Batch Process Research Articles

RZera: A customized and user-friendly software for the total scattering data reduction at CSNS

Reduction Zone Era (RZera), a customized and user-friendly software designed for the reduction of time-of-flight (TOF) data from the Multi-physics Instrument (MPI) at China Spallation Neutron Source (CSNS), is developed and presented in this paper. After inputting the required information, the software can transform and apply a series of corrections from raw data to user data that only reflects the intrinsic properties of the sample. The software has two modes: diffraction and pair distribution function reduction. Each mode is capable of processing batch data quickly. The nickel has been used to test the diffraction and the pair distribution function reduction mode separately. The diffraction result has been fitted by GSAS, and the pair distribution function result has been fitted by the DiffPy-CMI suite. All the fittings demonstrate the effectiveness of RZera. The future versions of RZera will integrate additional instrument data reduction capabilities, and incorporate advanced algorithms and features that address emerging challenges in neutron scattering research.

Stream-aware indexing for distributed inequality join processing

Inequality join is an operator to join data on inequality conditions and it is a fundamental building block for applications. While methods and optimizations exist for efficient inequality join in batch processing, little attention has been given to its streaming version, particularly to large-scale data-intensive applications that run on Distributed Stream Processing Systems (DSPSs). Designing an inequality join in streaming and distributed settings is not an easy task: (i) indexes have to be employed to efficiently support inequality-based comparisons, but the continuous stream of data imposes continuous insertions, updates, and deletions of elements in the indexes—hence a huge overhead for the DSPSs; (ii) oftentimes real data is skewed, which makes indexing even more challenging.To address these challenges, we propose the Stream-Aware inequality join (STA), an indexing method that can reduce redundancy and index update overhead. STA builds a separate in-memory index structure for hotkeys, i.e., the most frequently used keys, which are automatically identified with an efficient data sketch. On the other hand, the cold keys are treated using a linked set of index structures. In this way, STA avoids many superfluous index updates for frequent items. Finally, we implement four state-of-the-art inequality join solutions for a widely employed DSPS (Apache Storm) and compare their performance with STA on four real-world data sets and a synthetic one. The results of our experimental evaluation reveal that our stream-aware approach outperforms existing solutions.

Competitive sequestration behavior and mechanism of Cd2+, Pb2+and Ni2+ ions from single, binary and ternary metal laden solution by Hevea brasiliensis wood sawdust (HBS)

HeveaBrasiliensis wood sawdust (HBS), a lignocellulosic material, was employed for the eradication of Cd(II), Pb(II), and Ni(II) from synthetic single and multi-metalsystem. In the realm of multimetal adsorption, HBS exhibited the most substantial adsorption capacities for heavy metals, in the order: Pb(II) > Cd(II) > Ni(II). Experiments involving batch process were carried out to explore the rolesofpH, contact duration, HBS quantity, and preliminary metal concentrations on the adsorption capacity. Optimal conditions for biosorption of metal ions were determined as pH 5.0, dosage of 7 gL−1, and an equilibrium time of 90 min. The pseudo-second-order kinetics andLangmuirisotherm had a favorable alignment with the biosorption of all individual metal ions. Adsorption capacity was 8.45, 13.01 and 6.04 mg g− 1 for Cd, Pb and Ni respectively in single metal system. In binary and ternary system the capacity and adsorption percentages decreases significantly due the presence of competitive metal ions. HBS demonstrated effectiveness in treating real wastewater containing Cd2+, Pb2+and Ni2+ ionsin both single and mixed metal solutions, highlighting the necessity of thoroughly considering the impact of competitive adsorption. SEM with EDS results indicated single metal fixing in a multi-ion solution followed complex mechanisms. The surface area and functional group studies were conducted utilizing BET and a FTIR Spectrometer. Electrostatic attraction and complexation mechanisms were identified as responsible factor for capturing metal ions to the active surfaces of the bio sorbent. Cost analysis reflected the adsorbent cost was $0.021 per liter of mixed contaminated ternary effluent (30 mg L−1).

