Supervision Technology Research Articles

Expansion of Remote Supervision Technology in Community Corrections: Lessons Learned from One State During COVID-19

ABSTRACT The COVID-19 pandemic substantially disrupted community corrections work and forced agencies to adapt to continue maintaining public safety and helping clients navigate supervision. During this time, technology emerged as a key innovation to allow agencies to continue to achieve these mandates. The current study sought to better understand the role of technology in supervision during the pandemic with an emphasis on front-line officer perceptions. Using surveys of 184 probation and parole officers, we examined how officers adapted their supervision approach, the role of technology in this process, and the use of a mobile application to continue supervision during the pandemic. Study findings highlight pragmatic, risk-based adaptations that were made to supervision practices and ways technology was leveraged to mitigate the burdens of the pandemic. Additionally, we identified a number of barriers that hindered innovative efforts. This study adds to a growing discussion surrounding the promise of technological innovation in community corrections and how community corrections agencies can proactively prepare for future disruptions.

A novel method for rice identification: Coupling Raman spectroscopy with Fourier spectrum and analyzing with deep learning

The development of detection methods for food adulteration is of great significance for promoting innovation and development in food safety supervision technology. In this study, we proposed a novel model: Raman-Fourier-BiLSTM-CNN (RFBC), which combines Raman spectroscopy with deep learning to achieve precise identification of seven brands of japonica rice in the northeastern region of China. This model focuses on revealing the worth of the Fourier spectrum of Raman spectra. The raw Raman spectra and their Fourier spectra are cleverly coupled through a Bi-directional Long Short-Term Memory (BiLSTM) connection structure, and the resulting composite features are deeply analyzed by a Convolutional Neural Network (CNN). Compared to traditional algorithms, RFBC exhibits superior feature extraction capabilities. To further evaluate the recognition performance of RFBC, this study compared it with the RFBC model containing only the original Raman spectrum branch, as well as with machine learning models based on Principal Component Analysis (PCA), including Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), and eXtreme Gradient Boosting (XGBoost). The results show that, compared with the other five models, RFBC can accurately identify different brands of japonica rice, with significant advantages in accuracy, precision, recall, and other aspects. RFBC achieves a classification accuracy of 97.1 %, macro precision of 97.2 %, macro recall of 97.1 %, and macro F1-score of 97.2 %. The system proposed in this study can more accurately identify brands of japonica rice, providing strong technical support for combating counterfeit japonica rice products.

Study on the Legal Risks of Social Entities' Participation in Non-Emergency Transfers

Non-emergency transfer multi-level protection system is an important livelihood project in China and an important diversified support for a sound Chinese-style modern social security system, as well as an ideal governance tool to effectively solve the problems of non-emergency demand crowding out the emergency capacity, unbalanced transfer resources and differences in the quality of service, imperfect management mechanism and laws and regulations, and low social acceptance. We use structural theory to analyse the main problems in the current practice of China's non-emergency transfer security system, accordingly construct the non-emergency transfer security system, and put forward suggestions to promote the development of the system in a benign cycle. Improve the internal resource allocation mechanism to enhance the efficiency of non-emergency transfer services; focus on the cultivation of the talent team to steadily improve the quality of services; optimise the management system and strengthen the articulation mechanism to boost patients' beliefs in recovery; and strengthen the all-round management of information technology for market supervision to deepen the emotional communication and connection.

Pertanggungjawaban Konsultan Pengawas Jasa Konstruksi Terhadap Tindak Pidana Korupsi

