Processing Operations Research Articles

A Comprehensive Study on Phase Sensitive Amplification and Stimulated Brillouin Scattering in Nonlinear Fibers with Longitudinally Varying Dispersion

Fiber optic parametric and phase sensitive amplifiers (PSA) are interesting for modern day communication technologies due to their low noise and high gain amplification properties with a potential for all optical signal processing and wide band operation. PSAs are typically employed in either a single pump or dual pump configuration. In this article we explore the utilities of both configurations, however considering a fiber with a longitudinally varying dispersion profile. For the single pump case, PSA operation at large pump-signal detunings, that arise due to the longitudinal dispersion variation, were studied numerically, and recipes of using the system as a wide band wavelength selective filter were laid out. For the dual-pump case, emphasis was laid on achieving a larger signal gain, by reducing stimulated Brillouin scattering (SBS) that prevents large pump power transport through the nonlinear fiber. First, the effects of dispersion variation on the gain of a dual pump PSA were studied analytically and numerically in order to optimize the dispersion variation profile, neglecting SBS processes. Then we independently studied the SBS dynamics of the system numerically. A sinusoidally dispersion oscillating fiber (DOF) was found to be an optimal candidate with respect to its PSA and SBS performances. To establish this claim, we also experimentally compared the performance of an available DOF over a standard highly nonlinear fiber (HNLF) that has a constant dispersion profile and established its utility for designing a high gain PSA system, thanks to the SBS mitigation due to the longitudinal dispersion variation of the fiber.

Automating data analysis in scientific research of future education specialists using the Google Apps Script platform

Abstract. The article focuses on the role and potential of using the Google Apps Script platform as a tool for automating data analysis in scientific research for future specialists in the educational sphere. Thus, the article aims to systematize and summarize information about the possibilities and advantages of using digital tools, particularly the Google Apps Script platform, for automating data analysis in scientific research. The following research methods were employed: analysis and summarization of technological capabilities and practical cases using the Google Apps Script platform based on existing scientific and methodological sources, modelling, and synthesis of obtained data. This contributed to understanding and assessing the potential of the designated tool for use by future specialists in the educational sphere. The possibility of optimizing various stages of scientific research using Google Apps Script was thoroughly analysed, particularly in data filtering and summarization, statistical analysis, creating graphs, and reporting. Considering the prospects of applying the synthetic method in the research process, the role of the specified tool in creating and expanding data for objective analysis is emphasized. The Google Apps Script platform facilitates the effective combination of analytical capabilities and automation, allowing researchers to better synthesize the obtained results and react promptly to the identified trends. Such a comprehensive approach to data processing creates unique opportunities for innovative research in the field of education, making it more accessible and interactive for use by researchers and practitioners. As a result of the study, the significance of digital tools, including the Google Apps Script platform, in preparing future education professionals was highlighted. The advantages and possibilities of this tool for implementing innovative approaches in data analysis in the field of scientific research were pointed out, emphasizing its potential for developing new methodologies and approaches in this field. In the context of the on-going evolution of educational approaches, the Google Apps Script platform serves not only as a data analysis tool but also as a catalyst for innovative thinking. In summary, it is worth noting that Google Apps Script is a powerful tool that provides opportunities for automating complex data processing processes in scientific research, even when employing the synthetic method. The use of this tool opens up possibilities for efficient analysis and processing of large volumes of information, even when it is critical, overcoming this challenge in modern scientific work. Its capabilities enable researchers to focus on essential aspects of research, reducing time spent on routine data processing operations.

