Adaptive Time Research Articles

Fouling monitoring in a circulating fluidized bed boiler using direct and indirect model-based analytics

Fouling is a phenomenon where material accumulates on the exterior of convective heat exchangers (HX) and other surfaces. In boilers fired by waste-derived or biomass fuels, these surfaces are cleaned frequently to maintain adequate heat transfer between the flue gas and fluid. However, excess cleaning of HX surfaces wastes money and resources, and the common practice of soot removal at fixed time intervals is not an optimal strategy. An adaptive timing method would be beneficial; however, real-time knowledge of HX condition is hard to obtain.In this paper, we present (1) a state estimation approach for fouling monitoring in a Circulating Fluidized Bed (CFB) boiler, fusing knowledge from a physical model with process measurement data, and (2) a novel condition monitoring scheme based on modal-vibrational sensing, with potential for a directly estimating the degree of fouling on heating surfaces. The results are demonstrated on a full-scale commercial CFB. Combining physical models, machine learning, and modal analysis in mutually supporting ways provides a solid basis for future sootblowing optimization efforts and improved fouling management.

Annual availability assessment of a gas turbine power plant using Monte Carlo simulation based on fuzzy logic and an adaptive neuro-fuzzy repair time prediction system

Annual availability assessment of a gas turbine power plant using Monte Carlo simulation based on fuzzy logic and an adaptive neuro-fuzzy repair time prediction system

Phylogenomics revealed migration routes and adaptive radiation timing of Holarctic malaria mosquito species of the Maculipennis Group

BackgroundPhylogenetic analyses of closely related species of mosquitoes are important for better understanding the evolution of traits contributing to transmission of vector-borne diseases. Six out of 41 dominant malaria vectors of the genus Anopheles in the world belong to the Maculipennis Group, which is subdivided into two Nearctic subgroups (Freeborni and Quadrimaculatus) and one Palearctic (Maculipennis) subgroup. Although previous studies considered the Nearctic subgroups as ancestral, details about their relationship with the Palearctic subgroup, and their migration times and routes from North America to Eurasia remain controversial. The Palearctic species An. beklemishevi is currently included in the Nearctic Quadrimaculatus subgroup adding to the uncertainties in mosquito systematics.ResultsTo reconstruct historic relationships in the Maculipennis Group, we conducted a phylogenomic analysis of 11 Palearctic and 2 Nearctic species based on sequences of 1271 orthologous genes. The analysis indicated that the Palearctic species An. beklemishevi clusters together with other Eurasian species and represents a basal lineage among them. Also, An. beklemishevi is related more closely to An. freeborni, which inhabits the Western United States, rather than to An. quadrimaculatus, a species from the Eastern United States. The time-calibrated tree suggests a migration of mosquitoes in the Maculipennis Group from North America to Eurasia about 20–25 million years ago through the Bering Land Bridge. A Hybridcheck analysis demonstrated highly significant signatures of introgression events between allopatric species An. labranchiae and An. beklemishevi. The analysis also identified ancestral introgression events between An. sacharovi and its Nearctic relative An. freeborni despite their current geographic isolation. The reconstructed phylogeny suggests that vector competence and the ability to enter complete diapause during winter evolved independently in different lineages of the Maculipennis Group.ConclusionsOur phylogenomic analyses reveal migration routes and adaptive radiation timing of Holarctic malaria vectors and strongly support the inclusion of An. beklemishevi into the Maculipennis Subgroup. Detailed knowledge of the evolutionary history of the Maculipennis Subgroup provides a framework for examining the genomic changes related to ecological adaptation and susceptibility to human pathogens. These genomic variations may inform researchers about similar changes in the future providing insights into the patterns of disease transmission in Eurasia.

The practical utility of psychometric scales for the assessment of the impact of posttraumatic scars on mental health.

Scars are a natural consequence of the healing process, but with an impact on the psychological and social level for the individual, which can even lead to withdrawal and social stigmatization. We aimed to analyze the psychosocial impact determined by post-traumatic scars, using psychometric scales, to assess the effectiveness of the Mekereș' Psychosocial Internalization Scale (MPIS), and to identify relevant predictors of traumatic and surgical scar internalization. Our cohort included 293 participants, 149 women and 144 men, aged 18-64 years who were screened for scar characteristics and completed a set of psychological scales. We compared the results obtained in two subgroups: 153 subjects with posttraumatic scars and 140 with surgical scars. Relevant predictors for posttraumatic scar internalization (R 2 = 0.721) are adaptation time, age of the occurrence and subjective appraisal, while for the depression, and hopelessness relevant predictors are the subjective appraisal of the scars and the posttraumatic quality of life. The psychological and social reporting of the aftermath of trauma, that has been followed by scar-ring, is an indicator of how a person will react and could indicate the susceptibility to psycho-pathology.

