Product Failure Research Articles

Development of Biopredictive Dissolution Method for Extended-Release Desvenlafaxine Tablets.

This study aimed to develop a biopredictive dissolution method for desvenlafaxine ER tablets using design of experiments (DoE) and physiologically based biopharmaceutics modeling (PBBM) to address the challenge of developing generic drug products by reducing the risk of product failure in pivotal bioequivalence studies. For this purpose, a PBBM was developed in GastroPlus® and combined with a Taguchi L9 design, to evaluate the impact of different drug products (Reference, Generic #1 and Generic #2) and dissolution test conditions on desvenlafaxine release. The influence of the superficial area/volume ratio (SA/V) of the tablets was observed, mainly for Generic #1, which presented higher SA/V than the others, and a high amount of drug dissolved under similar test conditions. The dissolution test conditions of 900 mL of 0.9% NaCl and paddle at 50 rpm with sinker showed to be biopredictive, as it was possible to demonstrate virtual bioequivalence for all products, despite their release-pattern differences, including Generic #3 as an external validation. This approach led to a rational development of a biopredictive dissolution method for desvenlafaxine ER tablets, providing knowledge that may help the process of drug product and dissolution method development.

Validation of Artificial Intelligence Containing Products Across the Regulated Healthcare Industries.

The introduction of artificial intelligence / machine learning (AI/ML) products to the regulated fields of pharmaceutical research and development (R&D) and drug manufacture, and medical devices (MD) and in vitro diagnostics (IVD), poses new regulatory problems: a lack of a common terminology and understanding leads to confusion, delays and product failures. Validation as a key step in product development, common to each of these sectors including computerized systems and AI/ML development, offers an opportune point of comparison for aligning people and processes for cross-sectoral product development. A comparative approach, built upon workshops and a subsequent written sequence of exchanges, is summarized in a look-up table suitable for mixed-teams work. 1. A bottom-up, definitions led, approach which leads to a distinction between broad vs narrow validation, and their relationship to regulatory regimes. 2. Common basis introduction to the primary methodologies for software validation, including AI-containing software validation. 3. Pharmaceutical drug development and MD/IVD-specific perspectives on compliant AI software development, as a basis for collaboration. Alignment of the terms and methodologies used in validation of software products containing artificial intelligence/machine learning (AI/ML) components across the regulated industries of human health is a vital first step in streamlining processes and improving workflows.

Sensitive product feature integrity and confidentiality using blockchain-based internet of things (IoT) architecture

Small-scale third-party accessories vendors face numerous challenges such as (i) warranty period covered for sensitive product, (ii) malicious induction, and (iii) extract root cause of product failure after customer experienced in the real time usage. Therefore, an advanced industrial environment is necessary to resolve these limitations. It offers strong security levels as well as intelligent root cause detection over failure. In this work, propose a blockchain-based internet of things (IoT) architecture for industry 4.0, which includes a voting decision mechanism for IoT device authentication and fault classification based on device and product trust values, respectively. These values are regularly updated in the historical trust value and machinery database for every successful product transaction and identify a new failure cause to set feature for next iteration set from the defective industrial cloud database. The analytical model and simulation results for correct authentication ensure that the proposed architecture achieves 86% eligibility of accuracy across a high ratio of malicious attacks in less converged time.

Software Failure Log Analysis for Engineers—Review

The use of automated methods for log analysis is unavoidable in any large company; therefore, it has attracted attention from engineers and researchers. As a result, the number of articles in the field grows yearly and new approaches are frequently proposed. Unfortunately, published research works only sometimes meet the needs of engineers wishing to apply the methods in real-life systems. A common issue is that the method’s benefits often do not compensate for the effort required for its implementation and maintenance. Therefore, engineers must understand the pros and cons of full-scale applications, including the implementation details and the required effort. This work provides a comprehensive review of automated log analysis methods and aims to offer a guide for software engineers who fix integration and production failures. The article categorizes and provides an overview of existing methods and assesses their implementation and maintenance costs, as well as the feasibility of the methods. The article also identifies and describes the shortcomings of existing methods, including concept drift, which is not addressed with sufficient attention, as well as the lack of online benchmarks and the interpretation of the log sequence as a language, without an in-depth analysis of its properties. Despite growing efforts to provide feasible and widely adopted solutions, many reference implementations are unavailable. Consequently, the time and computation complexities differ between various implementations of the same approaches, making the results of research work difficult to replicate in real-life scenarios.

