Trustworthy Results Research Articles

Use of Technique of ‘Sampling’ in Urdu Research: An Analysis

Sampling is an important technique used in the modern research in social sciences. The technique covers a large population of research with ease. Its main kinds are probability and non-probability sampling, and both are used in research to draw reliable and trustworthy results. Sampling is used in literary and historical research also. The sub-techniques of non-probability sampling like purposive sampling, convenience sampling, judgement sampling have been used in Urdu research, but no researcher has used the term ‘sampling’ in his research report. Several researchers have clearly described their research methodology in their dissertations, still they have not mentioned the sampling there, despite they have used sampling in their research. Command over the use of sampling is need of the modern research, so it should be included in the curriculum of research methodology in universities. The article under study has used a sample of 50 theses out of total 150 theses, secured through systematic random sampling. The findings have been drawn from the sample and applied on the whole population. Conclusion has been presented based on findings.

Comments on Kämmerer, et al. (2023) regarding RT-PCR Testing

Kämmerer et al., (2023) reviewed the trustworthiness of Reverse Transcription – Polymerase Chain Reaction (RT-PCR) and sought to solve the serious problems that remain challenging its use and the significance of its application for the diagnosis of COVID-19 infections. They proposed certain modifications which they argued will enable more precise and trustworthy diagnostic results. However, this paper has a clear focus on the technical aspect of RT-PCR, which is only one piece of the COVID-19 puzzle, while there are other equally important pieces still missing. In order to establish what could correctly be judged as a true positive diagnosis, one sharply distinct from a false positive diagnosis, it is necessary to consider additional details. We believe their recommendations amount only to a partial solution to the deeper problems their work implicates. We propose to look at the larger perspective: to start from the very beginning, that is the definition of COVID-19 disease.

A model-based simulator for smart homes: Enabling reproducibility and standardization

Scientific contributions in the area of smart environments cover different tasks of ambient intelligence including action and activity recognition, anomaly detection, and automated enactment. Algorithms solving these tasks need to be validated against sensor logs of smart environments. In order to acquire these datasets, expensive facilities are needed, containing sensors, actuators and an acquisition infrastructure. Even though several freely accessible datasets are available, each of them features a very specific set of sensors, which can limit the introduction of novel approaches that could benefit of particular types of sensors and deployment layouts. Additionally, acquiring a dataset requires a considerable human effort for labeling purposes, thus further limiting the creation of new and general ones. In this paper, we propose a model-based simulator capable to generate synthetic datasets that emulate the characteristics of the vast majority of real datasets while granting trustworthy evaluation results. The datasets are generated using the eXtensible Event Stream – XES international standard commonly used for representing event logs. Finally, the datasets produced by the simulator are validated against two real scenario’s logs from the literature.

Development and validation of the steady state centrifuge experiment for the moisture retention curve of porous building materials

With the development of more efficient hygrothermal computer models, simulation studies have become increasingly important in the design of building components. To obtain trustworthy results from these studies, accurate hygric properties are required. The existing methods for moisture storage properties, however, are not very well suited to accurately measure moisture retention curves within a compact timeframe. To improve on this front, this paper introduces the steady state centrifuge technique, a common experiment in soil physics, for use on porous building materials. The laboratory centrifuge, used for the validation of this technique, is self-made to limit its cost and account for specific design choices. In the first part of the paper, the design of the laboratory centrifuge is described and all problems encountered during the development are explained and resolved. The two main problems are excessive heat generation by the motor and unwanted evaporation from the sample’s surfaces. The excessive heat generation is solved by extraction of heat both at the source, by using a ventilator, and at the rotor, by adding carefully positioned air extraction holes. The unwanted evaporation is eliminated by incorporating sample holders to shield the sample from the surrounding air. In the second part of the paper, the steady state centrifuge experiment is used to measure the desorption moisture retention curves of a ceramic brick starting from both saturated and capillary moisture content. The results are validated by their similarity to the curves obtained by mercury intrusion porosimetry. Besides providing accurate results, the determination of the full moisture retention curve requires only 1–2 weeks, which is significantly quicker than other common protocols, such as the pressure plates, which take about 2 months. Additionally, the ability to measure the desorption moisture retention curve from capillary moisture content as well as the limited cost of the centrifuge design (€6000) provide major advantages.

