Selection Stage Research Articles

SELF-DIRECTED LEARNING AND MOOC INTEGRATION INTO HIGHER EDUCATION EFL CLASSROOMS

This research includes the first cycle of an application based on the integration of a MOOC given in the field of “writing” into the formal education curriculum to reinforce classroom teaching and support the learning process to improve English writing skills. It was carried out in the spring semester of the 2021-2022 academic year with 14 students studying in an English preparatory program at a Turkish state university. In this study, qualitative research method was adopted and the action research design was applied. The implementation was carried out within the scope of the Reading/Writing course in the program in question. As data collection tools, a semi-structured interview form, Self-directed Learning Scale, and students’ course completion scores in their chosen MOOCs were utilized. In addition, the articles written by the students at the end of the term within the scope of classroom evaluation were also used to support the research data. The first data obtained in this direction show that the majority of the participants could not go beyond the course selection and registration stage. Although the students mostly did not have problems in accessing technology and showed self-directed learner characteristics, they did not follow or complete these courses due to lack of motivation, technological problems, heavy course loads and health problems.

The Good, The Bad, and The Ugly About Insta Shopping: A Qualitative Study

Instagram, as many social media platforms, has been increasingly used by users to shop for goods and products from business or other individuals. Recently, studies have shed lights on acceptance and usage of Insta shopping from users’ perspectives by following popular technology models, such as technology acceptance model (TAM) and unified theory of acceptance and use of technology (UTAUT). However, more rich and in-depth insights about using Instagram for commercial purposes within a certain context are yet to be discovered. Therefore, this study aims at discovering experiences and interactions with Insta shopping, the factors and the drivers that impact users’ acceptance of Insta shopping, the weight of each factor (degree of consensus among participants), and their direction (positive, negative, or both). The study followed a qualitative approach, by creating four homogeneous focus groups (six participants each) of IT students in United Arab Emirates (UAE) universities. The data analysis approach considered is an axial coding technique as part of the grounded theory, which includes open coding, axial coding, and selective coding stages. The results revealed that the time factor, trust in Insta shops (and its drivers such as reviews, word of mouth, trading license, and others), distrust (and its drivers such as fake comments and reviews, extremely low prices, and others), and the associated risks (financial for losing money, security because of online payments, and some privacy issues) can impact users’ behaviors toward Insta shopping. Also, the study classified participants’ viewpoints and experiences’ themes into advantages, disadvantages, and issues that are associated with Insta shopping. The study indicated theoretical and practical implications and suggests future research directions.

Proton Radiation Therapy for Stage IIA/IIB Testicular Seminoma.

Testicular seminoma affects young men and is associated with very favorable prognosis. The evolution in treatment paradigm has focused on minimizing acute and especially late toxicities. Following orchiectomy, while surveillance is favored in Stage I patients, radiotherapy (RT) is a standard treatment option for de novo or relapsed stage IIA or select non-bulky stage IIB disease. Despite low doses, standard RT fields to paraaortic and pelvic lymphatics using x-rays exposes a large volume of uninvolved normal tissue/viscera to excess dose. This young patient population is especially vulnerable to risks of late RT toxicities including secondary malignancy. Proton beam therapy (PBT) has dosimetric advantage over x-ray-based RT due to lack of exit dose, and comparative dosimetric/modeling studies show significant sparing of uninvolved abdominal/pelvic organs. However, there is scant reported clinical data at this time for PBT. We review our early institutional outcomes with PBT for testicular seminoma. Single institution retrospective review from a tertiary care center of patients treated with PBT from 2013-2022 for testicular seminoma. Recurrence free (RFS) and overall survival (OS) were calculated from the completion of PBT. Toxicities were graded (Gr) using CTCAE v5.0. Four patients underwent PBT, median age 39 (range 36-47). All were Stage I at diagnosis (pT1b n = 3; pT2 n = 1) and were treated for recurrent stage II disease (IIA n = 3; IIB n = 1) at a median of 34 months from orchiectomy (range 3 - 74 months). Nodal extent included 2 with multiple paraaortic nodes, 1 with solitary paraaortic node and 1 with solitary pelvic node. PBT was delivered with pencil-beam scanning, treating paraaortic + ipsilateral pelvic fields (20 Gy in 10 fractions), then sequential boost to involved nodes (10 -16 Gy in 5-8 fractions). Typically, PA or posterior oblique fields were used to minimize dose to out-of-field abdominal/pelvic viscera. Treatment was well tolerated with minimal acute toxicities: fatigue Gr 1 (n = 3), nausea Gr 1 (n = 3). No Gr 2 or higher acute toxicities or significant late toxicities were observed. At median follow up of 30 months (range 3 - 54), no recurrences were observed, and RFS and OS were 100%. Two patients are without evidence of disease > 4 years post-treatment. In this case series, PBT for retroperitoneal and pelvic metastases in Stage IIA/IIB testicular seminoma was associated with oncologic efficacy with minimal toxicity. PBT reduces unnecessary dose to abdominal/pelvic organs compared to x-ray techniques, which is advantageous in young patients who have anticipated long-term survival. This is one of the few series reporting clinical outcomes of PBT in the management of seminoma. Randomized comparisons with x-ray approaches are impractical given the relatively low volume of patients receiving RT in modern seminoma management, so it is essential to report and track longitudinal outcomes across institutions to validate this approach.

