Potential Flaws Research Articles

Analysis of the Impact of the Net Zero Movement on International Trade

The net-zero carbon emission mission raised by UNFCCC accelerated the already looming shortage of various key metals in the construction of renewable energy facilities, such as copper for the construction of solar panels. Using basic econometric tools through Stata, this paper studied the effect on copper prices related to the net-zero campaign, aiming at both establishing the validity of such an effect and quantifying its scale. Relevant data from FRED demonstrates that this effect drives up copper price at a high significance level and quantifies it to be 2440.636 USD per metric ton. This effect also seems to be lagging into future periods, and a staggered events analysis suggests that the growth in copper price due to the net-zero campaign will decelerate in the near future. Potential flaws are discussed, and several valid and valuable future research directions are also raised.

GLOSSARY FOR DENTAL PATIENT-CENTERED OUTCOMES

Dental patient-centered outcomes can improve the relevance of clinical study results to dental patients and generate evidence to optimize health outcomes for dental patients. Dental patient-reported outcomes (dPROs) are of great importance to patient-centered dental care. They can be used to evaluate the health outcomes of an individual patient about the impact of oral diseases and treatment, and to assess the quality of oral health care delivery for a health care entity. dPROs are measured with dental patient-reported outcome measures (dPROMs). dPROMs should be validated and tested before wider dissemination and application to ensure that they can accurately capture the intended dPROs. Evidence suggests inadequate dPRO usage among dental trials, as well as potential flaws in some existing dPROMs. This Glossary presents a collection of main terms in dental patient-centered outcomes to help clinicians and researchers read and understand patient-centered clinical studies in dentistry.

Study on the interlayer bonding state of an asphalt pavement based on the stacking peak ratio method

The interlayer bonding of an asphalt pavement significantly influences the mechanical properties and long-term durability of the pavement structure. In order to develop a rapid and comprehensive evaluation method for assessing the interlayer condition of asphalt pavements using 3D GPR, we propose the stacking peak ratio (SPR) method. This method involves stacking the ratio between the amplitude peak of the interlayer and that of surface reflection to evaluate the interlayer state. Comparative analysis with direct shear tests and field measurements leads us to draw several conclusions: the optimal test parameters for the SPR method are a stacking peak ratio at 35 ns with a residence time of 1 us; the SPR method is compatible with various bonding materials and pollution layers, demonstrating its ability to effectively assess the bonding state of asphalt pavements; field tests further validate that the SPR approach can identify insufficient layer bonding and predict potential flaws in advance. Through our test findings and data analysis, it is evident that this SPR approach provides theoretical support and technological assistance for promptly evaluating the bonding status in asphalt pavements.

Tools and Process of Defect Detection in Automated Manufacturing Systems

INTRODUCTION: A range of tools and technologies are at disposal for the purpose of defect detection. These include but are not limited to sensors, Statistical Process Control (SPC) software, Artificial Intelligence (AI) and machine learning (ML) algorithms, X-ray systems, ultrasound systems, and eddy current systems.
 OBJECTIVES: The determination of the suitable instrument or combination of instruments is contingent upon the precise production procedure and the category of flaw being identified. In certain cases, defects may necessitate real-time monitoring and analysis through the use of sensors and SPC software, whereas more comprehensive analysis may be required for other defects through the utilization of X-ray or ultrasound systems.
 METHODS: The utilization of AI and ML algorithms has gained significant traction in the realm of defect detection. This is attributed to their ability to process vast amounts of data and discern patterns that may have otherwise eluded detection. The aforementioned tools have the capability to anticipate potential flaws and implement pre-emptive measures to avert their occurrence. 
 RESULTS: The detection of defects in automated manufacturing systems is a continuous process that necessitates meticulous observation and examination to guarantee prompt and effective identification and resolution of defects. CONCLUSION: The utilization of suitable tools and technologies is imperative for manufacturers to guarantee optimal production quality and operational success.

