Dynamic Streaming Research Articles

Antecedent Hydrologic Conditions Reflected in Stream Lithium Isotope Ratios During Storms

AbstractAntecedent hydrological conditions are recorded through the evolution of dissolved lithium isotope signatures (Li) by juxtaposing two storm events in an upland watershed subject to a Mediterranean climate. Discharge and Li are negatively correlated in both events, but mean Li ratios and associated ranges of variation are distinct between them. We apply a previously developed reactive transport model (RTM) for the site to these event‐scale flow perturbations, but observed shifts in stream Li are not reproduced. To reconcile the stability of the subsurface solute weathering profile with our observations of dynamic stream Li signatures, we couple the RTM to a distribution of fluid transit times that evolve based on storm hydrographs. The approach guides appropriate flux‐weighting of fluid from the RTM over a range of flow path lengths, or equivalently fluid residence times. This flux‐weighted RTM approach accurately reproduces dynamic storm Li‐discharge patterns distinguished by the antecedent conditions of the watershed.

Fish and macroinvertebrates respond differently to seasonal drying in tropical non‐perennial streams

AbstractSurface water drying challenges the persistence of lotic biological assemblages in non‐perennial streams and rivers. However, the effects of natural hydrological disturbances on an assemblage depend on individual species characteristics. In this study, we investigated the structure and association of fish and macroinvertebrate assemblages during two phases in non‐perennial streams (flowing vs. disconnected pools). We sampled fish and macroinvertebrate assemblages in five non‐perennial stream reaches located in the Caatinga (Brazilian semi‐arid region). Fish species were resistant to hydrological dynamics in the streams. On the other hand, some macroinvertebrate taxa from the flowing phase disappeared, and new taxa colonized the disconnected pools. The absence of lotic insects following flow cessation and the colonization of disconnected pools by lentic taxa facilitated macroinvertebrate assemblages persistence in these dynamic streams. Our study showed that fish and macroinvertebrate assemblages respond differently to the same natural flow cessation, which leads us to predict that flow changes will yield different assemblages, depending on the taxa. Thus, it is crucial to consider multi‐assemblage responses to effectively manage and conserve non‐perennial stream ecosystems in a tropical semi‐arid region.

Dynamic stream partitioning for time-triggered traffic in Time-Sensitive Networking

Time-Sensitive Networking (TSN) is a promising network technology that can ensure bounded latency and jitter for industrial real-time scenarios. It establishes the IEEE 802.1 Qbv standard to precisely control the periodic transmission time of the time-triggered (TT) traffic. However, it is still a challenge to efficiently schedule the TT traffic in large-scale networks with high transmission performance. In this paper, we propose a novel method called dynamic stream partitioning (DSP) to solve this problem. It satisfies the IEEE 802.1 Qbv standard and can significantly reduce the scheduling time by reducing the number of constraints. Based on the DSP method, we analyze the impact of stream partitioning on the transmission performance of the TT traffic, and propose an indicator called Normalized Degradation of Performance (NDOP) to quantify it dynamically. Furthermore, we design a partitioning-aware dynamic routing (PADR) to expand the scheduling space. Integrated with NDOP and PADR, we propose a joint routing and dynamic stream partitioning (JR/DSP) algorithm. Extensive simulation experiments verify that the proposed JR/DSP algorithm has higher scalability, transmission performance, and schedulability compared to the static stream partitioning algorithms.

Multi-Resolution Odd Sketch for Mining Extended Jaccard Similarity of Dynamic Streaming sets

