Generation Of Streams Research Articles

Solar Cell Enhancement from Supercritical CO2 Dye Surface Modification of Mesoporous TiO2 Photoanodes.

In recent years, in an effort to reach Net Zero Emissions, there has been growing interest by various academic and industry communities to develop chemicals and industrial processes that are circular, sustainable and green. We report the rapid, simple and effective surface modification of a porous metal oxide with organic dyes using supercritical carbon dioxide (scCO2). Titanium dioxide (TiO2) photoanodes were coated in very short times, under mild conditions and the excess dye recovered afterwards for reuse. The process obviates the need for conventional toxic solvents, the generation of unwanted waste streams, and more importantly, we see an unexpected device performance enhancement of 212 and 163 % for TerCOOTMS, 2 a and TerCN/COOTBDMS, 4 dyes, respectively, when compared to the conventional solvent deposition method.

Improving agglomeration efficiency of aerosols with sizes less than 2.5 µm by generation of vortex streams in inhomogeneous ultrasonic field

In this paper a new approach to increase the agglomeration efficiency of finely dispersed aerosols by generating toroidal vortex streams in inhomogeneous ultrasonic field is proposed and studied. From the obtained experimental results (for two types of emitters) it could be established that the toroidal vortex streams generated in an inhomogeneous ultrasonic field provide an increase of the agglomeration efficiency when exposed to a gas-dispersed flow injected into the agglomeration chamber at a speed of up to 0.2 m/s. Further increase of the injected flow velocity leads to disruption of the structure of the generated vortex streams and agglomeration efficiency decreases.In the course of the analysis, it was found that the efficiency of agglomeration by toroidal vortex streams increases in direct proportion to the increase of the sound pressure level up to 165 dB. After that, no further increase in agglomeration efficiency is observed.By analyzing the agglomeration efficiency depending on particle size, it was possible to verify that the efficiency of agglomeration in comparison with agglomeration in a homogeneous ultrasonic field for droplets with a size of 0.2 … 0.6 μm is increased by 25 %, for 1.8 μm droplets by 20 %; and for droplets larger than 2.5 μm, the increase in efficiency is no more than 17 %.

Reliable Event Generation With Invertible Conditional Normalizing Flow.

Event streams provide a novel paradigm to describe visual scenes by capturing intensity variations above specific thresholds along with various types of noise. Existing event generation methods usually rely on one-way mappings using hand-crafted parameters and noise rates, which may not adequately suit diverse scenarios and event cameras. To address this limitation, we propose a novel approach to learn a bidirectional mapping between the feature space of event streams and their inherent parameters, enabling the generation of reliable event streams with enhanced generalization capabilities. We first randomly generate a vast number of parameters and synthesize massive event streams using an event simulator. Subsequently, an event-based normalizing flow network is proposed to learn the invertible mapping between the representation of a synthetic event stream and its parameters. The invertible mapping is implemented by incorporating an intensity-guided conditional affine simulation mechanism, facilitating better alignment between event features and parameter spaces. Additionally, we impose constraints on event sparsity, edge distribution, and noise distribution through novel event losses, further emphasizing event priors in the bidirectional mapping. Our framework surpasses state-of-the-art methods in video reconstruction, optical flow estimation, and parameter estimation tasks on synthetic and real-world datasets, exhibiting excellent generalization across diverse scenes and cameras.

