Research on the Effect of Microbial Fermentation Feeds on Slaughter Performance and Meat Quality in Pig with Large-scale Feeding

In this paper, Duroc-lanndrae-Yorkshire crossbred piggy of born at 75 days of age, bouncing with health and similar body weight were randomly divided into experimental group （136 pigs） and control group （136 pigs）. Pigs in control group were fed with conventional diets, and 4% microbial fermentation feeds were added to the basal diet of experimental group to research on the effect of performance and meat quality of pig breeding. The results indicated that after feeding 106 d, compared to control group, the slaughter performance, meat quality and content of fatty acid and amino acid of experimental group were all better, its slaughter rate increased 1.53%, the carcass weight increased 2.75% and the backfat thickness decreased 0.02 cm. In addition, the pork meat in test group hadn＇t antibiotic and heavy metal residues, while it had higher color score, lower pH45min and pH24h value, better tenderness and rate of cooked meat and higher content of fatty acids, polyunsaturated fatty acids and amino acids. Microbial fermentation feed using in test group can significantly improved the flavor and aroma of pork, and eating this pork conducive more to human health. Thus, the application of microbial fermenta- tion feed can provide a more safe, healthy and nutritious flavor pork for human beings.

Structure Design of Large-scale Fattening Pig House with Fermentation Bed

The fattening pig house with fermentation bed had an area of 2 100 m2, and the area of fermentation bed was 1 900 m2 with a utilization rate of 91.4%, which was 45% higher than that of conventional pig house with surrounding barrier. There was feeding trough around the house. The water troughs were set in the middle of the fermentation bed and of the feeding trough on the short sides of the house, separating feed and water. There were electric aluminum alloy shutters in both long sides of the house for ventilation, cooling and heat preservation. On both short sides, there were fans and wet curtains. The spray cooling devices were in- stalled outside the roof for cooling. The environmental control in the piggery, includ- ing light, temperature, water, humidity, carbon dioxide and ammonia, was realized to run by computer automatically. The coconut chaff and chaff configuration were used as mattress material, realizing the advantages of fermentation bed, such as no smell, zero emission, high-quality meat, saving labor, controlling disease, no drug residue, producing fertilizer, intelligent control, mechanized operation, etc.

Porcine Breeding Management in a Large-scale Piggery with Microbial Fermentation Bed

[Objective] The behavior of eating, drinking, defecating and peeing of 1 500 pigs in a large-scale microbial fermentation bed-equipped piggery was observed. We hoped to find some simple indicators that could reflect the health status of swinery and to provide experience for the swinery performance management in large-scale microbial fermentation bed-equipped piggery. [Method] The body weight （BW）, daily BW gain, feed intake and other indicators of different-day-old pigs were recorded in details. Based on the recorded data, the models between BW, BW gain, average daily feed intake and feed/gain ratio and growth days （d） were established. In addition, the incidences of pox-like macula （dermatitis）, diarrhea （gastrointestinal disease）, cough （respiratory disease）, stiff pig （malnutrition）, conjunctivitis （eye disease） and foot inflection （trauma） among fattening pigs were also investigated. [Result] The BW range, average BW, daily BW gain, breeding days, daily feed intake range, average daily feed intake, staged feed intake, accumulated feed intake, feed/gain ratio and accumulated feed/gain ratio of different-day-old pigs were studied, respectively. Four dynamic models were established for the growth of pigs： （1） the BW （y）-age （x） mod- el： y=0.758 9x-19.883 （3=0.993 7）; （2） the BW gain （y）-age （x） model： y=1.039 5x05051 （F=0.885 4）; （3） the average daily feed intake （y）-age （x） model： y=0.023 5x-0.334 3 （F=0.991 7）; （4） the feed/gain ratio （y）-age （x） model： y=0.022x＋0.427 8 （P=0.988 5）. Based on these models, the corresponding theoretical growth value of pigs at different growth stage could be predicted. The main diseases occurred among the swinery in the large-scale microbial fermentation bed piggery included pox-like macula （dermatitis）, diarrhea （gastrointestinal disease）, cough （respiratory disease）, stiff pig （mal- nutrition）, conjunctivitis （eye disease） and foot inflection （trauma）. The deadly infec- tious diseases had been not found among the pigs. [Conclusion] When the actual BW, BW gain, average daily feed intake and feed/gain ratio were all lower than the theoretical values predicted by the models, the management should be enhanced. The average daily feed intake of 60 to 65-day-old pigs was lower than the theoretic value, indicating that the pigs could not adapt nicely to the fermentation bed at the very early stage. When the pigs grew up to 70 to 75 d old, the average daily feed intake was higher than the theoretical value, indicating that the pigs had adapted to the fermentation bed. In particularly, average daily feed intake of 75-day-old pigs was higher than the theoretical value by 21%. It was suggested the fermentation bed was conducive to the growth of pigs. Considering the occurrence of diseases among pigs, the overall incidence was relatively low. The incidence of each disease was all lower than 10% with little difficulty in treating. If the management of mattress was strength- ened, such as paying attention to feeding and keeping water clean, many diseases could heal by themselves.

