Stream Types Research Articles

Do sediments of ephemeral and perennial streams show different impacts on water quality when subjected to the same drying conditions?

AbstractEmpirical evidence was studied to investigate whether ephemeral stream sediments have redeeming biological and physical attributes with respect to water quality, via a laboratory mesocosm study simulating stagnant pooled conditions in ephemeral and perennial streams. In addition to stream type, the effect of sediment quantity variation (sediment to water ratio) was also studied. From the water quality parameters tested (electrical conductivity, dissolved oxygen, pH, nitrogen species and phosphate), only electrical conductivity showed a significant difference between the two stream types irrespective of sediment to water ratio. However, the temporal water quality of a given stream type changed with sediment quantity in the mesocosm. Re‐flooding of mesocosms after complete drying did not result in blackwater conditions, but a similar spike in nutrient concentration was observed in both stream types. The absence of blackwater was attributed to the lack of addition of new organic matter and litter, as well as the saturation of dissolved oxygen in mesocosms, therefore, indicating that aeration and litter control could be used as mitigation methods for blackwater events. It was evident that water quality variations in ephemeral streams are purely based on the flow regime (hydrological flow conditions) and organic loading. No evidence was found for any unique biological and physical properties of ephemeral sediment that redeem water quality.

No Difference in Instream Decomposition Among Upland Agricultural and Forested Streams in Kenya

Expansion of agriculture is particularly worrying in tropical regions of the world, where native forests have been replaced by croplands and grasslands, with severe consequences for biodiversity conservation and ecosystem functioning. However, limited data exist on the effects of agriculture on the functioning of tropical streams. We conducted a leaf litter decomposition experiment in coarse- and fine-mesh litterbags using the three species of leaves (Eucalyptus globulus [non-native], Vernonia myriantha, and Syzygium cordatum [indigenous]) in three forested and agricultural streams to determine the effect of agriculture on instream leaf litter decomposition in headwater stream sites. We also examined the functional composition of macroinvertebrates in the streams through the contents of benthic kick samples. Agricultural streams had a less dense riparian canopy and smaller abundance of coarse organic particulate matter, and higher electric conductivity and suspended solids than forested streams. In terms of the effects of litter quality on decomposition rates, Vernonia had the fastest decomposition rates while Eucalyptus had the slowest in both forested and agricultural sites. Shredder invertebrates were less abundant in agricultural streams, and in both stream types, they were less diverse and abundant than other functional groups. Overall, leaf litter decomposition rates did not respond to agricultural land-use. The hypothesized negative effects of agriculture on organic matter processing were minimal and likely modulated by intact riparian zones along agricultural streams.

Effect of Particle Size and Shape on Wall Slip of Highly Filled Powder Feedstocks for Material Extrusion and Powder Injection Molding.

A necessity to distinguish between the influence of powder shape and size (particle size distribution) is especially demanding for highly filled metal powder feedstocks employed in additive manufacturing and powder injection molding. As their processability is evaluated through rheological behavior, the study focuses on the effect of powder size/shape on a wall slip, which is a typical phenomenon determining flow performance of these materials. Water and gas atomized 17-4PH stainless steel powders with D 50 of about 3 and 20 μm are admixed into a binder containing low-density polyethylene, ethylene vinyl acetate, and paraffin wax. Mooney analysis to intercept the slip velocity of 55 vol. % filled compounds reveals that wall slip effect appears to vary significantly with size and shape of metal powders-round shaped and large particles are the most prone to the wall slip. However, the evaluation is affected by the type of the flow streams resulting from the geometry of the dies-conical dies reduce the slip up to 60% in case of fine and round particles.

Online Bayesian learning for mixtures of spatial spline regressions with mixed effects

Classification and clustering methods based on univariate functions have been well developed. Recent work has extended the techniques to the domain of bivariate functions by incorporating the techniques based on mixtures of spatial spline regression with mixed-effects models. An Expectation Maximization (EM) algorithm is implemented to facilitate model inference. In this paper, we further extend the mixtures of spatial spline regression with mixed-effects model under the Bayesian framework to accommodate streaming image data. First, we derive a Markov chain Monte Carlo (MCMC) algorithm as an alternative approach to the EM algorithm to make inference on the model. However, MCMC is not scalable to streaming image data since it requires all observed information to update the posterior distribution of the parameters. To tackle this issue, we propose a sequential Monte Carlo (SMC) algorithm to analyse online fashion image data. The existence of model sufficient statistics improves the efficiency of the proposed online SMC algorithm. Instead of saving all batch data for inference, we only require storage of the model sufficient statistics and every data point is only used once, which is well suited for large-scale stream type data. In addition, the proposed algorithm provides an unbiased estimator of the marginal likelihood as a by-product of the approach, which can be used for model selection. Numerical experiments are used to demonstrate the effectiveness of our method. Our implementation is available at https://github.com/ShufeiGe/Online-Bayesian-learning-for-MMSRm.