Hybrid Efficient Stream Cipher KeyGenerator Based on LFSR's and Chaotic Map

Communication security that depends on chaos can be considered as a new approach which provides protection and security of communications and maintains confidentiality because Chaos theory can be implemented in cryptosystem successfully. A stream cipher, on the other hand, is a type of symmetric cryptosystem in which the plaintext is divided into small entities known as characters. The key in stream cipher is typically generated by a random bit generator. Many key stream generators employ linear feedback shift registers (LFSRs). LFSR systems are made up of a group of LFSR units and a combining function (CF) unit. The plaintext is encrypted one bit at a time. The key is fed into a random bit generator, which produces a long series of binary signals. This "key-stream" k is then combined with plaintext m, typically via a bit-wise XOR (Exclusive-OR modulo 2 addition), to produce the ciphertext stream, which employs the same random bit generator and seed. In this paper we will introduce a new stream cipher keygenerator which using a hybrid between chaotic and combination of Linear Feedback Shift Registers (LFSR's). The proposed generator can be used to protect different types of data files (text, image, audio and video). Many kinds of tests are applied to specifying the goodness of the proposed keygenerator. The results of testing prove the efficiency of the suggested system.

Economic Viability of Bamboo Dust Based Gasification Plant for a Paper Mill

Global concern over pollution and several related issues caused by increase in Green House Gas emission and consequently changes in climate have resulted in a paradigm shift in the approach towards development of the energy sector in many countries. Being relatively less expensive than other renewable sources like solar and wind energy and being geographically dispersed, biomass is a potential alternative for fossil fuels like coal to sustainably meet the increasing energy needs of the world. In addition to this, biomass is carbon dioxide neutral and is a reliable source of energy. In this paper a pilot project is conceptualized on the use of bamboo dust waste for Cachar Paper Mill located in the state of Assam, India. This paper mill is a source of biomass in itself as it generates about 35-40 tons of bamboo dust every day. This project is conceived to economically generate electricity by utilizing producer gas derived from a gasifier by means of a gas engine coupled with an alternator, thus contributing its share towards renewable energy in the country. An economic viability study has been carried out by calculating the cost of generation of electricity for the plant to produce this green energy as against that of convention coal fired stream generation. This paper further highlights how burning of reasonable quantity of coal can be avoided by replacing some quantum of electricity generation through renewable energy sources resulting in appreciable amount of cost savings.

Numerical investigation of nozzle length effects on Rayleigh jet breakup behaviors and droplet formation properties at different modulation amplitudes in liquid droplet radiator

Uniform droplet stream generation is required for a liquid droplet radiator to achieve the efficient heat dissipation of space nuclear power systems. In this study, Rayleigh jets breaking into continuous droplet streams at different modulation amplitudes are numerically simulated to investigate the nozzle length effects on jet breakup behaviors and droplet formation properties. First, simulated results confirm that the growth of secondary instability wave on jet free-surface causes the jet breakup pattern transition from downstream pinching to upstream pinching. As the modulation amplitude is from 0.04 up to 0.16, the nozzle length range where the upstream pinching occurs in the jet breakup is broadened from null to 1–5. The essential reason for the increase of jet breakup length is the reduction of fundamental amplitude and the increase of full velocity relaxation length. Then, a comprehensive prediction model for the velocity-modulated jet breakup length is developed in agreement with the experiments, and the average relative error is below 7.38 %. Finally, two types of satellite droplet formation regimes including rear-merging and forward-merging satellite droplets are identified by the oscillation mode of the jet tip. It is found that the primary droplet generation frequency is equal to the external disturbance frequency and independent of nozzle length and modulation amplitude. Increasing the nozzle length and modulation amplitude can both increase the satellite droplet diameter and primary droplet spacing, while the primary droplet diameter is almost constant at around 1.85.

Processing of Electronic Scrap with Ausmelt TSL Technology

Continued decreasing of the world’s average copper ore grades, coupled with a meteoric rise in the generation of consumer waste streams, has led to the processing of electronic scrap (e-scrap) and other secondaries assuming increasing importance within the global copper industry. Treatment of these secondary feeds has traditionally been carried out in the Blast furnace, Peirce-Smith converter and/or anode furnace. More recently however, bath smelting processes such as the Ausmelt TSL and Outotec Kaldo technologies have emerged as the preferred option for processing these materials due to their superior environmental performance and flexibility to operate under a wide range of conditions. Furthermore, the ability of these processes to handle a wide range of feed inputs make them ideally placed for the processing of not only e-scrap but also other waste streams from the clean energy transition such as end of life e-mobility batteries and photovoltaic systems. This paper focuses on the processing of electronic scrap and other copper secondaries with Ausmelt TSL technology and discusses specific issues facing secondary copper smelters in relation to impurity management and process offgas handling/cleaning.

