Principal Support Research Articles

Unlocking STEM Pathways: Revealing STEM Choices and Science Teachers Empowering Black Queer Students

Despite efforts to address racial disparities in STEM fields, little attention has been paid to the experiences and aspirations of queer and trans Black (QT Black) students in grades K-12. This study explored whether there were any significant differences in the choice of STEM majors between QT Black students and non-QT Black students. We found that Black QT students are less likely to choose STEM majors. Additionally, we found significant differences in science teachers’ perceptions of teaching, comparing between QT Black and non-QT Black students. Finally, we found that several factors predicted science-teacher perception of collective responsibility, perceptions of content professional learning community (PLC), self-efficacy, perception of content teacher expectations, and perceptions of principal support for teachers of QT Black and non-QT Black students. Implications for research and practice are discussed.

The Role of Cognitive Bias in Principal Decision Making: A Narrative Analysis of the Literature

This study aims to critically examine the role of cognitive biases in principal decision-making processes by synthesizing and analysing existing literature. It seeks to identify key cognitive biases affecting principal decisions, their impact on organizational outcomes, and potential mitigation strategies. A comprehensive narrative analysis of the literature was conducted. Relevant peer-reviewed articles, books, and book chapters published between 1970 and 2023 were systematically identified and reviewed using major academic databases such as Web of Science, JSTOR, and Google Scholar. The analysis revealed that cognitive biases, particularly confirmation bias, overconfidence, anchoring, and availability bias, significantly influence principal decision-making. These biases can lead to suboptimal decisions, affecting strategic planning, risk assessment, and overall organizational performance. The study also identified several debiasing techniques, including awareness training and structured decision-making frameworks, which show promise in mitigating the impact of cognitive biases. This study provides a comprehensive and up-to-date synthesis of research on cognitive biases in principal decision-making, offering a holistic view of the field’s current state. It uniquely combines insights from cognitive psychology, behavioural economics, and management science to provide a multidisciplinary perspective on the topic. The study contributes to both theory and practice by offering a structured understanding of how cognitive biases influence principal decision-making. It provides valuable insights for principals and organizations to improve their decision-making processes and highlights areas for future research in this critical field. The findings can inform the development of more effective principal training programs and decision support systems.

Distributed Estimation of Principal Support Vector Machines for Sufficient Dimension Reduction

The principal support vector machines method (Li et al., 2011) is a powerful tool for sufficient dimension reduction that replaces original predictors with their low-dimensional linear combinations while preserving the information for regression and classification. However, the computational burden of the principal support vector machines method constrains its use for massive data. To address this issue, we propose a naive and a refined distributed estimation algorithms for fast implementation when the sample size is large. Both distributed sufficient dimension reduction estimators exhibit the same statistical efficiency as when all the data is merged together, which provides rigorous statistical guarantees for their application to large-scale datasets, while the refined method requires smaller batch sample sizes and hence is more advantageous when memory limitations exist on distributed machines. The two distributed algorithms are further adapted to principal weighted support vector machines (Shin et al., 2017) for sufficient dimension reduction in binary classification. The statistical accuracy and computational complexity of our proposed methods are examined through comprehensive simulation studies and in a real data application with more than 600000 samples.

A Comprehensive Classification Approach by Integrating Principal Component Analysis and Support Vector Machines for Advanced Intrusion Detection Systems

A Comprehensive Classification Approach by Integrating Principal Component Analysis and Support Vector Machines for Advanced Intrusion Detection Systems

Machine learning-based football match prediction system

This study develops a machine learning-based system to predict English Premier League (EPL) outcomes, employing models such as Principal Component Analysis (PCA), K-Nearest Neighbors (KNN), Random Forests, and Support Vector Machines (SVM). The analysis covered a large dataset of matches, with the data normalized to ensure consistency and accuracy across models. Among the methods used, Random Forests showed the most robust performance in predicting match outcomes, particularly in forecasting wins and losses. However, both Random Forests and SVM encountered difficulties in accurately predicting draws, which points to areas where further refinement is needed. The prediction probabilities largely fell within a specific range, indicating the models' ability to identify patterns, but significant overfitting was observed in the models. This overfitting suggests that while the models performed well on the training data, they struggled to generalize to new, unseen data, highlighting the importance of implementing more effective regularization techniques to prevent overfitting and improve the models' overall predictive accuracy in real-world scenarios.