A wintertime thermal analysis of New Zealand Homestar certified apartments for older people

ABSTRACT The New Zealand Building Code mandates maintaining habitable spaces and bathrooms in elderly homes at a minimum internal temperature of 16°C with adequate ventilation. This study assesses the thermal wintertime performance of 40 subsidized apartments designed for older residents within a 7-Homestar certified building in Auckland. The building performs well, falling below the 16°C threshold only 5 per cent of the time across all units. However, inconsistencies exist, particularly on the top floor (Level 3), which is colder than other levels. This reveals a potential flaw in the typology approach of green certifications, like Homestar, where certifying a single dwelling within an apartment building does not guarantee uniform thermal performance across all units.

Documents without Borders: Beneath the Rubble

In my first few columns exploring the intricacies of international governmental information, I focused primarily on information provided by nations with strong institutions and long established international governmental organizations (IGOs). While I briefly mentioned potential flaws in information and data related to cultural preferences and willful misleading—I stayed away from discussing finding and evaluating information from nations with weak institutions. Government information specialists are well aware of the complexity of navigating a labyrinth of bureaucratic information that varies in how accessible it is, but what happens without even the assurance of that imperfect system? Nations that are mired in ongoing conflict, corruption, or whose institutions have been threatened by financial or climate crises face unique challenges in sharing or even preserving information. This column will look at examples of these situations, and strategies for accessing information that feels as though it is buried beneath the rubble.

Three novel methods for determining motor threshold with transcranial magnetic stimulation outperform conventional procedures.

Objective. Thresholding of neural responses is central to many applications of transcranial magnetic stimulation (TMS), but the stochastic aspect of neuronal activity and motor evoked potentials (MEPs) challenges thresholding techniques. We analyzed existing methods for obtaining TMS motor threshold and their variations, introduced new methods from other fields, and compared their accuracy and speed.Approach. In addition to existing relative-frequency methods, such as the five-out-of-ten method, we examined adaptive methods based on a probabilistic motor threshold model using maximum-likelihood (ML) or maximuma-posteriori(MAP) estimation. To improve the performance of these adaptive estimation methods, we explored variations in the estimation procedure and inclusion of population-level prior information. We adapted a Bayesian estimation method which iteratively incorporated information of the TMS responses into the probability density function. A family of non-parametric stochastic root-finding methods with different convergence criteria and stepping rules were explored as well. The performance of the thresholding methods was evaluated with an independent stochastic MEP model.Main Results. The conventional relative-frequency methods required a large number of stimuli, were inherently biased on the population level, and had wide error distributions for individual subjects. The parametric estimation methods obtained the thresholds much faster and their accuracy depended on the estimation method, with performance significantly improved when population-level prior information was included. Stochastic root-finding methods were comparable to adaptive estimation methods but were much simpler to implement and did not rely on a potentially inaccurate underlying estimation model.Significance. Two-parameter MAP estimation, Bayesian estimation, and stochastic root-finding methods have better error convergence compared to conventional single-parameter ML estimation, and all these methods require significantly fewer TMS pulses for accurate estimation than conventional relative-frequency methods. Stochastic root-finding appears particularly attractive due to the low computational requirements, simplicity of the algorithmic implementation, and independence from potential model flaws in the parametric estimators.

Optimized Deeplearning Algorithm for Software Defects Prediction

Accurate software defect prediction (SDP) helps to enhance the quality of the software by identifying potential flaws early in the development process. However, existing approaches face challenges in achieving reliable predictions. To address this, a novel approach is proposed that combines a two-tier-deep learning framework. The proposed work includes four major phases:(a) pre-processing, (b) Dimensionality reduction, (c) Feature Extraction and (d) Two-fold deep learning-based SDP. The collected raw data is initially pre-processed using a data cleaning approach (handling null values and missing data) and a Decimal scaling normalisation approach. The dimensions of the pre-processed data are reduced using the newly developed Incremental Covariance Principal Component Analysis (ICPCA), and this approach aids in solving the “curse of dimensionality” issue. Then, onto the dimensionally reduced data, the feature extraction is performed using statistical features (standard deviation, skewness, variance, and kurtosis), Mutual information (MI), and Conditional entropy (CE). From the extracted features, the relevant ones are selected using the new Euclidean Distance with Mean Absolute Deviation (ED-MAD). Finally, the SDP (decision making) is carried out using the optimized Two-Fold Deep Learning Framework (O-TFDLF), which encapsulates the RBFN and optimized MLP, respectively. The weight of MLP is fine-tuned using the new Levy Flight Cat Mouse Optimisation (LCMO) method to improve the model's prediction accuracy. The final detected outcome (forecasting the presence/ absence of defect) is acquired from optimized MLP. The implementation has been performed using the MATLAB software. By using certain performance metrics such as Sensitivity, Accuracy, Precision, Specificity and MSE the proposed model’s performance is compared to that of existing models. The accuracy achieved for the proposed model is 93.37%.

