Short Processing Time Research Articles

Enhanced Extraction of Polyphenols, Physicochemical Properties, and Microbial Control in Vitis vinifera L. Juice Using Ultrasound-Assisted Maceration

Polyphenols are essential bioactive compounds that contribute to the nutritional and sensory properties of grape juice and wine. This study investigates the impact of ultrasound-assisted maceration (UAM) compared to traditional maceration (TM) techniques, under both warm and cold conditions, on the polyphenol content, physicochemical properties, and microbial counts of juice from Vitis vinifera L. Ultrasound-assisted maceration significantly enhanced the extraction of polyphenols, including anthocyanins, flavonols, flavan-3-ols, and stilbenes, within a shorter processing time. The total polyphenol content increased up to 689.3 mg/L under UAM, while TM required extended maceration periods to achieve comparable results. In addition to polyphenol enrichment, UAM resulted in improved physicochemical properties, including higher extract content (% Brix) and increased turbidity (NTU), with minimal impact on pH and acidity levels. Microbial counts in juice remained low under UAM, indicating that this method may also have antimicrobial benefits due to the cavitation effects of ultrasound. Conversely, TM under warm conditions led to a reduction in extract content and nitrogen availability due to fermentation processes initiated during prolonged maceration. The findings highlight that UAM is a highly efficient technique for enhancing the polyphenol profile of grape juice while preserving key physicochemical parameters and microbial decontamination. This study provides valuable insights for the beverage industry, suggesting that UAM can be a sustainable and time-efficient alternative to traditional maceration methods for producing high-quality grape-based beverages.

Facile Synthesis of Nanostructured Lithium-Incorporated Titanium Oxides (Li-TiO x ) by Means of Wet Corrosion Process (WCP) and Their Potential Application for Batteries.

Currently, there is a growing demand for nanomaterials in the fields of materials and energy. Nanostructured metal oxides have been widely studied, owing to their unique and diverse physicochemical properties and potential applications in various fields. In recent years, considerable attention has been directed toward metal oxides, particularly lithium-incorporated titanium oxides (Li-TiO x ), owing to their exceptional safety profiles. This material has been used in automotive battery systems, which has prompted extensive research efforts to enhance its functional properties. In response to the demand for superior nanomaterials, this study attempts to fabricate nanostructured Li-TiO x using a wet corrosion process (WCP). WCP refers to a novel method for fabricating nanostructures that employ alkaline solutions. This technique offers numerous advantages, such as short processing times, high reproducibility, and low cost. As a result of experiments, nanostructured Li-TiO x were successfully fabricated using LiOH solutions ranging in concentration from 0.5 to 2 mol/L. The fabricated nanostructures exhibited superior characteristic properties, such as increased surface area and enhanced electrical properties, when compared with those of untreated titanium. This study demonstrates that WCP is a simple, versatile, and scalable method for producing nanostructured Li-TiO x tailored for battery applications.

Flow shop manufacturing layout as low carbon approach for application in bonding wire drawing industry

Modern manufacturing environments are yet to fulfil the evergrowing competitive supply and demand due to the limitation of conventional production approaches. A low-carbon approach incorporating the change from process to product-centric manufacturing systems could conserve energy while fulfilling supply and demand requirements in a competitive market. Current study had conducted a case study in a bonding wire factory that operates on process-oriented system with job shop layout. Preliminary data collection had assessed the existing practices on the factory's shop floor, and simulation model using WITNESS software was developed using this data. A job shop to flow shop conversion framework was developed with the integration of factors such as shop floor layout, order release, and dispatch rule play crucial roles in the conversion process of the production system. Based on the application of the conversion framework encompassing six future production system models, CL-SPT model which uses flow shop as layout, CONLOAD as order release and shortest processing time as the dispatch rule is selected as the best performing and economical production system. This model enhanced the output and average flow time by 2.7% and 27.7%, respectively, in tandem with low-carbon requisites while retaining machine utilization performance equivalent to the conventional system.

