Integration Technique Research Articles

Advance industrial monitoring of physio-chemical processes using novel integrated machine learning approach

With the rapid transition of Industry 4.0 to 5.0, modern industrial physio-chemical processes are characterized by two critical challenges: process safety and the quality of the final product. Traditional industrial monitoring methods have low reliability in accuracy and robustness, and they are inefficiently providing satisfactory results. This paper introduces a novel integration technique that employs machine learning (ML) to tackle the challenges associated with real industrial monitoring in physical and industrial processes. The proposed framework integrates distributed canonical correlation analysis - R-vine copula (DCCA-RVC), global local preserving projection (GLPP), and 2-Dimensional Deng information entropy (2-DDE). The framework's ability and productivity are assessed utilizing existing approaches such as wavelet-PCA, MRSAE, and DALSTM-AE and the new proposed novel integrated machine learning-based (DCCA-RVC) approach as benchmarks for model performance. The proposed novel approach has been validated by testing it on the ethanol-water system distillation column (DC) and Tennessee Eastman Process (TEP), utilizing it as actual industrial benchmarks. The results demonstrate that the novel integration ML-technique (DCCA-RVC) T22 – GLP monitoring graphs for the fault class type 1 in the distillation column showed a (FAR) of 0 %, a (FDR) of 100 %, a precision of 100 %, F1-score of 100 % and an accuracy of 100 %. However, for the TEP process failure event 13, the (FAR) was 0 %, the (FDR) was 99 %, the accuracy was 100 %, the F1-score was 99.5 %, and the accuracy was 99.5 %.

Biofuser: a multi-source data fusion platform for fusing the data of fermentation process devices.

In the past decade, the progress of traditional bioprocess optimization technique has lagged far behind the rapid development of synthetic biology, which has hindered the industrialization process of synthetic biology achievements. Recently, more and more advanced equipment and sensors have been applied for bioprocess online inspection to improve the understanding and optimization efficiency of the process. This has resulted in large amounts of process data from various sources with different communication protocols and data formats, requiring the development of techniques for integration and fusion of these heterogeneous data. Here we describe a multi-source fusion platform (Biofuser) that is designed to collect and process multi-source heterogeneous data. Biofuser integrates various data to a unique format that facilitates data visualization, further analysis, model construction, and automatic process control. Moreover, Biofuser also provides additional APIs that support machine learning or deep learning using the integrated data. We illustrate the application of Biofuser with a case study on riboflavin fermentation process development, demonstrating its ability in device faulty identification, critical process factor identification, and bioprocess prediction. Biofuser has the potential to significantly enhance the development of fermentation optimization techniques and is expected to become an important infrastructure for artificial intelligent integration into bioprocess optimization, thereby promoting the development of intelligent biomanufacturing.

Economical process design of reactive-extractive distillation combining variable pressure and heat integration for separating a ternary azeotropic mixture

Research and development of environmentally friendly processes that can generate significant profits has always been a focus of attention in pharmaceutical and chemical fields. In this study, a novel reactive-extractive pressure-swing distillation (REPSD) process was designed to separate tetrahydrofuran (THF)-methanol-water (TMW) ternary azeotropic mixtures. Ethylene oxide (EO) is reacted with water to form ethylene glycol (EG) in a reactive distillation (RD) column, which removes the water from the mixture. Then, the THF-methanol (TM) mixture is separated by extractive distillation (ED) and pressure-swing distillation (PSD). A multi-objective genetic algorithm (MUOGA) with total annual cost (TAC) and total capital cost (TCC) as objective functions, along with heat integration techniques were used to optimise the designed REPSD process. Optimal operating conditions to achieve maximum economic benefits were obtained for the process. Subsequently, the environmental benefits of the process were evaluated. The results showed that the proposed REPSD with heat integration can significantly reduce the TAC and CO2 emissions by 29.1 and 26.9 %, respectively, compared to existing optimal heat integration extractive distillation (HIED) processes. This study provides a new perspective on the separation and purification of ternary azeotropic mixtures.

Heterogeneous Integration of Wide Bandgap Semiconductors and 2D Materials: Processes, Applications, and Perspectives.