Green Software Engineering: Cloud-based Face Detection and Static Code Analysis

This paper presents an approach to green software engineering that integrates cloud-based face detection and static code analysis to promote sustainable software development. The proposed method uses OpenCV, a computer vision library, and a pre-trained Haar cascade classifier to detect faces in images. Faces are marked with green bounding frames that serve as visual indicators of their locations. In addition, the paper evaluates the quality of a distinct script file using Pylint library static code analysis techniques. The analysis evaluates code compliance with standards, identifies potential flaws, and identifies code odors. By integrating these practices, the proposed method seeks to reduce resource consumption, maximize energy efficiency, and enhance code maintainability, promoting environmentally friendly and sustainable software engineering practices. One outcome of our effort was creating the YasminNadiaArabcSocialMediaImages data collection, which includes faces of Arabic social media celebrities and is filled out to be accessible for public usage on the websites Kaggle and GitHub.

Existing Hypotheses about the Emergence of Nonsense Syllables in the Chant Tradition of Teretismata and Kratēmata in Byzantine Music

In the present essay, I review and evaluate the three main hypotheses about the historical background and emergence of the nonsense syllables in the chant tradition of teretismata and kratēmata in Byzantine music. The different historical hypotheses as to the historical roots and development of this singing practice are examined and analyzed thoroughly, namely those of Gregorios Stathis (1979, 2014), Diane Touliatos (1989), and Grigorios Anastasiou (2005). The aim of the analysis is to summarise and discuss the contribution of the up-to-date historical hypotheses to the theoretical approaches of the topic, including the identification of potential flaws, lacunae and inadequacies of their explanatory power. Touliatos takes antiquity as a starting point for her hypothesis and posits the roots of nonsense syllables in the music of Ancient Greece. After a historical gap of several centuries, those syllables reappear in the Byzantine music during the 14th c. Stathis and Anastasiou examine the phenomenon exclusively within the boundaries of Byzantine music. Before reviewing the three hypotheses concerning the historical development of nonsense syllables in Byzantine music, we must consider the sources on which the theories are postulated.

Application of Pseudo-Random Numbers in System Design

The purpose of the article is to study and search for optimal strategies for modelling information systems using pseudo-random numbers to improve the performance of information systems and to determine the optimal parameters of random number distributions; to analyze efficient algorithms for generating pseudo-random numbers. The research methods are based on the application of a systematic approach to analyzing the benefits of using pseudorandom numbers in the design of information systems. Scientific novelty. The article analyses the advantages of stochastic modelling of information systems using different approaches to generating pseudorandom numbers, and for the first time considers the application of a random decision tree forest for modelling queuing systems. Conclusions. The application of pseudorandom numbers in system design is an urgent problem that requires further research and development of methods. The design of efficient algorithms for generating, validating, and using pseudorandom values plays an essential role in many fields of science and technology. This approach allows for optimization and planning in the process of designing the system and is essential for validating and testing the system. By generating random variables, you can create different test scenarios and check how the system reacts to various conditions, which helps to identify potential errors, flaws, and problems in the system and ensures the quality of the design. Thus, the use of pseudo-random numbers in system design allows for randomness, risk analysis, optimization and planning, validation, and testing. This is an important aspect of system development that helps to ensure their reliability, performance, and efficiency.