Impact analysis of real and virtual concept drifts on the predictive performance of classifiers

In the domain of machine learning and predictive analytics, classifiers hold significant importance as they are instrumental in extracting valuable patterns and enabling precise predictions from data. However, data distributions in streams are rarely stable in real-world circumstances and may evolve over time. Concept drifts, or dynamic changes in the data, have a major impact on how well classifiers predict outcomes. Furthermore, virtual concept drifts have become equally essential when dealing with the development of virtual environments and synthetic data production tools. This study employs a range of conventional classifiers, and ensemble models to examine the effects of real and virtual concept drifts on the predictive performance of classifiers. In order to ensure the conclusions are applicable to a wide range of domains, the study will be undertaken on a variety of datasets. The generation of incremental data streams from historical records with drifting patterns will replicate real concept drifts. Synthetic data will be incorporated into the training and testing sets to introduce virtual concept drifts. The classifier models are then assessed using the benchmark metrics for performance on these datasets. Comparative evaluations are conducted to evaluate the classifiers’ robustness and adaptability in the face of real and virtual concept drifts. The results of this study will shed light on the performance of various classifiers and models in dynamic data environments while offering helpful insights to businesses for developing machine learning applications that are more resilient. The research examined the impact of real and virtual concept drifts on classifier performance, using three models: Streaming Random Patches (SRP), Adaptive Windowing (ADWIN), and Streaming Random Patches with Adaptive Windowing (SRP-ADWIN). The study found that the SRP-ADWIN ensemble model outperformed SRP and ADWIN individually, effectively adapting to changing data distributions, and showed improved predictive accuracy of 98.63% for virtual drift and 86.46% for real drift.

Automated workflow generation supporting the value stream design of reconfigurable robot assembly cells

The paper addresses the automated generation of value stream optimized assembly workflows for reconfigurable robot cells. The focus is on the generation of asynchronous concurrent action workflows from assembly task descriptions. The main motivation is to enable automated search for optimized solutions within the enormous design spaces of these systems which results from their structural and procedural configurability. The proposed approach conducts task assignment, task sequencing and logical task planning (AI Planning) for multiple agents. The approach combines existing methods from the distinct research fields of robot task sequencing and discrete task planning. The contribution of the research is twofold. Firstly, the paper proposes a generic domain meta model enabling automated workflow generation for multi-agent assembly tasks. Secondly, a workflow generation approach based on artificial intelligence planning combined with the game-theoretic concept of extensive form games is presented. The approach iteratively generates a cooperative strategy for acting agents which is subsequently optimized in terms of individual strategies. The obtained logical action plans are further transformed into parametric concurrent action schedules to support downstream design activities. Both the meta model and the workflow generation approach are applied in a case study and provide valuable information on achievable assembly speedup.

A Tensor-based t-SVD-LSTM Remaining Useful Life Prediction Model for Industrial Intelligence

The Industrial Internet of Things (IIoT) integrates smart sensors and actuators for the widespread digitization and enhancement of industrial and manufacturing processes. Smart equipment is used to improve the industrial intelligence and make industrial production more flexible, safer and more efficient. For complex equipment, product life-cycle management (PLM) including remaining useful life (RUL) is one of the essential issues for industrial intelligence. In this paper, a tensor-based remaining useful life prediction model is proposed to facilitate the life-cycle management, which combines features from time domain and frequency domain. For the characteristics of continuous generation of industrial data streaming, tensor singular value decomposition (t-SVD) is combined with long shortterm memory network (LSTM) method to predict the RUL of devices from high-order and high-noise time series data. Finally, experiments are carried out on three different data sets including the battery charge and discharge data set, the bearing acceleration life cycle data set, and the turbofan data set to measure the performance of the proposed model.