The California Environmental Flows Framework: Meeting the Challenges of Developing a Large-Scale Environmental Flows Program

Environmental flow programs aim to protect aquatic habitats and species while recognizing competing water demands. Often this is done at the local or watershed level because it is relatively easier to address technical and implementation challenges at these scales. However, a consequence of this approach is that ecological flow criteria are developed for only a few areas as dictated by funding and interest with many streams neglected. Here we discuss the collaborative development of the California Environmental Flows Framework (CEFF) as an example process for developing environmental flow recommendations at a statewide scale. CEFF uses a functional flows approach, which focuses on protecting a broad suite of ecological, geomorphic, and biogeochemical functions instead of specific species or habitats, and can be applied consistently across diverse stream types and spatial scales. CEFF adopts a tiered approach in which statewide models are used to estimate ecological flow needs based on natural functional flow ranges, i.e., metrics that quantify the required magnitude, timing, duration, frequency, and/or rate-of-change of functional flow components under reference hydrologic conditions, for every stream reach in the state. Initial flow needs can then be revised at regional, or watershed, scales based on local constraints, management objectives, and available data and resources. The third tier of CEFF provides a process for considering non-ecological flow needs to produce a final set of environmental flow recommendations that aim to balance of all desired water uses. CEFF was developed via a broad inclusive process that included technical experts across multiple disciplines, representatives from federal and state agencies, and stakeholders and potential end-users from across the state. The resulting framework is therefore not associated with any single agency or regulatory program but can be applied under different contexts, mandates and end-user priorities. The inclusive development of CEFF also allowed us to achieve consensus on the technical foundations and commitment to applying this approach in the future.

Variation in processes structuring fish assemblages as inferred from metacommunity analyses: Differences between headwater and adventitious streams within a river network

Community assembly is generally affected by the spatial position of local patches. Examining metacommunity processes and structures in small streams with different river network positions (e.g., headwater streams in the periphery of river networks vs adventitious streams in relatively central positions) may help us better understand how spatial position influences metacommunity structuring. In this study, based on data collected from sampling 24 headwater and 15 adventitious stream sites in the Xin’an Basin, China, we examined whether and how fish metacommunity processes and structures differed between the 2 stream types. We used distanced-based redundancy analysis coupled with forward selection to assess the relative roles of environmental and spatial factors in metacommunity processes, and we applied the elements of metacommunity structure analysis, extended by using different ordinations, to identify metacommunity structures. We found that the 2 types of streams were similar in environmental heterogeneity, but fish assemblages in adventitious streams were more dissimilar to one another than were assemblages in headwater streams and had more riverine immigrants. The amount of among-stream variation in species composition was low in both stream types. In headwater streams, assemblage–environment relationships were stronger than assemblage–space relationships, but we found the opposite pattern in adventitious streams. Species turnover was strong in both headwater and adventitious streams. However, assemblages in headwater streams showed both nested and quasi-nested structures, whereas the assemblages in adventitious streams were more consistent with Clementsian structure. Our results suggest that different metacommunity processes may occur in small streams with distinct river network positions and that extended elements of metacommunity structure analysis can help identify the underlying mechanisms that produce metacommunity structure. The effect of spatial position within river networks on stream fish metacommunity structuring appears to be weak, but our analyses do imply that river immigrants can influence species composition in adventitious streams to some extent.

Basin‐wide hydromorphological analysis of ephemeral streams using machine learning algorithms‡

