Stream Analysis Research Articles

S-Scheme TiO2/CuNb2O6 Nanocomposite Photocatalyst with Enhanced H2 Evolution Activity from Aqueous Glycerol Solution

Steering of photoexcitons via interface engineering of semiconductor photocatalysts is key to achieving a stable and efficient photocatalyst for continuous hydrogen production. Among them, the development of 1D nanostructures with several advantageous properties such as wide surface area, effective light harvesting, and increased electron moving kinetics has received special attention. In this work, 1D titania nanobars were successfully synthesized via the methanol-assisted solvothermal method, in which alcohol formed hydrogen bonds with nuclei, which consequently encouraged the growth of nanobars. Following this, the titania nanobars were integrated with solvothermally synthesized monoclinic CuNb2O6 nanoparticles. TEM analysis revealed the formation of TiO2 nanobars and their uniform interaction with CuNb2O6 nanoparticles. A S-scheme heterojunction is formed at the interface of TiO2 nanobars and CuNb2O6 nanoparticles. 1D morphology of titania and CuNb2O6 plays a key role in light absorption and boosts the photocatalytic activity by promoting the separation and transfer of photoexcited charge carriers. The electrons at the TiO2 conduction band and holes at the valance band of CuNb2O6 facilitated recombination at the heterojunction. The remaining electrons at the conduction band of CuNb2O6 and holes at the valance band of TiO2 tend to participate in redox reactions at their terminals. As the loading of CuNb2O6 increases, the rate of activity initially increases and then decreases, which is consistent with the trend for light penetration-dependent photocatalytic hydrogen evolution. The band edge potentials of TiO2 and CuNb2O6 were also revealed using VB-XPS and UPS analysis. In addition to catalytic stability, the effect of glycerol concentration, catalyst loading, time on stream, recyclability, and parametric analysis which aided in the discovery of ideal experimental conditions is revealed. Under optimal conditions, the photocatalyst showed an enhanced rate of H2 generation (146 mmol h–1 gcat–1) under simulated solar light irradiation. The solar to H2 conversion efficacy is also discussed with comparison.

TiO2 encased TaN nanoparticles heterojunction for photocatalytic H2 evolution from aqueous glycerol solution

Steering of photo excitons via interface engineering of semiconductor photocatalysts is key to achieving a stable and efficient photocatalyst for continuous hydrogen production. Among them, the development of binary nanocomposites have several advantages such as larger surface area, effective light harvesting, improved photoexcitons separation for red-ox reactions, and non-corrosive nature has received special attention. In this work, nanocomposite of metallic TaN nanoparticles attached with TiO2 layer was successfully synthesized using hydrothermal method. HR-TEM analysis revealed the formation of metallic TaN wrapped with TiO2 with irregular spherical morphology. Electrochemical analysis revealed that Schottky barrier is formed at the interface between TaN and TiO2, with the barrier potential also being dependent on the wrapped layer thickness. However, the hot electrons diffusion through TiO2 energy states can attend the photocatalytic reaction, only when they pass over the Schottky barrier. TaN plays a key role in boosting photocatalytic activity by promoting the separation and transfer of photoexcitons. As the thickness of the TiO2 layer increases, the rate of H2 production initially increases and then decreases. This is consistent with the trend for layer thickness-dependent photocatalytic hydrogen evolution. In addition to catalytic stability, time on stream, recyclability, and parametric analysis aided in optimization of ideal experimental conditions is revealed. Under optimal conditions, the photocatalyst showed an enhanced rate of H2 generation (18.2 mmol h−1. g−1cat) under simulated solar light irradiation.