Real-time multivariate statistical monitoring of biopharmaceutical processes with no prior product-specific history

Biologics drug substance manufacturing normally comprises several processing steps including cell culture, purification, etc. Real-time Multivariate Statistical Process Monitoring (RT-MSPM) is an integral need in order to achieve early fault detection and prevention to guarantee robust and efficient process performance. As a part of RT-MSPM framework, Batch Evolution Models (BEMs), which take into account the transient phase of a batch prior to reaching full maturity (steady state), are the standard methods for real-time early fault detection in batch processes. Prior product-specific knowledge and training dataset are critical in order to model time (batch maturity) dependencies. Unlike traditional settings, modern biopharmaceutical manufacturing and clinical facilities use modular designs to enable simultaneous production of multiple medicines and quickly pivot from producing one medicine to another where no prior product-specific historical data might be available. In this article, first, a framework is proposed to detect the steady state operation of a manufacturing scale cell culture bioreactor. This is critical to achieve the ultimate goal to demonstrate feasibility of developing a continuous statistical process monitoring framework to monitor the steady state operation of a batch with no prior product specific history. Steady state time series data from previous step are processed following a systematic approach. The requirements for statistical properties of the processed time series in order to leverage data from different products to monitor a new product are outlined. The new framework establishes a guideline to develop models using datasets from other products with different recipes to monitor new products with no prior product-specific history. A novel software product is developed to test the framework. The framework is successfully applied for real-time monitoring of fast-rate sensors of a manufacturing cell culture bioreactor.

Comparison of batch and continuous multi-column capture of monoclonal antibodies with convective diffusive membrane adsorbers

Protein A affinity membrane adsorbers are a promising alternative to resins to intensify the manufacturing of monoclonal antibodies. This study examined the process performance of convective diffusive membrane adsorbers operated in batch and continuous multi-column mode. Therefore, three different processes were compared regarding membrane utilization, productivity, and buffer consumption: the batch process, the rapid cycling parallel multi-column chromatography process, and the rapid cycling simulated moving bed process. The influence of the monoclonal antibody loading concentration (between 0.5 g L-1 and 5.2 g L-1) and the loading flow rate (between 1.25 MV min-1 and 10 MV min-1) on the monoclonal antibody binding behavior of the membrane adsorber were studied with breakthrough curve experiments. The determined breakthrough curves were used to calculate the monoclonal antibody dynamic binding capacity, the duration of the loading steps for each process, and the number of required membrane adsorbers for the continuous processes rapid cycling parallel multi-column chromatography and rapid cycling simulated moving bed. The highest productivity for the batch (176 g L-1 h-1) and rapid cycling parallel multi-column chromatography process (176 g L-1 h-1) was calculated for high monoclonal antibody loading concentrations and low loading flow rates. In contrast, the rapid cycling simulated moving bed process achieved the highest productivity (217 g L-1 h-1) for high monoclonal antibody loading concentrations and loading flow rates. Furthermore, due to the higher membrane utilization, the buffer consumption of the rapid cycling simulated moving bed process (1.1 L g-1) was up to 1.9 times lower than that of the batch or rapid cycling parallel multi-column chromatography operation (2.1 L g-1).

Adaptive two-dimensional subspace identification for monitoring batch processes with limited batch data

Data-driven based batch process monitoring is of critical importance in ensuring stable operating processes and consistent product quality. For long-duration batch processes, it is unrealistic to involve expensive data to train a statistical model for monitoring. To model the inherently batch-wise and variable-wise dynamics, nonlinearity, and time-varying characteristics, this paper proposes a local learning-based two-dimensional subspace identification (LL-2D-SID) scheme based on the similarity between the ongoing batch and the previous batches. The similarity is estimated by the extended extrapolative time-warping. Unlike the conventional statistical models using rich batch data, LL-2D-SID through online optimizing mechanism using limited batch data still has good prediction performance. The application of the sintering process in the polytetrafluoroethylene production has demonstrated that the LL-2D-SID based process monitoring scheme can not only accurately track temperature changes but also timely give fault alarms with a lower error alarm rate than the other SID-based process monitoring schemes.

Incremental minimum distortion embedding for online structural damage classification.

The damage detection problem in structures diminishes the maintenance cost and extends the life of the structures. Online structural damage classification is a relevant topic in structural health monitoring since monitoring the structure using sensors can help decision-makers can rely on empirical evidence for making informed choices regarding the maintenance, repair, or replacement of structural components. Sensors generate high-dimensional data. To address this challenge, employing dimensionality reduction methods becomes essential. These methods help obtain a low-dimensional representation of the data. Traditional dimensionality reduction methods are trained on a fixed data set, and incorporating new data may require retraining the entire model. This study employed the Minimum Distortion Embedding (MDE) method incrementally, ensuring the maintenance of a consistent embedding as new data becomes available. This incremental embedding took advantage of an anchor constraint to pin the existing embedding in place, then embed the new points. An online structural classification methodology was developed using the incremental MDE and a stream based hoeffding tree classifier. Stream data processing differs from batch processing, as it involves the real-time consumption of each incoming data. The developed methodology was tested with data obtained by accelerometers in a laboratory scaled jacket-type wind turbine foundation. The structural damage classification problems had 5 different classes including the undamaged class and 4 different damage classes. The damage corresponded to a 5mm crack in four different structural elements of the wind turbine foundation. The results indicate the good behavior of the damage classification methodology supported by the high values of the classification metrics obtained.

