Supervision Methods Research Articles

Pengawasan Penyelenggaraan Parkir Tepi Jalan Umum di Kawasan Wisata Jalan Tunjungan pada Dinas Perhubungan Kota Surabaya

The large number of public roadside parking arrangements in the Jalan Tunjungan Surabaya Tourist Area that are not in accordance with existing regulations has caused a lot of anxiety among road users. This research aims to analyze how supervision is carried out by the Surabaya City Transportation Department in the implementation of public roadside parking to find out whether existing supervision makes public roadside parking comply with existing regulations, increases PAD resulting from parking fees, increases parking attendant compliance, as well as sanctions. given for existing violations. This research uses a qualitative descriptive approach with primary and secondary data sources. Data collection methods used were observation, interviews and documentation. Data analysis techniques use data collection, data condensation, data presentation, and drawing conclusions. This research uses the theory of implementation of supervision according to Soekidjo Notoatmodjo which consists of three, namely the object of supervision, the method of supervision, and the process of supervision. The results of this research indicate that supervision of the implementation of public roadside parking in the Jalan Tunjungan Tourist Area is not yet fully optimal because it does not meet the criteria in terms of supervision objects and supervision methods. This is due to the low level of discipline of parking attendants, in addition to the lack of socialization regarding parking management regulations to the public so that complaints are rarely made by the public. However, the criteria for the supervision process show that the process of carrying out supervision by the Surabaya City Transportation Service is in accordance with the established flow and the imposition of sanctions for violations by parking attendants is determined in accordance with applicable regulations. It is hoped that the results of this research can make supervision more optimal.

Video-observed therapy (VOT) vs directly observed therapy (DOT) for tuberculosis treatment: A systematic review on adherence, cost of treatment observation, time spent observing treatment and patient satisfaction.

Tuberculosis (TB) treatment demands strict adherence to multidrug regimens. Directly Observed Therapy (DOT) poses challenges, especially regarding adherence. With the popularization of smartphones, Video-Observed Therapy (VOT) has emerged as a promising alternative, allowing healthcare providers to remotely supervise patients taking their medications via video calls. This systematic review critically assesses VOT's effectiveness compared to DOT, focusing on adherence, treatment costs, time spent supervising treatment, and patient satisfaction, aiming to optimize TB supervision methods worldwide. Only studies that met the following criteria were eligible for inclusion in the systematic review: randomized trials; studies that compared VOT to DOT; studies involving patients diagnosed with pulmonary or extrapulmonary tuberculosis; studies that reported any of the desired outcomes; full-text articles available for review; and studies conducted in the English language. We excluded studies with the following attributes: studies that lacked a control group; case series or case reports; and previous systematic reviews. The search engines and databases MEDLINE, Embase, and Cochrane were used to find studies comparing Video-Observed Therapy (VOT) to Directly Observed Therapy (DOT). The following search phrases were used to look for papers that contained them in their title or abstract: ("Electronic Directly Observed Therapy" OR "Video-observed therapy" OR "Telemedicine" OR "Wirelessly observed therapy" OR "Smartphone-enabled video-observed") AND ("TUBERCULOSIS"). A systematic review of the literature revealed the following findings: in all Randomized Controlled Trials (RCTs), video-observed therapy (VOT) demonstrated non-inferiority in terms of treatment adherence compared to traditional directly observed therapy (DOT); VOT reduced costs where these outcomes were assessed in the RCTs; the use of VOT reduced the amount of time healthcare professionals spent supervising treatment in RCTs evaluating this aspect; VOT contributed to higher treatment satisfaction in RCTs where this outcome was measured. In this systematic review we emphasize the importance of Video-Observed Therapy (VOT) in the digital age for patients that have access to internet. Our findings show that VOT is comparable to DOT in terms of treatment adherence, but it is also cost-effective, improves patient satisfaction and takes less time for healthcare professionals to supervise.