Induced magnetic transportation of Soret and dissipative effects on Casson fluid flow towards a vertical plate with thermal and species flux conditions

This research offers an analysis of the mixed convective transient boundary film Casson fluid stream and thermal distribution through a vertical sheet. Viscous dissipation and the Soret effect are introduced to support the flow in stimulating heat capacity. Unlike typical investigations, the present flow-formulated model is done to capture an induced magnetic field. The Boussinesq approximation is used to describe the nonlinear formulated partial derivatives governing the heat transfer fluid that is non-dimensionalized using suitable dimensionless quantities. The transformed partial derivative model is numerically solved via the spectral Chebyshev technique and the results of shear stress, current density, Nusselt number, and mass gradient are tabulated. The role of numerous terms on dimensionless flow rate, induced velocity, and heat transfer with species distribution is discussed in a very effective way. Velocity and temperature of liquid decline as boosting the material parameter. Enhancing values [Formula: see text] favor the flow momentum and oppose the temperature profile.

Dynamic conditional score model-based weighted incremental fuzzy clustering of consumer power load data

This study proposes a weighted incremental fuzzy C-mean power load clustering algorithm based on the dynamic conditional score model to solve the problems that the predominant power load data mining method only captures the mean characteristics of time series and ignores the heteroscedasticity and sequence correlations. Consequently, this method has unsatisfactory time series clustering and low clustering accuracy. A dynamic conditional score model is constructed to analyze and extract statistical characteristic parameters of a time series to calculate the autocorrelation value of the parameter series. A weighted fuzzy C-mean clustering analysis is performed, and the obtained data weight information is used as input for incremental clustering to improve the clustering accuracy. The DCS model parameter dataset and data weight information are combined, and the clustering analysis of the consumer power load data stream is performed. The power load time series of different companies is given, and the clustering validity indices are defined for the performance analysis to verify the proposed clustering algorithm. The experimental results show that the proposed algorithm achieves satisfactory clustering and improves the performance.

Alkaline electrolysis using CuOx cathode for the conversion of carbon dioxide into liquid fuels

Electrochemical reduction of CO2 is an effective method for storing intermittent renewable energy. This could result in fuel additives and chemical feedstocks such as alcohols. A challenge of electrochemical alcohol production is the transfer of electrons and protons, as well as the formation of C–C bonds. As of now, copper-based materials are the most commonly used and effective catalysts. Although CuOx is considered a promising catalyst for electrochemical CO2 reduction reactions (CO2RR), significant improvements in product selectivity are still needed. This paper presents some results obtained using copper oxide as a cathode, combined with 33% of ionomer, nickel iron as anode, and membrane Fumatech as electrolyte. As a result of physico-chemical experiments, morphological measurements of the cathode, electrochemical experiments carried out with a complete zero-gap cell operating under alkaline conditions, and gas-chromatographic (GC) analyses of the cathode outlet stream, we determined that methyl formate, ethanol, and propanol were mainly obtained at a rate of 116.3 μmol {text{g}}_{text{cat}}^{-1} , {text{h}}^{-{1}} during operation at 2.2 V.

DEEP REINFORCEMENT LEARNING-BASED OPTIMIZATION AND ENHANCEMENT OF MULTIMEDIA DATA: AN INNOVATIVE APPROACH

The rapid growth of multimedia data in various domains has necessitated the development of efficient techniques to enhance and optimize its quality. Traditional approaches often struggle to address the complexity and diversity of multimedia data, leading to suboptimal results. This paper presents a novel approach to tackle this challenge by leveraging the power of deep reinforcement learning (DRL). The proposed method utilizes DRL to learn and optimize multimedia data in an improvised manner. By employing a combination of convolutional neural networks and deep Q-networks, the model can effectively extract high-level features and make informed decisions to enhance the quality of multimedia data. The reinforcement learning framework enables the system to learn from its actions, continuously improving its performance through an iterative process. To evaluate the effectiveness of the proposed method, extensive experiments were conducted using a diverse set of multimedia datasets. The results demonstrate significant improvements in various quality metrics, including image resolution, video frame rate, and audio clarity. Additionally, the proposed approach exhibits robustness across different types of multimedia data, ensuring consistent enhancement performance across various domains. Furthermore, the computational efficiency of the proposed method is also highlighted, as it demonstrates faster convergence and lower computational overhead compared to traditional optimization methods. This makes the approach practical for real-time applications where multimedia data needs to be processed efficiently. Overall, this paper introduces an innovative framework that combines deep reinforcement learning with multimedia data optimization. The results indicate its potential for enhancing multimedia data quality, offering a promising solution to the challenges associated with traditional approaches. The proposed method not only improves the visual and auditory aspects of multimedia content but also provides a scalable and efficient solution for real-world applications in domains such as image processing, video streaming, and audio analysis.

