Supervision Strategies Research Articles

Strategy for Supervision of Islamic Religious Education in the Era of Information Technology Iin Banggai Regency

The impact of information technology has permeated various aspects of human life, including social, cultural, economic, political, and even educational elements. The education sector, influenced by technology, significantly affects the quality of education, including Islamic Education, where one crucial element is the presence of Islamic Education Supervisors. The role and responsibilities of Islamic Education Supervisors are to enhance the quality of Islamic Education and to exhibit adequate professionalism. Therefore, this study addresses the professionalism of Islamic Education Supervisors and the implementation of information technology in the duties of Islamic Education supervision in Banggai Regency. This study uses a qualitative approach. Data collection techniques include direct observation, in-depth interviews, and written document analysis. Data was analyzed using three stages: data reduction, presentation, and verification. This study found that the professionalism of Islamic Education supervision in the era of information technology in Banggai Regency is outstanding. This is proven by the research findings, which show that supervisors consistently carry out their duties despite many challenges and obstacles. The extensive supervisory area, the large number of supervised teachers, and the need for more supervisors present real challenges in performing their duties professionally. On the other hand, Islamic Education Supervisors still need to improve in aspects of information technology and discipline. Other findings indicate that in performing their supervisory duties, Islamic Education Supervisors in Banggai Regency use information technology in various strategies and methods.

Online Instructional Supervision in an Advanced Higher Education

The continuity of learning in graduate and post-graduate education was ensured through online instructional supervision during the COVID-19 pandemic. This initiative aligns with the fourth Sustainable Development Goal (SDG 4), emphasizing the preservation of quality education amidst unprecedented challenges. The pandemic-induced shift to online learning environments underscored the critical need for robust supervisory practices that promote equitable and inclusive education for all students in the graduate studies. Employing a phenomenological approach, this study explored the experiences of 15 internal stakeholders involved in online instructional delivery at a state-funded university. The thematic analysis yielded five key insights: the lived experiences of internal stakeholders during the pandemic, transformative shifts in work and learning dynamics, perceptions of the internal stakeholders on online supervision, strategies for effective online supervision, and the opportunities and challenges inherent in this modality. Despite inherent limitations, online instructional supervision in advanced higher education during the pandemic proved to be both complex and transformative. The study recommends ongoing investment in human capital and technological infrastructure by educational institutions to secure sustainable educational outcomes for graduate and post-graduate students in the long term.

Mastering Real-Time Data Processing Applications : Optimization Strategies for Peak Performance

This comprehensive article explores strategies for optimizing real-time data processing applications in the era of big data and distributed systems. It examines the growing demand for real-time analytics across industries and organizations' challenges in maintaining low latency and scalability. The article discusses key optimization techniques, including leveraging the Akka framework's actor model and supervision strategies, implementing efficient data ingestion through stream processing and partitioning, utilizing caching strategies, employing robust monitoring and auto-scaling mechanisms, ensuring fault tolerance through checkpointing and graceful degradation, and adopting asynchronous communication patterns. Throughout the article, real-world case studies and performance metrics demonstrate the significant improvements these strategies can bring to system throughput, latency, and resilience in various sectors such as finance, e-commerce, telecommunications, and manufacturing.

Analysis of implementation science strategies to train laparoscopic surgical skills among Liberian surgeons using ALL-SAFE.

Significant disparities exist in laparoscopic training opportunities for surgeons in low- and middle-income countries (LMICs). ALL-SAFE is an innovative, low-cost training system for LMIC surgeons' laparoscopic development. However, strategies to implement and scale ALL-SAFE are unstudied. We aimed to assess the impact of implementation science strategies on ALL-SAFE uptake in Liberia, a novel and low-resource context. This study used implementation science approaches to evaluate ALL-SAFE uptake in three Liberian hospitals: John F. Kennedy Medical Center, Phebe, and ELWA. Five validated implementation strategies of program orientation, local champions, network weaving, feedback mechanisms, and practical supervision were piloted. All five strategies were implemented at two hospitals, while four strategies, excluding clinician supervision, were implemented at the final site as a pseudo-control, to evaluate the impact of resource-intensive supervision. Participants included surgical consultants, residents, and medical students. Engagement was assessed with mixed methods including participant number, practice hours, module completion rates, and strategy feasibility through end-user interviews. Across three hospitals, 33 participants used ALL-SAFE for 87.8 total training hours (5,268min). Participant numbers varied across sites (NJFK = 20, 80%; NPhebe = 7, 88%; NELWA = 6, 86%), as did practice times (TotalJFK = 3,060min, Median time/ParticipantJFK = 103min; TotalPhebe = 1,434min, Median time/ParticipantPhebe = 126min; TotalELWA = 774min, Median time/ParticipantELWA = 100min). Sites with practical supervision demonstrated higher engagement than those without (P = 0.042). Interviews (n = 8) revealed positive perceptions toward program orientation, practical supervision, and network weaving. Participants recommended thirteen additional strategies for sustainability including translating ALL-SAFE skills to patient care. Our study is one of the first to evaluate implementation science strategies for laparoscopic training in sub-Saharan Africa. The implementation bundles of five validated strategies were considered feasible by Liberian surgeons in both urban and rural hospitals. Mixed methods suggested a positive association between engagement and the resource-intensive practical supervision strategy. Future studies should focus on quantifying individual strategy contributions with rigorous implementation designs and assessing sustainability strategies.

