Processing Mode Research Articles

Non-targeted mass spectrometry and feature-based molecular networking for determination of branched fatty acid esters of hydroxy fatty acids in milk.

Branched fatty acid esters of hydroxy fatty acids (FAHFAs) represent trace lipids with significant natural biological functions. While exogenous FAHFAs have been extensively studied, research on FAHFAs in milk remains limited, constraining our grasp of their nutritional roles. This study introduces a non-targeted mass spectrometry approach combined with chemical networking of spectral fragmentation patterns to uncover FAHFAs. Through meticulous sample handling and comparisons of various data acquisition and processing modes, we validate the method's superiority, identifying twice as many FAHFAs compared to alternative techniques. This validated method was then applied to different milk samples, revealing 45 chemical signals associated with known and potential FAHFAs, alongside findings of 66 ceramide/hexosylceramide (Cer/HexCer), 48 phosphatidyl ethanolamine/lyso phosphatidyl ethanolamine (PE/LPE), 21 phosphatidylcholine/lysophosphatidylcholine (PC/LPC), 16 phosphatidylinositol (PI), 7 phosphatidylserine (PS), and 11 sphingomyelin (SM) compounds. This study expands our understanding of the FAHFA family in milk and provides a fast and convenient method for identifying FAHFAs.

Long-lasting negativity in the left motoric brain structures during word memory inhibition in the Think/No-Think paradigm

In this study, we investigated the electrical brain responses in a high-density EEG array (64 electrodes) elicited specifically by the word memory cue in the Think/No-Think paradigm in 46 participants. In a first step, we corroborated previous findings demonstrating sustained and reduced brain electrical frontal and parietal late potentials elicited by memory cues following the No-Think (NT) instructions as compared to the Think (T) instructions. The topographical analysis revealed that such reduction was significant 1000 ms after memory cue onset and that it was long-lasting for 1000 ms. In a second step, we estimated the underlying brain generators with a distributed method (swLORETA) which does not preconceive any localization in the gray matter. This method revealed that the cognitive process related to the inhibition of memory retrieval involved classical motoric cerebral structures with the left primary motor cortex (M1, BA4), thalamus, and premotor cortex (BA6). Also, the right frontal-polar cortex was involved in the T condition which we interpreted as an indication of its role in the maintaining of a cognitive set during remembering, by the selection of one cognitive mode of processing, Think, over the other, No-Think, across extended periods of time, as it might be necessary for the successful execution of the Think/No-Think task.

Effect of CTC processing on quality characteristics of green tea infusion: A comparative study with conventional orthodox processing

Traditionally, green tea is manufactured using orthodox processing methods. The CTC (curl, tear, and crush) method, widely employed in black tea processing, has minimal application in green tea production. However, it exhibits potential in terms of extraction efficiency to infusion. This study investigates the CTC method for producing green tea (CGT) and compares the extraction efficiency of bioactives to infusion with orthodox green tea (OGT). Total polyphenol, catechins, caffeine, and theanine extraction efficiencies were evaluated in both CGT infusion (CGTI) and OGT infusion (OGTI), along with water extract, antioxidant activity, color parameters, and sensory attributes. Results showed the amount of the bioactive compounds extracted from 2.00 g OGT could be attained from 0.75 to 1.00 g of CGT. This indicates that CGT is 100 to 167% more efficient in terms of the contents of bioactives in the infusion than OGT. This finding corroborates the observations of higher antioxidant activity of CGT compared to OGT. CGTI achieved similar taste and color perceptions with half the amount of OGT or even less. This enhanced extraction efficiency, along with CGTI's taste and color characteristics, can facilitate its commercial production, benefiting consumers and the tea industry. Industrial relevanceThis study provides comprehensive information on CTC green tea to the tea industry, which is the first of its kind with such in-depth details. As evident, the CTC processing mode of green tea promises more affordability to its growing consumers leading to increased demand for this type of tea which will immensely benefit the tea industry.

The temporal dynamics of task processing and choice in a novel multitasking paradigm.