Effect of polishing on milling, nutritional properties, and optimization of polishing process parameters for kodo and browntop millets: A response surface methodology approach

AbstractMillets are termed ‘Nutri‐cereals’ as they possess exceptional nutrition profiles. They undergo several processing operations from farm to fork. Polishing is one of the important primary processing operations of millets that facilitates de‐branning. The current work aims to investigate the effect of polishing process parameters like grain's initial moisture content, the rotational speed of polisher, polishing time, on milling yield, nutritional losses of kodo, and browntop millets; to optimize the polishing process through response surface methodology. The polisher speed was found to influence milling and nutritional properties in both the millets whereas, the effect of grain's initial moisture was mostly observed in some responses of browntop millet. Kodo millet polishing at 10% initial moisture content, 1900 rpm for 2 min, and browntop millet polishing at 10% initial moisture content, 1300 rpm for 8 min were obtained as optimum polishing conditions. The degree of polishing and head grain yield at optimum conditions were in the range of 3.5%–4.0%, and 84%–92%, respectively. Less than 15% of nutritional losses were observed at optimum polishing conditions.Practical applicationsIndiscriminate polishing of millets has become most common industrial practice resulting in less head yield and high nutritional losses. Form the current study, both food processors and researchers get a brief knowledge on the effect of every polishing parameter such as grain moisture content before polishing, rotational speed of abrasive roller in polisher, time of polishing on milling losses and proximate compositions. The optimized process parameters can be directly employed in industrial polishing applications to produce polished grains with higher head yield and very minimal nutritional losses.

Fracture behavior of AZ31B magnesium alloy under high-temperature ultrasonic-assisted shearing with related ductile-brittle transformation mechanism

A wide width is one of the fundamental requirements for magnesium alloy sheets to fulfill the lightweight needs of the aerospace and rail transportation industries. Nevertheless, ensuring the dimensional integrity of wide-format magnesium alloy sheets is a challenge in most manufacturing facilities. Simultaneously, processing operations are susceptible to encountering production issues, including but not limited to significant loss of raw materials, elevated costs, environmental contamination, and safety risks. Therefore, to fulfill the operational demands of the hot rolling finishing line for wide sheets made of magnesium alloy, the present study employed ultrasonic-assisted shearing (UAS) to carry out a high-temperature shearing test investigation on the AZ31B magnesium alloy sheet. In this research, we employed scanning electron microscopy, optical microscopy, and a micro-hardness tester to investigate the relationship between ultrasonic amplitude and fracture behavior, deformation mechanism, and mechanical properties of magnesium alloy sheets following exposure to ultrasonic vibration. The fracture morphology of the sheet fracture demonstrates that the behavior of the magnesium alloy sheet fractures under ultrasonic vibration changes from ductile fracture under conventional shearing to brittle fracture under ultrasonic-assisted shearing. The primary cause of this transformation between ductile and brittle is the effect of ultrasonic vibration on the material's internal energy. High-energy ultrasonic amplitude action causes magnesium to absorb a significant amount of ultrasonic energy. This results in an increase in the number of dynamic re-crystallization grains in the material organization, a decrease in the number of twins, and significant refinement of the grains. The sheet's hardness also changes along the rolling and normal directions, with a uniform distribution. The magnesium alloy sheet's fracture quality is optimal when the ultrasonic amplitude is 10 μm in the UAS process. This treatment results in notable enhancements in the section's straightness and brightness. Our study aims to advance the mass manufacturing of wide-format magnesium alloy sheets by investigating a cost-effective and high-yield technique for shearing magnesium alloy sheets.

Image uncertainty components in video strain measurement of mortar destructive compression testing

This paper describes a video strain measurement solution developed for application in mortar destructive compression testing. Knowledge about the mechanical behavior of this type of construction material, namely up to its fracture, is still superficial due to the less common use of non-contact measurement methods. The performed research was focused on the determination of image coordinate accuracy, based on the experimental quantification of the identified main uncertainty components, using traceable reference patterns, and validated computational toolboxes dedicated to camera parameterization and digital image processing. The obtained results show the following uncertainty contributions: lens distortion - negligible; re-projection errors - 0.21 pixel; 0.10 pixel - spatial resolution; and digital image processing operations - 0.28 pixel. The combination of these uncertainty components resulted in an image coordinate standard uncertainty equal to 0.36 pixel, which was propagated (in addition to the scale coefficient measurement uncertainty) to the selected camera model – orthographic projection with uniform scaling – which supports the video strain measurement.