Cloud Detection Using a UNet3+ Model with a Hybrid Swin Transformer and EfficientNet (UNet3+STE) for Very-High-Resolution Satellite Imagery

It is necessary to extract and recognize the cloud regions presented in imagery to generate satellite imagery as analysis-ready data (ARD). In this manuscript, we proposed a new deep learning model to detect cloud areas in very-high-resolution (VHR) satellite imagery by fusing two deep learning architectures. The proposed UNet3+ model with a hybrid Swin Transformer and EfficientNet (UNet3+STE) was based on the structure of UNet3+, with the encoder sequentially combining EfficientNet based on mobile inverted bottleneck convolution (MBConv) and the Swin Transformer. By sequentially utilizing convolutional neural networks (CNNs) and transformer layers, the proposed algorithm aimed to extract the local and global information of cloud regions effectively. In addition, the decoder used MBConv to restore the spatial information of the feature map extracted by the encoder and adopted the deep supervision strategy of UNet3+ to enhance the model’s performance. The proposed model was trained using the open dataset derived from KOMPSAT-3 and 3A satellite imagery and conducted a comparative evaluation with the state-of-the-art (SOTA) methods on fourteen test datasets at the product level. The experimental results confirmed that the proposed UNet3+STE model outperformed the SOTA methods and demonstrated the most stable precision, recall, and F1 score values with fewer parameters and lower complexity.

Investigation, Evaluation, and Dynamic Monitoring of Traditional Chinese Village Buildings Based on Unmanned Aerial Vehicle Images and Deep Learning Methods

The investigation, evaluation, and dynamic monitoring of traditional village buildings are crucial for the protection and inheritance of their architectural styles. This study takes traditional villages in Shandong Province, China, as an example, employing UAV images and deep learning technology. Utilizing the YOLOv8 instance segmentation model, it introduces three key features reflecting the condition of traditional village buildings: roof status, roof form, and courtyard vegetation coverage. By extracting feature data on the condition of traditional village buildings and constructing a transition matrix for building condition changes, combined with corresponding manual judgment assistance, the study classifies, counts, and visually outputs the conditions and changes of buildings. This approach enables the investigation, evaluation, and dynamic monitoring of traditional village buildings. The results show that deep learning technology significantly enhances the efficiency and accuracy of traditional village architectural investigation and evaluations, and it performs well in dynamic monitoring of building condition changes. The “UAV image + deep learning” technical system, with its simplicity, accuracy, efficiency, and low cost, can provide further data and technical support for the planning, protection supervision, and development strategy formulation of traditional Chinese villages.

Compensation Mechanism for Ecological Diversity Governance in Chinese River Basins Application Based on Low-Carbon Water Quality Game Theory

Background: Taking the construction of ecological civilization as an important core of the overall layout of the “Five in One” and the coordinated promotion of the “Four Comprehensives” strategic layout, combined with the global concept of ecological environment governance, provides a new direction for the reform and innovation of China’s watershed ecological compensation system through diversified governance. Subjects and methods: This article focuses on ecological compensation in river basins, exploring collaborative cooperation models between upstream and downstream of the basin under the dual constraints of “low-carbon” and “water quality”, to achieve a “win-win” situation between the ecological environment of the basin and all parties involved in basin protection. Based on the theory of evolutionary game theory, the indicators of “carbon emissions” and “water quality” are introduced to construct a diversified ecological co-governance compensation strategy model for the watershed. By exploring the optimal strategies among various entities in the upstream and downstream of the watershed. Results: (1) In the initial stage, the optimal strategy for ecological compensation in the watershed among stakeholders cannot achieve a stable balance through their evolution; (2) In the mature stage, based on the regulation of the superior government, interest subjects will choose a mutual supervision strategy to avoid bearing high penalties, to achieve Pareto effectiveness in achieving a stable overall economy of the basin; (3) By paying for high-quality resources from downstream and implementing carbon trading among basin entities, we can promote internal resource digestion, reduce intermediate transaction costs, and deeply promote the overall green and sustainable development of the basin. Through analysis, it has been proven that the conclusion is effective, providing decision support for the diversified co-governance of watershed ecosystems. Based on building an ecological community with a shared future in the watershed, we aim to achieve the construction of a community with a shared future for mankind.

