Product Process Research Articles

PERANCANGAN USER INTERFACE DAN USER EXPERIENCE APLIKASI MONITORING EXPIRED MENGGUNAKAN METODE DOUBLE DIAMOND

Indomaret is one of the franchise business ventures which is a company owned by PT Indomarco Prismatama. At this time the process of monitoring products that are approaching the expiration date is still inefficient, often there are products whose expiration dates are missed and employees have difficulty finding products that will expire while there is no data and reminders about products that will expire. Therefore, a user interface design is needed before designing an application that can facilitate employees in managing expired products to be more efficient. In designing the user interface, researchers used the Double Diamond method which includes 4 stages, namely discover, define, develop, and deliver. For user experience testing, researchers adopted the User Experience Questionnaire (UEQ) method using 6 aspects with 26 items, including Attractiveness, Perpicuity, Effciency, Depandability, Stimulation, Novelty. The test results showed that t5 UEQ scales were on the "Excellent" assessment, namely on the Attractiveness, Efficiency, Dependability, Stimulation, Novelty scales. While one UEQ scale is at "Above Average", namely the Perspicuity scale.

Automated painting color matching technology based on semantic intelligence understanding

Painting color matching technology is widely used in the production and printing process of products. Traditional painting and color matching have been unable to meet market demands. Based on this, a large-scale corpus under the existing semantic intelligent understanding system is used as the knowledge source. The computer automated painting color matching model is constructed. It is applied in case studies to address issues such as unclear query intentions, mismatched system retrieval terms, and return errors caused by uncertain factors such as synonyms and polysemy. This provides new ideas for the application of semantic intelligence understanding and automated painting color matching technology. The experimental results showed that the precision, recall, and F1 of the method used in the research were 0.8639, 0.8026, and 0.8309, respectively, significantly superior to commonly used methods. This indicates that the proposed automated painting color matching technology based on semantic intelligent understanding has high performance, which can effectively meet the painting color matching requirements.

Redefining chimeric antigen receptor T-cell (CAR-T) regulation: China's responses to address secondary cancer risks of CAR-T therapy.

Since the United States Food and Drug Administration (FDA) approved the first chimeric antigen receptor T-cell (CAR-T) therapy in 2017, it has marked a major breakthrough in cancer treatment, leading to a surge in global research and applications in this field. In recent years, China has made rapid progress, quickly catching up through heavy investment in CAR-T construction, preparation processes, and treatment strategies. China's CAR-T therapy market is driven by substantial pharmaceutical investment targeting its vast population, yet high therapy costs remain uncovered by basic medical insurance. In November 2023, FDA issued a warning about the risk of secondary cancers in patients undergoing CAR-T therapy, sparking global concern. In fact, the China National Medical Products Administration (NMPA) preemptively implemented a series of measures to address the safety concerns of CAR-T therapy, emphasizing the risk of secondary cancers and advising lifelong monitoring as part of the approval process for CAR-T products. Nevertheless, additional regulatory measures are needed to address emerging risks, particularly the threat of secondary cancers. The authors believe that raising the standards for Investigational New Drug (IND) approval and establishing a dynamic reporting and feedback system based on real-world data will strengthen regulatory oversight and support the sustainable growth of the CAR-T industry in China.

A dynamic capability perspective on the impact of big data analytics and enterprise architecture on innovation: an empirical study

PurposeThe prevailing conceptualization of information system (IS) capabilities, rooted in the resource-based view (RBV) framework, tends to focus on unique firm resources. In the digital age, as emphasized by dynamic capabilities (DC), resource reconfiguration is critical in maintaining strategic advantage. This paper focuses on big data analytics capabilities (BDAC) from a DC perspective to present a novel conceptualization of BDAC–DC. We examine its effects on product, business model and business process innovation, including the effects of enterprise architecture (EA) on the BDAC business model innovation relationship.Design/methodology/approachThis research presents a novel DC-based BDAC conceptualization, operationalized as a hierarchical construct. A survey-based approach is used for data collection and data analysis is done using partial least squares structural equation modeling (PLS-SEM).FindingsThe novel conceptualization and the effects of BDAC DC on BDA sensing-seizing and reconfiguration capacities support BDAC’s functional and evolutionary roleplay. Empirical results confirm the positive effects of BDAC–DC on first-order value targets (innovation) and the moderating effects of EA.Research limitations/implicationsThe novel BDAC–DC conceptualization has several implications for BDAC, DC, EA and business value research. Practicing managers must adopt a multifaceted approach to BDAC development by considering non-technical and organizational factors, collaborate with their business counterparts to explore unique big data ideas, initiate proof-of-concept projects to secure support and allocate resources synchronously, considering a multidimensional view of the process, product and business model innovation.Practical implicationsPracticing managers must adopt a multifaceted approach to BDAC development by considering non-technical and organizational factors, collaborate with their business counterparts to explore unique big data ideas, initiate proof-of-concept projects to secure support and allocate resources synchronously, considering a multidimensional view of the process, product and business model innovation for synergistic outcomes.Originality/valueTo the best of our knowledge, this research is the first attempt toward DC-based BDAC conceptualization, empirical validation of first-order effects on various forms of innovation and the often-overlooked role of critical EA capability.