Prior math achievement and inventive production predict learning from productive failure

A frequent concern about constructivist instruction is that it works well, mainly for students with higher domain knowledge. We present findings from a set of two quasi-experimental pretest-intervention-posttest studies investigating the relationship between prior math achievement and learning in the context of a specific type of constructivist instruction, Productive Failure. Students from two Singapore public schools with significantly different prior math achievement profiles were asked to design solutions to complex problems prior to receiving instruction on the targeted concepts. Process results revealed that students who were significantly dissimilar in prior math achievement seemed to be strikingly similar in terms of their inventive production, that is, the variety of solutions they were able to design. Interestingly, it was inventive production that had a stronger association with learning from PF than pre-existing differences in math achievement. These findings, consistent across both topics, demonstrate the value of engaging students in opportunities for inventive production while learning math, regardless of prior math achievement.

Multi-level predictive maintenance of smart manufacturing systems driven by digital twin: A matheuristics approach

Digital twin technology is gradually being applied to smart manufacturing systems and is providing valuable information for predictive maintenance of swarms of machines, but also raises the need for more accurate and real-time decision making. However, there is still a shortage of research in this area. This paper proposes a general multi-level predictive maintenance decision-making framework driven by digital twin, considering component dependencies, the variable time scale of decisions, and comprehensive maintenance resources, in which an optimal maintenance schedule can be obtained in real time and then fed back to the physical space, so as to realize closed-loop control. A maintenance decision-making optimization model is then formulated based on integer linear programming to minimize total maintenance costs while meeting required production capacity. Further, a novel matheuristics algorithm (i.e., the interoperation of metaheuristics and mathematical programming techniques) is introduced for various maintenance decision scenarios. Finally, a case study of an offshore oil and gas production system consisting of eight subsea Christmas trees is examined, and the effects of changes in production capacity, failure thresholds, and maintenance resources on the multi-level optimization of decision-making solutions are discussed.

Assessing farmers’ perception based composite drought vulnerability in Godavari Middle Sub-basin, India

Climate change has increased the frequency and intensity of drought in various parts of the world. Godavari Middle Sub-basin of India has been experiencing significant change in rainfall, maximum and minimum temperature. Farmers of Sub-basin depend on mercy of rainfall for their cultivation. Occurrence of drought lead to farmers suicide due to crop failure and losses of agricultural production. Therefore, composite drought vulnerability assessment is essential in the Sub-basin. This paper makes an initial attempt to quantify composite vulnerability to drought using socio-economic and environmental indicators at household level. Stratified random sampling was utilized to collect primary data. A total number of 450 households were surveyed. The composite vulnerability index calculation was based on 5 domains namely economic, social, environmental, ecological, adaption and mitigation strategies. Results revealed that highest drought vulnerability was found in marginal farmers among all the types of farmers due to high climate change vulnerability and low adaptation strategies. Low level of education, low annual income and high dependency on local money lenders were the main causes of high drought vulnerability for marginal farmers. High adaptation strategies helped large farmer to mitigate the impact of drought. The adaptations and mitigation strategies encompassed change in crops, diversification of crops, keeping land unsown after the possibility of drought, change in traditional irrigation practices to sprinkler and drip irrigation, change in crop calendar and sowing of drought-tolerant crops. Community-based preparedness planning could be effective to mitigate the impact of climate change induced drought vulnerability in Godavari Middle Sub-basin.

Debt enforcement, financial leverage, and product failures: Evidence from China and the United States

AbstractBuilding on capital structure and product market interactions, and the role of debt enforcement in leveraged firms' investments, we examine whether cross‐country debt enforcement can produce different associations between financial leverage and product failures. Results show that different debt enforcement systems can generate opposite leverage effects. In countries with weak/nearly ineffective debt enforcement, financial leverage shows an incentive investment effect due to low default costs, and thus highly leveraged firms tend to invest more and are less likely to have product failures. Conversely, in countries with strict/effective debt enforcement, distressed companies tend to have an underinvestment effect and more product failures.