Adaptive Traffic Signal Timer for A Signal in Chennai Metropolitan City Using Python and OpenCV

Background: The current traffic control system in India’s metropolises is ineffective because of the randomization of traffic density patterns throughout the day. For a predetermined period of time, the traffic signal timers switch traffic’s direction. Vehicles must therefore wait for a long time even when there is little traffic. Objectives: To continuously adjust the traffic signal timer based on the varying real-time traffic density and to significantly lessen traffic congestion. Methods: An adaptive traffic light timer is offered by this research using image processing algorithms. The real-time image data will be analyzed by the Raspberry Pi and OpenCV, which will determine when to regulate traffic flow. Findings: The adaptive timer minimizes vehicle waiting time and increases the number of turns granted to cars in each lane to cross the signal, which results in an improvement in traffic flow. Novelty: The use of image processing algorithms, the Raspberry Pi, and OpenCV makes the proposed system highly efficient and effective in adapting to the real-time traffic conditions. The suggested methodology will be more affordable than the currently used methodologies. Keywords: Adaptive Traffic Signal Timer; Intelligent Traffic Control

Lateral Circulation in an Elongated Curved Tidal Channel

Abstract The dynamic theory of curvature-induced lateral circulation has been developed for open channel flows but not for oscillatory tides. A linear three-dimensional analytical model was developed to investigate the lateral circulation in an elongated tidal channel with mildly curved bends of which the radius of curvature is larger than the width. The curvature-induced lateral circulation has two components with the same amplitude, namely, a periodic component having an overtide frequency and a steady component. The combination of the two components allows the strength of the lateral circulation to vary periodically and the rotation direction to be unchanged during a tidal period. Friction modifies the strength and structure of the lateral circulation. The phase between the lateral flow and streamwise tidal flow decreases with increasing friction, indicating that the two flows are not necessarily in phase unless friction is strong. The lateral circulations driven by the Coriolis and curvature centrifugal forces augment each other during one phase and compete in the opposite phase, and the relative importance of the two circulations is determined by the Rossby number and friction. The adaptation time is the same for spinup and spindown of the curvature-induced lateral circulation and is determined by water depth and vertical eddy viscosity. The estimation of the adaptation time depends on the leading modes because the transition solution of the curvature-induced lateral circulation comprises a series of cosine modes. These results provide a theoretical basis for interpreting curvature-induced lateral circulation in tidal channels and coastal headlands. Significance Statement The dynamic theory of curvature-induced lateral circulation in a tidal flow remains unexplored. The purpose of this study is to understand the essentials of curvature-induced lateral circulation in an elongated tidal channel using a three-dimensional analytical model. The results showed that the curvature-induced lateral circulation has two components with the same amplitude: a periodic component having an overtide frequency and a steady component. This is significantly different from the curvature-induced lateral circulation associated with open channel flows, which is steady and in phase with the streamwise flow. Future work may show the role of curvature-induced lateral circulation in streamwise dynamics and mass transport.

Hybrid optimization algorithm for thermal displacement compensation of computer numerical control machine tool using regression analysis and fuzzy inference.

During the machining process, the computer numerical control machine is susceptible to variations in ambient temperature, cutting heat, and friction within the transmission parts, which generate different heat sources. These heat sources affect the machine structure in different ways, causing deformation of the machine and displacement of the tooltip and workpiece position, ultimately resulting in deviations in machining accuracy. The amount of thermal drift depends on several factors, including the material of the machine components, the cutting conditions, the duration of the machining process, and the environment. This study proposes a hybrid optimization algorithm to optimize the thermal variables of computer numerical control machine tool spindles. The proposed approach combines regression analysis and fuzzy inference to model the thermal behavior of the spindle. Spindle speed and 16 temperature measurement points distributed on the machine are input factors, while the spindle's axial thermal error is considered an output factor. This study develops a regression equation for each speed to account for the different temperature rise slopes and spindle thermal variations at different speeds. The experimental results show that the hybrid thermal displacement compensation framework proposed in this study effectively reduces the thermal displacement error caused by spindle temperature variation. Furthermore, the study finds that the model can be adapted to significant variations in environmental conditions by limiting the machining speed range, which significantly reduces the amount of data needed for model adaptation and shortens the adaptation time of the thermal displacement compensation model. As a result, this framework can indirectly improve product yield. The effects observed in this study are remarkable.