Thermodynamic Calculations of the Main Characteristics of the Combustion Process of Pyrotechnic Nitrate-Metallized Mixtures with Additives of Organic and Inorganic Substances under External Thermal Influences

The aim of the article is to determine the dependencies of the combustion product temperatures of four-component mixtures, the content of high-temperature condensate, and unoxidized metal on the excess oxidizer coefficient, the number of additives, and external pressure using thermodynamic calculations. Modern methods of physicochemical analysis (such as cinephotography and microcinephotography methods, X-ray analysis methods, non-contact and contact temperature measurement methods), nonlinear thermal conductivity and thermal stability methods, as well as mathematical and experimental-statistical modeling, were employed to investigate the influence of elevated heating temperatures (up to 800 K) and external pressures (up to 107 Pa) on the ignition and combustion processes of pyrotechnic products. Standard pyrotechnic equipment was used to test pyrotechnic product samples under specified conditions. Calculations based on models were conducted in real-time and dialog mode on computer devices that meet modern requirements for the use of specialized software. The results of thermodynamic calculations are presented for the dependencies of combustion product temperatures of multicomponent pyrotechnic mixtures based on powders of metal fuels (magnesium, aluminium), nitrate oxidizer (sodium nitrate), additives of organic and inorganic substances (paraffin, sodium fluoride), and the content of high-temperature condensate and unoxidized metal depending on the excess oxidizer coefficient α = 0.1…4.0, the amount of paraffin additive εp = 0…0.2, sodium fluoride additive εf = 0…0.06, and external pressure P = 105…107 Pa. Based on the obtained results of thermodynamic calculations, statistical models in the form of regression dependencies of the specified combustion characteristics on their technological parameters and external conditions have been developed. These models enable the determination of fire hazardous properties of mixtures in real-time dialog and PC-based scenarios, considering premature initiation of products based on them under external thermal actions.

Analysis of the evolution process of corrosion products of pure magnesium based on the phase angle

The process of immersion corrosion on pure magnesium in 0.1 M NaCl solution was investigated by the phase angle. Results show that the peak value of phase angle and responding frequency of peak can be used to characterize the evolution process of corrosion products. It is before 80 min that corrosion products increase on the surface until film layers formed. The theoretical relation between the phase angle and corrosion products was established and the simulated results had a good agreement with the validation experiment data. Moreover, the evolution mechanism of corrosion products was studied by Scanning Vibrating Electrode Technique.

The TPACK Physics Learning Video: Forming 21st Century Skills in Physics Learning

Abstract The 21st century learning process in schools emphasizes soft skills. To support the learning process in schools, learning videos are needed that present physics content in a learning process. This article publishes the results of research and development of physics learning videos. This physics learning video presents physics learning in stages that train creativity and critical thinking skills. The physics content is a Static Fluid presented with the TPACK framework. This physics learning video uses Lee & Owens research and development stages. Product feasibility is assessed using a questionnaire by experts. The components assessed are the suitability of physics content, the suitability of the learning process, the suitability of physics learning media on static fluid concepts. Sequentially, it received an assessment of 95.0% for physics content, 98.0% for the learning process, 100.0% for video media. The implementation of TPACK for the learning process in learning video products has made it easier for students to practice their critical thinking skills and increase their creativity.

