Developing an Intelligent Material Classification System for Plastic and Other Materials

Background: In the pharmaceutical industry, cleaning validation is a critical component of an effective quality assurance system to ensure compliance with the GMP requirements. FDA and EMA have guidance to perform cleaning validation, prevent cross-contamination and minimize patient safety risk but there is no regulatory guidance relating to microbiological aspects of cleaning validation, sampling techniques and establishing microbial contamination limits for pharmaceutical facilities. Therefore, there is a certain vacuum of regulations and basic principles, especially, methodologies to properly conduct cleaning validation focusing on microbiological contamination. Aim: This present work is dedicated to the development and validation of swab and rinse sampling methods of bioburden on manufacturing equipment surfaces in combination with microbiological testing methods, to demonstrate a suitable methodology in order to perform cleaning validation at an appropriate level in compliance with the GMP requirements. Methods: Swab and rinse sampling methods were used in this study. The swabbing procedure involved moistening the swab (10 cm) with the sterile saline and swabbing the area to be sampled in an overlapping zigzag pattern; Rinse sampling is performed with a fixed volume of sterile water for injection from a piece of equipment surface. Properly cleaned and sterilized coupons (100 cm2) of three different materials (plastic, glass, and stainless steel) were used and spiked (inoculated) with the fixed volume of the inoculum’s solution prepared by the working bacterial and fungi culture suspensions. After drying the surface, the swab and rinse sampling was performed to obtain test samples. Results and Discussion: Swab and rinse sampling methods were developed using five bacterial and fungal cultures and three different types of materials in order to obtain a good recovery (?37%) with high precision (RSD?15%). The recovery rates for all types of materials using the swab method ranged from 44 to 70%, while the rinse method was slightly lower, ranging from 35 to 57%. Depending on the type of coupon material, the recovery results of the test microorganisms are different. The decreasing tendency was revealed in the following order: plastic, stainless steel, and glass. Conclusions: The highest recovery was obtained in the case of E. coli for both sampling methods. The recovery rate for each test microorganism is higher for the swab sampling method (61 %) compared to the rinsing method (47 %). The proposed methodology can be used to control bioburden on the pharmaceutical equipment surfaces during microbiological monitoring process and successfully perform cleaning validation.

The pursuit of rapid, sensitive, and specific diagnostic methodologies is imperative across diverse applications, including the detection of pathogens and disease biomarkers, food safety testing and environmental monitoring. Point-of-care testing (POCT) is characterized by its portability, ease of use, rapidity, and affordability, emerging as an attractive alternative for traditional diagnostics. Over recent years, the incorporation of functional DNA (fDNA) into POC diagnostic devices has emerged as a groundbreaking advancement, significantly enhancing sensitivity, specificity, and user-friendliness. In this review, we explore the innovative applications of fDNA in POC devices, highlighting its potential to revolutionize diagnostics by providing rapid, portable, and precise solutions. We discuss the unique advantages of fDNA, including its stability in complex biological matrices and its ability to recognize a wide range of targets. Furthermore, we explore the potential synergy between fDNA and cutting-edge technologies, such as nanotechnology and artificial intelligence (AI), to forge a path toward more personalized and accessible healthcare solutions. Despite significant progress, challenges remain in translating these innovations from the bench to the clinic. This review aims to provide a comprehensive overview of the current status of fDNA-based POCT devices and future directions for their development, emphasizing their critical role in meeting the global demand for accessible, efficient, and precise diagnostic solutions.

With the development of network communication, computer technology and artificial intelligence, it has become an inevitable development trend to promote "intelligent buildings" characterized by intelligence, comfort and safety. In order to solve the shortcomings of the existing research on intelligent building control system, this paper discusses the functional equation of CAN algorithm, artificial intelligence fuzzy controller and intelligent building, and aims at the intelligent building control system based on artificial intelligence and CAN algorithm. Hardware settings and parameter settings are briefly introduced. And the work flow design of the intelligent building control system structure based on artificial intelligence and CAN algorithm is discussed, and finally the application of the intelligent building control system based on artificial intelligence and CAN algorithm is experimentally tested. The correct number of switch control by artificial intelligence and CAN algorithm in the intelligent building control system is relatively high. The correct number of switch control in the interference environment is less than that in the humid environment, and the number of switch operations for the control system is the least in the humid environment. The error rate of 500 and up to 2500 is less than 5%, thus verifying the superiority of the application of intelligent building control system based on artificial intelligence and CAN algorithm.