Solution for Determining Modulus of Elasticity of Natural Materials Using Vibrations of Non-Uniform Circular Cross-Section Cantilevers.

The article presents an original method for determining the modulus of elasticity of natural materials. A studied solution was based on vibrations of non-uniform circular cross-section cantilevers solved using Bessel functions. The derived equations, together with experimental tests, allowed for calculating the material's properties. Assessments were based on the measurement of the free-end oscillations in time using the Digital Image Correlation (DIC) method. They were induced manually and positioned at the end of a cantilever and monitored in time using a fast Vision Research Phantom v12.1 Camera with 1000 fps. GOM Correlate software tools were then used to find increments of deflection on a free end in every frame. It provided us with the ability to make diagrams containing a displacement-time relation. To find natural vibration frequencies, fast Fourier transform (FFT) analyses were conducted. The correctness of the proposed method was compared with a three-point bending test performed on a Zwick/Roell Z2.5 testing machine. The presented solution generates trustworthy results and can provide a method to confirm the elastic properties of natural materials obtained in various experimental tests.

A Decade of Intelligent Software Testing Research: A Bibliometric Analysis

It gets harder and harder to guarantee the quality of software systems due to their increasing complexity and fast development. Because it helps spot errors and gaps during the first phases of software development, software testing is one of the most crucial stages of software engineering. Software testing used to be done manually, which is a time-consuming, imprecise procedure that comes with errors and gaps and costs money, time, and effort. Currently, testing professionals routinely automate testing to obtain trustworthy results while saving time, cost, and labor. We’ve also moved the starting point of the software cycle to the developer, and made write tests before even writing code, or what’s known as TDD (Test Driven Development). The use of new artificial intelligence techniques will enable the generation of smart test cases to improve test quality and provide better coverage and accurate results. In this study, we used the Web of Science database to acquire bibliometric data about intelligent software testing papers which were conducted between 2012 and 2022, and we used Biblioshiny from the R bibliomerix package, alongside with VOSViewer in order to analyze the data and extract insights and answer research questions about the authors, articles, journals, organizations, and countries publishing in the field of intelligent software testing. The focus of this study is on scientific progress and collaborative trends in scholarly research, providing a blueprint for showcasing worldwide developments in the realm of intelligent software testing. By gaining a comprehensive understanding of the present state of research on the application of artificial intelligence in software testing, this study can offer valuable insights to software engineers, architects, and researchers in the field.

Modeling driver behavior in critical traffic scenarios for the safety assessment of automated driving

Objective Before market introduction, the safety of highly automated driving systems needs to be assessed prospectively. BMW has developed a holistic approach for the assessment of the traffic safety impact by these systems in which stochastic traffic simulations play a significant role. A driver behavior model which represents realistic driver behavior ranging from performance in non-critical everyday driving toward performance in critical situations is key for this approach. To ensure trustworthy results, validation of the driver model is needed. The paper aims at demonstrating that the presented driver model acts realistically in different critical real-world traffic scenarios. Methods BMW has been developing the Stochastic Cognitive Model (SCM) which models cognitive processes in traffic situations. These processes range from information acquisition by gaze behavior, mental representation of the environment, recognition of situations from the visual information and reaction to the situation. The driver model combines these cognitive processes with stochastic driver parameters to obtain a variation in driver behavior in simulations. Especially visual attention modeling is key to realistic traffic interactions in simulations as this is the input for the sequential cognitive processes, i.e., the recognition of situations and the reaction to the situation. Modeling of driver’s gaze behavior with SCM is thus shown in this paper. Results SCM is applied in three critical real-world traffic scenarios in which gaze behavior, brake reaction times and time-to-collisions are evaluated and compared to the real-world data. Due to the stochastic approach not only a single SCM agent but a collective of virtual SCM test drivers is assessed. Results show that SCM is capable to simulate the influence of visual inattention on collision risk. Conclusion Realistic driver behavior in simulations can be achieved by using SCM. Especially in the presented critical scenarios SCM is able to represent real-world driving behavior which is determined particularly by its gaze behavior and subsequent reaction. Driving performance varies over different SCM agents which mean that different driving behavior can be simulated with SCM as well. However, the investigation in this paper included only three real-world cases. Therefore, further critical, and additionally non-critical scenarios need to be investigated in the future.