Challenges and strategies of instructional supervision in senior secondary schools in Yola north local government area of Adamawa state

Challenges and strategies of instructional supervision in senior secondary schools in Yola north local government area of Adamawa state

Sector and employee withdrawal behavior: evidence from licensed public and private sector commercial bank staff of Sri Lanka

Employee well-being and organizational effectiveness are seriously threatened by withdrawal behavior in the ever-changing business environment. This study explores the complex link between the banking sector (public and private) and employees' withdrawal behavior. Further, which sector of banking staff has more withdrawal behavior is investigated in this study. Regression modeling and quantitative analysis are used in this study to provide persuasive insights into how the sector affects physical and psychological withdrawal behavior (WBP and WBS). Primary data is used in this study, collected through a Likert scale questionnaire from 131 banking staff. The results show a significant and positive relationship between sector and withdrawal behavior. Moreover, the results confirm that private-sector banking staff show higher levels of WBP and WBS than the public sector. Results of the study suggest that both sectors' top management ought to consider some strategic human resource management practices to retain their employees, especially private licensed banks must consider more impressive supervision and motivation strategies to reduce employee turnover because private sector banking staff have a higher level of withdrawal behavior than public sector banking staff.

Optimizing Internal Supervision: The Role of Time and Location in Enhancing Health Center Service Quality in Kendari

Introduction: The problem in this study is the data on the achievement of health center accreditation in Kendari City, namely: 10 intermediate health centers (66%), 2 basic (13%), 2 main (13%) and 1 plenary (6.6%). This data indicates that not all health centers have implemented service standards according to PMK 46 of 2015 concerning Health Center Accreditation Standards, this condition provides an illustration that technical guidance and supervision carried out at the Health Center have not been carried out optimally. The purpose of this study was to analyze the relationship between internal supervision strategies and improving the quality of health services as a managerial function at the Kendari City Health Center. Method: This type of research is quantitative Observational using Cross Sectional Study design. The population of the study were all health center leaders, Personal Health Efforts managers, Public Health Efforts managers and quality managers totaling 60 people at Kendari City health centers. The sampling method used the Total Sampling technique. Result: The results of the chi-square test on the supervision time variable obtained a value of X2 hit = 17.019, the location of supervision obtained a value of X2 hit = 13.081. Conclusion: There is a relationship between the time of supervision and the location of supervision with the improvement of service quality at the Kendari City Health Center.

The tripartite evolutionary game of enterprises' green production strategy with government supervision and people participation

Government supervision and people participation play vital roles in enterprises' green production strategy, but few studies have considered the strategy choices behind government supervision and people's participation. This study contended that whether an enterprise adopts the green production strategy is influenced not only by its responsible attitude facing social responsibility but also by the following two factors, the intensity of government supervision and whether people choose to participate in supervision or not when facing the enterprise adopting the green production strategy or not. We constructed a tripartite evolutionary game model to explore the behavioral evolution rules and evolutionary stability strategies of government supervision, people participation, and enterprises' green production. Then we employed numerical simulation to analyze how various factors influence the strategy selection of the government, people, and enterprises. The results show that if the utility values of government supervision and people participation are greater than 0, the enterprises will adopt the green production strategy. The system stability is affected by a synergistic relationship between participation cost, reputation benefit, and government subsidies, and an incremental relationship between enterprises' green production benefit, government subsidies, people's reported bonus, and enterprises' green production cost. This study provides a new theoretical perspective for research on government supervision, people participation, and enterprises' green production strategy, and the results provide important references for improving the enterprises' green production and urban environmental management.