Enhancing Online Job Shop Scheduling Efficiency: Simulation-Based Optimization Incorporating Human Productivity Factors and TOPSIS Ranking

Abstract The job shop scheduling problem is a classical optimization challenge aimed at determining the optimal processing order by assigning a set of resources to a corresponding set of operations. This article investigates various approaches to address the online job shop scheduling problem, employing a simulation-based study. Dispatching rules are applied to allocate resources to operations, with discrete event simulation used for problem assessment. The study also incorporates human productivity factors, specifically investigating the shortest processing time (SPT) dispatching rule in a separate scenario. Five distinct scenarios are simulated, including four dispatching rules (first in, first out; last in, first out; longest processing time; and SPT) and an additional scenario integrating the SPT rule and human productivity factors. The simulation results are used to compare makespan across these scenarios, revealing that the scenario involving the SPT dispatching rule and human productivity factors represents the shortest makespan. Through a TOPSIS technique-based ranking, considering makespan and cost as criteria, the study identifies the SPT rule and human productivity factors as the most efficient scenario. The findings imply that employing human productivity factors with effective dispatching rules, such as SPT, can significantly improve job shop scheduling operational efficiency and lead to more optimal results in both makespan and overall operational costs.

3D Scanning of Surgical Specimens to Improve Communication Between Surgeon and Pathologist: A Head and Neck Pilot Study

Background/Objectives Successful surgical outcomes in head and neck cancer depend on the accurate identification of resection margins. Effective communication between surgeons and pathologists is critical, but is often jeopardised by challenges in sampling and orienting anatomically complex specimens. This pilot study aims to evaluate the use of 3D scanning of surgical specimens as a tool to improve communication and optimise the pathology sampling process. Methods Two structured light 3D scanners, Cronos Dual and Optor Lab, were used to acquire 3D models of anatomical specimens in both preclinical (cadaver specimens) and clinical contexts (fresh surgical specimens). Surgical margins and critical points were annotated on the digital models. Acquisition quality, operating times and subjective feedback from surgeons and pathologists were evaluated. Results The Optor Lab scanner demonstrated superior image quality, shorter processing times and a more user-friendly interface than the Cronos Dual. Key challenges identified included specimen geometry, surface reflectivity and tissue stability. Feedback from both surgeons and pathologists was positive, highlighting the potential of 3D models to improve the surgical-pathology workflow. Conclusions 3D scanning of surgical specimens provides accurate, detailed digital models that can significantly enhance communication between surgeons and pathologists. This technology shows promise in improving pathological staging and clinical decision making, with further studies required to validate its integration into routine practice.

Rapid Visual Detection of Red Sea Bream Iridovirus Using a Novel Cross-Priming Amplification-Based Lateral Flow Assay.

Red sea bream iridovirus (RSIV) occurs mainly at high water temperatures and infects more than 30 different species of fish. In Asia, infected fish cause mass mortality every year. Molecular diagnostics is a technology that efficiently detects and identifies a wide range of fish pathogens through rapid and sensitive analysis of their genetic material. Rapid viral detection is essential for effective disease control. In this study, we developed and validated a cross-priming amplification-based lateral flow assay (CPA-LFA) suitable for field diagnosis owing to its short diagnostic time and simple diagnostic process. The CPA-LFA achieved optimal performance with concentrations of 4 mM MgSO4, 1.2 mM dNTPs and 0.7 M betaine, with the reaction conducted at 60°C for 60 min. The developed CPA-LFA could specifically identify RSIV without cross-reactivity with several pathogens commonly reported in various fish cell lines and fish. The 95% limit of detection (LOD95%) of CPA-LFA was 385.76 copies/μL, which was comparable to that of conventional polymerase chain reaction (PCR). Quantitative PCR (qPCR) was used to identify true-positive and true-negative samples from 210 fish samples (160 from cultured fish and 50 from artificially infected fish). Compared with qPCR, CPA-LFA classified six positive samples as false positives. The viral load of these samples was determined to be less than 195.1 copies/μL. The diagnostic sensitivity and specificity of CPA-LFA were 94.34% and 100%, respectively. Furthermore, inter-operator reproducibility testing yielded a kappa value of 1.0, indicating perfect agreement. Therefore, the novel CPA-LFA is especially well-suited for field diagnostics owing to its straightforward diagnostic procedure and capability to quickly and accurately detect RSIV.

