Processing Steps Research Articles

Comparative Study on Calcium-enriched Finger Millet Tisane: Optimizing Soaking and Roasting Methods

This study focuses on the development of a calcium-rich herbal tisane using finger millet, a grain known for its high calcium content, aimed at enhancing dietary calcium intake. Two distinct processing methods were employed to optimize the calcium content of the tisane. In Treatment 1, Finger millet was roasted and combined with spice powder, while in Treatment 2, Finger millet underwent soaking, cabinet drying, roasting, and then blending with spice powder. The calcium content and overall quality of both treatments were evaluated, revealing a marginal increase in calcium concentration in Treatment 2, attributed to the additional processing steps. Specifically, Treatment 2 yielded a maximum calcium content of 396 mg compared to 380 mg in Treatment 1, demonstrating the effectiveness of the additional soaking and drying steps in enhancing calcium bioavailability. The final product was further enriched with a blend of spices, including dry ginger, cinnamon, cardamom, Arjuna bark, fennel seeds, and Tulsi leaves, contributing to its healthful properties. Organoleptic evaluations, conducted to assess sensory attributes such as flavor, aroma, and overall acceptability, indicated that both treatments were well-received by consumers. This suggests the product's potential for consumer acceptance and market viability. The findings suggest that calcium-enriched Finger millet tisane could serve as a valuable functional beverage, particularly for populations at risk of calcium deficiency. Its nutrient profile, combined with favorable sensory attributes, positions it as a promising option for improving dietary calcium intake and promoting overall health. Further research on larger sample sizes and bioavailability studies could reinforce its potential as a functional health beverage.

Sky images based photovoltaic power forecasting: A novel approach with optimized VMD and Vision Mamba

As the global demand for sustainable energy sources continues to grow, accurate prediction of photovoltaic power generation is crucial for optimizing the utilization of solar resources and enhancing the efficiency of photovoltaic systems. To improve the accuracy of photovoltaic power forecasting, this paper proposes a novel hybrid predictive model that integrates Optimized Variational Mode Decomposition (VMD), Vision Mamba (Vim) for extracting features from sky images, and advanced mechanisms like Patch Embedding and Variate-wise Cross-Attention. Initially, the proposed model employs SAO-optimized VMD to decompose the photovoltaic power series into high, medium, and low-frequency components. Subsequently, these components are patched to serve as input for the subsequent layers. In the third step, exogenous variables, including meteorological and image data, are introduced and processed through Variate Embedding combined with cross-attention mechanisms to capture the intricate interactions between these variables. Finally, by integrating the outputs from all processing steps through normalization and feed-forward layers, the final predictive results are produced. Experimental evaluations across different seasons demonstrate significant enhancements in forecasting accuracy, with the model achieving Root Mean Square Error (RMSE) values of 0.3587 in spring, 0.4376 in summer, 0.3544 in autumn, and 0.3493 in winter. Similarly, Mean Absolute Error (MAE) and Mean Squared Error (MSE) across these seasons underscore the model's effectiveness. This model offers new technical means for photovoltaic power forecasting and provides valuable decision support for the optimization and management of photovoltaic power systems.

Thermochemical reactions in tea drying shape the flavor of tea: A review

Drying is the final and essential step in tea processing. It contributes a lot to the formation of tea flavor quality by a series of complicated and violent thermochemical reactions, such as degradation reaction, Maillard reaction, redox reaction, isomerization reaction, etc. However, the mechanism of specific thermochemical reaction is unclear. Here, by comprehensively summarizing the thermochemical reactions of the main chemicals, including polyphenols, lipids, amino acids and carbohydrates, etc., during tea drying with particularly focus on their contributions of thermal drying on the flavor including color, aroma, and taste, we found that thermal degradation is the dominant thermochemical reaction, directly affecting the taste and color of tea, and thermal oxidation of lipids and Maillard reaction mainly contribute to form tea aroma. More interesting was that high temperature enhanced nucleophilicity of phenolics, allowing them to easily trap carbonyl substances to form small molecular adducts (i.e. EPSFs) or polymers, which could interfere other thermochemical reactions, and then alter the flavor quality of tea. Over all, this review provides updated scientific evidence for in-depth exploration of thermochemical reactions towards tea precision processing.