This study investigated the temporal dynamics of task performance and voluntary task choice within a multitasking paradigm in which the task-related processing outcomes themselves determined the to-be-performed task. In the novel forced-no-go trials, the stimulus for one task required an overt response, but the stimulus for the other task was associated with a no-go response. Task performance results showed that participants often processed the no-go task's stimulus before switching to the go-task. Dual-task interference effects and switch costs indicated various forms of multitasking interference, with their underlying causes appearing to overlap, as engagement in parallel processing seemed to be limited by switch-related reconfiguration processes. Intermixing free-choice trials, where both stimuli were associated with overt responses, revealed costs associated with switching between processing modes, providing new evidence that the distinctions between free and forced task goals stem from differences in their internal representations rather than alterations in processing due to different presentations in the environment. Task choice results align with this perspective, demonstrating a preference for repeating a free- over a forced-choice task. Furthermore, these free-choice results illuminate the interplay of cognitive (task-repetition bias) and environmental constraints (first-task bias) in shaping task choices: It appears that task-specific information increases goal activations for both task goals concurrently, with participants favoring central processing of the second- over the first-presented task to optimize their behavior when shorter central processing is required (task repetition). Overall, this study offers new insights into the dynamics of task processing and choice in environments requiring the balance of multiple tasks.

Improving Urban Mapping Accuracy: Investigating the Role of Data Acquisition Methods and SfM Processing Modes in UAS-Based Survey Through Explainable AI Metrics

In this study, we investigated the accuracy of surface models and orthophoto mosaics generated from images acquired using different data acquisition methods at different processing levels in two urban study areas with different characteristics. Experimental investigations employed single- and double-grid flight directions with nadir and tilted (60°) camera angles, alongside the Perimeter 3D method. Three processing levels (low, medium, and high) were applied using SfM software, resulting in 42 models. Ground truth data from RTK GNSS points and aerial LiDAR surveys were used to assess horizontal and vertical accuracies. For the horizontal accuracy test, neither the oblique camera angle nor the double grid resulted in an improvement in accuracy. In contrast, when examining the vertical accuracy, it was concluded that for several processing levels, the tilted camera angle yielded better results, and in these cases, the double grid also improved accuracy. Feature importance analysis revealed that, among the four variables, the data acquisition method was the most important factor affecting accuracy in two out of three cases.

Regulation of Fat Content in Triticale Based on Optimization of Technological Processing Modes

Introduction The aim of the study was to regulate the fat content based on the optimization of technological processing modes. A promising method of preparing grain for animal feeding is its preliminary germination. Sprouted grain is a dietary food product, as it increases the content of proteins, essential amino acids, and macro- and microelements in comparison with non-sprouted grain. Methods The purpose of this study was to regulate the fat content based on the optimization of technological processing modes. Regression equations describing the dependences of the quality indicators of the triticale grain germination modes on the factors tw, τ, w and tg affecting them were investigated and obtained. During the study of triticale grains, it was found that the most significant parameter affecting the germination process is the fat content. The optimization of the germination modes of triticale Leather grain was carried out by the method of nonlinear programming. Results During the study, it was found that the maximum fat content during germination of triticale grain was observed with the following technological parameters of germination: grain temperature = 30°C; germination time = 24 h; grain moisture = 13%; and grain temperature = 26°C. When controlling the fat content in triticale based on the optimization of technological modes, the target function was 2.83%. Conclusion The presented results and conclusion will allow to regulate the fat content in triticale grain during germination.

Application of neural network algorithm in computer data information processing system

In order to solve the problem that the data processing frequency of the traditional centralized data processing system is low, which leads to the poor feedback effect of massive data, this paper proposes a computer data information processing system based on neural network algorithm. Based on the original system hardware, the system replaces the data processor and increases the total number of the processor, so as to realize the distributed synchronous processing of massive data. In the aspect of software design, the interaction between system units and modules is formed through the protocol to improve the data communication mode of the system; Calculate the Euclidean distance and set the distributed processing mode of the system; According to the definition of cloud computing, the classification function is used to determine the constraints, establish the processing frequency equation, and realize the rapid processing of massive data. The experimental results show that the processing frequency of the system designed in this paper is more than 50 % higher than that of the traditional system, which solves the problem of low processing frequency caused by weak analysis ability and slow response speed in the traditional system. Conclusioncompared with the traditional system, the system designed in this paper has faster processing frequency for massive data and better feedback effect to users.