Wide-bandgap semiconductors (WBGs) are crucial building blocks of many modern electronic devices. However, there is significant room for improving the crystal quality, available choice of materials/heterostructures, scalability, and cost-effectiveness of WBGs. In this regard, utilizing layered 2D materials in conjunction with WBG is emerging as a promising solution. This review presents recent advancements in the integration of WBGs and 2D materials, including fabrication techniques, mechanisms, devices, and novel functionalities. The properties of various WBGs and 2D materials, their integration techniques including epitaxial and nonepitaxial growth methods as well as transfer techniques, along with their advantages and challenges, are discussed. Additionally, devices and applications based on the WBG/2D heterostructures are introduced. Distinctive advantages of merging 2D materials with WBGs are described in detail, along with perspectives on strategies to overcome current challenges and unlock the unexplored potential of WBG/2D heterostructures.

Power quality improvement of grid‐connected solar power plant systems using a novel fractional order proportional integral derivative controller technique

AbstractRecently, there has been a push to integrate renewable energy system (RES) into grid‐connected load system in enhancing reliability and reducing losses. However, integrating these systems introduces power quality (PQ) issues, especially with non‐linear, critical, and imbalanced loads. Addressing this, a hybrid mantis search‐reptile search algorithm (HMS‐RSA) combined with a unified power quality conditioner (UPQC) to mitigate PQ problems related to current and voltages in RES systems. In other words, the UPQC, enhanced by fractional order proportional integral derivative controller parameters tuned using the proposed HMS‐RSA assists in enhancing the power quality. The approach has been validated by connecting a non‐linear load to the system, which typically creates PQ issues. The proposed method is implemented in MATLAB/Simulink and their performance is analysed in three scenarios, such as sag, swell, and disturbance, and the total harmonic distortion is evaluated to quantify improvements in PQ. Finally, the proposed method is compared with existing approaches, such as ant colony optimization (ACO), artificial bee colony optimization (ABC), and bacterial foraging optimization (BFO). The method also outperforms ACO, ABC, and BFO in terms of convergence speed and effectiveness in mitigating PQ issues.

A comprehensive review of integrated management strategies for damping-off disease in chili.

Damping-off disease in chili (Capsicum annum L.) cultivation is a significant global issue, severely affecting seeds, seedlings, and young plants, regardless of the location of cultivation, whether in greenhouses or open fields. Despite chili being a widely popular vegetable used in various cuisines globally, farmers face challenges in meeting the growing demand due to the extensive damage caused by this disease, ranging from 20 to 85%. The shelf life and quality of mature pods are also severely affected. Damping-off disease is mainly caused by soil-borne fungus from the Pythium species, with additional contributions from Phytophthora, Fusarium, and Rhizoctonia species. These pathogens' adaptability to diverse environmental conditions and resistance to synthetic fungicides make controlling damping-off on a commercial scale challenging. However, integrated disease management has shown promising results as a remedial approach. In this review, we discuss the current state of chili diseases, the nature of the pathogens causing damping-off, the epidemiology of the disease, and various control mechanisms. In this review, we broadly discuss the current state of chili diseases, the nature of the pathogens causing damping-off, the epidemiology of the disease, and various control mechanisms. Furthermore, we highlight the importance and efficacy of integrated disease management techniques, along with future prospects in unexplored areas, such as host-pathogen interaction and sustainable disease control measures. The information in this review aims to assist chili growers in understanding the epidemiology and management of damping-off in chili cultivation.

Pollution of organophosphorus pesticides in the Dongting Lake, China and its relationship with dissolved organic matter: Occurrence, source identification and risk assessment

The escalating global demand for food and industrialization has placed significant pressure on the integrity and management of inland lake ecosystems. Herein, the organophosphorus pesticides (OPPs) pollution status and their relationship with dissolved organic matter (DOM) in Dongting Lake were investigated to identify the ecological risks and potential sources of OPPs. The total concentrations of 18 detected OPPs were in the range of 13.49–375.24 ng/L, with higher concentration observed in east and west lake regions. Among these, fenthion was the dominant contributor, accounting for 64% of total OPPs, posing significant ecological risk to aquatic organisms. Nearly all of sites showed high combined risk of total OPPs. Parallel factor analysis (PARAFAC) and fluorescence regional integration (FRI) technique showed that DOM was mainly composed of terrestrial humic-like and tryptophan-like substances. Moreover, correlation analysis revealed a close association between DOM optical parameters and OPP concentrations. Specifically, OPPs exhibited a significantly positive correlation with tyrosine-like substances, while displaying a negative correlation with fulvic acid-like substances. These results indicated that OPP concentrations may decrease with increasing humification levels and declining tyrosine-like substance contents. This study underscores the critical role of DOM in assessing the occurrence and sources of OPPs in aquatic environments, providing valuable insights for effective environmental management strategies.

Public perspectives on the benefits and harms of lung cancer screening: A systematic review and mixed-method integrative synthesis.