AbstractSustainable river management now encompasses a much wider concept that includes hydromorphological and fluvial habitat studies. In ephemeral streams, the geomorphological characterization of channels is complex due to episodic flows and riparian vegetation dynamics. Stream channel survey and classification at the watershed scale provide the basis for geomorphological conservation, process interpretation, assessing sensitivity to disturbance, and identifying reaches that supply and store sediment. Here, we present a stream classification based on a two‐step approach: (1) automatic river segmentation based on spatial variability in channel/valley morphology from topographic measurements (LiDAR, light, detection and ranging), and (2) fluvial landform and vegetation density mapping derived from multispectral open‐source satellite images (Sentinel‐2) using support vector machine (SVM) and Random Forest (RF) algorithms. These analyses provide continuous, quantitative spatial values of geometric (channel/valley width, slope gradient, and route distance), landform (active channel and gravel bars with five densities of vegetation cover), and hydraulic (specific stream power) variables. Four stream types were identified in the Rambla de la Viuda catchment (~1500 km2), an ephemeral gravel‐bed river in eastern Spain. The spatial distribution of channel types is explained by differences in geometry (active channel width, valley width, and slope gradient) and a hydraulic parameter (specific stream power). The landforms/vegetation patterns provided insight on causal relationships between erosion and deposition processes during high flow periods and the time since the most recent large disruptive flood event. Channel type distribution provided first‐order predictions about the location of reaches that supply and store sediment and thus information on sediment continuity along the river. Dam effects on downstream reaches resulted in geomorphological disequilibrium, producing narrowing of the active channel, slope reduction, and a decrease of gravel bar areal extension. The proposed catchment scale analysis provides a comprehensive and replicable methodology for environmental planning in Mediterranean ephemeral streams to guide further hydromorphological surveys at the reach scale.

Untangling multi‐scale habitat relationships of an endangered frog in streams to inform reintroduction programs

AbstractSuccessful reintroductions of endangered species rely on comprehensive knowledge of habitat requirements across spatial and temporal scales. To inform designs of reintroduction programs, we studied habitat selection of the federally endangered Sierra Nevada yellow‐legged frog (Rana sierrae) in streams in its northern range. We quantified multi‐scale habitat use across diverse streams, habitat types within streams, and seasonal flows. In one intermittent stream, we compared habitat selection between wild and captive‐reared frogs that were released as a population augmentation. We analyzed habitat selection of seasonal habitat unit types (e.g., pool, riffle, cascades) and dimensions, and microhabitat hydrology and cover. R. sierrae appeared able to meet its ecological requirements in a variety of habitats. Study streams ranged from third‐order perennial streams to first‐order intermittent headwater creeks. Perennial streams retained a variety of habitats across flows whereas intermittent streams dried to just a few pools. Frogs used all seasonal habitat types but selected deeper pools most often relative to available habitats in intermittent streams and selected riffles or showed no preference in perennial streams. Frogs avoided fast deep‐water microhabitats, but, otherwise, preferred flowing water or deeper water without flow. Our results suggest that diverse streams can be considered candidates for reintroductions. Within intermittent streams, deeper perennial pools may provide more stable release sites, whereas non‐pool habitats may be safer in perennial streams with fish. Importantly, captive‐reared frogs selected similar habitats as wild frogs, suggesting that captivity does not alter habitat selection behaviors post‐release and that reintroduction designs for this species can be based on wild frog preferences. By improving our understanding of how habitat use varies among different types of streams and differs seasonally in response to changing habitat conditions, our results inform the design of effective reintroduction programs for frogs in streams.

Towards zero carbon dioxide concentration in sweet natural gas product from amine sweetening plant

This work presents a two-step method to reduce CO2 concentration of sweet natural gas product from amine sweetening plant via amine blending (Step 1) followed by minor process modification (Step 2). In Step 1, an industrial natural gas sweetening plant was simulated using Aspen HYSYS and the simulation results were validated against the plant data. Afterwards, different blends of methyl diethanolamine and monoethanolamine (MDEA-MEA) and methyl diethanolamine and diethanolamine (MDEA-DEA) were investigated. Then the optimum amine blend of 28 wt.% MDEA and 10 wt.% MEA was reported. The optimum amine blend achieved a significant reduction in CO2 concentration of sweet natural gas of 99.9% compared to the base case (plant data). In Step 2, two types of amine stream splits, i.e., lean amine stream split and semi-lean amine stream split were studied. The study covered split stream amount, absorber recycle stage, and regenerator stage withdrawal. Both types of stream splits attained a significant reduction in CO2 concentration of sweet natural gas product and amine circulation rate compared to Step 1. However, the semi-lean amine stream split was superior to lean amine split with 69.1% and 63.6% reduction in CO2 concentration of sweet natural gas and lean amine circulation rate, respectively.

