Applications In Industry Research Articles

Tuning of FOPID parameters combined with swarm intelligent algorithm

This paper delves into the parameter tuning of fractional-order PID (FOPID) controllers. FOPID controllers, with additional integral and derivative orders compared to traditional PID controllers, possess enhanced capabilities in handling complex systems. However, effective tuning of its five parameters is challenging. To address this, multiple intelligent algorithms are investigated. The improved sparrow search algorithm (ISSA) utilizes Chebyshev chaotic mapping initialization, adaptive t-distribution, and the firefly algorithm to overcome the limitations of the basic algorithm, showing high accuracy, speed, and robustness in multi-modal problems. The grey wolf optimizer (GWO), inspired by the hunting behavior of grey wolves, has procedures for encircling, hunting, and attacking but may encounter local optima, and several improvement methods have been proposed. The genetic algorithm, based on the survival of the fittest principle, involves encoding, decoding, and other operations. Taking vehicle ABS control as an example, the genetic algorithm-based FOPID controller outperforms the traditional PID controller. In conclusion, different algorithms have their own advantages in FOPID parameter tuning, and the selection depends on system characteristics and control requirements. Future research can focus on further algorithm improvement and hybrid methods to achieve better control performance, providing a valuable reference for FOPID applications in industry.

Effect of graphene oxide on hydration activity of spherical magnesium oxide

The spherical morphology of magnesium oxide (MgO) exhibits a high porosity and an extensive specific surface area, contributing to its significant hydration activity. These characteristics make it a valuable material across various applications in the construction industry. Graphene oxide (GO) is rich in functional groups, which have been increasingly utilized in recent years for the surface modification of various materials. In this study, spherical MgO was synthesized, and GO was applied to its surface using a wet mixing method to investigate the effects of GO on the hydration performance of MgO and to analyze the underlying mechanisms. The results indicate that within the range of 0.0% to 0.6% GO content, the hydration activity of MgO first decreases and then increases with increasing GO dosage. GO tends to adsorb onto the MgO surface during hydration, initially inhibiting early hydration reactions. However, when the GO content exceeds 0.3%, free GO in the system complexes with Mg2+ ions in the solution, which subsequently promotes the hydration of MgO.

Superior Flame Retardancy, Mechanical and Dielectric Properties of Epoxy Resin Composites Based on Molybdenum Disulfide‐Melamine Trimetaphosphate Crystallite Composites

ABSTRACTTo improve the flame retardancy, mechanical and dielectric properties of epoxy resin (EP), promoting the application in aeronautic industry and other fields, molybdenum disulfide‐melamine trimetaphosphate (MoS2‐MAP) crystallite composites are synthesized. The chemical, crystalline structure and thermal stabilities of MoS2‐MAPs are thoroughly characterized by Fourier transform infrared (FTIR), X‐ray diffraction (XRD), scanning electron microscope (SEM), and thermo‐gravimetric analysis (TGA) techniques, respectively. The morphologies of EP/MoS2‐MAP composites are assessed by SEM, thermal stability by TGA, fire retardancy by limiting oxygen index test (LOI), vertical burning standard test (UL‐94), and cone calorimeter tests, mechanical properties by stress strain tests, and dielectric properties by impedance analyzer. Compared with pure EP, the mechanical, dielectrical properties, thermal stabilities and flame retardancy of EP/MoS2‐MAP composites can be significantly improved due to the good dispersibility of MoS2 and MAP in matrix. While, the properties of EP/MoS2‐MAP composites can be further adjusted by controlling the mass raito of MoS2 and MAP in MoS2‐MAP crystallite. EP with 4 wt% of MoS2‐MAP‐50% (P content: 0.30%) shows the lowest dielectric constant and dielectric loss values on the basis of good flame retardancy and mechanical properties. The results show that the MoS2‐MAP crystallite composites can promote the application of EP composites in aeronautical devices, radar detection and other fields.

Potential of New Plant Sources as Raw Materials for Obtaining Natural Pigments/Dyes

Natural dyes can be extracted from fruits, flowers, leaves, and roots. Exploring new sources of natural dyes, especially from underutilized plants, emerges as a promising strategy. The main advantages of exploiting unconventional plants include local availability, specialty food production, cultural significance, sustainable production, technological feasibility, and new fundamental insights. Finding and exploiting such underutilized plants is significant as unfavorable climatic and human conditions put natural vegetation at risk worldwide. Thus, this study aims to review plants with potential applications as natural dyes and pigments, highlighting their potential applications, benefits, and prospects. An integrative review was conducted by searching Web of Science, ScienceDirect, and SpringerLink for all studies published up to December 2024. For this review, a total of 133 references that presented the information and data of interest to the authors were selected. This review highlighted their potential applications in food, cosmetic, pharmaceutical, and textile industries. Despite the growing interest in natural dyes, challenges related to their stability, seasonality, and extraction efficiency continue to limit their commercial use. However, advancements in extraction technologies have improved the applicability of these compounds. Additionally, utilizing underexplored plant sources presents a strategic opportunity to diversify dye production, reduce reliance on traditional sources, and promote more sustainable practices.