Regulation of tapioca starch 3D printability by yeast protein: Rheological, textural evaluation, and machine learning prediction

This article investigated the effects of yeast protein (YP) on gel rheology, texture, and 3D printability of tapioca starch and the feasibility of Principal component analysis (PCA) and support vector machine (SVM) algorithms for classification and prediction of printability. The results indicated that increasing YP content enhanced the viscosity, storage and loss moduli, and hardness, thereby improving extrudability and supportability of 3D printing. The addition of 15% YP exhibited the best 3D printing performance, but excessively high YP addition hindered ink extrusion. PCA analysis based on rheological and texture indices categorized the ink's 3D printing performance into four classes: poor support and low printing accuracy; good support but low printing accuracy; good support and high printing accuracy; and non-smooth extrusion. Furthermore, SVM algorithm used texture data to predict printability classification, with the highest prediction accuracy (91.67%) achieved at polynomial kernel among four different kernel functions. These results confirm that YP can serve as a potential ink for 3D printing and underscore SVM's efficacy in predicting ink's 3D printing performance.

KINERJA GURU BERSERTIFIKAT PENDIDIK DALAM MENINGKATKAN MOTIVASI BELAJAR SISWA DI SEKOLAH DASAR

This study aims to analyze the performance of certified teachers in improving student learning motivation at SD Negeri 4 Cigadung. This research uses a descriptive qualitative approach, data collected through observation, in-depth interviews, and documentation. The results show that certified teachers have four basic competencies which include pedagogic, personality, social, and professional competencies well. Teachers use various strategies to improve students' learning motivation, including identification of students' characteristics, appropriate lesson planning and thorough evaluation. Despite this, students' learning motivation still varies, with some aspects needing improvement such as focus and independence. Principal support through performance appraisals and professional development encouragement contributed to this improvement. In conclusion, although the performance of certified teachers has shown consistent efforts, there is still room for improvement in increasing students' learning motivation at SD Negeri 4 Cigadung.

Pushing the Survival Bar Higher: Two Decades of Innovation in Lung Transplantation.

Survival after lung transplantation has significantly improved during the last two decades. The refinement of the already existing extracorporeal life support (ECLS) systems, such as extracorporeal membrane oxygenation (ECMO), and the introduction of new techniques for donor lung optimization, such as ex vivo lung perfusion (EVLP), have allowed the extension of transplant indication to patients with end-stage lung failure after acute respiratory distress syndrome (ARDS) and the expansion of the donor organ pool, due to the better evaluation and optimization of extended-criteria donor (ECD) lungs and of donors after circulatory death (DCD). The close monitoring of anti-HLA donor-specific antibodies (DSAs) has allowed the early recognition of pulmonary antibody-mediated rejection (AMR), which requires a completely different treatment and has a worse prognosis than acute cellular rejection (ACR). As such, the standardization of patient selection and post-transplant management has significantly contributed to this positive trend, especially at high-volume centers. This review focuses on lung transplantation after ARDS, on the role of EVLP in lung donor expansion, on ECMO as a principal cardiopulmonary support system in lung transplantation, and on the diagnosis and therapy of pulmonary AMR.

Recognition of Rice Species Based on Gas Chromatography-Ion Mobility Spectrometry and Deep Learning

To address the challenge of relying on complex biochemical methods for identifying rice species, a prediction model that combines gas chromatography-ion mobility spectroscopy (GC-IMS) with a convolutional neural network (CNN) was developed. The model utilizes the GC-IMS fingerprint data of each rice variety sample, and an improved CNN structure is employed to increase the recognition accuracy. First, an improved generative adversarial network based on the diffusion model (DGAN) is used for data enhancement to expand the dataset size. Then, on the basis of a residual network called ResNet50, a transfer learning method is introduced to improve the training effect of the model under the condition of a small sample. In addition, a new attention mechanism called Triplet is introduced to further highlight useful features and improve the feature extraction performance of the model. Finally, to reduce the number of model parameters and improve the efficiency of the model, a method called knowledge distillation is used to compress the model. The results of our experiments revealed that the recognition accuracy for identifying the 10 rice varieties was close to 96%; hence, the proposed model significantly outperformed traditional models such as principal component analysis and support vector machine. Furthermore, compared to the traditional CNN, our model reduced the number of parameters and number of computations by 53% and 55%, respectively, without compromising classification accuracy. The study also suggests that the combination of GC-IMS and our proposed deep learning method had better discrimination abilities for rice varieties than traditional chromatography and other spectral analysis methods and that it effectively identified rice varieties.