Supervisi Pendidikan di Sekolah Inklusif: Tantangan dan Solusinya

Educational supervision in inclusive schools faces various challenges, particularly in addressing the diverse needs of students, the lack of specialized training for teachers, and the limitations of resources and supporting facilities. The aim of this research is to identify the challenges of supervision in inclusive schools and to seek effective solutions to improve the quality of supervision within the context of inclusive education. This study employs a library research method, using data sources from books, journals, and relevant previous studies. Data analysis is conducted qualitatively through literature review. The findings indicate that enhancing teacher competency through continuous training, implementing collaborative supervision methods, and strengthening supporting facilities are effective solutions to address the challenges of supervision in inclusive schools. In conclusion, educational supervision in inclusive schools can operate more optimally when supported by a comprehensive approach, both in terms of teacher competency development and the provision of adequate facilities

Progressive Supervision via Label Decomposition: An long-term and large-scale wireless traffic forecasting method

Long-term and Large-scale Wireless Traffic Forecasting (LL-WTF) is pivotal for strategic network management and comprehensive planning on a macro scale. However, LL-WTF poses greater challenges than short-term ones due to the pronounced non-stationarity of extended wireless traffic and the vast number of nodes distributed at the city scale. To cope with this, we propose a Progressive Supervision method based on Label Decomposition (PSLD). Specifically, we first introduce a Random Subgraph Sampling (RSS) algorithm designed to sample a tractable subset from large-scale traffic data, thereby enabling efficient network training. Then, PSLD employs label decomposition to obtain multiple easy-to-learn components, which are learned progressively at shallow layers and combined at deep layers to effectively cope with the non-stationary problem raised by LL-WTF tasks. Finally, we compare the proposed method with various state-of-the-art (SOTA) methods on three large-scale WT datasets. Extensive experimental results demonstrate that the proposed PSLD significantly outperforms existing methods, with an average 2%, 4%, and 11% performance improvement on three WT datasets, respectively. In addition, we built an open source library for WT forecasting (WTFlib) to facilitate related research, which contains numerous SOTA methods and provides a strong benchmark. Experiments can be reproduced through https://github.com/Anoise/WTFlib.

An Intelligent Decision-Making Approach for Multi-Ship Traffic Conflict Mitigation from the Perspective of Maritime Surveillance

Potential multi-ship conflict situations in coastal or near-shore port areas have always been one of the important factors affecting ship navigation safety and a key target of maritime traffic regulatory authorities. In recent years, with the continuous development and integration of various emerging technologies in the maritime field, maritime traffic supervision has also shown a trend of intelligent and autonomous development. The traditional supervision method dominated by human experience is evolving towards data and model-driven practices. In order to solve the problem of ship navigation safety supervision under multi-ship conflict scenarios, it is urgent to build an intelligent conflict mitigation decision-making model. Therefore, this paper designs a novel risk mitigation decision-making model for multi-ship conflict scenarios from the perspective of maritime supervision. The model proposed in this paper first extracts high-density ship clusters based on AIS (Automatic Identification System) data and uses the MCD (Mean Core Density) and PRM (Proportion of Relative Motion) as feature indicators to further mine potential multi-ship conflict scenarios. Finally, a global optimization decision-making model is constructed to effectively mitigate conflict risks. Experimental verification shows that the intelligent decision-making model for the mitigation of maritime traffic conflict proposed in this paper can autonomously identify conflict scenarios and make reasonable decisions in real time. It can effectively ensure the navigation safety of ships in multi-ship conflict scenarios and further improve the supervision level of maritime departments.

Investigation on Factors Affecting Cognitive Skills in Detection of Driving Fatigue

Road safety is a worldwide concern where a high number of fatalities and injuries are caused by traffic accidents. One major issue contributing to these incidents is driving while fatigued, which has been recognized as a contributing factor to accidents in many countries. Being tired while driving is a state that encompasses both physical and mental exhaustion and can greatly impair a person's ability to drive safely. Fatigue negatively affects a driver's cognitive abilities, resulting in decreased performance behind the wheel. The aim of this study is to analyse the effect of driving duration, body mass index (BMI), types of roads and gender factor in causing fatigue while driving. Cognitive skills of drivers while driving was measured through the electroencephalogram (EEG) signal gathered from Emotiv EPOC device to measure brain activity. Relationship between cognitive skills and fatigue signalling is then analysed using regression analysis in Design-Expert software. Results indicated that as mental fatigue increases, there is a noticeable decrease in the relative power of beta rhythm, supporting previous research findings. Results on correlation show that the longer the drivers drove, the wearier they become. Obese people experience more fatigue when driving than those with a normal or overweight BMI. Driving on straight roads resulted in greater fatigue compared to driving on winding ones, with female participants indicating higher levels of tiredness than male participants. These insights are crucial for road safety authorities to tailor their supervision methods based on individual characteristics and to design effective driver fatigue warnings.