Static and Fatigue Properties of Rhenium-Alloyed Inconel 718 Produced by Powder Bed Fusion Additive Manufacturing

Inconel 718 (In718) is the most widely used nickel-based alloy in additive manufacturing due to its favorable processability. However, In718’s high-temperature performance is not suited for the most demanding applications in the aerospace industry. Therefore, in this study, Inconel 718 powder was coated with 3% wt. rhenium (In718-Re) using AM’s in situ alloying capabilities to improve high-temperature properties. The proposed alloy’s mechanical performance was evaluated, focusing on the effects of post-process heat treatment and hot isostatic pressing following the laser-based powder bed fusion of metals (PBF-LB/M) processing. Static tensile tests conducted at room temperature and elevated temperatures (650 °C and 760 °C) demonstrated that the alloy has comparable strength to pure In718 according to ASTM F3055-14a—an ultimate tensile strength of 1247 MPa, yield strength of 909 MPa, and almost 2× higher elongation of 23.8%. Fatigue tests at room temperature indicated a fatigue limit below 400 MPa for 107 cycles. Fractographic analysis revealed that fatigue performance was primarily impacted by a lack of fusion defects inherent to the PBF-LB/M process, highlighting the need for optimized powder preparation and processing parameters to minimize defect formation. While rhenium addition shows limited benefits in Inconel 718, this study underscores the potential of in situ alloying through powder surface modification as a flexible method for incorporating high-melting-point elements into nickel-based alloys for tailored alloy design in additive manufacturing.

Hybrid quantum neural networks show strongly reduced need for free parameters in entity matching

Modern technology and scientific experiments increasingly generate larger and larger amounts of data. This data is sometimes redundant, incomplete or inaccurate and needs to be cleaned and merged with other data before becoming useful for scientific exploration. Hence, entity matching, i.e. the process of linking data about a given entity gathered from multiple data sets, is a major problem in artificial intelligence with applications in science and industry. Typical methods for entity matching either use specialized algorithms or supervised machine learning. Although the problem has been well studied on classical computers, it is unclear how quantum approaches would tackle these challenges. In this paper, we evaluate quantum machine learning algorithms for entity matching on a hand-crafted data set and compare them to similar classical algorithms. We do this by implementing a neural network with a classical embedding layer and extending it with quantum layers. Our experimental results suggest that our hybrid quantum neural network reaches similar performance as classical approaches while requiring an order of magnitude fewer parameters than its classical counterpart. Furthermore, we also show that a model trained on a quantum simulator is portable and thus transferable to a real quantum computer. From a practical perspective and as long as quantum hardware is a scarce resource, experiments, e.g. addressing performance, can profit from producing good initial configurations for quantum neural networks via a simulator, thus only leaving the fine-tuning to quantum computations.

In Situ Growth of Iron‐Based MOFs on Carboxylated Cotton Fabrics for Removal of Rhodamine B by Photocatalysis

ABSTRACTThere are tremendous potential in utilizing metal–organic frameworks (MOFs) for enhancing the removal of organic dyes from wastewater. However, the recovery of powdered catalysts poses a significant challenge. In this study, four different iron‐based metal–organic frameworks (Fe‐MOFs) were synthesized by growing them in situ on carboxylated cotton fabrics. Scanning electron microscopy (SEM), X‐ray diffraction (XRD), and X‐ray photoelectron spectroscopy (XPS) were employed to conduct a comprehensive characterization of the Fe‐MOFs. The dye removal rate of Fe‐MOF@Cotton was meticulously determined by comprehensively investigating various factors including the concentration of the dye and hydrogen peroxide (H2O2), the pH value of the solution, and the temperature. Extensive catalytic degradation experiments were conducted to assess the performance of Fe‐MOF@Cotton. The results vividly demonstrated that Rhodamine B (RhB) was photocatalytically degraded by Fe‐MOF@Cotton with remarkable efficiency in the presence of H2O2. Notably, the four MOF fabric composites were capable of degrading over 97% of RhB with a concentration of 20 mg/L. Moreover, these composites exhibited excellent reusability, strongly indicating their significant potential for application in the dye wastewater treatment industry. This not only highlights the effectiveness of Fe‐MOF@Cotton in dye removal but also offers a promising avenue for addressing the challenges associated with dye‐contaminated wastewater treatment.