Edge-Based Video Stream Generation for Multi-Party Mobile Augmented Reality

Edge-Based Video Stream Generation for Multi-Party Mobile Augmented Reality

Filter generators with increased resistance against algebraic attacks

Filter generators form one of the best known and most studied classes of key stream generators used in synchronous stream ciphers. Each such generator consists of a binary linear shift register with a primitive feedback polynomial and a nonlinear Boolean function, which has a number of requirements related to the condition of generator security against known attacks. One such requirement is the high algebraic immunity of the generator's filter function; this parameter characterises security filter generator against modern algebraic attacks. In certain cases, this requirement is too restrictive in sense of practicality, as it increases the computational or circuit complexity of the key stream generation algorithm. This makes the actuality of increasing the resistance of filter generators with fixed filter functions, that have limited (low) algebraic immunity. The paper proposes to solve this problem by modifying the feedback function of the linear shift register. the security of the proposed generators against algebraic attacks is investigated and is shown that (under certain natural conditions) such generators are more secure at the same initial state length as compared to traditional filter generators. The proposed solution seems to be useful for practical application in advanced hardware-oriented stream ciphers design.

SO2-Tolerant Electrocatalytic Reduction of CO2 from Simulated Industrial Flue Gas

The electrochemical reduction of CO2 using copper-based electrocatalysts offers a route to produce high-value multicarbon (C2+) products from renewable electricity (Nat. Catal. 4, 952-958 (2021); Nature 614, 262-269 (2023)). To date, the efficient electrocatalytic conversion of CO2 to multicarbon products has only been possible when using impurity-free CO2 sources, such as from direct air capture. The generation of such high-grade CO2 streams is expensive, accounting for almost half of the total energy required for both capture and electroreduction processes (Nat. Catal. 4, 952-958 (2021)). Conversely, capturing CO2 from point sources, such as industrial flue gas, is more efficient due to the higher concentration of CO2 in the feed. However, trace amounts of sulfur dioxide (10 ~ 400 ppm SO2) inherently present in these streams will significantly degrade the CO2 conversion process. All previous attempts to convert CO2 with SO2 present in the feed have resulted in immediate catalyst poisoning and an irreversible loss of CO2 conversion activity.In this study, we designed a modified catalyst layer to react a stream of dilute CO2 containing 400 ppm SO2 to multicarbon products with high stability and performance metrics that match or exceed those achieved with pure CO2 streams. Driven by density function theory and COMSOL simulations, we designed an ionomer:copper:polytetrafluoroethylene (PTFE) (ICP) electrode that features both hydrophobic and highly-charged hydrophilic domains to limit water adsorption and promote CO2 over SO2 transport near the electrochemically active sites. This deactivates the SO2 poisoning mechanism, thus enabling stable and efficient CO2 conversion (see figure). Our approach achieved a sustained C2+ Faradaic efficiency (FE) of 50% for the initial 160 hours at 100 mA cm-2.In order to improve the overall C2+ current efficiency (jC2+) towards industrial scales, we applied our strategy in high-surface-area copper electrodes. We achieved CO2 conversion in the presence of 400 ppm SO2 with a C2+ FE of 76% at a current density of 700 mA cm-2, surpassing what can be achieved in existing integrated CO2 capture-electrolysis systems that use pure CO2. Overall, our approach provides a fully 140-fold increase in performance (FEC2+ × jC2+) compared to the best prior CO2 conversion systems with added SO2 (Nat. Nanotechnol. doi: 10.1038/s41565-022-01286-y (2023); J. Am. Chem. Soc. 141, 9902-9909 (2019)). These findings represent an important advancement in the field of CO2 conversion and highlight the potential of our strategy for industrial-scale applications. Figure 1

Hydrogen Production Via Mediated Electrolysis of Biomass from Distillery Whisky Waste Streams

