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Background. Insulin, commonly used for diabetes treatment, needs better ways to improve its effectiveness and safety due to its challenges with poor permeability and stability. Various system has been developed for oral peptide delivery. The non-targeted system can prevent gastric and enzymatic degradation of peptides but cannot increase the bulk transport of peptides across the membrane. However, the non-selectivity is the limitation of the existing system. Numerous carbohydrate-binding receptors overexpressed on intestinal macrophage cells (M-cells) of gut-associated lymphoid tissue. It is the most desirable site for receptor-mediated endocytosis and lymphatic drug delivery of peptides. Objective. The prime objective of the study was to fabricate mannose ligand conjugated nanoparticles (MNPs) employing a quality-by-design approach to address permeability challenges after oral administration. Herein, the study's secondary objective of this study is to identify the influencing factor for producing quality products. Considering this objective, the Lymphatic uptake of NPs was selected as a quality target product profile (QTPP), and a systematic study was conducted to identify the critical formulation attributes (CFAs) and critical process parameters (CPP) influencing critical quality attributes (CQAs). Mannosylated Chitosan concentrations (MCs) and TPP concentrations were identified as CFAs, and stirring speed was identified as CPP. Methods. MNPs were prepared by the inotropic gelation method and filled into the enteric-coated capsule to protect from acidic environments. The effect of CFAs and CPP on responses like particle size (X) and entrapment (Y) was observed by Box-Behnken design (BBD). ANOVA statistically evaluated the result to confirm a significant level (p < 0.05). The optimal conditions of NPs were obtained by constructing an overlay plot and determining the desirability value. HPLC and zeta-seizer analysis characterized the lyophilized NPs. Cell-line studies were performed to confirm the safety and M-cell targeting of NPs to enhance Insulin oral bioavailability. Results. The morphology of NPs was revealed by SEM. The developed NPs showed a nearly oval shape with the average size, surface potential, and % drug entrapment were 245.52 ± 3.37 nm, 22.12 ± 2.13 mV, and 76.15 ± 1.3%, respectively. MTT assay result exhibited that MNPs safe and Confocal imaging inference that NPs selectively uptake by the M-cell. Conclusion. BBD experimental design enables the effective formulation of optimized NPs. The statistical analysis estimated a clear assessment of the significance of the process and formulation variable. Cell line study confirms that NPs are safe and effectively uptake by the cell. [ABSTRACT FROM AUTHOR]

BACKGROUND: Cassava processing is a crucial source of livelihood for rural farmers and processors in Nigeria and Cameroon. This study investigated the varietal effect on the processing productivity of women farmer processors within their working environment and compared this with the food product quality as evaluated by the processors and the field yield. Field trials were established in Nigeria (Benue and Osun state) and Cameroon (Littoral region). Eight cassava genotypes were evaluated. These eight varieties included newly bred genotypes, commercial checks and varieties provided and preferred by the processors. The roots of these genotypes were harvested and processed into gari and eba by processors. The time of each processing step was recorded. Processors assessed the quality of the roots and food products using pairwise ranking. RESULTS: In the field trials in Cameroon and Nigeria (Benue state), the newly bred genotypes showed superior performance in terms of dry matter content and fresh and dry yield. During processing, genotypes showed significant variation for most assessed parameters in both countries. Some newly bred varieties exhibited lower productivity that can make them more prone to drudgery than the local commercial checks and the varieties provided and preferred by the processors. Newly bred varieties were mostly ranked higher or equal to processors' preferred varieties concerning fresh root and food product quality. In the Cameroon location there were significant varietal differences in processing productivity and drudgery index which suggest that the particular processing methods there ‐ such as pressing methods and fermentation time ‐ cause these varietal differences to matter more. CONCLUSIONS: The varieties that were tested were observed to differ in yield, product quality, processing productivity, and potential drudgery levels. Some breeders' germplasms displayed a combination of increased yields and good product quality and good processor productivity. Those varieties that showed low processor productivity should be avoided during selection to avoid increased labour burden and associated drudgery of women processors. Further research is recommended to enhance food product color, latent culinary qualities, and processing productivity of newly bred varieties to improve acceptability and reduce processing drudgery for women. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

The comparability assessment of a biological product after implementing a manufacturing process change should involve a risk-based approach. Process changes may occur at any stage of the product lifecycle: early development, clinical manufacture for pivotal trials, or post-approval. The risk of the change to impact product quality varies. The design of the comparability assessment should be adapted accordingly. A working group reviewed and consolidated industry approaches to assess comparability of traditional protein-based biological products during clinical development and post-approval. The insights compiled in this review article encompass topics such as a risk-evaluation strategy, the design of comparability studies, definition of assessment criteria for comparability, holistic evaluation of data, and the regulatory submission strategy. These practices can be leveraged across the industry to help companies in design and execution of comparability assessments, and to inform discussions with global regulators. [ABSTRACT FROM AUTHOR]

Geographical indications (GIs) aim to protect the names of specific high‐quality products (food and wine) to preserve and promote the uniqueness linked to their geographical origin and traditional know‐how. EU and extra‐EU countries register domestic products with GIs. This study disentangles the effects of such registrations on the quality of imports using country‐level product import data for the fruit, vegetable, coffee, tea, and spice sectors. Our results show that the registration of domestic products as GIs enhances the quality of imported goods only if the importing country has a lower level of quality of domestic production. The introduction of GIs into high‐quality domestic markets can discourage import quality upgrades. This is because domestic producers may prefer to compete for quality rather than price, and imported goods represent a less expensive alternative to high‐quality national goods for consumers. Conversely, in countries where domestic product quality is lower, the introduction of GIs may enhance import quality upgrading because the diffusion of GIs induces domestic consumers to become more demanding in terms of quality for foreign products. [ABSTRACT FROM AUTHOR]