Length of Adaptation Has No Effect on the Threonine Requirement Determined in Healthy Young Adult Males Using the Indicator Amino Acid Oxidation Method

BackgroundThe indicator amino acid oxidation (IAAO) method is minimally invasive; therefore, it is applicable to study the amino acid (AA) requirements of individuals in various age groups. However, the accuracy of this method has been criticized because of the 8 h (1 d) protocol, which has been suggested to be too short an adaptation time for estimating AA requirements. ObjectivesThe IAAO method was used to determine whether 3 or 7 d of adaptation to each threonine intake alters the threonine requirement in adult men compared to 1 d of adaptation. MethodsEleven healthy adult men (19–35 y, body mass index (BMI) 23.4 in kg⋅m−2) were studied at 6 threonine intakes; each intake was studied over a 9 d period. Following 2 d of pre-adaptation to adequate protein intake (1.0 g·kg−1⋅d−1), subjects received experimental diets containing the randomly assigned test threonine intake (5, 10, 15, 20, 25, or 35 mg·kg−1·d−1) for 7 d. IAAO studies were performed on days 1, 3, and 7 of adaptation to the experimental diet. The rate of release of 13CO2 from the oxidation of L-[1-13C]phenylalanine (F13CO2) was measured, and the threonine requirement was determined by applying mixed-effect change-point regression to the F13CO2 data in R version 4.0.5. The 95% confidence interval (CI) was calculated using parametric bootstrap, and the requirement estimates on days 1, 3, and 7 were compared using analysis of variance (ANOVA). ResultsThe mean threonine requirements (upper, lower 95% CI) for days 1, 3, and 7 were 10.5 (5.7, 15.9), 10.6 (7.5, 13.7), and 12.1 (9.2, 15.0 mg·kg−1·d−1), respectively; and these requirements were not statistically different (P = 0.213). ConclusionsWe demonstrated that the short, 8 h IAAO protocol results in a threonine requirement that is not statistically different from that obtained on days 3 or 7 of adaptation in healthy adult males.This trial was registered at www.clinicaltrials.gov as NCT04585087.

Effect of antioxidant dental gel on the adaptation of the oral mucosa to removable dentures.

Determination of the effect of a gel with natural astaxanthin on the timing of adaptation of the oral mucosa to removable dentures. The gel made from a natural antioxidant reduces the risk of stomatitis, values of hygienic indices and traumatic factor. Aims: Reduction of the period of adaptation to removable dentures in patients with partial adentia. The objective was to evaluate the effect of antioxidant gel on the oral mucosa. Patients and Methods. The state of oropharyngeal microbiocenosis was studied in 105 patients aged 30-65 years old who were divided into three groups: the main group - 45 patients; comparison group - 35 patients; control group - 25 patients. The groups were comparable by age; gender differences were not taken into account. Patients in the main group received astaxanthin gel for 7 days after the delivery of the removable denture. Patients in the comparison group did not receive astaxanthin gel after the delivery of the removable denture. Clinical indices were used to assess oral hygiene: oral hygiene index plaque index API, gingivitis index, papillary-marginal-alveolar - (PMA); gingival sulcus bleeding index – SBI. We used a therapeutic and prophylactic agent in the form of a gel containing natural astaxanthin - 0.026%, vitamin E - 2.2%, Coenzyme Q - 1.5%. Result. According to the results of the analysis of the visual control and the results of photometric registration of the speed of healing and adaptation of the patient to the prosthesis, the data obtained differed in the patients of the 1st and 2nd groups. Visual control data of the dynamics of inflammatory changes in the oral mucosa development, received by the results of semi-quantitative evaluation of the severity of edema and hyperemia indicate significant differences in the rate of their reduction depending on the type of the applied tactics. In the case of group 1, prophylactic gel was applied from the time of denture fitting, and in group 2, only denture correction was performed. Thus, the index of hyperemia intensity testified to the dis-appearance of signs of the latter in Group 1 (gel with natural astaxanthin) - by the 4th day of observation, in Group 2 - by the 10th day after prosthesis correction. Complete disappearance of the signs of edema, respectively, was ascertained: in Group 1 - on the 4-5th day of observation and in Group 2 - by the 10th-11th day, at that, edema was retained 1-2 days longer. The method of photoplanimetric registration of the healing rate objectively confirmed the results obtained. Conclusion. Studies of the effectiveness of the gel containing natural astaxanthin indicate improved oral hygiene parameters of patients and reduced concentration of bacterial endotoxin and plasmalogen metabolites of oral microorganisms after 7 days of using the gel. Prophylactic dental gel with astaxanthin reduces the rate of adaptation to removable dentures by 1.7 - 2 times and reduces the risk of denture stomatitis due to wound healing, anti-inflammatory and anti-microbial action.