NanoTrans: an integrated computational framework for comprehensive transcriptome analysis with nanopore direct RNA sequencing

Nanopore direct RNA sequencing (DRS) provides the direct access to native RNA strands with full-length information, shedding light on rich qualitative and quantitative properties of gene expression profiles. Here with NanoTrans, we present an integrated computational framework that comprehensively covers all major DRS-based application scopes, including isoform clustering and quantification, poly(A) tail length estimation, RNA modification profiling, and fusion gene detection. In addition to its merit in providing such a streamlined one-stop solution, NanoTrans also shines in its workflow-orientated modular design, batch processing capability, all-in-one tabular and graphic report output, as well as automatic installation and configuration supports. Finally, by applying NanoTrans to real DRS datasets of yeast, Arabidopsis, as well as human embryonic kidney and cancer cell lines, we further demonstrate its utility, effectiveness, and efficacy across a wide range of DRS-based application settings.

SiO2‐GaN Interface Improvement by Wet Cleaning and In Situ Annealing for GaN MOS Transistors

Herein, ex situ wet cleaning and in situ high‐temperature annealing of GaN surfaces prior to low pressure chemical vapor deposition (LPCVD) of the SiO2 gate oxide, aiming at effective SiO2‐GaN interface engineering for channel improvement of metal–oxide semiconductor (MOS) transistors, are investigated. Additionally, the combination of in situ annealing and gate oxide deposition in an LPCVD tool provides the advantage of an industrially preferred batch process. A strong impact of the pretreatments on the interface state density and flatband voltage of the fabricated n‐type GaN MOS capacitors is demonstrated. Combined HF wet cleaning and NH annealing result in a low peak interface state density and a close to ideal C–V curve with a nearly ideal flatband voltage . Furthermore, the I–V characteristics exhibit a positive voltage shift of the current onset and substantially reduced I‐V hysteresis, i.e., negligible temporary charging. Physical root causes are assumed to be reduced contamination due to nondestructive yet efficient HF cleaning combined with subsequent high temperatures and the reduction of near‐interface, quasi‐permanent traps due to the saturation of dangling bonds by the annealing in hydrogen‐containing atmosphere.

Automated fiducial-based alignment of cryo-electron tomography tilt series in Dynamo

With the advent of modern technologies for cryo-electron tomography (cryo-ET), high-quality tilt series are more rapidly acquired than processed and analyzed. Thus, a robust and fast-automated alignment for batch processing in cryo-ET is needed. While different software packages have made available several approaches for automated marker-based alignment of tilt series, manual user intervention remains necessary for many datasets, thus preventing high-throughput tomography. We have developed a MATLAB-based framework integrated into the Dynamo software package for automatic detection of fiducial markers that generates a robust alignment model with minimal input parameters. This approach allows high-throughput, unsupervised volume reconstruction. This new module extends Dynamo with a large repertory of tools for tomographic alignment and reconstruction, as well as specific visualization browsers to rapidly assess the biological relevance of the dataset. Our approach has been successfully tested on a broad range of datasets that include diverse biological samples and cryo-ET modalities.

IsolateR: an R package for generating microbial libraries from Sanger sequencing data.

Sanger sequencing of taxonomic marker genes (e.g. 16S/18S/ITS/rpoB/cpn60) represents the leading method for identifying a wide range of microorganisms including bacteria, archaea, and fungi. However, the manual processing of sequence data and limitations associated with conventional BLAST searches impede the efficient generation of strain libraries essential for cataloging microbial diversity and discovering novel species. isolateR addresses these challenges by implementing a standardized and scalable three-step pipeline that includes: (1) automated batch processing of Sanger sequence files, (2) taxonomic classification via global alignment to type strain databases in accordance with the latest international nomenclature standards, and (3) straightforward creation of strain libraries and handling of clonal isolates, with the ability to set customizable sequence dereplication thresholds and combine data from multiple sequencing runs into a single library. The tool's user-friendly design also features interactive HTML outputs that simplify data exploration and analysis. Additionally, in silico benchmarking done on two comprehensive human gut genome catalogues (IMGG and Hadza hunter-gather populations) showcase the proficiency of isolateR in uncovering and cataloging the nuanced spectrum of microbial diversity, advocating for a more targeted and granular exploration within individual hosts to achieve the highest strain-level resolution possible when generating culture collections. isolateR is available at: https://github.com/bdaisley/isolateR.