MoS2-ZnO Nanocomposite Mediated Immunosensor for Non-Invasive Electrochemical Detection of IL8 Oral Tumor Biomarker

In order to support biomolecule attachment, an effective electrochemical transducer matrix for biosensing devices needs to have many specialized properties, including quick electron transfer, stability, high surface area, biocompatibility, and the presence of particular functional groups. Enzyme-linked immunosorbent assays, gel electrophoresis, mass spectrometry, fluorescence spectroscopy, and surface-enhanced Raman spectroscopy are common techniques used to assess biomarkers. Even though these techniques provide precise and trustworthy results, they cannot replace clinical applications because of factors such as detection time, sample amount, sensitivity, equipment expense, and the need for highly skilled individuals. For the very sensitive and targeted electrochemical detection of the salivary oral cancer biomarker IL8, we have created a flower-structured molybdenum disulfide-decorated zinc oxide composite on GCE (interleu-kin-8). This immunosensor shows very fast detection; the limit of detection (LOD) for interleukin-8 (IL8) detection in a 0.1 M phosphate buffer solution (PBS) was discovered to be 11.6 fM, while the MoS2/ZnO nanocomposite modified glassy carbon electrode (GCE) demonstrated a high catalytic current linearly from 500 pg to 4500 pg mL-1 interleukin-8 (IL8). Therefore, the proposed biosensor exhibits excellent stability, high accuracy sensitivity, repeatability, and reproducibility and shows the acceptable fabrication of the electrochemical biosensors to detect the ACh in real sample analysis.

Differentially private tree-based redescription mining

Differential privacy provides a strong form of privacy and allows preserving most of the original characteristics of the dataset. Utilizing these benefits requires one to design specific differentially private data analysis algorithms. In this work, we present three tree-based algorithms for mining redescriptions while preserving differential privacy. Redescription mining is an exploratory data analysis method for finding connections between two views over the same entities, such as phenotypes and genotypes of medical patients, for example. It has applications in many fields, including some, like health care informatics, where privacy-preserving access to data is desired. Our algorithms are the first tree-based differentially private redescription mining algorithms, and we show via experiments that, despite the inherent noise in differential privacy, it can return trustworthy results even in smaller datasets where noise typically has a stronger effect.

The effect of regular exercise on depression, anxiety, treatment motivation and mindfulness in addiction: a randomized controlled trial

ABSTRACT Objective The aim of this study is to examine the effect of regular exercise on depressive anxiety symptoms, treatment motivation and mindfulness levels in individuals who are alcohol or substance dependent. Methods The study had two arms and was randomized controlled: (A) Patients assigned to standard medical treatment, (B) Patients who receive standard treatment are also given access to supervised group exercise. Patients in the exercise group were given exercise for 3 weeks. Hamilton Anxiety (HAM-A), Hamilton Depression Scale (HAM-D), Treatment Motivation Scale (TMQ) and Mindful Attention Awareness Scale (MAAS) were administered to the patients at the beginning and end of the treatment. Results There was a decrease in HAM-A, HAM-D scores, an increase in TMQ and MAAS scores, and improvement after treatment in both groups. However, there was a significant change in HAM-A, HAM-D and TMQ scores in the group that added exercise to their treatment compared to the group that received only standard therapy. There was no significant difference in MAAS scores. Conclusion It was discovered as a result that including exercise in addiction therapy could enhance the therapeutic response. Longer-term studies with greater sample sizes must be planned and carried out to produce more trustworthy results.

RTD3 – Real Time Driver Drowsiness Detection System Using CNN on Inception V3 Model

This paper suggests a behaviourally based driver sleepiness detection system. Faces provide data that may be used to gauge sleepiness levels. The level of tiredness may be determined by extrapolating a variety of facial functions from the face. They include head movements, yawning, and eye blinks. Yet, since it needs accurate and reliable algorithms, creating a sleepiness detection system that produces accurate and trustworthy results is a difficult issue. In the past, a wide range of methods have been explored to detect driver sleepiness. The most recent growth in deep learning requires that those algorithms be reviewed to evaluate their efficacy in detecting sleepiness. In this study, the eyes the body feature that matters the most—are used to identify driver sleepiness. The algorithm has been taught to examine the face, isolate the ROI (Region of Interest), or the eyes, and determine whether they are open or closed. When the eyelids are closed continuously for a certain amount of time, the alarm begins to beep to warn the driver. CNN serves as our classifier (Convolution Neural Networks). After a comprehensive review of the literature, we conclude that, in order to get the best outcomes, CNN is the best method to use when compared to vector system approaches, Markov models, etc. The InceptionV3 model, which is a trained image recognition model, is the one employed in this instance. Also, we go through the installation, training, and testing accuracy methodologies.