Study on the synergistic mechanism of proline in the treatment of high-salt phenolic wastewater by short-time aerobic digestion process.

High salt concentrations pose a significant challenge to the efficiency of activated sludge (AS) in phenolic wastewater treatment. As a cellular osmoprotectant, proline (Pro) has the capacity to increase the salt tolerance of microbes in AS, hence improving the efficiency of phenolic wastewater degradation. Nevertheless, the precise mechanism behind this enhancement remains ambiguous. This study utilized short-time aerobic digestion (STAD) to examine the kinetics of phenol degradation (250-750mg/L) by AS under high-salinity stress (2-8%), with the inclusion of Pro (115-575mg/L) as an auxiliary agent. The process was optimized via response surface methodology (RSM), and the mitigating effect of Pro on microorganisms in AS subjected to salt stress was evaluated. The results demonstrated that the addition of 468mg/L Pro substantially improved the ability of AS to withstand high-salinity wastewater with high phenol concentrations, which had a salinity of 5.1% and a phenol concentration of 531mg/L. The addition led to a mitigation rate of the phenol degradation constant k0 of 38.59 ± 1.54%, resulting in enhanced degradation of chemical oxygen demand (COD), NH4+-N, and NO3--N. In addition, the prolonged presence of Pro increased AS dehydrogenase activity (DHA) by 24.82% after 30days. Microbial community analysis demonstrated that Pro promoted the proliferation of functional microorganisms such as Proteobacteria, Firmicutes, Acinetobacter, and Comamonas. These bacteria have essential functions in the elimination of phenol and organic matter, as well as the absorption of nitrogen. This study emphasizes the impact of Pro as a compatible solute in the treatment of high-salinity and high-phenol wastewater in the STAD process.

Stellar parameter prediction and spectral simulation using machine learning. A systematic comparison of methods with HARPS observational data

We applied machine learning to the entire data history of ESO’s High Accuracy Radial Velocity Planet Searcher (HARPS) instrument. Our primary goal was to recover the physical properties of the observed objects, with a secondary emphasis on simulating spectra. We systematically investigated the impact of various factors on the accuracy and fidelity of the results, including the use of simulated data, the effect of varying amounts of real training data, network architectures, and learning paradigms. Our approach integrates supervised and unsupervised learning techniques within autoencoder frameworks. Our methodology leverages an existing simulation model that utilizes a library of existing stellar spectra in which the emerging flux is computed from first principles rooted in physics and a HARPS instrument model to generate simulated spectra comparable to observational data. We trained standard and variational autoencoders on HARPS data to predict spectral parameters and generate spectra. Convolutional and residual architectures were compared, and we decomposed autoencoders in order to assess component impacts. Our models excel at predicting spectral parameters and compressing real spectra, and they achieved a mean prediction error of $ K for effective temperatures, making them relevant for most astrophysical applications. Furthermore, the models predict metallicity M/H ) and surface gravity ($ g$) with an accuracy of $ dex and $ dex, respectively, underscoring their broad applicability in astrophysical research. Moreover, the models can generate new spectra that closely mimic actual observations, enriching traditional simulation techniques. Our variational autoencoder-based models achieve short processing times: 779.6 second on a CPU and 3.97 second on a GPU. These results demonstrate the benefits of integrating high-quality data with advanced model architectures, as it significantly enhances the scope and accuracy of spectroscopic analysis. With an accuracy comparable to the best classical analysis method but requiring a fraction of the computation time, our methods are particularly suitable for high-throughput observations such as massive spectroscopic surveys and large archival studies.

Advances in the Application of Infrared in Food Processing for Improved Food Quality and Microbial Inactivation.