With the existing big data intelligent classification algorithm for German professional vocabulary, the vocabulary classifier constructed has difficulty distinguishing the sample set in the hyperplane, resulting in large errors in the vocabulary classification results. To improve the accuracy of German professional vocabulary classification results, an intelligent classification method for German professional vocabulary memory big data is designed based on the cross-border e-commerce background. Calculate the data structure similarity of German professional vocabulary, extract the normalized information of German professional vocabulary, establish a vocabulary classifier based on memory big data, and design an intelligent classification algorithm of memory big data. The experimental results show that the accuracies of vocabulary classification obtained by this method in the four datasets are 98.7%, 99.1%, 97.8% and 99.4%, which are higher than those of the other three algorithms. From the above data, it can be seen that the German professional vocabulary intelligent classification algorithm has higher accuracy and achieves the optimization goal.

Correlating ignition mechanisms of aluminum-based reactive materials with thermoanalytical measurements

Damage in materials is a complex and stochastic process bridging several time and length scales. This dissertation focuses on investigating the damage process in a particular class of precipitate-hardened aluminum alloys which is widely used in automotive and aerospace applications. Most emphasis in the literature has been given either on their ductility for manufacturing purposes or fracture for performance considerations. In this dissertation, emphasis is placed on using nondestructive evaluation (NDE) combined with mechanical testing and characterization methods applied at a scale where damage incubation and initiation is occurring. Specifically, a novel setup built inside a Scanning Electron Microscope (SEM) and retrofitted to be combined with characterization and NDE capabilities was developed with the goal to track the early stages of the damage process in this type of material. The characterization capabilities include Electron Backscatter Diffraction (EBSD) and Energy Dispersive Spectroscopy (EDS) in addition to X-ray micro-computed tomography ([mu]-CT) and nanoindentation, in addition to microscopy achieved by the Secondary Electron (SE) and Back Scatter Electron (BSE) detectors. The mechanical testing inside the SEM was achieved with the use of an appropriate stage that fitted within its chamber and is capable of applying both axial and bending monotonic and cyclic loads. The NDE capabilities, beyond the microscopy and [mu]-CT, include the methods of Acoustic Emission and Digital Image Correlation (DIC). This setup was used to identify damage precursors in this material system and their evolution over time and space. The experimental results were analyzed by a custom signal processing scheme that involves both feature-based analyses as well as a machine learning method to relate recorded microstructural data to damage in this material. Extensions of the presented approach to include information from computational methods as well as its applicability to other material systems are discussed.

A study on emission of phthalate esters from plastic materials using a passive flux sampler

The focus of this study is to investigate novel and precise solitary wave solutions of the modified Kawahara (MK) equation using recent and accurate computational techniques. The MK equation, a nonlinear partial differential equation, has significant applications in various fields, including fluid dynamics and plasma physics The comprehension of nonlinear wave phenomena relies heavily on solitary wave solutions, making it necessary to develop accurate and efficient computational techniques for their identification. In this study, the Khater II method is employed as a computational technique, while the variational iteration method is used as a numerical scheme.The Khater II method is a powerful computational technique that has exhibited promising outcomes in solving nonlinear wave equations. It uses an analytical framework to transform the partial differential equation into a set of ordinary differential equations that are amenable to straightforward solutions, thereby enabling the construction of exact solutions. Conversely, the variational iteration method is a numerical scheme that enhances solution accuracy through iterative approximations.By using the Khater II method as a computational technique and the variational iteration method as a numerical scheme, this study identifies novel and precise solitary wave solutions of the MK equation. These solutions provide valuable insights into the dynamics and behavior of nonlinear waves across various applications. The effectiveness of the employed techniques is demonstrated by comparing them with other commonly utilized computational methods for solving the MK equation. The results indicate that the combined application of the Khater II method and the variational iteration method yields more accurate and efficient solutions. This heightened accuracy and efficiency are critical for comprehending and predicting the behavior of the studied model in real-world applications. Thus, this study underscores the significance of employing recent and accurate computational techniques, such as the Khater II method and the variational iteration method, to identify novel and precise solitary wave solutions of the MK equation. The obtained solutions contribute to an improved understanding of the model’s behavior and hold practical implications across diverse scientific and engineering domains.