Deconstructing emotion regulation in schizophrenia: The nature of abnormalities at the selection and implementation stages.

Difficulties with emotion regulation are observed across psychiatric diagnoses, including psychotic disorders. Past studies using trait self-report indicate that people with schizophrenia (SZ) are less likely to use adaptive emotion regulation strategies and more likely to use maladaptive emotion regulation strategies than controls (CN). However, more recent evidence using ecological momentary assessment (EMA) indicates that regulation effectiveness and adaptiveness may vary across strategies. The present study aimed to systematically understand abnormalities in state-level emotion regulation strategy selection, effectiveness, and adaptiveness in SZ compared to CN using EMA. Participants (n = 50 SZ; n = 53 CN) completed 6 days of EMA surveys assessing emotional experience, emotion regulation, and symptoms. Results indicated that SZ selected interpersonal emotion regulation and avoidance more often than CN, while both groups selected reappraisal and distraction more often than avoidance and suppression. Overall, strategies were effective at reducing negative emotion and adaptive for reducing delusions over time. Reappraisal, avoidance, and suppression all significantly down-regulated delusions over time. Although some selection abnormalities were present in terms of rate of selection and effort exertion, people with SZ select strategies which are effective and adaptive in the short term. The present results have implications for how cognitive therapy for psychosis may target delusions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Large Environment Indoor Localization Leveraging Semi-Tensor Product Compression Sensing

The sparsity of the localization problem makes the Compression Sensing (CS) theory suitable for indoor localization in Wireless Local Area Networks (WLAN). However, in practice, we find that the location errors and computing complexity increase significantly as the dimensionality of the sparse vector and measurement matrix are high in a large environment, so most CS-based localization techniques are accompanied by coarse localization and AP selection stages. Therefore, in this paper, we first deduced the relationship between the number of Access Points (APs) and the dimensionality of the sparse vector theoretically to give the guideline that the number of sub-databases and APs should be obtained. Then an Adaptive Intuitionistic Fuzzy C-ordered Mean (AIFCOM) clustering is designed for the data with outliers in the environment with multipath effects. Finally, in the fine localization stage, we propose a Semi-tensor Product Compression Sensing (STP-CS) model to construct the measurement matrix, compared with the traditional CS model, our model not only remains more number of APs, but also decreases the dimensionality of measurement matrix, which can reduce the storage space and improve localization accuracy simultaneously.

Investigating the Health Status of the Working Environment of Employees of the Islamic Republic of Iran News Network (IRINN) Using the Grounded Theory Method

Background: The employees of the IRINN are among the employees operating in the creative industries. Contrary to their important role, it seems necessary for them to benefit from favorable health conditions in their working environment. Objectives: This study aims to evaluate the health of the work environment at IRINN. Methods: This cross-sectional study was conducted from September 2021 to August 2022 at the IRINN. The statistical population of the research consisted of 17 managers and editors from different sections of the news network who were interviewed as experts to identify the health components of the IRINN. After this, the collected information was coded and prioritized using the grounded theory method. Results: Three categories of components affecting the health of the working environment of the IRINN were identified in the open coding stage, which were classified into three related sub-categories. The highest frequency sub-category was related to the survey and field investigation, increasing productivity of the employees, and inappropriate building. In the axial coding stage, the components of the coding paradigm were identified and analyzed in six categories. In the selective coding stage, the validity of these relationships among the above-said categories was checked. Conclusions: Based on the results obtained from the grounded theory method, the conditions of the working environment at the IRINN require fundamental changes from the point of view of the building, management structure, health education programs, and, last but not least, modification of common practices.

