Input Stream Research Articles

Exergy analysis of process configurations for phenol recovery from wastewater

Wastewater with high phenol content, primarily generated by the petrochemical, coal, oil, and gas industries, must be treated before disposal to meet global regulations and prevent environmental harm. Liquid-liquid extraction is a well-studied method employed to address this need. Despite the extensive focus on to energy performance, exergy analysis has not yet been incorporated. Therefore, this study aims to compare four process configurations by integrating an exergetic approach with economic and emissions analyses to quantify thermodynamic efficiency, total annual cost, and CO2 emissions. Process simulations were conducted using Aspen Plus V.12, and the economic analysis was performed using the Aspen Process Economic Analyzer. The input streams of all configurations were 100 ton/h of wastewater containing 275 kg/h of phenol and 266 kg/h of hydroquinone, with pure methyl iso-butyl ketone (MIBK) as the makeup solvent. Extraction and mixing exhibited the lowest exergy destruction, while distillation accounted for approximately 55 % of the total exergy destruction. Configurations that incorporated direct medium-pressure steam input resulted in greater exergy destruction, leading to reduced exergy efficiency, but a lower total annual cost. The most economical configuration was achieved by heat integration of De Dietrich® process with low-pressure steam (C-IE lps), benefiting from exergy analysis insights. The exergy efficiency of this configuration increased from 33 % to 38 %, and the total annual cost decreased by 183 thousand USD per year compared to the initial De Dietrich® process (configuration C). Similarly, CO2 emissions were reduced to the lowest level, reaching 4.87 kg CO2 /h.

Bidirectional temporal and frame-segment attention for sparse action segmentation of figure skating

Temporal action segmentation is a task for understanding human activities in long-term videos. Most of the efforts have been focused on dense-frame action, which relies on strong correlations between frames. However, in the figure skating scene, technical actions are sparsely shown in the video. This brings new challenges: a large amount of redundant temporal information leads to weak frame correlation. To end this, we propose a Bidirectional Temporal and Frame-Segment Attention Module (FSAM). Specifically, we propose an additional reverse-temporal input stream to enhance frame correlation, learned by fusing bidirectional temporal features. In addition, the proposed FSAM contains a Multi-stage segment-aware GCN and decoder interaction module, aiming to learn the correlation between segment features across time domains and integrate embeddings between frame and segment representations. To evaluate our approach, we propose the Figure Skating Sparse Action Segmentation (FSSAS) dataset: The dataset comprises 100 samples of the Olympic figure skating final and semi-final competition, with more than 50 different men and women athletes. Extensive experiments show that our method achieves an accuracy of 87.75 and an edit score of 90.18 on the FSSAS dataset.

Identification of Yarrowia lipolytica as a platform for designed consortia that incorporate in situ nitrogen fixation to enable ammonia-free bioconversion

Bioconversion processes require nitrogen for growth and production of intracellular enzymes to produce biofuels and bioproducts. Typically, this is supplied as reduced nitrogen in the form of ammonia, which is produced offsite from N2 and H2 via the Haber-Bosch process. While this has revolutionized industries dependent on fixed nitrogen (e.g., modern agriculture), it is highly energy-intensive and its reliance on natural gas combustion results in substantial global CO2 emissions. Here we investigated the feasibility of in situ biological nitrogen fixation from N2 gas as a strategy to reduce greenhouse gas impacts of aerobic bioconversion processes. We developed an efficient and cost-effective method to screen fungal bioconversion hosts for compatibility with the free-living diazotrophic bacterium Azotobacter vinelandii under nitrogen fixing conditions. Our screening revealed that the genus Yarrowia is particularly enriched during co-culture experiments. Follow-up experiments identified four Y. lipolytica strains (NRRL Y-11853, NRRL Y-7208, NRRL Y-7317, and NRRL YB-618) capable of growth in co-culture with A. vinelandii. These strains utilize ammonium secreted during diazotrophic fixation of N2, which is provided as a component of the air input stream during aerobic fermentation. This demonstrates the feasibly of in situ biological nitrogen fixation to support heterotrophic fermentation processes for production of fuels and chemicals.