Identifying synthetic variables influencing the reproducible microfluidic synthesis of ZIF nano- and micro-particles

The reproducible synthesis of nano- and -micro particles is a critical step in multiple synthetic and industrial processes. Microfluidic synthesis offers numerous advantages for development of precise, reliable, and industrially useful synthesis protocols. However, the influence of different synthetic variables on reproducibility in microfluidic synthesis is underexplored. In this work, we systematically study the influence of key synthetic parameters on the microfluidic synthesis of four Zeolitic Imidazolate Frameworks (ZIFs): ZIF-7, ZIF-8, ZIF-9, and ZIF-67. Utilizing a coiled tube microfluidic setup, we explore the influence of key synthetic parameters such as reagent concentration, stoichiometry, aging time, and nine modulators (pH-altering agents, surfactants, and polar polymers). Most importantly, we evaluate the impact of these variables in combination with the role of mixing, using the Dean flow model. Lastly, we focus on the reproducibility problems that a coiled reactor presents at specific flowrates and how these problems can be recognized and avoided. Overall, the insights collected from this work inform the design of synthetic protocols and enhance material reproducibility and control in microfluidic nano- and micro-particle synthesis.

Multi-loaded PLGA microspheres as neuroretinal therapy in a chronic glaucoma animal model.

This work focused on the co-encapsulation and simultaneous co-delivery of three different neuroprotective drugs in PLGA (poly(lactic-co-glycolic acid) microspheres for the treatment of glaucoma. For formulation optimization, dexamethasone (anti-inflammatory) and ursodeoxycholic acid (anti-apoptotic) were co-loaded by the solid-in-oil-in-water emulsion solvent extraction-evaporation technique as a first step. The incorporation of a water-soluble co-solvent (ethanol) and different amounts of dexamethasone resulted critical for the encapsulation of the neuroprotective agents and their initial release. The optimized formulation was obtained with 60mg of dexamethasone and using an 80:20 dichloromethane:ethanol ratio. In the second step in the microencapsulation process, the incorporation of the glial cell line-derived neurotrophic factor (GDNF) was performed. The final prototype showed encapsulation efficiencies for each component above 50% with suitable properties for long-term application for at least 3months. Physicochemical studies were performed by SEM, TEM, DSC, XRD, and gas chromatography. The evaluation of the kinetic release by the Gallagher-Corrigan analysis with Gorrasi correction helped to understand the influence of the co-microencapsulation on the delivery of the different actives from the optimized formulation. The final prototype was tested in a chronic glaucoma animal model. Rats received two intravitreal injections of the neuroprotective treatment within a 24-week follow-up study. The proposed formulation improved retinal ganglion cell (RGC) functionality examined by electroretinography. Also, it was able to maintain a neuroretinal thickness similar to that of healthy animals scanned by in vivo optical coherence tomography, and a higher RGC count on histology compared to glaucomatous animals at the end of the study.

Employee Salary Prediction in HRMS Using Regression Models

The study uses regression models to examine AI model engagement in employee salary prediction. Salary prediction is significantly critical for both employees and employers. Employees try to get maximum benefit for the services they provide, and employers emphasize achieving organizational goals through optimized employee salaries. Inconsistency in employee salaries may cause organizations financial losses and organizational objectivity failure. Historically, salary was determined by historical data, market surveys, and personal judgment, which often resulted in inconsistencies and biases. With the availability of large datasets from Human Resource Management Systems and advanced machine learning algorithms, there is an opportunity to enhance the fairness of Salary Prediction. To overcome this problem in organizations, we have proposed a regression model for salary prediction with a promising accuracy rate of 99% with regression models. The methodology includes data processing steps including EDA, data standardization, feature correlation, and feature engineering to enhance the accuracy of the models. This study used Random Forest Regressor, Gradient Boosting Regressor, and Light Gradient Boosting Machine Regressor models for employee salary prediction. This research paper provides a valuable understanding of HR analytics for HR professionals and organizations for salary prediction of employees in an organization. Also, this research investigates the use of Machine learning algorithms to predict employee salaries while comparing employee performance and eliminating biases. The aim is to develop robust, data-enriched frameworks in HRMS for organizations for accurate and transparent salary prediction.