Estimating the Jaccard similarity between two or more streaming sets is a fundamental problem with many applications, such as mining the co-occurrences of host communication behaviors in IP networks and the co-purchasing behaviors of online users in E-commence systems. For a “streaming” data set, its elements arrive sequentially at the stream processing engine, whose working memory is limited in size. To meet the resource constraint, the data set must be stored as a compressed format called a “sketch”, so that millions or even billions of such sets can be held in the memory of the engine. To strike a balance between memory cost and similarity estimation accuracy, many sketching algorithms were proposed, such as MinHash, HyperLogLog and theta sketch. As far as we know, most of previous solutions fail to handle the fully dynamic streaming sets that allow both the insertion and the deletion of elements. Although PCSA <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> , and virtual odd sketch (VOS) partially solves the deletion problem, their memory efficiency and estimation accuracy can be fundamentally improved. In this paper, we propose a multi-resolution odd sketch (MROS), which allows more accurate similarity estimation with less memory consumption. Its design is to encode a streaming set into multiple layers of odd sketches with exponentially reducing sampling probabilities. No matter a set is small or large, we can pick a suitable sampling probability to accurately estimate its cardinality. Next, we present an algorithm to estimate the extended Jaccard similarity of multiple streaming sets. Their compressed MROS summaries are merged by bitwise XOR, which in turn helps estimate the cardinality of symmetric difference of the multiple sets. Then, using these estimated cardinalities as observations, we estimate the size of an arbitrary set expression, which is connected by union <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\bigcup$</tex-math></inline-formula> , intersection <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\bigcap$</tex-math></inline-formula> , relative complement <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\setminus$</tex-math></inline-formula> , and symmetric difference <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\Delta$</tex-math></inline-formula> . Our evaluation results show that its accuracy outperforms MinHash, KMV, PCSA <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> and VOS, and it can support element deletion.

Online Boosting Adaptive Learning under Concept Drift for Multistream Classification

Multistream classification poses significant challenges due to the necessity for rapid adaptation in dynamic streaming processes with concept drift. Despite the growing research outcomes in this area, there has been a notable oversight regarding the temporal dynamic relationships between these streams, leading to the issue of negative transfer arising from irrelevant data. In this paper, we propose a novel Online Boosting Adaptive Learning (OBAL) method that effectively addresses this limitation by adaptively learning the dynamic correlation among different streams. Specifically, OBAL operates in a dual-phase mechanism, in the first of which we design an Adaptive COvariate Shift Adaptation (AdaCOSA) algorithm to construct an initialized ensemble model using archived data from various source streams, thus mitigating the covariate shift while learning the dynamic correlations via an adaptive re-weighting strategy. During the online process, we employ a Gaussian Mixture Model-based weighting mechanism, which is seamlessly integrated with the acquired correlations via AdaCOSA to effectively handle asynchronous drift. This approach significantly improves the predictive performance and stability of the target stream. We conduct comprehensive experiments on several synthetic and real-world data streams, encompassing various drifting scenarios and types. The results clearly demonstrate that OBAL achieves remarkable advancements in addressing multistream classification problems by effectively leveraging positive knowledge derived from multiple sources.

Real-Time Twitter Trends Analysis Using Latent Dirichlet Allocation and Machine Learning

As far as social media is concerned twitter has become a major source of public data in the form of tweets. Twitter is an emerging source of large textual data(big data).People can easily express their opinions , reviews, interests and tastes about a particular event, topic ,product etc., occurring worldwide. This makes twitter a good source of valuable data which can be further used to perform sentiment analysis, know trending topics of public intrests and public opinion on a particular product or event which can be beneficial for business growth and political parties to know public choices and they can take actions accordingly. The extraction of meaningful insights from the vast and dynamic stream of social media data is greatly facilitated by the implementation of topic modeling and sentiment analysis on Twitter tweets. The identification of recurring themes or subjects within a collection of tweets, which is the essence of topic modeling, serves to reveal patterns in discussions, thereby aiding in the comprehension of prevalent topics and trends. Concurrently, sentiment analysis, through the evaluation of the emotional tone of tweets, enables the discernment of whether the expressed sentiments are positive, negative, or neutral. The combination of these techniques provides researchers and businesses with valuable perspectives on public opinions, emerging issues, and user sentiments, thereby empowering them to make informed decisions and develop effective engagement strategies in the ever-evolving landscape of social media. But twitter data(tweets) are unstructured ,contains noisy data, urls , stop words, re-tweets video ,emoji etc., which need to be cleaned and preprocessed to perform proper sentiment analysis and use it to extract meaningful effective insights from it. This paper focuses on various methods of topic modeling and discovering latent topics ,text-mining approaches ,micro-blogging methods used in various researches .This paper focuses on latent dirichlet allocation method of topic modeling and text-mining to discover latent topics in tweets, micro-blogging ,text-mining approaches .A proper survey is done on previous researches on this topic in this article. Keywords:-LDA, text-mining, topic modeling, micro-blogging ,machine learning ,sentiment analysis, NLP.