Fuel-flexible hydrogen generation methods, such as electrochemical conversion of waste biomass, offer a route to sustainable production of hydrogen (in addition to steam or water electrolysis using renewable electricity [1]) whilst valorising feedstocks that are often overlooked. [2] In this research, the authors explore the potential of a novel, two-stage electrolysis process to convert biomass-containing solid and liquid distillery waste products into hydrogen, using a phosphomolybdic acid (H3[PMo12O40] or PMA) catalyst, at lower operating voltages (<0.95 V) than proton exchange membrane (PEM) water electrolysers (1.5 – 1.6 V). [3]Firstly, an overview of the processes, energy usage and waste stream generation at the Isle of Raasay Distillery (Hebrides, Scotland) will be provided, before results from the characterization of whisky waste products are introduced. This provides a detailed compositional analysis of both solid (draff/spent barley) and liquid (pot ale and spent lees) wastes, as well as residual alcohol analysis of the liquid wastes, and their potential for conversion to hydrogen.Subsequently, the concept of thermal digestion of each waste-type, using the Keggin-type polyoxometalate PMA catalyst to abstract protons and electrons from biomass, will be outlined. Finally, details of electrolysis of the PMA-biomass solutions using a PEM flow cell will be provided, including electrochemical data (AC impedance spectroscopy, linear sweep voltammetry and potentiostatic operation) in order to determine the optimal operating conditions for the process, in addition to the respective yield of hydrogen from each biomass source. Prospects for upscaling the process will also be discussed. References Götz, M., Lefebvre, J., Mörs, F., McDaniel Koch, A., Graf, F., Bajohr, S. Reimert, R. and Kolb, T., Renew. Energ., 85, 1371 – 1390 (2016).Liu, W., Cui, Y., Du, X., Zhang, Z., Chao, Z. and Deng, Y., Energy Environ. Sci., 9, 467 – 472 (2016).Oh, H., Choi, Y., Shin, C., Nguyen, T. V. T., Han, Y., Kim, H., Kim, Y. H., Lee, J-W., Jang, J-W. and Ryu, J., ACS Catal. 10, 2060 – 2068 (2020).

Shaping Future Foods through Fermentation of Side Streams: Microbial, Chemical, and Physical Characteristics of Fermented Blends from Sunflower Seed Press Cake and Cheese Whey.

The current food system suffers from the inefficient use of resources, including the generation of side streams of low economic value that still contain nutritional components. One potential approach to reach a more sustainable food system is to reintroduce such side streams into a circular value chain and valorise them in novel food products, preferably in an unrefined or minimally refined manner. Blending side streams from different industries might be a suitable way to improve the nutritional value of the final matrix. In this study, sunflower seed press cake and cheese whey were combined to obtain matrices containing valuable proteins, structuring polysaccharides, as well as lactose and minerals facilitating fermentation with three different co-cultures of lactic acid bacteria and yeasts. Fermentation for 48 h at 26 °C decreased the pH from ~6.3 to ~4.7 and enhanced the storage stability of the blends with no effect on their rheological properties and microstructure. This research demonstrates the potential of fermentation as a mean to stabilise side stream blends while only minimally affecting their physical appearance.

Analysis of E-commerce Live Streaming and Traffic Generation

The Internet has become an integral part of our society. This paper focuses on TikTok, one of today's most popular Internet platforms, and analyzes its e-commerce live streaming structure and traffic generation methods. Through a comprehensive examination of TikTok's current status and prospects, this study aims to deepen our understanding of the current Internet model and identify emerging trends. TikTok's commercial liquidity is essential to its success as a commercial platform. However, balancing commercial interests with user experience has challenged the platform's development. The key to TikTok's growth is creating a sticky user base and fostering a unique platform culture. Rather than prioritizing commercial gain over user experience, TikTok aims to engage users in the commercial process, which is critical for sustainable development. Overall, this paper sheds light on the complexities of e-commerce live streaming and traffic generation on TikTok and highlights the platform's unique approach to balancing commercial interests with user experience.