Understanding Residents' and Supervisors' Views on Developing Support-Autonomy Balance Through Supervision Methods in Postgraduate Dental Training: AQualitative Study.

Supervisors continuously need to decide when to provide clinical opportunities for unsupervised patient care to facilitate residents' development in the complex clinical learning context. The aim of this study is to explore residents' and supervisors' views and understanding of the influence of clinical supervision on affording a balanced support-autonomy from the cognitive apprenticeship (CA) theoretical lens. Residents and supervisors, representing all disciplines, participated in five focus groups and four semi-structured individual interviews. Purposive and convenience sampling methods were used for recruiting participants. The reflexive thematic analysis approach was used for inductive data analysis. A total of 15 residents and 8 supervisors participated in this study. All agreed that CA teaching methods can be applied across all levels. Participants experienced changes in their supervision methods and supervisor-resident interactions at different levels of training. They related the selection of supervision to task-, resident- and supervisor-related factors. Learning facilitating factors in clinical learning environment were identified and suggestions to enrich residents' learning experiences were also reported. The current study found that a one-size-fits-all paradigm may not be effective for clinical supervision. It contributes to our understanding of how the CA model may be used to guide supervisor behaviour and how such practices can be modified to residents' level of development and competencies.

3D voxel reconstruction from single-view image based on cross-domain feature fusion

The single-view 3D voxel reconstruction approach that relies on deep learning is inherently constrained by its single-input nature, which fails to account for the disparities in data distribution between the image and voxel domains. This leads to insufficient model details in the reconstruction. Currently, there is an absence of an end-to-end single-view 3D voxel reconstruction technique that effectively addresses the data distribution variances across domains while streamlining the training and inference processes. To mitigate the challenges arising from the disparities in data distribution across various domains, we introduce a novel single-view 3D voxel reconstruction model that leverages cross-domain feature fusion, termed SV3D-CDFF. SV3D-CDFF utilizes cross-domain feature clustering to mitigate the impact of data distribution disparities and employ a feature supervision method to learns voxel features . Additionally, it incorporates attention mechanism to fuse image and voxel features and utilizes residual network for 3D voxel reconstruction. Quantitative and qualitative experimental results demonstrate that SV3D-CDFF effectively integrates image and voxel features, eliminates data distribution disparities across various domains, and improves the quality of reconstructed voxel models in terms of overall structure and local detail. SV3D-CDFF outperforms existing state-of-the-arts methods in terms of the IoU and F-Score, with improvements of 0.005 and 0.045, respectively.

Biomedical literature mining: graph kernel-based learning for gene–gene interaction extraction

The supervised machine learning method is often used for biomedical relationship extraction. The disadvantage is that it requires much time and money to manually establish an annotated dataset. Based on distant supervision, the knowledge base is combined with the corpus, thus, the training corpus can be automatically annotated. As many biomedical databases provide knowledge bases for study with a limited number of annotated corpora, this method is practical in biomedicine. The clinical significance of each patient’s genetic makeup can be understood based on the healthcare provider’s genetic database. Unfortunately, the lack of previous biomedical relationship extraction studies focuses on gene–gene interaction. The main purpose of this study is to develop extraction methods for gene–gene interactions that can help explain the heritability of human complex diseases. This study referred to the information on gene–gene interactions in the KEGG PATHWAY database, the abstracts in PubMed were adopted to generate the training sample set, and the graph kernel method was adopted to extract gene–gene interactions. The best assessment result was an F1-score of 0.79. Our developed distant supervision method automatically finds sentences through the corpus without manual labeling for extracting gene–gene interactions, which can effectively reduce the time cost for manual annotation data; moreover, the relationship extraction method based on a graph kernel can be successfully applied to extract gene–gene interactions. In this way, the results of this study are expected to help achieve precision medicine.