Food processing is a fundamental requirement for extending the shelf life of food products, but it often involves heat treatment, which can compromise organoleptic quality while improving food safety. Infrared (IR) radiation has emerged as a transformative technology in food processing, offering a rapid, energy-efficient method for inactivating microbial cells and spores while preserving the nutritional and sensory attributes of food. Unlike traditional heating methods, IR technology enhances heating homogeneity, shortens processing time, and reduces energy consumption, making it an environmentally friendly alternative. Additionally, IR processing minimizes water usage, prevents undesirable solute migration, and maintains product quality, as evidenced by its effectiveness in applications ranging from drying fruits and vegetables to decontaminating meat and grains. The advantages of IR heating, including its precise and even heat diffusion, ability to retain color and nutrient content, and capacity to improve the microbial safety of food, position it as a promising tool in modern food preservation. Nevertheless, there are gaps in knowledge with respect to optimal application of IR in foods, especially in the maintenance product quality and the impact of factors such as IR power level, temperature, wavelength (λ), food depth, and target microorganisms on the applicability of this novel technology in food systems. Recent research has attempted to address challenges to the application of IR in food processing such as its limited penetration depth and the potential for surface burns due to high energy which has delayed the widespread utilization of this technology in food processing. Thus, this review critically evaluates the application of IR in food safety and quality, focusing on factors that affect its effectiveness and its use to moderate food quality and safety while comparing its advantages/disadvantages over traditional thermal processing methods.

Utilizing Artificial Intelligence (AI) in Healthcare Insurance to Transform Risk Assessment, Claims Processing, and Fraud Detection

Artificial Intelligence (AI) is changing the face of healthcare insurance by innovating key operations such as risk assessment, claim processing, and fraud detection. By applying advanced algorithms in machine learning, insurers are able to reach a more accurate risk profiling and thus offer personalized insurance pricing based on individual health conditions and lifestyles. AI- powered automated claims processing enhances operational efficiency, shortens processing time, and minimizes errors. Additionally, fraud detection systems powered by AI proactively identify suspicious activities and save insurers and policyholders from financial loss. The paper discusses the various dimensions in which AI is making an impact on the health insurance ecosystem, highlighting the potential benefit to insurers through cost savings and improved decision-making while offering customers speedier services and personalization. The paper further points to some key regulatory challenges that will need consideration in terms of data privacy, algorithmic transparency, and ethical considerations to ensure equity and compliance in the adoption of AI technologies in health insurance. Keywords: Artificial Intelligence, Health Insurance, Risk Profiling, Claims Processing, Fraud Detection, Pricing of Insurance, Machine Learning, Regulatory Challenges, Data Privacy, Operational Efficiency

Extraordinary Mechanical Properties of a Field‐Assisted Sintering Technique/Spark Plasma Sintering Consolidated Stainless Steel with High Copper Content of 9 wt%

The solidification behavior of a novel X1CrCuNiN 18‐9‐6 (concentrations in wt%) stainless steel is studied by thermodynamic calculations and corresponding microstructure investigations. The thermodynamic calculations of the X1CrCuNiN 18‐9‐6 steel predict a metastable austenitic structure, which is verified by microstructural analyses. In the as‐cast state before heat treatment, a few copper precipitates, mostly over 50 μm in size, are visible, which are located exclusively in the interdendritic regions. The electrode inert gas atomization process is applied to produce a steel powder using a pre‐material in as‐cast state with significantly increased Cu content of 9 wt%. After atomization, despite the rapid cooling, micrometer‐sized copper precipitates form again, which are homogeneously distributed in the microstructure, but are mostly less than 10 μm in size and thus much finer than in the initial cast state. The short processing times during field‐assisted sintering technique/spark plasma sintering makes it possible to produce a bulk material with a porosity of less than 1%. Miniature samples produced from the as‐sintered material exhibit uniform elongation values of 30%–40% with a tensile strength of about 640 MPa under tensile loading conditions.