Pemodelan Dinamis Proses Produksi Batch Satu Tahap Dalam Satu Mesin Dengan Memperhatikan Waktu Loading dan Waktu Proses

In the batch process, the company uses the same process facilities to produce various types of products. This allows businesses to change production in response to changes in market demand. Batch processes are widely used in industry. In many cases, batch processes provide several benefits over continuous processes, especially when the industry wants to ensure and maintain the quality standards of its products. This paper proposes a behavioral model of a single-stage batch processing system, using the language of system dynamics. When run, the behavioral model will provide an overview of how the system behaves. The system dynamics language is a formulation of a system of ordinary differential equations that is extracted from the real system being studied, in the form of a stock and flow diagram. The computation of this diagram is further implemented using software. Because it is actually a calculus method, the system dynamics language provides output data much faster than discrete simulation modeling. In this paper, the system behavior modeled is mainly loading, processing and unloading activities. The verification results carried out by testing it on two different case examples show that the model can sufficiently describe the behavior of the conceptual system logically so that the model can enrich experimental tool options related to batch production processes in the future.

Sample-by-sample Power Quality Disturbance classification based on Sliding Window Recursive Discrete Fourier Transform

Power Quality Disturbances (PQDs) are caused by large-scale grid connections of nonlinear loads and Distributed Generations (DG). They affect the electrical and electronics equipment performance and may cause serious accidents and economic losses. The classification of PQDs becomes a key issue for end-users in order to enhance the Power Quality (PQ) and comprises an important step in monitoring the Electric Power System (EPS). Accurate PQDs classification is crucial for improving the quality of power supply, monitoring the condition of power equipment, and mitigating power grid issues. In the literature, several methods for detecting and classifying PQDs have been proposed. However, these methods consume a significant amount of processing time due to the detection and sample storage steps required to classify each disturbance class. In this work, we propose a method for classifying PQDs on a sample-by-sample basis. The innovation of this method lies in its ability to classify PQDs for each individual sample, thereby eliminating the need for window-based (batch) processing. To do so, it is applied the Sliding Window Recursive Discrete Fourier Transform (SWRDFT) to real-time estimation of PQ parameters. For the classification stage, decision tree, Artificial Neural Networks (ANN), Ensemble and the Naive Bayes classifiers were employed. The proposed method using Ensemble achieved the most satisfactory results for six different types of PQDs (oscillatory transients, harmonics, notches, sags, spikes and swells), in which oscillatory transients and spikes were identified in the first monitored samples (sample 2 and 11 respectively), sags were identified in the 14th sample counting from the beginning of the disturbance, notches were captured from the 35th sample, and harmonics and swells were identified after the 113th sample.

Faster (Multi) Document Summarization using PRIMERA & PEGASUS

Text summarization is super important when it comes to dealing with loads of information that's available on the internet and in archives. Trying to summarize all that stuff manually would be impossible because there's just so much of it. That's why automatic summarization techniques have become a big deal. These techniques basically condense documents by picking out the most important ideas. At first, people were mainly focused on summarizing individual documents, but now the latest research is all about summarizing groups of documents. In our study, we've come up with this cool new way to combine the Primera and Pegasus models. This combo not only improves the quality of the summaries, but it also speeds up the whole process. And guess what? We can even summarize multiple documents at the same time thanks to some batch processing techniques that make everything run smoother.