A Framework for Adversarial Streaming Via Differential Privacy and Difference Estimators

Classical streaming algorithms operate under the (not always reasonable) assumption that the input stream is fixed in advance. Recently, there is a growing interest in designing robust streaming algorithms that provide provable guarantees even when the input stream is chosen adaptively as the execution progresses. We propose a new framework for robust streaming that combines techniques from two recently suggested frameworks by Hassidim et al. (NeurIPS 2020) and by Woodruff and Zhou (FOCS 2021). These recently suggested frameworks rely on very different ideas, each with its own strengths and weaknesses. We combine these two frameworks into a single hybrid framework that obtains the “best of both worlds”, thereby solving a question left open by Woodruff and Zhou.

GROUP GAMMA-DISTRIBUTION AND NEURAL NETWORK IN OF THE LATEST TELECOMMUNICATION TRAFFIC MODELING

This article considers queue formation in the M/D/1 system with statistical characteristics of the first two orders that areclose to real, as the purpose of telecommunication traffic modeling . The input stream to the system is considered to be a group stream with constant parameters of the package and distance between arrivals, influenced by gamma distribution. These parameters are determined by a neural network trained to determine parameters of such input streams according to statistical characteristics of the queue at various loads of device. The results obtained demonstrate a good ap-proximation with the use of gamma-ray fluxes. Parameters are evaluated with the use of the neural network. The practical usefulness of the considered approach and the prospects of using neural net-works for practical tasks in solving which queue theory is used are provided.

Tuning transition metals layered-electroplated on bimetallic MxCu1−x crystallites (M = Fe, Co, Ni, and Zn) to boost ammonia yield in electrocatalytic reduction of nitrate wastewaters

Nitrate-containing wastewaters have been recognized as an important source for recovering valuable ammonia. This work targets integrating a series of transition metals (M = Fe, Co, Ni, and Zn) onto Cu crystallites through a layered-plating method. The strategy to promote the nitrate reduction reaction (NO3-RR) involves tuning M surfaces in specific ratios for the hydrogenation of nitrogenous species on MxCu1−x electrodes. Electrochemical analysis and operando Raman spectra identified that a solid-state Cu2O-to-Cu0 transition acted as the primary mediator, while its high corrosion resistance protected the M metals or metal oxides from inactivation in nitrate-to-ammonia pathways. Among bimetals, FeCu was the best combination, with the order of performance in constant potential electrolysis, Fe0.36Cu0.64 > Ni0.73Cu0.27 > Co0.34Cu0.66 > Zn0.64Cu0.36. The collaboration of Cu and M in deoxygenating nitrate and subsequently hydrogenating NOx at respective overpotentials is key to enhancing ammonia yield. Nitrate removal (96 %), NH3 selectivity (93 %), and Faradaic efficiency (92 %) were optimized on Fe0.36Cu0.64 electrode at −0.6 V (vs. RHE). A steady yield as high as 14,080 μg h−1 mg−1 was achieved at 30 mA cm−2 using a real water sample (NO3- ∼ 500 mg-N L−1, pH 4) as the input stream, continuously operated for 96 h.

Separation and concentration of CO2 from air using a humidity-driven molten-carbonate membrane

Separation processes are substantially more difficult when the species to be separated is highly dilute. To perform any dilute separation, thermodynamic and kinetic limitations must be overcome. Here we report a molten-carbonate membrane that can ‘pump’ CO2 from a 400 ppm input stream (representative of air) to an output stream with a higher concentration of CO2, by exploiting ambient energy in the form of a humidity difference. The substantial H2O concentration difference across the membrane drives CO2 permeation ‘uphill’ against its own concentration difference, analogous to active transport in biological membranes. The introduction of this H2O concentration difference also results in a kinetic enhancement that boosts the CO2 flux by an order of magnitude even as the CO2 input stream concentration is decreased by three orders of magnitude from 50% to 400 ppm. Computational modelling shows that this enhancement is due to the H2O-mediated formation of carriers within the molten salt that facilitate rapid CO2 transport.

