Validation Data Research Articles

Prediction of Glass Transition Temperature of Polymers Using Simple Machine Learning.

Polymer materials have garnered significant attention due to their exceptional mechanical properties and diverse industrial applications. Understanding the glass transition temperature (Tg) of polymers is critical to prevent operational failures at specific temperatures. Traditional methods for measuring Tg, such as differential scanning calorimetry (DSC) and dynamic mechanical analysis, while accurate, are often time-consuming, costly, and susceptible to inaccuracies due to random and uncertain factors. To address these limitations, the aim of the present study is to investigate the potential of Simplified Molecular Input Line Entry System (SMILES) as descriptors in simple machine learning models to predict Tg efficiently and reliably. Five models were utilized: k-nearest neighbors (KNNs), support vector regression (SVR), extreme gradient boosting (XGBoost), artificial neural network (ANN), and recurrent neural network (RNN). SMILES descriptors were converted into numerical data using either One Hot Encoding (OHE) or Natural Language Processing (NLP). The study found that SMILES inputs with fewer than 200 characters were inadequate for accurately describing compound structures, while inputs exceeding 200 characters diminished model performance due to the curse of dimensionality. The ANN model achieved the highest R2 value of 0.79; however, the XGB model, with an R2 value of 0.774, exhibited the highest stability and shorter training times compared to other models, making it the preferred choice for Tg prediction. The efficiency of the OHE method over NLP was demonstrated by faster training times across the KNN, SVR, XGB, and ANN models. Validation of new polymer data showed the XGB model's robustness, with an average prediction deviation of 9.76 from actual Tg values. These findings underscore the importance of optimizing SMILES conversion methods and model parameters to enhance prediction reliability. Future research should focus on improving model accuracy and generalizability by incorporating additional features and advanced techniques. This study contributes to the development of efficient and reliable predictive models for polymer properties, facilitating the design and application of new polymer materials.

What is the relation between training transfer evaluation factors and training transfer when measured in the Spanish Public Administration?

The European Commission will reinforce the value of adult training through an evaluation of the impact of actions. Among the existing options in evaluating the impact of actions, training transfer is crucial in determining the degree to which the learning acquired during training is used at work. We validated a predicting transfer model in the Spanish Public Administration (SPA) by analyzing, through structural equation modeling (SEM), results of the application of the Factors for Predicting Transfer (FPT) questionnaire applied after the training, and the Scale of Efficacy (CdE) applied four months after the training (n = 726). Although satisfaction and motivation to transfer were not related to transfer, content relevance and accountability were. This study widens the traditional contexts of the study of training transfer and provides valid and reliable empirical data on transfer embedded in a new context, the Spanish Public Administration. This can help further develop the field of international human resource management (IHRM) by exploring transfer in different cultural contexts.

Prognostic significance of an innovative staging system based on the logarithmic odds of positive lymph nodes for resectable gastroesophageal cancer after neoadjuvant chemoradiation: a population-based study with external validation of data

BackgroundAfter receiving neoadjuvant chemoradiation, the number of examined lymph nodes in resectable gastroesophageal cancer (GEC) will decrease, this may not accurately determine the N staging. So our study evaluates the clinical significance of a new staging model based on the logarithmic odds of positive lymph nodes (LODDS) in patients with GEC after receiving neoadjuvant chemoradiation.MethodsA total of 1 130 patients with pathologically diagnosed GEC who received neoadjuvant chemoradiation from 2004 to 2019 included in the National Cancer Institute Surveillance, Epidemiology, and Results (SEER) database were selected for analysis. Lymph nodes were staged according to the AJCC TNM staging system (eighth edition) and LODDS. Patient prognosis across the two systems were evaluated by the Kaplan–Meier method, differences in node staging were evaluated by the Akaike information criterion and Bayesian information criterion. In addition, 914 patients from our center were externally validated.ResultsCompared to the traditional TNM staging system, the new TLODDSM staging system was comprised of stage I, stage II, stage IIIA, stage IIIB, and stage IVA, and decision curve analysis showed that the new staging system had higher benefits for different decision thresholds than the old staging system. The Akaike information criterion and Bayesian information criterion of the new staging system was lower than those of the old staging system, indicating the sensitivity of the TLODDSM staging system for predicting the prognosis of patients was higher. In addition, stage-IIIB or -IVA patients in the new staging system benefited from adjuvant chemotherapy. The externally validated data from our center supported this conclusion.ConclusionsCompared to the TNM staging system, the TLODDSM staging system has significant advantages in predicting prognosis of patients with GEC who have completed neoadjuvant chemoradiation, guiding the adjuvant chemotherapy for patients.