A-195 Combination of Lean Mindset, Robotics and Digitalization allowed us to manage a 2.5 times increase in workload in our central reference lab

Abstract Background Our Agilus Diagnostics, Gurugram, India is a Central Reference Laboratory and part of the pan-India network over 400 lab in India. Our Gurugram lab delivers 3,200 tubes in the serum work area in 17 hour operations daily and is ISO 15189 and CAP accredited.In Jan 2023, our lab started to support Govt PHC Program samples and our workload increased to 8000 tubes. This was a clear operational and clinical challenge for us with a 2.5 x jump in workload. To achieve this goal, a lean mindset with process improvement steps were to be applied required across the total testing process. This lean approach was supported by cobas ® Lab Automation systems managed by navify ® Lab Operations informatics automation solution. This poster explains our journey of planning, implementing, and delivering on our goal of predictable operational efficiency when faced with this challenge. Methods We took 4 major steps We redesigned lab processes using lean process improvement operational dashboards. This poster will show the metric we track which help eliminate waste in the system. We optimized our total lab automation solution with smart load balancing across cobas ® Pro integrated analyzers and staff friendly recursive workflow. Clear and strict use of off peak hours for maintenance and QC procedures and standardized predictable schedule of operation. This poster will share our time table with key milestones across the 24 hours We built on the Informatics solution using 5 layered Auto verification rules based decision framework. IQC, Systems Flags check, Sample Quality checks, Biological Reference check, custom Auto verification Range, Critical ranges Delta check Criteria, Clinical Correlation Rule are all part of the auto verification logic framework. Results We were able to deliver the following results.Lean process design led to 53% simplification. Number of process steps reduced to 29 from 62 steps across the total testing process. Total Lab Automation has helped improve 90 percentile production TAT by 46% from 250 mins to 135 mins Our 90 % TAT CV has significantly reduced making our production predictable. Over 17,500 results / day reported are subject to auto verification with 87 % of all results auto-verified and auto-approved. Not a single additional employee was added in the analytical or post analytical areas of the lab operations Conclusions Lean Mindset complemented by Total Laboratory Automation along with Decision Automation on an Informatics solution has enhanced lab performance in terms of TAT, Efficiency, and Staff Motivation. This poster will also demonstrate using incremental effectiveness analysis comparative data of key performance indicators, such predictability in production, and efficiency of workforce, senior staff time saved, before and after technology deployment. The challenge of increase in sample workload is real in India and this approach allows for a sustainable way to ensure patient impact with minimum burden on our workforce.

Detection and Signal Processing for Near‐Field Nanoscale Fourier Transform Infrared Spectroscopy

AbstractResearchers from a broad spectrum of scientific and engineering disciplines are increasingly using scattering‐type near‐field infrared spectroscopic techniques to characterize materials non‐destructively with nanoscale spatial resolution. However, a sub‐optimal understanding of a technique's implementation can complicate data interpretation and act as a barrier to entering the field. Here the key detection and processing steps involved in producing scattering‐type near‐field nanoscale Fourier transform infrared spectra (nano‐FTIR) are outlined. The self‐contained mathematical and experimental work derives and explains: i) how normalized complex‐valued nano‐FTIR spectra are generated, ii) why the real and imaginary components of spectra qualitatively relate to dispersion and absorption respectively, iii) a new and generally valid equation for spectra which can be used as a springboard for additional modeling of the scattering processes, and iv) an algebraic expression that can be used to extract an approximation to the sample's local extinction coefficient from nano‐FTIR. The algebraic model for weak oscillators is validated with nano‐FTIR and attenuated total reflectance Fourier transform infrared (ATR‐FTIR) spectra on samples of polystyrene and Kapton and further provides a pedagogical pathway to cementing some of the technique's key qualitative attributes.