The instability of product quality and low printing efficiency are the main obstacles to the widespread application of 3D printing in the manufacturing industry. Optimizing printing parameters can substantially improve product quality and printing efficiency. However, existing methods for optimizing process parameters primarily rely on computationally expensive numerical simulations or costly physical experiments, which cannot balance model accuracy and experiment cost. To the best of our knowledge, almost no relevant papers have been found to address the issues of product quality and printing efficiency in 3D printing from experimental data‐driven perspective. In this paper, we propose a method that integrates multiobjective Bayesian optimization (MOBO) with experimental data‐driven, aiming at obtaining more accurate optimization results at a lower cost. Distinguishing from previous studies, the proposed method utilizes experimental data instead of predicted values to update the model and find the optimal process parameters based on expected hypervolume improvement. The results of the 3D printing case study show that the proposed method can better model and optimize the highly fluctuating 3D printing process and obtain the optimal process parameters at a much lower cost. In addition, confirmatory experiments verify that the proposed method achieves higher printing efficiency while maintaining product quality. [ABSTRACT FROM AUTHOR]

Herbal products comprise a wide spectrum of locally, nationally or internationally commercialized commodities. As these products have an increasingly important position in healthcare systems worldwide, a detailed product quality assessment is of crucial importance. For the quality evaluation of commercial herbal products, a wide range of methods were used, from simpler, quicker, and cost-effective HPTLC, to hyphenated methods with MS or NMR, where more precise quantification or specific structural information is required. Additionally, most of the methods have been coupled with chemometric tools, such as PCA, or PDA, for the multivariate analysis of the high amount of data generated by chromatograms, electropherograms or spectra. The chemical methods have revealed the widespread presence of low or variable quality herbal products in the marketplace. The majority of analytical investigations present major, qualitative and quantitative, inter-product variations of their chemical composition, ranging from missing ingredients, to strikingly and unnaturally high concentrations of some compounds. Moreover, the inter-batch quality variations were frequently reported, as well as the presence of some undesirable substances. The chemical analysis of herbal products is a vital component to raise the overall awareness of quality in the herbal market and generate a quality driven approach. [ABSTRACT FROM AUTHOR]

The Cyprus Continuously Operating Natural Hazards Monitoring and Prevention System, abbreviated CyCLOPS, is a national strategic research infrastructure devoted to systematically studying geohazards in Cyprus and the Eastern Mediterranean, Middle East, and North Africa (EMMENA) region. Amongst others, CyCLOPS comprises six permanent sites, each housing a Tier-1 GNSS reference station co-located with two calibration-grade corner reflectors (CRs). The latter are strategically positioned to account for both the ascending and descending tracks of SAR satellite missions, including the ESA's Sentinel-1. As of June 2021, CyCLOPS has reached full operational capacity and plays a crucial role in monitoring the geodynamic regime within the southeastern Mediterranean area. Additionally, it actively tracks landslides occurring in the western part of Cyprus. Although CyCLOPS primarily concentrates on geohazard monitoring, its infrastructure is also configured to facilitate the radiometric calibration and geometric validation of Synthetic Aperture Radar (SAR) imagery. Consequently, this study evaluates the performance of Sentinel-1A SAR by exploiting the CyCLOPS network to determine key parameters including spatial resolution, sidelobe levels, Radar Cross-Section (RCS), Signal-to-Clutter Ratio (SCR), phase stability, and localization accuracy, through Point Target Analysis (PTA). The findings reveal the effectiveness of the CyCLOPS infrastructure to maintain high-quality radiometric parameters in SAR imagery, with consistent spatial resolution, controlled sidelobe levels, and reliable RCS and SCR values that closely adhere to theoretical expectations. With over two years of operational data, these findings enhance the understanding of Sentinel-1 SAR product quality and affirm CyCLOPS infrastructure's reliability. [ABSTRACT FROM AUTHOR]

The development of the textile and apparel (T&A) industry has led to an increasing focus on recycling used products. Remanufactured product quality raises consumer concerns, and blockchain can effectively solve this problem. We establish a closed-loop supply chain (CLSC) in which a manufacturer, a retailer, or a third-party recycler collects used T&A products to examine the most efficient recycling mode with and without blockchain and the impact of blockchain on CLSC decisions. The results show that (1) if the manufacturer's recycling cost coefficient is relatively low, used T&A products are collected directly by the manufacturer. Otherwise, the responsibility for recycling used T&A products falls to the retailer or the third-party recycler. It is noteworthy that the manufacturer's choice of recycling mode remains unchanged whether a blockchain is implemented or not. (2) The implementation of blockchain by the manufacturer and the retailer can increase profits and consumers also benefit when the cost of validating blockchain units remains below a certain threshold. (3) When the recycling cost coefficient exceeds a certain threshold, the implementation of blockchain increases prices and recycling rates. These findings offer CLSC members' management insights into how to select the optimal recycling mode and the consequences of implementing blockchain. [ABSTRACT FROM AUTHOR]

Surface defect detection is an essential task for ensuring the quality of products. Many excellent object detectors have been employed to detect surface defects in resent years, which has achieved outstanding success. To further improve the detection performance, a defect detector based on state‐of‐the‐art YOLOv8, named improved YOLOv8 by neck, head and data (NHD‐YOLO), is proposed. Specifically, YOLOv8 from three crucial aspects including neck, head and data is improved. First, a shortcut feature pyramid network is designed to effectively fuse features from backbone by improving the information transmission. Then, an adaptive decoupled head is proposed to alleviate the feature spatial misalignment between the classification and regression tasks. Finally, to enhance the training on small objects, a data augmentation method named selective small object copy and paste is proposed. Extensive experiments are conducted on three real‐world datasets: detection dataset from Northeastern University (NEU‐DET), printed circuit boards from Peking University (PKU‐Market‐PCB) and common objects in context (COCO). According to the results, NHD‐YOLO achieves the highest detection accuracy and exhibits outstanding inference speed and generalisation performance. [ABSTRACT FROM AUTHOR]