This study examines the liability of construction service supervisory consultants in criminal acts of corruption, with a focus on criminal law enforcement mechanisms, forms of liability, and recommendations for strengthening the supervision system and preventing corruption in the construction sector. Using normative legal research methods, this study examines primary, secondary, and tertiary legal materials relevant to the research topic.The approaches used include statute approach, conceptual approach, and case approach. The analysis is conducted qualitatively with descriptive analytical method to produce a comprehensive picture of the problem under study.The results show that the criminal law enforcement mechanism against supervisory consultants involved in corruption involves a complex process, from investigation to court decisions, with the main challenge being proving the involvement of consultants in corruption schemes. The form of liability of supervisory consultants can be in the form of individual, corporate, or in solidum criminal liability, with sanctions that include imprisonment, fines, and state loss recovery obligations.The research also produces recommendations for strengthening the supervision system and preventing corruption, which include the implementation of technology in project supervision, increasing transparency through e-procurement, strengthening the whistleblowing system, improving the quality and independence of supervisory consultants, as well as regulatory reform and law enforcement.The research conclusions emphasize the importance of a holistic approach in addressing corruption in the construction sector, incorporating legal, management, technological and ethical aspects. The implementation of the proposed recommendations is expected to contribute to the creation of a cleaner, more efficient, and higher quality construction industry in Indonesia.

Research on blockchain integrated monitoring and supervision technology

With the development and progress of science and technology , the blockchain monitoring platform can reflect the dynamics of monitored objects in real time intuitively and accurately, and has become the main technical means for modern management, monitoring and supervision.Especially with the continuous development of blockchain technology, blockchain integrated monitoring and supervision technology has become more widely used.However, judging from the actual operation of the current blockchain monitoring and supervision platform, there are still many problems that need to be solved.Some of the problems have even caused the blockchain monitoring and supervision work can not operate efficiently.Blockchain monitoring and supervision personnel at all levels are also needed.Actively seek effective countermeasures and suggestions to improve the level of blockchain monitoring and supervision, demonstrating the importance of blockchain integrated monitoring and supervision technology.

Utilization of Digital Technologies in Doctoral Thesis Supervision at the University of Nairobi: Implications for Quality Assurance

The utilization of online digital technologies in higher education teaching and learning has gained impetus in the post-COVID-19 era. A number of higher educational institutions have invested in capacity-building programmes related to the utilization of online digital technologies for teaching and learning. However, the utilization of these technologies in doctoral supervision is hampered by challenges related to access to digital devices, internet connectivity and the general attitude among digital technology users. Furthermore, despite supervisors’ efforts to reinforce mentoring, the lack of in-person engagement between supervisors and students portends a greater challenge. Therefore, the study sought to determine the influence of doctoral students’ gender on their thesis progression, the relationship between the frequency of engaging supervisors and thesis progression, the preferred mode of supervisor engagement and the level of student utilization of digital technologies in their doctoral work. The study sampled 109 students from a population of 150 PhD students of the University of Nairobi. Data were gathered through questionnaires and interviews that were administered to doctoral students and their supervisors respectively. The results showed a weak negative correlation between student and supervisor engagement and their stage in theses writing. The study also established low utilization of digital technologies in doctoral thesis supervision. Nonetheless, the results showed that student-supervisor engagement has an impact on their progression and that a significant number of doctoral students were comfortable with engaging their supervisors through a combination of face-to-face/in-person and virtual/online sessions. The study recommends that universities should adopt a blended doctoral supervision model to leverage the strengths of both in-person and virtual student-supervisor engagement. Further, there is a need to train both students and supervisors in the use of digital tools to enhance their engagement

Research and postgraduate supervision during the Coronavirus-19 pandemic: Lessons learned

The Coronavirus-19 (COVID-19) pandemic imposed new demands for conducting research and postgraduate supervision online. Face-to-face interactions between supervisors and research participants were limited. Consequently, there was heightened attention to the use of information communications technology (ICTs) to mitigate the disruptions. There is not much research on postgraduate supervision during COVID-19. This interpretative-phenomenological qualitative case study analysed innovative strategies that nine purposely selected supervisors reported to have used to manage postgraduate research and supervision during the COVID-19 pandemic. Data were collected through online open-ended questionnaires sent to the supervisors. The constructivist theory underpinned this study. Results showed that while the pandemic was devastating, academic institutions learnt many good lessons regarding leveraging digital technologies for postgraduate students’ supervision. Innovative online supervision pedagogies were invaluable for both supervisors and students. Therefore, during the post COVID-19 era, supervisors must continue leveraging the use of digital technologies in postgraduate supervision to reduce costs and increase master’s and doctoral students’ throughputs and outputs.