Assessment of the Damage Incurred by biodiversity and ecological degradation via HEC-HMS simulations and of Stream Flow Assessment and Analysis (SAAS).A case study.

Findings on the quality of River Ikpoba samplings is an essential matter because water resources management coupled with hydro-chemical interfaces is of the essence, especially for monitoring and discovering of surface-water contaminants origins. Thus, River Ikpoba samplings were scrutinized using Stream Flow Simulation Model (SFSM) and Multivariate Statistical techniques. Runoff occasion due to land use change shows increase in curve number at peak runoff of 11.60m3/s, while the maximum rainfall is greater in the circumstances up to 141.0 mm. All though the year, not only that envisaged medians always greater than the mean monthly scrutinized, but the anticipated minimum temperature is also bigger, i.e. even more unadventurous prognosis of temperature result in warmer climate scenario in the study region. As the computed runoff occasion p-value (0.998) is greater than the significance level alpha=0.05, one cannot reject the null hypothesis H0. Hence, it was concluded that the simulated flow data are closely related to the observed flow data. Likewise, an increase in curve number (from 74.58 to 79.99) resulted in a corresponding increase in outflow (from 8.00cms to 10.350cms) was observed. Indicating, with increasing curve number, the peak discharge and direct runoff volume also increases proportionately.

The Energy Stream Analysis Method for Integrated Energy Systems

This research presents a novel method for analyzing energy flows in integrated energy systems (IES) using a likelihood-based approach. The proposed method incorporates the concept of Probabilistic Load Flow (PLF) from power systems into the analysis of IES likelihood energy flow points. By assessing the qualitative impact of uncertainties on the probability distribution of the state quantities in integrated energy systems encompassing electricity, air, and heat, this method enables a comprehensive evaluation of the load and output of wind electric fields. The outcomes of this research contribute to establishing a solid foundation for real-time network planning, steady-state analysis, optimized operation, control, and safety analysis of integrated energy systems.

FcγRIIA-specific DARPins as novel tools in blood cell analysis and platelet aggregation

Fc receptors are involved in a variety of physiologically and disease-relevant responses. Among them, FcγRIIA (CD32a) is known for its activating functions in pathogen recognition and platelet biology, and, as potential marker of T lymphocytes latently infected with HIV-1. The latter has not been without controversy due to technical challenges complicated by T-B cell conjugates and trogocytosis as well as a lack of antibodies distinguishing between the closely related isoforms of FcγRII. To generate high-affinity binders specific for FcγRIIA, libraries of designed ankyrin repeat proteins (DARPins) were screened for binding to its extracellular domains by ribosomal display. Counterselection against FcγRIIB eliminated binders cross-reacting with both isoforms. The identified DARPins bound FcγRIIA with no detectable binding for FcγRIIB. Their affinities for FcγRIIA were in the low nanomolar range and could be enhanced by cleavage of the His-tag and dimerization. Interestingly, complex formation between DARPin and FcγRIIA followed a two-state reaction model, and discrimination from FcγRIIB was based on a single amino acid residue. In flow cytometry, DARPin F11 detected FcγRIIA+ cells even when they made up less than 1% of the cell population. Image stream analysis of primary human blood cells confirmed that F11 caused dim but reliable cell surface staining of a small subpopulation of T lymphocytes. When incubated with platelets, F11 inhibited their aggregation equally efficient as antibodies unable to discriminate between both FcγRII isoforms. The selected DARPins are unique novel tools for platelet aggregation studies as well as the role of FcγRIIA for the latent HIV-1 reservoir.

