Miracle-dz/UniFAR-FTUs huggingface dataset:
Customer Interaction in Software Development: A Comparison of Software Methodologies Deployed in Namibian Software Firms

Software methodologies provide guidelines for the development of software applications. Studies reveal that customer interaction in the software development process improves the chances that software applications will meet customers’ needs. Despite a number of software methodologies introduced and a comparison of these methodologies, there is a dearth of studies that empirically investigate customer interaction between these software methodologies within the Namibian context. The purpose of this study was to examine the differences in customer interaction between software methodologies deployed in Namibian software firms. The study adopted a qualitative, case study approach. Data was collected through standardized, open‐ended interviews. The findings show that the methodologies deployed in Namibian software firms include the waterfall model, Scrum, iterative model, eXtreme Programming (XP), and rapid application development (RAD). The findings also reveal that although there was in‐depth customer interaction in Scrum, the iterative model, XP and RAD, customer interaction in the software development process could also be challenging. The findings provide useful insights in software methodologies deployed in Namibian software firms and the experience within the Namibian context. An implication for software project managers and software developers is that customer interaction should be properly managed to ensure that the software methodologies for improving software development processes are effectively deployed.
Updated Estimates of Frequencies of Pipeline Failures Caused by Geohazards

This paper provides an updated compilation of geohazardrelated pipeline failure frequencies for onshore hydrocarbon gathering and transmission pipelines, with a particular emphasis on the analysis of data from Western Europe, Western Canada, the US, and South America. The results will be of interest to owners, operators, regulators and insurers who wish to calibrate estimates of geohazard failure frequency and risk on planned and operating pipelines, particularly for pipelines traversing mountainous terrain. It concludes with an estimate of the global annual frequency of failures caused by geohazards on hydrocarbon gathering and transmission pipelines, and postulates that this failure frequency should continue to decline when measured on a per kilometer basis due to ongoing improvements in geohazard recognition, routing and design of new pipelines, and improvements to integrity management practices for operating pipelines.
A Kriging Method for Modeling Cycle Time-Throughput Profiles in Manufacturing

A Kriging Method for Modeling Cycle Time-Throughput Profiles in Manufacturing Amirmahdi Tafreshian In semiconductor manufacturing, the steady-state behavior of a wafer fab system can be characterized by its cycle time-throughput profiles. These profiles quantify the relationship between the cycle time of a product and the system throughput and product mix. The objective of this work is to efficiently generate such cycle time-throughput profiles in manufacturing which can further assist decision makings in production planning. In this research, a metamodeling approach based on Stochastic Kriging model with Qualitative factors (SKQ) has been adopted to quantify the target relationship of interest. Furthermore, a sequential experimental design procedure is developed to improve the efficiency of simulation experiments. For the initial design, a Sequential Conditional Maximin algorithm is utilized. Regarding the follow-up designs, batches of design points are determined using a Particle Swarm Optimization algorithm. The procedure is applied to a Jackson network, as well as a scale-down wafer fab system. In both examples, the prediction performance of the SKQ model is promising. It is also shown that the SKQ model provides narrower confidence intervals compared to the Stochastic Kriging model (SK) by pooling the information of the qualitative variables. “The mysteries of eternity are known neither to you nor me the enigma can be read neither by you nor me behind the veil a discourse goes on about me and you when the veil disappears there remain neither you nor me”
Alcohol Use Disorder Is Associated with Higher Risks of Adverse Brain Outcomes

Alcohol use disorder (AUD) is on the ascendancy in the US older adult population, while the association between AUD and adverse brain outcomes remains inconclusive. The objective of this work is to investigate the associations between AUD with the onset of Alzheimer's disease and Parkinson's disease. In a retrospective cohort design using US insurance claim data (2007-2020), 129,182 patients with AUD were matched with 129,182 controls by age, sex, race, and clinical characteristics. After adjusting for covariates, AUD was associated with a higher risk of Alzheimer's disease (female adjusted HR=1.78, 95% CI: 1.68-1.90, P<0.001; male adjusted HR=1.80, 95% CI: 1.71-1.91, P<0.001) and a higher risk of Parkinson's disease (female adjusted HR=1.49, 95% CI: 1.32-1.68 P<0.001; male adjusted HR=1.42, 95% CI: 1.32-1.52, P<0.001) in the overall sample. In separate analyses of Black, White, and Hispanic patients, those with AUD had higher risk of Alzheimer's disease (adjusted HRs[≥]1.58; Ps[≥]0.001). A significantly elevated risk for Parkinson's disease was found only in the White subpopulation (female adjusted HR=1.55, 95% CI: 1.36-1.77, P<0.001; male adjusted HR=1.45, 95% CI: 1.33-1.57, P<0.001). Alcohol use disorder is associated with Alzheimer's disease. Alcohol use disorder is associated with Parkinson's disease in White persons. Cognitive screening and neurological examination among older adults with severe problematic alcohol use hold the promise for early detection of Alzheimer's disease and Parkinson's disease.
Conceptualization of Health Literacy from A Nursing Perspective