Support vector machine in the elementomic evaluation of arugula (Eruca Sativa) and lettuce (Lactuca sativa) grown in soils from a decommissioned mining area

Potentially toxic metals (PTMs) in plants are absorbed and accumulated in various parts of the plants. Due to the risk associated with consuming vegetables contaminated with PTMs, it is essential to determine the distribution of these metals in plants. Vegetables grown in soils contaminated by PTMs from a decommissioned manganese mine in Bahia, Brazil, were assessed for metal uptake and distribution. This study focused on Arugula (Eruca sativa) and lettuce (Lactuca sativa), chosen for their metal bioaccumulation properties and their role in the human diet. The metal concentration in the Arugula followed the order Fe > Mn > Cu > Cr > Tl > Co > Cd > Pb, while in lettuce, it was Mn > Fe > Cr > Tl > Co > Cu > Cd > Pb. A significant positive correlation between Mn and other metals suggests a common contamination source. The findings highlight a food safety risk from consuming these vegetables. Combining the PTMs bioaccumulation coefficient with Principal Component Analysis (PCA) and Support Vector Machines (SVM) effectively distinguished between plant types and parts, providing valuable insights into PTMs transfer from soil to the food chain

Work motivation profiles among inclusive school teachers of students with intellectual and developmental disabilities: A mixed-method study

This study aimed to identify the profiles of work motivation among teachers of students with intellectual and developmental disabilities and investigate the associated factors. Through surveying 972 and interviewing 10 teachers from Chinese inclusive primary schools, this mixed-method study identified three work motivation profiles: de-motivated, meaning-seeking, and deep internalization, with the first profile exhibiting the least adaptive value and the third demonstrating the most adaptive value. Length of service and in-service training influenced teachers' profile membership. School policies, principal support, professional support, and collaboration with students’ parents contributed to differentiating the three profiles. Implications and limitations are discussed.

Assessing digital leadership of junior high school principals

Digital leaders are not necessarily experts in computer programming; rather, they are individuals capable of leveraging information technology to achieve organizational goals in the current era of digitalization. This research analyzes the digital leadership aspects or dimensions of junior high school principals in Sukabumi City, West Java, Indonesia. Specifically, it examines whether the digital leadership of these principals has met the target, is on target, or still falls short in enhancing the digital competence of 21st-century teachers. The study employs a qualitative descriptive design, utilizing interviews and observations as instruments. Data were collected from three junior high schools, involving three principals and nine teachers in Sukabumi City. The qualitative analysis was conducted using Atlas.Ti version 8. The research findings reveal several obstacles in the implementation of digital initiatives, including budget constraints, insufficient digital competence among teachers, and uneven infrastructure, all of which have hindered effectiveness in meeting the targets. Consequently, the study suggests that principal support, increased teacher literacy, and the formation of innovation teams are crucial for effective digital leadership and the successful achievement of school program targets.

Principal Support on Teacher Professional Development and Mediating Role of Career Calling: Higher Education Vocational College Teachers Perceptions

Principal Support on Teacher Professional Development and Mediating Role of Career Calling: Higher Education Vocational College Teachers Perceptions

Raman spectroscopy in lung cancer diagnostics: Can an in vivo setup compete with ex vivo applications?

Lung carcinoma remains the leading cause of cancer death worldwide. The tactic to change this unfortunate rate may be a timely and rapid diagnostic, which may in many cases improve patient prognosis.In our study, we focus on the comparison of two novel methods of rapid lung carcinoma diagnostics, label-free in vivo and ex vivo Raman spectroscopy of the epithelial tissue, and assess their feasibility in clinical practice. As these techniques are sensitive not only to the basic molecular composition of the analyzed sample but also to the secondary structure of large biomolecules, such as tissue proteins, they represent suitable candidate methods for epithelial cancer diagnostics.During routine bronchoscopy, we collected 78 in vivo Raman spectra of normal and cancerous lung tissue and 37 samples of endobronchial pathologies, which were subsequently analyzed ex vivo. Using machine learning techniques, namely principal component analysis (PCA) and support vector machines (SVM), we were able to reach 87.2% (95% CI, 79.8–94.6%) and 100.0% (95% CI, 92.1–100.0%) of diagnostic accuracy for in vivo and ex vivo setup, respectively. Although the ex vivo approach provided superior results, the rapidity of in vivo Raman spectroscopy might become unmatchable in the acceleration of the diagnostic process.

