefore, considering the acceptance of consumers, it could be concluded that the 3 g/100 g and 6 g/100 g addition of PGR flour successfully substituted wheat flour in bread, and these results provided a thought to future research about the use of PGR in the bakery industry.


The rise of deep learning has caused a paradigm shift in robotics research, favoring methods that require large amounts of data. Unfortunately, it is prohibitively expensive to generate such data sets on a physical platform. Therefore, state-of-the-art approaches learn in simulation where data generation is fast as well as inexpensive and subsequently transfer the knowledge to the real robot . Despite becoming increasingly realistic, all simulators are by construction based on models, hence inevitably imperfect. This raises the question of how simulators can be modified to facilitate learning robot control policies and overcome the mismatch between simulation and reality, often called the “reality gap.” We provide a comprehensive review of sim-to-real research for robotics, focusing on a technique named “domain randomization” which is a method for learning from randomized simulations. Given that machine learning has achieved super-human performance in image classification  and gameThere is a consensus that further increasing the simulator’s accuracy alone will not bridge this gap . LookingIn this paper, we first introduce the necessary nomenclature and mathematical fundamentals for the problem . Next, wWe begin our discussion by defining critical concepts and nomenclature used throughout this article.t. The environment, also called domain, is characterized by its parameters ϕ . The domain parameters determine the transition probability density function s0 is drawn from the start state distribution γ ∈ , the system forms a MDP described by the tuple Markov Decision Processes (MDPs): Consider a discrete-time dynamical systemot = fobs (st).Reinforcement Learning (RL): The goal of a RL agent is to maximize the expected (discounted) return, a numeric scoring function which measures the policy’s performance. The expected discounted return of a policy p(ξ) is the key difference compared to the standard MDP formulation. It enables the learning of robust policies, in the sense that these policies work for a whole set of environments instead of overfitting to a particular problem instance.Domain randomization: When augmenting the RL setting with domain randomization, the goal becomes to maximize the expected (discounted) return for a distribution of domain parametersModern research on learning from (randomized) physics simulations is based on solid foundation of prior work . ParametThe roots of randomized simulations trace back to the invention of the Monte Carlo method , which cInspired by these early approaches, the systematic analysis of randomized simulations for robot learning has become a highly active research direction. Moreover, the prior work above also falsifies the common belief that domain randomization originated recently with the rise of deep learning. Nevertheless, the current popularity of domain randomization can be explained by its widespread use in the computer vision and locomotion communities as well as its synergies with deep learning methods. The key difference between the early and the recent domain randomization methods  is that Simulators can be obtained by implementing a set of physical laws for a particular system. Given the challenges in implementing an efficient simulator for complex systems, it is common to use general purpose physics engines such as ODE, DART, Bullet, Newton, SimBody, Vortex, MuJoCo, Havok, Chrono, RaiSim, PhysX, FleX, or Brax. These simulators are parameterized generative models, which describe how multiple bodies or particles evolve over time by interacting with each other. The associated physics parameters can be estimated by system identification , which gSeveral comparisons between various physics engines were made . HoweverApart from the physics engines listed above, there is an orthogonal research direction investigating human-inspired learning of the physics laws from visual input  as well Learning from randomized simulations entails significant design decisions:Which parameters should be randomized? Depending on the problem, some domain parameters have no influence  while others are pivotal . It is recommended to first identify the essential parameters . For exaWhen should the parameters be randomized? Episodic dynamics randomization, without a rigorous theoretical justification, is the most common approach. Randomizing the domain parameters at every time step instead would drastically increase the variance, and pose a challenge to the implementations since this typically implies recreating the simulation at every step. Imagine a stack of cubes standing on the ground. If we now vary the cubes’ side lengths individually while keeping their absolute positions fixed, they will either lose contact or intersect with their neighboring cube(s). In order to keep the stack intact, we need to randomize the cubes with respect to their neighbors, additionally moving them in space. Executing this once at the beginning is fine, but doing this at every step creates artificial “movement” which would almost certainly be detrimental. Orthogonal to the argumentation above, alternative approaches apply random disturbance forces and torques at every time step. In these cases, the