Continuous flow production of bioactive ceria quantum dots: New paradigms to the effect of process parameters on surface oxygen vacancy tuning

Precisely monitored nucleation-growth kinetics for governing the size, surface chemistry, and sought-after attributes of quantum dots (QDs) for large-scale manufacturing remains a formidable challenge. This study evinces the importance of ultrafast mixing and high heating rates for rapid nucleation, particularly in synthesizing monodispersed QDs with enriched surface defects. Recently, cerium oxide (CeO2) nanostructures have gained prominence in antioxidant therapy owing to the co-existence of Ce3+ and Ce4+. However, current batch processes lack scalability, reproducibility, and control over the reaction kinetics, key for fine-tuning the surface defect-driven properties of CeO2 nanostructures. Addressing this knowledge gap, we demonstrate a unique, sustainable, continuous flow platform that allows simultaneous engineering of surface oxygen vacancies (VO●) and regulates the size of L arginine functionalized CeO2 QDs (VO●-rich l-arg-CeO2 QDs). Introduction of the helical coil reactor regulated by dean vortices yields monodispersed QDs with enhanced Ce3+/Ce4+ratio and VO● at the surface. Through various experimental methodologies, we showed how adjusting temperature, flowrate, and pH enables achieving the desirable size (3 nm), thereby bestowing an optimal surface Ce3+ and VO● fractions, pivotal for size-dictated photo-response, physiochemical properties, and biofunctionality of the QDs. The abundant surface VO● (72 %) entails narrowing of the band gap (∼ 2.5 eV), resulting in unprecedented photothermal response (ΔT = 19.7 ± 0.6 °C) and photoluminescence, features not typically found in defect-free conventional CeO2 nanostructures. With a strategic combination of process parameters, the defect-rich material system displayed excellent biocompatibility (95.6 %), and antioxidant efficacy on human keratinocyte (HaCaT) cells, and long-term stability (ζ = -29±1.5 mV) in suspensions, even after 120 days. This economical, high throughput continuous flow platform for fabricating biofunctionalized VO●-rich l-arg-CeO2 QDs outperforms conventional batch processes, opening numerous possibilities for lab-to-clinic translation in the fight against oxidative stress-related disorders.

НАУКОВО-МЕТОДИЧНІ ЗАСАДИ РЕСТРУКТУРИЗАЦІЇ ВИРОБНИЧОГО ПРОЦЕСУ

Batch production is typical for engineering enterprises connected with aeronautical engineering manufacturing. One of its main problems is the need to periodically change the production pattern which arises as a result of the instability of the products range, changes in volume of series, moral and physical wear of technological equipment. Such a change is carried out in the course of reconstruction and technical re-equipment in order to improve the efficiency of material flows.The main characteristics of material flows are determined by a spatial component of a flexible manufacturing system pattern, set by the machine tools laying according to the organization of batch process. The bulk of the cost of final product manufacturing is associated with the arrangement of material flows. In order to reduce the total costs it is necessary to improve the pattern of production by establishing a technology-oriented machine tools laying. In this case, there is a need to develop scientific and methodological basis of restructuring the manufacturing process.One of the most effective approaches to the design of machine building plants with a batch type of manufacturing is the use of program-target method based on the methodology of choosing the best technological solutions.Modern methodology of batch technologies design is based on the system approach to the analysis and synthesis of the basic structure of the manufacturing processes, which allows to apply mathematical simulation.The orientation of each of the sites on the end result leads to a substantial reduction of manufacturing costs, which significantly simplifies solving problems of division and coordination of labor on the principles of self-organization and self-regulation.The proposed mathematical model of manufacturing costs optimization is designed in accordance with all the aforementioned principles. The problem being solved is multiobjective. That is why the mathematical model has several goal functions them requiring finding peak values and some of them requiring finding the bottom values.The model can be applied in organizing the aeronautical engineering manufacturing, which requires effective remounting during the change of product range thus ensuring the required flexibility of manufacturing.

Active-Learning Reliability Analysis of Automotive Structures Based on Multi-Software Interaction in the MATLAB Environment

The reliability design of automotive structures is characterized by numerous variables and implicit responses. The traditional design of experiments for metamodel construction often requires manual adjustment of model parameters and extensive finite element analysis, resulting in inefficiency. To address these issues, active learning-based reliability methods are effective solutions. This study proposes an active-learning reliability analysis method based on multi-software interaction. Firstly, through secondary development of different software and MATLAB (version 2023a)’s batch processing function, a multi-software interactive reliability analysis method is developed, achieving automation in structural parametric design, finite element analysis and post-processing. This provides a more efficient and convenient platform for the implementation of active learning. Secondly, the polynomial chaos–kriging (PCK) active-learning method is introduced, combining the advantages of polynomial chaos expansion (PCE) and kriging. The PCK method captures the global behavior of the computational model using regression-based PCE and local variations using interpolation-based kriging. This metamodel is constructed with fewer training samples, effectively replacing the real multi-dimensional implicit response relations, thereby improving the efficiency of modeling and reliability analysis. Finally, the specific implementation scheme is detailed. The accuracy and efficiency of the proposed method are verified by a reliability engineering example of body-in-white bending and torsional stiffness.