MOTIVATIONS AND EXPECTATIONS FOR VIRTUAL GIFT-GIVING IN DOUYIN LIVE STREAMS

Mobile live streaming recently advanced as popular form of content creation for streamers on short-video apps like Douyin and TikTok to make money through virtual gifts from viewers. Virtual gifts are primarily used to reward streamers for their content or performance in a live stream, yet on Douyin social aspects seem to be more crucial for virtual gifting. We are looking at three types of live streams by young male hosts (= streamers) on Douyin to analyze the motivations of female viewers to watch and make virtual gifts to the hosts and to explore resulting power relations between hosts and viewers. We conducted 12 semi-structured interviews with female of all three live streams who sent virtual gifts to hosts. We find female viewers send virtual gifts, for example, because they wanted to make the host happy or to help them achieve an assigned virtual gifts task. We did not find aspects of power imbalances regarding the monetary or social value of virtual gifts. While interviewees do not expect anything from the hosts in return for their virtual gifts, they appreciate online gestures, like a host blowing a kiss. In contrast, we observed viewers send virtual gifts to help a host win a live stream competition which holds implicit expectations. Overall, we find female viewers of male-hosted Douyin live streams mainly use virtual gifts as a social means of appreciation and support for friendship in a parasocial online interaction.

An overview of complex data stream ensemble classification

With the rapid development of information technology, data streams in various fields are showing the characteristics of rapid arrival, complex structure and timely processing. Complex types of data streams make the classification performance worse. However, ensemble classification has become one of the main methods of processing data streams. Ensemble classification performance is better than traditional single classifiers. This article introduces the ensemble classification algorithms of complex data streams for the first time. Then overview analyzes the advantages and disadvantages of these algorithms for steady-state, concept drift, imbalanced, multi-label and multi-instance data streams. At the same time, the application fields of data streams are also introduced which summarizes the ensemble algorithms processing text, graph and big data streams. Moreover, it comprehensively summarizes the verification technology, evaluation indicators and open source platforms of complex data streams mining algorithms. Finally, the challenges and future research directions of ensemble learning algorithms dealing with uncertain, multi-type, delayed, multi-type concept drift data streams are given.

Numerical study of enhanced Rayleigh streaming in resonant cylindrical shells

We have numerically demonstrated a type of enhanced Rayleigh streaming in a resonant cylindrical shell (CS) by using the limiting velocity method. It is found that the main characteristics of the resonant streaming field resemble those of classical Rayleigh streaming in a rigid cylindrical cavity. Nevertheless, the maximum velocity of the demonstrated streaming in the resonant CS is significantly higher than that of classical Rayleigh streaming in a rigid cylindrical cavity under the same acoustic conditions. In addition, the velocity of the demonstrated streaming at the low resonant frequency is higher than that at the high resonant and non-resonant frequencies in the shell. The enhancement of the demonstrated streaming originates from the resonant excitation of non-leaky circumferential modes intrinsic to the CS, characterizing with the enhanced gradient acoustic fields in the viscous boundary layer around its inner surface. This type of streaming may be of interest for acoustofluidic devices in many applications such as particle manipulation and liquid mixing.

CO2 capture using ionic liquid-based hybrid solvents from experiment to process evaluation

The CO2 absorption capacity in three hybrid solvents based on butyl-3-methylimidazolium acetate ([Bmim][OAc]) and three different cosolvents (Dimethyl ethers of polyethylene glycol (DEPG250), propylene carbonate, and water) was investigated and compared, and [Bmim][OAc]-DEPG250 shows the highest CO2 absorption capacity. The effects of the mass ratio of [Bmim][OAc]-DEPG250 and temperature on their CO2 absorption capacity, density, and viscosity were further investigated. In addition, the absorption capacities of N2 and CO2 in [Bmim][OAc]-DEPG250 with the simulated flue gas as a feed gas were studied and compared with that using the pure gas as a feed gas. Thermodynamic models were used to represent the experimental data, and then process simulation and evaluation were carried out. The results show that the addition of DEPG250 dramatically decreases the viscosity, while the absorption capacity of hybrid solvents is still comparable with pure [Bmim][OAc]. The type of gas stream, that is, pure gas or gas mixture, has a negligible effect on the N2 and CO2 absorption capacity. The simulation results show that [Bmim][OAc]-DEPG250 only utilizes less than 50% of the heating duty of aqueous amine solution because of the low-pressure desorption and preheating with waste heat in this hybrid solvent-based process. The CO2 capture cost of using this [Bmim][OAc]-DEPG250 reduces by 11% compared with that of using aqueous amine solution due to the significant decrease (by 52%) in utility cost.