Strengthening Character Education Through the Application of Religious Culture to Support the Pancasila Student Strengthening Project (P5) in Junior High Schools

This study aims to describe the forms of character education strengthening programs and the inhibiting and supporting factors for the implementation of religious culture to support the project of strengthening the Pancasila student profile (P5) at SMP Negeri 2 Takengon. This qualitative research was conducted at SMP Negeri 2 Takengon. This research is qualitative, holistic, and descriptive in terms of words and language in a special, natural context, and it utilizes various natural methods. This research will be conducted at SMP Negeri 2 Takengon. The data collection techniques are observation, in-depth interviews, and documentation review. Data analysis techniques are data reduction, data modeling, and conclusion verification. The results of this study are the school's efforts to strengthen character education through the application of religious culture to support the P5 project at SMP Negeri 2 Takengon Forms of strengthening character education through the application of religious culture to support the project to strengthen the profile of Pancasila students (P5) at SMP Negeri 2 Takengon including morning greeting activities, tahsin and tahfidz activities, women's activities/rohis, speech activities, literacy, Yasinan and Friday alms and activities to commemorate Islamic holidays. These activities have been proven to strengthen students' character and can support the project of strengthening the profile of Pancasila students. The supporting factors so that these activities can be carried out properly and continuously to strengthen the character of students are the principal's commitment and support from the teacher council, adequate facilities and infrastructure, and good management. The students' enthusiasm in participating in various religious and cultural activities. The factors that hinder the strengthening of students' character through religious culture to support the project of strengthening the profile of Pancasila students are the family or community environment, peers, the presence of activity coaches, and the lack of mastery of reading the Quran.

Principal Support for Teacher Vitality: An Empirical Analysis in Low-Performing Schools

This article draws on a teacher survey (n = 789) to examine the relationship between teacher vitality and two conversational approaches—transformative leadership conversation and controlling conversation. The analyses use latent structural equation modelling. The sample is restricted to teachers in schools with an accountability rating of “low performing” so that principal/teacher conversations can be examined in schools that potentially have the most to gain from strong teacher vitality. Results indicate that the use of transformative leadership conversation had a direct positive relationship with teacher vitality (β = .26) and an indirect relationship (β = .22) with it through teacher need satisfaction. Controlling conversation had a negative relationship with teacher vitality, but it did not have a statistically significant relationship with need satisfaction.

Navigating the Leadership Challenges and Support System for Assistant School Principals in Senior High School

Purpose: The role of assistant school principals (ASPs) is becoming vital in educational leadership, particularly in the context of senior high school education in the Philippines. This study was conducted to uncover insights from the ASPs’ shared experiences and propose support strategies to enhance their school leadership practices for better learning outcomes in senior high school. Methodology: This research utilized a descriptive case study using an unstructured interview as the primary procedure for data gathering. Separate face-to-face interviews with the informants were conducted to gather data for this investigation. The informants in this study were five assistant principals in the Division of Sorsogon purposefully selected as to the length of service, place of assignment, and willingness to participate until the completion of this study. Results: Findings revealed that the ASPs had varied educational backgrounds and professional experiences, suggesting no universal path to becoming an administrator. Nevertheless, the skills and insights gained from previous roles can be beneficial in assuming the new administrative roles. This study also revealed that ASPs in senior high school had a broad set of responsibilities. However, the primary focus was on instructional leadership. It also reflected that ASPs encountered various challenges related to a lack of training and continuous professional development programs; establishing authority and cultivating principal support; conducting classroom observation due to the enormous number and diversity of teachers; organizing immersion programs for TVL students due to financial constraints and availability of industry partners; and disruption in their professional and personal lives. The findings highlight the support that ASPs need to enhance their school leadership. Conclusion: ASPs are the school leaders’ partners in attaining the school’s mission and vision. However, comprehensive support and professional development opportunities are needed to empower assistant principals to fulfill their roles and enhance school leadership effectively.

