Product Failure Research Articles

Progress and optimization of CRISPR-Cas9/12 application in CAR-T therapy

CAR-T therapy and CRISPR/Cas9 gene editing technology have shown great potential in cancer treatment and gene editing, respectively. Combining these two technologies can significantly improve the effect of cancer treatment. However, CAR-T therapy has problems such as complex production, toxicity and functional failure, while CRISPR/Cas9 technology faces challenges such as off-target effects. This study first reviewed the basic principles of CAR-T therapy and CRISPR/Cas9 technology and their current application status, analyzed the feasibility of applying CRISPR/Cas9 technology to CAR-T therapy, including optimization strategies and examples, and focused on how to reduce off-target effects and enhance T cell function and persistence. The results showed that the application of CRISPR-Cas9/12 technology in CAR-T therapy significantly improved the accuracy of gene editing, therapeutic effects and the realization of personalized medicine. Nevertheless, these advances are also accompanied by ethical issues, especially in areas related to human genes. Therefore, future research needs to pay close attention to and address ethical issues while improving therapeutic effects.

Mitigating mitochondrial dysfunction and neuroinflammation by hematoma aspiration in a new surgical model for severe intracerebral hemorrhage.

Intracerebral hemorrhage (ICH) is associated with a large hematoma that causes compression, increased intracranial pressure (IICP), midline shift, and brain herniation, and may ultimately lead to death. Urgent surgical removal of the large hematoma can ameliorate these injuries, which would be life-saving, but has not improved clinical outcome. A suitable animal model that mimics the clinically relevant human severe ICH injury requiring surgical hematoma evacuation is urgently needed. Here, we established a novel model of severe ICH in rats allowing aspiration of the hematoma and studying the effects of mitochondrial dysfunction in ICH. Severe ICH was induced by intra-striatal injection of 0.6 U of collagenase in 3 μl sterile saline over 15 min. Aspiration of approximately 75 % of the total hematoma was performed 6 h after induction of severe ICH. The effects of hematoma aspiration on hematoma volume, mortality, oxidative stress, ATP levels, mitochondrial dysfunction, and neurological function were measured in rats. Severe ICH induction increased hematoma volume, neurological deficits, and mortality. Hematoma aspiration abolished mortality and significantly reduced hematoma volume, and neurological deficits. In addition, hematoma aspiration ameliorated the pronounced mitochondrial dysfunction responsible for the failure of energy production and excessive oxidative stress associated with severe hemorrhagic injury. Hematoma aspiration also modulated mitochondrial biogenesis and mitophagy, thereby promoting mitochondrial homeostasis. Markers of neuroinflammation, including iNOS, MMP9, and MPO, were elevated in severe ICH but attenuated by hematoma aspiration. This study established an animal model of severe ICH and provides valuable insights into the complex pathogenesis of severe ICH. The results showed that hematoma aspiration effectively ameliorates mitochondrial dysfunction, oxidative stress, and neuroinflammation, highlighting its potential as a therapeutic intervention.

Based on Productive Failure Theory Apply in STEM Learning

From the perspective of existing research results, the application of effective failure theory in education has been proven to promote students' conceptual understanding, transfer, and problem-solving abilities. In terms of the scope of research implementation, most studies have focused on single disciplinary approaches to solving fixed answers, with little in-depth research in interdisciplinary fields, especially STEM education. Kapur's four student behaviors need to be adapted and improved to facilitate teachers to construct frameworks more accurately.

Investigating learner characteristics and processes in Productive Failure and Vicarious Failure to design adaptive guidance