Study on the Whole Process Supervision Technology of Digital Intelligent Enabling for Building Safety and Quality Appraisal Institutions

In order to fully implement the "Notice of The General Office of the State Council on Printing and Printing the Special Rectification Work Plan for the Safety of Self-built Houses", in view of the endless collapse of houses such as self-built houses, the traditional supervision methods of housing safety appraisal agencies are dishonest and the agency appraisal reports are distorted. Based on the principle of "the whole process of Internet + digital intelligence", this paper develops a set of digital and intelligent regulatory software system suitable for regulatory departments. It mainly introduces the basic application functions of this system, and takes the whole-area appraisal project of Taizhou City as an example to show that this regulatory system manages in all aspects such as human, machine, material, law and environment. The system has the advantages of thoughtful use steps, simple steps, scientific and accurate. At the end of this paper, the benefits and promotion value of the whole process of intelligent enabling supervision system are summarized and analyzed.

Identification and quantification of viable Lacticaseibacillus rhamnosus in probiotics using validated PMA-qPCR method.

The identification and quantification of viable bacteria at the species/strain level in compound probiotic products is challenging now. Molecular biology methods, e.g., propidium monoazide (PMA) combination with qPCR, have gained prominence for targeted viable cell counts. This study endeavors to establish a robust PMA-qPCR method for viable Lacticaseibacillus rhamnosus detection and systematically validated key metrics encompassing relative trueness, accuracy, limit of quantification, linear, and range. The inclusivity and exclusivity notably underscored high specificity of the primers for L. rhamnosus, which allowed accurate identification of the target bacteria. Furthermore, the conditions employed for PMA treatment were fully verified by 24 different L. rhamnosus including type strain, commercial strains, etc., confirming its effective discrimination between live and dead bacteria. A standard curve constructed by type strain could apply to commercial strains to convert qPCR Cq values to viable cell numbers. The established PMA-qPCR method was applied to 46 samples including pure cultures, probiotics as food ingredients, and compound probiotic products. Noteworthy is the congruity observed between measured and theoretical values within a 95% confidence interval of the upper and lower limits of agreement, demonstrating the relative trueness of this method. Moreover, accurate results were obtained when viable L. rhamnosus ranging from 103 to 108 CFU/mL. The comprehensive appraisal of PMA-qPCR performances provides potential industrial applications of this new technology in quality control and supervision of probiotic products.

Optimizing Juvenile Probation: A Concurrent Triangulation Study on Supervision Practices and Recidivism in Kenya

The primary objective of probation supervision is to ensure compliance with probation conditions, improve public safety, and rehabilitate and reintegrate offenders back into a pro-legal and social lifestyle. However, statistics in Kenya point to consistently high rates of recidivism in the past two decades. Despite this, limited information exists on the effect of juvenile supervision practices on recidivism in the country. This study aimed to evaluate the existing juvenile probation supervision practices and their outcomes in Nairobi County, Kenya. The study sampled 226 respondents comprising juvenile probationers and probation practitioners. Using a concurrent triangulation mixed-method approach, three probation supervision practices, namely, frequency and mode of supervision, supervision skills and workload, were examined. The outcomes for each of the three practices were varied, with frequency and mode of supervision having a reducing effect on recidivism but not significant. Supervision skills and probation officers’ workload significantly impacted recidivism. Collectively, the centrality of the existing probation supervision practices in curtailing juvenile reoffending behavior was evidenced. However, a number of challenges, including limited specialized training and mentorship in juvenile matters, resource constraints and overwhelming workloads, limited optimal outcomes. The study recommends consistent juvenile-focused training on supervision skills, clear guidelines to sustain manageable caseloads, and leveraging technology for administrative tasks and remote supervision.