Health as an inseparable part of human beings needs to be maintained to achieve a complete human health degree. The role of health literacy in attaining optimum health is significant. When associated with nursing, health literacy must be interpreted as a part of the role and function of nursing. However, to understand health literacy, it is necessary to study it from the aspect of scientific formation itself and a nursing perspective. This review proposes an alternative conceptualization of health literacy from a nursing perspective. This review used an integrative search through four databases: ScienceDirect, ProQuest, SAGE Journal, and Google Scholar. Search using various combinations of keywords with the help of Boolean operators, including: health literacy, nursing perspective, nursing, and conceptualization combined as MESH terms. The inclusion criteria are peer-reviewed articles in English that discuss health literacy and nursing perspective. Articles published within the last six years (2017-2022). Research such as literature reviews, dissertations, editorials, commentaries, and other expert opinions are excluded. Ten articles were considered in this literature review. We describe the conceptualization of health literacy from the nurse's point of view, the predictors that influence it, the dimensions surrounding health literacy, the implication of health literacy, and how nurses will participate in supporting this health literacy. In the end, this conceptualization will be used as an illustration material to integrate the concept of health literacy into various problems that become nursing tasks.
Adherence trajectory as an on-treatment risk indicator among drug-resistant TB patients in the Philippines

Introduction: High levels of treatment adherence are critical for achieving optimal treatment outcomes among patients with tuberculosis (TB), especially for drug-resistant TB (DR TB). Current tools for identifying high-risk non-adherence are insufficient. Here, we apply trajectory analysis to characterize adherence behavior early in DR TB treatment and assess whether these patterns predict treatment outcomes. Methods: We conducted a retrospective analysis of Philippines DR TB patients treated between 2013 and 2016. To identify unique patterns of adherence, we performed group-based trajectory modelling on adherence to the first 12 weeks of treatment. We estimated the association of adherence trajectory group with six-month and final treatment outcomes using univariable and multivariable logistic regression. We also estimated and compared the predictive accuracy of adherence trajectory group and a binary adherence threshold for treatment outcomes. Results: Of 596 patients, 302 (50.7%) had multidrug resistant TB, 11 (1.8%) extremely drug-resistant (XDR) TB, and 283 (47.5%) pre-XDR TB. We identified three distinct adherence trajectories during the first 12 weeks of treatment: a high adherence group (n=483), a moderate adherence group (n=93) and a low adherence group (n=20). Similar patterns were identified at 4 and 8 weeks. Being in the 12-week moderate or low adherence group was associated with unfavorable six-month (adjusted OR [aOR] 3.42, 95% CI 1.90 - 6.12) and final (aOR 2.71, 95% 1.73 - 4.30) treatment outcomes. Adherence trajectory group performed similarly to a binary threshold classification for the prediction of final treatment outcomes (65.9 % vs. 65.4 % correctly classified), but was more accurate for prediction of six-month treatment outcomes (79.4% vs. 60.0% correctly classified). Conclusions: Adherence patterns are strongly predictive of patient treatment outcomes. Trajectory-based analyses represent an exciting avenue of research into TB patient adherence behavior seeking to inform interventions which rapidly identify and support patients with high-risk adherence patterns.
A Method of Constructing Power System Dispatcher Personas Based on Convolutional Neural Network

The recommendation algorithm has been applied in many fields and achieved pretty results in recent years. The personas for a large number of dispatchers in the power system are an indispensable step in the process of intelligent recommendation. In order to recommend dispatchers of different levels according to different operation scenarios, the aim of dispatcher personas is the classification of business level. In this paper, we focused on constructing dispatcher personas of Chinese power system. Unlike of the previous research work, we used convolutional neural network(CNN) to create dispatcher personas. The contributions of this paper are reflected in the following aspects: 1. dataset were built automatically based on internal data; 2. a CNN text classifier were trained to construct the dispatcher personas; 3. the effectiveness of the model is proved by experiments on real data. The experimental results show that the method proposed in the paper is superior to the baseline method and the average accuracy is 96%.
Characteristics of non-equilibrium condensing flow in three-dimensional supersonic rectangular nozzles