Process control of rice protein enzymolysis by field monitoring

By devising a novel near-infrared (NIR) in-situ online monitoring system, this study proposed a new process control method for rice protein (RP) enzymatic hydrolysis in order to address the challenge of monitoring certain chemical indices in real-time. The endpoint of the enzymatic hydrolysis reaction and the flexible switching point of dual-enzyme hydrolysis were determined through real-time spectrum collection. Correction and prediction models were constructed to predict the angiotensin I-converting enzyme (ACE) inhibitory activity of RP hydrolysis as well as the endpoint and the dual-enzyme flexible switching point at different substrate concentrations in an attempt to establish the new control system. At the RP substrate concentrations of 35, 40, and 45 g/L, the R2 of predicted and measured ACE inhibitory activity values under hydrolysis with a single alcalase were 0.8852, 0.8360, and 0.8613, respectively. In addition, the ACE inhibitory activity of dual-enzyme hydrolysis showed a high growth trend before and after the flexible switching point, and the hydrolysis time was significantly shortened at the same endpoint threshold as that of single alcalase hydrolysis. The results indicated that this method is capable of accurately determining and regulating the endpoint and adaptable transition point of RP hydrolysis. Consequently, this approach holds promise for the regulation of food basic materials during large-scale processing operations.

Harnessing Solar Energy: A Novel Hybrid Solar Dryer for Efficient Fish Waste Processing

Facing severe climate change, preserving the environment, and promoting sustainable development necessitate innovative global solutions such as waste recycling, extracting value-added by-products, and transitioning from traditional to renewable energy sources. Accordingly, this study aims to repurpose fish waste into valuable, nutritionally rich products and extract essential chemical compounds such as proteins and oils using a newly developed hybrid solar dryer (HSD). This proposed HSD aims to produce thermal energy for drying fish waste through the combined use of solar collectors and solar panels. The HSD, primarily composed of a solar collector, drying chamber, auxiliary heating system, solar panels, battery, pump, heating tank, control panel, and charging unit, has been designed for the effective drying of fish waste. We subjected the fish waste samples to controlled drying at three distinct temperatures: 45, 50, and 55 °C. The results indicated a reduction in moisture content from 75.2% to 24.8% within drying times of 10, 7, and 5 h, respectively, at these temperatures. Moreover, maximum drying rates of 1.10, 1.22, and 1.41 kgH2O/kg dry material/h were recorded at 45, 50, and 55 °C, respectively. Remarkable energy efficiency was also observed in the HSD’s operation, with savings of 79.2%, 75.8%, and 62.2% at each respective temperature. Notably, with an increase in drying temperature, the microbial load, crude lipid, and moisture content decreased, while the crude protein and ash content increased. The outcomes of this study indicate that the practical, solar-powered HSD can recycle fish waste, enhance its value, and reduce the carbon footprint of processing operations. This sustainable approach, underpinned by renewable energy, offers significant environmental preservation and a reduction in fossil fuel reliance for industrial operations.

Metabolomics of chemical constituents as a tool for understanding the quality of fruits during development and processing operations

SummaryFruit is a crucial component of our diet, and breeding to produce nutrient‐rich cultivars necessitates a thorough examination of their metabolite composition. Fruit contains hundreds of different chemicals, including both primary and secondary (or specialised) metabolites. The structural diversity of health promoting and sensory attributes imparting metabolites and their complex interactions during fruit growth and developments were uncovered by high‐throughput metabolomics. Both targeted and non‐targeted metabolomics approach are used for the comprehensive analysis of small molecules from various complex matrices, addressing genetic, environment effect, metabolic programming, etc. With advances of mass spectrometer (MS), targeted metabolomics with non‐targeted metabolomics, MS‐based fingerprinting, profiling or imaging strategies and pseudo‐targeted are being employed to elucidate novel bioactive compounds. The exemplary reports cover a wide range of fruit metabolic implications, addressing the impact of processing on fruit flavour quality, the identification of sensory and nutraceutical biomarkers in fruits, metabolic alterations in fruits during post‐harvesting and future prospects. The examination of metabolomics profiles of multiple fruit species is anticipated to be benefitted for the expansion of fruits in terms of nutritional and health benefits aspects in the upcoming years.