Bidirectional LSTM with saliency-aware 3D-CNN features for human action recognition

Deep convolutional neural network (DCNN) and recurrent neural network (RNN) have been proved as an imperious research area in multimedia understanding and obtained remarkable action recognition performance. However, videos contain rich motion information with varying dimensions. Existing recurrent based pipelines fail to capture long-term motion dynamics in videos with various motion scales and complex actions performed by multiple actors. Consideration of contextual and salient features is more important than mapping a video frame into a static video representation. This research work provides a novel pipeline by analyzing and processing the video information using a 3D convolution (C3D) network and newly introduced deep bidirectional LSTM. Like popular two-stream convent, we also introduce a two-stream framework with one modification; that is, we replace the optical flow stream by saliency-aware stream to avoid the computational complexity. First, we generate a saliency-aware video stream by applying the saliency-aware method. Secondly, a two-stream 3D-convolutional network (C3D) is utilized with two different types of streams, i.e., RGB stream and saliency-aware video stream, to collect both spatial and semantic temporal features. Next, a deep bidirectional LSTM network is used to learn sequential deep temporal dynamics. Finally, time-series-pooling-layer and softmax-layers classify human activity and behavior. The introduced system can learn long-term temporal dependencies and can predict complex human actions. Experimental results demonstrate the significant improvement in action recognition accuracy on different benchmark datasets.

A Comparative Study of Self Confidence among Tribal and Non-tribal 12th Students

The Research aims to find out the self-confidence among gender, types of ethnicity and types of streams. So, investigator selected three groups are gender second types of ethnicities and last one is types of stream, all groups have 360 peoples. Data were collected from Tapi districts. Scale was use for data collection is personal datasheet and self confidence scale developed by Dr Rekha Gupta in 2005. 2x2x3 factorial design was used and data were analysis by ‘F’ test. Result show, there is significant effect on self confidence among male and female students (A). The Non-tribal student’s self-confidence is better than tribal Students (B). There is significant effect on self confidence among arts, science and commerce students(C). The interaction between gender and types of ethnicities was significant effect on self confidence (AxB). The interaction between Gender and Types of Streams was no significant effect on self confidence (AxC). The interaction between types of ethnicity and types of streams was significant effect on self confidence (BxC). The interaction between gender, types of ethnicity and types of streams was significant effect on self confidence (AxBxC).

Technical note: Modified simple average internal rate of return

One of the most attractive indexes to select the best investment decision is internal rate of return (IRR) measure, but it has some significant faults both in practice and theoretically. Multiple attempts have been made to resolve the IRR challenges, but none of them are perfect. This article presents the Modified Simple Average Internal Rate of Return criterion as a profitability index for calculating a unique rate of return for all various types of cash flow streams so that obtained results are consistent with the net present value method in accept/reject decisions. The presented method is simple to compute and is capable of resolving all known IRR defects including when the resulting rate is greater than −1.

Модель процесса доставки пакетов по каналу передачи данных в условиях компьютерных атак нарушителя

Objective: To analyze the impact of computer attacks on the performance quality of data transmission channels and channeling systems. It is also necessary to take into account the capabilities of an intruder to introduce malware into channeling systems when committing a computer attack. Methods: To determine the required design ratios, several options for setting various distribution functions characterizing the parameters used as input data and types of inbound streams have been considered, taking into account the parameters of the intruder’s computer attack model set by the values of the probability of successful attack. Mathematical modeling is carried out using the method of topological transformation of stochastic networks. The exponential, momentum and gamma distributions are considered as distribution functions of random variables. The solutions are presented for inbound streams corresponding to the Poisson, Weibull, and Pareto models. Results: The proposed approach makes it possible to assess the performance quality of data transmission channels in the context of computer attacks. These assessments make it possible to analyze the state and develop guidelines for improving the performance quality of communication channels against the destructive information impact of the intruder. Various variants of the functions of random variables distribution and various types of the inbound stream were used for modeling, making it possible to compare them, as well as to assess the possibility of using them in channels that provide users with different services. Practical importance: The modeling results can be used to build communication management decision support systems, as well as to detect attempts of unauthorized access to the telecommunications resource of transportation management systems. The proposed approach can be applied in the development of threat models to describe the capabilities of the intruder (the ‘Intruder Model’).