Statistical learning shapes pain perception and prediction independently of external cues.

The placebo and nocebo effects highlight the importance of expectations in modulating pain perception, but in everyday life we don't need an external source of information to form expectations about pain. The brain can learn to predict pain in a more fundamental way, simply by experiencing fluctuating, non-random streams of noxious inputs, and extracting their temporal regularities. This process is called statistical learning. Here, we address a key open question: does statistical learning modulate pain perception? We asked 27 participants to both rate and predict pain intensity levels in sequences of fluctuating heat pain. Using a computational approach, we show that probabilistic expectations and confidence were used to weigh pain perception and prediction. As such, this study goes beyond well-established conditioning paradigms associating non-pain cues with pain outcomes, and shows that statistical learning itself shapes pain experience. This finding opens a new path of research into the brain mechanisms of pain regulation, with relevance to chronic pain where it may be dysfunctional.

Statistical learning shapes pain perception and prediction independently of external cues

The placebo and nocebo effects highlight the importance of expectations in modulating pain perception, but in everyday life we don’t need an external source of information to form expectations about pain. The brain can learn to predict pain in a more fundamental way, simply by experiencing fluctuating, non-random streams of noxious inputs, and extracting their temporal regularities. This process is called statistical learning. Here, we address a key open question: does statistical learning modulate pain perception? We asked 27 participants to both rate and predict pain intensity levels in sequences of fluctuating heat pain. Using a computational approach, we show that probabilistic expectations and confidence were used to weigh pain perception and prediction. As such, this study goes beyond well-established conditioning paradigms associating non-pain cues with pain outcomes, and shows that statistical learning itself shapes pain experience. This finding opens a new path of research into the brain mechanisms of pain regulation, with relevance to chronic pain where it may be dysfunctional.

Comparing the Representation of a Simple Visual Stimulus across the Cerebellar Network.

The cerebellum is a conserved structure of the vertebrate brain involved in the timing and calibration of movements. Its function is supported by the convergence of fibers from granule cells (GCs) and inferior olive neurons (IONs) onto Purkinje cells (PCs). Theories of cerebellar function postulate that IONs convey error signals to PCs that, paired with the contextual information provided by GCs, can instruct motor learning. Here, we use the larval zebrafish to investigate (1) how sensory representations of the same stimulus vary across GCs and IONs and (2) how PC activity reflects these two different input streams. We use population calcium imaging to measure ION and GC responses to flashes of diverse luminance and duration. First, we observe that GCs show tonic and graded responses, as opposed to IONs, whose activity peaks mostly at luminance transitions, consistently with the notion that GCs and IONs encode context and error information, respectively. Second, we show that GC activity is patterned over time: some neurons exhibit sustained responses for the entire duration of the stimulus, while in others activity ramps up with slow time constants. This activity could provide a substrate for time representation in the cerebellum. Together, our observations give support to the notion of an error signal coming from IONs and provide the first experimental evidence for a temporal patterning of GC activity over many seconds.

A PINN-DeepONet framework for extracting turbulent combustion closure from multiscalar measurements

In this study, we develop a novel framework to extract turbulent combustion closure, including closure for species chemical source terms, from multiscalar and velocity measurements in turbulent flames. The technique is based on a physics-informed neural network (PINN) that combines models for velocity and scalar measurements and a deep operator network (DeepONet) to accommodate spatial measurements and experimental parameters as separate input streams. An additional key innovation is the estimate of the unconditional means of the species’ chemical source terms as additional “observations” to constrain the prediction of these rates. This estimate is based on a convolution of the means of species reaction rates conditioned on principal components of the multiscalar data and the joint probability density functions of these principal components. The PINN-DeepONet method is implemented on the so-called Sydney flames, where training is carried out on 3 flames and validated on 4 flames. The results show that, despite the limited samples of experimental parameters, including the inlet flow and the fuel jet recess length within the air flow, the PINN-DeepONet approach can construct velocity and scalar fields along with important closure terms for turbulent transport and reaction rates.