BackgroundWe investigate learner characteristics and learning processes in two instructional designs with preparatory activities prior to instruction: Productive Failure (PF) and Vicarious Failure (VF). The aim is to develop a first theoretical understanding of PF and VF learners that can inform directions for the design of adaptive support. MethodsBased on an experimental study with 110 secondary school students in mathematics, we examine the effect of students’ learner characteristics in PF (students generating own solution attempts) and VF (students studying these attempts). We assess the learning processes based on think-aloud data from 15 PF-VF-model-observer-pairs, analyzing frequencies and sequences of cognitive processes, using the HeuristicsMiner algorithm. ResultsMathematical ability was related to PF and VF students' learning, prior knowledge and mathematical self-concept to their learning processes. While both groups showed large overlaps in the frequencies and sequences of learning processes, VF students displayed a more analytical and PF students a more solution-oriented approach to the task. Metacognitive planning and evaluation behavior was relevant for VF students’ learning, depending on prior knowledge and ability. For PF students, it depended on mathematical self-concept. ConclusionsPF and VF students’ learning as well as frequencies and sequences of their learning processes relates to cognitive and affective learner characteristics. Metacognitive planning and evaluation behavior may require adaptive guidance specifically for learners with low mathematical self-concept in PF or prior knowledge and ability in VF.

Crack Control in Additive Manufacturing by Leveraging Process Parameters and Lattice Design.

This study investigates the design of additive manufacturing for controlled crack propagation using process parameters and lattice structures. We examine two lattice types-octet-truss (OT) and diamond (DM)-fabricated via powder bed fusion with Ti-6Al-4V. Lattice structures are designed with varying densities (10%, 30%, and 50%) and process using two different laser energies. Using additive-manufactured specimens, Charpy impact tests are conducted to evaluate the fracture behavior and impact energy levels of the specimens. Results show that the type of the lattice structures, the density of the lattice structures, and laser energy significantly influence crack propagation patterns and impact energy. OT exhibits straighter crack paths, while DM demonstrates more random fracture patterns. Higher-density lattices and increased laser energy generally improve the impact energy. DM consistently outperformed OT in the impact energy for angle specimens, while OT showed superior performance in stair specimens. Finally, a case study demonstrates the potential for combining OT and DM structures to guide crack propagation along predetermined paths, offering a novel approach to protect critical components during product failure.

Designing branching scenarios to support clinical reasoning in dental education

Branching scenarios are educational tools where dental students can explore their decision making in a clinical environment without risking harm to patients. By incorporating productive failure, adaptive learning and serious gaming elements into their design, they offer interactive and personalised learning environments. We aimed to design a branching scenario based on a patient presenting with dental pain and evaluate its implementation in a classroom setting. We employed a validated serious gaming model as the framework for our design and mapped a decision tree on a scalable whiteboard for the case. A survey platform was used to build the scenario using rule-based logic. The scenario included multiple ‘learning points’ with tailored feedback. Students can track how many learning points they have ‘unlocked’ and are encouraged to revisit previously uncovered pathways. Classroom observations, learning analytics and student feedback have been collected to evaluate the activity. Preliminary feedback has informed improvements to the branching scenario design and educators have found the learning analytics can be used to tailor future teaching sessions. Future steps include useability testing and design improvements based on student feedback.

ANÁLISE HAZOP DE UMA UNIDADE DE DESTILAÇÃO ATMOSFÉRICA DE PETRÓLEO E SIMILARIDADE EM PROCESSOS DE PRODUÇÃO DE COMBUSTÍVEIS SUSTENTÁVEIS PARA AVIAÇÃO (SAFs)

The production of petroleum products stands out worldwide, among the production processes of non-renewable energies, as the best known worldwide. In this work, an atmospheric distillation column that processed 100,000 barrels/day (662.5 m3/h) of crude oil was simulated using the CHEMCAD® software. From this procedure, the Process Flow Diagram (PFD) and the Piping and Instrumentation Diagram (PI&D) were obtained for the unit, corresponding to its automation. Subsequently, a HAZOP (Hazard and Operability Study) analysis was conducted in order to identify risks in the unit that, although not dangerous, could compromise its ability to achieve its productivity. The main risks of the process were related to fires, explosions and environmental contamination from leaks and ruptures in pipes, pumps, heat exchangers, the column itself, among other equipment. The HAZOP analysis was able, through the use of the guide words, to identify the possible operational risks arising from deviations in the operating intentions, such as the possibility of fires, explosions, environmental contamination and its consequences. It was possible to identify the risks that are associated with the selection of materials, the mechanical design of the equipment and the specifications of the accessories. Therefore, the study carried out is an important survey to reduce possible failures in the oil industry. And, this study being a deepening of the distillation processes for petroleum products. It is understood that its deepening, as a result of the development of new renewable products, especially sustainable aviation fuels (SAFs), due to being similar purification processes, employ the same methodology to identify possible failures in production.