Screening for lung cancer with low dose computed tomography aims to reduce lung cancer mortality, but there is a lack of knowledge about how target populations consider its potential benefits and harms. We conducted a systematic review of primary empirical studies published in any jurisdiction since 2002 using an integrative meta-synthesis technique. We searched six health and social science databases. Two reviewers independently screened titles, abstracts, and potentially eligible full-text studies. Quantitative assessments and open-ended perspectives on benefits and harms were extracted and convergently integrated at analysis using a narrative approach. Study quality was assessed. The review included 26 quantitative, 18 qualitative, and 5 mixed methods studies. Study quality was acceptable. Lung cancer screening was widely perceived to be personally beneficial for early detection and reassurance. Radiation exposure and screening accuracy were recognised as harms, but these were frequently considered to be justified by early detection of lung cancer. Stigma, anxiety, and fear related to screening procedures and results were pervasive among current smokers. People with low incomes reported not participating in screening because of potential out-of-pocket costs and geographic access. Populations targeted for lung cancer screening tended to consider screening as personally beneficial and rationalised physical, but not psychological, harms. Screening programmes should be clear about benefits, use non-stigmatising design, and consider equity as a guiding principle.

Numerical Integration Techniques: A Comprehensive Review

Numerical integration is a fundamental concept in computational mathematics and plays a crucial role in various scientific and engineering disciplines. This paper provides a comprehensive review of numerical integration techniques, their applications, comparative analysis, and conclusions. The discussed methods include the trapezoidal rule, Simpson’s rule, Gaussian quadrature, and Widdle’s method methods. The accuracy, efficiency, and limitations of each method are evaluated through theoretical analysis and practical examples.

Precision-induced localized molten liquid metal stamps for damage-free transfer printing of ultrathin membranes and 3D objects

Transfer printing, a crucial technique for heterogeneous integration, has gained attention for enabling unconventional layouts and high-performance electronic systems. Elastomer stamps are typically used for transfer printing, where localized heating for elastomer stamp can effectively control the transfer process. A key challenge is the potential damage to ultrathin membranes from the contact force of elastic stamps, especially with fragile inorganic nanomembranes. Herein, we present a precision-induced localized molten technique that employs either laser-induced transient heating or hotplate-induced directional heating to precisely melt solid gallium (Ga). By leveraging the fluidity of localized molten Ga, which provides gentle contact force and exceptional conformal adaptability, this technique avoids damage to fragile thin films and improves operational reliability compared to fully liquefied Ga stamps. Furthermore, the phase transition of Ga provides a reversible adhesion with high adhesion switchability. Once solidified, the Ga stamp hardens and securely adheres to the micro/nano-membrane during the pick-up process. The solidified stamp also exhibits the capability to maneuver arbitrarily shaped objects by generating a substantial grip force through the interlocking effects. Such a robust, damage-free, simply operable protocol illustrates its promising capabilities in transfer printing diverse ultrathin membranes and objects on complex surfaces for developing high-performance unconventional electronics.

Finite-time output feedback control for a class of stochastic low-order nonlinear systems with output function

This paper studies the problem of finite-time output feedback stabilisation for a class of stochastic low-order nonlinear systems with output function. Under the assumption that output function is continuous, by generalising the adding a power integrator technique, constructing a new implementable reduced-order observer and using the stochastic finite-time stability criterion, the maximal open sector Ω of output function is given. As long as output function belongs to any closed sector included in Ω, an output feedback controller can be developed to guarantee finite-time stability in probability of the closed-loop system. A simulation example is provided to verify the effectiveness of the proposed design method.

Employing Multi-Omics Analyses to Understand Changes during Kidney Development in Perinatal Interleukin-6 Animal Model.

Chronic kidney disease (CKD) is a leading cause of morbidity and mortality globally. Maternal obesity during pregnancy is linked to systemic inflammation and elevated levels of the pro-inflammatory cytokine interleukin-6 (IL-6). In our previous work, we demonstrated that increased maternal IL-6 during gestation impacts intrauterine development in mice. We hypothesized that IL-6-induced inflammation alters gene expression in the developing fetus. To test this, pregnant mice were administered IL-6 or saline during mid-gestation. Newborn mouse kidneys were analyzed using mRNA-seq, miRNA-seq and whole-genome bisulfite-seq (WGBS). A multi-omics approach was employed to quantify mRNA gene expression, miRNA expression and DNA methylation, using advanced bioinformatics and data integration techniques. Our analysis identified 19 key genes present in multiple omics datasets, regulated by epigenetics and miRNAs. We constructed a regulatory network for these genes, revealing disruptions in pathways such as Mannose type O-glycan biosynthesis, the cell cycle, apoptosis and FoxO signaling. Notably, the Atp7b gene was regulated by DNA methylation and miR-223 targeting, whereas the Man2a1 gene was controlled by DNA methylation affecting energy metabolism. These findings suggest that these genes may play a role in fetal programming, potentially leading to CKD later in life due to gestational inflammation.