Outdoor River Activities: Relations with Geological Background and Extreme Events in the Perspective of Geoeducation

As for sport climbing, also for outdoor activities such as rafting and canyoning, a strong connection with geofeatures on a long and short time scale exists. For investigating this, three river segments were selected in the Sesia hydrographic basin (Sesia Val Grande UNESCO Global Geopark, Northwest Italy). Here, commercial rafting and canyoning activities are popular, and important geological features are present (Sesia Supervolcano, Insubric Line). The segments were investigated using partly IDRAIM (system for stream hydromorphological assessment, analysis, and monitoring). Bedrock features, confinement, sinuosity, bankfull bottom, morphological units, and steepness were characterized. The segments were hence divided in several reaches. Correlations on the long time scale were found mainly between the geology and bankfull bottom configuration, while sinuosity allowed us to highlight the possible structural control on the genesis of gorges. Moreover, the analysis of changes in a short time scale along the river segments after the extreme meteorological event occurred on 2–3 October 2020, highlighted a rapids difficulty variation, channels diversion, and woody–rocky debris accumulation in the bankfull, deeply influencing river activities. Through the proposed approach, new frontiers in the outreach of geosciences could be opened, especially in virtue of the several Sustainable Development Goals that could be achieved through river outdoor activities, among the others new employment opportunities for local operators.

XR Technology Deployment in Value Creation

With increasing computing power and data transmission performance of information technologies, the application scenarios for Extended Reality (XR) technologies in industries are growing. Despite the ongoing scientific investigation of industrial XR applications for over 25 years, these technologies are still considered emerging. Within this paper, we present an industry- and business-process agnostic approach for classifying the deployment purposes of XR technologies in value creation. We identified two major research streams regarding the role of XR technologies in value creation: (1) the research initiatives focusing on business-process-specific use case analysis and (2) industry-oriented research reviews. This results in limited identification of suitable application scenarios for new use cases and restricted transferability of the existing use case to future deployments. First, we provide a qualitative analysis of the current research streams. Then, in the second step, by abstracting the XR technology from the existing business processes and the industry-specific context, the generic purposes for XR technologies in value creation are identified and defined. The summary of these deployment purposes results in a taxonomy that enables the identification and transfer of potential use cases of XR technologies in value creation.

An Enhanced Version of MDDB-GC Algorithm: Multi-Density DBSCAN Based on Grid and Contribution for Data Stream

With the continuous enrichment of big data technology application scenarios, the clustering analysis of a data stream has become a research hotspot. However, the existing data stream clustering algorithms usually have some defects, such as inability to cluster arbitrary shapes, difficulty determining some important parameters, and “static” clustering. In this study, a novel algorithm MDDSDB-GC is innovated. It selected MDDB-GC as the original algorithm that cannot deal with a data stream. In MDDSDB-GC, the calculation methods of contribution, grid density, and migration factor are effectively improved, and other parts are adjusted accordingly. The experiments show that MDDSDB-GC retains the advantage of MDDB-GC and obtains the ability to cluster an analysis for a data stream. At the same time, it effectively overcomes the above conventional defects, and its overall performance is better.

Fuzzy based optimized itemset mining in high dimensional transactional database using adaptable FCM

One of the extremely deliberated data mining processes is HUIM (High Utility Itemset Mining). Its applications include text mining, e-learning bioinformatics, product recommendation, online click stream analysis, and market basket analysis. Likewise lot of potential applications availed in the HUIM. However, HUIM techniques could find erroneous patterns because they don’t look at the correlation of the retrieved patterns. Numerous approaches for mining related HUIs have been presented as an outcome. The computational expense of these methods continues to be problematic, both in terms of time and memory utilization. A technique for extracting weighted temporal designs is therefore suggested to rectify the identified issue in HUIM. Preprocessing of time series-based information into fuzzy item sets is the first step of the suggested technique. These feed the Graph Based Ant Colony Optimization (GACO) and Fuzzy C Means (FCM) clustering methodologies used in the Improvised Adaptable FCM (IAFCM) method. The suggested IAFCM technique achieves two objectives: optimal item placement in clusters using GACO; and ii) IAFCM clustering and information decrease in FCM cluster. The proposed technique yields high-quality clusters by GACO. Weighted sequential pattern mining, which considers facts of patterns with the highest weight and low frequency in a repository that is updated over a period, is used to locate the sequential patterns in these clusters. The outcomes of this methodology make evident that the IAFCM with GACO improves execution time when compared to other conventional approaches. Additionally, it enhances information representation by enhancing accuracy while using a smaller amount of memory.