Fast Algorithm of Passive Bistatic Radar Detection Based on Batches Processing of Sparse Representation and Recovery

In the passive bistatic radar (PBR) system,methods exist to address the issue of detecting weak targets without being influenced by non-ideal factors from adjacent strong targets. These methods utilize the sparsity in the delay-Doppler domain of the cross ambiguity function (CAF) to detect weak targets. However, the modeling and solving of this method involve substantial memory consumption and computational complexity. To address these challenges, this paper establishes a target detection model for PBR based on batch processing of sparse representation and recovery. This model partitions the CAF into blocks, identifies blocks requiring processing based on the presence of targets, and improves the construction and utilization of the measurement matrix. This results in a reduction in the computational complexity and memory resource requirements for sparse representation and recovery, and provides favorable conditions for parallel execution of the algorithm. Experimental results indicate that the proposed approach increases the number of blocks by a factor of four, and reduces the number of real multiplications by approximately an order of magnitude. Hence, compared with the traditional approach, the proposed approach enables fast and stable detection of weak targets.

Distributed Batch Learning of Growing Neural Gas for Quick and Efficient Clustering

Growing neural gas (GNG) has been widely used in topological mapping, clustering and unsupervised tasks. It starts from two random nodes and grows until it forms a topological network covering all data. The time required for growth depends on the total amount of data and the current network nodes. To accelerate growth, we introduce a novel distributed batch processing method to extract the rough distribution called Distributed Batch Learning Growing Neural Gas (DBL-GNG). First, instead of using a for loop in standard GNG, we adopt a batch learning approach to accelerate learning. To do this, we replace most of the standard equations with matrix calculations. Next, instead of starting with two random nodes, we start with multiple nodes in different distribution areas. Furthermore, we also propose to add multiple nodes to the network instead of adding them one by one. Finally, we introduce an edge cutting method to reduce unimportant links between nodes to obtain a better cluster network. We demonstrate DBL-GNG on multiple benchmark datasets. From the results, DBL-GNG performs faster than other GNG methods by at least 10 times. We also demonstrate the scalability of DBL-GNG by implementing a multi-scale batch learning process in it, named MS-DBL-GNG, which successfully obtains fast convergence results. In addition, we also demonstrate the dynamic data adaptation of DBL-GNG to 3D point cloud data. It is capable of processing and mapping topological nodes on point cloud objects in real time.

Robust Structure-Aware Graph-based Semi-Supervised Learning: Batch and Recursive Processing

Graph-based semi-supervised learning plays an important role in large scale image classification tasks. However, the problem becomes very challenging in the presence of noisy labels and outliers. Moreover, traditional robust semi-supervised learning solutions suffers from prohibitive computational burdens thus cannot be computed for streaming data. Motivated by that, we present a novel unified framework robust structure-aware semi-supervised learning called Unified RSSL (URSSL) for batch processing and recursive processing robust to both outliers and noisy labels. Particularly, URSSL applies joint semi-supervised dimensionality reduction with robust estimators and network sparse regularization simultaneously on the graph Laplacian matrix iteratively to preserve the intrinsic graph structure and ensure robustness to the compound noise. First, in order to relieve the influence from outliers, a novel semi-supervised robust dimensionality reduction is applied relying on robust estimators to suppress outliers. Meanwhile, to tackle noisy labels, the denoised graph similarity information is encoded into the network regularization. Moreover, by identifying strong relevance of dimensionality reduction and network regularization in the context of robust semi-supervised learning (RSSL), a two-step alternative optimization is derived to compute optimal solutions with guaranteed convergence. We further derive our framework to adapt to large scale semi-supervised learning particularly suitable for large scale image classification and demonstrate the model robustness under different adversarial attacks. For recursive processing, we rely on reparameterization to transform the formulation to unlock the challenging problem of robust streaming-based semi-supervised learning. Last but not least, we extend our solution into distributed solutions to resolve the challenging issue of distributed robust semi-supervised learning when images are captured by multiple cameras at different locations. Extensive experimental results demonstrate the promising performance of this framework when applied to multiple benchmark datasets with respect to state-of-the-art approaches for important applications in the areas of image classification and spam data analysis.