Monitoring organic micropollutants in stormwater runoff with the method of fingerprinting

The ecological state of receiving water bodies can be significantly influenced by organic micropollutants that are emitted via stormwater runoff. Reported efforts to quantify the emission of micropollutants mainly focus on sampling at combined sewer overflows and storm sewer outfalls, which can be challenging. An alternative method, called fingerprinting, was developed and tested in this study. The fingerprinting method utilizes wastewater treatment plant (WWTP) influent samples and derives the proportion of stormwater in a sample. This is achieved by comparing the wet weather vs dry weather concentrations of substances-tracers which are present only in wastewater. It is then possible to estimate the concentration of organic micropollutants in stormwater runoff from measurements in the influent of a WWTP based on a mass balance. In this research, the fingerprinting method was applied in influent samples obtained in five WWTPs in the Netherlands. In total, 28 DWF and 22 WWF samples were used. The chosen tracers were ibuprofen, 2-hydroxyibuprofen, naproxen and diclofenac. Subsequently, the concentration in stormwater runoff of 403 organic micropollutants was estimated via the WWF samples. The substances that were present and analyzed included glyphosate and AMPA, 24 out of 254 pesticides, 6 out of 28 organochlorine pesticides, 45 out of 63 pharmaceuticals, 15 out of 15 PAHs, 2 of the 7 PCBs, and 20 of 33 other substances (e.g. bisphenol-A). A comparison with findings from other studies suggested that the fingerprinting method yields trustworthy results. It was also noted that a representative and stable dry weather flow reference concentration is a strict requirement for the successful application of the proposed method.

Diverse title generation for Stack Overflow posts with multiple-sampling-enhanced transformer

Stack Overflow is one of the most popular programming communities where developers can seek help for their encountered problems. Nevertheless, if inexperienced developers fail to describe their problems clearly, it is hard for them to attract sufficient attention and get the anticipated answers. To address such a problem, we propose M3NSCT5, a novel approach to automatically generate multiple post titles from the given code snippets. Developers may take advantage of the generated titles to find closely related posts and complete their problem descriptions. M3NSCT5 employs the CodeT5 backbone, which is a pre-trained Transformer model with an excellent language understanding and generation ability. To alleviate the ambiguity issue that the same code snippets could be aligned with different titles under varying contexts, we propose the maximal marginal multiple nucleus sampling strategy to generate multiple high-quality and diverse title candidates at a time for the developers to choose from. We build a large-scale dataset with 890,000 question posts covering eight programming languages to validate the effectiveness of M3NSCT5. The automatic evaluation results on the BLEU and ROUGE metrics demonstrate the superiority of M3NSCT5 over six state-of-the-art baseline models. Moreover, a human evaluation with trustworthy results also demonstrates the great potential of our approach for real-world applications.

Artificial intelligence centric scientific research on COVID-19: an analysis based on scientometrics data

With the spread of the deadly coronavirus disease throughout the geographies of the globe, expertise from every field has been sought to fight the impact of the virus. The use of Artificial Intelligence (AI), especially, has been the center of attention due to its capability to produce trustworthy results in a reasonable time. As a result, AI centric based research on coronavirus (or COVID-19) has been receiving growing attention from different domains ranging from medicine, virology, and psychiatry etc. We present this comprehensive study that closely monitors the impact of the pandemic on global research activities related exclusively to AI. In this article, we produce highly informative insights pertaining to publications, such as the best articles, research areas, most productive and influential journals, authors, and institutions. Studies are made on top 50 most cited articles to identify the most influential AI subcategories. We also study the outcome of research from different geographic areas while identifying the research collaborations that have had an impact. This study also compares the outcome of research from the different countries around the globe and produces insights on the same.