Research and evidence-based standards paving the way for a digital and sustainable transformation of the built environment

PurposeResearchers and standardisation bodies are key to accelerating societal transition and contributing to society’s demands for sustainability, resilience and digitalisation. Standards are the agreed-upon best practices published by national or international bodies and are key enablers of transformation. Scholars have not yet identified a relationship between standards and facilities management (FM) research. The purpose of this paper is to investigate the role of formal standards in FM research.Design/methodology/approachA literature review was conducted to identify journal articles addressing standards and FM. A total of 198 journal articles published from 2010 to 2021 were identified. After screening these articles, 27 journal articles were considered the most relevant for data analysis.FindingsThe findings show that the role of standards in research can be analysed thematically, categorically, textually, methodologically and directionally. Standards are relevant to research by defining terms, creating backgrounds, guiding research, supporting the development of new standards and encouraging more collaboration between research and standardisation. Some studies have shown how standards influence research, but only a few have explored how research influences standards.Research limitations/implicationsThis research provides examples that inspire stronger collaboration between people and processes in research and standardisation.Originality/valueThe articles collected and analysed in this study comprise original research. A limited preliminary study of ten core articles was presented at the International Council for Research and Innovation in Building and Construction World Congress 2022. This presentation of this work provides an expanded framework for analysing the roles of standards in research. This framework includes (1) categorical analysis of research and standardisation streams; (2) thematic analysis of the topic of interest; (3) textual analysis of the use of the term “standard”; (4) methodological analysis of the influence of standards on the research method; and (5) directional analysis of the intended audience.

Building an Acceleration Ladder with Tidal Streams and Pulsar Timing

We analyse stellar streams in action-angle coordinates combined with recent local direct acceleration measurements to provide joint constraints on the potential of our galaxy. Our stream analysis uses the Kullback–Leibler divergence with a likelihood analysis based on the two-point correlation function. We provide joint constraints from pulsar accelerations and stellar streams for local and global parameters that describe the potential of the Milky Way (MW). Our goal is to build an “acceleration ladder,” where direct acceleration measurements that are currently limited in dynamic range are combined with indirect techniques that can access a much larger volume of the MW. To constrain the MW potential with stellar streams, we consider the Palomar 5, Orphan, Nyx, Helmi, and GD1 streams. Of the potential models that we have considered here, the preferred potential for the streams is a two-component Staeckel potential. We also compare the vertical accelerations from stellar streams and pulsar timing, defining the function , where Φ is the MW potential determined from stellar streams and α 1 pulsar z is the vertical acceleration determined from pulsar timing observations. Our analysis indicates that the Oort limit determined from streams is consistently (regardless of the choice of potential) lower than that determined from pulsar timing observations. The calibration we have derived here may be used to correct the estimate of the acceleration from stellar streams.

Numerical investigation of submerged impinging jet based on different turbulent models

In today’s world, the loss of effective storage capacity caused by reservoir siltation has reached 50 billion cubic meters per year. As a high impact and simple water jet technology, submerged jet impact can have a powerful washout effect on submerged sediment. On this article, the submerged impinging jet is numerically computed with importing the Wray–Agarwal (W-A) turbulence models in FLUENT. Then, the streaming field analysis of the jet under different conditions is carried out and compared to the PIV test outcomes. To validate the effectiveness of the W-A turbulence models for predicting the streaming field of submerged impinging jet. The results show that, relative to other turbulence models, the calculated findings of the Wray–Agarwal turbulence model are in much closer accordance with the test data.