Gate location (GL) is one of the most important criteria in the injection molding technology to produce a good quality product. The best GL will bring many benefits to the manufacturing process such as reducing cycle time, minimizing defects of the product, and decreasing capital cost. In this study, an automatic gate location analysis tool (AGLA) is introduced to investigate the best gate location for designing the injection mold. EasyFill Advanced software was used to conduct experiments on analyzing the filling process of melting plastic material into the mold cavity. Acrylonitrile butadiene styrene (ABS) is the polymer material that is used in the study to produce the plastic product. The gate locations are located in different positions to analyze the product characteristics that will occur during the injection molding process such as weld lines, short shot, warpage, shrinkage, and air traps. Three simulation cases were set up and conducted including case 1 with the suggestion from EasyFill software, case 2 with the gate location located on the middle point of the long edge side, and case 3 with the gate location located on the middle point of the short edge side. The warpage defects are recorded with a minimum value of 0.464 mm belonging to case 1 smaller than that of 0.481 mm in case 2 and 0.482 mm in case 3, respectively. The method is a good solution for engineers in designing and developing a new mold for injection molding manufacturing. [ABSTRACT FROM AUTHOR]

Conventional quality control methodologies are inadequate for fully elucidating the aberrant patterns of product quality. A multitude of factors influence product quality, yet the limited number of controlled quality characteristics is insufficient for accurately diagnosing quality abnormalities. Additionally, there are asymmetries in data collection, data pre-processing, and model interpretation. In this context, a quality anomaly recognition and diagnosis model for the complex product manufacturing process is constructed based on a deep residual network, support vector machine (SVM), and Shapley additive explanation (SHAP). Given the numerous complex product quality characteristic indexes and unpredictable accidental factors in the production process, it is necessary to mine the deep relationship between quality characteristic data and quality state. This mining is achieved by utilizing the strong feature extraction ability of the deep residual shrinkage network (DRSN) through self-learning. The symmetry of the data within the model has also been taken into account to ensure a more balanced and comprehensive analysis. The excellent binary classification ability of the support vector machine is combined with the DRSN to identify the quality anomaly state. The SHAP interpretable model is employed to diagnose the quality anomaly problem of a single product and to identify and diagnose quality anomalies in the manufacturing process of complex products. The effectiveness of the model is validated through case analysis. The accuracy of the DRSN-SVM quality anomaly recognition model reaches 99%, as demonstrated by example analysis, and the model exhibits faster convergence and significantly higher accuracy compared with the naive Bayesian model classification and support vector machine classification models. [ABSTRACT FROM AUTHOR]

Maintaining exceptional product quality and boosting processing efficiency requires precise evaluation of various aspects of the turning process, including the cutting depth, feed rate, and size of the workpiece. This article presents a novel approach for observing the turning process state using modulation signal bispectrum (MSB) and motor current signals. A nonlinear model was established that clarifies the load torque oscillations during turning, which in turn affects the amplitude and phase modulation of the motor stator current. Random noise can be efficiently minimized using the MSB algorithm, allowing the extraction of the current-modulation characteristic sideband phase and amplitude from the collected current signal. This technique enables clear representation and enhanced monitoring of load torque changes throughout the turning process. The proposed method was validated via mathematical simulations and universal lathe tests, with the results indicating that the MSB phase and amplitude values effectively capture both dynamic and static torque alterations during the turning operation, making this approach a valuable tool for overseeing the turning process. [ABSTRACT FROM AUTHOR]

Seafood is one of the most traded food commodities in the world. Traditionally, frozen seafood is evaluated using destructive chemical methods, which are complicated and time-consuming. Therefore, this study proposed a noninvasive method for evaluation of frozen seafood, focusing on fluorescence fingerprints (FFs). FFs, also known as an excitation–emission matrix (EEM), refer to an arranged fluorescence spectrum of emission stimulated by different excitation wavelengths. As FFs is a set of fluorescence spectra acquired at consecutive excitation wavelengths, it produces a three-dimensional diagram that is composite fluorescence of every fluorophore, similar to a fingerprint. Compared with conventional fluorescence spectroscopy, the FFs technique can measure complex samples that contain many fluorophores, because it includes emission spectra excited at many different excitation wavelengths. In this review, the applications of FFs technology for nondestructive prediction of seafood quality are summarized. Postmortem metabolism and seafood freshness indices are discussed comprehensively. We have also introduced multidimensional fluorescence imaging, a combination of FFs and imaging techniques for visualizing seafood product quality. Finally, the advantages of the FFs technique for nondestructive evaluation of seafood quality, even at the frozen state, over traditional methods and previous fluorescent spectroscopic techniques are enumerated. [ABSTRACT FROM AUTHOR]

Water content plays a crucial role in the retrogradation of rice starch. The relationship between the water content of rice starch gels and their structural changes during retrogradation is studied. The results indicate that the gels have three distinct types of water: structural constitution water, starch intermolecular bound water, and free water. The rate of retrogradation increases as the water content increases. Nevertheless, when the water content is lower (1:1) or higher (1:6) than optimal, the starch molecules show limited aggregation and recrystallization, which retards the rate of retrogradation. With the aggregation and recrystallization of starch and the inward contraction of the gel structure, the mobility of water is restricted, and the binding of water to the starch is enhanced. With the sustained contraction of the gel structure, the water‐holding capacity of the gel is weakened. By analyzing the experimental results, water distribution, transformation, and migration in rice starch during retrogradation are delineated. These findings provide a theoretical basis for controlling the retrogradation of rice starch and improving product quality. [ABSTRACT FROM AUTHOR]