A Comprehensive Evaluation of Agricultural Drought Vulnerability Using Fuzzy‐AHP‐Based Composite Index Integrating Sensitivity and Adaptive Capacity

ABSTRACTWith increasing extreme weather events, ground water crisis and population expansion, crop stress and production failure have emerged as critical challenges. Agricultural drought vulnerability (ADV) at local and regional scales has become a global concern as it is directly related to food security, hunger issues and poverty. The Kangsabati river basin is one of the major drought‐prone river basin in the eastern India and frequently affected by the reduction of crop production or crop failure because of fluctuation of monsoonal rainfalls, poor irrigation system and harsh edaphic factors. In this context, this study focuses on assessing agricultural vulnerability in the Kangsabati basin using multi‐sensor datasets and geospatial techniques. The ADV has been assessed through multi‐source data sets covering meteorological, agricultural, soil and socio‐economic aspects using a powerful, systematic, and flexible decision‐making fuzzy‐based analytic hierarchy process (fuzzy‐AHP) technique. The ADV index is a functional product of two composite indices: the sensitivity index (SI) and the adaptivity index. The SI is derived from components like the intensity of agricultural drought index, groundwater stress, soil erosion, percentage of cultivators, marginal workers and agricultural land. Adaptive capacity depends upon human, financial, physical, infrastructural and natural capital. Each index was derived considering various factors using fuzzy‐AHP methods for weightage calculation. The composite indices revealed the variation of resource distribution precisely in each geographically distinct zone. The study shows that almost 60% of the highly sensitive zone is situated in the upper basin region characterised by undulating lands. A large part of the entire basin (48%) is moderately drought‐sensitive. The result also shows that a significant part (35%) of the upper and middle basin is highly vulnerable to agricultural drought. In contrast, the lower basin exhibits low to very low levels of vulnerability to drought. The results indicate that even though some areas are moderate to less sensitive, the vulnerability of agricultural drought has become high due to their limited adaptive capacity. The comprehensive framework developed for assessing ADV has the potential for region‐specific policy implementation and sustainable growth.

A Method for Predicting the Remaining Service Life of Finite Data Products Based on Gaussian Stochastic Processes

There is a problem of poor predictive performance when predicting the remaining service life of products. Therefore, this paper proposes a finite data product remaining service life prediction method based on Gaussian stochastic processes. Analyze the types of product failure and degradation under limited data, and determine the factors affecting the degradation performance of product life through constant stress accelerated degradation tests, step stress accelerated degradation tests, and sequential stress accelerated aging tests; using the Wiener process to analyze the performance degradation characteristics of products, setting product failure thresholds, defining the remaining life of the product based on the degradation process, and using equal interval partitioning methods to determine the product health index, and clarifying the product failure process under limited data; by calculating the mathematical expected value, the numerical characteristics of the remaining life of the product are defined. Variance and moments are used to determine the predicted second-order central moment and origin moment. Based on the monotonic increasing characteristics of the inverse Gaussian process, the probability distribution of the random variable of product failure is determined after the Gaussian process. Under limited data, the actual working time of the product and the degradation amount at the time scale are clarified, and a preset threshold is set for the degradation amount. Build a residual life prediction model for product degradation and output the prediction results. The experimental results show that this method can accurately predict the remaining service life of the product and has good predictive performance.