Assessing the linkage among green finance, technology, and education expenditure: evidence from G7 economies.

The growth of green finance is a multifaceted system, including the interaction of three spheres: the economy, the environment, and the finance sector. Spending on education is a singular intellectual contribution to a society's attempts to achieve sustainability through the application of skills, the provision of consultancies, the delivery of training, and the dissemination of knowledge. University scientists sound the first warnings about environmental problems and help lead the charge toward transdisciplinary technological solutions. Researchers are compelled to look into the environmental crisis because it has become a worldwide concern that needs regular examination. In this research, we examine how the GDP per capita, green financing, health expenditure, educational expenditure, and technology in the G7 economies affect the growth of renewable energy (Canada, Japan, Germany, France, Italy, UK, and the USA). The research makes use of panel data from the year 2000 through the year 2020. Long-term correlations between the variables are estimated using the CC-EMG in this study. The study's trustworthy results were determined using a combination of AMG and MG regression calculations. The research shows that the growth of renewable energy is positively affected by green finance, educational spending, and technology but negatively affected by GDP per capita and health expenditure. The growth of renewable energy is also positively affected by the influence of the term "green financing" on such variables as GDP per capita, health expenditure, educational expenditure, and technological advancement. The estimated outcomes also provide significant policy implications for the chosen and other developing economies in scheming a suitable route to a sustainable environment.

Anomaly Detection in Batch Manufacturing Processes Using Localized Reconstruction Errors From 1-D Convolutional AutoEncoders

Multivariate batch time-series data sets within Semiconductor manufacturing processes present a difficult environment for effective Anomaly Detection (AD). The challenge is amplified by the limited availability of ground truth labelled data. In scenarios where AD is possible, black box modelling approaches constrain model interpretability. These challenges obstruct the widespread adoption of Deep Learning solutions. The objective of the study is to demonstrate an AD approach which employs 1-Dimensional Convolutional AutoEncoders (1d-CAE) and Localised Reconstruction Error (LRE) to improve AD performance and interpretability. Using LRE to identify sensors and data that result in the anomaly, the explainability of the Deep Learning solution is enhanced. The Tennessee Eastman Process (TEP) and LAM 9600 Metal Etcher datasets have been utilised to validate the proposed framework. The results show that the proposed LRE approach outperforms global reconstruction errors for similar model architectures achieving an AUC of 1.00. The proposed unsupervised learning approach with AE and LRE improves model explainability which is expected to be beneficial for deployment in semiconductor manufacturing where interpretable and trustworthy results are critical for process engineering teams.

The Effects of Percutaneous Coronary Intervention on Biomarkers and Quality of Life in Patients With Chronic Total Coronary Artery Obstruction.

Chronic total occlusion (CTO) is an angiographic picture of total occlusion without blood flow which is estimated to have lasted at least 3 months. This study attempted to provide an overview of the levels of matrix metalloproteinase-9 (MMP-9), soluble suppression tumorigenicity 2 (sST2), and N-terminal pro-B-type natriuretic peptide (NT-pro-BNP) as remodeling, inflammatory, and atherosclerotic markers, as well as changes in the angina severity in patients with CTO who underwent percutaneous coronary intervention (PCI) compared to those without PCI. This study is a preliminary report with quasi-experimental design study with a pre-test and post-test approach to compare PCI's effect in CTO patients towards changes in MMP-9, sST2, NT-pro-BNP levels, and changes in the angina severity. Twenty subjects underwent PCI and 20 subjects with optimal medical therapy, who were then assessed at baseline and 8 weeks after intervention. The results of this preliminary report showed that decreased MMP-9 (pre-test: 12.07 ± 1.27 ng/mL vs. post-test: 9.91 ± 5.19 ng/mL, P = 0.049), sST2 (pre-test: 37.65 ± 20.00 ng/mL vs. post-test: 29.74 ± 15.17 ng/mL, P = 0.026) and NT-pro-BNP (pre-test: 0.63 ± 0.23 ng/mL vs. post-test: 0.24 ± 0.10 ng/mL, P < 0.001) levels were found after 8 weeks of PCI compared to those without such intervention. The levels of NT-pro-BNP were lower in the PCI group (0.24 ± 0.10 ng/mL) than in the non-PCI group (0.56 ± 0.23 ng/mL; P < 0.001). Moreover, there was an improvement of angina severity in PCI group than without PCI (P < 0.039). Although this preliminary report found a significant decrease in MMP-9, NT-pro-BNP, and sST2 levels in CTO patients who had undergone PCI compared to those without PCI, as well as improved angina severity in these patients, this study still has limitations. The number of samples was so small that similar studies with larger sample sizes or multicenter investigations are required to deliver more trustworthy and useful results. Nevertheless, we encourage this study as a preliminary baseline for further studies in the future.