Sustainable recycling of pure aluminum from waste chips under supergravity-enhanced separation: A cleaning process

Large quantities of chips containing high levels of aluminum are always inevitably produced during machining process of aluminum products, which is a valuable renewable resource. In this paper, an environmental-friendly method for direct and continuous recovery of same-level recycled Al from waste chips under supergravity-induced was proposed. The oxide film covering the surface of the molten Al-chips was easily disrupted under super-gravity, and subsequently almost all of Al melt detached from the oxide film and flowed rapidly through the microporous ceramic foam filter, with a yield ratio of more than 97 %. During this process, all fine broken oxide-film particles and large amounts of primary iron-rich particles in the melt were captured in the complex channels of the filter, resulting in clean 1xxx series recycled Al with free inclusions and impurity iron content of less than 0.28 wt%. In addition, a sustainable process and a continuous centrifugal unit for recycling aluminum chips were designed, the economic and environmental advantages of which demonstrate the feasibility of sustainable regeneration of aluminum chip resources on an industrial scale via supergravity technique.

Property Effect and Mechanism of Sodium Salicylate on Magnesium-based Curing Plugging Materials

Abstract Lost circulation is a common problem in drilling and completion. Magnesium-based curing material has the advantages of effective plugging and formation damage control, and controlling curing time can extend the use. In this paper, the main agent of magnesium-based curing materials is magnesium salt, the influence of sodium salicylate with different dosage on the curing time in different curing states was analyzed, the optimal addition was determined, and the curing product and hydration process were analyzed under the optimal addition to determine the retarding effect of sodium salicylate. In static curing, the optimal addition of AR grade sodium salicylate was 15-17%, and the curing time was 3-4 h. The dynamic curing time at atmospheric pressure was obviously less than the static curing time, but clear right-angle thickening could be seen. The optimal addition of industrial grade sodium salicylate was 18%, and the static curing time was about 3 h. At high temperature and high pressure, it’s proved that pressure had little effect on curing time, but the curing time decreased significantly under high temperature environment. Through SEM, XRD and TGA, it was found that sodium salicylate was more conducive to the growth of the cured product, and more 517 phases was generated, but it did not affect the acid dissolution, with the acid dissolution rate reached more than 95%, which could realize the reservoir protection. By studying the hydration process, it’s showed that sodium salicylate could delay the hydration and reduce the heat release by about 2/3, and the hydration process was controlled by the phase boundary reaction. The retarding effect of sodium salicylate for magnesium-based curing material can guide the efficient application in different environments.

Adapting the GPT engine for proactive customer insight extraction in product development

Understanding the perceptions of consumers is widely recognized as a critical element in the innovation of new products. Traditional techniques used by companies to collect essential consumer insights have largely remained unchanged. Common practices like interviews and surveys have distinct limitations. Interviews may not always capture the precise needs of consumers due to communication barriers, and surveys tend to encourage incremental changes rather than radical innovations. Service industries face further complexity as they navigate customer feedback on the more intangible aspects of service quality. This study highlights the pioneering use of GPT-3.5 Turbo, a tool known for its exceptional ability to delve into the nuances of conversational context and process data in a chat-centric manner, thereby enhancing the extraction of Voice of the Customer (VoC). Its capability to handle large volumes of data in multiple languages leads to a more thorough and inclusive VoC analysis. The study links these technological advancements with Lean Six Sigma 4.0, suggesting that incorporating GPT-3.5 Turbo could significantly improve the customer-focused strategies in the current industrial landscape. This breakthrough in VoC analysis suggests possibilities for more perceptive, immediate data-driven approaches in customer service, and lays a stronger groundwork for decisions in product evolution and process enhancement. The paper concludes by urging further investigation to confirm these initial results and to explore the ethical aspects of employing such advanced natural language processing technologies.

Application of metal additive testing and thermal conductivity technology in optimizing product appearance design in intelligent manufacturing industry

Metal additive manufacturing technology has the advantages of rapid manufacturing and personalized production, but due to the complexity of the manufacturing process and differences in material characteristics, products made with metal additives often have appearance defects and process performance issues. Therefore, the purpose of this study is to explore the application of metal additive detection technology in product appearance design and process performance optimization. This study proposes a comprehensive detection and optimization method that utilizes non-destructive testing techniques to comprehensively inspect metal components. By identifying and locating these issues, the quality status of the components can be accurately understood. Based on the test results, a series of appropriate optimization measures have been taken to improve the appearance and process performance of the product, adjust process parameters, improve the forming quality and surface smoothness of the components, thereby achieving better appearance effects. Metal additive testing technology can effectively detect and locate appearance defects in products, and by optimizing process parameters and material selection, it can significantly improve the appearance quality and process performance of products, providing support for improving the reliability and production efficiency of industrial manufacturing.