Public acceptance of nuclear waste disposal sites: a decision-making process utilising the ‘veil of ignorance’ concept

This study demonstrates that a decision-making process utilising ‘the veil of ignorance’ concept, defined in process terms as beginning from a blank slate encompassing the entire country as potential sites and shortlisting candidate sites based on scientific (geological) safety, promotes public acceptance of siting a repository for the geological disposal of high-level radioactive waste and fosters procedural fairness. A hypothetical scenario experiment was conducted in Japan, manipulating the site selection process by setting two conditions—one being the application/proposal condition that the Japanese government currently employs, such as an application by municipalities or a proposal by the government, and the other being the veil of ignorance condition, in which multiple candidate areas are selected from a blank slate for the entire land area based purely on geological factors. Three stages of acceptance were presumed—at the level of general management policy, the site selection process itself with a specified decision policy, and the siting of a repository in their area of residence. Two hypotheses were tested: (a) the veil of ignorance condition will be evaluated as a more acceptable and fairer procedure and will engender increased national consensus than the application/proposal condition at the site selection and repository siting stages, and (b) procedural fairness and national consensus will impact acceptance at each stage; these variables at each stage help shape the same variables in the next stage. The results supported these hypotheses. This study discusses the importance of the site selection process, beginning where any de facto site can be a candidate and shortlisting the candidate sites based on scientific criteria.

Assessing cognitive workloads of assembly workers during multi-task switching

Complex assembly tasks with multiple manual operations and steps often require rapid judgment and action under time pressure and cause most human-related errors. The task switching and action transitions are major sources of these errors. This study intends to implement an electroencephalography (EEG) approach to quantitatively evaluate the mental workload during task switching and transition. The time–frequency and spectrum analysis were utilized to compute and reflect the task demand between the intervals of individual tasks. This study developed an experiment to validate the proposed assessment approach and benchmark the results with the National Aeronautics and Space Administration task load index (NASA-TLX) subjective evaluation scale analysis. The results show that the average value of the power spectral densities (PSDs) of the gamma band signal of the AF4 channel and the beta band signal of Channel F3 show distinctive signal patterns among task stages and intervals. During the interval between the idling stage and the part selection stage, the peak of the PSD envelope increased from 18 to 27 Hz, suggesting advanced cognition increases the mental workload of the interval between different tasks. Therefore, the task switching period cannot be regarded as rest and need to be optimized with better task organization.

ROBOUT: a conditional outlier detection methodology for high-dimensional data

This paper presents a methodology, called ROBOUT, to identify outliers conditional on a high-dimensional noisy information set. In particular, ROBOUT is able to identify observations with outlying conditional mean or variance when the dataset contains multivariate outliers in or besides the predictors, multi-collinearity, and a large variable dimension compared to the sample size. ROBOUT entails a pre-processing step, a preliminary robust imputation procedure that prevents anomalous instances from corrupting predictor recovery, a selection stage of the statistically relevant predictors (through cross-validated LASSO-penalized Huber loss regression), the estimation of a robust regression model based on the selected predictors (via MM regression), and a criterion to identify conditional outliers. We conduct a comprehensive simulation study in which the proposed algorithm is tested under a wide range of perturbation scenarios. The combination formed by LASSO-penalized Huber loss and MM regression turns out to be the best in terms of conditional outlier detection under the above described perturbed conditions, also compared to existing integrated methodologies like Sparse Least Trimmed Squares and Robust Least Angle Regression. Furthermore, the proposed methodology is applied to a granular supervisory banking dataset collected by the European Central Bank, in order to model the total assets of euro area banks.