Application of electrical resistivity tomography (ERT) for vertical high-density polyethylene (HDPE) membrane detection

At landfill sites, uncontrolled leachate is a serious cause of groundwater and soil contamination at landfills and can occur extensively when there are looploopholesin the vertical HDPE (High-density polyethylene) membrane. For this reason, detecting HDPE membranes is necessary. The conventional electrode running poles approach is limited, it reflects the distribution of resistivity in 2D subsurface profiles, which is difficult to deploy electrodes above vertical HDPE membrane. According to the structural characteristics and field conditions of vertical HDPE membranes, the two-row current and potential (CP) electrode array is proposed to be applied to the testing of vertical HDPE membranes. The forward geoelectric model of the vulnerability was built using pyGimli, and data on 1519 apparent resistivity were acquired. The previous knowledge without the vulnerability was taken into consideration as the constraint condition, and the regional control was added to conduct the inversion, all using the least square approach. The model was used to analyze data on the detection of vertical HDPE membranes in a landfill In Yancheng, and potential flaws were discovered. Results show that the CP electrical measuring device can be efficient, quick, and accurate in the positioning of vertical HDPE membrane hole detection, and it is a cost-effective method. To verify the results, in the vertical HDPE membrane, through the Wenner device, the inversion of the 2D profile, and low resistivity anomalies are detected. This is thought to be through the leachate of leakage of HDPE membrane resistance rate is about 1.8 ohm. The findings demonstrate that the vertical HDPE membrane susceptibility may be located and detected using a CP electrical measuring instrument, which is a practical and efficient approach.

Finland’s Election: Ideology, Power, and Critique behind the Loss of Sanna Marin in The Guardian

Finland’s prime minister election result is out, impacting a huge shock in European politics. This study examines The Guardian's coverage of the Finnish Prime Minister Sanna Marin's loss in the recent election, utilizing Ruth Wodak's approach to Critical Discourse Analysis. The study aims to uncover underlying ideologies, power relations, and potential critiques in The Guardian's coverage. The article analyzes The Guardian's April 4th, 2023, publication titled "Why did Sanna Marin lose Finland's election?" and discusses the reasons behind Marin's defeat. The article frames Marin's loss as a "shock," suggesting potential flaws in the electoral system or an unfair outcome. The article discusses various factors that led to Marin's defeat, such as her approach to the economy, foreign policy, and immigration, highlighting their potential impact on voter behavior. Additionally, the article suggests that Marin's loss may indicate a shift towards more conservative values in Finnish society, highlighting a rise in anti-immigration sentiment and a focus on economic issues. The study also considers power relations in the coverage, highlighting the opposition's successful framing of Marin as an out-of-touch elite, which may have played a significant role in swaying voters. However, the study notes that this approach only examined a single source. Further research involving multiple media sources and a mixed investigative lens is necessary to understand discursive issues comprehensively. Keywords: Finland’s Election, Sanna Marin, Discourse Historical Approach, The Guardian

Non-Destructive Testing in Concrete Maturity Modeling and Master Curve Development

Assessing concrete quality as construction goes on provides early warnings of potential flaws and leads to timely corrections in mix proportioning and placement techniques. Compressive strength and maturity modeling are among the most common parameters used by the concrete industry. Past studies indicated that non-destructive methods, NDTs, relate well to maturity and concrete strength predictions. In this study, the hydration temperature–time history of concrete was explored in defining “master curves” for concrete maturity for the first time. Well-accepted NDTs, such as ultrasonic pulse velocity and resonant frequency, were used in this effort. The study findings indicated that the novel approach of “master curves” for the maturity of concrete can be defined and follow a generalized logarithmic form. The best fit models relating NDT response and the maturity temperature–time product provided a high coefficient of determination (i.e., in almost all cases above 0.9 and p < 0.05), thus resulting in a very good fit. The shift factors for each mixture’s maturity function in relation to the master curve were related to concrete properties. The shifted maturity functions from the concrete mixtures included in the study had a perfect transition to the master curve (i.e., all the shifted data overlap the master curve trend line with an R2 = 1). The NDTs’ ability to capture the hydration temperature-time history was assessed with impeded sensors into the concrete mixtures. This approach has provided strength prediction models with a high accuracy (i.e., good agreement between observed and predicted strength values with R2 = 0.93). The proposed NDT-based maturity modeling through “master curve” development provides significant benefits in relation to traditional maturity modeling since it offers the opportunity to: (i) predict strength without having to repeat maturity testing each time a producer adjusts mixture proportioning to fine tune mix design; (ii) save testing time and cost due to reduced maturity evaluation from the use of master curves; and (iii) be able to quickly predict without further testing what the strength gain will be due to variations in mixture proportioning. The ability to monitor concrete maturity, and thus strength, with NDTs in reinforced concrete is of particular interest since using cores is problematic due to the presence of reinforcement.