Differences in Self-Rated Worker Outcomes Across Stress States: An Interim Analysis of Hybrid Worker Data

Stress experiences can have dire consequences for worker performance and well-being, and the social environment of the workplace is a key contributor to worker experience. This study investigated the relationship between hybrid workers’ self-ratings of productivity, mood, and stress with perceptions of positive (eustress) and negative (distress) stress states. We hypothesized that self-ratings would vary across combinations of eustress and distress experiences and that these differences would differ based on the social context. Ecological momentary assessments (EMA) were used to obtain ecologically valid data at four data points each workday across a 4-month study period in a cohort of seven office workers. Findings aligned with the Yerkes–Dodson law, such that higher states of arousal were associated with greater self-perceived productivity, and higher stress magnitudes were found when distress existed. Compared to other states, eustress was associated with higher productivity in work-related activities and better mood across all activity types.

Analysis of Policy Implementation and Availability of Infrastructure on the Implementation of Early Breastfeeding Initiation After Cesarean Section Delivery in Private Hospitals in Kudus District

Early initiation of breastfeeding (IMD) is placing the infant on the mother's chest or abdomen and allowing the infant to seek the mother's nipple within the first hour of birth. Caesarean section (SC) poses challenges to the implementation of Early Breastfeeding Initiation (IMD) in both internationally and nationally recommended hospital programs for maternal and infant care. The purpose of this study was to analyze policy implementation and analyze the availability of hospital facilities in the implementation of Early Breastfeeding Initiation (IMD) after Sectio Caesarean delivery. The research design was descriptive qualitative. The main informants of the study were SPOg doctors, implementing midwives and the Head of the Central Surgical Unit. Supporting informants are laboring mothers, central surgery unit nurses, pediatricians, hospital management. Data collection using in-depth interviews and observation. Data validity strategy using source triangulation. There is a policy contained in the decree of the hospital director and the hospital SOP as a work instruction for the implementing unit, monitoring is carried out every delivery and the division of tasks is carried out situationally. The number, competence and infrastructure are available well enough to support the implementation of IMD after Sectio Caesarea delivery in the hospital

Management Analysis of Tied Infaq and Sadaqah on the Tahfidz Quran Scholarship Program (Study of Yakesma Branch in Riau Islands)

The challenges faced in the management of Islamic philanthropic institutions include the potential for large infaq and sadaqah and the importance of infaq and sadaqah for the purpose of community empowerment, the dominance of the distribution of funds that are still consumptive so that they still do not empower the community, and the lack of accountability reporting to donors which can reduce their level of trust in Islamic philanthropic institutions. This research is important to gain an in-depth understanding of the effective management of tied infaq and alms through the Tahfidz Quran scholarship program, improve the effectiveness and efficiency of the management of tied infaq and alms funds, and provide recommendations for improving accountability and developing tied infaq and alms-based programs. This qualitative research with a case study approach analyzes the management of tied infaq and sadaqah in the Tahfidz Quran scholarship program run at the national amil zakat institution, YAKESMA Riau Islands Branch. Research data were obtained through interviews, field observations, and documentation studies related to the management carried out. The data was then analyzed descriptively by triangulating to ensure data validity and credibility. The theoretical framework of management, the concept of infaq and sadaqah, and SWOT analysis were used to interpret the factual findings comprehensively. The results show that efforts to overcome existing problems are expected to increase the effectiveness and accountability of the management of tied infaq and sadaqah funds and the management of Islamic philanthropic institutions in supporting programs with various other empowerment goals in the future

Analysis of Halal Literacy, Halal Awareness, Motivation and Religiousity of SMES on Interest in Halal Certification in Banjarmasin