A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.

Insights into microbiota community dynamics and flavor development mechanism during golden pomfret(Trachinotus ovatus)fermentation based on single-molecule real-time sequencing and molecular networking analysis

Popular fermented golden pomfret(Trachinotus ovatus)is prepared via spontaneous fermentation;however,the mechanisms underlying the regulation of its flavor development remain unclear.This study shows the roles of the complex microbiota and the dynamic changes in microbial community and flavor compounds during fish fermentation.Single-molecule real-time sequencing and molecular networking analysis revealed the correlations among different microbial genera and the relationships between microbial taxa and volatile compounds.Mechanisms underlying flavor development were also elucidated via KEGG based functional annotations.Clostridium,Shewanella,and Staphylococcus were the dominant microbial genera.Forty-nine volatile compounds were detected in the fermented fish samples,with thirteen identified as characteristic volatile compounds(ROAV>1).Volatile profiles resulted from the interactions among the microorganisms and derived enzymes,with the main metabolic pathways being amino acid biosynthesis/metabolism,carbon metabolism,and glycolysis/gluconeogenesis.This study demonstrated the approaches for distinguishing key microbiota associated with volatile compounds and monitoring the industrial production of high-quality fermented fish products.

Assessment of Wet Season Precipitation in the Central United States by the Regional Climate Simulation of the WRFG Member in NARCCAP and Its Relationship with Large-Scale Circulation Biases

Assessment of past-climate simulations of regional climate models(RCMs)is important for understanding the reliability of RCMs when used to project future regional climate.Here,we assess the performance and discuss possible causes of biases in a WRF-based RCM with a grid spacing of 50 km,named WRFG,from the North American Regional Climate Change Assessment Program(NARCCAP)in simulating wet season precipitation over the Central United States for a period when observational data are available.The RCM reproduces key features of the precipitation distribution characteristics during late spring to early summer,although it tends to underestimate the magnitude of precipitation.This dry bias is partially due to the model’s lack of skill in simulating nocturnal precipitation related to the lack of eastward propagating convective systems in the simulation.Inaccuracy in reproducing large-scale circulation and environmental conditions is another contributing factor.The too weak simulated pressure gradient between the Rocky Mountains and the Gulf of Mexico results in weaker southerly winds in between,leading to a reduction of warm moist air transport from the Gulf to the Central Great Plains.The simulated low-level horizontal convergence fields are less favorable for upward motion than in the NARR and hence,for the development of moist convection as well.Therefore,a careful examination of an RCM’s deficiencies and the identification of the source of errors are important when using the RCM to project precipitation changes in future climate scenarios.