Science and Math Teachers’ Satisfaction Level towards the Electronic Educational Supervision

The study aimed to identify the satisfaction level of Jordanian science and mathematics teachers toward electronic educational supervision and their relationship with educational supervisors. The study population included all Jordanian teachers of science and mathematics. The sample size of the study was 184 male and female teachers. The data was collected using a questionnaire that the researchers built and ensured its validity and reliability, and then published it to the study community electronically according to the snowball method. The study concluded that the average level of Jordanian science and mathematics teachers’ satisfaction with electronic supervision and their relationship with educational supervisors was within the middle category. The results indicated statistically significant differences in the satisfaction of Jordanian science and mathematics teachers with the educational supervisors of the school type in favor of private schools. The results did not indicate statistically significant differences in the level of Jordanian teachers’ satisfaction with the electronic supervision of the rest of the study variables (the teacher’s gender, the number of years of experience, and the teacher’s possession of technical skills). Accordingly, the study reached several recommendations: maintaining a culture that fosters distance learning by promoting mechanisms for the use of technology in teaching, learning, and supervision at the course level or at the school level annually.

Research on “multi-blind” working mechanism of Internet+ coal mine safety supervision

PurposeEffective safety supervision plays a crucial role in ensuring safe production within coal mines. Conventional coal mine safety supervision (CMSS) in China has suffered from the problems of power-seeking, excessive resource consumption and poor timeliness. This paper aims to explore the Internet+ CMSS mode being emerged in China.Design/methodology/approachThe evolution of CMSS systems underwent comprehensive scrutiny through a blend of qualitative and quantitative approaches. First, evolutionary game theory was used to analyze the necessity of incorporating Internet+ technology. Second, a system dynamics model of Internet+ CMSS was crafted, encompassing a system flow diagram and equations for various variables. The model was subsequently simulated by taking the W coal mine in Shanxi Province as a representative case study.FindingsIt was revealed that the expected safety profit from the Internet+ mode is 296.03% more than that from the conventional mode. The precise dissemination of law enforcement information was identified as a pivotal approach through which the Internet+ platform served as a conduit to foster synergistic collaboration among diverse elements within the system.Practical implicationsThe outcomes of this study not only raise awareness about the potential of Internet+ technology in safety supervision but also establish a vital theoretical foundation for enhancing the efficacy of the Internet+ CMSS mode. The significance of these findings extends to fostering the wholesome and sustainable progress of the coal mining industry.Originality/valueThis research stands out as one of the limited studies that delve into the influence of Internet+ technology on CMSS. Building upon the pivotal approach identified, to the best of authors’ knowledge, a novel “multi-blind” working mechanism for Internet+ CMSS is introduced for the first time.

EfficientUNet: An efficient solution for breast tumour segmentation in ultrasound images

AbstractThe accurate segmentation of breast tumours is important for the diagnosis and treatment of breast cancer. When using the classic U‐Net, Attention‐UNet, and UNet++ to segment the tumour, there are problems of oversegmentation, incorrect segmentation, and poor edge continuity. In this paper, an effective tumour segmentation method, EfficientUNet, is proposed. EfficientUNet adopts a step‐by‐step enhancement method, combining ResNet18, a channel attention mechanism and deep supervision. ResNet18, as the encoder of the whole network, solves the problem of gradient disappearanceand improves the feature extraction ability of the model. The channel attention module makes the model more accurate in tumour edge processing. The deep supervision technology accelerates the model training and provides the convergence direction for the model. In addition, it is found that when adjusting the size of the image, the method of image filling before clipping (or zooming) is more conducive to model learning than the direct interpolation method. And a comparative experiment wasperformed on dataset B. Compared to U‐Net, Attention‐UNet and UNet++, EfficientUNet has the highest performance. Finally, the ablation experiment also indicated the effectiveness of each module in EfficientUNet.

The role of blockchain technology in advancing sustainable energy with security settlement: enhancing security and efficiency in China’s security market

Blockchain technology has the potential to revolutionize securities settlement systems, offering an efficient, reliable, and cost-effective alternative to traditional methods. Its features, including distributed data authenticity, programmability, and scalability, can enhance security and efficiency in China’s securities market, while promoting a sustainable energy future. However, to fully leverage the benefits of blockchain in securities registration and settlement, it is crucial to address algorithmic loopholes and operational risks associated with smart contracts. Establishing blockchain technical standards and rules is also necessary to ensure smooth system operation. Furthermore, given the uncertainty of the final settlement time point, adherence to decentralization principles and the incorporation of embedded technology for supervision are essential. Legislative measures are required to regulate smart contracts and mitigate systemic risk effectively. This will ensure a stable settlement time expectation and enable fair allocation of legal responsibility among the involved parties. Additionally, other regulatory approaches should be implemented to provide effective supervision and adapt to the rapid development of blockchain. By addressing challenges and risks, blockchain’s full potential can be realized, enabling a sustainable energy future, while enhancing security and efficiency in China’s securities market.