Micro, Small, and Medium-Sized Enterprises (MSMEs) play a significant role in Banjarmasin City's local economic development. Along with this growth, halal products have great potential to gain the support of consumers whose awareness of halal products is increasing. Halal factors are becoming increasingly important to consumers when making routine product purchases. This study examines how the motivation,religiosity, halal literacy, and halal awareness of MSME (Micro, Small, and Medium Enterprises) actors affect their desire to get halal certification in Banjarmasin City. This study is conducted using field research using a quantitative descriptive technique. Purposive sampling, a non-probability sampling technique, selects samples based on the requirement to meet specific objectives, such as identifying MSMEs in Banjarmasin for a minimum of one year. A questionnaire is used for data collection, and the sample size is 96. Using SPSS 25.0 for Windows applications,the following data analysis procedures were employed: hypothesis testing, data validity analysis, and descriptive data analysis. The findings indicate that MSMEs' interest in Halal Certification in Banjarmasin City is significantly influenced, at least in part, by the factors of Halal Literacy, Halal Awareness, Motivation, and Religiosity. The factors significantly impacting MSMEs' interest in halal certification in Banjarmasin City simultaneously include motivation,religiosity, halal literacy, and halal awareness.

Peran Kepemimpinan Kepala Sekolah dan Kinerja Guru dalam Meningkatkan Kualitas Mutu Pendidikan pada SDI Cokroaminoto Surabaya

The principal has an important role which is very helpful to the Cokroaminoto Islamic elementary school. in improving the quality of education, even though his role is only as PLT Principal, he carries out his duties well. Efforts to improve the quality of education are one of the factors in creating ideal education. This research wants to find out more about "The Role of Principal Leadership and Teacher Performance in Improving the Quality of Education" in accordance with the main problem raised, namely the relationship between principal leadership and teacher performance in improving the quality of education at the Cokroaminoto Surabaya Islamic elementary school. There are several factors that influence the increase in educational progress, including: a) competent teachers, b) facilities and infrastructure, c) implementation of curriculum and innovations implemented, d) student involvement, e) evaluation or supervision carried out by the Head of School, f) and administration education. The aim of this research is to find out the role of the school principal as a leader, who has motivator competencies and indicators for educational staff who contribute to improving the quality of education. This research method uses a qualitative approach and uses a case study research design. Data collection techniques used (1) in-depth interviews, (2) non-participant observation, (3) documentation studies. Data validity techniques use credibility, transferability, dependability and confirmability. Data analysis techniques use data reduction, data presentation, verification or conclusions. The conclusion of this research is that there is a link between school leadership and teacher performance in an effort to improve the quality of education at the Cokroaminoto Islamic elementary school, Surabaya.

A Minimal Nontrivial Example of Higher Order Evidence.

Higher order evidence (evidence about evidence) allows epidemiologists and other health data scientists to account for measurement error in validation data. Here, to illustrate the use of higher order evidence, we provide a minimal nontrivial example of estimating the proportion and show how higher order evidence can be used to construct sensitivity analyses. The proposed method provides a flexible approach to account for multiple levels of distortion in the results of epidemiologic studies.

Implementasi Algoritma You Only Look Once (YOLO) untuk Mendeteksi Bahasa Isyarat SIBI

The Indonesian Sign Language System (SIBI) is a translator of sign language into text or speech. This research aims to bridge communication between ordinary people and speech impaired people through the introduction of SIBI sign language using the YOLO algorithm. This research uses 24 alphabets which are divided into 4 groups, where each alphabet has 20 image data which is divided into 70% train data, 25% valid data, and 5% test data. The train data was then added with augmented data from Roboflow which was then carried out using a training process using a batch number of 16 and epochs of 100. The results of the research show that the YOLO algorithm can detect SIBI sign language alphabet gestures using confusion matrix testing and achieve quite good performance, as shown by the results F1 Score: Group 1 was 90.90%, Group 2 was 97.1%, Group 3 was 90.90%, and Group 4 was 83.8%. Other factors such as hand size, lighting conditions, and variations in data position also affect detection accuracy. A limitation in this research is that the alphabets J and Z were not included because these two alphabets not only use shape patterns, but also gesture patterns.