Prediction of Response to Neoadjuvant Chemoradiotherapy Combined with Pembrolizumab in Esophageal Squamous Cell Carcinoma with CT/FDG PET Radiomic Signatures Based on Machine Learning Classification

PALACE-1 trial has confirm that the addition of pembrolizumab to neoadjuvant chemoradiotherapy (NCRT) improves the pathological complete response(pCR) for esophageal squamous cell carcinoma (ESCC), which might be a novel treatment strategy for ESCC. In the present study, we aim to establish a machine learning model to predict the local response to NCRT+ pembrolizumab for ESCC by using pretreatment 18-fluorodeoxyglucose positron emission tomography (FDG PET) and contrast-enhanced plan CT images. A total of 65 cases treated with NCRT+ pembrolizumab followed by surgery were prospectively enrolled for analysis from 2019-2022. Each patient contains a contrast-enhanced plan CT and FDG PET images. 52 patients were randomly divided into training set and 13 patients were used as test set. The Extraction of radiomics features was performed using an open-source Python library PyRadiomics automatically. Features were computed according to the radiologist-drawn ROIs on both CT and PET images. In the feature selection stage least absolute shrinkage and selection operator (LASSO) was utilized on CT features and PET features separately. Four different machine learning models were implemented: Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF) and XGBoost (XGB). The features selected by LASSO regression were used as model input and the output of the model is "pCR" or "non-pCR". To find the optimal parameter, the 5-fold cross-validation method was used in the training stage. In this study, we use accuracy, sensitivity and specificity as the metrics to evaluate the performance of the model on the testing cohort. The predictive performance of the model was assessed using the area under curve (AUC) of the receiver operating characteristics curve (ROC). Of the 65 cases treated with NCRT+pembrolizumab, 35 patients archived pCR (53.8%), and 30 archived non-pCR. 1684 radiomics features were extracted from each case, and half of them (842 features) were from CT and others were from PET. Among the machine learning models mentioned above SVM achieves the most promising performance on the evaluation metrics. Accuracy, sensitivity, specificity and AUC score on test set were 0.692, 0.833, 0.571 and 0.786 for CT features and 0.615, 0.667, 0.571 and 0.762 for PET features, respectively. For CT+FDG PET fused features accuracy, sensitivity, specificity and AUC score on test set were 0.769, 0.667, 0.857 and 0.833. In this study, we performed several different machine learning models to predict the response to NCRT+ pembrolizumab among ESCC based on the extracted radiomics features from CT and FDG PET images. The best-performing model based on radiomics features of CT and PET images could identify non-pCR to NCRT + pembrolizumab in EC patients.

ALGORITHM FOR ASSESSING THE ECONOMIC EFFICIENCY OF THE ORGANIZATION OF MULTIMODAL LONG-DISTANCE PASSENGER TRANSPORTATION

One of the directions of development of the transport complex of the Russian Federation is the development of multimodal transport technologies and infrastructure to ensure multimodal transportation. The article presents an economic assessment of measures for the organization of multimodal long-distance passenger transportation. It is shown that the proposed methodology is aimed at meeting the basic requirements for the organization of passenger transportation, which are to obtain the maximum income when performing multimodal transportation in the direction, and to maximize the number of passengers transported in order to better meet passenger demand.
 Purpose is to study the impact of multimodal transportation on the increase of passenger turnover at the landfill of JSC “FPC”. Since modern requirements for the passenger transportation system dictate the need to create a multimodal transport system based on the integration of passenger rail transport with other modes of transport, it is necessary to show the importance of organizing multimodal transportation in accordance with passenger demand. To consider the matter of necessity to determine the main parameters that affect the choice of multimodal routes.
 Methodology. Complement existing approaches to the organization of multimodal transportation. Methods of economic theory for determining the effectiveness of the organization of long-distance multimodal passenger transportation.
 Results. The criteria of multimodal transportation have been defined.An algorithm for assessing the economic efficiency of the organization of a multimodal route is proposed, which allows to estimate the expected profit from transportation organization, as well as predicting transportation impact on transportation process indicators.
 Practical implication. The recommendations and approaches proposed in the algorithm developed by the author can be used for the development of multimodal routes. The proposed algorithm allows performing a preliminary analysis of its economic feasibility yet at the preliminary selection stage for multimodal long-distance passenger transportation organization.