Image Encryption Using Quantum 3D Mobius Scrambling and 3D Hyper-Chaotic Henon Map.

In encryption technology, image scrambling is a common processing operation. This paper proposes a quantum version of the 3D Mobius scrambling transform based on the QRCI model, which changes not only the position of pixels but also the gray values. The corresponding quantum circuits are devised. Furthermore, an encryption scheme combining the quantum 3D Mobius transform with the 3D hyper-chaotic Henon map is suggested to protect the security of image information. To facilitate subsequent processing, the RGB color image is first represented with QRCI. Then, to achieve the pixel-level permutation effect, the quantum 3D Mobius transform is applied to scramble bit-planes and pixel positions. Ultimately, to increase the diffusion effect, the scrambled image is XORed with a key image created by the 3D hyper-chaotic Henon map to produce the encrypted image. Numerical simulations and result analyses indicate that our designed encryption scheme is secure and reliable. It offers better performance in the aspect of key space, histogram variance, and correlation coefficient than some of the latest algorithms.

Application of Big Data Technology Combined with Clustering Algorithm in Manufacturing Production Analysis System

Production data analysis is crucial for production planning in the manufacturing industry, and accurate and a comprehensive and accurate analysis can improve production planning. To analyze the production of manufacturing industry, the research proposes the big data technology research method of the set clustering algorithm. In the process of this research method, the K-means clustering algorithm is first used to build the production measurement data system, and the Apache Hadoop Big data framework is applied. Then it introduces the Apriori algorithm for data association mining, and finally uses the genetic algorithm for production scheduling in view of big data analysis. The experiment showcases that the research method achieved a support level of 0.002 with 729 association rules when testing the number of association rules. When conducting data throughput testing, the research method achieved a data throughput of 7789 threads/s when the number of threads reached 8 in the Sleep scenario. In the analysis error testing, the error rate of the research method in the retained data fluctuates around 3.1%. When testing the number of processing operations in the process, the maximum error in the analysis results of the processing operations in the research method is 2. The results indicate that the research method possesses exceptional computational performance, carrying out manufacturing production analysis effectively and efficiently. The manufacturing production analysis system designed by the research institute offers valuable reference solutions for the informationization and intelligent development of the manufacturing industry.

Interpreting Rhythm as Parsing: Syntactic-Processing Operations Predict the Migration of Visual Flashes as Perceived During Listening to Musical Rhythms.

Music can be interpreted by attributing syntactic relationships to sequential musical events, and, computationally, such musical interpretation represents an analogous combinatorial task to syntactic processing in language. While this perspective has been primarily addressed in the domain of harmony, we focus here on rhythm in the Western tonal idiom, and we propose for the first time a framework for modeling the moment-by-moment execution of processing operations involved in the interpretation of music. Our approach is based on (1) a music-theoretically motivated grammar formalizing the competence of rhythmic interpretation in terms of three basic types of dependency (preparation, syncopation, and split; Rohrmeier, 2020), and (2) psychologically plausible predictions about the complexity of structural integration and memory storage operations, necessary for parsing hierarchical dependencies, derived from the dependency locality theory (Gibson, 2000). With a behavioral experiment, we exemplify an empirical implementation of the proposed theoretical framework. One hundred listeners were asked to reproduce the location of a visual flash presented while listening to three rhythmic excerpts, each exemplifying a different interpretation under the formal grammar. The hypothesized execution of syntactic-processing operations was found to be a significant predictor of the observed displacement between the reported and the objective location of the flashes. Overall, this study presents a theoretical approach and a first empirical proof-of-concept for modeling the cognitive process resulting in such interpretation as a form of syntactic parsing with algorithmic similarities to its linguistic counterpart. Results from the present small-scale experiment should not be read as a final test of the theory, but they are consistent with the theoretical predictions after controlling for several possible confounding factors and may form the basis for further large-scale and ecological testing.