Stability of cloud detection methods for Land Surface Temperature (LST) Climate Data Records (CDRs)

The stability of a climate data record (CDR) is essential for evaluating long-term trends in surface temperature using remote sensing products. In the case of a satellite-derived CDR of land surface temperature (LST), this includes the stability of processing steps prior to the estimation of the target climate variable. Instability in the masking of cloud-affected observations can result in non-geophysical trends in a LST CDR. This paper provides an assessment of cloud detection performance stability over a 25-year LST CDR generated using data from the second Along-Track Scanning Radiometer (ATSR-2), the Advanced Along-Track Scanning Radiometer (AATSR), the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Sea and Land Surface Temperature Radiometer (SLSTR). We evaluate three cloud detection methodologies, one fully Bayesian, one naïve probabilistic and the operational threshold-based cloud mask provided with each sensor, at four in-situ ceilometer sites. Of the 12 algorithm-site combinations assessed, only two (17 %) were stable across the full timeseries with respect to both cloud contamination and missed clear-sky observations. Five (42 %) were stable with respect to missed clear-sky observations only. The associated impacts on LST trends in the CDR could be as large as (+/−)0.73 K per decade (0.43 K per decade above the target stability), which means that attention needs to be paid to this aspect of stability in order to understand uncertainty in long-term observed trends. Given that cloud detection stability has not to our knowledge been previously assessed for any target climate variable, this conclusion may apply more broadly to other satellite-derived CDRs.

Improving the Performance of LiFePO4 Cathodes with a Sulfur-Modified Carbon Layer

LiFePO₄ (LFP) cathodes are popular due to their safety and cyclic performance, despite limitations in lithium-ion diffusion and conductivity. These can be improved with carbon coating, but further advancements are possible despite commercial success. In this study, we modified the carbon coating layer using sulfur to enhance the electronic conductivity and stabilize the carbon surface layer via two methods: 1-step and 2-step processes. In the 1-step process, sulfur powder was mixed with cellulose followed by heat treatment to form a coating layer; in the 2-step process, an additional coating layer was applied on top of the carbon coating layer. Electrochemical measurements demonstrated that the 1-step sulfur-modified LFP significantly improved the discharge capacity (~152 mAh·g−1 at 0.5 C rate) and rate capability compared to pristine LFP. Raman analyses indicated that sulfur mixed with a carbon source increases the graphitization of the carbon layer. Although the 2-step sulfur modification did not exceed the 1-step process in enhancing rate capability, it improved the storage characteristics of LFP at high temperatures. The residual sulfur elements apparently protected the surface. These findings confirm that sulfur modification of the carbon layer is effective for improving LFP cathode properties, offering a promising approach to enhance the performance and stability of LFP-based lithium-ion batteries.

Documenting research in simulation science to enhance understanding for reusability.

One goal of Open Science is to promote reusability, which requires understanding and documentation. Reusability spans a spectrum from the straightforward reuse of existing materials to the extraction and adaptation of specific elements, depending on the maturity of the reused research and the research context. Beyond knowledge, understanding is crucial for enabling reusability. Simply reading an article is often insufficient; thus, publishing the underlying data and software is recommended. While the FAIR principles (Findable, Accessible, Interoperable, Reusable) facilitate the discovery and legal reuse of data and software through metadata, they fall short of promoting comprehensive understanding. By applying insights from the epistemology of simulation to computational mechanics research cases, we tested our hypothesis that the simulation process involves critical components beyond software and data that are essential to understand for reuse. Our findings indicate that reusability in these cases predominantly involves adjusting existing methodologies-a combination of different process steps from an epistemological perspective. Therefore, it is imperative to document not only learned knowledge but also the decisions regarding adjustments, assumptions and applications that concern the entire simulation process. Documentation is vital for understanding and herby enabling true reusability in scientific research, aligning with philosophical considerations of transparency and the nature of scientific knowledge.

The lingering safety menace: A 10-year review of enteral misconnection adverse events and narrative review.

In 2011, "Tubing Misconnections: Normalization of Deviance" reported >100 cases of enteral tubing misconnections leading to patient harm. Despite development of safer enteral device connectors, 96 new cases of enteral misconnections have been published since 2011. Publication and safety databases were searched for reports from 2011 to 2023. Reported misconnections lead to death in 4% of the cases and survival with harm were reported in 69% of cases. Reported misconnections occurred more often in infants and children than in adults. This article outlines why these misconnections happen, the history of the issue and development of safer connector standards, the safety threats and recommendations associated with the new cases, current conversion rates, and process steps, education, and resources for the conversion to safer connectors for enteral nutrition devices.

Research on the calibration method of winding deformation tester

Abstract The focus of this paper is the calibration method and process for winding deformation tester, encompassing its working principle, metrological characteristics, calibration conditions and measurement methods. The calibration conditions are divided into environmental conditions and measurement standards and other equipments. The measurement methods are divided into indication error of scanning frequency and indication error of amplitude ratio. The key steps in the calibration process are elaborated, including the selection of standard equipments, error analysis, data processing. The method’s feasibility is verified through experiments.