Adaptive step-size forward advection method for aerosol process simulation

ABSTRACT Outdoor aerosol processes are often associated with disasters and diseases, which threaten human life and health. Outdoor aerosols are a fluid system affected by meteorological conditions and three-dimensional complex terrain. Their variable wind speed and direction and complex terrain boundary conditions make simulating advection processes difficult. Based on incompressible flow conditions, we designed an adaptive time step algorithm for forward advection for the rapid simulation of aerosol processes. The method is based on the first-order forward semi-Lagrangian advection method with unconditional mass conservation. The first-order truncated error coefficient function theory generates an adaptive time step to control the accuracy of forward advection. Smoke aerosol simulation experiments in two small outdoor scenes were designed, and the effects of the traditional backward advection and forward fixed step methods were compared with the algorithm in this study. The proposed simulation method showed improved accuracy compared with the other two methods in experimental scenarios; moreover, compared with those of the traditional backward method, the computation time was significantly reduced and the conservation of mass was significantly improved. Thus, the proposed method is a fast simulation method for outdoor aerosol numerical prediction.

Optimizing Protein Production in Therapeutic Phages against a Bacterial Pathogen, Mycobacterium abscessus

Therapeutic phages against pathogenic bacteria should kill the bacteria efficiently before the latter evolve resistance against the phages. While many factors contribute to phage efficiency in killing bacteria, such as phage attachment to host, delivery of phage genome into the host, phage mechanisms against host defense, phage biosynthesis rate, and phage life cycle, this paper focuses only on the optimization of phage mRNA for efficient translation. Phage mRNA may not be adapted to its host translation machinery for three reasons: (1) mutation disrupting adaptation, (2) a recent host switch leaving no time for adaptation, and (3) multiple hosts with different translation machineries so that adaptation to one host implies suboptimal adaptation to another host. It is therefore important to optimize phage mRNAs in therapeutic phages. Theoretical and practical principles based on many experiments were developed and applied to phages engineered against a drug-resistant Mycobacterium abscessus that infected a young cystic fibrosis patient. I provide a detailed genomic evaluation of the three therapeutic phages with respect to translation initiation, elongation, and termination, by making use of both experimental results and highly expressed genes in the host. For optimizing phage genes against M. abscessus, the start codon should be AUG. The DtoStart distance from base-pairing between the Shine-Dalgarno (SD) sequence and the anti-SD sequence should be 14–16. The stop codon should be UAA. If UAG or UGA is used as a stop codon, they should be followed by nucleotide U. Start codon, SD, or stop codon should not be embedded in a secondary structure that may obscure the signals and interfere with their decoding. The optimization framework should be generally applicable to developing therapeutic phages against bacterial pathogens.

Port investments to address diversified risks under risk-sensitive behavior: Prevention or adaptation?

The hub location and key socioeconomic functions of seaports in the global supply chain make them vulnerable to the dual challenges of congestion and climate change. Appropriate scale and timing of prevention and adaptation can be targeted to reduce the probability of congestion and potential damage from climate change-related risks. We propose a two-period model to explore the optimal scale and timing of joint prevention and adaptation for port authorities (PAs) with risk-averse behavior. Our analysis shows that private PAs are better off making preventive efforts early and adaptive efforts late, while publicly owned PAs are better off making adaptive efforts early. Resource allocation for prevention and adaptation is strongly influenced by risk-sensitive behavior. As budgets increase, less risk-averse PAs prioritize congestion prevention followed by climate change adaptation, while more risk-averse PAs prioritize climate change adaptation followed by congestion prevention as budgets increase. The findings in this study provide significant managerial insights for decision-makers and policy-makers in ports and governments.