Linking Dynamic Water Storage and Subsurface Geochemical Structure Using High‐Frequency Concentration‐Discharge Records

AbstractShifts in water fluxes and chemical heterogeneity through catchments combine to dictate stream solute export from the Critical Zone. The ways in which these factors emerge in resultant concentration‐discharge (C‐Q) relationships remain obscure, particularly at the timescale of individual precipitation and discharge events. Here we take advantage of a new high‐frequency, multi‐element and multi‐event stream C‐Q data set. The stream solute concentrations of seven major ions were recorded every 40 min over five flood events spanning one hydrologic year in a French agricultural watershed (Orgeval) using a lab‐in‐the‐field deployment we refer to as a “River Lab.” We focus attention on the recession periods of these events to consider how geochemical heterogeneity within the catchment translates into dynamic stream solute concentrations during shifts in water storage. We first show that for C‐Q relationships resulting from data acquisition over multiple flood events, lumping all trends together can lead to biases in characteristic C‐Q parameters. We then reframe C‐Q relationships using a simple recession curve analysis to consider how hydrological processes produce chemical mixing of distinct solute pools immediately following discharge events. We find three distinct classes of behavior among the major solutes, none of which can be interpreted based on water storage changes alone. The shape of C‐Q relationships for each solute can then be related to their vertical zonation in the subsurface of Orgeval, and to the capacity for subcomponents of these distributions to be readily mobilized during a discharge event.

Revisiting maximum satisfiability and related problems in data streams

We revisit the maximum satisfiability problem (Max-SAT) in the data stream model. In this problem, the stream consists of m clauses that are disjunctions of literals drawn from n Boolean variables. The objective is to find an assignment to the variables that maximizes the number of satisfied clauses. Chou et al. (FOCS 2020) showed that Ω(n) space is necessary to yield a 2/2+ε approximation of the optimum value; they also presented an algorithm that yields a 2/2−ε approximation of the optimum value using O(ε−2log⁡n) space.In this paper, we not only focus on approximating the optimum value, but also on obtaining the corresponding Boolean assignment using sublinear o(mn) space. We present randomized single-pass algorithms that w.h.p.1 yield:•A 1−ε approximation using O˜(n/ε3) space and exponential post-processing time•A 3/4−ε approximation using O˜(n/ε) space and polynomial post-processing time. Our ideas also extend to dynamic streams. However, we show that the streaming k-SAT problem, which asks whether one can satisfy all size-k input clauses, must use Ω(nk) space.We also consider the related minimum satisfiability problem (Min-SAT), introduced by Kohli et al. (SIAM J. Discrete Math. 1994), that asks to find an assignment that minimizes the number of satisfied clauses. For this problem, we give a O˜(n2/ε2) space algorithm, which is sublinear when m=ω(n), that yields an α+ε approximation where α is the approximation guarantee of the offline algorithm. If each variable appears in at most f clauses, we show that a 2fn approximation using O˜(n) space is possible.Finally, for the Max-AND-SAT problem where clauses are conjunctions of literals, we show that any single-pass algorithm that approximates the optimal value up to a factor better than 1/2 with success probability at least 2/3 must use Ω(mn) space.

Low hyporheic denitrification in headwater streams revealed by nutrient injections and in situ gas measurements

Stream networks can retain or remove nutrient pollution, including nitrate from agricultural and urban runoff. However, assessing the location and timing of nutrient uptake remains challenging because of the hydrological and biogeochemical complexity of dynamic stream ecosystems. We used a novel approach to continuously characterize the biological activity in a stream with in situ measurement of dissolved gases by membrane inlet mass spectrometry (MIMS). In a headwater stream in western France, we compared in situ measurements of O2, CO2, N2, and N2O (the main gases associated with respiration, including denitrification) with more traditional laboratory incubations of collected sediment. The in situ measurements showed near-zero denitrification in the stream and the hyporheic zone. However, the laboratory incubations showed a low but present denitrification potential. This demonstrates how denitrification potential is not necessarily expressed in field hydrological and geochemical conditions. In situ measurements are thus crucial to quantify expressed rates of nutrient removal. Broader application of in situ gas measurement based on technologies such as MIMS could enhance our understanding of the spatiotemporal distribution of stream and hyporheic processes and overall nutrient retention at stream network scales.

