Input Categories Research Articles

CIPP 평가모형에 기반한 의료기관 인증평가 대비 역량 강화 교육에 대한 연구

Objectives In this study, we developed and applied an education program for the purpose of strengthening the competency of employees preparing for the medical institution certification evaluation, analyzed by dividing it into the categories of context, input, process, and product according to Stufflebeam's CIPP evaluation model, and confirm the results of capacity strengthening through education. Methods 101 outpatient department-nursing staff at C city-based a teritiary hospital applied an education program to prepare for medical institution certification evaluation and investigated the evaluation and perception before and after education according to the CIPP evaluation model. Results As a result of systematically evaluating thecompetency enhancement education program for preparing for certification evaluation of medical institutions based on the CIPP model, it was confirmed that it was appropriate for each step. Participants' perceptions before and after education were compared, and the average score of perception after education was significantly improved to confirm the effectiveness of the education program. Conclusions The results of this study are expected to be used as basic data for planning, such as improving medical institutions' attitudes toward medical institution certification evaluation, establishing internal policies, and developing certification evaluation education programs.

Economic Impact of Biotech and Nanotech Approaches in Chickpea (Cicer arietinum L.) Cultivation

Background: Chickpea cultivation plays a vital role in India's agricultural economy, yet it faces challenges like low yields, high input costs, and environmental concerns. Emerging biotechnological and nanotechnological approaches offer potential solutions to enhance productivity and sustainability in legume cultivation. This study evaluates the economic and environmental impacts of these technologies in comparison to conventional farming methods. Methods: A cost-benefit analysis was conducted for chickpea cultivation using both conventional and biotech/nanotech methods across key input categories. A regression model was developed to assess the impact of factors like farm size, irrigation access, and labour costs on profitability. Additionally, a scenario analysis was performed to project economic and environmental outcomes under varying levels of technology adoption. Results: Biotech/nanotech methods demonstrated a significant yield increase of 33% (1.6 tons/hectare vs. 1.2 tons/hectare), resulting in a 60% higher net return (₹48,800 vs. ₹30,500 per hectare). The cost-benefit ratio (CBR) improved from 2.03 (conventional) to 2.56 (biotech/nanotech). Environmental savings amounted to ₹1,800 per hectare through reduced pesticide and water usage. Scenario analysis showed that high adoption rates (90%) could lead to additional revenue of ₹17,000 per hectare and environmental savings of ₹16,200. These results indicate that biotech and nanotech approaches can significantly improve the economic and environmental sustainability of chickpea farming in India.

The impact of economic sanctions on cancer diagnosis and treatment in Iran: a qualitative study

BackgroundThe economic sanctions imposed on Iran have had a significant impact on the country’s healthcare system. The sanctions have affected the availability and affordability of cancer diagnosis and treatment services, leading to a range of challenges for patients with cancer. This study aimed to explore these challenges and gain a deeper understanding of the impact of economic sanctions on cancer care in Iran.MethodsThe research method involved qualitative in-depth interviews with 53 experts, including clinical and basic sciences specialists, who were selected through purposeful sampling. The data were analyzed through qualitative content analysis using MAXQDA 2020.ResultsA total of 900 codes were identified, categorized into three primary groups—input, process, and output—and further divided into 13 subcategories. Within the input category related to health systems providing cancer care to patients, several critical issues emerged. These included instability in financing mechanisms and regulations, shortages of materials, low-quality imported materials, high material prices, and loss of human resources. Meanwhile, in the process of delivering diagnostic and treatment procedures to cancer patients, concerns centered around delayed and inadequate care, non-adherence to guidelines, limited international collaborations, and reduced provider satisfaction. Finally, during the output phase, the main subcategories extracted were lower effectiveness, increased adverse events, and increased equity gaps.ConclusionsAlthough economic sanctions against Iran have not officially targeted healthcare and access to drugs, they have restricted access to standard equipment and modern technologies, leading to increased delays, misdiagnosis, and diagnostic and therapeutic side effects in cancer patients to the point of death.

Building 1D lattice models with $G$-graded fusion category

We construct a family of one-dimensional (1D) quantum lattice models based on GG-graded unitary fusion category \mathcal{C}_G𝒞G. This family realize an interpolation between the anyon-chain models and edge models of 2D symmetry-protected topological states, and can be thought of as edge models of 2D symmetry-enriched topological states. The models display a set of unconventional global symmetries that are characterized by the input category \mathcal{C}_G𝒞G. While spontaneous symmetry breaking is also possible, our numerical evidence shows that the category symmetry constrains the models to the extent that the low-energy physics has a large likelihood to be gapless.