PRINCIPAL'S CLINICAL SUPERVISION: TEACHERS' PERSPECTIVES ON INSTRUCTIONAL FEEDBACK

The purpose of this study was to investigate the instructional feedback given by Principals during classroom observations. A qualitative approach was used, with semi-structured interviews conducted to gather data. Content analysis was employed to analyze the data. The participants were 20 elementary school teachers from Surabaya city in the 2023-2024 academic year, selected through purposive sampling. The findings revealed that Principals showed insufficient competence in supervision, conducting classroom observations only once a year as a formal procedure. Compared to educational supervisors, Principals were found to need greater proficiency in managing instructional issues faced by teachers due to their familiarity with teachers and knowledge of school activities. The study highlighted that teachers did not find the feedback provided by Principals beneficial, as it was seen as general advice. Therefore, it is essential for administrators to learn effective supervision methods to support instructional improvements in schools. Principals should understand how their role in the supervision system impacts the feedback they provide to teachers.

The Impacts of Regulatory Approaches to Carbon Quotas on Third-Party Logistics Low-Carbon Financing Strategies and Emission Reduction Effects

Carbon emission reduction is an important issue for sustainable development. The logistics industry is a key area for carbon emission reduction. The modern logistics supply chain includes logistics parks (fourth-party logistics, 4PL) and small, medium and micro logistics enterprises settled in the parks (third-party logistics, 3PL). Facing the pressure of the need for sustainable development, 3PL enterprises need to invest a lot of money in green transformation. However, 3PL enterprises are subject to serious financial constraints. In order to break the capital constraints, 3PL enterprises can raise funds from banks or from 4PL financing. Under the carbon quota policy, the government can regulate the 4PL or the 3PL. Therefore, this paper uses the Stackelberg game model to build a green financing strategy model of small and medium-sized logistics enterprises considering different supervision methods under carbon quotas, explores the optimal emission reduction decision-making process of small and medium-sized logistics enterprises, and provides solutions to the financing problems of small and medium-sized logistics enterprises in realizing sustainable development. The study found that the decisions of enterprises under different governmental supervision methods are affected by carbon quotas, and the government’s supervision of 3PL is more conducive to carbon emission reduction; in this scenario, the 4PL financing strategy is more likely to be adopted compared with bank financing, because 4PL charge lower service fees, thus encouraging 3PL to increase their low-carbon investment. Finally, this paper puts forward two different carbon emission supervision methods and considers the green financing services of 4PL; this provides references for government supervision and the sustainable development strategies of logistics enterprises.

Incorporating long-tail data in complex backgrounds for visual surface defect detection in PCBs

AbstractHigh-quality printed circuit boards (PCBs) are essential components in modern electronic circuits. Nevertheless, most of the existing methods for PCB surface defect detection neglect the fact that PCB surface defects in complex backgrounds are prone to long-tailed data distributions, which in turn affects the effectiveness of defect detection. Additionally, most of the existing methods ignore the intra-scale features of defects and do not utilize auxiliary supervision strategies to improve the detection performance of the network. To tackle these issues, we propose a lightweight long-tailed data mining network (LLM-Net) for identifying PCB surface defects. Firstly, the proposed Efficient Feature Fusion Network (EFFNet) is applied to embed intra-scale feature associations and multi-scale features of defects into LLM-Net. Next, an auxiliary supervision method with a soft label assignment strategy is designed to help LLM-Net learn more accurate defect features. Finally, the issue of inadequate tail data detection is addressed by employing the devised Binary Cross-Entropy Loss Rank Mining method (BCE-LRM) to identify challenging samples. The performance of LLM-Net was evaluated on a homemade dataset of PCB surface soldering defects, and the results show that LLM-Net achieves the best accuracy of mAP@0.5 for the evaluation metric of the COCO dataset, and it has a real-time inference speed of 188 frames per second (FPS).