Fire and Water: Geometallurgy and Extractive Metallurgy

There is no doubt that mineralogy, texture, and microfabric, as primary characteristics of an ore, affect mineral processing operations. Their direct effects on extractive metallurgical processes and the associated optimization potential, however, are less well documented. Here, we examine the status of geometallurgical approaches in extractive metallurgy by focusing on the effects of primary ore characteristics in hydrometallurgical and pyrometallurgical processes. Two selected case studies illustrate the linkages. Using quantitative data analytics on ores and concentrates, the possibilities for optimized and sustainable metal extraction and waste valorization are discussed.

Transformation of Local Government Technical Management Unit Waste Management Implements Regional Public Service Agency to Overcome Waste Problems in Indonesia

Financing in local governments is an important point in dealing with the waste problem in Indonesia. Converting waste into goods or materials that can be used in other forms is a solution to the waste problem. At the Bagendung Cilegon TPSA, waste can be processed into solid jumputan fuel which is a companion fuel to coal, of course this process requires quite a lot of capital and operational costs, while regional budget funding will not be sufficient. So it requires a financing scheme outside the regional budget but still one chain of command of the regional government, the Regional Public Service Agency (BLUD) which is the best choice to overcome financing problems in the region. By implementing flexible BLUD, you can overcome various problems at UPTD related to financial governance, one of which is financing. In the UPTD transformation to implement BLUD, there are several requirements that must be met, namely: substantive requirements, technical requirements and administrative requirements. These three conditions must be met which will then be reviewed by the regional government assessment team. In this research, it was conducted qualitatively, using primary and secondary data, obtained by in-depth interviews and data collection and documents, there were 8 informants from the regional government, environmental services and UPTD TPSA Bagendung, Cilegon city. The aim of this research is to understand the process of transforming UPTD into UPTD BLUD, how to fulfill all requirements and understand the commitment of all relevant stack holders to changes in BLUD policy. UPTD BLUD will have a lot of flexibility in managing revenue so that it is hoped that TPSA waste processing operations can run according to government targets. In the formation of BLUDs, there is autonomous regional government participation and policy which greatly influences whether or not BLUDs are formed in a region. Commitment and understanding of BLUDs is very important so that harmonization occurs in all lines of regional government

Decomposing Neural Circuit Function into Information Processing Primitives.

It is challenging to measure how specific aspects of coordinated neural dynamics translate into operations of information processing and, ultimately, cognitive functions. An obstacle is that simple circuit mechanisms-such as self-sustained or propagating activity and nonlinear summation of inputs-do not directly give rise to high-level functions. Nevertheless, they already implement simple the information carried by neural activity. Here, we propose that distinct functions, such as stimulus representation, working memory, or selective attention, stem from different combinations and types of low-level manipulations of information or information processing primitives. To test this hypothesis, we combine approaches from information theory with simulations of multi-scale neural circuits involving interacting brain regions that emulate well-defined cognitive functions. Specifically, we track the information dynamics emergent from patterns of neural dynamics, using quantitative metrics to detect where and when information is actively buffered, transferred or nonlinearly merged, as possible modes of low-level processing (storage, transfer and modification). We find that neuronal subsets maintaining representations in working memory or performing attentional gain modulation are signaled by their boosted involvement in operations of information storage or modification, respectively. Thus, information dynamic metrics, beyond detecting which network units participate in cognitive processing, also promise to specify how and when they do it, that is, through which type of primitive computation, a capability that may be exploited for the analysis of experimental recordings.