Comparative Analysis of Evolutionary Methods in Topological Optimization of Truss Structures: Progressive Directional Selection versus Evolutionary Structural Optimization

The main difference between evolutionary methods and traditional structural topological optimization methods is that the former may not always converge to an optimized solution and can be challenging to implement. This work presents two heuristic and evolutionary methods, which aim to analyze truss structures obtained by the topological optimization process using the PDS and ESO methods, employing a ground structure type structure as the initial design domain. The Progressive Directional Selection (PDS) method is based on selecting a certain number of elements, established by the user, that most contribute to supporting the loads applied in an initial design domain. The selection stages involve a progressive number of selection steps, each eliminating a certain number of less efficient elements. Convergence occurs when the selected elements are the same in a certain number of consecutive selection stages, also pre-established by the user. The Evolutionary Structural Optimization (ESO) method is a technique where elements that contribute less to supporting loads in an initial domain are removed through a rejection rate. If removing elements with the same rejection rate value is no longer possible, an evolution rate value can be added to remove more inefficient elements. ESO convergence is achieved when the rejection rate evolves to a user-established value. The optimized structures obtained by both methods efficiently support the applied loads. The PDS method stands out for reaching optimized truss structures with a specific number of elements that ESO cannot obtain, but the former stands out for its shorter processing time.

Use of mean apparent propagator (MAP) MRI in patients with acute ischemic stroke: A comparative study with DTI and NODDI

PurposeTo evaluate the Mean Apparent Propagator (MAP) MRI for processing multi-shell diffusion imaging in patients with acute ischemic stroke (AIS) and correlate to diffusion tensor imaging (DTI) and neurite orientation and dispersion density imaging (NODDI). MethodsWe enrolled patients with AIS from 1/2022 to 4/2024 who underwent multi-shell diffusion imaging on a 3.0-Tesla scanner to generate DTI, NODDI and MAP measures. Mean intensity and standard deviation (SD) were calculated for the infarcted regions-of-interest in b0, fractional anisotropy (FA), mean diffusivity (MD), intra-cellular volume fraction (ICVF), free water fraction (FWF), and orientation dispersion index (ODI), return to the origin probability (RTOP), return to the plane probability (RTPP), return to the axis probability (RTAP), propagator anisotropy (PA), q-space Mean Square Displacement (QMSD), and non-Gaussianity (NG). ResultsTwenty-two patients were included with an average age of 69.5 ± 13.5, mean NIHSS of 12.4 ± 7.7, and median infarct of 73.3 ± 10.1 ml. ICVF was correlated with RTPP (ρ = 0.82, p < 0.01), RTAP (ρ = 0.76, p < 0.01) and RTOP (ρ = 0.79, p < 0.01), ODI with PA (ρ = −0.83, p < 0.01), FWF with RTOP (ρ = −0.73, p < 0.01), RTAP (ρ = −0.69, p < 0.01), and RTPP (ρ = −0.73, p < 0.01), MD with RTPP (ρ = −0.80, p < 0.01), RTOP (ρ = −0.79, p < 0.01), and RTAP (ρ = −0.77, p < 0.01), FA with RTAP (ρ = 0.77, p < 0.01), RTOP (ρ = 0.67, p = 0.01), PA (ρ = 0.74, p < 0.01), and SD PA (ρ = 0.85, p < 0.01). Multivariable linear regression identified the SD QMSD (β = 0.406, p = 0.008), thrombectomy (β = 0.481, p = 0.002), and infarct volume (β = 0.292, p = 0.051) as predictive of stroke severity based on NIHSS. ConclusionsGiven its short processing time, MAP MRI is a valuable alternative with potential for clinical use in AIS.