Modeling Multi-Output Back-Propagation DNN for Forecasting Indonesian Export-Import

Introduction/Main Objectives: International trade through the mechanisms of exports and imports plays a significant role in the Indonesian economy, making the timely availability of export and import value data crucial. Background Problems: Export and import values are influenced by inflation and exchange rate factors. Novelty: This study identifies two categories of variables, namely output (export value and import value) and input (inflation rate and the exchange rate of the Rupiah against the US Dollar). Research Methods: the research approach utilizes a Multi-output Deep Neural Network (DNN) with a Back-propagation algorithm to model the input-output relationship. The method can provide forecasting results for two or more bivariate or multivariate output variables. Finding/Results: The modeling analysis results indicate that the optimal model network structure is DNN (3.4). This model successfully predicts output 1 (export value) and output 2 (import value) with Mean Absolute Percentage Error (MAPE) rates of 13.76% and 13.63%, respectively. Additionally, the forecasting results show predicted export and import values for November to be US$ 16,208.13 billion and US$ 15,105.33 billion, respectively. These findings offer important insights into the direction of Indonesia's international trade movement, which can serve as a basis for future economic decision-making.

Can ChatGPT Match the Experts? A Feedback Comparison for Serious Game Development

This paper investigates the potential and validity of ChatGPT as a tool to generate meaningful input for the serious game design process. Baseline input was collected from game designers, students and teachers via surveys, individual interviews and group discussions inspired by a description of a simple educational drilling game and its context of use. In these mixed methods experiments, two recent large language models (ChatGPT 3.5 and 4.0) were prompted with the same description to validate findings with expert participants. In addition, the impact on the models’ suggestions from integrating the expert’s role (e.g., "Answer as if you were a teacher.", "game designer", or a "student") into the prompt was investigated. The findings of these comparative analyses show that the input from both human expert participants and large language models can produce overlapping input in some expert groups. However, experts put emphasis on different categories of input and produce unique viewpoints. This research opens the discussion on the trustworthiness of large language model generated input for serious game development.

TPRO-NET: an EEG-based emotion recognition method reflecting subtle changes in emotion

Emotion recognition based on Electroencephalogram (EEG) has been applied in various fields, including human–computer interaction and healthcare. However, for the popular Valence-Arousal-Dominance emotion model, researchers often classify the dimensions into high and low categories, which cannot reflect subtle changes in emotion. Furthermore, there are issues with the design of EEG features and the efficiency of transformer. To address these issues, we have designed TPRO-NET, a neural network that takes differential entropy and enhanced differential entropy features as input and outputs emotion categories through convolutional layers and improved transformer encoders. For our experiments, we categorized the emotions in the DEAP dataset into 8 classes and those in the DREAMER dataset into 5 classes. On the DEAP and the DREAMER datasets, TPRO-NET achieved average accuracy rates of 97.63%/97.47%/97.88% and 98.18%/98.37%/98.40%, respectively, on the Valence/Arousal/Dominance dimension for the subject-dependent experiments. Compared to other advanced methods, TPRO-NET demonstrates superior performance.

Is Service Orientation Benefitting Manufacturing Exports from Low-Middle Income Countries? Firm-level Empirical Evidence from WBES Data

Servicification of manufacturing, generally understood as the increasing use of service inputs in the production process of goods, is often associated with high-income countries. Recent empirical research on servicification and its impact on manufacturing exports across low-middle income countries is, however, relatively scarce. The current article empirically tests the servicification for low-middle income countries using the World Bank Enterprise Survey (WBES) database over 2006–2021. A fixed effect model with country-sector-year fixed effects is used to study the impact of servicification on existing exporters (intensive margin), and a Logit model is estimated to determine the corresponding impact on the decision to export (extensive margin). While the positive influence of servicification on manufacturing exports is confirmed, different service categories (e.g., transport & communication, information & technology, research & development, legal, finance and related services) are found to play an essential role in determining the decision to enter the export market, while leaving a weaker impact on existing exporters. The findings suggest that increasing the use of different categories of service inputs may differently influence firm-level export behaviour. Liberalization of services, in the era of Industry 4.0, is expected to further enhance this interlinkage. These inevitable dynamics require preparations by firm-level decision-makers to benefit from the emerging opportunities.