METHOD OF THE SERVER HORIZONTAL LOAD BALANCING FOR REDUCING ENERGY CONSUMPTION

Background. Server horizontal load balancing is a crucial aspect of modern computing systems, particularly in cloud computing environments. The efficient management of incoming flows of applications is essential to ensure optimal resource utilization and minimize energy consumption. This study focuses on developing a method for managing the incoming flow of applications to reduce energy consumption in server horizontal load balancing. Objective. The primary objective is to develop a method for managing the incoming flow of applications to reduce energy consumption in server horizontal load balancing. This involves identifying the maximum permissible number of applications that can simultaneously enter the system for service, ensuring that the volume of resources used is close to the total maximum possible amount of resources. The method aims to minimize the variance of the elements of the sequence of maximum allowable numbers of applications and the dispersion of the elements of the sequences of volumes of resources used. Methods. The method involves several key steps: Input Load Smoothing Scheme: A static control method is proposed to smooth the incoming load. This involves developing a scheme for smoothing the incoming load, which is a set of values of the maximum allowable number of requests (sequence {ki}) arriving at the system input for a small time interval ∆ti. The sequence is selected to ensure that the volume of resources used is close to the total maximum possible amount of resources. Genetic Algorithm: The selection of the sequence {ki} is carried out using a genetic algorithm. The algorithm involves crossover, mutation, and selection operations to minimize the variance of the elements of the sequence and the dispersion of the elements of the sequences of volumes of resources used. Resource Allocation: The method involves allocating resources for the maintenance of a given type of service. The parameters of the server, which are characterized as the resources of the system serving the applications, are usually calculated for the average values of the parameters of the input stream. Delay Introduction: To manage the application processing process and prevent resource shortages, a delay is introduced for a part of the applications that coincide with a surge in load. The delay time is determined so that delayed applications do not enter the system until the previous burst of load is successfully serviced in the resource-consuming functional block. Results. The results of the study include the development of a method for managing the incoming flow of applications to reduce energy consumption in server horizontal load balancing. The method involves the use of a genetic algorithm to select the sequence {ki} that minimizes the variance of the elements of the sequence and the dispersion of the elements of the sequences of volumes of resources used. Conclusions. The study concludes that the proposed method for managing the incoming flow of applications can effectively reduce energy consumption in server horizontal load balancing. The method involves the use of a genetic algorithm to select the sequence {ki} that ensures efficient use of system resources and minimizes the variance of the elements of the sequence and the dispersion of the elements of the sequences of volumes of resources used. The method can be applied in various scenarios where efficient use of system resources is crucial, such as in cloud computing environments.

Analysis of methods and algorithms for processing unstructured text data based on JSON technology

The object of research is the process of automating systems for structuring data from several sources. The subject of the research is methods and algorithms for implementing a complete system for automated and parallel processing, validation and structuring of data. One of the most problematic areas is the merging of databases with different structures and several common fields into a generalized structure. The research was aimed at developing a system to increase the efficiency of automation of big data processing. As a result of the work, optimization methods were studied, the influence of their internal parameters on the operation of algorithms was analyzed, their main advantages and disadvantages were determined, and software was developed in which the corresponding methods were implemented. An algorithm for structuring data before processing has been obtained. Data structuring is achieved by performing the «mapping» operation. Mapping can take place by indexes of already cleaned data or using a defined dictionary with a given set of keys, which allows not to care about the sequence of storing values and their possible shift. The practical significance of the developed system lies in the improvement of methods of collecting and processing information for the purpose of its further validation, cleaning and accumulation in the following categories: geographic addresses and geo-coordinates, validation and automated addition of a mobile phone number to the international format, processing of car numbers (in modern and outdated format), VIN code of the engine and car brand, validation of urls of social networks, passport data and processing of personal data. Compared to similar methods for processing large volumes of data, the possibility of splitting the input file or stream into separate parts was used, the cleaned data from which is combined at the end of the system operation. Thanks to this, it is possible to process data whose size exceeds the available volume of the device's RAM, and the method of working with loosely structured text files in CSV format has been improved.