Bioinsecticidal activity of cajeput oil to pyrethroid-susceptible and -resistant mosquitoes

Mosquito-borne diseases are a significant threat to human health. The frequent and repetitive application of insecticides can result in the selection of resistant mosquito populations leading to product failures for reducing community disease transmission. It is important that new interventions are discovered and developed for reducing mosquito populations and, in turn, protecting human health. Plant essential oils are promising chemical interventions for reducing mosquito populations. The myrtle family, Myrtaceae, has numerous species to be studied as potential bioinsecticides. Here, we combined toxicological, biochemical, and neurophysiological approaches to provide evidence for cajeput oil and terpene constituents to elicit bioinsecticidal activity to pyrethroid-susceptible and -resistant Aedes aegypti. We show cajeput oil terpenes to enhance cAMP production, increase ACh levels, inhibit in vivo and in vitro AChE activity, and disrupt spike discharge frequencies of the mosquito CNS. This study presents the first report on the bioinsecticidal activity of cajeput oil terpenes to pyrethroid-susceptible and -resistant mosquitoes and provides comparative data for the octopaminergic system as a putative molecular target for the bioinsecticides with implications for resistance management.

Price and quality competition while envisioning a quality-related product recall

Many product recalls are caused by quality-related product failures. When such recalls occur, the effects may not only be limited to the firm selling the product but also extend to competing firms in the category. This paper analyzes quality and pricing strategies for competing firms facing the risk of a severe quality-related recall making the product hazardous and leading to its removal from the market. We develop a two-stage Nash game where the probability of recall depends on the firms’ chosen quality investments, and either firm can experience a recall. We consider a utility-based model where consumers’ sensitivity to price and quality can change following the recall. Our results indicate that the competitor should lower its price after a recall if consumers’ price sensitivity changes enough and may increase or keep its price the same otherwise. Surprisingly, considering the risk of a recall does not always lead firms to enhance their product quality. If the change in consumer quality sensitivity is low enough, firms adopt an inferior product quality level than when they overlook the product recall risk, even if consumer quality sensitivity increases and/or consumer price sensitivity decreases after the recall. These results can help companies plan their pricing and quality decisions in competitive industries with potential product quality failures leading to recalls.

The post-heroism of Stuur Groete aan Mannetjies Roux and Verraaiers

There is much scholarship on the linkages between Afrikaner nationalism and South African (Afrikaans-language) filmmaking. Within the context of a sustained post-apartheid renegotiation of Afrikaans or Afrikaner nationalism in the popular imagination, in this article we argue that two feature film historical dramas from the production company Bosbok Ses Films, Stuur groete aan Mannetjies Roux (2013) and Verraaiers (2013), resonate thematically and aesthetically with Thomas Elsaesser’s notion of post-heroic cinema. While a number of pre-1994 Afrikaans-language films celebrated Afrikaner nationalism as personified in the figure of the hero, this article positions and uses Elsaesser’s post-heroism as a critical lens through which to demonstrate the ways in which these two films call attention to a post-hero whose actions and behaviour (often inadvertently) renders a productive renegotiating of the hero figure within a post-apartheid cinematic context. To supplement Elsaesser, we also draw on Johan Degenaar’s writing on political pluralism. In this article, we find that an Elsaesserian post-heroic approach to the two films allows the following constitutive components of post-heroic cinema to surface: atemporality as opposed to linear narrative time, parapraxis (productive failure) as opposed to traditional iterations of heroic acts and valour, and conceiving of the film screen as a surface in flux as opposed to the screen as a mirror. The article’s contribution to existing scholarship on contemporary Afrikaans-language cinema is three-fold: it is the first to utilise an Elsaesserian approach to Afrikaans film and as such to foreground and investigate the figure of the post-hero, it provides a critical account of two independently-made feature films that remain under-researched in current South African film scholarship, and it contributes to discourse around the ways in which popular media inform and respond to the renegotiation of Afrikaans (or Afrikaans) identity.