Penyesuaian Diri pada Menantu Perempuan yang Tinggal di Rumah Mertua

Marriage is a bond between two individuals who dicide to live together. Many couples begin their married life by living with their parents, resulting in more complex dynamics and adjustment processes. This research employs a qualitative phenomenological method with three participants. Data collection techniques include interviews. Data validity is tasted through member checks and expert judgment. The result of this study indicate that participants are able to adjust to living with their in-laws, with supporting factors including physical condotions, development and maturity, psychological state, as well as cultural and religious conditions. Conflicts encountered by daughters-in-law living with their in-laws include misunderstandings about household cleanliness, differences in daily activities, and child-rearing practices.

Automatic elimination of invalid impact-echo signals for detecting delamination in concrete bridge decks based on deep learning

The impact-echo (IE) method is effective for evaluating invisible defects. However, it might return misleading results when its signals are invalid. This challenge aggravates when the tests are conducted using robotic devices that automatically collect massive data. This study proposes an automatic method to eliminate invalid signals based on the ResNet model. First, the signals are visualized into two-dimensional images as the input for ResNet. The input data can then be classified into valid and invalid data via the ResNet model, which is trained with 11,290 signals and tested with 5664 signals. Finally, defects can be detected using the dominant frequencies of the valid-class data. A case study with IE data from two concrete bridges was employed to validate the feasibility of the proposed approach. The results indicate that the method can achieve an average accuracy of 90.6% for eliminating invalid signals and significantly improve the IE test accuracy.

History Extended: Migration of Systema Dipterorum into TaxonWorks

Now 40-year old, Systema Dipterorum was originally created in a FileMaker™ database by F.C. Thompson and is currently maintained by N.L. Evenhuis and T. Pape (Evenhuis and Pape 2024a). Recent conversations as to its future led to an effort to transition Systema Dipterorum into TaxonWorks. A web-based platform tailored to the requirements of biodiversity-informatics scientists, TaxonWorks meets the evolving needs of Systema Dipterorum. TaxonWorks provides tools designed to facilitate data migration, management, validation, and packaging for exports, such as Darwin Core-Archives (DwC-A) and Catalogue of Life Data Package (CoL-DP) formats. While TaxonWorks offers batch upload functionality, the intricate nature of the Systema Dipterorum database necessitated specialized handling, i.e., a custom migration script. A TaxonWorks version of Systema Dipterorum is now accessible for preview and validation. This preview is particularly valuable for nomenclature validation, as TaxonWorks includes many tools ensuring compliance with the rules of Zoological Nomenclature (International Commission on Zoological Nomenclature 1999). Additionally, this preview version now directly feeds the pipeline serving Systema Dipterorum to the Catalogue of Life. By migrating Systema Dipterorum into TaxonWorks, a wide range of utilities expanding the decades of effort behind the data are unlocked. TaxonWorks enables the creation of paper-style catalogs and the sharing of data via its public interface, TaxonPages. A test version of Systema Dipterorum stored in TaxonWorks and rendered with TaxonPages is now available on-line (Evenhuis and Pape 2024b). The data are also accessible via a JavaScript Object Notation (JSON) API, enabling computing (e.g., scripted synonymy queries) against the TaxonWorks dataset. Complexity and diversity of the data are illustrated in Fig. 1. While many technical hurdles are resolved, major challenges remain in building a new community around Systema Dipterorum, one that will collectively take over the efforts of the few curators who have worked for so long on this broadly used resource.

Dynamic Modeling of a Thermochemical System for Solar Fuel Production Based on an Open-Source Framework

This paper describes the dynamic process model for a solar fuels synthesis plant currently being built in Germany. The open-source, equation-based modelling language Modelica is used as the foundation. In this plant, biogas and steam are reformed, producing synthesis gas and subsequently turned into synthetic crude oil. This work contains the necessary model setups for the thermal energy storage, solar-absorbing gas receiver and reforming reactor to consider the thermohydraulic, thermochemical and radiative interactions occurring in the process. The remaining infrastructure is modeled with the Modelica Standard Library. A way to fit these models with experimental data for validation is also outlined.