Growth period determination and color coordinates visual analysis of tomato using hyperspectral imaging technology

Growth period determination and color coordinates prediction are essential for comparing postharvest fruit quality. This paper proposes a tomato growth period judgment and color coordinates prediction model based on hyperspectral imaging technology. It utilizes the most effective color coordinates prediction model to obtain a color visual image. Firstly, hyperspectral images were taken of tomatoes at different growth periods (green-ripe, color-changing, half-ripe, and full-ripe), and color coordinates (L*, a*, b*, c, h) were obtained using a colorimeter. The sample set was divided by the sample set partitioning based on joint X-Y distances (SPXY). The support vector machine (SVM), K-nearest neighbors (KNN), and linear discriminant analysis (LDA) were used to discriminate growth period. Results show that the LDA model has the best prediction effect with a prediction set accuracy of 93.1%. In addition, effective wavelengths were selected using competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), and chromaticity prediction models were established using partial least squares regression (PLSR), multiple linear regression (MLR), principal component regression (PCR) and support vector machine regression (SVR) Finally, the color of each pixel of the tomato is calculated using the optimal model, generating a visual distribution image of the color coordinate. The results showed that hyperspectral imaging can non-destructively detect tomatoes’ growth stage and color coordinates, providing great significance for designing a tomato quality grading system.

Predicting regional sustainable development to enhance decision-making in brownfield redevelopment using machine learning algorithms

Brownfield redevelopment and land sustainable management have many interconnected aspects, which could be evaluated by regional development. Most contaminated sites in Taiwan often lack comprehensive consideration during remediation for land reuse planning; therefore, they are operated independently from regional development. Regional development is a dynamic process; however, there is a research gap in understanding how regional development can serve as an incentive for brownfield redevelopment. This study established a model for assessing regional sustainability using machine learning (ML-RSD), that model used principal component analysis (PCA) and support vector machine (SVM) to predict the comprehensive evaluation rating of regional development at the township level and to explore the trends of regional governance sustainability. The results of ML-RSD that are compared to commonly used methods (driver-pressure-state-impact-response and entropy weight method) were consistent. Indicators such as the protection of vulnerable groups (SDG1), food security (SDG2), good health and well-being (SDG3), inclusive and equitable quality education (SDG4), environmental protection (SDG6), accessible and reliable energy (SDG7), and sustainable economic growth (SDG8) were identified as the main factors affecting regional development. Additionally, the model achieved an accuracy value exceeding 80%. Additionally, the results showed that there was no significant correlation between the delisted site (completed remediation) and regional development in southern Taiwan. The model underscored the overall lower comprehensive sustainable values for regions affected by inadequate land rehabilitation governance (SDG15). The results present that brownfield redevelopment strategies should enhance their effectiveness in land adaptation and integrating brownfield redevelopment with regional sustainable development is essential for it to become a win–win solution.

Critical Analysis of Job Satisfaction among Special Education Consultants, Trainers and Teachers: Evidence from Province of Sindh

A study sought to evaluate the characteristics that influence work satisfaction among special education teachers, consultant and trainers. The survey included 323 full-time teachers and 207 professionals employed in special education institutions in Karachi, Sindh. Working in special schools, connections with colleagues, job stability, pupil-professional ratio, accomplishment, principal support, and working-class reputation were all factors that influenced job satisfaction. Special allowances, physical amenities, resource acquisition, pay, and prospects for advancement were all factors that contributed to work unhappiness. Competitive compensation, equal advancement chances, positive connections with coworkers, supportive supervisors, and participation in decision-making are all strategies for increasing workplace satisfaction. This investigation utilized a descriptive survey plan, a cost-effective method for collecting large amounts of data from a large population. The research data was analyzed using descriptive statistics like percentages, means, and frequency distribution tables to present the findings. The study reveals a correlation between workload, work environment, organizational support, and job satisfaction in professionals and teachers working with special needs students. Organizational support significantly predicts job satisfaction. However, research on Pakistan's impact is limited. Recommendations included improving working conditions, recognizing and promoting employees appropriately, and offering suitable teaching and learning materials from diverse stakeholders. Key words:, , , , ,