distribution over disturbance magnitudes is chosen to be constant until the randomization scheme is updated. To the best of our knowledge, event-triggered randomization has not been explored yet.How should the parameters be randomized? Answering this question is what characterizes a domain randomization method . There aWhat about physical plausibility? The application of pseudo-random color patterns, e.g., Perlin noise , has becEven in the case of rigid body dynamics there are notable differences between physics engines, as was observed by Coining the term “reality gap,” The Simulation Optimization Bias (SOB) ; 2021b iTo obtain a quantitative estimate of the transferability, With robot navigation in mind, There are several research areas that overlap with sim-to-real in robot learning, more specifically domain randomization . In the The key idea behind curriculum learning is to increase the sample efficiency by scheduling the training process such that the agent first encounters “easier” tasks and gradually progresses to “harder” ones. Hence, the agent can bootstrap from the knowledge it gained at the beginning, before learning to solve more difficult task instances. Widely known in supervised learning , curricuVia this duality one can view domain randomization as a special form of meta learning where the robot’s task remains qualitatively unchanged but the environment varies. Thus, the tasks seen during the meta training phase are analogous to domain instances experienced earlier in the training process. However, when looking at the complete procedure, meta learning and domain randomization are fundamentally different. The goal of meta learning, i.e., Inspired by the human ability to quickly master new tasks by leveraging the knowledge extracted from solving other tasks, meta learning  seeks toThe term transfer learning covers a wide range of machine learning research, aiming at using knowledge learned in the source domain to solve a task in the target domain. Rooted in classification, transfer learning is categorized in several subfields by for example differentiating 1) if labeled data is available in the source or target domain, and 2) if the tasks in both domains are the same . Domain When executing a controller on a physical device operating at high frequencies, it is of utmost importance that the forward pass finishes with the given time frame. With deep Neural Network (NN) policies, and especially with ensembles of these, this requirement can become challenging to meet. Distilling the knowledge of a larger network into a smaller one reduces the evaluation time. Knowledge distillation  has beenThe term robustness is overloaded with different meanings, such as the ability to (quickly) counteract external disturbances, or the resilience against uncertainties in the underlying model’s parameters. The field of robust control aims at designing controllers that explicitly deal with these uncertainties . Within The goal of system identification is to find the set of model parameters which fit the observed data best, typically by minimizing the prediction-dependent loss such as the mean-squared error. Since the simulator is the pivotal element in every domain randomization method, the accessible parameters and their nominal values are of critical importance. When a manufacturer does not provide data for all model parameters, or when an engineer wants to deploy a new model, system identification is typically the first measure to obtain an estimate of the domain parameters. In principle, a number of approaches can be applied depending on the assumptions on the internal structure of the simulator. The earliest approaches in robotics recognized the linearity of the rigid body dynamics with respect to combinations of physics parameters such as masses, moments of inertia, and link lengths, thus proposed to use linear regression , and latThe well-established field of adaptive control is concerned with the problem of adapting a controller’s parameters at runtime to operate initially uncertain or varying systems . A prominent method is model reference adaptive control, which tracks a reference model’s output specifying the desired closed-loop behavior. Model Identification Adaptive Control (MIAC) is a different variant, which includes an online system identification component that continuously estimates the system’s parameters based on the prediction error of the output signal . Given tSimulators are predominantly used as forward models, i.e., to make predictions. However, with the increasing fidelity and expressiveness of simulators, there is a growing interest to also use them for probabilistic inference . In the ξ ∼ p(ξ) at the beginning of each simulated rollout. A randomization scheme is categorized as adaptive if the domain parameter distribution is updated during learning, otherwise the scheme is called static. The main advantage of adaptive schemes is that they alleviate the need for hand-tuning the distributions of the domain parameters, which is currently a decisive part of the hyper-parameter search in a static scheme. Nonetheless, the prior distributions still demand design decisions. On the downside, every form of adaptation requires data from the target domain, typically the real robot, which is significantly more expensive to obtain. Another approach for learning robust policies in simulation is to apply adversarial disturbances during the training process. We classify