In advanced manufacturing processes, high-dimensional (HD) streaming data (e.g., sequential images or videos) are commonly used to provide online measurements of product quality. Although there exist numerous research studies for monitoring and anomaly detection using HD streaming data, little research is conducted on feedback control based on HD streaming data to improve product quality, especially in the presence of incomplete responses. To address this challenge, this article proposes a novel tensor-based automatic control method for partially observed HD streaming data, which consists of two stages: offline modeling and online control. In the offline modeling stage, we propose a one-step approach integrating parameter estimation of the system model with missing value imputation for the response data. This approach (i) improves the accuracy of parameter estimation, and (ii) maintains a stable and superior imputation performance in a wider range of the rank or missing ratio for the data to be completed, compared to the existing data completion methods. In the online control stage, for each incoming sample, missing observations are imputed by balancing its low-rank information and the one-step-ahead prediction result based on the control action from the last time step. Then, the optimal control action is computed by minimizing a quadratic loss function on the sum of squared deviations from the target. Furthermore, we conduct two sets of simulations and one case study on semiconductor manufacturing to validate the superiority of the proposed framework. [ABSTRACT FROM AUTHOR]

Recent research provides strong evidence that fouling in ultra‐high‐temperature (UHT) plants for milk is largely impacted by formation of bubbles on hot heat‐exchanger surfaces. The causal relationship between formation of bubbles and fouling is explained, and the impact of the variables—temperature, pressure and dissolved air, is clarified. By elevating the back pressure significantly, according to theoretically based mathematical equations, the formation of bubbles can be prevented, thus inhibiting the bubble‐related fouling. Equations and calculation results, based on a presented calculation tool, of three design examples, are provided to support designs of plants for suppressed or prevented bubble‐related fouling. [ABSTRACT FROM AUTHOR]

Despite unprecedented quality improvement and defect prevention measures, producing a complete lot of perfect items is still highly unlikely due to the degradation of machines and processes over time. The previous studies on supply chains with defective/imperfect items have explored options such as process quality improvement investments, reworking, selling at a salvage value, or disposing of these items at an additional cost. This paper, contrary to existing literature, categorizes defective items into three types based on the level of imperfection: slightly, moderately, and highly defective items, and determines the optimal lot size for perfect and defective items. The focus is to develop a supply chain model for a single manufacturer and multiple retailers in the presence of defective items and manage defective products based on quality to generate profit from low-defective products rather than traditional defect rate reduction methods. Numerical examples and sensitivity analyses are presented to demonstrate the effectiveness of the proposed model in improving the total profit of the supply chain by optimizing production quantities. [ABSTRACT FROM AUTHOR]

The coupled systems with numerous interdependent variables pose considerable control and stability issues in commercial chemical processes. These systems exhibit complex interactions, time delays, parameter uncertainties and disturbances, which can lead to poor performance and reduced productivity. Conventional single-loop controllers struggle to effectively manage the interactions between variables, leading to poor disturbance rejection and tracking accuracy. A more robust and efficient control strategy is essential to tackle these limitations. Hence, a robust decentralized PID controller design is proposed to address the shortcomings of coupled industrial chemical processes. The aim is to enhance control system performance by achieving better disturbance rejection and tracking accuracy, thereby improving process efficiency and product quality. The complementary sensitivity function is exploited to attain this, and a novel graphical tuning method is proposed for obtaining optimal PID controller parameters. The use of the H ∞ robust criterion ensures robust stability and minimizes the influence of external disturbances. The proposed approach offers an efficient means to design robust decentralized controllers for complex chemical processes. The simulation results demonstrate the effectiveness of the method compared to existing controllers. Numerically, the proposed controller is able to attain almost a 20 % reduction in integral absolute error and a 30 % decrease in the settling time for all cases compared to existing controllers. The graphical tuning method enables efficient parameter optimization, resulting in faster response times and reduced settling time. This work represents a significant step toward achieving enhanced control system performance and stability in coupled industrial chemical processes. [ABSTRACT FROM AUTHOR]

BACKGROUND: Licorice extract is an important raw material for food additives and medicine. The quality of licorice extract is dictated by the drying process. The commonly used drying methods of licorice extract are not efficient in obtaining high‐quality products so alternative techniques need to be developed and researched. In this study, ultrasound‐assisted vacuum drying (UAVD) was first utilized to improve drying efficiency and produce a higher‐quality product. The changes in water mobility of licorice extract during drying were characterized using low‐field nuclear magnetic resonance. In addition, the effects of ultrasonic power on the drying dynamics, the contents of liquiritin and glycyrrhizic acid, the antioxidant capacity and the microstructure formation of licorice extract during the whole drying process were investigated. RESULTS: The drying times for licorice extract to reach equilibrium moisture content were reduced by 9.09–69.70% with UAVD at 40–200 W compared with that without ultrasonic treatment (0 W). Moreover, the proportions of bound water and semi‐bound water in fresh concentrate were 3.75% and 96.25%. It was also found that high ultrasonic power promoted the flow of water and the formation of porous structure in licorice extract, which led to the improvement of drying efficiency. The contents of liquiritin (2.444%) and glycyrrhizic acid (6.514%) were retained to a large degree in the dried product at an ultrasonic power of 80 W. The DPPH inhibition rate of UAVD samples with different ultrasonic powers ranged from 84.07 ± 0.46% to 90.65 ± 0.22%. CONCLUSION: UAVD has the advantages of high efficiency and low energy consumption, which may be an alternative technology for vacuum drying widely used in industry. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Background: For a specific purpose involving the pH of menstrual blood and the pH of the vagina during menstruation, the current work describes the development of pH-responsive superadsorbent polymer (SAP)-coated menstrual pads. Acrylic acid (AA) and methyl methacrylate (MMA) were used to create a pH-responsive polymer, which was then applied layer by layer to sanitary napkins. Using FTIR, the chemical composition and structure were examined. By examining tests for absorbent capacity, swelling capacity, water retention, sorption under load, strike through, wicking height, and re-wetness under load, the performance studies of coated pads were contrasted with those of menstrual pads that are sold commercially. Additionally, the coated pad's pH response was examined to determine how stable the polymer was against pH variation. Results: The use of SAP in this study resulted in a higher free swelling capacity (102.22 g/g) for tap water and absorption under load in sanitary napkins. The absorption capacity of SAP ranges from 8 to 24 h, with values ranging from 14.84 to 132.41 g/g. Similarly, wet back and striking through analysis indicated superior outcomes in comparison with commercial products. The pH of the prepared SAP helps to maintain the necessary acidity level (pH 3.02) for the vagina during menstruation. Conclusions: Based on comparison results from FTIR, absorbency capacity, rewet, strike-through, and pH, the report recommends using the pH-responsive SAP to coat the top layer of feminine napkins. [ABSTRACT FROM AUTHOR]