Qualitative Analysis of Newborn Hearing Screening Program in Iran.

Early diagnosis of hearing loss and timely interventions are important to minimize the consequences of this condition, especially for children. This research was conducted to analyze the newborn hearing loss screening program in Iran. This qualitative study was conducted using the content analysis method and based on the CIPP model in 2023. The snowball method was used to recruit a sample with maximum diversity. The criteria for selecting people for interviews included having at least three years of experience in the newborn hearing loss screening program and sufficient knowledge in the field. To ensure the reliability of the results, four criteria proposed by Lincoln and Guba were used. Data analysis was conducted by MAXQDA2022 software. In the current research, using content analysis in the form of the CIPP model, based on the viewpoints of the interviewees (40 people), the management requirements of the newborn hearing loss screening program were categorized into the four main categories of context (texture), input, process, and output. Eight subcategories were identified in the context dimension, four subcategories in the input dimension, seven subcategories in the process dimension, and four subcategories in the output dimension. According to the findings of this research, in order to properly implement the newborn hearing loss screening program, there is a need to conduct pilot studies, need assessments, evidence-based programs, and epidemiological studies and to prioritize services and resources. Also, communication between service delivery levels needs to be improved, and attention should be paid to personnel motivation and screening programs.

Cross-movie prediction of individualized functional topography.

Participant-specific, functionally defined brain areas are usually mapped with functional localizers and estimated by making contrasts between responses to single categories of input. Naturalistic stimuli engage multiple brain systems in parallel, provide more ecologically plausible estimates of real-world statistics, and are friendly to special populations. The current study shows that cortical functional topographies in individual participants can be estimated with high fidelity from naturalistic stimuli. Importantly, we demonstrate that robust, individualized estimates can be obtained even when participants watched different movies, were scanned with different parameters/scanners, and were sampled from different institutes across the world. Our results create a foundation for future studies that allow researchers to estimate a broad range of functional topographies based on naturalistic movies and a normative database, making it possible to integrate high-level cognitive functions across datasets from laboratories worldwide.

Machine learning analysis to interpret the effect of the photocatalytic reaction rate constant (k) of semiconductor-based photocatalysts on dye removal

Photocatalytic reactions with semiconductor-based photocatalysts have been investigated extensively for application to wastewater treatment, especially dye degradation, yet the interactions between different process parameters have rarely been reported due to their complicated reaction mechanisms. Hence, this study aims to discern the impact of each factor, and each interaction between multiple factors on reaction rate constant (k) using a decision tree model. The dyes selected as target pollutants were indigo and malachite green, and 5 different semiconductor-based photocatalysts with 17 different compositions were tested, which generated 34 input features and 1527 data points. The Boruta Shapley Additive exPlanations (SHAP) feature selection for the 34 inputs found that 11 inputs were significantly important. The decision tree model exhibited for 11 input features with an R2 value of 0.94. The SHAP feature importance analysis suggested that photocatalytic experimental conditions, with an importance of 59%, was the most important input category, followed by atomic composition (39%) and physicochemical properties (2%). Additionally, the effects on k of the synergy between the metal cocatalysts and important experimental conditions were confirmed by two feature SHAP dependence plots, regardless of importance order. This work provides insight into the single and multiple factors that affect reaction rate and mechanism.

Ensemble Learning-Based Seismic Response Prediction of Isolated Structure Considering Soil–Structure Interaction

To accurately and rapidly predict seismic responses, including the maximum displacement (MaxD) and maximum acceleration (MaxA), of the isolated structure considering the soil–structure interaction (SSI), five ensemble learning models, i.e. random forest (RF), gradient boosting regression tree (GBRT), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM) and stacking model, are constructed. Firstly, a total of 96 000 nonlinear time history analyses of the isolated structure considering the SSI are conducted with the aid of OpenSees. The generated database is used for training and testing ensemble learning models. The ensemble learning models have 12 input variables in four categories, i.e. ground motion parameters, structural parameter, isolation parameters and soil parameter, and two output variables, i.e. MaxD and MaxA. The study shows that all ensemble learning models have excellent prediction performance for both training and testing datasets. The determination coefficients are larger than 0.96 and root-mean-square errors (RMSEs) are relatively small. Among the five ensemble learning models, the stacking model exhibits the best performance. In addition, the calculation method of feature importance score for the stacking model is provided. According to the feature importance analysis, the ground motion parameters have greater impact on seismic responses than other three categories of inputs. Finally, six ground motions are randomly selected to verify the generalization ability of the proposed ensemble learning models. The results show that the stacking model has a favorable generalization ability with relatively small prediction errors.