Enhanced Interlayer Adhesion of 3D Printed Poly(lactide acid) with Ultralow Hyperbranched Poly(ester imide)s

The layer-by-layer process of fused deposition modeling (FDM) is well-known for introducing anisotropy in 3D printed parts due to weak interlayer adhesion. Moreover, the existing physical blend modification methods still face the side effects caused by the heavy use of additives, such as high cost, poor biocompatibility and weakening of primary properties. In this study, hyperbranched polymers poly(ester imide)s (HBPEIs) were blended into the poly(lactic acid) (PLA) matrix to enhance the interlayer adhesion of 3D printed parts. The application effect of HBPEIs in different processing modes was discussed. The effect of different amounts of HBPEIs on the mechanical properties of PLA specimens was investigated. Results showed that by adding just 0.1 wt% HBPEIs, the maximum tensile strength for samples printed with a 90° raster orientation increased from 23.1 MPa to 46.6 MPa while maintaining an isotropic behavior at around 95.9 %. Microstructures, rheological behaviors, and thermal properties of the printed specimens were characterized and compared to understand the underlying enhancement mechanism. It suggested that HBPEIs significantly improved interlayer adhesion by promoting chain diffusion and cold-crystallization between layers. This study showed that using ultralow additives can significantly enhance the interlayer adhesion in FDM parts.

Study of operational modes of cool-down and warm-up processes for the ESS large-scale 20 K helium refrigeration system

In the beginning, the European Spallation Source (ESS) will install two hydrogen moderators where the nuclear heating is estimated to be 6.7 kW at 5 MW proton beam power. The ESS cryogenic moderator system (CMS) has the function to circulate a subcooled liquid hydrogen through the moderatos. It is cooled through a heat exchanger by a large-scale 20 K helium refrigeration system with a cooling capacity of 30.3 kW at 15K, which is called Target Moderator Cryoplant (TMCP). The commissioning of the TMCP cold box and the two compressor skids was completed in 2019. Cryogenic transfer lines with a length of 300 m (CTL) and a valve box located next to the CMS cold box were installed during the summer of 2022. In this paper, the TMCP cool-down and warm-up processes have been studied and the operational parameters have been optimized based on the result of the CMS cool-down simulation.

The effect of plasma electrolytic oxidation of aluminum alloys on the structure and properties of protective oxide-ceramic coatings

The results of the studies of electrical modes of the high-frequency high-voltage plasma electrolytic oxidation (PEO) on the properties of the formed protective coatings on the B95 T1 and D16ч T aluminum alloys are presented. The control of the PEO process was carried out by changing the amplitude values of anode and cathode voltage pulses. The local mechanical characteristics and the structure of the PEO coatings were studied using instrumentedindentation and scanning electron microscopy. As a result of the studies, optimal modes of the PEO process were determined: potentiodynamic modes with a smooth decrease in the amplitude of anode voltage pulses at a speed of 5 V/min and a smooth increase in the amplitude of cathode voltage pulses at a speed of 1 V/min. The coatings formed in the optimal modes of the PEO were characterized by a reduced number of defects (microcracks, craters, pores) and high values of hardness of 27.5–27.8 GPa and elastic modulus of 286–309 GPa averaged over their thickness. These characteristics turned out to be higher by 33–45% and 15–30%, respectively, compared with a hardness and elastic modulus of the coatings, formed in other studied PEO modes.