Control-oriented modeling of direct-heat co-current rotary dryers for energy demand flexibility

The contribution presents a new control-oriented model for direct-heat co-current rotary dryers with a focus on demand-side flexibility considering the variation of material throughput and heat source. The dryer model is based on partial differential-algebraic equations covering time- and location-dependent gas and particle velocities taking phase interactions into account. In addition, a steady-state model of a heat supply unit covering electricity-based and chemical heat sources is presented. The model is qualitatively compared with measured data and models from literature showing good overall agreement. Furthermore, simulations of the flexibility mechanisms are performed and their effect on temperature and humidity profiles as well as input and output streams of the dryer are analyzed. On this basis, requirements to process control are discussed. The simulations show that the variation of the throughput is associated with significant changes to the gas input stream, while only small changes are observed for the variation of the energy carrier.

EVIDENCE FOR AUDITORY STIMULUS-SPECIFIC ADAPTATION BUT NOT DEVIANCE DETECTION IN LARVAL ZEBRAFISH BRAINS.

Animals receive a constant stream of sensory input, and detecting changes in this sensory landscape is critical to their survival. One signature of change detection in humans is the auditory mismatch negativity (MMN), a neural response to unexpected stimuli that deviate from a predictable sequence. This process requires the auditory system to adapt to specific repeated stimuli while remaining sensitive to novel input (stimulus-specific adaptation). MMN was originally described in humans, and equivalent responses have been found in other mammals and birds, but it is not known to what extent this deviance detection circuitry is evolutionarily conserved. Here we present the first evidence for stimulus-specific adaptation in the brain of a teleost fish, using whole-brain calcium imaging of larval zebrafish at single-neuron resolution with selective plane illumination microscopy. We found frequency-specific responses across the brain with variable response amplitudes for frequencies of the same volume, and created a loudness curve to model this effect. We presented an auditory 'oddball' stimulus in an otherwise predictable train of pure tone stimuli, and did not find a population of neurons with specific responses to deviant tones that were not otherwise explained by stimulus-specific adaptation. Further, we observed no deviance responses to an unexpected omission of a sound in a repetitive sequence of white noise bursts. These findings extend the known scope of auditory adaptation and deviance responses across the evolutionary tree, and lay groundwork for future studies to describe the circuitry underlying auditory adaptation at the level of individual neurons.

Laser-sound reproduction by pulse amplitude modulation audio streams

Recently, the possibility to reproduce complex continuous acoustic signals via pulsed laser-plasma sound sources was demonstrated. This was achieved by optoacoustic transduction of dense laser pulse trains, modulated via single- or multi-bit Sigma–Delta, in the air or on solid targets. In this work, we extend the laser-sound concept to amplitude modulation techniques. Particularly, we demonstrate the possibility of transcoding audio streams directly into acoustic pulse streams by analog pulsed amplitude modulation. For this purpose, an electro-optic modulator is used to achieve pulse-to-pulse amplitude modulation of the laser radiation, similarly to the multi-level Sigma–Delta method. The modulator is directly driven by the analog input stream through an audio interface. The performance of the system is evaluated at a proof-of-principle level for the reproduction of test audio signals such as single tones, double tones and sine sweeps, within a limited frequency range of the audible spectrum. The results are supported by computational simulations of the reproduced acoustic signals using a linear convolution model that takes as input the audio signal and the laser-generated acoustic pulse profile. The study shows that amplitude modulation allows for significant relaxation of the laser repetition rate requirements compared to the Sigma–Delta-based implementation, albeit at the potential cost of increased distortion of the reproduced sound signal. The nature of the distortions is analyzed and a preliminary experimental and computational investigation for their suppression is presented.