End-to-end child-adult speech diarization in naturalistic conditions of preschool classrooms

Speech and language development are early indicators of overall analytical and learning ability in pre-school children. Early childhood researchers are interested in analyzing naturalistic versus controlled lab recordings to assess both quality and quantity of such communication interactions between children and adults/teachers. Unfortunately, present-day speech technologies are not capable of addressing the wide dynamic scenario of early childhood classroom settings. Due to diversity of acoustic events/conditionsin daylong audio streams, automated speaker diarization technology is limited and must be advanced to address this challenging domain for audio segmentation and meta-data information extraction. We investigate a Deep Learning-based diarization solution for segmenting classroom interactions of 3–5 year-old children engaging with teachers. Here, the focus is on speaker-label diarization which classifies speech segments as belonging to either Adults or Children, partitioned across multiple classrooms. Our proposed ECAPA-TDNN model achieves a best F1-score of 65.5% on data from two classrooms, based on open dev and test sets for each classroom. Also, F1-scores for individual speaker labels provide a breakdown of performance across naturalistic child classroom engagement. The study demonstrates the prospects of addressing educational assessment needs through communication audio stream analysis, while maintaining both security and privacy of all children and adults.

An Environmental Analysis of Live Streaming in Social Media Commerce for Beauty Industry in Indonesia

Livestreaming is becoming one of the promotion strategies that companies used to promote the products in this advanced technology development. One of the industry that actively promote the products through live streaming is Beauty Industry. The beauty industry often used few platforms to do the livestreaming, for instance social media. The purpose of this study is to understand that live streaming is becoming one of the digital marketing strategy for beauty industry that has impacted to the industry and also to discuss the underlying potential and understand the weaknesses to avoid in the future. To achieve these goals, data must be collected and then analysed. This research uses a descriptive qualitative method and the data obtained will be analyzed using the SWOT matrix (Strengths, Weaknesses, Opportunities, Threats). The output of this research is expected to be adapted by sellers who utilize video streaming as one of the digital marketing and this research focused on two social commerce platforms, Tiktok & Instagram.

Analisa Waste Man hours Pada Pekerjaan Modifikiasi Structure Bulkhead Body STA 2598 Pesawat 747-400 Di PT. GMF Aeroasia Tbk

This study aims to determine the cause of the actual man hours that did not reach the target in the interim modification work which refers to the modification of the STA 2598 bulkhead body structure on the 747-400 aircraft at PT. GMF Aeroasia Tbk. Tangerang. The type of research used in this study is a qualitative and quantitative method which refers to data taken from 15 aircraft that have performed aircraft maintenance at PT. GMF Aeroasia Tbk. By analyzing the man hours that have been partnered with the SAP application, value stream mapping analysis techniques are obtained to find out the source of the problem from the man hours delay with the stages of making current state mapping in current conditions, making process activity mapping to map existing activities at the time of work. takes place based on VA, NVA and NNVA and obtained from the results of the study the biggest cause of out of target in manhours in the modification of the structure of the bulkhead body STA 2598 on the 747-400 aircraft that was treated at PT. GMF AeroAsia Tbk is waste over processing which reaches 21 hours 38 minutes with a percentage of 14.74%, waste waiting as much as 1 hour 14 minutes with a percentage of 0.84% and a total waste motion of 30 minutes with a percentage of 0.34% with a total waste during work lasted for 23 hours 22 minutes with a percentage of 15, 97% with the proposed improvement by increasing the effectiveness of duration manhours which previously reached 145 hours 4 minutes, to 121 hours 12 minutes. Keywords: VSM, Maintenance, Waste Analysis, Manhours, PAM

Food and social media: a research stream analysis

Interest in food and online communication is growing fast among marketing and business scholars. Nevertheless, this interest has been not exclusive to these areas. Researchers from different disciplines have focused their research on different concepts, target populations, approaches, methodologies, and theoretical backgrounds, making this growing body of knowledge richer, but at the same time difficult to analyze. In order to have a broader overview of this topic, this study analyzes the existent literature regarding food and social media in social sciences in order to identify the main research streams and themes explored. With this purpose, the present paper uses bibliometric methods to analyze 1356 journal articles by means of factor and social network analysis. The study contributes by revealing 4 clusters containing 11 dominant research streams within the social sciences, determining the linkages among the main research discourses, and recommending new future topics of research.