Recent advances and mechanisms of microbial bioremediation of nickel from wastewater.

The global concern over emerging pollutants, characterized by their low concentrations and high toxicity, necessitates effective remediation strategies. Among these pollutants, pharmaceutical and personal care products, pesticides, surfactants, and persistent organic pollutants have gained significant attention. These contaminants are extensively distributed within aquatic ecosystems, posing threats to both human and aquatic physiological systems. Nickel, a valuable metal renowned for its corrosion-resistant properties, is widely utilized in various industrial processes, leading to the generation of nickel-containing waste streams, including batteries, catalysts, wastewater, and electrolyte bleed-off. Contamination of soil, water, or air by these waste materials can have adverse effects on the environment and human health. This review article focuses on the recent advancements in environmental and economic implications associated with the removal of nickel from diverse waste sources. Physicochemical technologies employed for treating different nickel-containing effluents and wastewater are discussed, alongside bioremediation techniques and the underlying mechanisms by which microorganisms facilitate nickel removal. The recovery of nickel from waste materials holds paramount importance not only from an economic standpoint but also to mitigate environmental impacts.

5-bit all-optical quantum random number generator based on a time-multiplexed optical parametric oscillator.

Random numbers are of critical importance in many applications, including secure communication, photonics computing and cryptography. Due to the non-deterministic nature of the quantum processes, a degenerate optical parametric oscillator (DOPO) constitutes a solution to produce true randomness. Nevertheless, one of the existing challenges for DOPO in this field is bit sequence scalability. Here, we experimentally report on the generation of 5-bit random number streams in a time-multiplexed femtosecond DOPO system. A multi-pass cell is added to elongate the OPO cavity to scale up the bit sequences. To this end, for a ∼15 m long all free space OPO cavity, resonating 5 signal pulses with a repetition rate of 50 MHz is demonstrated. The above-threshold binary phase nature originates from vacuum fluctuations of a DOPO ensuring the randomness of the system. The phase state of the output is characterized by the interference pattern between the output pulses and the fundamental pump pulses. Different bit sequences are presented here by turning on and off the OPO. Conditional probability is performed to verify the randomness of the output for 1200 bits. Our scheme provides a new direction for an all-optical random number generator.

Adaptive Fast XGBoost for Multiclass Classification

The popularization of sensoring and connectivity technologies like 5G and IoT are boosting the generation of data streams. Such kinds of data are one of the last frontiers of data mining applications. However, data streams are massive and unbounded sequences of non-stationary data objects that are continuously generated at rapid rates. To deal with these challenges, the learning algorithms should analyze the data just once and update their classifiers to handle the concept drifts. The literature presents some algorithms to deal with the classification of multiclass data streams. However, most of them have high processing time. Therefore, this work proposes a XGBoost-based classifier called AFXGB-MC to fast classify non-stationary data streams with multiple classes. We compared it with the six state-of-the-art algorithms for multiclass classification found in the literature. The results pointed out that AFXGB-MC presents similar accuracy performance, but with faster processing time, being twice faster than the second fastest algorithm from the literature, and having fast drift recovery time.

Physics-driven Machine Learning for the Prediction of Coronal Mass Ejections’ Travel Times

Coronal Mass Ejections (CMEs) correspond to dramatic expulsions of plasma and magnetic field from the solar corona into the heliosphere. CMEs are scientifically relevant because they are involved in the physical mechanisms characterizing the active Sun. However, more recently, CMEs have attracted attention for their impact on space weather, as they are correlated to geomagnetic storms and may induce the generation of solar energetic particle streams. In this space weather framework, the present paper introduces a physics-driven artificial intelligence (AI) approach to the prediction of CMEs’ travel time, in which the deterministic drag-based model is exploited to improve the training phase of a cascade of two neural networks fed with both remote sensing and in situ data. This study shows that the use of physical information in the AI architecture significantly improves both the accuracy and the robustness of the travel time prediction.