Convex integration solution of two-dimensional hyperbolic Navier–Stokes equations*

Abstract Hyperbolic Navier–Stokes equations replace the heat operator within the Navier–Stokes equations with a damped wave operator. Due to this second-order temporal derivative term, there exist no known bounded quantities for its solution; consequently, various standard results for the Navier–Stokes equations such as the global existence of a weak solution, that is typically constructed via Galerkin approximation, are absent in the literature. In this manuscript, we employ the technique of convex integration on the two-dimensional hyperbolic Navier–Stokes equations to construct a weak solution with prescribed energy and thereby prove its non-uniqueness. The main difficulty is the second-order temporal derivative term, which is too singular to be estimated as a linear error. One of our novel ideas is to use the time integral of the temporal corrector perturbation of the Navier–Stokes equations as the temporal corrector perturbation for the hyperbolic Navier–Stokes equations.

Oscillatory and Asymptotic Criteria for a Fifth-Order Fractional Difference Equation

In this paper, using the properties of the conformable fractional difference and fractional sum, we initially establish some oscillatory and asymptotic criteria for a fifth-order fractional difference equation. Several critical inequalities, the Riccati transformation technique, and the integral technique are used in the deduction process. We provide some example to test the results. The established criteria are new results in the study of oscillation, and can be extended to other types of high-order fractional difference equations as well as fractional differential equations with more complicated forms.

An Analytical Review on Predictive Fault Tolerance for Industrial IoT Using Advanced Learning Techniques

This article presents a thorough examination of hybrid machine learning methodologies employed to improve fault tolerance in Industrial Internet of Things (IIoT) systems. These hybrid models enhance fault detection precision and resilience by integrating various machine learning techniques, surpassing conventional methods. The research commences with a comprehensive literature review of contemporary fault tolerance techniques, subsequently presenting a comparison of various datasets and a proposal for hybrid predictive algorithms. The efficacy of these algorithms is subsequently assessed in comparison to cutting-edge techniques. Principal findings underscore the enhanced precision of hybrid models in real-time applications, their diminished computing complexity, and their scalability in dynamic industrial settings. Nonetheless, issues pertaining to integration, data reliance, and real-time implementation are also addressed. The article concluded by outlining prospective research methods, encompassing sophisticated integration techniques, automated model optimisation, adaptive self-learning frameworks, and methodologies to safeguard data privacy. This paper outlines a framework for future advancements in fault tolerance, with considerable implications for both academia and industry.

Machine learning based intratumor heterogeneity signature for predicting prognosis and immunotherapy benefit in stomach adenocarcinoma

Stomach adenocarcinoma (STAD) is a prevalent malignancy that is highly aggressive and heterogeneous. Intratumor heterogeneity (ITH) showed strong link to tumor progression and metastasis. High ITH may promote tumor evolution. An ITH-related signature (IRS) was created using as integrative technique including 10 machine learning methods based on TCGA, GSE15459, GSE26253, GSE62254 and GSE84437 datasets. The relevance of IRS in predicting the advantages of immunotherapy was assessed using a number of prediction scores and three immunotherapy datasets (GSE78220, IMvigor210 and GSE91061). Vitro experiments were performed to verify the biological functions of AKR1B1. The RSF + Enet (alpha = 0.1) projected model was proposed as the ideal IRS because it had the highest average C-index. The IRS demonstrated a strong performance in serving as an independent risk factor for the clinical outcome of STAD patients. It performed exceptionally well in predicting the overall survival rate of STAD patients, as seen by the TCGA cohort’s AUC of 1-, 3-, and 5-year ROC curves, which were 0.689, 0.683, and 0.669, respectively. A low IRS score demonstrated a superior response to immunotherapy, as seen by a lower TIDE score, lower immune escape score, greater TMB score, higher PD1&CTLA4 immunophenoscore, higher response rate, and improved prognosis. Common chemotherapeutic and targeted treatment regimens had lower IC50 values in the group with higher IRS scores. Vitro experiment showed that AKR1B1 was upregulated in STAD and knockdown of AKR1B1 obviously suppressed tumor cell proliferation and migration. The present investigation produced the best IRS for STAD, which may be applied to prognostication, risk stratification, and therapy planning for STAD patients.