A Review of the Technological Aspects and Process Optimization of Bioethanol Production From Corn Stover Biomass: Pretreatment Process, Hydrolysis, Fermentation, Purification Process, and Future Perspective

ABSTRACTBioethanol, a sustainable energy solution derived from renewable biomass, has gained prominence, with corn stover emerging as a substantial biomass resource in Indonesia. Corn stover, a corn residue, is one of the top three agricultural wastes worldwide and is abundantly available. However, a significant portion of corn stover is burned in fields rather than utilized for bioethanol production, whereas it has potential as a bioethanol feedstock. As the world strives to realize sustainable and environmentally friendly energy security, bioethanol production from corn stover can be one of the solutions to be developed. Nonetheless, the current immaturity of bioethanol production technology is one of the causes of large‐scale production failure. The present paper comprehensively reviews the technological aspects and process optimization of bioethanol production using corn stover as a feedstock comprising pretreatment, hydrolysis, fermentation, and bioethanol purification processes. According to our critical review, ammonia fiber expansion (AFEX) pretreatment is the most effective conventional pretreatment, with glucose yield up to 90%. Moreover, ultrasound appears to be the most viable option for nonconventional pretreatment of corn stover for producing bioethanol. However, combining ultrasound pretreatment and dilute aqueous ammonia produced 80.6% sugar output. Furthermore, enzymatic hydrolysis emerges as the most effective saccharification, yielding up to 81.39%. Moreover, the fermentation process of corn stover with the saccharification and co‐fermentation (SScF) method and the process optimization with response surface methodology (RSM) could produce bioethanol with a concentration of up to 59.8 g/L and 92.07% ethanol yield, respectively. This review also reveals that pervaporation for the purification process is the best choice for producing bioethanol with high purity up to > 99%. In addition, this method could reduce the energy used by 6.6% lower, 24.2% lower carbon footprint, and have the lowest total capital and production costs compared to conventional molecular sieves and extractive distillation. We believe this review article can provide a reference for selecting the best bioethanol production process from corn stover for further research.

Decomposing the Cognition-influential Aspects of Instructional Designs A Systematic Review of Induced Causal Relationships by the Constructional Components of the Direct Instruction and Productive Failure Designs

A substantial debate on the effectiveness of two instructional designs, namely “Productive Failure (PF)” and “Direct Instruction (DI)” has been prolonged for almost two decades. Indeed, from many studies we have seen if being deliberately designed and carried out, then both designs can be sophisticated not only in instructional procedures but also in how each function takes effect on learning. The constituting components could each be specified preceding the instruction as a design, but these activities carry its own goal in facilitating learning. In this article, we summarized the reasoning in the specifications of connection, construction and criticization. Thus, from our review, we debated how instructional stages, problem-led learning and failure-driven learning can each deliver good outcomes for learners.

The Competition between Productive Failure and Direct Instruction Is not Determinable: A Review of the Deviations and Reasons

Teaching the learner new information demands careful implementation. The implementational protocols are derived from instructional ideologies and purpose of the design. It not only requires the instructors to grasp the essence of instructional designs without an extemporaneous amending or exchanging of the theoretically proven meaningful teaching events, but also, a precise actualization of structure and approach as well as its behind ideology is demanded. Those rationales and references for instructors to determine when and how to implement instruction, what practice or what strategies are needed to be taught ought to be precise enough to separately explain their advantages and prediction of outcomes in learning. In this article, we explored how both problem-proceeding learning and guidance-proceeding learning are beyond control regarding the deviated cognitive processing those instructional procedures can possibly elicited.

Genotype‐phenotype associations in individuals with Diamond Blackfan anaemia

AbstractIntroductionDiamond Blackfan anaemia (DBA) is a rare disorder characterized by failure of red blood cell production, congenital abnormalities and cancer predisposition, primarily caused by pathogenic germline variants in genes encoding ribosomal proteins.MethodsWe conducted a genotype‐phenotype and outcome study of 121 patients with DBA spanning the 20‐year history of the National Cancer Institute's Inherited Bone Marrow Failure Syndromes study. Patient phenotypes were compared by large versus small ribosomal protein genes, across genes with >5 cases (RPS19, RPS29, RPS26 and RPL35A) and by type of pathogenic variants (hypomorphic versus null, large deletions versus others).ResultsA pathogenic germline variant was identified in 71% of patients (n = 86/121) from 54 families. After adjusting for multiple testing, we found that patients with RPS29 variants were least likely to need treatment for anaemia while those with large ribosomal protein subunit variants had a higher proportion of intellectual disability and gastrointestinal abnormalities compared with small ribosomal protein subunit variants (p < 3.5 × 10−4). There were no statistically significant differences in overall survival or cancer incidence among patients with large or small ribosomal subunit genes.ConclusionThis detailed genotype‐phenotype study of DBA improves our understanding of the role of germline genetics in the clinical manifestations that may help guide the management of people with DBA.