Effect of structure of metal-free nitrogen-doped coal-based carbon on catalytic oxygen reduction

In carbon-based oxygen reduction reaction (ORR) catalysts, metal-free nitrogen-doped carbon-based materials generally have higher catalytic stability compared to metal catalysts with reduced catalytic activity due to the occurrence of metal agglomeration during the catalytic reaction. In this paper, a metal-free nitrogen-doped carbon-based ORR catalyst NCOH5-900 with a half-wave potential of 0.846 V and onset potential of 0.94 V was prepared using coal as the carbon source, dicyandiamide as the nitrogen source and KOH as the activator. The material has good ORR catalytic performance with half-wave potential and onset potential comparable to Pt/C catalyst with the same mass loading, with better resistance to methanol toxicity and catalytic stability. The physical and chemical properties of the prepared catalyst were controlled by adjusting the amount of nitrogen doping and activation temperature to establish the relationship between the material structure and the ORR performance. The results suggest that for the metal-free nitrogen-doped carbon-based material, the good ORR catalytic performance is attributed to the abundant edge defects, high graphitization, good mixed structure of micro/mesopores, large nitrogen content, and high ratio of pyridine to graphitic nitrogen. Finally, the catalytic mechanism of NCOH5-900 for ORR is investigated using in-situ attenuated total reflection Fourier infrared (ATR-FTIR) and in-situ Raman, confirming the four-electron reduction process of O2 via a continuous dual step of two-electron transfer process.

Speech recognition inspired features for acoustic emission

For the application of acoustic emission methods in the field of condition monitoring, extracting meaningful information out of signals is an essential step in signal processing and mandatory for machine learning. However, when dealing with acoustic emission signals, the computational intensity of extracting features is becoming more important due to its large amount of data. The methodology in this study, hence, investigates established methods from descriptive statistics as typically used in acoustic emission and techniques commonly employed in speech recognition. Thereby, the objective is to improve the feature extraction of acoustic emission signals, emphasizing the synergies between the methods of both approaches. Extracting traditional acoustic emission features based on descriptive statistics alone usually yields robust features capturing the significant patterns within the time and frequency domain. However, traditional acoustic emission features may neglect more detailed information present in the signals due to averaging or mere evaluation of extreme values. To address this limitation, the methodology is extended by incorporating techniques from the field of speech recognition, such as applying window functions and digital filter banks to calculate the so-called cepstral coefficients. When comparing machine learning models that incorporate these cepstral coefficients alongside those that do not, this approach demonstrates that speech recognition techniques can result in more effective models. Here, the basic concept for speech recognition-based feature extraction and its implementation are presented, and necessary considerations and techniques are discussed, which are crucial for performant signal processing, particularly for real-time monitoring applications. The methodology is demonstrated, using acoustic emission signals derived from industrial-scale processes , such as e.g. milling, drilling or friction stir welding.

Impact of the asylum determination process on mental health in the UK and EU+: a systematic review and thematic synthesis

IntroductionPeople seeking asylum are at increased risk of mental health difficulties due to premigration and postmigration experiences. The objective of this review was to understand how asylum determination process in...

An analytical model for estimating process parameters input in L-DED based on bead geometry

Additive manufacturing of metal alloys via laser Directed Energy Deposition (L-DED) has been gaining popularity due to its potential to repair and create new features/components, enabling new applications for built parts. The success of L-DED operations hinges on the precise control of printing parameters, including laser power, scanning speed, and powder feed rate. These parameters significantly influence heat distribution during printing, directly impacting the quality of the resulting parts. Thus, defining an efficient methodology to find a good correlation between these parameters for the printing process is crucial to boost part production, as it reduces the time-consuming trial-and-error parameter tuning process. In this context, our study introduces an analytical model that predicts printing parameters based on the deposited material volume along track lines. Deposition was carried in stainless steel 316L with different values for laser power (ranging from 500 to 750W with 50W increments), scanning speed (from 400 to 700mm/min with 100mm/min increments), and powder feed rate (6.4,8.0 and 10.0g/min). The experimental data verified the effectiveness of the proposed model, demonstrating its potential to standardize the first step of printing process and expedite the initial search for optimal printing parameters in L-DED. The model provided accurate initial estimates of laser power, with a maximum relative error of 12%, particularly for the optimum mass flow rate (ṁ) of 8.0g/min. Beyond its benefits to the L-DED process, this analytical solution contributes to experimental practices by offering an efficient method for predicting material deposition volume during printing. Thus, our work underscores the significance of optimizing printing parameters to achieve high-quality parts and provides a valuable reference for future research and studies in the field of L-DED.