Rancang Bangun Sistem Monitoring Kinerja Fresh Water Generator Menggunakan Arduino Mega 2560

Fresh water is a basic need of the ship's crew in supporting the creation of smooth ship operating activities, therefore it is also necessary to have an auxiliary aircraft that can produce its own fresh water on board to reduce ship operating costs, which is called the Fresh Water Generator (FWG). FWG is an auxiliary aircraft that can convert seawater into fresh water by evaporation and condensation processes. Therefore, it is necessary to have a tool to monitor the performance of the fresh water generator. The purpose of this research is none other than to monitor temperature conditions, water TDS, and conditions on the input and output streams of the fresh water generator evaporator filter to be more optimal and efficient and to determine the reliability of the fresh water generator performance equipment system using a long distance in order to create better and higher quality fresh water production on board. This research uses a prototyping method system. After designing and testing the tool monitoring system for the performance of fresh water generators using Arduino mega 2560 based on LoRa Ra-02, this system is one of the new technologies that can monitor the performance of fresh water generators to be more efficient and reliable. Then the readings of all sensors can work properly with an average percentage error on the Max 6675 temperature sensor of 0.58%, pressure transmiter sensor 1 of 2.04%, pressure transmiter sensor 2 of 2.04%, and TDS sensor of 1,70% and the distance to communication between the receiver and transceiver reaches a range of 200 meters without obstacles and 97 meters through obstacles around.

Biological computations: Limitations of attractor-based formalisms and the need for transients

Living systems, from single cells to higher vertebrates, receive a continuous stream of non-stationary inputs that they sense, for e.g. via cell surface receptors or sensory organs. By integrating these time-varying, multi-sensory, and often noisy information with memory using complex molecular or neuronal networks, they generate a variety of responses beyond simple stimulus-response association, including avoidance behavior, life-long-learning or social interactions. In a broad sense, these processes can be understood as a type of biological computation. Taking as a basis generic features of biological computations, such as real-time responsiveness or robustness and flexibility of the computation, we highlight the limitations of the current attractor-based framework for understanding computations in biological systems. We argue that frameworks based on transient dynamics away from attractors are better suited for the description of computations performed by neuronal and signaling networks. In particular, we discuss how quasi-stable transient dynamics from ghost states that emerge at criticality have a promising potential for developing an integrated framework of computations, that can help us understand how living system actively process information and learn from their continuously changing environment.

Plant wide modelling and thermodynamic optimization of a petroleum refinery for improvement potentials

In this work, plant wide modelling of a petroleum refinery was conducted for the optimization. An Aspen HYSYS® model of a petroleum refinery was used for collection of the information to perform the thermodynamic (exergy) analysis. The plant wide model consisted of alkylation unit, reformer, naphtha hydrotreater, catalytic cracker, diesel hydrotreater, isomerization unit, hydrocracker, and kerosene hydrotreater. Seventeen (17) independent input streams and twenty-eight (28) output streams of the plant were considered for the analysis. The physical and chemical exergies of the streams were summed for assessing the overall exergy efficiency, exergy destruction, and improvement potentials of the plant. Exergy efficiency of the plant was 91.38 % with exergy destruction of 704054.64 kW and exergetic improvement potential of 60707.50 kW. The current study gives an insight into the plant-wide analysis ranging from physical exergies of the process streams and utilities to the chemical exergies of the fuels (product) produced in the refineries.

Near-infrared-based sortability of polyester-containing textile waste

Through textile recycling, resources can be saved, and landfilling can be avoided. However, determined input streams are needed for high-quality recycling processes. The aim of this study is to investigate the detection, prediction and sorting of polyester-containing textile waste using near-infrared spectroscopy. In experimental trials, polyester-containing textiles with different fibre blends are scanned, and the spectra are recorded. The results are evaluated in a linear regression model and transferred to an industrial-scale sorting plant. Due to the different peaks in the spectra of the investigated fibre types and a visible correlation of the absorption intensity between the blend ratios, the use of NIR spectroscopy is very promising for application in the sorting of polyester-containing textiles.