Associations Between Neighborhood Resources and Youths’ Response to Reward Omission in a Task Modeling Negatively Biased Environments

Neighborhoods provide essential resources (eg, education, safe housing, green space) that influence neurodevelopment and mental health. However, we need a clearer understanding of the mechanisms mediating these relationships. Limited access to neighborhood resources may hinder youths from achieving their goals and, over time, shape their behavioral and neurobiological response to negatively biased environments blocking goals and rewards. To test this hypothesis, 211 youths (aged ∼13.0 years, 48% boys, 62% identifying as White, 75% with a psychiatric disorder diagnosis) performed a task during functional magnetic resonance imaging. Initially, rewards depended on performance (unbiased condition); but later, rewards were randomly withheld under the pretense that youths did not perform adequately (negatively biased condition), a manipulation that elicits frustration, sadness, and a broad response in neural networks. We investigated associations between the Childhood Opportunity Index (COI), which quantifies access to youth-relevant neighborhood features in 1 metric, and the multimodal response to the negatively biased condition, controlling for age, sex, medication, and psychopathology. Youths from less-resourced neighborhoods responded with less anger (p< .001, marginal R2= 0.42) and more sadness (p< .001, marginal R2= 0.46) to the negatively biased condition than youths from well-resourced neighborhoods. On the neurobiological level, lower COI scores were associated with a more localized processing mode (p= .039, marginal R2= 0.076), reduced connectivity between the somatic-motor-salience and the control network (p= .041, marginal R2= 0.040), and fewer provincial hubs in the somatic-motor-salience, control, and default mode networks (all pFWE< .05). The present study adds to a growing literature documenting how inequity may affect the brain and emotions in youths. Future work should test whether findings generalize to more diverse samples and should explore effects on neurodevelopmental trajectories and emerging mood disorders during adolescence. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. One or more of the authors of this paper received support from a program designed to increase minority representation in science. We actively worked to promote sex and gender balance in our author group. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group.

Joint computational and optimized resource allocation in edge computing networks

Fog computing has several undeniable benefits, such as enhancing near-real-time response, reducing transmission costs, and facilitating IoT analysis. This technology is poised to have a significant impact on businesses, organizations, and our daily lives. However, mobile user equipment struggles to handle the complex computing tasks associated with modern applications due to its limited processing power and battery life. Edge computing has emerged as a solution to this problem by relocating processing to nodes at the network’s periphery, which have more computational capacity. With the rapid evolution of wireless technologies and infrastructure, edge computing has become increasingly popular. Nevertheless, managing fog computing resources remains challenging due to resource constraints, heterogeneity, and distant nodes. For delay-sensitive intelligent IoT applications within the fog computing architecture, cooperation and communication processing resources in 6 G and future networks are essential. This study proposes a joint computational and optimized resource allocation (JCORA) technique to accelerate the processing of data from intelligent IoT sensors in a cell association environment. The proposed technique utilizes an uplink and downlink power allocation factor and the shortest job first (SJF) task scheduling system to optimize user fairness and decrease data processing time. This is a complex assignment due to several non-convex limitations. The suggested JCORA-SJF model simultaneously optimizes time partitioning, computing task processing mode selection, and target sensing location selection to maximize the weighted total of task processing and communication performance. The simulation results demonstrate the effectiveness of the proposed JCORA-SJF algorithms, and the system’s scalability is also examined.

Experimental and numerical simulation study on flexural performance of high-strength reinforced concrete beams under static loading

In recent years, high-strength steel bars (HSSB), such as hot-rolled ribbed bars (HRB) and heat-treated ribbed bars (HTRB), have been promoted and used in reinforced concrete (RC) structures worldwide. There is still some controversy in existing research regarding the crack development process, failure modes, and the applicability of current code-specified calculation formulas for high-strength RC beams. The study of the mechanical properties of high-strength RC beams is of great significance in promoting the development of construction engineering science. In order to investigate the flexural behavior of high-strength RC beams, four-point bending static loading tests were carried out on 11 groups of RC beams with different parameters (reinforcement strength, concrete strength, and reinforcement ratio). The test results indicate that the crack development process and failure mode of high-strength RC beams exhibit obvious bending failure characteristics. Increasing reinforcement strength and reinforcement ratio significantly improves the bearing capacity of the beams, while the impact of increasing concrete strength on the beam's capacity is relatively small. The applicability of the calculation formulas in the current codes was analyzed, it was found that the yield moment formula in the current code accurately predicts the yield moment of high-strength RC beams, and the formula for calculating the maximum crack width was modified by proposing the maximum crack width adjustment factor kω. Subsequently, the failure process of high-strength RC beams was simulated using explicit dynamic finite element software LS-DYNA. Parameterized analyses were conducted to broaden the research scope. The ultimate moment prediction factor ku was proposed and a formula that can accurately predict the ultimate moment of high-strength RC beams was established.