Barberry Anthocyanins: Recent Advances in Extraction, Stability, Biological Activities and Utilization in Food Systems- A Review

Abstract Barberry anthocyanins have attracted more attention in the food industry due to their natural red colourants and beneficial characteristics. These compounds exhibit a range of biological activities, such as antioxidant, antibacterial, anti-diabetic, anticancer and neuroprotective effects, which indicates their potential role in the development of functional foods and therapeutic formulations. To extract the optimal yield of these valuable pigments from barberry fruit, it is essential to use the most suitable extraction method. While traditional methods like solvent extraction are commonly used, novel techniques such as pulsed electric field processing, ultrasound-assisted extraction, and high-pressure carbon dioxide extraction offer advantages like reduced solvent demand, shorter processing time, higher yields and lower energy consumption. Despite the numerous beneficial features of anthocyanins, their application is restricted due to their low stability and bioavailability, which is affected by environmental factors such as pH, temperature, light, oxygen, enzymatic activity, and ascorbic acid. Strategies to enhance their structural stability, including copigmentation and encapsulation, are necessary to expand their further application in the food industry.

Scalable Anomaly Detection with Machine Learning: Techniques for Managing High-Dimensional Data Streams

The increase in big data values from industries, especially in Analytics, Risk, and Management Information systems, offers a great catchment and, at the same time, books with a lot of potential hurdles. This paper provides the conceptual basis for combining unsupervised and deep learning for real-time anomaly detection in high feature-space trajectories and offers practical applications for several industries. Autoencoders, Isolation Forests, and RNNs are tested for their performance and compared with the PCA in finance, manufacturing, healthcare, and cybersecurity applications. Other benefits are measured in specifics, such as cutting fraud detection mistakes by 25% and increasing the effectiveness of predictive maintenance. Further, the examined study focuses on problems like big data, real-time streaming data, distributed computing, edge computing, and incremental learning. Some key contributions are the scalable methods for IoT system-driven scenarios and the possibility of quickly recognizing anomalies with low latency. The paper also outlines emerging aspects of data quality, model interpretability, data privacy, and possible strategies such as Explainable AI and data masking. Examples from fraud detection and equipment failure prediction further explain the various opportunities these models offer for making decisions more effectively in complex and evolving environments.

Fatigue behavior of friction stir welded AA6061 alloy using brass insert

Aluminum alloys, broadly used in aerospace and automotive, are particularly susceptible to fatigue failures. The grain refinement characteristics can improve the fatigue behavior of aluminum alloys, which can be achieved using friction stir welding (FSW). The primary aim of this study is to examine how incorporating a brass insert influences the fatigue crack growth behavior of AA6061-T6 alloy welded through FSW, comparing welds with and without the insert. Microstructural analysis showed fine recrystallized grains are obtained for both welds. However, welding with the insert exhibited smaller grains. Moreover, robust intermetallics are formed for welding with insert due to the intermixing reaction at FSW temperature, which improves mechanical properties such as hardness and tensile strength. The findings on fatigue indicate that the fatigue resistance of the weld with insert is significantly high, which can be attributed to the increased grain boundaries and development of strong intermetallic compounds, which hindered the crack propagation. Fractographic analysis of the fracture surfaces indicated the presence of striation marks in the weld with the insert, which slowed crack propagation and prolonged fatigue life. The findings suggest real-world applications in industries, where improving the fatigue life and structural reliability of welded aluminum components is critical.

Dynamic reconfiguration of application and communication in Industry 4.0 with Digital Twin for enhancing flexibility

Abstract Industry 4.0 emphasizes the flexibility of production systems, requiring dynamic adaptation to varied product requirements. This paper presents a framework for managing production systems by integrating application and communication systems. Central to it is the use of Asset Administration Shell (AAS) to create Digital Twins of assets, enabling continuous adaptation and optimization of production processes through effective resource management. The paper discusses a use case involving negotiation between communication and application systems, proposing a model-based solution leveraging AAS. It details the implementation of the proposed model with AAS components, providing an analysis of the use case and an automation solution. Validation results show how the AAS can improve the performance and dependability of wireless communication systems.

Unlocking the potential of l-α-glycerylphosphorylcholine in the food industry: From safety approvals to market prospects.