Impact of Instructional Supervision Strategies on Teacher Performance in Hinabangan Samar District 1

The effectiveness of instructional supervision strategies on teacher performance is a critical area of educational research. This study aimed to determine the effectiveness of different instructional supervision strategies employed by school heads and their impact on the instructional performance of teachers in the Hinabangan Samar District I. This study utilized the descriptive-correlational research design using the researcher-developed survey questionnaire administered to 81 public school teacher-respondents who were randomly sampled. The high effectiveness of school heads' instructional supervision practices in the Hinabangan Samar District I as evidenced by a composite mean of 4.82 highlights the positive influence of strong leadership and supportive practices. The very high positive correlation observed in the Hinabangan Samar District I reinforce the importance of robust supervision systems in educational settings. School heads should prioritize regular classroom observations, provide constructive feedback, and offer professional development opportunities. Additionally, fostering a collaborative culture among teachers can further enhance the effectiveness of instructional supervision.

Influence of Head Teachers’ Leadership Practices on Pupils’ Learning Outcomes in Public Primary Schools in Narok North Sub County, Kenya

This study sought to investigate the influence of head teachers’ instructional leadership practices on pupils’ learning outcomes/expectations in public primary schools in Narok North Sub-County, Kenya. This was specifically addressed by seeking to establish the influence of instructional supervision and motivational strategies on pupils’ learning outcomes/expectations in public primary schools in Narok North Sub-County, Kenya. The study employed the descriptive survey design. The target population constituted 54 public primary schools in Narok North Sub-County presented by 54 headteachers and 100 grade 5 teachers. Random sampling design was used to sample 129 respondents (45 head teachers and 84 teachers) from the target population. The descriptive analysis was used to establish the perceived level of head teachers’ instructional supervision, motivational strategies and pupils’ learning outcomes. Multiple Linear regression analysis was used to establish the influence of head teachers’ instructional supervision, motivational strategies and resource provision on the pupils’ academic performance and if the influences explained was statistically significant. Multiple Linear regression analysis was used to establish the influence of head teachers’ instructional supervision and motivational strategies on the pupils’ academic performance and if the influences explained was statistically significant. The study revealed that headteachers’ instructional leadership practices consisted of classroom visitation, delegation of teaching duties, evaluation of records of work, holding conferences with teachers, lesson preparations and evaluation of pupils’ exercise books which considerably improved the learning outcomes of grade 5 Pupils in all the five subjects. Finally, the study recommended that Re-engineering workshops by the Ministry of Education should emphasize on curriculum instructional supervision leadership practices and learners’ motivational strategies with a view to improving the quality of Grade 5 Pupils’ learning outcomes in all the five subjects’ areas.

Associations of Racial Equity Training, Policies, and Practices With Routine Supervision Strategies in Community Corrections

Community corrections agencies across the United States are adopting racial equity as a priority in their polices, practices, and staff training, though how racial equity can be translated into the routine practices of probation and parole officers has not been widely discussed. This study examines the associations of perceived racial equity priorities on the supervision strategies reported by probation and parole officers in an online survey ( N = 1054). Approximately one-third of officers reported that their agencies prioritized racial equity. Racial equity scores were stronger in juvenile agencies and in agencies that prioritized evidence-informed practices. Racial equity was associated with reduced accountability-oriented case management practices. Results of this study suggest that racial equity policies and practices may impact client outcomes through changes in general supervision strategies. More research is needed to identify race-conscious strategies that community corrections officers can employ to meet the needs of people involved in the legal system.

An investigation into China’s online catering food safety governance efficacy based on the strategies of frequent supervision and strict penalty

An investigation into China’s online catering food safety governance efficacy based on the strategies of frequent supervision and strict penalty

Clean energy ship application to promote carbon neutrality in global maritime industry: State governance or international supervision？

The global maritime industry is facing the goal to achieve "carbon neutrality", and substituting traditional ship fuel with clean energy is the most important solution. Whether to promote clean-energy ships through state governance or unified international supervision to achieve carbon neutrality has been a long-standing debate. This study employs both a tripartite evolutionary game model and a quadrilateral evolutionary game model to compare the effectiveness of state governance and unified international supervision in promoting clean-energy ships. The tripartite model simulates interactions between local governments, shipping companies, and shippers, revealing that high subsidy costs reduce local governments' incentives to promote clean-energy ships. The quadrilateral model introduces international supervision, demonstrating that a unified international oversight mechanism can accelerate the global maritime industry's progress toward carbon neutrality. Numerical simulations indicate that lowering the purchase costs of clean-energy ships and introducing government subsidies significantly influence the adoption of clean-energy ships, while supervision costs impact the effectiveness of international regulations. These findings provide critical insights for policymakers aiming to balance financial incentives and global supervision strategies to foster low-carbon development in the maritime sector.