Party Proximities in Voting Advice Applications – Identifying Structural Breaks in Data from the German Wahl-O-Mat

Abstract Voting Advice Applications (VAAs), such as the German Wahl-O-Mat (WOM), have been examined extensively by researchers in various ways. Here, a subset of research uses data from the Wahl-O-Mat to investigate party relations. While these investigations focus on a limited number of WOM editions, I explicitly investigate the persistence of party relations in the WOM in a broad statistical analysis over 63 editions of the WOM in this exploratory research paper. At first glance, I find that intuitive differences in the overall proximities are observable. However, while many arguments exist that corroborate the suitability of the WOM data for the investigation of party relations, I identify statistically significant lower proximities for the six largest German parties in WOM editions from 2009 onward. Based on a review of changes in the design framework of the WOM and comparative data analyses based on the Chapel Hill Expert Survey and the Manifesto data set, these lower proximities are apparently linked to changes in the design of the WOM. As a result, I discuss the general ability of data from the WOM to give reliable insights into party relations. I argue that the WOM might roughly represent overall party relations but, due to a non-proportionality in the shift of the distances, potential design changes have distorting impacts on the relations between parties that make comparisons between different editions of the WOM problematic. This should be a starting point to investigate other VAAs for similar patterns to reevaluate the validity of data from VAAs for the examination of party relations in general.

Evaluation of the Xpert Carba-R assay for quantifying carbapenemase-producing bacterial load in stool samples.

The spread of Carbapenemase-producing Organisms (CPO) remains a major threat globally. Within clinical settings, the existing method of determining gene load involves traditional culture to determine bacterial load and polymerase-chain-reaction-based Xpert Carba-R Assay to determine carbapenemase gene type. However, there is a need for a fast and accurate method of quantifying CPO colonisation to study the risk of persistent CPO carriage. This study evaluated the accuracy of Xpert Carba-R Ct value in estimating carbapenamase producing bacterial loads in stool samples. Stool samples were obtained from an ongoing study investigating the household transmission of CPO in Singapore. Stool samples lacking carbapenemase producing organisms were spiked with organism carrying a single carbapenemase gene (blaKPC, blaNDM, blaVIM, blaOXA-48(-like) or blaIMP-1) and serially diluted before being subjected to Xpert Carba-R assay and traditional culture. Standard curves with regression lines showing correlation between Ct values and plate counts were generated. The standard curves were validated with stool samples collected from patients. The limit of detection of blaNDM, blaKPC, and blaOXA-48 was approximately 103 cfu/mL, while that of blaIMP-1 and blaVIM was approximately 104 cfu/mL. Validation of the blaNDM and blaOXA-48 curves revealed average delta values of 0.56 log(cfu/mL) (95% CI 0.24-0.88) and 0.80 log(cfu/mL) (95% CI 0.53-1.07), respectively. Our validation data for stool positive for blaNDM and blaOXA-48-type suggests that bacterial loads can be estimated within a reasonable range of error.

Classification of Indonesia False News Detection Using Bertopic and Indobert

In the current global era, the development of technology and information is very rapid, so it is very easy to get information/news from the internet. Because of the ease of getting this information, there is a lot of circulating fake news (hoaxes), the news is not filtered so anyone can spread news that is not clear in content. This can lower a person's credibility in the professional world, cause division, threaten physical and mental health, and can also result in material losses. Based on this, to stop the spread of hoaxes is to detect them as early as possible and block them. This detection can use deep learning methods which are also one of the architectures of transformers, namely a combination of BERTopic which is used to find important words from the news narrative, then the words are combined into the narrative and classified using Indo Bidirectional Encoder Representation from Transformer (IndoBERT). For experiments, the author uses a dataset taken from the kaggle.com website entitled Indonesia False News (HOAX) dataset. This study uses a learning rate of 1e-5, a batch size of 16 and using 5 epochs so that the f1-Score results are 92% for validation data and 91% for testing data.