This research investigates the control of thickness and weight in plastic extrusion vacuum-thermoforming products to identify optimal key parameters for cost reduction and energy savings. The initial step involves identifying crucial influencing factors. In this step, the Delphi technique was employed through a questionnaire administered to a panel of expert scholars to ensure minimal error and maximal reliability in determining key influencing factors. Consensus was sought to establish appropriateness and consistency. Subsequently, the Taguchi method was applied for quality design and planning of the extrusion vacuum-forming process. The experimental design parameters were selected using an L18 (21 × 37) orthogonal array, and the desired quality characteristics were determined. Comparative analysis of quantitative production data from two consecutive experiments was conducted, and based on F-values and contribution analysis, the combination of control factors maximizing the Signal-to-Noise (S/N) ratio was identified. The objective is to seek optimal parameters for improving the quality of the plastic polypropylene (PP cup lid) manufacturing process, reducing process variability, and identifying the most robust production conditions. Through multiple actual production prediction experiments, it was determined that five control factors, "polypropylene new material ratio," "T-die lips adjustment thickness", "mirror wheel temperature stability", "molding vacuum pressure time", and "forming mold area design", contribute to the maximization of the S/N ratio, i.e., minimizing variability. Statistical validation confirms a significant improvement in product quality and weight control. Noteworthily, the quality control model and experimental design parameters established in this study are also applicable to other plastic products and bio-based materials, such as PET, HIPS, and biodegradable PLA lids with added calcium carbonate. The results of the experimental production demonstrate its ability to consistently control product weight within the range of 3.4 ± 0.1 g, approaching the specified tolerance limits. This capability results in approximately 2.6% cost savings in product weight, contributing significantly to achieving a company's KPI goals for environmental conservation, energy efficiency, and operational cost reduction. Therefore, the findings of this study represent a substantial and tangible contribution. [ABSTRACT FROM AUTHOR]

In the highly competitive field of material manufacturing, stakeholders strive for the increased quality of the end products, reduced cost of operation, and the timely completion of their business processes. Digital twin (DT) technologies are considered major enablers that can be deployed to assist the development and effective provision of manufacturing processes. Additionally, knowledge graphs (KG) have emerged as efficient tools in the industrial domain and are able to efficiently represent data from various disciplines in a structured manner while also supporting advanced analytics. This paper proposes a solution that integrates a KG and DTs. Through this synergy, we aimed to develop highly autonomous and flexible DTs that utilize the semantic knowledge stored in the KG to better support advanced functionalities. The developed KG stores information about materials and their properties and details about the processes in which they are involved, following a flexible schema that is not domain specific. The DT comprises smaller Virtual Objects (VOs), each one acting as an abstraction of a single step of the Industrial Business Process (IBP), providing the necessary functionalities that simulate the corresponding real-world process. By executing appropriate queries to the KG, the DT can orchestrate the operation of the VOs and their physical counterparts and configure their parameters accordingly, in this way increasing its self-awareness. In this article, the architecture of such a solution is presented and its application in a real laser glass bending process is showcased. [ABSTRACT FROM AUTHOR]

Surface-to-volume ratio (SVR) during deep-fat frying affects the rates of momentum, heat, and mass transfer, leading to influencing the final product quality. Mathematical modeling of simultaneous transport phenomena was developed to investigate the influence of the SVR on oil velocity and temperature, as well as product temperature, moisture, and oil distributions, during the frying of potato strips. Considering three different SVRs (3, 3.73, and 5.5 cm−1), the maximum oil velocity ranged from 5 to 27 mm s−1, coinciding with upward trends of the oil temperature. The temperature at both the top and bottom surfaces of the potato strips increased rapidly up to the evaporation temperature. It remained constant for some time due to the onset of the surface evaporation and then increased until the end of the frying. Results also showed that the oil uptake intensified sharply by increasing the SVR during the initial 90 s of the frying, followed by an upward trend until the end of the process but at a lower rate. The developed model could help to assess various product and oil variables during frying to better control the process to produce low-fat fried products. [ABSTRACT FROM AUTHOR]

In addition to probiotics, some inactivated bacteria, their components, and metabolites also have many probiotic functions, which can be collectively called postbiotics Postbiotics mainly come from inactive components in probiotics, which can play a probiotic role. It has the advantages of high safety, good stability and wide applicability, and has many probiotic effects such as improving gastrointestinal function, anti-inflammatory and bacteriostatic, immune regulation, and relieving obesity. It has broad application prospects in the fields of food, medicine, and animal husbandry production. At present, postbiotics has been gradually used in animal production, and has shown many functions, such as improving livestock and poultry production performance, improving animal product quality and preventing and treating diseases, but its application has not been popularized. In this paper, the research progress of postbiotics in livestock and poultry production was summarized, in order to provide a reference for its further wide application in animal husbandry. [ABSTRACT FROM AUTHOR]