Ethics in research and publication of research articles

Science aims at promoting knowledge by gathering and discovering the objective truth, the facts that are independent of human interests, their values, ideology and biases. The way in which scientists come to this goal is through the universally accepted and thoroughly regulated processes – the scientific method. There is no clear definition which will answer the question what is unethical in biomedical research. All people recognize some common ethical norms but different individuals interpret, apply, and balance these norms in different ways in light of their own values and life experiences. Generally, it can be said that unethical behaviour in science is any significant mistreatment of intellectual property or participation of other parties, deliberately hampering the research process or distortion of scientific evidence,as well as all the behaviours that affect the integrity of scientific practice. Given theimportance of the primary goal of scientific enterprise, that is search for truth and trustworthy results, ethics in science has increasingly come into focus. There are several reasons why it is important to adhere to ethical norms in research. Norms promote the aims of research, such as knowledge and truth, variety of moral and social values and help to build public support for research. This paper analyzes the major principles of ethical conduct in science and closely related topics on ghost authorship, conflict of interest, co-authorship assignment, redundant/repetitiveand duplicate publications. Furthermore, the paper provides an insight into the fabrication and falsification of data, as the most common forms of scientific fraud.

Role of renewable energy and fiscal policy on trade adjusted carbon emissions: Evaluating the role of environmental policy stringency

There are scant empirical studies investigating the impact of fiscal policy and environmental policy stringency on consumption-based carbon emissions, especially within the context of the BRICS countries. To fill the voids in prior studies, the current study evaluates the effect of fiscal policy and environmental stringent policy on consumption-based carbon emissions. The study also incorporates other drivers of fiscal consumption-based carbon emissions (CCO2) emissions, such as disintegrated energy and economic growth, using data from 1990 to 2019. Since second-generation econometric methodologies such as augmented mean group (AMG) and common correlated effect means group (CCEMG) were used in the empirical research, this work presents trustworthy and solid empirical evidence. The quantile regression performs better when dealing with unobserved heterogeneity for each cross-section. Quantile regression gives more consistent and trustworthy results than traditional econometric approaches because heterogeneity and non-normality are identified in the dataset. The results from these estimators show that economic growth, nonrenewable energy, and government expenditure intensify CCO2 emissions while taxation revenue, environmental policy stringency and renewable energy mitigate CCO2 emissions. Furthermore, the panel causality test results show that taxation revenue, government expenditure, environmental policy stringency, and renewable energy can predict CCO2 emissions. Policy recommendations are put forward based on these results.

Multi-Scale Fusion Localization Based on Magnetic Trajectory Sequence

Magnetic fingerprint has a multitude of advantages in the application of indoor positioning, but as a weak magnetic field, the dynamic range of the data is limited, which exerts direct influence on the positioning accuracy. Aiming at resolving the problem wherein the indoor magnetic positioning results tremendously rest with the magnetic characteristics, this paper puts forward a method based on deep learning to fuse the temporal and spatial characteristics of magnetic fingerprints, to fully explore the magnetic characteristics and to obtain stable and trustworthy positioning results. First and foremost, the trajectory of the acquisition area is extracted by adopting the ameliorated random waypoint model, and the simulation of pedestrian trajectory is completed. Then, the magnetic sequence is obtained by mapping the magnetic data. Aside from that, considering the scale characteristics of the sequence, a scale transformation unit is designed to obtain multi-scale features. At length, the neural network self-attention mechanism is adopted to fuse multiple features and output the positioning results. By probing into the positioning results of dissimilar indoor scenes, this method can adapt to diverse scenes. The average positioning error in a corridor, open area and complex area reaches 0.65 m, 0.93 m and 1.38 m respectively. The addition of multi-scale features has certain reference value for ameliorating the positioning performance.