An adaptive time stepping scheme for rate-independent systems with nonconvex energy

Abstract We investigate a local incremental stationary scheme for the numerical solution of rate-independent systems. Such systems are characterized by a (possibly) nonconvex energy and a dissipation potential, which is positively homogeneous of degree one. Due to the nonconvexity of the energy, the system does in general not admit a time-continuous solution. In order to resolve these potential discontinuities, the algorithm produces a sequence of state variables and physical time points as functions of a curve parameter. The main novelty of our approach in comparison to existing methods is an adaptive choice of the step size for the update of the curve parameter, depending on a prescribed tolerance for the residua in the energy-dissipation balance, and in a complementarity relation concerning the so-called local stability condition. It is proven that, for tolerance tending to zero, the piecewise affine approximations generated by the algorithm converge (weakly) to a so-called parametrized balanced viscosity solution. Numerical experiments illustrate the theoretical findings, and show that an adaptive choice of the step size indeed pays off as they lead to a significant increase of the step size during sticking and in viscous jumps.

Second-order partitioned method and adaptive time step algorithms for the nonstationary Stokes-Darcy equations

Second-order partitioned method and adaptive time step algorithms for the nonstationary Stokes-Darcy equations

Optimal design and control loop selection for a STATCOM wide-area damping controller considering communication time delays

Wide-area damping controllers (WADC) have been shown to be effective in damping interarea oscillation modes. The varying nature of power system operating point requires the design of an adaptive WADC. Majority of the existing control design techniques rely on empirical approaches at one or more of their design stages, which makes it difficult to implement in adaptive control techniques. Therefore, the purpose of this study is to provide a systematic procedure based entirely on numerical approaches for designing a STATCOM-based WADC. The proposed procedure involving control loop selection, control design and adaptive time delay compensation, can then be implemented in adaptive WADC schemes. Geometric analysis has been used to choose the STATCOM location. Then, a novel optimization-based signal selection methodology is proposed. The proposed methodology employs a combination of multiple input signals to optimally generate a signal with the required modal objectives like enhancing observability and reducing interaction between different control loops. After that, another optimization problem is formulated to tune the controller parameters using integral of time multiplied by absolute error (ITAE) as the optimization objective. Both optimization problems have been evaluated using two optimization techniques: Particle swarm optimization (PSO) and self-adaptive multi-population elitist Jaya algorithm (SAMPE-JAYA). Finally, an adaptive delay compensator is designed to mitigate the effects of communication time delays. The proposed control strategy is applied on the IEEE 68-bus system and the controller performance is evaluated by modal analysis and multiple time-domain simulation scenarios.

A Next POI Recommendation Based on Graph Convolutional Network by Adaptive Time Patterns

Users’ activities in location-based social networks (LBSNs) can be naturally transformed into graph structural data, and more advanced graph representation learning techniques can be adopted for analyzing user preferences, which benefits a variety of real-world applications. This paper focuses on the next point-of-interest (POI) recommendation task in LBSNs. We argue that existing graph-based POI recommendation methods only consider user preferences from several individual contextual factors, ignoring the influence of interactions between different contextual information. This practice leads to the suboptimal learning of user preferences. To address this problem, we propose a novel method called hierarchical attention-based graph convolutional network (HAGCN) for the next POI recommendation, a technique which leverages graph convolutional networks to extract the representations of POIs from predefined graphs via different time patterns and develops a hierarchical attention mechanism to adaptively learn user preferences from the interactions between different contextual data. Moreover, HAGCN uses a dynamic preference estimation to precisely learn user preferences. We conduct extensive experiments on real-world datasets to evaluate the performance of HAGCN against representative baseline models in the field of next POI recommendation. The experimental results demonstrate the superiority of our proposed method on the next POI recommendation task.

Prediction of NOx emission concentration from coal-fired power plant based on joint knowledge and data driven

Accurate NOx concentration prediction is of great significance for the pollutant emission control and safe operation of coal-fired power plants. The global properties of the research object cannot be adequately described by a single data driven model, which hinders generalization performance. We propose a NOx emission concentration prediction method based on joint knowledge and data driven. First, we introduce a knowledge driven combined feature selection method to provide a global feature basis for data driven modeling. Second, we enable adaptive decomposition of the variational modal decomposition (VMD) using the modal energy difference and sample entropy. The method can extract deep time-frequency information in nonlinear and non-smooth features. Finally, we use the Informer combined with an adaptive time series segmentation method to predict NOx concentration. The experimental results indicate that the proposed method predicts the NOx concentration better than several comparative models.