This study examines the complex relationship among brand association, image, loyalty, and attachment and perceived product quality. It also examines how brand likeability moderates this relationship. The study investigates these dynamics to determine how consumer-brand interactions affect product quality perceptions. Customers evaluate product quality beyond intrinsic qualities in competitive markets. Brand association, image, loyalty, and attachment form these values. Understudied is brand likeability's moderating role in these correlations. Analyzing consumerbrand interactions on product quality fills this gap. This study uses a quantitative-methods approach. A varied sample of 270 consumers is asked about brand association, image, loyalty, attachment, product quality, and likeability. Analysis of direct and moderated effects uses structural equation modeling (SEM). The study found that brand association, image, loyalty, and attachment significantly impact perceived product quality. Consumers with strong positive associations, a positive brand image, great brand loyalty, and a profound emotional commitment to a brand see its items as higher quality. In particular, brand likeability moderates these relationships. Brand likeability enhances the effects of brand association, image, loyalty, and attachment on product quality. Brand-related characteristics greatly influence customer product quality perceptions when they like a brand. This study provides valuable insights for brand managers and marketers. First, it stresses the necessity of positive brand associations, images, loyalty, and attachment to boost product quality. Second, brand likeability amplifies these brand variables. Brands should provide engaging, realistic, and emotionally appealing experiences to increase likeability. Understanding these relationships can help marketers brand and communicate products, influencing consumer quality perceptions. This study adds to marketing literature by analyzing how brand connection, image, loyalty, and attachment affect product quality. Brand likeability moderates consumer-brand interactions, revealing their complexity. This research helps brands and marketers improve branding and consumer interactions, enhancing product quality and brand success. [ABSTRACT FROM AUTHOR]

This study aims to analyze the factors influencing consumers' perceptions of brands and their purchasing decisions. The research method employed was a survey of a number of consumers purchasing products from various brands. The results of the study indicate that consumer trust, product quality, service, and brand image play significant roles in determining purchasing decisions. To enhance consumers' perceptions of brands, companies need to focus on building a positive brand image, increasing consumer trust through brand transparency and consistency, and improving the quality of their services and products. A holistic approach that considers all these elements is considered crucial to enhancing the effectiveness of marketing strategies and brand management. However, this study has limitations, and future research should involve a broader demographic variation to better understand the impact of external factors on consumer behavior comprehensively. [ABSTRACT FROM AUTHOR]

Background: Company performance is a result of operational activities in using the company's resources. The successful contribution of employees will have an impact on the level of customer satisfaction, where satisfied customers will minimize the level of complaints and retention levels and play a role in assessing company performance as evaluation material for company management. Purpose: The purpose of this research is to determine the influence of service quality and product quality on company performance at The Royal Pita Maha. Method: This research is a type of associative research with a quantitative approach. The data collection method was carried out by direct observation, interviews and distributing questionnaires to 160 respondents, namely employees of The Royal Pita Maha who were selected using a non-probability sampling method with a quota sampling technique. The research instrument was tested using validity and reliability tests, then data analysis was carried out using multiple linear regression using SPSS. Results: The research results show that service quality has a positive and significant effect on company performance, as well as product quality has a positive and significant effect on company performance. Conclusion Based on the research results, it is hoped that company management can pay more attention to the company's operational activities through employee contributions in providing services so as to create a sense of security and comfort for customers and pay attention to the ingredients of the products provided so that they remain safe and last a long time so that the company's performance assessment can be even better. [ABSTRACT FROM AUTHOR]

Weld seam detection is particularly crucial within industrial systems, as it promptly reflects the quality of product fabrication and performance, ensuring the final metallurgical product's quality while mitigating potential losses stemming from product quality issues. This paper proposed the InfoFPN Cross Refinement Network (InfoCRNet) for the weld seam detection, achieving an accuracy rate of 98,90% and F1 score of 98,85 %. [ABSTRACT FROM AUTHOR]

Honey has great benefits to humans, both as medicine and food. It contains glucose, fructose and water in addition to small quantities of proteins, minerals, organic acids, and vitamins. Vacuum cooling is a rapid cooling technique based on the principle of evaporation, which lowers the boiling point of water based on the vacuum pressure inside a cooler room. A prototype vacuum-cooling jet water system was used to process the honey. Raw honey obtained from the forests of Riau was used in the present study. This study aimed to determine the effect of jet water vacuum cooling with variations in volume (V/V), capacity (12.5%, 25%, and 50%) on the quality of honey (Apis dorsata Fabricus) to accelerate the cooling process and preserve product quality. The cooling mechanical analysis parameters reviewed included the processing time and the lowest maximum pressure. The parameters of the quality of honey observed included moisture content, viscosity, density and total dissolved solids. As a result, the vacuum cooling process can suppress damage to the quality of honey owing to the heating process. At the cooling time and lowest maximum pressure value, the mechanical aspects of the water jet system vacuum cooling process showed the maximum results. The water contents of 12.5% (V/V), 25% (V/V) and 50% (V/V) filling were 13.69%, 12.89%, and 14.25%, respectively. The viscosities at 12.5% (V/V), 25% (V/V) and 50% (V/V) filling were 2.604 Pa.s, 2.355 Pa.s, and 1.644 Pa.s, respectively. Density with 12.5% (V/V), 25% (V/V) and 50% (V/V) filling were 1.52 g/cm3, 1.53 g/cm3 and 1.50 g/cm3, respectively. Total dissolved solid values of honey with 12.5 % (V/V), 25 % (V/V), and 50 % (V/V) fillings were 78.16%, 78.16% and 77.66%, respectively. Water jet vacuum cooling with a 25% chamber volume capacity accelerates honey with the best total dissolved solids, density and moisture content. Vacuum cooling can be used to improve product quality, shorten handling time, extend product shelf life and improve security. [ABSTRACT FROM AUTHOR]