Fluid flow inside a supersonic nozzle with a rectangular cross-section induces secondary flow due to the shape of the upper wall curvature. The secondary flow forms longitudinal vortices near the wall of the nozzle corner which destabilizes the nozzle outlet flow due to the early transition of the flow from laminar to turbulent. It is known that non-equilibrium homogeneous condensation occurs in a supersonic rectangular nozzle due to the rapid expansion of the condensable fluid with large latent heat such as water vapor. Homogeneous condensation takes place through the formation of water nuclei from its vapor when foreign nuclei are absent in the flow. The irreversibility of the non-equilibrium condensation process causes loss in flow transportation energy in the form of total pressure loss. Although numerous researches have been conducted about the effect of non-equilibrium condensation process in supersonic nozzles, however, the interactions between nozzle shape, non-equilibrium condensation, and longitudinal vortices have not been studied convincingly. Therefore, numerical simulations have been carried out for two supersonic three-dimensional rectangular nozzles in order to understand the effect of nozzle shape and non-equilibrium condensation on the longitudinal vortices at the nozzle corner. Furthermore, total pressure distribution in the nozzle was analysed to understand the effect of degree of supersaturation and expansion rate on the total pressure loss. In this research, characteristics of simulated flow fields including total pressure loss were in good agreement with the published literature.
Research on Mongolian-Chinese Neural Machine Translation Based on CSGAN and Co-training

Aiming at the problems of poor translation quality and scarcity of Mongolian-Chinese parallel corpus resources in Mongolian-Chinese neural machine translation, this paper proposes a method based on conditional sequence of generative adversarial networks (CSGAN) and collaborative training. This method uses the CSGAN network based on adversarial training to train the translation model and obtains a better translation. At the same time, in order to make full use of the existing corpus resources, a collaborative training method is adopted on the basis of the above translation model to integrate the translation into the translation results of the high-resource corpus, and further improve the performance of Mongolian-Chinese machine translation. By comparing the BLEU values of different translation models, it is shown that using CSGAN-based and collaborative training methods can significantly improve the quality of Mongolian-Chinese translation models.
Sub-nanometer-sized carbon nanoparticle shows higher biocompatibility to DNA than nanometer-sized nanoparticles

Nanomaterials have been extensively studied for their potential applications in biomedical research, but the concerns of nanotoxicity still remain. A systematic understanding of how the geometrical factors of nanomaterials, especially the dimensional size, regulating the toxicity is critical for the development of biomedical materials, which has yet to be documented. In this work, we have studied how the ultra-small-sized C28 nanoparticles bind to and regulate the structure of double stranded (dsDNA) by molecular dynamics simulations. Our results show that the C28 molecule can strongly bind to the nucleic acid terminal or the minor groove. It is interesting to observe a high sequence preference for C28 binding which prefers to bind to GC-rich than AT-rich segments. The C28 binding does not cause evident structural distortion and has little effect on the global motion of dsDNA, showing relatively higher biocompatibility than most reported nanomaterials that are usually at the nanometer size. These results reveal a fact that the nanomaterials toxicity is highly correlated to their size where sub-nanometer sized materials may have higher biocompatibility. This could provide deep understanding of how to control the biomaterials effects and might further extend applications of nanomaterials in biomedical research.
A method of estimation for the area and direction of pipe crack

Crack direction will affect the ultrasonic wave reflected from it. Firstly, the correlation between crack direction and the reflected energy of ultrasonic is analyzed. Then, a mathematical model of crack direction and its reflected energy is developed due to the theory and methods of function approximation. Finally, aiming at the problem that the crack area must be known when using the model to estimate the crack direction, By observing the probability distribution of reflected wave energy of cracks with different areas and directions at different measurement distances, Using its law and mathematical model, an algorithm for estimating crack area and crack direction is designed. According to the related experiments, the study confirmed that both the estimation errors of the crack area and direction by the method are 5.25% and 8.21%, respectively.
PVDF piezoelectric polymers : characterization and application to thermal energy harvesting

This work deals with the characterization of piezoelectric polymers PVDF and its composites with shape memory alloys, for thermal energy harvesting applications. First, we discuss current advancements on energy harvesting technologies as well as their economical interests. Typical values of energy that can be generated are given together with energies typically needed for applications.Particular attention is given to the functioning principles of pyroelectric and piezoelectric materials. PVDF and shape memory alloy NiTiCu are also introduced.Custom characterization techniques are introduced to characterize PVDF piezoelectric properties relevant to generator applications and to evaluate its suitability for thermal energy harvesting. Since PVDF is a very flexible material, four-point bending, tube bending and a tensile machine experiments are used to study its piezoelectric response in quasi-static mode, as well as changes in piezoelectric properties with increased strain. Self-discharge measurements under various applied electric fields, temperatures and strains are performed to study the stability of material.A concept of composite energy harvesting, utilizing two materials of different families, is introduced. Here, we propose the coupling of piezo-/pyroelectric material and shape memory alloy. The pure pyroelectric voltage is combined with generated piezoelectric voltage, induced by shape memory alloy transformation, to increase the total energy generated by the system during heating. The proof of concept is shown first for ceramic PZT-based semi-flexible material and then for fully flexible PVDF.Finally, a power management circuit was designed and integrated with the PVDF energy harvester. High generated voltage peaks at heating are lowered by a two-step buck converter to a useful stable output voltage. Output energy are used to power a wireless emission card. Thus, a complete power generation chain from temperature variations to data emission is presented.The results of this work concern a wide range of applications, especially modern autonomous wireless sensors and Internet of Things objects, with low profile, high mechanical flexibility and low maintenance costs.
Erratum: “Identification of Faint Chandra X-Ray Sources in the Core-collapsed Globular Cluster NGC 6397: Evidence for a Bimodal Cataclysmic Variable Population” (2010, ApJ, 722, 20)