Retraction Note: Intelligent root cause detection in Industry 4.0: a secure cross-validation of blockchain architecture for product failure checker

Retraction Note: Intelligent root cause detection in Industry 4.0: a secure cross-validation of blockchain architecture for product failure checker

Defining quality by quantifying degradation in the mechanical recycling of polyethylene

Polyolefins have a multitude of uses across packaging, automotive and construction sectors. Their resistance to degradation during reprocessing enables recyclability, but variability in recycled polymer feedstocks renders it difficult to assure their manufacturing suitability. The lack of quality control methods has disabled circular economy pathways; product failure is costly, wasteful and time-intensive. Using rheology-simulated and extrusion-based recycling experiments, we explore the degradation pathways of high-density polyethylene (HDPE). Chain scission dominates during the initial degradation of HDPE, and increasing exposure to O2 shifts the dominant mechanism to long-chain branching. Importantly, extending this method to post-consumer recyclate (PCR), the results show potential as a methodology to assess recyclate quality to enable a circular plastics economy. In this study, we establish the validity of this rheology simulation to define a characteristic degradation parameter, relating it to the structural evolution under different environments defined for virgin HDPE and post-consumer recyclate (PCR).

The strength of stance: The impact of brand activism on resistance to negative information, purchasing, and premium paying intents across different types of failures

This paper represents a pioneering exploration of how brand activism influences consumer responses to performance and value-related failures. Specifically, across two experiments, the research explores how brand activism influences consumer resistance to negative information, purchase intent, and willingness to pay more, in response to product, service, and value-related failures. Study 1 (N = 237) reveals that consumers who are more involved in activism and are exposed to a product failure from activist brands exhibit higher resistance to negative information compared to those who encounter product failures from non-activist brands. This suggests that activist branding may protect companies by cultivating a resilient, loyal consumer base in such adverse situations. Study 2 (N = 261) extends these findings by examining whether value-related (vs service-related) failures impact the buffering effect generated by a brand’s activist stance. Results indicate that activist brands particularly benefit from a protective buffer in the context of service failure scenarios. However, this buffer does not extend to value-related failures, leading to harsher consumer judgments and intentions. The research significantly contributes to brand activism literature by uniquely integrating the themes of brand activism and brand transgressions, exploring how brand activism affects consumer responses to both performance and value-related failures.

Transgenic zebrafish as a model for investigating diabetic peripheral neuropathy: investigation of the role of insulin signaling.

Diabetic peripheral neuropathy (DPN), a complication of diabetes mellitus (DM), is a neurodegenerative disorder that results from hyperglycemic damage and deficient insulin receptor (IR) signaling in peripheral nerves, triggered by failure of insulin production and insulin resistance. IR signaling plays an important role in nutrient metabolism and synaptic formation and maintenance in peripheral neurons. Although several animal models of DPN have been developed to identify new drug candidates using cytotoxic reagents, nutrient-rich diets, and genetic manipulations, a model showing beneficial effects remains to be established. In this study, we aimed to develop a DPN animal model using zebrafish to validate the effects of drug candidates on sensory neuropathy through in vivo imaging during the early larval stage. To achieve this, we generated Tg (ins:gal4p16);Tg (5uas:epNTR-p2a-mcherry) zebrafish using an enhanced potency nitroreductase (epNTR)-mediated chemogenetic ablation system, which showed highly efficient ablation of pancreatic β-cells following treatment with low-dose metronidazole (MTZ). Using in vivo live imaging, we observed that sensory nerve endings and postsynaptic formation in the peripheral lateral line (PLL) were defective, followed by a disturbance in rheotaxis behavior without any locomotory behavioral changes. Despite defects in sensory nerves and elevated glucose levels, both reactive oxygen species (ROS) levels, a primary cause of DPN, and the number of ganglion cells, remained normal. Furthermore, we found that the activity of mTOR, a downstream target of IR signaling, was decreased in the PLL ganglion cells of the transgenic zebrafish. Our data indicates that peripheral neuropathy results from the loss of IR signaling due to insulin deficiency rather than hyperglycemia alone.