Digitalization of Web-Based Employee Selection System at Bank Woori Saudara Diponegoro Branch Bandung City

Employee selection is an activity to select workers according to the needs of the company, to select employees according to the needs there must be prior selection. In the selection of employees, the problem that is felt by the company is that managing application files requires a long time of about 2-3 weeks, limited storage capacity in the archive space so that there is a buildup of application files and a lack of transparency to applicants regarding information on passing the selection stage. As for the applicants themselves, they must wait for information to pass the selection stage with a long time of about 1-2 months, so that applicants feel there is no certainty. To overcome these problems, namely by making a digitization project for an employee selection system in order to solve problems. The goal for the company is to make it easier for companies to find the required human resources, to make it easier for HRD to manage files, as well as transparency of information for applicants regarding each passing selection stage. As for the applicants themselves, namely to make it easier to find out information on job vacancies in the company, make it easier for applicants to collect files, and make it easier to find out information on passing the selection stage. The web-based selection system digitization project was created using XAMPP and Wordpress CMS which is supported by the results of the project's trial with alpha and beta testing. The HRD staff is expected to be able to understand the use of the project and can be operated by the manual book so that it can be carried out according to the procedure.
 
 Keywords: Employee Selection, Selection System, Applicant Files, Digitalization.

Multi-source variational mode transfer learning for enhanced PM2.5 concentration forecasting at data-limited monitoring stations

Hybrid methods combining data decomposition with deep learning have recently exhibited remarkable performance in PM2.5 concentration forecasting. However, these methods still encounter limitations when confronted with monitoring sites lacking adequate historical data. To overcome this challenge, this study proposes a novel methodological framework named as Multi-source Variational Mode Transfer Learning (MSVMTL) that integrates data decomposition, deep learning, and multi-source transfer learning strategies. The framework consists of four stages: source domain selection, data decomposition by Variational Mode Decomposition (VMD), mode sequence clustering, and multi-source mode transfer learning. The source domain selection stage utilizes EMS (Euclidean Distance and Maximum Mean Discrepancy Distance) to identify the most suitable source domain for knowledge transfer. The mode sequence clustering employs the LDDK algorithm (Largest Triangle Three Buckets, Dynamic Time Warping, Dynamic Time Warping Barycenter Averaging, and K-means) to cluster modes from VMD-derived domains. The multi-source mode transfer stage combines VMD with pre-training and fine-tuning, leveraging source domain knowledge for target domain prediction. To validate the proposed framework, a case study, including multiple sets of comparative experiments and ablation study, was conducted using data from 12 air quality monitoring sites in Beijing, China. The experimental results demonstrate that EMS, LDDK, and multi-source mode transfer learning strategy all achieved excellent performance, and the presented MSVMTL significantly enhances the prediction accuracy of PM2.5 concentrations at monitoring sites with limited historical data.

Is energy aid allocated fairly? A global energy vulnerability perspective

The escalating climate change crisis is causing many developing countries to become more vulnerable to energy-related challenges, highlighting the issue of fairness in allocating energy aid. Given this context, it is important to ensure countries that face higher energy vulnerabilities receive a greater share of energy aid funding. This study aims to assess the impact of energy vulnerability on energy aid allocation using a double-hurdle model and a panel of 124 countries from 2002 to 2019. We rely on an index of energy vulnerability to understand the differences in the amount of aid available to different energy-vulnerable recipients, and study the transmission mechanisms underlying this relationship. Our results suggest that the allocation of energy aid has been largely fair. Specifically, energy-vulnerable countries are more likely to be selected as recipients of energy aid during the selection stage, and subsequently receive a greater share of this aid during the allocation stage. However, this effect is heterogeneous and asymmetric. Donors tend to allocate energy aid to countries characterized by relatively low quality of government, low income levels, and limited access to energy assistance. Furthermore, disparities exist in the impact of energy vulnerability on different types of energy support. Specifically, countries with higher energy vulnerability receive a greater proportion of energy policy and energy distribution aid, but are allocated a relatively small share of aid to their non-renewable energy-generation sectors. Additionally, the relationship between energy vulnerability and renewable energy generation aid is not statistically significant. Our results highlight the importance of considering energy vulnerability when allocating energy assistance, and have both scholarly and practical significance. Our findings also have important policy implications for donors by providing guidance on how to promote a fairer allocation of energy aid.