Relationships among para-social interaction, perceived benefits, community commitment, and customer citizenship behavior: Evidence from a social live-streaming platform

Encouraging online consumers to participate in the value enhancement process of products and stimulating users to engage in spontaneous and voluntary behaviors, the so-called Customer Citizenship Behaviors (CCBs), can significantly contribute to enhancing the competitiveness of online communities. Nowadays, there has been no systematic discussion on enhancing product value through value co-creation. Based on the Social Exchange Theory (SET), and take the current new type of online community, the social live-streaming community, as the research object, this study investigates how Para-social Interaction (PSI) between users and streamers affects users' perceived benefits, whether perceived benefits are related to users' community commitment and the relationship between community commitment and CCB from the perspective of value co-creation. Three hundred and forty-seven valid samples from China were obtained using TikTok and subsequently analyzed via Partial least squares structural equation modeling (PLS-SEM). The analysis results show that PSI positively influences users' perceived benefits, and among the perceived benefits, hedonic and self-esteem benefits can positively influence users' community commitment. When users' community commitment to live-streamers exists, it can effectively incentivize users to develop CCB. Theoretically, research findings enrich the study of online community value co-creation, para-social interaction, and citizenship behavior and provide recommendations for operating social live-streaming platforms.

Application of mechanical crack defect detection based on thermal energy parameter simulation and sensing images in product process design

Application of mechanical crack defect detection based on thermal energy parameter simulation and sensing images in product process design

Recyclability of wastepaper containing cellulose nanofibers

Cellulosic/lignocellulosic nanofibers (CNF/LCNF) are value-added products which recently have been considered widely. This study focused on valorization of waste by making CNF from white cutting wastepaper (WCW), which was confirmed by the transmission electron microscope images. The CNF present in the paper recycling process showed significant effects on the final product and process aspects. Addition of 5% CNF (based on dry weight of pulp) to the recycled fibers improved the strength properties as well as the fines retention. But in contrast, the drainage decreased from 303 to 188 mL CSF. Finally, considering the importance of wastepaper history on the quality of recycling process and final products, as a new vision, this study focused on recyclability of wastepaper which has experienced CNF as an additive in their history of papermaking. In this respect, the papers reinforced with nanofibers in their history, after several recycling, experienced less reduction in the tensile and tear indices and fines retention. In summary, it seems that the presence of CNF in the wastepaper can slow down the reduction of product density and process properties during several recycling cycles and so restore their potential for subsequent papermaking cycles.

Role of In Vitro Tests in the Characterisation of Locally Applied, Locally Acting Drugs in the Throat: Application to Flurbiprofen.

Background/Objectives: For locally applied, locally acting generic drug products, comparison to an originator product based on systemic exposure is usually not feasible due to low plasma concentrations and inadequate reflection of local exposure at the site of action. Where a validated PD model exists, a comparative clinical study can be performed in healthy subjects; where no surrogate endpoint is available, patients with the relevant indication need to be enrolled, with all the associated factors which could result in lack of sensitivity. Even though the need for alternative in vitro approaches has been acknowledged by both industry and regulatory bodies, the complexity of in vivo drug delivery processes makes the development of guidance documents particularly difficult. Our objective was to present in vitro approaches less classically used and to address in vivo relevance of the selected tests. Methods: This article analyses current regulatory approaches in Europe and the U.S., and highlights the key advantages of in vitro tests in terms of their sensitivity, reliability, reproducibility and in vivo relevance using locally applied flurbiprofen in various formulations. Results: The in vitro esophageal retention (IVOR) model demonstrates that the first 6-10 min after application of different flurbiprofen formulations is important for their comparison and also offers the best correlation with in vivo data using the partial area under the concentration-time curves (pAUCs). Rheological evaluations further demonstrated that the mucoadhesive properties of the gel spray formulation are based on interaction with mucin. Conclusions: Designing a relevant in vitro test requires adequate evaluation of the complexity of the drug substance, drug product, dosing conditions and delivery processes.

Prediction of Potato Volume by Neural Network Regression Model

Potatoes are essential for meeting the world's dietary requirements due to their high concentration of carbohydrates, vitamins, and minerals. Potato production is plentiful worldwide, but postharvest processing losses have reduced potato quality. Potatoes can be graded manually, which is time-consuming and labor-intensive. In this study, a neural network regression model was developed to predict the volume of potatoes using 2D images, which can be used as a parameter for grading potatoes automatically. The image processing method was used to extract features like the major axis, minor axis, and roundness of 400 potato samples, and each potato's volume was thoroughly measured using the water displacement method, and a dataset was generated. These attributes were used to create the neural network regression model and predict the volume. The results revealed that the mean squared error (MSE) was 83.93, the root mean squared error (RMSE) was 9.16, and R2 was approximately 0.882. The potatoes were categorized into three groups: small, medium, and large, based on their size. The utilized model demonstrated an overall accuracy rate of 91.4%. Specifically, it achieved 90% and 85% accuracy for small-sized and medium-sized potatoes, respectively, whereas the model displayed a decrease in accuracy from 60% to 70% for large-sized potatoes. To summarize, the suggested neural network regression model provided an efficient approach to volume prediction using 2D images to improve the grading process of agricultural products.