How can blockchain technology promote food safety in agricultural market?-an evolutionary game analysis.

The governance of agricultural food safety issues is closely linked to social interests. To promote food safety supervision in the Chinese agricultural markets under the background of blockchain application, this paper develops a partnership comprising vendors, consumers, and the government. Using the theory of evolutionary game combined with the actual situation of China, the evolutionary process simulations of three participants prove that the tripartite subjects can realize a stable state under the specific relationship. Impact investigation results of typical influential factors indicate the following: (1) The behavior of vendors depends on the government's supervision and consumers' reporting attitude. Limiting the penalty amount for vendors to 66.7% of speculative gains can shorten the processing time for vendors to comply with the law. (2) Consumers play a vital role in food safety supervision of the agricultural market. The penalty for consumers should be limited to 1/3 of the reward amount. (3) The government's incentive-oriented and punishment-inhibited policies can promote blockchain technology in supervision. Punishment-inhibited and key influencing parameters can cooperate in obtaining the maximum regulatory benefits. The results of this study have certain reference values for promoting policy formulation and implementing blockchain technology in agricultural food safety supervision.

Research on Anti-Monopoly Regulations Against Algorithmic Price Discrimination

In the era of the digital economy, the algorithm is the core of productivity. As for the price, operators use algorithms to collect consumer data and customize “differentiated” prices for specific consumers, so as to gain or maintain competitive advantages. Clarifying the connotation, extension, and comprehensive influence of algorithmic price discrimination is the foundation of legal regulation. Illegal algorithmic price discrimination cannot be regulated spontaneously by the market alone, which needs legal intervention. This differential treatment of algorithmic price discrimination is prone to trigger serious unfair competition, resulting in an anti-competitive effect, so anti-monopoly regulation is necessary and reasonable. Information technologies such as data and algorithms further challenge traditional anti-monopoly regulation. In legislation, the definition of relevant markets, the determination of market dominance, the judgment of legitimate reasons, and the identification of the competition blockade impact are all vague. In supervision, the backward supervision technology and the supervisors that need to improve their ability also limit supervision efficiency. Therefore, we should first grasp the development status and value goal of China’s digital economy, and then promote and amend the existing legislation from three aspects, including subject, behavior, and result. Finally, we should not only improve the new mode of pre-regulation and post-regulation step by step, and regulate technology with technology, but also adjust the supervision allocation and supervision subjects, aiming to build a new pattern of anti-monopoly joint governance.

An Operation Scheme Generation Method for Nuclear Power Plant Operation under the Condition of No Operating Procedures Guided

Large-scale, complex, and high-risk industrial systems such as nuclear power plants have developed detailed operating procedures. Under the expected conditions, the operators operate the system according to the operating procedures to avoid human error. However, under complex and unfamiliar conditions, once the guidance of operating procedures is lost, serious consequences may be caused. This paper proposes a No-Procedure guided Operation Supervision Technology (NoP-OST). The key idea is to identify the success paths by a forward-searching approach from source to sink functions. On this basis, the success paths are combined to generate various operation schemes to achieve the main objectives of the system. The paper illustrates the application of NoP-OST through the case of loss of the Residual Heat Removal System (RHRS) in a Pressurized Water Reactor (PWR) nuclear power plant. The research results show that the method proposed in this paper can provide a valuable operational reference for complex systems by making use of the available functions of the system.