Особенности расчета начальных (максимальных) цен контрактов при осуществлении государственных закупок

Justification of the initial (maximum) contract price is one of the main tools used at the stage of supplier selec-tion. Establishing the marginal price of the contract has a significant impact on the quality of work performance, qualification of the supplier and, as a consequence, on the efficiency of budget financing utilization as a whole. The aim of the study is to analyze the correctness of the methods used in practice to calculate the initial (maxi-mum) prices of contracts enshrined in the regulatory legal acts of the Russian Federation. Thus, the methods of analysis, synthesis, abduction and comparison of the obtained mathematical calculations were used in the work. The documentation attached to the notices published in the Unified Information System justifying the cal-culation of the marginal price of various contracts has been analyzed. In the course of the analysis, the author calculated the coefficients necessary to decide on the acceptability of the values applied by customers at the stage of supplier selection, in particular, the coefficients of variation and mean square deviation used in the method of comparable market prices. The study identified cases of incorrect application of mathematical justi-fication models in determining the amount of funding aimed at realizing the objectives of budgetary institutions. Such incorrect application of models can lead to an increase in the cost of work and, as a result, the risks of unfair behavior of participants in the contract model.

Evaluation of the lead and chromium removal capabilities of Bacillus subtilis-induced food waste compost-based biomedia

Food waste compost (FWC) is a sustainable recycling approach employed in soil media, offering extensive advantages to urban areas by promoting resource circulation and effectively managing water pollution. To improve value, Bacillus subtilis (B. subtilis)-induced FWC-based biomedia (BIBMFWCs) was produced via a secondary treatment involving selective meso-thermophilic stages. During the production of BIBMFWCs, physicochemical properties were found to have favorable characteristics for the efficient removal of metal ions. The produced organic-carbonate complex structure demonstrated the synergistic effect involving simultaneous sorption/precipitation mechanisms for the removal of Pb(II) and Cr(III). Also, the dose of B. subtilis has an impact on the pseudo-second-order (PSO) and intra-particle diffusion (IPD) reaction, leading to distinct removal capacities for Pb(II) and Cr(III) [24.26–24.74 mg g−1 in Pb(II) and 12.7–23.93 mg g−1 in Cr(III)]. Furthermore, B. subtilis, an inducing mediator for microbial metabolites, exhibits the potential to facilitate the removal of Pb(II) and Cr(III) through biological modification of raw materials, which are transformed, facilitating the presence of hydroxyl groups, immobilizing metal ions, and enabling ion exchange via biogenic carbonate formation processes. Finally, the developed BIBMFWCs could be used as a nature-based solution (NBS) material without in-situ pH control.

Bentuk Optimalisasi Guru Penggerak di Sekolah Dasar Negeri 3 Sadeng

This article discusses the "Empowering Pioneer Teachers" program implemented at Public Elementary School 3 Sadeng, aiming to enhance the quality of education and nurture holistic student development. The program is rooted in education that embraces diversity and adapts to changing times. Pioneer teachers are selected based on their enthusiasm, dedication, adaptability, in addition to academic qualifications. The article reviews the stages of selection, intensive training, and program implementation within classrooms. The program emphasizes the development of interpersonal competencies and innovation in teaching methodologies. Support from the school, parents, and local community plays a crucial role in the program's success. Its impact is evident in improved student learning outcomes, social skills, creativity, and the cultivation of an inclusive and collaborative learning environment. Through this article, it is hoped that the experience of Public Elementary School 3 Sadeng can inspire other educational institutions. Empowering pioneer teachers yields positive effects throughout the school community and has the potential to guide elementary education towards a brighter and more equitable future.

Early diagnosis of liver disease using improved binary butterfly optimization and machine learning algorithms