Taguchi Approach to Defect Analysis on Electric Motor Conversion at PT. Electric Vehicle Trimotorindo

Maintaining product quality can reduce the risk of defective products. The survival of a company is heavily reliant on product quality. In order to improve performance and reduce defects in the product process, PT. Electric Vehicle Trimotorindo employs the Taguchi approach in conjunction with the QC system. The goal of this research is to determine the product process of PT. Trimotorindo Electric Vehicle, identify the highest rejection rate, evaluate process capability, and propose improvements using the Kaizen system. This study uses the Taguchi system to optimize the product process of PT. Trimotorindo Electric Vehicles. After collecting data from the taguchi system, it can be concluded that the highest position of blights in a conversion process is in the electric current string.

Electrooxidation of glycerol to formic acid coupled with Energy-Saving hydrogen production over ruthenium doped Cobalt-Based nanosheet arrays

The utilization of small molecule oxidation reactions as a substitute for the sluggish oxygen evolution reaction (OER) in traditional water electrolysis represents an innovative approach to achieve efficient hydrogen production while concurrently generating value-added chemicals. Herein, we report an energy-saving H2 production system utilizing electrooxidation of glycerol to generate valuable formate. Cobalt–ruthenium oxide nanosheet arrays (CoRuO/CF) and cobalt–ruthenium bimetallic nanoparticles wrapped by nitrogen-doped carbon nanosheet arrays (CoRuCN/CF) were synthesized for glycerol oxidation reaction (GOR) and hydrogen evolution reaction (HER), respectively, using Co-ZIF nanosheets precursor material supported on copper foam (Co-ZIF/CF) as the substrate. Remarkably, the hybrid electrolysis system can achieve a current density of 10 mA cm−2 with only 1.19 V, which is significantly lower than that required by traditional water-splitting systems. This work demonstrates the promising potential of efficiently utilizing biomass energy and achieving a sustainable production process for fine chemical products.

Research on Critical Quality Feature Recognition and Quality Prediction Method of Machining Based on Information Entropy and XGBoost Hyperparameter Optimization

To address the problem of predicting machining quality for critical features in the manufacturing process of mechanical products, a method that combines information entropy and XGBoost (version 2.1.1) hyperparameter optimization is proposed. Initially, machining data of mechanical products are analyzed based on information entropy theory to identify critical quality characteristics. Subsequently, a quality prediction model for these critical features is established using the XGBoost machine learning framework. The model’s hyperparameters are then optimized through Bayesian optimization. This method is applied as a case study to a medium-speed marine diesel engine piston. After the critical quality characteristics in the machining process are identified, the machining quality of these vital characteristics is predicted, and the results are compared with those obtained from a machine learning model without hyperparameter optimization. The findings demonstrate that the proposed method effectively predicts the machining quality of mechanical products.

Cross-contamination Risk Assessment using FMEA tool

In pharmaceutical manufacturing facilities, products with different therapeutic properties and potencies are manufactured in shared spaces, which poses a potential risk for cross-contamination. During the manufacturing process, cross-contamination may occur due to the uncontrolled release of dust, gases/vapors, mix-ups with other materials, residues on equipment, and contamination from operator’s clothing. By implementing adequate facility design and manufacturing operations aligned with Quality Risk Management principles, the risk of cross-contamination can be controlled. If a shared facility is used for manufacturing pharmaceutical products, strategic controls for identifying and managing the risks of cross-contamination should be in place. This article provides a structured and systematic framework for the risk-based identification of the worst-case product and manufacturing process among the products manufactured in the solid oral facility, addressing the high risk of cross-contamination. Keywords: Cross-contamination, Contamination, Risk Assessment, Formal risk assessment, Quality Risk Management (QRM), Failure Mode Effect Analysis (FMEA), Oral Solid Dosage (OSD), Worst case, Contamination Control Strategies (CCS)