Determinant- and derivative-free quantum Monte Carlo within the stochastic representation of wavefunctions

Abstract Describing the ground states of continuous, real-space quantum many-body systems, like atoms and molecules, is a significant computational challenge with applications throughout the physical sciences. Recent progress was made by variational methods based on machine learning (ML) ansatzes. However, since these approaches are based on energy minimization, ansatzes must be twice differentiable. This (a) precludes the use of many powerful classes of ML models; and (b) makes the enforcement of bosonic, fermionic, and other symmetries costly. Furthermore, (c) the optimization procedure is often unstable unless it is done by imaginary time propagation, which is often impractically expensive in modern ML models with many parameters. The stochastic representation of wavefunctions (SRW), introduced in (Atanasova et al 2023 Nat. Commun. 14 3601), is a recent approach to overcoming (c). SRW enables imaginary time propagation at scale, and makes some headway towards the solution of problem (b), but remains limited by problem (a). Here, we argue that combining SRW with path integral techniques leads to a new formulation that overcomes all three problems simultaneously. As a demonstration, we apply the approach to generalized ‘Hooke’s atoms’: interacting particles in harmonic wells. We benchmark our results against state-of-the-art data where possible, and use it to investigate the crossover between the Fermi liquid and the Wigner molecule within closed-shell systems. Our results shed new light on the competition between interaction-driven symmetry breaking and kinetic-energy-driven delocalization.

The Value Attribution Solution: A Novel Approach to Financial Allocation and Performance Evaluation in Organizations

This paper introduces the Value Attribution Solution , a novel approach to financial allocation and performance tracking that enables organizations to monitor and allocate both costs and revenues at an unprecedented level of detail. By leveraging advanced data integration techniques and multi-dimensional analysis, the framework provides organizations with real-time insights into value creation and resource utilization across various operational dimensions. This research presents the theoretical foundations of the solution, details its methodology and implementation process, and discusses its potential impact on organizational performance and decision-making. Through a series of case studies and empirical analyses, we demonstrate the solution's effectiveness in enhancing financial performance, operational efficiency, and strategic planning across diverse industries. The solution represents a significant advancement in management accounting and financial control systems, offering a powerful tool for organizations seeking to optimize their resource allocation and value creation processes in an increasingly complex and dynamic business environment.

Designing cost-effective data solutions: Best practices from multi-million dollar projects involving cross-functional teams and diverse data sources

This paper explores the design and implementation of cost-effective data solutions, focusing on multi-million dollar projects that involve cross-functional teams and diverse data sources. The study aims to identify best practices and technological advancements that enhance data management efficiency, ensuring scalability, security, and compliance. Employing a comprehensive review of existing literature and case studies, the paper examines the challenges and opportunities in integrating technologies such as AI, machine learning, cloud computing, and blockchain. Key findings highlight the importance of cross-functional collaboration in addressing data management complexities and the adoption of agile methodologies to enhance adaptability and efficiency. The study also underscores the significance of advanced data integration techniques and the application of circular economy principles to promote sustainability in data management. The conclusion provides actionable recommendations for organizations seeking to optimize their data solutions, emphasizing the need for strategic investment in emerging technologies and a robust data governance framework. By offering insights into future trends and research directions, this paper contributes to the ongoing discourse on developing cost-effective, secure, and sustainable data solutions in an increasingly digitalized world. Keywords: Cost-Effective Data Solutions, Cross-Functional Teams, Data Integration, Cybersecurity, Artificial Intelligence, Circular Economy Principles

Investigation of optical soliton solutions for the cubic-quartic derivative nonlinear Schrödinger equation using advanced integration techniques

This paper aims to investigate optical soliton solutions in the context of the cubic-quartic derivative nonlinear Schrödinger equation with differential group delay, incorporating perturbation terms for the first time. Motivated by the need to better understand soliton dynamics in advanced optical communication systems, we employ three integration techniques: the direct algebraic approach, Kudryashov’s method with an addendum, and the unified Riccati equation expansion method. Our study reveals that, by appropriately selecting parameter values, the resulting solutions include Jacobi elliptic functions that describe straddle solitons, bright, dark, and singular solitons. We also identify the conditions under which these soliton pulses can exist. Furthermore, we provide numerical simulations to illustrate these solutions under specific parameter settings, highlighting their potential applications in optical fiber systems.