Deep Learning-based Depth Estimation Methods from Monocular Image and Videos: A Comprehensive Survey

Estimating depth from single RGB images and videos is of widespread interest due to its applications in many areas, including autonomous driving, 3D reconstruction, digital entertainment, and robotics. More than 500 deep learning-based papers have been published in the past 10 years, which indicates the growing interest in the task. This paper presents a comprehensive survey of the existing deep learning-based methods, the challenges they address, and how they have evolved in their architecture and supervision methods. It provides a taxonomy for classifying the current work based on their input and output modalities, network architectures, and learning methods. It also discusses the major milestones in the history of monocular depth estimation, and different pipelines, datasets, and evaluation metrics used in existing methods.

Issues in China's Alignment with DEPA Data Cross-Border Regulation

In the global digital governance game, the Digital Economy Partnership Agreement (DEPA) features innovative designs for digital trade, aligning with China's goals of internal data sharing openness and external breaking of "rule locks". However, China currently exhibits significant legislative differences in the "Data Issues" section of DEPA's fourth module, adopting stricter regulatory measures for cross-border data flow and data localization. Addressing DEPA's "principle & exception" regulatory standards, this paper argues that legitimate public policy objectives can be aligned with DEPA through proportionality principles, WTO general exception clauses, etc. By innovating customs supervision methods and following the path of the Hainan Free Trade Port trial, connecting international trade new rules, orderly implementing law enforcement systems and supporting mechanisms to ensure data security and orderly flow, China can gradually align with DEPA.

Reining in the Behemoths for the Common Good? An Analysis of State Control of State-Owned Enterprises and the Pathway to Better Governance in China

AbstractThe state-owned enterprise (SOE) is the most significant business form in China. In the planned economy era, the state employed SOEs to implement economic policy and to provide employment opportunities. After entering the marketization era, the state still expects SOEs to preserve the state assets and fulfill political goals, notwithstanding the governance issues facing SOEs. SOEs do not generally perform as well as private corporations worldwide and the existing literature attempts to explain the reasons for poor governance problems from many different perspectives. SOEs in China have many distinctive characteristics compared to their counterparts overseas. The ways in which the state exercises its control over SOEs are the primary reasons for creating the vital distinctive characteristics that invite SOEs’ poor governance problems in China. The three most significant problems are discussed here. First, over-politicization and bureaucracy lower SOEs’ operational efficiency. Second, the board of directors (BOD) fails to perform its traditional role in corporate governance in serving the corporate interests. Third, the comprehensive involvement of the Communist Party in China (CPC) needs to be regulated and the accountability of internal organs should be extended to the party committees within the companies. We submit that the existing SOE supervision and control methods need to be changed to clarify the for-profit purpose of SOEs. The BOD needs uninterrupted business management power and the party activities should follow party rules and company law to make party cadres accountable for their business decisions.

Webly Supervised Knowledge-Embedded Model for Visual Reasoning.

Visual reasoning between visual images and natural language remains a long-standing challenge in computer vision. Conventional deep supervision methods target at finding answers to the questions relying on the datasets containing only a limited amount of images with textual ground-truth descriptions. Facing learning with limited labels, it is natural to expect to constitute a larger scale dataset consisting of several million visual data annotated with texts, but this approach is extremely time-intensive and laborious. Knowledge-based works usually treat knowledge graphs (KGs) as static flattened tables for searching the answer, but fail to take advantage of the dynamic update of KGs. To overcome these deficiencies, we propose a Webly supervised knowledge-embedded model for the task of visual reasoning. On the one hand, vitalized by the overwhelming successful Webly supervised learning, we make much use readily available images from the Web with their weakly annotated texts for an effective representation. On the other hand, we design a knowledge-embedded model, including the dynamically updated interaction mechanism between semantic representation models and KGs. Experimental results on two benchmark datasets demonstrate that our proposed model significantly achieves the most outstanding performance compared with other state-of-the-art approaches for the task of visual reasoning.