Structure and immunomodulatory activity of Lentinus edodes polysaccharides modified by probiotic fermentation

Plant-based fermentations provide an untapped source for novel biotechnological applications.In this study,a probiotic named Lactobacillus fermentum 21828 was introduced to ferment Lentinus edodes.Polysaccharides were extracted from fermented and non-fermented L.edodes and purified via DEAE-52 and Sephadex G-100.The components designated F-LEP-2a and NF-LEP-2a were analyzed by FT-IR,HPGPC,HPAEC,SEM,GC-MS and NMR.The results revealed that probiotic fermentation increased the molecular weight from 1.16×10^(4) Da to 1.87×10^(4) Da and altered the proportions of glucose,galactose and mannose,in which glucose increased from 45.94%to 48.16%.Methylation analysis and NMR spectra indicated that F-LEP-2a and NF-LEP-2a had similar linkage patterns.Furthermore,their immunomodulatory activities were evaluated with immunosuppressive mice.NF-LEP and F-LEP improved immune organ indices,immunoglobulin(Ig G and Ig M)and cytokines concentrations;restored the antioxidation capacity of liver;and maintained the balance of gut microbiota.F-LEP displayed better moderating effects on the spleen index,immunoglobulin,cytokines and the diversity of gut microbiota than NF-LEP(200,400 mg/kg).Our study provides an efficient and environment-friendly way for the structural modification of polysaccharides,which helps to enhance their biological activity and promote their wide application in food,medicine and other fields.

Effects of Rosa roxburghii&edible fungus fermentation broth on immune response and gut microbiota in immunosuppressed mice

With the rise of probiotics fermentation in food industry,fermented foods have attracted worldwide attention.In this study,protective effects of Rosa roxburghii&edible fungus fermentation broth(REFB)on immune function and gut health in Cyclophosphamide induced immunosuppressed mice were investigated.Results showed that REFB could improve the immune organ index,and promote the proliferation and differentiation of splenic T lymphocytes.In addition,it attenuated intestinal mucosal damage and improved intestinal cellular immunity.REFB administration also up-regulated the expression of IL-4,INF-γ,TNF-α,T-bet and GATA-3 mRNA in small intestine.Furthermore,administration of REFB modulated gut microbiota composition and increased the relative abundance of beneficial genus,such as Bacteroides.It also increased the production of fecal short-chain fatty acids.These indicate that REFB has the potential to improve immunity,alleviate intestinal injury and regulate gut microbiota in immunosuppressed mice.

Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems

Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational resources.Furthermore,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables efficiently.This paper is dedicated to addressing the challenges posed by SLMOPs.To start,we introduce innovative objective functions customized to mine maximum and minimum candidate sets.This substantial enhancement dramatically improves the efficacy of frequent pattern mining.In this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific dimensions.Additionally,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero variables.This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value.We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs.The results demonstrate that our approach achieves competitive solutions across various challenges.

The Advantages of Methane Production by Combined Fermentation of Lignite and Wheat Straw

Biogasification of coal is important for clean utilization of coal. Experiments on the fermentation of single lignite, single straw and their mixture were performed to explore the variation characteristics of gas production potential, microbial community and methanogenic metabolic pathways of mixture. Research has shown that mixed fermentation of lignite and straw significantly promoted biomethane production. The abundance of hydrolytic acidifying functional bacteria genera (Sphaerochaeta, Lentimicrobium) in mixed fermentation was higher than that in the fermentation of single lignite and single straw. The abundance of some key CAZy metabolic enzyme gene sequences in mixed fermentation group was increased, which was favorable to improve methane production. Aceticlastic methanogenesis was the most critical methanogenic pathway and acetic acid pathway was more competitive in methanogenic mode during peak fermentation. Macrogenomics provided theoretical support for the claim that mixed fermentation of coal and straw promoted biomethane metabolism, which was potentially valuable in expanding methanogenesis from mixed fermentation of lignite with different biomasses.

Postbiotics from Saccharomyces cerevisiae fermentation stabilize microbiota in rumen liquid digesta during grain-based subacute ruminal acidosis(SARA) in lactating dairy cows