FAST: A Forecasting Model With Adaptive Sliding Window and Time Locality Integration for Dynamic Cloud Workloads

The workload predictor has attracted attention as a key component of the proactive service operation management framework. However, the request and resource workloads of cloud applications are highly dynamic. Existing approaches decompose the original workload into trend, seasonal, and random components, establish models accordingly, and then combine all outputs to generate results. Indeed, the random component usually has significant heteroscedasticity and noise, having little or even a negative effect on model accuracy improvement. In our model, trend and seasonal components are seen as macro workload changes, and the micro workload changes are obtained by an adaptive sliding window algorithm. Therefore, we propose an ensembling model named FAST for Forecasting workloads with Adaptive Sliding window and Time locality integration. Notably, we propose an adaptive sliding window algorithm that considers trend correlation, time correlation, and random fluctuations of workload for online regression to achieve higher accuracy with lower overhead; and for the error-based integration strategy, we propose a time locality concept for local-predictor behavior and develop a multi-class regression algorithm for model integration. Finally, we conduct experiments on Google cluster trace datasets which show FAST has better accuracy than all other state-of-the-art models for dynamic workloads.

An Adaptive Multiple Backtracking UKF Method Based on Krein Space Theory for Marine Vehicles Alignment Process

The large misalignment errors of horizontal and azimuthal angle cannot be converged rapidly on the sea surface due to extra disturbance of linear and angular motions caused by wind and waves, which increases the time cost of marine vehicles alignment process and ultimately limits the SINS quick working capability. In order to solve the above problem, an adaptive multiple backtracking UKF method based on Krein space theory is proposed in this paper. Firstly, considering time-varying calibration errors of inertial sensors, a novel robust posterior filtering equation in Krein space is designed to improve the matching degree between the physical process and the mathematical model. Moreover, combined with the multiple forward and reverse navigation processes, an adaptive calculation times control method in backtracking UKF based on the innovation is constructed to solve the nonlinear fast alignment problem in the case of large misalignment angle errors with the purpose of further intelligently shortening the alignment time and ensuring alignment accuracy simultaneously in different periods and working environments. The physical experiment results demonstrate that the horizontal and azimuthal alignment accuracy of the proposed algorithm are improved compared with standard CKF alignment algorithm.

Time-course effects of different fiber-rich ingredients on energy values, microbiota composition and SCFA profile in growing pigs.

This study was to investigate time-course effects of different types of dietary fiber on the energy values, fecal microbiota and short-chain fatty acid (SCFA) concentration in growing pigs. A total of 24 barrows (initial body weight, 19.8±0.5kg) were assigned to 4 dietary treatments based on body weight (BW) in a completely randomized design, including a basal diet (CON) and 3 fiber-rich diets replacing corn, soybean meal and soybean oil in the CON diet with 20% sugar beet pulp (SBP), defatted rice bran (DFRB) or soybean hull (SBH), respectively. Fresh feces were sampled on d 7, 14 and 21, followed by 5d total feces and urine collections. The results showed that there were no differences in DE and ME between any of the fiber ingredients on d 7, 14 or 21. However, fiber inclusion decreased the DE and ME of the diet (P<0.05) regardless of the time effect. Principal coordinate analysis (PCoA) revealed distinctly different microbial communities on the DFRB diet and SBH diet across different times (P<0.05) and the fecal microbiota of the 4 diet groups demonstrated notably distinct clusters at each time point (P<0.05). With adaptation time increased from 7 to 21d, cellulose-degrading bacteria and SCFA-producing bacteria (e.g., Ruminococcaceae _UCG-014, Rikenellaceae _RC9_gut_group and Bifidobacterium) increased in the fiber inclusion diets, and pathogenic genera (e.g., Streptococcus and Selenomonas) were increased in the basal diet (P<0.05). Furthermore, the gut microbiota of growing pigs adapted more easily and quickly to the SBP diet compared to the DFRB diet, as reflected by the concentration of propionate, butyrate, isovalerate and total SCFA which increased with time for growing pigs fed the DFRB diet (P<0.05). Collectively, our results indicated at least 7d adaptation was required to evaluate the energy values of fiber-rich ingredients, as the hindgut microbiota of growing pigs may need more time to adapt to a high fiber diet, especially for insoluble dietary fiber.