Understanding the role of magnesium stearate in lowering punch sticking propensity of drugs during compression

The sticking of active pharmaceutical ingredient (API) to the surfaces of compaction tooling, frequently referred to as punch sticking, causes costly downtime or product failures in commercial tablet manufacturing. Magnesium stearate (MgSt) is a common tablet lubricant known to ameliorate the sticking problem, even though there exist exceptions. The mechanism by which MgSt lowers punch sticking propensity (PSP) by covering API surface is sensible but not yet experimentally proven. This work was aimed at elucidating the link between PSP and surface area coverage (SAC) of tablets by MgSt, in relation to some key formulation properties and process parameters, namely MgSt concentration, API loading, API particle size, and mixing conditions. The study was conducted using two model APIs with known high PSPs, tafamidis (TAF) and ertugliflozin-pyroglutamic acid (ERT). Results showed that PSP decreases exponentially with increasing SAC by MgSt. The composition of material stuck to punch face was also explored to better understand the onset of punch sticking and the impact of possible MgSt-effected punch conditioning event.

The effect of long-term spaceflight on drug potency and the risk of medication failure

Pharmaceuticals selected for exploration space missions must remain stable and effective throughout mission timeframes. Although there have been six spaceflight drug stability studies, there has not been a comprehensive analytical analysis of these data. We sought to use these studies to quantify the rate of spaceflight drug degradation and the time-dependent probability of drug failure resulting from the loss of active pharmaceutical ingredient (API). Additionally, existing spaceflight drug stability studies were reviewed to identify research gaps to be addressed prior to exploration missions. Data were extracted from the six spaceflight studies to quantify API loss for 36 drug products with long-duration exposure to spaceflight. Medications stored for up to 2.4 years in low Earth orbit (LEO) exhibit a small increase in the rate of API loss with a corresponding increase in risk of product failure. Overall, the potency for all spaceflight-exposed medications remains within 10% of terrestrial lot-matched control with a ~1.5 increase in degradation rate. All existing studies of spaceflight drug stability have focused primarily on repackaged solid oral medications, which is important because non-protective repackaging is a well-established factor contributing to loss of drug potency. The factor most detrimental to drug stability appears to be nonprotective drug repackaging, based on premature failure of drug products in the terrestrial control group. The result of this study supports a critical need to evaluate the effects of current repackaging processes on drug shelf life, and to develop and validate suitable protective repackaging strategies that help assure the stability of medications throughout the full duration of exploration space missions.

Inclusive diets within planetary boundaries

Inclusive diets within planetary boundaries

Key Failure Mode Identification Method of LED System Based on Fuzzy TOPSIS

The premise of the system reliability analysis is to effectively discern the patterns of product failures at all levels. To deal with the problem that the diversity of failure modes of complex products represented by LED systems cannot be analyzed comprehensively, in this paper, a key failure mode identification method based on Fuzzy TOPSIS is proposed. First, the construction method of the failure relevance tree is given to support the comprehensive identification of potential failure modes. Then, the decision rule of node failure weight is mined by the rough set method, the systematic evaluation of the node failure weight is realized with the TOPSIS method, and the key failure modes are identified. Finally, a case validation of the proposed method in this paper is performed.

Challenges and opportunities to build quantitative self-confidence in biologists

Abstract New graduate students in biology programs may lack the quantitative skills necessary for their research and professional careers. The acquisition of these skills may be impeded by teaching and mentoring experiences that decrease rather than increase students’ beliefs in their ability to learn and apply quantitative approaches. In this opinion piece, we argue that revising instructional experiences to ensure that both student confidence and quantitative skills are enhanced may improve both educational outcomes and professional success. A few studies suggest that explicitly addressing productive failure in an instructional setting and ensuring effective mentoring may be the most effective routes to simultaneously increasing both quantitative self-efficacy and quantitative skills. However, there is little work that specifically addresses graduate student needs, and more research is required to reach evidence-backed conclusions.