A novel end-to-end vision-based architecture for agricultural human–robot collaboration in fruit picking operations

In the agricultural sector worldwide, there is a need for more human labor in fruit harvesting and processing operations, requiring advanced technologies like human–robot collaboration (HRC), where robots collaborate with humans to improve safety, efficiency, and productivity. In this context, we propose a new end-to-end vision-based detection, tracking, and classification (DTC) architecture enabling robots to assist human pickers in fruit-picking operations. The proposed solution includes using RGB-D camera perception, customized convolutional and recurrent neural network models, and pose estimation to detect, track, and classify the activities of human pickers from continuous streaming data captured in different seasons. It also provides a module for location estimation of the human pickers, allowing the robot to navigate to the estimated goal and assist them in real-time with logistic tasks during harvesting. The authors contribute a new video dataset of real fruit-picking operations under suboptimal conditions, which is used to evaluate the effectiveness of the proposed solution. Simulation results, obtained from the collected experimental dataset, show that the overall time required to compute insights-to-action is around six seconds for challenging scenarios such as multiple human pickers in the scene. This research aims to establish a baseline, end-to-end AI pipeline of insight predictions to prescriptive robot actions in fruit-picking operations to reduce workers’ working time and physical fatigue in logistics operations in the field. The authors expect this study to be used further to improve and develop anticipatory scheduling of robots methodology to save time and energy for pickers in logistics activities. The vision-based DTC architecture presents a reliable and cost-effective solution to address the scarcity of human labor in agriculture by facilitating the collaboration between humans and robots in fruit-picking tasks, as shown by simulations in the ROS-Gazebo framework.

Engineering Properties, Processing and Value Addition of Tamarind: A Review

Tamarind is widely consumed as fruit and spices in India. Tamarind contains pulp, seeds, shell and fibres. The pulp, which makes up between 30 and 50% of the mature fruit, is rich in reducing sugars, pectin, protein, fiber, and cellulose substances. Study of various physical engineering properties such as moisture content, fruit size, length, width, thickness, and weight (pulp, seed, shell, etc.) is important for designing the post-harvest machineries of tamarind. Major unit operations for processing of tamarind includes drying, dehulling, deseeding, pressing and storage. Traditional and mechanical approaches are used for these operations. Mechanical approaches of tamarind processing are carried out by dehuller and deseeder machinery. One of the crucial unit procedures in the processing of tamarind is deseeding which can be done by deseeder, mechanically. Processing of raw tamarind into value-added goods may increase its worth in addition to increase in shelf life. Additionally, it boosts the income of producers and processors. Value added products of tamarind are pulp, tamarind juice concentrate, tamarind pulp powder, tamarind pickle, tamarind jam, tamarind syrup, tamarind candy, tamarind kernel powder, dried fruit block, tamarind chutney and beverages. Tamarind is also rich in major amino acids phytochemicals and hence it carries the properties of antidiabetic, antibacterial, antivenomic, antioxidant. This paper provides an overview of the engineering properties, processing technologies, value added products, technologies and machineries developed/available for tamarind and its health benefits which will further help in machinery, protocol, technology and product design and development.

Numerical analysis for MHD blood-nanofluid flow through a non-linearly stretched sheet interpolated in a permeable medium along heat generation

In this study, steady and incompressible MHD movement of a viscous nanofluid past a non-linear stretching plate has been investigated mathematically by considering heat generation. Blood is employed as the base fluid. Mathematical modeling and analysis are attended in the presence of the permeable medium. Specifically, influences of porosity and heat generation are explored. Numerical solution of the enlisted mathematical model is carried out via an in-built bvp4c tool of MATLAB software. Impact of various parameters associated with the system of ordinary differential equations (ODEs) is reported through graphical depicturation and scrutinized thoroughly. Computed outcomes demonstrated that the increment in the porous media parameter caused a reduction in the velocity profile, while converse outcomes were sighted for the temperature and mass distribution. Temperature profile shows a boost with an increase in heat generation parameter whereas concentration profile indicates a non-monotonic behavior. Analogy between the results obtained in this study and already published work has been established. Thus, by adjusting the controlling parameters, the heating/cooling mechanism can be created. This will help the engineers in designing the application that needs high-temperature processing operations.

Berries as Foods: Processing, Products, and Health Implications.