Towards automated physical fatigue monitoring and prediction among construction workers using physiological signals: An on-site study

Physical fatigue has been recognized as a serious health and safety risk among construction workers. As a result, numerous studies have endeavored to monitor/predict it using physiological measures. While the results are promising, their methodologies seem inappropriate. First, many studies utilized inappropriate benchmarking methods for physical fatigue monitoring. Importantly, a few of them utilized physical exertion scales as a surrogate for physical fatigue benchmarking. Second, many of them collected data in highly structured tasks in controlled environments. To assess these potential flaws, this research monitored fourteen construction workers' fatigue onsite by gathering physiological measures and fatigue data simultaneously. The results show that while the physical exertion scale was on average moderately correlated with a valid physical fatigue scale (average correlation coefficient 0.65), correlation coefficients varied widely among workers with the lowest of 0.05 and the highest of 0.89. This variation could be attributed to numerous factors including nature of the task, pacing and breaks during work, and individual factors. This might suggest that the physical exertion scale cannot serve as a good surrogate for physical fatigue. Additionally, the results found that workers’ physiological measures were weakly correlated to fatigue than previous laboratory studies. Overall, this study contributes to the body of knowledge by highlighting the methodological issues in the previous studies related to physical fatigue monitoring using physiological measures and the need to re-evaluate the usefulness of the measures, entailing appropriate methods. More importantly, the current study has challenged the status quo for monitoring/predicting fatigue using physiological measures.

Induction Programs’ Effectiveness in Boosting New Teachers’ Instruction and Student Achievement: A Critical Review

Induction programs are a set of systematic structures aimed at for supporting and guiding novice teachers and in helping them to learn responsibilities and to improve practices related to the effective teaching process and its impact on students’ achievement. The purpose of this critical review was to examine ten empirical studies conducted between 2000 and 202, in order1 to determine the impact of induction programs on teachers' self-efficacy and teaching practices as well as students’ learning and achievement. These studies were selected based on their direct relevance to the induction programs for novice teachers; and they are distinguished by the clarity of the methods used, the results, and the presence of experimental evidence. In addition, the quality of these studies must be high. Most of the studies empirically support the claim that induction programs for new teachers positively impact both the instructional practices and the students’ achievement. However, some of the studies showed no significant relationship between them. Therefore, this review attempts to reconcile the results of these studies that were showed by each study, and highlight the elements that contribute to variations in the results, including potential flaws in research design or other shortcomings, if any. The most prominent results of this review were that eight studies, which demonstrated that induction programs have a positive effect on teachers' classroom practices and students’ achievement; while two studies indicated that there was no relationship between teachers’ induction and students’ achievement. Finally, the relevant questions not addressed in the literature are suggested for promoting additional research that would demystify some of the ambiguities among the existing studies.

Combatting over-specialization bias in growing chemical databases

BackgroundPredicting in advance the behavior of new chemical compounds can support the design process of new products by directing the research toward the most promising candidates and ruling out others. Such predictive models can be data-driven using Machine Learning or based on researchers’ experience and depend on the collection of past results. In either case: models (or researchers) can only make reliable assumptions about compounds that are similar to what they have seen before. Therefore, consequent usage of these predictive models shapes the dataset and causes a continuous specialization shrinking the applicability domain of all trained models on this dataset in the future, and increasingly harming model-based exploration of the space.Proposed solutionIn this paper, we propose cancels (CounterActiNg Compound spEciaLization biaS), a technique that helps to break the dataset specialization spiral. Aiming for a smooth distribution of the compounds in the dataset, we identify areas in the space that fall short and suggest additional experiments that help bridge the gap. Thereby, we generally improve the dataset quality in an entirely unsupervised manner and create awareness of potential flaws in the data. cancels does not aim to cover the entire compound space and hence retains a desirable degree of specialization to a specified research domain.ResultsAn extensive set of experiments on the use-case of biodegradation pathway prediction not only reveals that the bias spiral can indeed be observed but also that cancels produces meaningful results. Additionally, we demonstrate that mitigating the observed bias is crucial as it cannot only intervene with the continuous specialization process, but also significantly improves a predictor’s performance while reducing the number of required experiments. Overall, we believe that cancels can support researchers in their experimentation process to not only better understand their data and potential flaws, but also to grow the dataset in a sustainable way. All code is available under github.com/KatDost/Cancels.