Hierarchical robot learning method for industrial fluorescent penetrant inspection

Fluorescent Penetrant Inspection (FPI) is a Non-Destructive Testing (NDT) method, extensively used to evaluate components for identifying defects across a broad range of industries. FPI process remains a manual visual inspection, where the operator by means of fluorescent dye, that penetrates discontinuities on the component, aims to distinguish between indications that are relevant (i.e., can be associated with surface defects) and non-relevant (i.e., can be associated to insufficient wash-off, dust or other non-relevant factors). The FPI process can be decomposed into the following steps: (a) thorough visual examination of the component, (b) executing manual wiping-off of the fluorescent dye with a brush, of all areas that require interrogation for potential indications, and (c) disposition of the inspected components. The number of those areas on the component that require interrogation and hence to be wiped-off is unknown a priori of the inspection and varies depending on the condition of the part. As a result, replacing this manual wipe-off step by a robot requires tedious manual programming of an excessive number of robot paths to assure reach of the robot to the entire surface of the part as well as safe robot motion. In addition, these robot motions are part specific and thus not transferrable to other geometries of components, making scaling of this technology across manufacturing industry not possible. In this paper, we propose a hierarchical robot learning method to address the challenge of reducing manual robot programming and enable the scaling of this automated NDT technology. The proposed method integrates and fuses Deep Reinforcement Learning (DRL), Screw Linear Interpolation (ScLERP) and Learning from Demonstration (LfD), enabling an autonomous generation of brushing strokes with a six-degrees of freedom (DoF) industrial manipulator, and automating the wipe-off step of the FPI process. Using this approach, a robot learning policy is generated for the wipe-off motion in a simulated industrial robotic cell at first and then the policy is transferred to the real system for validation.

Quality Assurance in Preschool Teacher Training According to the Aun-Qa Approach

Ensuring the quality of teacher training programs is an important aspect of organizing higher education activities at universities and is the primary factor in improving the quality of school education. Currently, many countries around the world regard quality assurance in training programs as a crucial step in the educational reform process, contributing to the overall development of the nation. This article focuses on surveying and researching the content of quality assurance in pedagogical universities. The content of quality assurance in pedagogical universities according to the AUN-QA approach covers all activities that these universities implement to maintain, improve, and enhance their training quality. Researching the AUN-QA approach for quality assurance in teacher training in general, and specifically in preschool teacher training, serves as a method to enhance training quality and plays an important role in the trends of internationalization, competition, and global cooperation in education. All these studies represent significant theoretical and practical findings that align with modern trends and the current requirements for fundamental and comprehensive reform in education and training. Therefore, identifying the research issue "Quality Assurance in Preschool Teacher Training According to the AUN-QA Approach" has highlighted new points and made direct contributions to improving the quality of preschool teacher training to meet societal needs and current international integration.

Third-octave analyses describing two perceptual dimensions of sound reproduction and the resulting overall perceived dissimilarity between loudspeakers or headphones.

Many objective measurements have been proposed to evaluate sound reproduction, but it is often difficult to link measured differences with the differences perceived by listeners. In the literature, the best correlations with perception were obtained for measures involving an auditory model. The present study investigated simpler measurements to highlight the signal processing steps required to make the link with perception. It is based on dissimilarity evaluations from two previous studies: 1 study comparing 12 single loudspeakers using 3 musical excerpts, 1 study comparing 21 headphones using 2 musical excerpts, and both studies highlighting 2 perceptual dimensions associated with the relative strengths of bass and midrange. The objective approach compared several signal analyses computing the dissimilarity between the spectra of the recorded sound reproductions. The results show that a third-octave analysis can accurately describe the overall dissimilarity between the loudspeakers or headphones and the two underlying perceptual dimensions.