Liver disease in patients is on the rise due to environmental factors like toxic gas exposure, contaminated food, drug interactions, and excessive alcohol use. Therefore, diagnosing liver disease is crucial for saving lives and managing the condition effectively. In this paper, a new method called Liver Patients Detection Strategy (LPDS) is proposed for diagnosing liver disease in patients from laboratory data alone. The three main parts of LPDS are data preprocessing, feature selection, and detection. The data from the patient is processed, and any anomalies are removed during this stage. Then, during feature selection phase, the most helpful features are chosen. A novel method is proposed to choose the most relevant features during the feature selection stage. The formal name for this method is IB2OA, which stands for Improved Binary Butterfly Optimization Algorithm. There are two steps to IB2OA, which are; Primary Selection (PS) step and Final Selection (FS) step. This paper presents two enhancements. The first is Information Gain (IG) approach, which is used for initial feature reduction. The second is implementing BOA's initialization with Optimization Based on Opposition (OBO). Finally, five different classifiers, which are Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naive Bayes (NB), Decision Tree (DT), and Random Forest (RF) are used to identify patients with liver disease during the detection phase. Results from a battery of experiments show that the proposed IB2OA outperforms the state-of-the-art methods in terms of precision, accuracy, recall, and F-score. In addition, when compared to the state-of-the-art, the proposed model's average selected features score is 4.425. In addition, among all classifiers considered, KNN classifier achieved the highest classification accuracy on the test dataset.

New protocol for rapid cassava multiplication in field conditions: a perspective on speed breeding.

Despite the economic and social importance, high-yielding cassava cultivars are only released after extensive research, mainly due to the low multiplication rate. This study aimed to assess the impact of using smaller-sized seed cuttings treated with agrochemicals (8MP) compared to the conventional planting size (16 cm) on genetic parameters, agronomic performance, and the ranking of cassava clones based on yield and growth attributes. The evaluation was carried out in clonal evaluation trial (CET), preliminary yield trial (PYT), and uniform yield trials (UYT). Additionally, a new selection scheme for cassava breeding programs was proposed. A total of 169 clones were evaluated, including 154 improved clones at different stages of selection and 15 local varieties used as checks. Field trials were conducted using both sizes of propagative material (8MP and 16 cm) in each phase of the breeding program. The data were analyzed using mixed models, considering the random effects of genotype and genotype-environment interaction (G×E) to determine variances and heritabilities. Bland-Altman concordance and correlation analysis of selection indices were employed to examine the consistency in the ranking of cassava clones using different seed cutting sizes. The distribution of variance components, heritabilities, means, and range of the 8MP and 16 cm trials in different phases of the cassava breeding program exhibited remarkable similarity, thereby enabling a comparative assessment of similar genetic effects. With a selection intensity of 30%, the concordance in clone ranking was 0.41, 0.57, and 0.85 in CET, PYT, and UYT trials, respectively, when comparing the selection based on 8MP and 16 cm trials. It is worth noting that the ranking of the top 15% remained largely unchanged. Based on the findings, proposed changes in the cassava selection scheme involve increasing the number of trials starting from the CET phase, early incorporation of G×E interaction, elimination of the PYT trial, reduction of the breeding cycle from 5 to 3 years, and a decrease in the time required for variety development from 11 to 9 years. These modifications are expected to lead to cost reduction and enhance the effectiveness of cassava breeding programs.

An Automated Procedure for Continuous Dynamic Monitoring of Structures: Theory and Validation

IntroductionStability diagrams are a helpful tool for operational modal analysis to obtain the physical modes of a structure. These modes can be defined by visualizing stable columns formed by consistently identified modes over a range of model orders. Extracting these modes manually becomes an obstacle if continuous identification of modal parameters is required.Materials and methodsIn this paper, a procedure is configured to automatically interpret the stability diagrams constructed with the identification results of the SSI-COV/ref algorithm. This procedure is based on some methodologies found in the literature, which follow three stages. First, a stability diagram cleaning stage is defined where modal validation criteria and partitioning clustering algorithms are used to detect spurious modes. Second, a mode grouping stage based on a hierarchical clustering algorithm is implemented to form sets of modes that share similar modal information. Finally, a selection stage is applied to define representative modal parameters from the set of physical modes.ResultsThe proposed procedure is validated by simulating a beam-type structural model with ten degrees of freedom affected by ambient temperature functions. Natural frequencies computed for the DT140 and DT220 datasets collection with the frequency-domain decomposition method agree with the computed ones with the proposed procedure, presenting MAC coefficients higher than 0.97. A total of 192 datasets are simulated, and the acceleration responses are polluted with two noise levels, SRN = 40 [dB] and SNR = 20 [dB].ConclusionsFor the analyzed beam, the modal tracking results showed that the procedure could perform continuous identification automatically. The variations in the natural frequencies are correlated to the variations in the ambient temperature functions.