Background Subacute ruminal acidosis(SARA)is a common metabolic disorder of high yielding dairy cows,and it is associated with dysbiosis of the rumen and gut microbiome and host inflammation.This study evaluated the impact of two postbiotics from Saccharomyces cerevisiae fermentation products(SCFP)on rumen liquid associated microbiota of lactating dairy cows subjected to repeated grain-based SARA challenges.A total of 32 rumen cannulated cows were randomly assigned to 4 treatments from 4 weeks before until 12 weeks after parturition.Treatment groups included a Control diet or diets supplemented with postbiotics(SCFPa,14 g/d Original XPC;SCFPb-1X,19 g/d Nutri Tek;SCFPb-2X,38 g/d Nutri Tek,Diamond V,Cedar Rapids,IA,USA).Grain-based SARA challenges were conducted during week 5(SARA1)and week 8(SARA2)after parturition by replacing 20%DM of the base total mixed ration(TMR)with pellets containing 50%ground barley and 50%ground wheat.Total DNA from rumen liquid samples was subjected to V3–V416S r RNA gene amplicon sequencing.Characteristics of rumen microbiota were compared among treatments and SARA stages.Results Both SARA challenges reduced the diversity and richness of rumen liquid microbiota,altered the overall composition(β-diversity),and its predicted functionality including carbohydrates and amino acids metabolic pathways.The SARA challenges also reduced the number of significant associations among different taxa,number of hub taxa and their composition in the microbial co-occurrence networks.Supplementation with SCFP postbiotics,in particular SCFPb-2X,enhanced the robustness of the rumen microbiota.The SCFP supplemented cows had less fluctuation in relative abundances of community members when exposed to SARA challenges.The SCFP supplementation promoted the populations of lactate utilizing and fibrolytic bacteria,including members of Ruminococcaceae and Lachnospiraceae,and also increased the numbers of hub taxa during non-SARA and SARA stages.Supplementation with SCFPb-2X prevented the fluctuations in the abundances of hub taxa that were positively correlated with the acetate concentration,andα-andβ-diversity metrics in rumen liquid digesta.Conclusions Induction of SARA challenges reduced microbiota richness and diversity and caused fluctuations in major bacterial phyla in rumen liquid microbiota in lactating dairy cows.Supplementation of SCFP postbiotics could attenuate adverse effects of SARA on rumen liquid microbiota.

Large-scale model testing of high-pressure grouting reinforcement for bedding slope with rapid-setting polyurethane

Bedding slope is a typical heterogeneous slope consisting of different soil/rock layers and is likely to slide along the weakest interface.Conventional slope protection methods for bedding slopes,such as retaining walls,stabilizing piles,and anchors,are time-consuming and labor-and energy-intensive.This study proposes an innovative polymer grout method to improve the bearing capacity and reduce the displacement of bedding slopes.A series of large-scale model tests were carried out to verify the effectiveness of polymer grout in protecting bedding slopes.Specifically,load-displacement relationships and failure patterns were analyzed for different testing slopes with various dosages of polymer.Results show the great potential of polymer grout in improving bearing capacity,reducing settlement,and protecting slopes from being crushed under shearing.The polymer-treated slopes remained structurally intact,while the untreated slope exhibited considerable damage when subjected to loads surpassing the bearing capacity.It is also found that polymer-cemented soils concentrate around the injection pipe,forming a fan-shaped sheet-like structure.This study proves the improvement of polymer grouting for bedding slope treatment and will contribute to the development of a fast method to protect bedding slopes from landslides.

Influence of nitrogen status on fermentation performances of non-Saccharomyces yeasts:a review

Nitrogen,one of the most crucial nutrients present in grapes and musts,plays a key role in yeast activities during alcoholic fermentation.Such influences are imposed on yeast growth and fermentation performances including the formation of secondary metabolites.Saccharomyces cerevisiae,the main yeast responsible for fermentation,has been studied extensively regarding nitrogen impacts.On the other hand,a similar study for non-Saccharomyces yeasts,whose contributions to winemaking have gradually been acknowledged,remains to be fully explored,with a few studies being reported.This review starts by discussing nitrogen impacts on non-Saccharomyces yeast growth and fermentation kinetics in different case scenarios,then proceeds to summarize the nitrogen preferences of individual yeast strains with regulation mechanisms elucidated by recent studies.Detailed discussions on the influences on the production of volatile compounds and proposed pathways therein are made,followed by future work suggested as the final section.In summarizing the nitrogen impacts on non-Saccharomyces yeasts throughout alcoholic fermentation,this review will be helpful in obtaining a more comprehensive view on these non-conventional wine yeasts in terms of nutrient requirements and corresponding volatile production.Research gaps will therefore be elucidated for future research.