Berries are highly regarded as flavorful and healthy fruits that may prevent or delay some chronic diseases attributed to oxidative stress and inflammation. Berries are low in calories and harbor diverse bioactive phytochemicals, antioxidants, dietary fibers, and vitamins. This review delves into the main characteristics of fresh berries and berry products as foods and the technologies associated with their production. The main effects of processing operations and related variables on bioactive components and antioxidants are described. This review critically discusses why some health claims based on in vitro antioxidant data and clinical studies and intervention trials are difficult to assess. The review suggests that the beneficial health effects of berries are derived from a multifactorial combination of complex mixtures of abundant phenolic components, antioxidants, and their metabolites acting synergistically or additively with other nutrients like fibers and vitamins and possibly by modulating the gut microbiota.

Youtiao: Effect of ingredients and processing on quality attributes

AbstractBackground and ObjectivesYoutiao (oil stick), a popular food product in East and Southeast Asia, is a deep‐fried stick of wheat flour dough. The basic ingredients include wheat flour, water, leavening agent, and frying oil. The ingredients are mixed to form a dough, which is proofed and shaped before deep‐frying. There have been variations in the quality of youtiao from the market as the production is upscaling toward the industrial level. Consumers demand healthier formulations of youtiao. A better understanding of property–quality relationships of youtiao components is essential to meet these demands. This mini‐review summarizes the sensory quality and flavor attributes of youtiao. The effects of basic and optional ingredients, chemical components, and processing on the quality of youtiao are presented. Quality evaluation of youtiao and the occurrence of chemical toxins are also described. The property–quality relationships of youtiao components are discussed. Future research directions on how to improve the youtiao quality are suggested.FindingsYoutiao contains a range of chemical toxins including polycyclic aromatic hydrocarbons, acrylamide, 5‐hydroxymethylfurfural, 3‐monochloropropane‐1,2‐diol fatty acid esters, and aluminum. The textural, sensory, and chemical properties of youtiao are affected by the interactions between basic ingredients, optional ingredients, and processing operations. Suitable wheat varieties and frying oils should be chosen to make youtiao with desirable sensory properties. Optional ingredients, such as starch‐based nonwheat flours, hydrocolloids, and antioxidants, showed potential to improve various aspects of youtiao quality. Frozen dough technology is promising for industrial‐scale production of youtiao.ConclusionsVariation in the quality of youtiao from different manufacturers is large. There are many research opportunities to improve the quality of youtiao.Significance and NoveltyYoutiao represents a typical model system of deep‐fried cereal foods, which are under‐studied as compared with other cereal and grain‐based foods. This review systematically analyzed different aspects of youtiao quality for the first time, and may stimulate further interest in the studies of fried cereal and grain‐based foods.

Assessing the environmental impacts of copper cathode production based on life cycle assessment.

The demand for copper is growing considerably in parallel with economic and technological development. The rate increase in copper consumption in Iran increases pressure on the numerous unexploited mines in southeast Iran and causes the environmental crisis alongside the northern Levar wind in this area. Given this, this study systematically explored the environmental impacts of a one-ton copper cathode processing operation from a cradle-to-gate perspective, using life cycle assessment (LCA). Moreover, the release of greenhouse gases and the energy consumption during the copper cathode production were also assessed. The results indicated that sulfuric acid use in the smelting and extraction stages, metal leaching from tailings, and CO2 dominated more than 50% of contributions to freshwater and marine ecotoxicity, human toxicity, and global warming. The energy analysis revealed 88.92% of crude oil use especially for the electrowinning stage, which should be promoted technologically. For global warming, the indirect CO2 emission from electricity consumption using fossil fuels was the main contributor (94.56%). Therefore, shifting from conventional energy systems to renewable energy systems could alleviate the overall environmental impact. For a 0.57-ton sulfuric acid effluent per one ton of copper cathode production, its treatment and reuse in the process is recommended. Summing up, the results of this study provide the environmental hot spots for copper cathode production for further investigation. Integr Environ Assess Manag 2024;20:1180-1190. © 2023 SETAC.