Evaluation of TOPAS MC tool performance in optical photon transport and radioluminescence-based dosimetry

Radiation therapy plays a pivotal role in modern cancer treatment, demanding precise and accurate dose delivery to tumor sites while minimizing harm to surrounding healthy tissues. Monte Carlo simulations have emerged as indispensable tools for achieving this precision, offering detailed insights into radiation transport and interaction at the subatomic level. As the use of scintillation and luminescence dosimetry becomes increasingly prevalent in radiation therapy, there arises a need for validated Monte Carlo tools tailored to optical photon transport applications. In this paper, an evaluation process of the TOPAS (TOol for PArticle Simulation) Monte Carlo tool for Cerenkov light generation, optical photon transport and radioluminescence based dosimetry is presented. Three distinct sources of validation data are utilized: one from a published set of experimental results and two others from simulations performed with the Geant4 code. The methodology employed for evaluation includes the selection of benchmark experiments, making use of opt3 and opt4 Geant4 physics models and simulation setup, with observed slight discrepancies within the calculation uncertainties. Additionally, the complexities and challenges associated with modeling optical photons generation through luminescence or Cerenkov radiation and their transport are discussed. The results of our evaluation suggests that TOPAS can be used to reliably predict Cerenkov generation, luminescence phenomenon and the behavior of optical photons in common dosimetry scenarios.

Validation of the Basic Psychological Needs Satisfaction scale in transport for urban commuters (BPNS-TUC) in Spain

The aim of this paper is to present a validity and reliability study of the translation into Spanish and adaptation to the transport planning domain of the Basic Psychological Needs Satisfaction scale to assess autonomy, competence, and relatedness satisfaction of urban commuters associated to the use of habitual travel modes, expanding past studies that only focused on a particular population of study and type of travel behavior. An explanatory analysis of the main relations found is also reported, including micro-mobility and shared travel options. To this end, data collected by an online survey during the summer of 2021 from 949 respondents who provided valid data is used. The average age of respondents was 42, and women were slightly underrepresented (40.4%). Factor structure is analyzed and a three-factor ESEM model supports the definition of the constructs. Internal consistency indices show adequate values for the three factors. No statistically significant differences of factor mean values are observed according to sex. The youngest respondents associate the highest values of satisfaction to travel modes when commuting, while the old adults score the lowest values. Active travel modes are associated to the highest values of satisfaction. On the other hand, urban bus is related to the lowest values of satisfaction. Private e-scooter and shared mobility are associated with a higher satisfaction than driving a private car. Future work and practical applications are identified.

Artificial Intelligence-Based Improvement of Empirical Methods for Accurate Global Solar Radiation Forecast: Development and Comparative Analysis

Artificial intelligence (AI) technology has expanded its potential in environmental and renewable energy applications, particularly in the use of artificial neural networks (ANNs) as the most widely used technique. To address the shortage of solar measurement in various places worldwide, several solar radiation methods have been developed to forecast global solar radiation (GSR). With this consideration, this study aims to develop temperature-based GSR models using a commonly utilized approach in machine learning techniques, ANNs, to predict GSR using just temperature data. It also compares the performance of these models to the commonly used empirical technique. Additionally, it develops precise GSR models for five new sites and the entire region, which currently lacks AI-based models despite the presence of proposed solar energy plants in the area. The study also examines the impact of varying lengths of validation datasets on solar radiation models’ prediction and accuracy, which has received little attention. Furthermore, it investigates different ANN architectures for GSR estimation and introduces a comprehensive comparative study. The findings indicate that the most advanced models of both methods accurately predict GSR, with coefficient of determination, R2, values ranging from 96% to 98%. Moreover, the local and general formulas of the empirical model exhibit comparable performance at non-coastal sites. Conversely, the local and general ANN-based models perform almost identically, with a high ability to forecast GSR in any location, even during the winter months. Additionally, ANN architectures with fewer neurons in their single hidden layer generally outperform those with more. Furthermore, the efficacy and precision of the models, particularly ANN-based ones, are minimally impacted by the size of the validation data sets. This study also reveals that the performance of the empirical models was significantly influenced by weather conditions such as clouds and rain, especially at coastal sites. In contrast, the ANN-based models were less impacted by such weather variations, with a performance that was approximately 7% better than the empirical ones at coastal sites. The best-developed models, particularly the ANN-based models, are thus highly recommended. They enable the precise and rapid forecast of GSR, which is useful in the design and performance evaluation of various solar applications, with the temperature data continuously and easily recorded for various purposes.