A detection model of aggressive driving behavior based on hybrid deep learning

&lt;p&gt;Modern transportation faces a crucial challenge in ensuring road safety by addressing driving behavior concerns. This paper introduces an innovative deep learning model derived from a cellphone-collected Driving Behavior dataset, focusing on detecting and classifying aggressive driving. Using a cohort-based dataset, a hyper-deep learning model categorizes drivers into normal, slow, and aggressive groups. The system employs pre-processing methods and two methodologies, directly inputting data and incorporating feature selection. The hyper-CNN-Dense model, used for training, shows promising results. Feature selection techniques like SVD6 and MI6 achieve optimal outcomes, with a 100% accuracy rate in detecting aggressive driving. Notably, SVD6 boasts a short processing time of just 43 seconds. This research successfully identifies aggressive driving behavior with impeccable accuracy and in a remarkably short timeframe.&lt;/p&gt;

On the control of thickness in gas blow metal sheet forming processes: A review

Although the advantages of formability are notable, blow-forming processes, which are classified as sheet metal stretching processes, present limits in terms of uniformity of the thicknesses of the produced product. This is a review paper that collects the numerical and experimental studies of the literature on the new metal sheet forming techniques to improve formability and production efficiency. In particular, the focus is on (a) the multi-phase forming technique and (b) the techniques involving a blank with a profile of variable initial thickness. These techniques promote a strategic decrease of the thickness in some areas of the part to be formed to improve the final distribution of thicknesses in those most critical areas of the component to be made. The paper presents the finite element modelling works of the literature to predict the accuracy of a formed part in terms of the uniformity of its thickness that avoids breakups; thus, allowing to design and establish the feasibility of new forming techniques. The parameters influencing the forming processes were analysed by comparing the results of the finite element simulations in terms of the thickness distribution of the finished product and forming times. This paper presents also the experimental forming tests that verified these forming techniques and provided a thickness profile that reduces maximum thinning and forming times in comparison with the traditional processes. The conclusion of the paper demonstrates that these forming techniques allow to obtain a more uniform thickness of the formed part in a shorter processing time.

Cold Plasma Techniques for Sustainable Material Synthesis and Climate Change Mitigation: A Review

In recent years, the emission of greenhouse gases (GHGs) has increased significantly, contributing to global warming. Among these GHGs, CH4, CO2, and CO are particularly potent contributors. Remediation techniques primarily rely on materials capable of capturing, storing, and converting these gases. Catalytic processes, particularly heterogeneous catalysis, are essential to chemical and petrochemical industries as well as environmental remediation. Due to the growing demand for catalysts, efforts are being made to reduce energy consumption and make technologies more environmentally friendly. Green chemistry emphasizes minimizing the use of hazardous reactants and harmful solvents in chemical processes. Achieving these principles should be paired with processes that reduce time and costs in catalyst preparation while improving their efficiency. Non-thermal plasma (NTP) has been widely used for the preparation of supported metal catalysts. NTP has attracted significant attention for its ability to improve the physicochemical properties of catalysts, enhancing process efficiency through low-temperature operation and shorter processing times. NTP has been applied to various catalyst synthesis techniques, including reduction, oxidation, metal oxide doping, surface etching, coating, alloy formation, surface treatment, and surface cleaning. Plasma-prepared transition-metal catalysts offer advantages over conventionally prepared catalysts due to their unique material properties. These properties enhance catalytic activity by lowering the activation energy barrier, improving stability, and increasing conversion and selectivity compared to untreated samples. This review demonstrates how plasma activation modifies material properties and, based on extensive literature, illustrates its potential to combat climate change by converting CO2, CH4, CO, and other gases, showcasing the benefits of plasma-treated materials and catalysts. A succinct introduction to this review outlines the advantages of plasma-based synthesis and modification over traditional synthesis techniques. The introduction also highlights the various types of plasma and their physical characteristics across different factors. Additionally, this review addresses methods by which materials are synthesized and modified using plasma. The latter section of this review discusses the use of non-thermal plasma for greenhouse gas mitigation, covering applications such as the dry reforming of CH4, CO and CH4 oxidation, CO2 reduction, and other uses of plasma-modified catalysts.