Exploration and Practice of AI Behavior Analysis Technology in Prison Safety Management

With the continuous development of science and technology, the popularity of prison security video surveillance system is getting higher and higher. The prison security management department needs to use more advanced and intelligent technology to identify these information when facing massive video images. From the development history of AI technology and the application status of related technologies, the innovative application of AI technology in smart prisons and the application of intelligent security system in prison security management as examples, this paper puts forward the research significance of AI technology in prison security management, and analyzes the necessity of adopting AI technology in prison security management and its results. Through the application of AI intelligent camera, face recognition technology, UAV and other advanced technologies in intelligent supervision, and the analysis of different perimeter control scenarios, it is concluded that the AI analysis system with intelligent synchronous early warning function makes prison information collection more convenient, information recognition more intelligent, and improves the efficiency of prison information collection. The application of AI behavior analysis technology in prison security management improves the accuracy of information data identification, improves the efficiency of prison security management, and provides reference value for building smart prisons.

A Study on Monitoring and Supervision of Ship Nitrogen-Oxide Emissions and Fuel-Sulfur-Content Compliance

Regulations for the control of air-pollutant emissions from ships within pollutant emission control areas (ECAs) have been issued for several years, but the lack of practical technologies and fundamental theory in the implementation process remains a challenge. In this study, we designed a model to calculate the nitrogen-oxide-emission intensity of ships and the sulfur content of ship fuels using theoretical deduction from the law of the conservation of mass. The reliability and availability of the derived results were empirically evaluated using measurement data for NOx, SO2, and CO2 in the exhaust gas of a demonstration ship in practice. By examining the model and the measured or registered fuel-oil-consumption rates of ships, a compliance-determination workflow for NOx-emission intensity and fuel-sulfur-content monitoring and supervision in on-voyage ships were proposed. The results showed that the ship fuel’s NOx-emission intensity and sulfur content can be evaluated by monitoring the exhaust-gas composition online and used to assist in maritime monitoring and the supervision of pollutant emissions from ships. It is recommended that uncertainties regarding sulfur content should be considered within 15% during monitoring and supervision. The established model and workflow can assist in maritime monitoring. Meanwhile, all related governments and industry-management departments are advised to actively lead the development of monitoring and supervision technology for ship-air-pollutant control in ECAs, as well as strengthening the quality management of ships’ static data.

TAGU-Net: Transformer Convolution Hybrid-Based U-Net With Attention Gate for Atypical Meningioma Segmentation

Meningioma is derived from the cap cells that reside on the arachnoid membrane. The atypical meninges of Grade II, a classification established by the World Health Organization, are included in one of the grades of meningioma. It has been discovered that early surgical resection significantly reduces the recurrence rate and mortality of tumors. Accurate segmentation of magnetic resonance images of brain tumors is crucial for diagnosing and treating atypical meningiomas. However, the traditional automatic segmentation framework heavily relies on convolution. The convolution-based segmentation network has limitations such as the size of the convolution kernels, a restricted receptive field, and a lack of spatial aggregation ability. To overcome these limitations, this paper presents a novel hybrid architecture named TAGU-Net, which combines Transformer and convolution based on U-Net with an attention gate. The TAGU-Net architecture extracts features of different resolution feature scales using convolutional neural network and Transformer. This approach effectively captures the image’s long-distance dependency and global characteristics in the encoder stage, relying on the global self-attention mechanism of the Transformer. Additionally, the inductive bias of the convolution neural network is combined to enhance the local modeling information and improve the model’s overall modeling ability. In the decoder phase, the attention gate is introduced to adaptively learn the skip connection information and up-sampling information in the network. This information is weighted and fused to highlight important features and suppress irrelevant features. To obtain better model training and avoid the vanishing gradient, deep supervision technology is used in the training process. Supplementary loss is added in some stages to supervise the training and achieve the best effect of atypical meningioma segmentation. The proposed method is evaluated on both the private atypical meningioma dataset and the publicly available BraTs2018 dataset.TAGU-Net has achieved Dice Scores of 97.67% and 97.62% and Jaccard index of 96.35% and 95.35% on the private atypical meningioma dataset and BraTs2018 dataset respectively, which is a state-of-the-art segmentation result beyond existing methods. According to the research results, the TAGU-Net model significantly improves atypical meningioma segmentation and can effectively assist doctors in processing MRI images.