Pharmaceutical science encompasses meticulous processes, from initial pharmacological testing to distribution and pharmacovigilance. Each stage demands unwavering precision and adherence to regulatory standards to ensure the quality and safety of medicinal products. This article delineates the multifaceted landscape of pharmaceutical regulations, focusing on Good Manufacturing Practice (GMP) standards as a cornerstone of quality assurance. GMP standards, encompassing GxP principles, mandate adherence to rigorous protocols throughout the drug lifecycle. Within the European Union (EU), GMP standards are codified in directives and regulations governing medicinal products for human and veterinary use. Volume 4 of Eudralex serves as a compendium of EU regulations, providing comprehensive guidelines for pharmaceutical manufacturing. Mutual Recognition Agreements on GMP inspections between the EU and other jurisdictions aim to facilitate trade and regulatory cooperation, exemplifying international collaboration in pharmaceutical oversight. Recent agreements between the EU and the US FDA streamline inspection processes, reducing redundant inspections and expediting market access for pharmaceutical products. Moreover, electronic systems such as EudraGMDP enhance transparency and regulatory compliance within the pharmaceutical industry. By evaluating international GMP practices, this article underscores the importance of harmonizing regulatory standards for Good Manufacturing Practices globally. In conclusion, adherence to GMP standards not only ensures the quality of pharmaceutical products but also plays a pivotal role in the development of pharmacy practices worldwide. [ABSTRACT FROM AUTHOR]

Twin-screw granulation (TSG) is an emerging continuous wet granulation technique that has not been widely applied in the industry due to a poor mechanistic understanding of the process. This study focuses on improving this mechanistic understanding by analyzing the effects of the mixing dynamics on the granule quality attributes (PSD, content uniformity, and microstructure). Mixing is an important dynamic process that simultaneously occurs along with the granulation rate mechanisms during the wet granulation process. An improved mechanistic understanding was achieved by identifying and quantifying the physically relevant intermediate parameters that affect the mixing dynamics in TSG, and then their effects on the granule attributes were analyzed by investigating their effects on the granulation rate mechanisms. The fill level, granule liquid saturation, extent of nucleation, and powder wettability were found to be the key physically relevant intermediate parameters that affect the mixing inside the twin-screw granulator. An improved geometrical model for the fill level was developed and validated against existing experimental data. Finally, a process map was developed to depict the effects of mixing on the temporal and spatial evolution of the materials inside the twin-screw granulator. This process map illustrates the mechanism of nucleation and the growth of the granules based on the fundamental material properties of the primary powders (solubility and wettability), liquid binders (viscosity), and mixing dynamics present in the system. Furthermore, it was shown that the process map can be used to predict the granule product quality based on the granule growth mechanism. [ABSTRACT FROM AUTHOR]

Starch extraction from tubers and roots has long been an essential process, playing a crucial role in diverse industries ranging from alimentary to pharmacology. This review explores the different methods employed in starch extraction, including traditional techniques and the most innovative mechanical strategies. The methods show a good improvement in many aspects, such as an improvement in the efficiency of the process and an improvement in the yield, showing a value of 10.0–65.0% depending on the starch source. On the other hand, solvents such as NaOH are used in many mechanical processes for alkaline digestion to improve the extraction time. Ethanol and K2S2O5 concentrations of 0.5% and 0.8% were used to prevent oxidation and modify some properties of the extracted starch. The use of many solvents has improved the optimization of the processes, providing the final extracted starch with more advantages and better quality. However, using enzymes such as cellulase in new and innovative ways has provided more advantages and a better efficiency and yield than the other methods. Each method has its advantages and challenges, highlighting the importance of understanding the diversity of different approaches and their impact on the yield, sustainability, environmental considerations, and quality of the extracted starch. As the world looks for more ecological approaches, this review shows the importance of critically evaluating the yield, efficiency, and environmental implications of the extraction methods, providing us with more ways of evaluating the methods used for starch extraction. The ecological impact is a crucial point when evaluating the innovation of a new extraction process, which is why methods such as ultrasound and pulsed electric-field-assisted techniques have been proposed. These methods have been presented as sustainable techniques called green technologies, offering more approaches and different advantages than the other methods. This review intends to investigate the complexities and considerations of starch extraction, providing a solid basis for decision-making regarding starch extraction. In a time where sustainability and product quality are crucial elements of industrial strategy formulation, an in-depth understanding of these methods becomes imperative to the development of responsible practices and efficiency in starch extraction. [ABSTRACT FROM AUTHOR]

The manufacturing industry is always exploring techniques to optimize processes, increase product quality, and more accurately identify defects. The technique of deep learning is the strategy that will be used to handle the issues presented. However, the challenge of using AI in this domain is the small and imbalanced dataset for training affected by the severe shortage of defective data. Moreover, data acquisition requires significant labor, time, and resources. In response to these demands, this research presents an intelligent internet of things (IoT) framework enriched by generative adversarial network (GAN). This framework was developed in response to the needs outlined above. The framework applies the IoT for real‐time data collection and communication, while GAN are utilized to synthesize high‐fidelity images of manufacturing defects. The quality of the GAN‐synthesized image is quantified by the average FID score of 8.312 for non‐defective images and 7.459 for defective images. As evidenced by the similarity between the distributions of synthetic and real images, the proposed generative model can generate visually authentic and high‐fidelity images. As demonstrated by the results of the defect detection experiments, the accuracy can be enhanced to the maximum of 96.5% by integrating the GAN‐synthesized images with the real images. Concurrently, this integration reduces the occurrence of false alarms. [ABSTRACT FROM AUTHOR]

In recent decades, laser additive manufacturing has seen rapid development and has been applied to various fields, including the aerospace, automotive, and biomedical industries. However, the residual stresses that form during the manufacturing process can lead to defects in the printed parts, such as distortion and cracking. Therefore, accurately predicting residual stresses is crucial for preventing part failure and ensuring product quality. This critical review covers the fundamental aspects and formation mechanisms of residual stresses. It also extensively discusses the prediction of residual stresses utilizing experimental, computational, and machine learning methods. Finally, the review addresses the challenges and future directions in predicting residual stresses in laser additive manufacturing. [ABSTRACT FROM AUTHOR]