Tuning spike-like morphologies in Silicon by sustainable fs-laser processing in air for enhanced light absorption

In this work, we demonstrate that ultrafast laser processing (1030nm, 290fs) of silicon in ambient air strongly improves the material’s performance in terms of absorption, both in the visible and near infrared spectral range, which paves the way for further studies on increasing the sub-bandgap absorption after texturing, suggesting the material developed as a sustainable substitute for black silicon processed in greenhouse gases atmospheres. Our approach is based on the fabrication of spike-like morphologies in ambient air and the subsequent annealing of the material by pulsed laser melting or rapid thermal annealing to recover its crystalline phase. In particular, the influence of three main processing parameters (fluence, pulse number and repetition rate) on the properties of the spike-like structures has been investigated, each of them revealing the possibility of a direct control on the size, shape and period of the spikes, and achieving a total tuning range of the period from 4μm to 14μm for a single laser wavelength. Macroscopic areas have been fabricated using short processing times, yielding absorption values A>94% over the UV–VIS-NIR spectral range (250 nm - 1100nm) without hyperdoping, and A≥20% for longer wavelengths up to 2500nm, while preserving the electrical performance of pristine silicon.

Effect of Different Pretreatments on Drying Characteristics and Physicochemical Properties of Apricot (Prunus armeniaca L.)

ABSTRACTIn this study, apricot samples were dried by using different drying methods (tray drying, microwave drying, and freeze drying). Moreover, different pretreatments (infrared, ultrasound, and treatment with dipping green tea extract) were also applied to the samples. The effect of process conditions on physical and chemical quality of dried apricot samples were investigated. It was aimed to come of age of these technologies as an alternative to conventional pretreatments. Among all drying techniques, the shortest processing time was achieved by microwave drying at 270 W. Midilli was found to be the best model that fits the moisture ratio values among 10 thin layer models. Freeze dried samples had the lowest water activity which ranged from 0.2260 to 0.4868 and rehydration capacity of dried samples were found to be between 2.40 and 3.09. Besides, it was found that the total phenolic content of dried apricots varied between 99.24 and 512.60 mg GAE/100 g dry matter. In addition, the maximum antioxidant activity values for microwave drying (270 W) were obtained in the green tea extract pretreated samples and calculated to be 1.85 ± 0.01 mg TE/g dry matter. NMR and SEM analysis were also applied to the samples. To sum up, it can be declared that microwave technology and green tea extract pretreatments were superior and had beneficial impacts on the end product's quality.

Enhancing Pharma Manufacturing Efficiency: Integrating Lean Six Sigma and Fuzzy FMEA for Waste Reduction

In pharmaceutical manufacturing, inefficiencies such as waiting times, excessive material usage, and packaging defects can significantly impact productivity and quality. This study adopts a Lean Six Sigma approach, integrating lean manufacturing and six sigma methodologies, to systematically address these challenges. Through Process Activity Mapping (PAM), it was determined that value-added (VA) activities account for approximately 63% of total production activities, while non-value-added (NVA) and essential non-value-added (ENVA) activities contribute about 34% and 4%, respectively. Critical waste was identified using the genba shikumi method, followed by Failure Modes and Effects Analysis (FMEA) to determine Risk Priority Numbers (RPNs). Fuzzy logic was applied to prioritize the suggested improvements for more accurate risk assessments. Key recommendations based on Fuzzy RPN rank include, enhancing bulk product quality before printing, implementing rigorous inspections of the printing process, optimizing machine utilization, and adjusting production schedules using the Shortest Processing Time (SPT) method.

Research on the Influence of Short Video Content on Users: A Case Study of Film Commentary

The theme of this paper is to study the impact of short video platforms on users’ behaviors, and film interpretation is chosen as an example. As a new medium of information dissemination, short video platforms have developed at a very fast speed in recent years. While it has brought a lot of benefits to the public, it has also brought bad far-reaching effects on the people. The main feature of short video platforms is their short video production time and simple process; Second, the speed and scope of information dissemination of short video platforms is extremely fast, small to different cities, and large to different countries. Among them, the TikTok platform is the most typical example. TikTok short videos are not only spread in China but also respected by other countries abroad. The short video propagated in the short video platform can transmit a large amount of information at home and abroad in only ten seconds of video. This research helps short video platforms to improve and increase consumer satisfaction. It can also play a role in promoting things and raising awareness among the users.