The Supervisory Relationship and Contextual Stressors

The effectiveness of supervision throughout the student writing process depends on several key factors: regular academic meetings, transparent expectations, progress documentation, and the cultivation of an inclusive research environment. To effectively address unconscious biases and inequities within the supervisory relationship, a gender-responsive approach is crucial. Implementing gender-sensitive supervision strategies is essential for mitigating the negative effects of gender-based interaction patterns on supervisees, which, in turn, enhances the overall quality of research. By fostering a more equitable supervisory environment, these strategies contribute to improved outcomes and a more supportive research process.

Improving image-based weld defects detection under interference information via a novel semantic supervision strategy

ABSTRACT Weld defects of skin-skeleton structures are invisible and image-based visual inspections using deep learning neural networks are in demand. The main limitation of previous detection algorithms is interference information in images. The reflective light and uneven brightness distribution hinder algorithms to achieve higher detection reliability. In this work, a novel supervision strategy for defects detection algorithms was proposed to break the limitation. A new semantic gate convolution block was developed to help the neural networks to distinguish between targets and interference. The block utilised semantic segmentation labels and a gate function to control the information transmitted to the detection output. Additionally, a new category-pixel-accuracy loss function was adopted to improve the effectiveness of semantic features. The new function reduced the effects of negative pixels to avoid over emphasis on semantic completeness, thus the ineffective features of interference could be eliminated. The results indicated that the back-propagated gradients from the supervision part taught the backbone networks to focus on correct objects instead of interference. To testify the validity of the current method, the algorithms was verified in burns and collapse defects. The detection accuracy under the new semantic supervision reached 99.1%, which was superior to common CNN.

Low-carbon operation strategy of virtual power plant considering progressive demand response

Adapting virtual power plants (VPPs) to the development of new power systems has gradually become a trend. Meanwhile, the economical and effective use of demand-side resources in VPPs has become a research hotspot in the field of new energy. This paper proposes a low-carbon operation strategy with progressive demand response (DR). A DR model id established by evaluating the scheduling potential of low-carbon and flexible loads and the DR transfer of different users motivated by incentive mechanisms. Because the changing carbon price in carbon trading also affects the response transfer amount, it is beneficial to reduce the operating cost of the energy system by using consumers’ surplus value according to consumer psychology to guide energy users to participate in DR. Finally, wind, light, user load data and VPP internal unit output are optimized in rolling mode over multiple time scales, and the optimal adjustment results of the previous day are taken as the basis. At the same time, the output deviation penalty function is added to further reduce the system cost. The nonlinear model of DR cost is transformed into a mixed integer linear model, which is modeled on MATLAB software and solved by a CPLEX optimization solver. The case analysis shows that the progressive DR strategy may increase the total operating cost of the system, but reduce the cost of the carbon emission. The proposed model and method can not only improve the feasibility and effectiveness of market supervision strategy, but also provides a useful reference for operators and demand-side users in energy systems to choose strategies.

Role Exchange-Based Self-Training Semi-Supervision Framework for Complex Medical Image Segmentation.

Segmentation of complex medical images such as vascular network and pulmonary tracheal network requires segmentation of many tiny targets on each tomographic section of the 3-D medical image volume. Although semantic segmentation of medical images based on deep learning has made great progress, fully supervised models require a great amount of annotations, making such complex medical image segmentation a difficult problem. In this article, we propose a semi-supervised model for complex medical image segmentation, which innovatively proposes a bidirectional self-training paradigm, through dynamically exchanging the roles of teacher and student by estimating the reliability at the model level. The direction of information and knowledge transfer between the two networks can be controlled, and the probability distribution of the roles of teacher and student in the next stage will be jointly determined by the model's uncertainty and instability in the training process. We also resolve the problem that loosely coupled networks are prone to collapse when training on small-scale annotated data by proposing asymmetric supervision (AS) strategy and hierarchical dual student (HDS) structure. In particular, a bidirectional distillation loss combined with the role exchange (RE) strategy and a global-local-aware consistency loss are introduced to obtain stable mutual promotion and achieve matching of global and local features, respectively. We conduct detailed experiments on two public datasets and one private dataset and lead existing semi-supervised methods by a large margin, while achieving fully supervised performance at a labeling cost of 5%.