Flaws and uncertainties in pandemic global excess death calculations.

Several teams have been publishing global estimates of excess deaths during the COVID-19 pandemic. Here, we examine potential flaws and underappreciated sources of uncertainty in global excess death calculations. Adjusting for changing population age structure is essential. Otherwise, excess deaths are markedly overestimated in countries with increasingly aging populations. Adjusting for changes in other high-risk indicators, such as residence in long-term facilities, may also make a difference. Death registration is highly incomplete in most countries; completeness corrections should allow for substantial uncertainty and consider that completeness may have changed during pandemic years. Excess death estimates have high sensitivity to modeling choice. Therefore different options should be considered and the full range of results should be shown for different choices of pre-pandemic reference periods and imposed models. Any post-modeling corrections in specific countries should be guided by pre-specified rules. Modeling of all-cause mortality (ACM) in countries that have ACM data and extrapolating these models to other countries is precarious; models may lack transportability. Existing global excess death estimates underestimate the overall uncertainty that is multiplicative across diverse sources of uncertainty. Informative excess death estimates require risk stratification, including age groups and ethnic/racial strata. Data to-date suggest a death deficit among children during the pandemic and marked socioeconomic differences in deaths, widening inequalities. Finally, causal explanations require great caution in disentangling SARS-CoV-2 deaths, indirect pandemic effects, and effects from measures taken. We conclude that excess deaths have many uncertainties, but globally deaths from SARS-CoV-2 may be the minority of calculated excess deaths.

Different augmentations of absorbed hydrogen under elastic straining in high-pressure gaseous hydrogen environment by as-quenched and as-tempered martensitic steels: combined experimental and simulation study

Hydrogen embrittlement (HE) behavior of as-quenched and as-tempered martensitic steels under elastic straining in a high-pressure gaseous hydrogen environment was studied for the first time. The difference in the total content of defects acting as H trap sites has the highest contribution to the increase in absorbed hydrogen content under the elastic loading. The hydrogen-induced fracture occurred in the as-quenched specimen during elastic straining in hydrogen environment, while the tempered specimen did not fracture under the elastic loading. Higher accumulation of the dislocations near the main crack initiation sites (prior austenite grain boundaries (PAGBs)) in the as-quenched specimen will be the main source of the hydrogen to the potential flaw to crack to initiate. H accumulated near PAGBs enhances dislocation slip along {011} planes and helps transgranular crack propagation. Due to less possibility to provide the critical local amount of hydrogen at PAGBs for the crack to initiate, the as-tempered specimen remained unfractured.

Clinicians' Guide to Artificial Intelligence in Colon Capsule Endoscopy-Technology Made Simple.

Artificial intelligence (AI) applications have become widely popular across the healthcare ecosystem. Colon capsule endoscopy (CCE) was adopted in the NHS England pilot project following the recent COVID pandemic's impact. It demonstrated its capability to relieve the national backlog in endoscopy. As a result, AI-assisted colon capsule video analysis has become gastroenterology's most active research area. However, with rapid AI advances, mastering these complex machine learning concepts remains challenging for healthcare professionals. This forms a barrier for clinicians to take on this new technology and embrace the new era of big data. This paper aims to bridge the knowledge gap between the current CCE system and the future, fully integrated AI system. The primary focus is on simplifying the technical terms and concepts in machine learning. This will hopefully address the general "fear of the unknown in AI" by helping healthcare professionals understand the basic principle of machine learning in capsule endoscopy and apply this knowledge in their future interactions and adaptation to AI technology. It also summarises the evidence of AI in CCE and its impact on diagnostic pathways. Finally, it discusses the unintended consequences of using AI, ethical challenges, potential flaws, and bias within clinical settings.