Prediction and aggregation of regional PV and wind generation based on neural computation and real measurements

With the increasing integration of renewable energy sources (RES) in electricity grid networks, the power supply system has been changed to be decentralised. The complexity of the grids increases. So the estimation and prediction of regional renewable power integration is essential for grid stability and planning. However, the power prediction from single virtual power plant (VPP) is no longer suitable for the large scale regional renewable power generation. This paper proposed a prediction model based on neural computation. Various architectures of artificial neural network (ANN) are studied. Different input features are investigated and uncertainty of the prediction results are assessed. The relative mean absolute error (rMAE) to installed capacity can reach 2.5% in summer and 0.5% in winter for PV power prediction. By using the reduced mean values of input categories features the rMAE can reach 1.7% and 4.9% for PV and wind prediction for the whole year respectively.

Educational Application for Institute

Abstract: Physical schoolroom learning today isn't longer applicable to the younger generations. Net and distance learning which is usually called online education plays a significant role in the country's education system. Simply, online education provides a sample of advantages to young learners.Institute Management Systems helps lecturers to complete grade books, track students’ group activities, take quizzes, input category notes, produce lesson plans and elaborated reports, and communicate with different workers members, students, and fogeys all via e-mail. It conjointly helps Students to access assignments and tests, and consider group action records, grades, report cards, and progress reports all online. They can also communicate through mail and forums with lecturers and different students online. The form contained queries of open sort associate degree queries within thestyle of an assessment scale. The obtained results have shown that students and lecturers square measure responsive to the advantages of project teaching andparticularly emphasize the contribution to the development of scholar’s skills.

Developing machine learning models for wheat yield prediction using ground-based data, satellite-based actual evapotranspiration and vegetation indices

Timely and accurate crop yield estimation is important for adjusting agronomic management and enseuring agricultural sustainability. Machine learning (ML) algorithms provide new opportunities to integrate agronomic information with ground-based and satellite data and develop flexible yield predictive models. In particular, satellite-based vegetation indices and evapotranspiration provide robust proxies for crop yield estimations in the absence of measurements; nevertheless, most prior model development efforts have focused on using only vegetation indices due to the simplicity of the process. Additionally, the contribution of input categories (i.e., field, meteorological, and satellite data) and the use of appropriate proxies, aligned with the crop growth stages, in developing yield predictive models have not been adequately investigated. To address these challenges, we employed two ML techniques, Random Forest (RF) and extreme gradient boosting algorithm (XGB), to estimate wheat yield using meteorological variables, satellite-driven actual evapotranspiration (ETa), and vegetation indices (VIs). ETa was separately computed using the surface energy balance concept and the METRIC model. The models were first trained and tested in the study area using three input combinations: i) meteorological variables, ii) satellite data, and iii) an ensemble of meteorological and satellite data. Then, the best-performing model was further evaluated using two independent datasets. We found ETa to be particularly important in improving the accuracy of the model predictions. Among the vegetation indices, EVI, EVI2, and NDVI during May, and among the meteorological data, growing degree days during the grain filling stage plus minimum temperature in the stem elongation stage had the highest contributions to yield predictions. Both ML algorithms generated relatively accurate results, where XGB was marginally more accurate than RF, considering an average mean absolute error of 0.39 t ha-1 for XGB and 0.50 t ha-1 for RF. Normalized root-mean-square errors of the ensemble, satellite-derived and meteorological-derived models in XGB were 0.05, 0.07, and 0.10, respectively. Nevertheless, both algorithms’ performances deteriorated in predicting the yield values beyond the range of the training set, though XGB could handle the extrapolation process more efficiently than RF.

Pattern Recognition in Breast Cancer Using Machine Learning

Breast Cancer is the most often identified cancer among women and major reason for increasing mortality rate among women. As the diagnosis of this disease manually takes long hours and the lesser availability of systems, there is a need to develop the automatic diagnosis system for early detection of cancer. Data mining techniques contribute a lot in the development of such system. For the classification of benign and malignant tumor we have used classification techniques of machine learning in which the machine is learned from the past data and can predict the category of new input. This paper is a relative study on the implementation of models using Logistic Regression, Support Vector Machine (SVM) and Latent Dirichlet Allocation (LDA) is done on the dataset taken from the UCI repository. With respect to the results of accuracy, precision, sensitivity, specificity and False Positive Rate the efficiency of each algorithm is measured and compared. Breast Cancer is the most leading malignancy affecting 2.1 million women each year which leads to greatest number of deaths among women. Early treatment not only helps to cure cancer but also helps in prevention of its recurrence. And hence this system mainly focuses on prediction of breast cancer where it uses different machine learning algorithms for creating models like decision tree, logistic regression, random forest which are applied on pre-processed data which suspects greater accuracy for prediction. Amongst all the models, Random Forest Classification leads to best accuracy with 98.6% techniques