Рассмотрена проблема совершенствования роторных окорочных станков, которые во всех технологиях лесопереработки лесопромышленных стран используются в обязательном порядке. Несмотря на достаточно отработанную конструкцию, тем не менее, основные технологические операции станка не оснащены современными адаптивными системами автоматического управления (САУ). Ранее были предложены разработки на основе пневмогидропривода с использованием автоматического управления на основе нечеткой логики. В предложенной системе автоматического управления выполняется стабилизация заданного усилия прижима режущего инструмента – короснимателя. Однако заданное усилие зависит от ряда технологических параметров, которые характеризуются неопределенностью, и проблема управления заданным прижимом инструмента осталась нерешенной. Таким образом определилась цель исследований, которая заключалась в создании интеллектуальной системы автоматического управления заданным прижимом короснимателя окорочного станка. Решались следующие задачи: 1) разработка схемы интеллектуального управления короснимателем; 2) разработка схемы обобщенной интеллектуальной системы управления в виде нейронечеткой сети; 3) постановка задачи управления заданным прижимом инструмента; 4) обоснование входных и выходных переменных задачи (фаззификация); 5) разработка базы правил нечеткой системы; 6) выполнение нечетких выводов для промежуточных и заключительного слоев сети в среде Matlab; 7) реализация модели интеллектуальной системы в среде Matlab+Simulink. Результатами работы является модель интеллектуальной системы управления короснимателем и ее программная реализация в среде Simulink для использования в практике проектирования роторных окорочных станков. The problem of improving the rotary debarkers, which are used without fail in all timber processing technologies of the timber industry countries, is considered. Despite the sufficiently developed design, nevertheless, the main technological operations of the machine are not equipped with modern adaptive automatic control systems (ACS). Previously, developments based on a pneumatic hydraulic drive were proposed using automatic control based on fuzzy logic. In the proposed automatic control system, the stabilization of a given pressing force of the cutting tool – the debarker is performed. However, the given force depends on a number of technological parameters, which are characterized by uncertainty, and the problem of controlling the given clamping of the tool remains unsolved. Thus, the goal of the research was determined, which was to create an intelligent system for automatic control of a given pressure of the debarker staple lifter. The following tasks were solved: 1) development of an intelligent control scheme for the debarker; 2) development of a diagram of a generalized intelligent control system in the form of a neuro-fuzzy network; 3) setting the task of controlling the given clamping of the tool; 4) justification of the input and output variables of the problem (fuzzification); 5) development of a fuzzy system rule base; 6) execution of fuzzy conclusions for intermediate and final layers of the network in the Matlab environment; 7) implementation of the model of an intelligent system in the Matlab + Simulink environment. The results of the work are a model of an intelligent control system for the debarker and its software implementation in the Simulink environment for use in the practice of designing rotary debarkers.

Electrochemical discharge machining (ECDM) is recognized as a successful method for processing non-conductive materials through thermal erosion and chemical dissolution. This article aims at the development of a finite element modeling (FEM) based three-dimensional (3D) thermal model for estimating the temperature distribution for scrutinizing the material removal rate (MRR) in electrochemical discharge drilling of silica glass material. The predicted FEM results are verified against existing literature and experimental results during micro-drilling operation. Simulations for evaluating the parametric studies such as the effect of applied voltage, electrolyte concentration, electrolyte type, energy partition on MRR is performed. Results revealed that the experimental values of MRR exhibit fare agreement with the predicted FEA results. Experiments are performed using adaptive tool feed based closed-loop ECDM machining that retracts the tool in an upward direction once its contact is detected with the work material. Tool contacts often result in a high tool wear rate (TWR). Moreover, the response surface methodology (RSM) based mathematical models are established to correlate the input variable’s relationship with the response parameters. Tool feed rate, applied voltage, and electrolyte concentration are chosen as numerical input variables while electrolyte type and tool material are chosen as categorical variables. MRR, TWR, and a number of tool contacts (NTC) are chosen as response parameters. A number of tool contacts are selected since it influences both MRR and TWR. The model’s adequacy is checked using the analysis of variance (ANOVA) and found significant for all the responses. The multi-response optimization of distinctive input variables is acquired by utilizing the desirability function (obtained 0.8646) for maximizing the MRR and minimizing the both TWR and NTC.

The production of complex machine components requires advanced and accurate techniques. Achieving optimal quality through 3D printing involves carefully examining the process parameters. However, many studies have not thoroughly explored the impact of these parameters on parts produced using Fused Deposition Modeling (FDM) 3D printing. This study evaluates how process parameters and material variations affect the dimensional accuracy of printed parts. The study focuses on input variables such as material type, infill density, infill pattern, and raster angle. Using the fractional L9 Taguchi method, the optimal settings identified were PLA+ material, 80% infill density, an infill grid pattern, and a 0° raster angle, resulting in a 1.39%-dimensional deviation and an S/N ratio of -3.29 dB. ANOVA analysis reveals material type as the most significant factor, contributing 49.81% to performance. These findings, complemented by statistical analyses, can guide decision-making in industrial applications and serve as a reference for selecting FDM 3D printing settings related to dimensional accuracy to print components in the industry.