ENVIRONMENTALLY SAFE TECHNOLOGY OF STARCH NITRATE PRODUCTION

This article is devoted to development of an environmentally safe technology for production of an explosive substance – starch nitrate. The aim of the development was to create the technology for starch nitrate production, which reduces the man-made burden on the environment, due to the absence of acidic wastes discharged into the environment, and also ensures the chemical stability of starch nitrate. This is achieved by starch nitration with highly concentrated nitric acid without adding sulfuric acid. To work out this process in laboratory conditions, nitration of starch was carried out with 98 %, 90 % and 85 % nitric acids. Obtained starch nitrate was precipitated from the solution in nitric acid into the aqueous solution of nitric acid of lower concentration, separated from the precipitation medium, washed with water, dried, and its chemical stability was determined. The following portions of starch nitrate were precipitated in media prepared from drains formed after washing previous portions of starch nitrate by adding 98 % nitric acid to them. Concentrations of nitric acid in the initial and spent precipitation media were determined. Based on the results of the experimental studies, the modes of the technological process were established, which allows effective regeneration of the presipitation medium after presipitation of starch nitrate, which excludes the ingress of acid waste into the environment, and ensure high chemical stability of the obtained starch nitrate. The technological scheme for the continuous production of starch nitrate according to environmentally safe technology using a drum vacuum filter was developed, in which all starch nitrate stabilization operations are combined.

Silicate brick production using anhydrite raw material

Purpose: The aim of this work is to substantiate recycling of anhydrite raw materials for the production of silicate products and the development of methods for preparing source materials, compositions and process modes.Methodology/approach: Selection and research of basic materials, methods of their preparation for the production of silicate products; justification of process methods and assessment of quality parameters of materials with the required properties.Research findings: The possibility of utilizing acid fluoride during the synthesis of calcareous-siliceous binder used to produce silicate bricks during autoclave processing. The obtained sand-lime brick samples possess the following physical and mechanical properties: 1750 to 1900 kg/m3 average density, 10 to 12.5 MPa compressive strength, 16 or 17 % water absorption. The samples meet the requirements for sand-lime bricks, namely strength, density, water absorption and correspond to grades 100 and 125.Practical implications: The process modes of autoclaving are detected and the main operational characteristics of sand-lime brick are determined. Methods proposed for preparing raw materials and process conditions of silicate wall materials allow solving environmental problems and saving natural limestone raw materials.Novelty: Sand-lime brick production is based on anhydrite raw materials; suggested dependencies make it possible to control the composition and properties of silicate materials.

UNRAVELING THE USE OF COGNITIVE AND METACOGNITIVE LEARNING STRATEGIES AMONG MOROCCAN EFL FIRST-SEMESTER UNIVERSITY LEARNERS

This study, drawing on the theory of metacognition as a lively research area in the field of educational psychology, is intended to explore the cognitive and metacognitive learning strategies (CMLSs) tapped by the English department first-semester university students in their learning practices. It is an attempt to evaluate the extent to which EFL learners resort to these ‘high-level’ heuristics for executing a diversity of learning-related tasks. To achieve this stated objective, this exploratory study targeted 63 Moroccan English department students. The data were gathered through the use of a ‘self-report questionnaire’ that addressed the learners’ recourse to (meta) cognitive learning strategies (CMLSs) along the continuum of their academic studies at the first-semester level. The outcomes reached manifestly exhibit that heavy reliance on cognitive strategies (CSs) and reduced dependency on metacognitive strategies (MSs) prototypically characterized the learners’ adopted processing modes in tackling differing learning tasks relatable to studying English as a foreign language (EFL). Hence, some actionable recommendations as well as a few limitations encountered in conducting this study are explicitly put forward.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/lit/0757/a.php" alt="Hit counter" /&gt;&lt;/p&gt;