With the exacerbation of global population aging, age-related neurodegenerative disorders have been posing an increasing public health concern. l-α-Glycerylphosphorylcholine (l-α-GPC) has demonstrated significant therapeutic potential for mental health-related disorders and possesses promising market prospects. Recently, l-α-GPC has been successively approved as a new food resource in Canada (2023) and in China (2024). These policies pointed out the recognized safety and utility of l-α-GPC. The utilization of l-α-GPC in dietary supplements and health foods could be a convenient option for early intervention strategies to potentially delay or mitigate the progression of neurodegenerative disorders. Additionally, other unique nutritional benefits of l-α-GPC have been highlighted, further expanding its application in food industry. Encouraged by the policy incentives, there is likely to be a new upsurge in the research interest surrounding l-α-GPC. To fully capitalize on these emerging opportunities, we present a comprehensive review of l-α-GPC. The chemical properties, pharmacological characteristics, safety assessments, and preparation methods of l-α-GPC were summarized. A brief outlook on the future perspectives and unsolved challenges was also proposed.

Physical and Structural Properties of Chitosan–Squid Gelatin Hydrogels

The development of functional hydrogels is currently receiving great attention. In this study, a squid by-product, gelatin (SG)–acetic acid solution, was added to a commercial chitosan (CH)–acetic acid solution to develop an antioxidant hydrogel. The CH–SG mass ratios evaluated were 1:0, 2:1, and 1:2. Glutaraldehyde was used as cross linker. The effects of the SG addition to the hydrogel on different properties (physical in general, stability in aqueous media at pH 7.2, swelling, textural profile, and antioxidant) were evaluated. The interaction of CH and SG was established by scanning electron microscope microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), and nuclear magnetic resonance spectroscopy (NMR). As a result, the addition of SG decreased the resistance to flow, hardness, chewiness, and stability, but increased the springiness, resilience, and antioxidant properties of CH hydrogels. The SEM analysis revealed that the CH-GS hydrogel showed a relatively more porous structure. FTIR and NMR analyses suggested a good compatibility of the components due mainly to an increased hydrogen bond formation. The present results suggest that CH could establish a valuable interaction with SG, so that a new hydrogel with enhanced textural and antioxidant properties would be produced, which would enable its potential application in biomedical and food industries.

Characterization of microbiota signatures in Iberian pig strains using machine learning algorithms

BackgroundThere is a growing interest in uncovering the factors that shape microbiome composition due to its association with complex phenotypic traits in livestock. Host genetic variation is increasingly recognized as a major factor influencing the microbiome. The Iberian pig breed, known for its high-quality meat products, includes various strains with recognized genetic and phenotypic variability. However, despite the microbiome’s known impact on pigs’ productive phenotypes such as meat quality traits, comparative analyses of gut microbial composition across Iberian pig strains are lacking. This study aims to explore the gut microbiota of two Iberian pig strains, Entrepelado (n = 74) and Retinto (n = 63), and their reciprocal crosses (n = 100), using machine learning (ML) models to identify key microbial taxa relevant for distinguishing their genetic backgrounds, which holds potential application in the pig industry. Nine ML algorithms, including tree-based, kernel-based, probabilistic, and linear algorithms, were used.ResultsBeta diversity analysis on 16 S rRNA microbiome data revealed compositional divergence among genetic, age and batch groups. ML models exploring maternal, paternal and heterosis effects showed varying levels of classification performance, with the paternal effect scenario being the best, achieving a mean Area Under the ROC curve (AUROC) of 0.74 using the Catboost (CB) algorithm. However, the most genetically distant animals, the purebreds, were more easily discriminated using the ML models. The classification of the two Iberian strains reached the highest mean AUROC of 0.83 using Support Vector Machine (SVM) model. The most relevant genera in this classification performance were Acetitomaculum, Butyricicoccus and Limosilactobacillus. All of which exhibited a relevant differential abundance between purebred animals using a Bayesian linear model.ConclusionsThe study confirms variations in gut microbiota among Iberian pig strains and their crosses, influenced by genetic and non-genetic factors. ML models, particularly CB and RF, as well as SVM in certain scenarios, combined with a feature selection process, effectively classified genetic groups based on microbiome data and identified key microbial taxa. These taxa were linked to short-chain fatty acids production and lipid metabolism, suggesting microbial composition differences may contribute to variations in fat-related traits among Iberian genetic groups.

Force-Field Benchmark for Polydimethylsiloxane: Density, Heat Capacity, Isothermal Compressibility, Viscosity and Thermal Conductivity.