Integrating risk management in implementing circular economy principles in the healthcare sector: a case study from Indonesia

Purpose The purpose of this study is to explore the integration of risk management and circular economy (CE) principles within the healthcare sector to promote sustainability and resilience. Specifically, the study aims to demonstrate how risk management can support the transition to a circular economy in healthcare supply chains. By integrating risk management practices with CE principles, healthcare organizations can identify potential risks and opportunities associated with circular initiatives. Design/methodology/approach This study adopts a qualitative research approach, using a case study methodology with semi-structured interviews conducted at primary care facilities to understand the application of CE principles in practice. The study uses fuzzy logic methods to assess and mitigate risks associated with strategies promoting CE principles. Additionally, key performance indicators are identified to evaluate the effectiveness and enhance the resilience of these strategies within healthcare supply chains. Findings The study highlights the critical role of robust risk management strategies in facilitating the transition to a circular economy within healthcare organizations. Primary care facilities, which are critical to frontline healthcare delivery, are particularly vulnerable to product shortages due to supply risks. This study focuses on critical protective equipment, specifically latex gloves and assesses operational risks, including supply, demand and environmental risks, using a fuzzy logic-based model. Import delays were found to be a moderate risk, typically occurring once a year. The research highlights critical KPIs for a successful CE transition within healthcare supply chains, such as on-time delivery and service quality, which are directly related to the risk of supply chain disruption. In addition, the study highlights the significant impact of other CE strategies on healthcare supply chains, including localized production and manufacturing, innovation in product development, reverse logistics, closed-loop supply chains and the adoption of lean principles. Practical implications This study provides valuable insights for healthcare organizations to optimize resource efficiency, reduce waste and promote circularity in their operations. By implementing the proposed solutions and focusing on the identified KPIs, organizations can develop strategies to achieve sustainability goals and enhance resilience in healthcare supply chains. Originality/value This study contributes to the literature by demonstrating the application of risk management in facilitating the transition to a circular economy in the healthcare sector. The use of fuzzy logic methodology offers a novel approach to assessing and mitigating risks associated with critical product failures in supply chain activities. The study’s findings provide practical guidance for healthcare organizations seeking to integrate circular economy principles and improve sustainability performance.

Mathematical modelling of induction motors taking into account design-parameter asymmetry

Due to their numerous technical and economic advantages, induction machines (IM) with squirrel-cage rotors are important components of many industrial processes. Their reliability, durability, and ease of operation make them indispensable in various industries, ensuring stable and efficient equipment performance. However, to minimise the risk of costly production failures caused by sudden stoppages, it is important to implement effective diagnostic and monitoring methods. The most common type of fault in squirrel-cage induction motors is the breakage of cage bars and end ring segments, particularly in motors with high inertia loads and frequent stops and starts. Regular maintenance and the implementation of modern control systems in the industry will help ensure the reliable and uninterrupted operation of these machines. In the technical literature, various diagnostic methods have been proposed. Some are based on analysing data collected from the induction motor (IM), detecting characteristic perturbations that indicate faults in the machine. Other methods involve comparing the data obtained from the actual induction motor with its digital model. It is clear that accurate and adequate IM models are necessary for the development and optimisation of fault diagnosis methods. This paper focuses on developing a mathematical model of an induction motor (IM) in a stator-fixed coordinate system, where the squirrel-cage rotor of the IM is represented as a multiphase, symmetrically distributed winding system in space. The adequacy of the obtained model is demonstrated. The developed model allows for the analysis of various types of asymmetries in both stator and rotor of squirrel-cage induction motors

Machine learning technologies for predicting and preventing production failures

Machine learning technologies for predicting and preventing production failures