An Ensemble Learning Approach for Fast Disaster Response using Social Media Analytics

Natural disaster happens, as a result of natural hazards that cause financial, environmental or human losses. Natural disasters strike unexpectedly, affecting the lives of tens of thousands of people. During the flood, social media sites were also heavily used to disseminate information about flooded areas, rescue agencies, food and relief centres. This work proposes an ensemble learning strategy for combining and analysing social media data in order to close the gap and progress in catastrophic situation. To enable scalability and broad accessibility of the dynamic streaming of multimodal data namely text, image, audio and video, this work is designed around social media data. A fusion technique was employed at the decision level, based on a database of 15 characteristics for more than 300 disasters around the world (Trained with MNIST dataset 60000 training images and 10000 testing images). This work allows the collected multimodal social media data to share a common semantic space, making individual variable prediction easier. Each merged numerical vector(tensors) of text and audio is sent into the K-CNN algorithm, which is an unsupervised learning algorithm (K-CNN), and the image and video data is given to a deep learning based Progressive Neural Artificial Search (PNAS). The trained data acts as a predictor for future incidents, allowing for the estimation of total deaths, total individuals impacted, and total damage, as well as specific suggestions for food, shelter and housing inspections. To make such a prediction, the trained model is presented a satellite image from before the accident as well as the geographic and demographic conditions, which is expected to result in a prediction accuracy of more than 85%.

The gamut of artificial intelligence in oculoplasty

Artificial intelligence (AI) is taking its grasp over health-care system and ophthalmology as one of the most dynamic streams is largely influenced by AI. AI over the past few decades has made a huge impact in the bailiwick of oculoplasty. AI-based imaging softwares have made easier the diagnosis and management of several orbital and eyelid pathologies by its accuracy and reproducibility. AI also has made possible real-time tracking of deep orbital structures through navigation-guided technologies which have made orbital surgeries safer and easier. This article is a meta-analysis of several articles which have discussed applications and impact of AI-based software in diagnosis and management planning of periorbital and eyelid pathologies and also articles on navigation-guided orbital surgeries.

Streamline Effect Improvement of Additive Manufactured Airfoil Utilizing Dynamic Stream Control Procedure

In the era of fast transport, to create inventive stream flow management solutions that are capable of diminishing the aerodynamic drag of the vehicles, there is a need to modify the flow characteristics over the vehicle by deferring or expelling the position of the flow partition. The objective of this study involves the parameterized design of an airfoil utilizing the Bezier curve technique with the assistance of the simulation program. For flow regulations, synthetic jet modules are ingrained at different percentages of the chord to manage the stall characteristics. The parametrization system, combined with the stream control method, can give a much better insight into flow re-energization and pave some way for the reduction of the wake. Digital fabrication technique (3d printing or Rapid Prototyping method) is used to fabricate the end product for aerodynamic testing. The comparative outcome showed a reduction in drag at certain angles of attack due to the surface finish obtained. By comparing the results, the aerodynamic efficiency showed a significant rise of 13.05% at lower angles of attack when compressed gas was used in the synthetic jet closer to the frontier edge of the airfoil. Near the stall angle of attack, the coefficient of lift (Cl) and coefficient of drag (Cd) values showed no progress.

Adaptive VR Video Data Transmission Method Using Mobile Edge Computing Based on AIoT Cloud VR

Aiming at the high requirements of cloud service-based virtual reality in AIoT for data transmission rate and delay sensitivity, a cloud VR system scheme based on MEC (Mobile Edge Computing) is proposed, which mainly incorporates viewpoint-based VR video data processing and hybrid digital-to-analog (HDA) transmission optimization and can be served for AIoT transmission filed. Firstly, a learning-driven multiaccess MEC offloading strategy is designed, in which the VR terminal automatically selects the optimal MEC server for task offloading, thereby effectively improving network efficiency and reducing service delay. Secondly, the progressive transmission of the VR data is realized through viewpoint-aware dynamic streaming based on RoI (region of interest) and the priorities of different objects. The transmission priority of each object in the scene is determined through the ROI layering, which effectively solves the contradiction between the large data volume in the VR scenes and the network bandwidth limitation when applied in AIoT domain, and further improves the real-time performance of the system. Then, the HDA (hybrid digital-analog) technique is introduced to optimize the transmission. Finally, the base station protocol stack is modified on the basis of the LTE (Long-Term Evolution) system, and the MEC technology is integrated to realize a complete cloud VR system in AIoT. The experimental results show that compared with other advanced schemes, the proposed scheme can achieve more robust and efficient data transmission performance and provide better VR user experience.