Polysiloxanes are versatile polymeric materials with widespread applications in industries ranging from electronics to biomedical devices because of their unique thermal and viscoelastic properties. Accurate molecular simulations of polysiloxanes are essential for understanding their broad applications from a microscopic perspective. However, the accuracy of these simulations is highly dependent on the quality of the force fields used. In this work, we present a comprehensive benchmark and development of force fields tailored for polydimethylsiloxane, which is one of the most widely used polysiloxane materials. Our focus is on their performance in predicting key thermophysical properties including density, heat capacities, isothermal compressibility, and transport properties such as viscosity and thermal conductivity. Experimental measurements are performed in parallel to rigorously validate simulation outcomes. Existing force fields for polydimethylsiloxane, including those derived for organic and inorganic systems, are systematically evaluated against experimental data to identify limitations in accuracy and transferability. Simulation results are compared extensively with experimental observations across a range of temperatures and pressures, revealing the strengths and shortcomings of these commonly utilized force fields for polydimethylsiloxane. Discrepancies between force field predictions and experimental measurements are analyzed in detail for thermodynamic and transport properties of polydimethylsiloxane. This benchmark study serves as a critical assessment of current force fields for polydimethylsiloxane and offers guidelines for their further development, enabling more reliable simulations of polysiloxane-based materials for various industrial applications.

Ultrasonic-assisted plasma-activated water extraction of polysaccharide from Hemerocallis citrina Baroni: Structural characterization and antioxidant mechanism in vitro.

Natural polysaccharides derived from Hemerocallis citrina Baroni exhibit significant biological activity. To enhance extraction efficiency and antioxidant activity, ultrasonic-assised plasma-activated water (PAW) was utilized to extract polysaccharides from Hemerocallis citrina Baroni. PAW demonstrated enhanced permeability and diffusion capabilities due to a reduced percentage of fully hydrogen-bonded structures, along with optimal pH (3.4) and conductivity (157.20μS/cm). Compared to other methods, PAW significantly improved the extraction efficiency of polysaccharides to 38.24%. Hemerocallis citrina Baroni polysaccharides predominantly consisted of α-pyranose sugars with sugar uronic acids, and ultrasound-assisted PAW did not alter their major functional groups. The extracted Hemerocallis citrina Baroni polysaccharides exhibited a semi-rigid triple-helical structure. Importantly, these polysaccharides displayed strong antioxidant activity, with ABTS+, DPPH, and O2- radical scavenging rates of 66.31%, 65.98%, and 47.73% respectively. This study provides an effective method for extracting natural plant polysaccharides and offers valuable insights into their potential applications in the food industry and beyond.

Current advances in the biosynthesis and sustainable production strategies of carotenoids and their multifaceted applications in the food industry: A comprehensive review

Current advances in the biosynthesis and sustainable production strategies of carotenoids and their multifaceted applications in the food industry: A comprehensive review

Response mechanisms of lactic acid bacteria under environmental stress and their application in the food industry

Response mechanisms of lactic acid bacteria under environmental stress and their application in the food industry

Innovative encapsulation of Dysphania ambrosioides essential oil and α-terpinene with gum arabic and inulin: Enhancing antibacterial activity, stability, and bioavailability.

Dysphania ambrosioides essential oil (EO) possesses significant antibacterial and antioxidant properties. However, its application as a food preservation agent is limited due to high volatility and instability. Given the industrial relevance of this EO, developing new products that incorporate microencapsulated D. ambrosioides EO is recommended. This study addresses these challenges by encapsulating the EO using inulin and gum arabic (IN/GA) biopolymers, known for their biocompatibility and biodegradability. We systematically evaluated the encapsulation efficiency and structural properties of the resulting microcapsules. Advanced characterization techniques, including FT-IR, SEM, and EDX, were used to analyze the chemical interactions and morphological characteristics of the microcapsules. The thermal stability of the microcapsules was assessed using TGA, while their stability and bioaccessibility were evaluated under simulated in vitro digestion conditions. The formulation (C1) used in this study demonstrated a high encapsulation efficiency (88 %). The IN/GA formulations successfully microencapsulated EO and α-terpinene, producing microcapsules with high stability (>80 %) and bioaccessibility (>40 %). These microcapsules showed controlled release during digestion and exhibited strong antibacterial activity against Staphylococcus aureus and Escherichia coli. These findings suggest that inulin and gum arabic are effective macromolecules for stabilizing this EO, offering valuable potential applications in the food industry.

Recent trends in co-encapsulation of probiotics with prebiotics and their applications in the food industry

Recent trends in co-encapsulation of probiotics with prebiotics and their applications in the food industry