We report and correct a zero-point error in the HST ACS / WFC F658N ( H α ) magnitudes for stars in the globular cluster NGC 6397 presented in the published article ( hereafter C10 ) . The H α magnitudes reported in Table 1 of C10 are too faint by 3 mag and consequently the horizontal axes of Figures 3 and 4 ( right panels ) and Figure 7 in C10 are shifted by this amount. This error does not affect any of the conclusions of C10, which are based on relative values of the H α magnitudes. However, the correct calibration of the H α magnitudes is necessary for other applications, such as for comparison of the H α photometry of other clusters with that reported for NGC 6397 by C10 and for scaling the exposure times for other clusters from those of HST program GO-10257 ( PI: Anderson ) using the H α magnitudes
EEG Signal Classification Based on Neural Network with Depthwise Convolution

Brain-Computer Interface (BCI) have attracted a lot of attention recently as it enables people to communicate between the human and the machine by electroencephalography (EEG) signals decoding. EEG signals classification is important for BCI system. Convolution neural network (CNN) can automatically extract features and enhance the classification accuracy. However, limited EEG data easily leads to over-fitting of neural network. In this study, we proposed two novel neural networks (DCNN and DSCNN) based on depthwise convolution for EEG signals classification. The proposed models used depthwise convolution to learn a spatial filter and fuse channels information. And we performed multiple experiments to evaluate the EEG signal classification performance of the proposed CNN models on multiple sets of public datasets. The proposed DCNN method achieved 88.33% and 78.00% on the dataset III and the dataset IV from BCI competition II. The proposed DSCNN method could also achieve very competitive accuracy. In addition, the proposed models exhibited a significant ability in reducing model parameters and mitigating over-fitting.
Antimicrobial Activity of Lactobacillus plantarum in Fermented Chicken Egg towards Pathogenic Bacteria

Fermentation technology using L. plantarum with different incubation times affects the amount of L. plantarum containing antimicrobials in fermented chicken eggs. The purpose of this study was to determine the effect of incubation time on the antimicrobial activity of fermented chicken eggs. This study was conducted experimentally using a completely randomized design (CRD). The treatment was carried out by fermentation with an incubation temperature of 37°C with different incubation times of 0, 48 and 96 hours. The measured parameters in this study were testing for total lactic acid bacteria by using the pour plate method and the antimicrobial activity test for fermented eggs was carried out using the agar spotted on the lawn method. The results showed that the number of lactic acid bacteria in chicken eggs was optimum at an incubation temperature of 37°C for 96 hours. The antimicrobial activity of fermented chicken eggs was optimum at an incubation temperature of 37°C for 96 hours as shown from the formation of a clear zone on Muller Hinton Agar (OXOID CM0337). The improvement of total lactic acid bacteria and antimicrobial activity in fermented chicken eggs was optimum at an incubation temperature of 37°C for 96 hours of incubation time.
Research and Implementation of Containerization for Virtual Instrumentation Measurement

The instrumentation measurement system has fruitful achievements in various fields with cloud computing technology. However, the instrumentation cloud needs more PaaS (Platform-as-a-Service) capabilities to improve development and IT operations efficiency by expanding distributed measurement systems. In recent years, container and cloud-native technology growth have brought developing chances for instrumentation cloud. We propose a novel container and instrumentation communication architecture to apply container technology to virtual instrumentation. Firstly, we analyze the critical technologies of Container-Instrumentation communication architecture and give the solution of its implementation. Secondly, we develop and deploy the web backend and frontend to visualize measurement data with Kubernetes. Finally, completing the IV sweep measurement on the web demonstrates the feasibility of building the measurement system in a cloud-native way. So, the container-instrumentation architecture provides practical significance for constructing measurement systems and improving the instrumentation cloud capabilities with cloud-native architecture in the future.