An edge–cloud integrated framework for flexible and dynamic stream analytics

With the popularity of Internet of Things (IoT), edge computing and cloud computing, more and more stream analytics applications are being developed including real-time trend prediction and object detection on top of IoT sensing data. One popular type of stream analytics is the recurrent neural network (RNN) deep learning model based time series or sequence data prediction and forecasting. Different from traditional analytics that assumes data are available ahead of time and will not change, stream analytics deals with data that are being generated continuously and data trend/distribution could change (a.k.a. concept drift), which will cause prediction/forecasting accuracy to drop over time. One other challenge is to find the best resource provisioning for stream analytics to achieve good overall latency. In this paper, we study how to best leverage edge and cloud resources to achieve better accuracy and latency for stream analytics using a type of RNN model called long short-term memory (LSTM). We propose a novel edge–cloud integrated framework for hybrid stream analytics that supports low latency inference on the edge and high capacity training on the cloud. To achieve flexible deployment, we study different approaches of deploying our hybrid learning framework including edge-centric, cloud-centric and edge–cloud integrated. Further, our hybrid learning framework can dynamically combine inference results from an LSTM model pre-trained based on historical data and another LSTM model re-trained periodically based on the most recent data. Using real-world and simulated stream datasets, our experiments show the proposed edge–cloud deployment is the best among all three deployment types in terms of latency. For accuracy, the experiments show our dynamic learning approach performs the best among all learning approaches for all three concept drift scenarios.

DASH Streaming traffic influence over energy efficient ethernet to improve energy savings

Dynamic Streaming over HTTP (DASH) is the main standard used in online video streaming services, given that more than 1.2 billion pay subscribers around the world use this standard. This fact entails billions of streaming connections between video servers and client displays. These devices involved in the streaming connection use an Ethernet Interface Card that consumes energy. In order to reduce the energy consumption, IEEE proposed the 802.3az Energy Efficient Ethernet standard, with a mechanism to make the network card change to a low power consumption mode when it is not in transmission mode. This behavior will be beneficial for services where traffic is sent in bursts, for instance video packet bursts like in video streaming over Real Time Transport Protocol in IPTV or the widely used DASH standard. Therefore, in this study the Ethernet traffic pattern when transmitting online video content using DASH is characterized in order to analyze the efficiency of the IEEE 802.3az standard under this video streaming scenario, and to verify the convenience of activating this energy saving alternative at the network interface of billions of client devices. The experiments have been conducted using a test-bed consisting of a full DASH streaming architecture, comparing different video segment sizes and changing the available bandwidth during the experiments in different scenarios in order to analyze the effect of the DASH content segment size on the Ethernet traffic pattern to identify the trade-off between energy efficiency, the energy savings, and the impact on the performance of the dynamic adaptation on the video streaming and reproduction.

On the Relation Between Active Network Length and Catchment Discharge.

The ever‐changing hydroclimatic conditions of the landscape induce ceaseless variations in the wet channel length (L) and the streamflow (Q) of a catchment. Here we use a perceptual model to analyze the links among (and the drivers of) four descriptors commonly used to characterize discharge and active length dynamics in streams, namely the L(Q) relationship and the cumulative distributions of local persistency, flowrate and active length. The model demonstrates that the shape of the L(Q) law is defined by the cumulative distribution of the specific subsurface discharge capacity along the network, a finding which provides a clue for the parametrization of L(Q) relations in dynamic streams. Furthermore, we show that L(Q) laws can be constructed combining the streamflow distribution with disjoint active length data. Our framework links previously unconnected formulations for characterizing stream network dynamics, and offers a novel perspective to describe the scaling between wet length and discharge in rivers.

Inference Acceleration with Adaptive Distributed DNN Partition over Dynamic Video Stream

Deep neural network-based computer vision applications have exploded and are widely used in intelligent services for IoT devices. Due to the computationally intensive nature of DNNs, the deployment and execution of intelligent applications in smart scenarios face the challenge of limited device resources. Existing job scheduling strategies are single-focused and have limited support for large-scale end-device scenarios. In this paper, we present ADDP, an adaptive distributed DNN partition method that supports video analysis on large-scale smart cameras. ADDP applies to the commonly used DNN models for computer vision and contains a feature-map layer partition module (FLP) supporting edge-to-end collaborative model partition and a feature-map size partition (FSP) module supporting multidevice parallel inference. Based on the inference delay minimization objective, FLP and FSP achieve a tradeoff between the arithmetic and communication resources of different devices. We validate ADDP on heterogeneous devices and show that both the FLP module and the FSP module outperform existing approaches and reduce single-frame response latency by 10–25% compared to the pure on-device processing.