Reliability analysis of intelligent manufacturing systems based on improved FMEA combined with machine learning

Along with the booming of intelligent manufacturing, the reliability management of intelligent manufacturing systems appears to be becoming more significant. Failure mode and effects analysis (FMEA) is a prospective reliability management instrument extensively utilized to manage failure modes of systems, products, processes, and services in various industries. However, the conventional FMEA method has been criticized for its inherent limitations. Machine learning can handle large amounts of data and has merits in reliability analysis and prediction, which can help in failure mode classification and risk management under limited resources. Therefore, this paper devises a method for complex systems based on an improved FMEA model combined with machine learning and applies it to the reliability management of intelligent manufacturing systems. First, the structured network of failure modes is constructed based on the knowledge graph for intelligent manufacturing systems. Then, the grey relation analysis (GRA) is applied to determine the risk prioritization of failure modes. Hereafter, the k-means algorithm in unsupervised machine learning is employed to cluster failure modes into priority classes. Finally, a case study and further comparative analysis are implemented. The results demonstrate that failure modes in system security, production quality, and information integration are high-risk and require more resources for prevention. In addition, recommendations for risk prevention and monitoring of intelligent manufacturing systems were given based on the clustering results. In comparison to the conventional FMEA method, the proposed method can more precisely capture the coupling relationship between the failure modes compared with. This research provides significant support for the reliability and risk management of complex systems such as intelligent manufacturing systems.

Optical fiber-based open source low cost portable spectrometer system

This article explores the development of a small, compact fiber-based spectrometer system designed to overcome the limitations of standard spectrometers, such as the high cost and restricted accessibility.Operated by a Raspberry Pi, the fiber-based spectrometer system uses the increased computing power to provide versatile modes of operation and powerful data processing, while maintaining a small size. Specifically crafted for basic chemistry and biology lab setups, where fibers allow measurements in different conditions, and customization enables fluorescence, light scattering, and absorption measurements.The system is adaptable and versatile, offering ease of modification and adaptation for a broad range of applications.

Research and Application Progress of Laser-Processing Technology in Diamond Micro-Fabrication.

Laser-processing technology has been widely used in the ultra-precision machining of diamond materials. It has the advantages of high precision and high efficiency, especially in the field of super-hard materials and high-precision parts manufacturing. This paper explains the fundamental principles of diamond laser processing, introduces the interaction mechanisms between various types of lasers and diamond materials, focuses on analyzing the current development status of various modes of laser processing of diamond, briefly discusses the relevant applications in diamond cutting, micro-hole forming, and micro-groove machining, etc., and finally discusses the issues, challenges, and potential future advancements of laser technology in the field of diamond processing at this point.

The Future Higher Education in Albania in the Digital Era–Challenges and Opportunities

Over the last decade, Albania made genuine attempts to adopt its education system after the European model considering the implementation and integration of ICT as a functional skill of digital literacy to face, among others, the challenges of current globalization. But the Covid-19 crisis evidenced that such a revolutionizing of the education system was applied to ICT incorporation and implementation into a curriculum of merely conventionally-based teaching and learning process. Precisely, the research question addresses the evolution of the higher education in Albania against the backdrop of lessons learned from the pandemic crisis in view of an effective and qualitative education system while making the most of technological and digitalized tools. The study is based on a qualitative analysis offering a theoretical approach of primary and secondary sources and continues with the empirical research conducted in 2023 - a survey questionnaire regarding students’ perceptions of the blended mode of teaching and learning process and also a comparative assessment with the one conducted in 2020 regarding their attitudes of online delivery. Hence, by considering the analysis in two quite differing contexts, the aim is to explore how have students’ outlooks changed regarding the mode of delivery of the teaching and learning process since the pandemic period to date. The study also includes in-field expertise acquired to that aim from the University of Münster (Germany) in the framework of the RESILIENCE Project.