A probabilistic uncertain linguistic FMEA model based on the extended ORESTE and regret theory

As a means of identifying, preventing, and controlling potential system failures, failure mode and effect analysis (FMEA) has been extensively applied in many industries. However, the classical FMEA method has several defects in actual situations, such as being unable to express uncertain information, overlooking the weights of FMEA experts and risk factors (RFs), and without taking into account the experts’ psychological behaviors. To break through these constraints, a novel FMEA model based on probabilistic uncertain linguistic term sets (PULTSs), Organísation, rangement et Synthèse de données relarionnelles (in French) (ORESTE) and regret theory (RT) is proposed in this paper. First, PULTSs are introduced to express FMEA experts’ assessments under uncertainty. Then, the maximizing consensus model with PULTSs context (PUL-MCM) is constructed to obtain the weights of FMEA experts. Moreover, direct assessment weighting and maximizing deviation method (MDM) are utilized to determine the subjective and objective weights of RFs respectively, so as to reflect the importance of RFs comprehensively. Afterward, the extended ORESTE and RT are combined to prioritize FMs, which not only considers the psychological behaviors of experts and the detailed relations between failure modes (FMs) but avoids the information loss caused by the defuzzification of subjective weights of RFs. Finally, an example of the pasting process failures in lead-acid battery production is presented to illustrate the feasibility and rationality of the novel FMEA model, while sensitivity and comparative analyses are also performed to further demonstrate the flexibility and superiority of the novel FMEA model.

Screw Absence Classification on Aluminum Plate via Features Based Transfer Learning Models

Screw is one of the important elements in every industry. Present of screw play an important role in which it holds the product in its own position and prevent loosen or collision with the case which will cause the small components or compartment fall off from its original position and lead to product failure. With the rise of revolution 4.0 in the industry, it helps to reduce the labor cost and human error. The main purpose of this study is to create a robust classification model used for machine vision detection – absence and present of screw, which could be adapted into respective robotics application system. 6 degree of freedom UR robot, Universal Robot is used to collect the custom dataset in TT Vision Technologies Sdn Bhd. The collected dataset is then classified into two categories, named as absent and present. Pretrained dataset, ImageNet is used to ease the training process in this research. Transfer learning model is used to extract the features which used to feed into different machine learning models. Each machine learning models undergoes hyperparameters tunning to achieve best classification accuracy. Samling ratio of 60:20:20 is used to separate the data in training, validation and testing respectively before fed into different ml models

Risk Management-Obstetrics and Gynecology Perspective.

The Obstetrics and Gynecology physician's likelihood to experience medical malpractice claims are higher than in other medical specialties. We will review the basic principles of health care risk management, the role of the risk manager, and the importance of health care risk management in risk mitigation for obstetrics and gynecology physicians. Attention is focused on medical record documentation, disclosure of adverse events, second victim programs, grievance management techniques, alternative dispute resolution concepts, regulatory inquiries including state licensure investigations, product failures, and electronic media strategies. Concluding, health care risk management may be used as a claim avoidance tool and provider protective vehicle for physicians.

Cross‐version defect prediction using threshold‐based active learning

AbstractBecause defects in software modules (e.g., classes) might lead to product failure and financial loss, software defect prediction enables us to better understand and control software quality. Software development is a dynamic evolutionary process that may result in data distributions (e.g., defect characteristics) varying from version to version. In this case, effective cross‐version defect prediction (CVDP) is not easy to achieve. In this paper, we aim to investigate whether the defect prediction method of the threshold‐based active learning (TAL) can tackle the problem of the different data distribution between successive versions. Our TAL method includes two stages. At the active learning stage, a committee of investigated metrics is constructed to vote on the unlabeled modules of the current version. We pick up the unlabeled module with the median of voting scores to domain experts. The domain experts test and label the selected unlabeled module. Then, we merge the selected labeled module and the remaining modules with pseudo‐labels from the current version into the labeled modules of the prior version to form enhanced training data. Based on the training data, we derive the metric thresholds used for the next iteration. At the defect prediction stage, the iterations stop when a predefined threshold is reached. Finally, we use the cutoff threshold of voting scores, that is, 50%, to predict the defect‐prone of the remaining unlabeled modules. We evaluate the TAL method on 31 versions of 10 projects with three prevalent performance indicators. The results show that TAL outperforms the baseline methods, including three variations methods, two common supervised methods, and the state‐of‐the‐art method Hybrid Active Learning and Kernel PCA (HALKP). The results indicate that TAL can effectively address the different data distribution between successive versions. Furthermore, to keep the cost of extensive testing low in practice, selecting 5% of candidate modules from the current version is sufficient for TAL to achieve a good performance of defect prediction.