Large machine tools are critically affected by ambient temperature fluctuations, impacting their performance and the quality of machined products. Addressing the challenge of accurately measuring thermal effects on machine structures, this study introduces the Machine Tool Integrated Inverse Multilateration method. This method offers a precise approach for assessing geometric error parameters throughout a machine's working volume, featuring a low level of uncertainty and high speed suitable for effective temperature change monitoring. A significant innovation is found in the capability to automatically realise the volumetric error characterisation of medium- to large-sized machine tools at intervals of 40–60 min with a measurement uncertainty of 10 µm. This enables the detailed study of thermal errors which are generated due to variations in ambient temperature over extended periods. To validate the method, an extensive experimental campaign was conducted on a ZAYER Arion G™ large machine tool using a LEICA AT960™ laser tracker with four wide-angle retro-reflectors under natural workshop conditions. This research identified two key thermal scenarios, quasi-stationary and changing environments, providing valuable insights into how temperature variations influence machine behaviour. This novel method facilitates the optimization of machine tool operations and the improvement of product quality in industrial environments, marking a significant advancement in manufacturing metrology. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to assess the impact of incorporating foxnut powder (FP) into bakery products to evaluate their effect on product quality and nutritional characteristics. Design/methodology/approach: Samples of refined flour (control) and refined wheat flour with varying levels of FP were prepared for each bakery item. Sensory evaluations using a nine-point hedonic scale were conducted. Different concentrations of FP (20% for cakes, 12.5% for bread and 12.5% for doughnuts) were tested to achieve sensory acceptability. Findings: The addition of FP at specified concentrations achieved sensory acceptability in the tested bakery items, significantly impacting overall acceptability. Incorporating FP led to textural attribute alterations, including increased hardness, gumminess and chewiness, alongside reduced cohesiveness and elasticity. Color properties were influenced, affecting lightness, redness and yellowness of the bakery items. Proximate composition analysis highlighted shifts in moisture, protein, fiber, fat and ash content between control and accepted samples. Mineral content analysis revealed notable differences in calcium, potassium, iron, magnesium and sodium between control and accepted samples. Originality/value: These findings demonstrate the potential of FP to enhance bakery products, offering promising industrial applications in producing nutritionally enriched and visually appealing baked products. [ABSTRACT FROM AUTHOR]

Detecting surface defects in strip steel is significantly important for improving production efficiency and product quality. Herein, a novel surface defect detection of strip steel method based on You Only Look Once (YOLO)v5s‐GC is proposed. First, a ResNet‐Mini is developed to preclassify the original dataset to reduce the number of calculations. Subsequently, image preprocessing is conducted to enhance the defect features, which consists of two steps: the first is combining the ResNet‐Mini network weights with Grad‐CAM to crop defective areas and remove background interference and the second is applying the OTSU and normal distribution enhancement algorithm to extract the feature grayscale. Furthermore, a size enhancement strategy is adopted to obtain larger data sizes that could simulate an actual application scenario with a large‐area saccade of strip steel. Finally, the cross‐stage partial module of YOLOv5s is replaced with GhostBottleneck, and the convolutional block attention module is added to the detection neck. The average accuracy of YOLOv5s‐GC for detecting six types of strip steel is 82.4%, marking an increase of 7.0% over YOLOv5s. In addition, the calculation amount of YOLOv5s‐GC is reduced by 48%, and the inference speed is increased by 24%. [ABSTRACT FROM AUTHOR]

Analyzing vaccine stability under different storage and transportation conditions is critical to ensure that effectiveness and safety are not affected by distribution. In a simulation of the last mile in the supply chain, we found that shock and vibration had no effect on Ad26.ZEBOV/ MVA-BN-Filo Ebola vaccine regimen quality under refrigerated conditions. [ABSTRACT FROM AUTHOR]

Prebiotic galacto‐oligosaccharides (GOSs) are synthesized by transgalactosylation of lactose using β‐galactosidases. Well‐established, large‐scale production of GOS is performed by stirred‐tank reactors (STR) operating in batch fashion. Main manufacturing expenses are attributed to enzyme purchase costs and the downstream operations required for subsequent enzyme inactivation and removal. In the last decades, several studies and patents have appeared on free enzyme membrane reactor (FEMR) as an economic alternative to STR due to enhanced enzyme recovery and reuse. In this review, we attempt to summarize the key design aspects, the catalytic performance and efficiency of FEMR compared to STR technology, and the main concerns related to stable product quality over long‐term operation. [ABSTRACT FROM AUTHOR]

To ensure the high quality of biopharmaceutical products, it is imperative to implement specialized unit operations that effectively safeguard the structural integrity of large molecules. While lyophilization has long been a reliable process, spray drying has recently garnered attention for its particle engineering capabilities for the pulmonary route of administration. However, maintaining the integrity of biologics during spray drying remains a challenge. To address this issue, we explored a novel dehydration system based on aerosol-assisted room-temperature drying of biological formulations recently developed at Princeton University, called Rapid Room-Temperature Aerosol Dehydration. We compared the quality attributes of the bulk powder of biopharmaceutical products manufactured using this drying technology with that of traditional spray drying. For all the fragment antigen-binding formulations tested, in terms of protein degradation and aerosol performance, we were able to achieve a better product quality using this drying technology compared to the spray drying technique. We also highlight areas for improvement in future prototypes and prospective commercial versions of the system. Overall, the offered dehydration system holds potential for improving the quality and diversity of biopharmaceutical products and may pave the way for more efficient and effective production methods in the biopharma industry. [ABSTRACT FROM AUTHOR]