Effect of Biostimulants on Leafy Vegetables (Baby Leaf Lettuce and Batavia Lettuce) Exposed to Abiotic or Biotic Stress under Two Different Growing Systems

Plant biostimulants are substances or microorganisms aimed at promoting plant growth by increasing the mineral nutrition efficiency, tolerance to environmental stress, and crop quality traits. This new category of crop inputs has been capturing the interest of both researchers and agriculture takeholders in light of the promising effects they could have on crop productivity and sustainability. This study investigated a variety of biostimulants for their effect on germination rates, plant health, chlorophyll fluorescence parameters, SPAD index, and growth of baby leaf lettuce and Batavia lettuce submitted to biotic (absence/presence of Pythium ultimum in the growing medium) or abiotic (0, 40, 80, and 120 mM NaCl L−1 concentrations and −0.5, −2, −4, and −6 kPa water potentials) stresses when grown in a greenhouse under conventional and organic cultivation. The results obtained show that lettuce response to biostimulants was influenced by the type or level of stress applied and the growing system used. The effects of the tested biostimulants varied from strongly detrimental to strongly beneficial.

Disentangling modular Walker-Wang models via fermionic invertible boundaries

Walker-Wang models are fixed-point models of topological order in $3+1$ dimensions constructed from a braided fusion category. For a modular input category $\mathcal M$, the model itself is invertible and is believed to be in a trivial topological phase, whereas its standard boundary is supposed to represent a $2+1$-dimensional chiral phase. In this work we explicitly show triviality of the model by constructing an invertible domain wall to vacuum as well as a disentangling generalized local unitary circuit in the case where $\mathcal M$ is a Drinfeld center. Moreover, we show that if we allow for fermionic (auxiliary) degrees of freedom inside the disentangling domain wall or circuit, the model becomes trivial for a larger class of modular fusion categories, namely those in the Witt classes generated by the Ising UMTC. In the appendices, we also discuss general (non-invertible) boundaries of general Walker-Wang models and describe a simple axiomatization of extended TQFT in terms of tensors.

Step-by-Step Process for Assessing the Economic Impact of Regional Medical Campuses in Canada

Background: Regional medical campuses (RMCs) create positive economic impacts in communities and small cities. RMCs increase educational capacity, medical services, and address the shortage or maldistribution of physicians in rural areas. Our paper answers the question: How do you assess the economic impact of a RMC? Methods: The Canadian Input-Output (I-O) model and the Simplified American Council on Education (ACE) model are adapted to assess the economic impact of an individual RMC using a step-by-step process. The models are tested using data from three Canadian RMCs. Results: A comparison of the two models found similarities with data requirements and spreadsheet calculations. However, the Canadian I-O model spreadsheet is linked to Statistics Canada multipliers and calculations are more complex. Outputs are calculated for multiple economic variables. The Simplified ACE model, in contrast, uses a single multiplier and provides a single number by input category and a cumulative total of all impacts. Conclusion: Both models successfully assess economic impacts of RMC. The step-by-step process allows RMC administrators and others to understand the limitations of each model, but also facilitates an in-house economic assessment of RMC. The authors provide guidance on choosing the best model.

A descriptive analysis of olfactory sensation and memory in Drosophila and its relation to artificial neural networks

This article provides a background and descriptive analysis of insect memory and the coding of olfactory sensation in Drosophila, presenting graphs and summary statistics from a large dataset of neurons and synapses that was recently made publicly available and also discussing findings from the existing empirical literature. Some general principles from Drosophila olfaction are discussed as they apply to the design of analogous systems in artificial neural networks: (1) the networks used for coding are shallow; (2) the level of connectedness varies widely across neurons in the same layer; (3) much communication is between neurons in the same layer; (4) in most olfactory learning, the manner in which sensory inputs are represented in stored memory is largely fixed, and the learning process involves developing positive or negative associations with existing categories of inputs.