Student‐generated metaphor in research supervision: An arts‐based method

AbstractPrevious research has highlighted the impact and importance of the use of client‐generated metaphor in psychotherapy (Cott, 2020). While researching this topic, we began to incorporate student‐generated metaphor into the research supervision process. This was found to enhance the supervision relationship, providing a quick and immediate insight into the supervisee's lived experience and pastoral needs at each stage of the research journey. This work developed into an arts‐based research supervision method, which reduced the power differential in the supervisory relationship, and made space for the supervisee to communicate their learning experience on their own terms. In this paper, we will set out the rationale for using student‐generated metaphor in research supervision and share further student feedback on this process, including future ideas for research and practice. We argue that theory and research on research supervision often neglect its pastoral component, and that providing excellent pastoral support to postgraduate researchers is key. The paper will conclude by making the case that the use of student‐generated metaphor in research supervision can be a simple and effective way to bring the supervisory relationship into focus.

Utilizing excitation-emission matrix fluorescence spectroscopy and convolutional neural networks for dark tea brand and aging period identification

This study utilized excitation-emission matrix (EEM) fluorescence spectroscopy in conjunction with convolutional neural networks (CNNs) to identify the dark tea brands and their aging periods, which were both key factors of market value. The adaptation of EEM data to the CNN model was examined, encompassing the configuration of the data structure and the implementation of data augmentation. Three typical CNN models including AlexNet, GoogLeNet, and ResNet were evaluated. ResNet was selected because it demonstrated the highest classification accuracy on the test set and superior clustering performance in the t-SNE visualization of extracted features. Analysis of feature contribution suggested that the CNN model exhibited enhanced sensitivity towards the boundaries of the fluorescence region. Furthermore, transfer learning was proved to be effective in facilitating adaptation of dark tea recognition models to databases with different sample sizes, which was meaningful for the application of this method in dark tea market supervision. Finally, the multi-task model was developed and achieved a 96.9 % accuracy in brand identification and 87.5 % in aging period determination, suggesting the advantages of multi-task learning in enhancing model generalization and training efficiency.

Hierarchy-Aware and Label Balanced Model for Hierarchical Text Classification

Hierarchical text classification, where labels can be modeled as a hierarchical structure, is a special multi-label text classification sub-task. Current methods mainly improve model performance by modeling label dependencies. The model HGCLR with excellent performance obtains hierarchy-aware text representations through contrastive learning, but the degree of hierarchical awareness is insufficient. To address this problem, a new multi-label negative supervision method is proposed to drive text representations of samples with more different labels farther away. In addition, to tackle the problem of label imbalance in hierarchical text classification, asymmetric loss is employed to compute the classification loss, so that the model focuses on learning from difficult samples and the contributions of positive and negative labels to the loss function tend to be balanced. In summary, based on HGCLR, by adding multi-label negative supervision and replacing the classification loss with asymmetric loss, we propose a hierarchy-aware and label balanced model (HALB) for hierarchical text classification. Experimental results demonstrate that HALB outperforms several classical models for hierarchical text classification. HALB achieves the best results on both Micro-F1 and Macro-F1 and obtains average improvements of 0.51% and 1.28% respectively on four datasets compared with the strongest baseline model HGCLR.

Educating custom officers supervisors: how do they experience their role?

PurposeThis study aims to examine customs officers’ perception of the role as supervisor before and after completing formal training in practice supervision. Owing to changes in the education of customs officers, their role as supervisors has changed, necessitating formal training.Design/methodology/approachA questionnaire with three open-ended questions was given to all participants to answer before and immediately after the training. Responses were given anonymously. Additionally, free text of the participants’ reflections on the supervision method and challenges within supervision was analysed.FindingsFour categories, including subcategories, emerged from the thematic analysis: competence in practice supervision; socialising and integration; motivation; and frame conditions. Analysis of the feedback shows that the education is experienced/perceived as a contribution to their personal development, learning and motivation, leading to more reflection on their own practice and a stronger focus on contributing to the students’ learning process and shaping them as future colleagues.Originality/valueTo the authors’ knowledge, there are no similar studies within this group, and the results of this study may lead to greater awareness regarding the supervisor role and the quality of customs and border management education and, thereby, higher quality in professional practice as customs officers.