Impacts of Device Caching of Content Fractions on Expected Content Quality

Motivated by the dynamic streaming feature that allows a video stream to consist of multiple chunks having different qualities, this study explores caching of fractions of a video content, not caching of an entire content, to increase the expected video quality. We show that the highest-quality content is better to be cached and propose the caching policy of video chunks having different qualities. Extensive performance evaluations are conducted to show that caching of content fractions, rather than an entire content, can improve the expected video quality especially when the channel condition is sufficiently good to cooperate with nearby BS or helpers.

High Median Utility Itemset Mining for recovering streaming window transaction using novel Modified Heap‐based Optimization

AbstractHigh utility itemsets (HUIs) are items in the dynamically streaming transaction list that generate a high‐profit margin. Many of the real‐time applications depend on finding HUIs from the transaction list. However, HUI mining is time‐consuming and results in high complexity due to memory requirements, a large search space, and the cost of HUI estimation. To overcome these issues we have proposed a novel High Median Utility Itemset Mining (HMUIM) approach. This approach utilizes the HMUI‐Miner which effectively ignores the unnecessary itemsets, that is, items with less profit, and mines the HUIs from the database. Furthermore, it has the limitation of deleting the transaction list based on the sliding window size used while conducting a dynamic streaming dataset. To tackle this issue we proposed a novel Modified Heap‐based Optimizer (MHBO) algorithm which effectively copies the HUIs transaction list and preserves it for further process. The MHBO is the combination of Heap Based Optimizer and nine fuzzy rules. The fuzzy rules are used to analyze the priority of the transaction list and based on that it ignores the transaction list with low priorities. Experimental analysis is performed for the proposed method on the real‐time dataset and compared with HUI and HAUIM approaches. The proposed method reduces the execution time and memory usage based on the minimum threshold. Meanwhile, the proposed MHBO approach is compared with iMEFIM, REX, and FCHUIM state‐of‐art works based on the sliding window concept. The execution time to store the replicated copies of original data is less for our proposed method.

Co3O4@Zn-BTC MOF as a novel nano-photocatalyst for degradation of toluene from ambient air

ABSTRACT The present work aimed to synthesise and utilise a novel Co3O4@Zn-BTC Metal-Organic Framework (MOF) photocatalyst to decompose toluene in a dynamic stream under visible light. Nano-catalyst was prepared by a simple and facile solvothermal procedure and was characterised via various techniques included of PXRD, FE-SEM, TEM, BET, UV-VIS with DRS, and FT-IR. To optimise the effects of process parameters, namely relative humidity, toluene input concentration and flow rate on toluene degradation efficiency, the process was modelled using response surface methodology based on central composite design (RSM-CCD). RSM-CCD approach developed a quadratic polynomial model with R2 = 0.9989, adjusted R2 = 0.9980 and p-value <0.001. The optimal values of the input variables based on the obtained model were found to be 20 ppm vapour concentration, 30% RH and 15 ml/min. Under this condition, 40% Co3O4@Zn-BTC MOF showed the highest photocatalytic efficiency (64.1%) in toluene mineralisation compared to other photocatalysts (40% > 60% > 20% > 10% Co3O4@Zn-BTC > Zn-BTC). Among the studied variables, humidity (%) had the greatest efficacy on the decomposition efficiency of toluene. These outcomes could be attributed to the assembly of Co3O4 nanoparticles with Zn-BTC to form a heterogeneous structure of Co3O4@Zn-BTC MOF that expands the light absorption in the visible scope. The findings also demonstrated that the 40% Co3O4@Zn-BTC MOF photocatalyst possesses excellent stability in long-term exposure to toluene removal. Also, any by-products at the output of the photocatalytic system were not detected by GC-MS. The study shows promising ability of the MOF in photocatalytic oxidation of toluene and provides valuable information for further practical applications of the system in air purification.