With the rapid development of its unique advantages, nonwoven fabric has become the leading material of automotive textiles. Nonwoven fabric has become an ideal material for automobiles and replaced traditional textiles and plastic products. This paper takes a car as the research model. A DRL‐2B thermal conductivity tester was used to test thermal conductivity of the material based on the plate method. Material systems and structures of new types of heat insulation composite materials were determined according to test results and the theory of heat transfer. Then, the final new type of heat insulation material was determined by calculating and comparing average heat transfer coefficients and heat transfer between traditional insulation materials and new types of heat insulation materials. The calculated results were verified by experiments in this paper. All the test results show that the new type of composite insulation material is significantly better than traditional insulation materials, and the test results match the calculated results.

Agricultural parks consist of many functional parts for crops cultivation, including irrigation systems, environmental monitoring equipment, and other important infrastructure. In order to prevent and respond to various technical failures, accurate and real-time monitoring needs to be carried out. This article is based on big data and artificial intelligent technology, and conducted in-depth mining on it. It adopts an intelligent fault diagnosis and management system based on a combination of machine learning and big data analysis. By combining discrete data with intelligent algorithms, the fault diagnosis process is optimized to improve the real-time and accuracy of the system. By automatically collecting and analyzing various information during the production process, scientific decision-making basis is provided for enterprise management to reduce human errors and improve operational efficiency. This article also optimizes and improves existing user side fault diagnosis and intelligent management technologies. Our results indicate that the fault prediction accuracy achieved in this article is significantly improved. The research results can provide theoretical basis and practical guidance for the transformation, upgrading, and development model innovation of agricultural parks in China.

Quantum image processing represents a pivotal field in quantum remote sensing[1-3]. Dr. Siwen Bi and his research team pioneered the concept of quantum image data processing, having dedicated nearly 15 years to this technological advancement. This study focuses on developing core technologies for quantum-enhanced image processors, with particular emphasis on optimizing quantum enhancement algorithms, refining image processing methodologies, and implementing hardware solutions. The research aims to enhance both efficiency and quality in remote sensing and aerospace imaging applications[4-5], while providing efficient and precise image enhancement solutions for medical imaging, industrial inspection, remote sensing processing, and surveying fields. This study integrates quantum computing with traditional image processing technologies to develop quantum image enhancement algorithms, which have been applied to diverse imaging data types including remote sensing images, aerospace imagery, and medical imaging[6-8]. Through systematic research and experiments[9-11], we analyze key technologies in image enhancement processes such as quantum states, quantum superposition, quantum parallel computing, deep learning optimization, and hardware architecture design. Simultaneously, performance evaluations of various algorithms and hardware configurations were conducted using a combination of simulations and experimental methods. The research demonstrates that quantum image enhancement algorithms exhibit significant advantages over traditional methods in terms of processing accuracy, speed, and efficiency. Notably, image quality shows marked improvement during complex image processing. Furthermore, hardware-optimized quantum image processors can effectively support real-time processing of large-scale imaging data. As a cutting-edge image processing method, quantum image enhancement technology has demonstrated remarkable potential in remote sensing, aerospace, and medical fields[12-15]. Quantum computing offers innovative approaches to image processing, where the integration of deep learning and quantum computing can significantly enhance both efficiency and quality. With continuous advancements in quantum imaging hardware, this technology is poised to provide more efficient and precise solutions for image processing applications across various industries.

Mixed plastic wastes recovered from dumpsites may be utilized as densified solid fuel. However, it is commonly known that plastic materials may not bind together under compaction. Corn stover can be used as natural binder in this case. It can improve chemical composition (reducing sulfur and chlorine content) and mechanical strength of the fuel pellet. In this paper, densification of mixed plastic wastes and corn stover was investigated. Compression pressure was conducted at 150 MPa. The pellet size was 8 mm in diameter and 20 mm long. Effects of moisture content (5-20%), types of material, and preheating temperatures (75 and 100°C) on the fuel properties were studied. The pellets from mixed materials were found to have higher calorific value, carbon content and durability index than corn stover pellet.