Effects of streamflow isotope sampling strategies on the calibration of a tracer‐aided rainfall‐runoff model

AbstractIsotopes are increasingly used in rainfall‐runoff models to constrain conceptualisations of internal catchment functioning and reduce model uncertainty. However, there is little guidance on how much tracer data is required to adequately do this, and different studies use data from different sampling strategies. Here, we used a 7‐year time series of daily stable water isotope samples of precipitation and streamflow to derive a range of typical stream sampling regimes and investigate how this impacts calibration of a semi‐distributed tracer‐aided model in terms of flow, deuterium and flux age simulations. Over the 7 years weekly sampling facilitated an almost identical model performance as daily, and there were only slight deteriorations in performance for fortnightly sampling. Monthly sampling resulted in poorer deuterium simulations and greater uncertainty in the derived parameter sets ability to accurately represent catchment functioning, evidenced by unrealistic reductions in the volumes of water available for mixing in the saturation area causing simulated water age decreases. Reducing sampling effort and restricting data collection to 3 years caused reductions in the accuracy of deuterium simulation, though the deterioration did not occur if sampling continued for 5 years. Analysis was also undertaken to consider the effects of reduced sampling effort over the driest and wettest hydrological years to evaluate effects of more extreme conditions. This showed that the model was particularly sensitive to changes in sampling during dry conditions, when the catchment hydrological response is most non‐linear. Across all dataset durations, sampling in relation to flow conditions, rather than time, revealed that samples collected at flows >Q50 could provide calibration results comparable to daily sampling. Targeting only extreme high flows resulted in poor deuterium and low flow simulations. This study suggests sufficient characterization of catchment functioning can be obtained through reduced sampling effort over longer timescales and the targeting of flows >Q50.

Can Multipath TCP Be Robust to Cyber Attacks? A Measuring Study of MPTCP with Active Queue Management Algorithms

With the development of social networks, more and more mobile social network devices have multiple interfaces. Multipath TCP (MPTCP), as an emerging transmission protocol, can fit multiple link bandwidths to improve data transmission performance and improve user experience quality. At the same time, due to the large-scale deployment and application of emerging technologies such as the Internet of Things and cloud computing, cyber attacks against MPTCP have gradually increased. More and more network security research studies point out that low-rate distributed denial of service (LDDoS) attacks are relatively popular and difficult to detect and are recognized as one of the most severe threats to network services. This article introduces six classic queue management algorithms: DropTail, RED, FRED, REM, BLUE, and FQ. In a multihomed network environment, we perform the performance evaluation of MPTCP under LDDoS attacks in terms of throughput, delay, and packet loss rate when using the six algorithms, respectively, by simulations. The results show that in an MPTCP-enabled multihomed network, different queue management algorithms have different throughput, delay, and packet loss rate performance when subjected to LDDoS attacks. Considering these three performance indicators comprehensively, the FRED algorithm has better performance. By adopting an effective active queue management (AQM) algorithm, the MPTCP transmission system can enhance its robustness capability, thus improving transmission performance. We suggest that when designing and improving the queue management algorithm, the antiattack performance of the algorithm should be considered: (1) it can adjust the traffic speed by optimizing the congestion control mechanism; (2) the fairness of different types of data streams sharing bandwidth is taken into consideration; and (3) it has the ability to adjust the parameters of the queue management algorithm in a timely and accurate manner.

Long short-term memory self-adapting online random forests for evolving data stream regression

Evolving data stream, especially with concept drift is generally accepted as a challenging data type for regression task, because it usually makes machine learning models trained on old data not adapting to new data, and leads to dramatic performance degradation as a result. Moreover, the behavior of a data stream may change in different modes and therefore introduces various concept drifts, e.g., abrupt, incremental, gradual, recurring, even more, complex concept drifts. Although there are some algorithms that can adapt to stationary data streams or a specific type of concept drift in non-stationary data streams, a wide range of practical applications call for machine learning regression models to handle multi-type of data streams. In this work, we propose an online learning strategy called adaptive long and short-term memories online Random Forests regression(ALSM-RFR), where an adaptive memory activation mechanism is designed to make the model switch adaptively between long-term and hybrid (long-term plus short-term) memory modes in the face of stationary data streams or non-stationary data streams with different types of concept drift. In particular, leaf and tree weights in random forests are used to learn information at different timescales, namely, long-term and short-term memories. Moreover, we devise an adaptive memory activation mechanism to formulate the switch decision of memory modes as a classification problem. Numerical experiments show remarkable improvements of the proposed method in the adaptability of stream types and predictive accuracy in data streams across several real datasets and synthetic datasets, compared to the state-of-the-art online approaches. Besides, the convergence and the influence of the parameters involved in our method are evaluated.