The Influence of Gratering, Fermentation and Variety on the Physio-Chemical Quality of Cassava Starch

The study was conducted to determine the influence of gratering and fermentation parameters on the physicochemical quality of starch obtained from two cassava varieties in Sierra Leone (i.e., SLICASS 11 and SLICASS 6). Fresh cassava roots harvested from the Department of Agricultural Engineering were peeled, washed and grated before fermentation and starch extraction. Fermentation was carried out under separate aerobic and anaerobic conditions for a period of Seven days. Physicochemical analyses were conducted at the Postharvest Food and Bioprocess Engineering Laboratory of the Department of Agricultural and Bio-System Engineering, Njala University to determine the solubility, water absorption capacity and swelling power of starch extracts obtained from various experimental treatments. Fermentation method and duration had significant effects on the solubility, water absorption capacity (WAC) and swelling power (SP) of starch extracts. Maximum solubility and WAC were recorded on the fifth day, for both cassava varieties tested, with apparent significant difference resulting from the two gratering bands (i.e., with 1.5-inch nail hole and 4-inch nail hole sizes, respectively). While swelling power increased consistently with temperature for starch obtained from SLICASS-11 variety, an irregular pattern was observed for SLICASS-6 variety. A multiple correlation analysis proposes a significant and weak correlation between temperature, WAC (+0.150) and swelling power (+0.048). Also multiple correlation analyses suggest a significant correlation between fermentation period, the functional properties of starch extracts obtained from both fermentation methods and cassava varieties (i.e., solubility (−0.226), water absorption capacity (+0.301) and swelling power (+0.329)).

Microbial metabolic interaction in fermentation ecosystem and cooperation in flavor compounds formation of Chinese cereal vinegar

Shanxi aged vinegar(SAV)is a famous cereal vinegar in China,which is produced through a solid-state fermentation where diverse microbes spontaneously and complex interactions occur.Here,combined with the metatranscriptomics,the microbial co-occurrence network was constructed,indicating that Lactobacillus,Acetobacter and Pediococcus are the most critical genera to maintain the fermentation stability.Based on an extensive collection of 264 relevant literatures,a transport network containing 2271 reactions between microorganisms and compounds was constructed,showing that glucose(84%of all species),fructose(67%)and maltose(67%)are the most frequently utilized substrates while lactic acid(64%),acetic acid(45%)are the most frequently occurring metabolites.Specifically,the metabolic influence of species pairs was calculated using a mathematical calculation model and the metabolic influence network was constructed.The topology properties analysis found that Lactobacillus was the key role with robust metabolic control of vinegar fermentation ecosystem and acetic acid and lactic acid were the main metabolites with feedback regulation in microbial metabolism of SAV.Furthermore,systematic coordination of positive and negative impacts was proved to be inevitable to form flavor compounds and maintain a natural microbial ecosystem.This study provides a new perspective for understanding microbial interactions in fermented food.

Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

A semantic vector map-based approach for aircraft positioning in GNSS/GPS denied large-scale environment

Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

Impact of sourdough fermentation on nutrient transformations in cereal-based foods:Mechanisms,practical applications,and health implications

Sourdough is often considered a healthy choice and quality improver for use in cereal production due to its unique microbial composition and fermentation properties.During sourdough fermentation of cereals,biotransformation of nutrients occurs,resulting in notable changes to proteins,carbohydrates,fats,vitamins,and minerals.Each nutrient undergoes specific transformations,providing various advantages for human health.Proteins undergo hydrolysis to produce small molecular weight peptides and amino acids that are more easily digested and absorbed by the human body.Carbohydrates break down to improve the digestibility and absorption of cereals and lower the glycemic index.Fatty acids experience oxidation to produce new substances with health benefits.Additionally,the application of sourdough fermentation can enhance the texture,flavor,and nutritional value of cereal foods while also extending their shelf life and improving food safety.In conclusion,sourdough fermentation has a broad range of applications in cereal food processing.Further research is encouraged to investigate the mechanisms and processes of sourdough fermentation to develop even more nutritious,healthy,and flavorful cereal-based foods.

A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.