these perturbations as a form of domain randomization, since they either depend on a highly stochastic adversary learned jointly with the policy, or directly contain a random process controlling the application of the perturbation. Adversarial approaches may yield exceptionally robust control strategies. However, without any further restrictions, it is always possible to create scenarios in which the protagonist agent can never win, i.e., the policy can not learn the task. Balancing the adversary’s power is pivotal to an adversarial domain randomization method, adding a sensitive hyper-parameter.We distinguish between static , adaptivAnother way to distinguish domain randomization concepts is the representation of the domain parameter distribution. The vast majority of algorithms assume a specific probability distribution  independently for every parameter. This modeling decision has the benefit of greatly reducing the complexity, but at the same time severely limits the expressiveness. Novel LFI methods  estimateApproaches that sample from a fixed domain parameter distribution typically aim at performing sim-to-real transfer without using any real-world data . Since rMore than a decade ago, The work by Combining Generative Adversarial Networks (GANs) and domain randomization, Static domain randomization  is inhervia Stein Variational Policy Gradient  (Gradient . Mehta eh (REPS)  to updath (REPS) , a step-via model-free RL, the authors created an online variant of the original method. The sim-to-real experiments were carried out using MPC where (only) the model parameters are updated based on the result from the LFI routine. via nonlinear MPC while using the unscented transform to simulate different domain instances for the control horizon. Hence, this algorithm allows one to calibrate the uncertainty as the system evolves with the passage of time, attributing higher costs to more uncertain paths. For performing the essential LFI, the authors build upon the work of via adversarial optimization, leading to significant drops in accuracy  assumptions can be met. Adaptive domain randomization methods, for example, will require operating the physical device in order to collect real-world data. After all, we can expect that approaches with randomization will be more robust than the ones only trained on a nominal model. This has been shown consistently . HoweverDomain randomization is one way to successfully transfer control policies learned in simulation to the physical device, but by no means the only way.In order to cope with the inaccuracies of a simulator, via forces on a unit mass-spring-damper system. The constrains and nullspace behavior are abstracted away from the agent, making the RL problem easier and the policy more transferable.Learning from randomized simulations still offers abundant possibilities to enable or improve the sim-to-real transfer of control policies. In the following section, we describe multiple opportunities for future work in this area of research.Creating randomizable simulation environments is time-intensive, and the initial guesses for the domain parameters as well as their variances are typically very inaccurate. It is of great interest to automate this process grounded by real-world data. One viable scenario could be to record an environment with a RGBD camera, and subsequently use the information to reconstruct the scene. Moreover, the recorded data can be processed to infer the domain parameters, which then specifies the domain parameter distributions. When devising such a framework, we could start from prior work on 3D scene reconstruction tDeep NNs are by far the most common policy type, favored because of their flexibility and expressiveness. However, they are also brittle w.r.t. changes in their inputs . Due to Continuing the direction of adaptive domain randomization, we are convinced that neural LFI powered by normalizing flows are auspicious approaches. The combination of highly flexible density estimators with widely applicable and sample-efficient inference methods allows one to identify multi-modal distributions over simulators with very mild assumptions . By intrAnother promising direction for future research is the combination of simulated and real-world data collection with explicit consideration of the different costs when sampling from the two domains, subject to a restriction of the overall computational budget. One part of this problem was already addressed by The problem settings considered in the overwhelming majority of related publications, are (continuous) control tasks which do not have a sequential nature. In contrast, most real-world tasks such as the ones posed at the DARPA Robotics Challenge  consist We chose the references based on multiple criteria: 1) Our primary goal was to covering all milestones of the sim-to-real research for robotics. 2) In the process, we aimed at diversifying over subfields and research groups. 3) A large proportion of papers came to our attention by running Google Scholar alerts on “sim-to-real” and “reality gap” since 2017. 4) Another source were reverse searches starting from highly influential publications. 5) Some papers came to our attention because of citation notifications we received on our work. 6) Finally, a few of the selected publications are recommendations from reviewers, colleagues, or researchers met at conferences. 7) Peer-reviewed papers were strongly preferred over pre-prints.