Land Use Land Cover Analysis for Godavari Basin in Maharashtra Using Geographical Information System and Remote Sensing

The dynamic transformation of land use and land cover has emerged as a crucial aspect in the effective management of natural resources and the continual monitoring of environmental shifts. This study focused on the land use and land cover (LULC) changes within the catchment area of the Godavari River, assessing the repercussions of land and water resource exploitation. Utilizing LANDSAT satellite images from 2009, 2014, and 2019, this research employed supervised classification through the Quantum Geographic Information System (QGIS) software’s SCP plugin. Maximum likelihood classification algorithm was used for the assessment of supervised land use classification. Seven distinct LULC classes—forest, irrigated cropland, agricultural land (fallow), barren land, shrub land, water, and urban land—are delineated for classification purposes. The study revealed substantial changes in the Godavari basin’s land use patterns over the ten-year period from 2009 to 2019. Spatial and temporal dynamics of land use/cover changes (2009-2019) were quantified using three Satellite/Landsat images, a supervised classification algorithm and the post classification change detection technique in GIS. The total study area of the Godavari basin in Maharashtra encompasses 5138175.48 hectares. Notably, the built-up area increased from 0.14% in 2009 to 1.94% in 2019. The proportion of irrigated cropland, which was 62.32% in 2009, declined to 41.52% in 2019. Shrub land witnessed a noteworthy increase from 0.05% to 2.05% over the last decade. The key findings underscored significant declines in barren land, agricultural land, and irrigated cropland, juxtaposed with an expansion in forest land, shrub land, and urban land. The classification methodology achieved an overall accuracy of 80%, with a Kappa Statistic of 71.9% for the satellite images. The overall classification accuracy along with the Kappa value for 2009, 2014 and 2019 supervised land use land cover classification was good enough to detect the changing scenarios of Godavari River basin under study. These findings provide valuable insights for discerning land utilization across various categories, facilitating the adoption of appropriate strategies for sustainable land use in the region.

Study on Independent Supervision Function of Design Assurance System

The effective operation of a design assurance system cannot be achieved without the effective performance of the independent supervision function.As one of the core functions of the design assurance system,the purpose of the independent supervision function is to ensure that the system operates within the scope of procedures and manuals.At present,the function of independent supervision is a difficult and confusing issue for various original equipment manufacturers as well as suppliers,and there is an urgent requirement to put forward relevant requirements and form relevant methods.Based on the above mentioned objective,the basic requirements of the independent supervision function of design assurance system were studied,the problems and deficiencies in the organization,staffing,and methods existing in the current independent supervision function were analyzed,the improvement suggestions and measures for the performance of the independent supervision function from the aspects of the organization,staffing,procedures,and suppliers were put forward.The present work and conclusions provide guidance and direction for the effective operation of the design assurance system.

Training and Implementation of Subjective Questions Scoring System Based on the Baidu Qianfan Model Platform

Leveraging the Baidu Qianfan model platform,this paper designs and implements a highly efficient and accurate scoring system for subjective questions,focusing primarily on questions in the field of computer network technology.The system enhances the foundational model by utilizing Qianfan’s training tools and integrating advanced techniques,such as supervised fine-tuning.In the data preparation phase,a comprehensive collection of subjective data related to computer network technology is gathered,cleaned,and labeled.During model training and evaluation,optimal hyperparameters and tuning strategies are applied,resulting in a model capable of scoring with high accuracy.Evaluation results demonstrate that the proposed model performs well across multiple dimensions-content,expression,and development scores-yielding results comparable to those of manual scoring.

Research on Product Quality Law System

Product quality law refers to the combination of various economical relationships and legal norms in the process of production,circulation and administration.It combines the operation of the market with the state supervision.Studying on the legislation style will be of theoretical and practical significance.

Research on effectiveness of coal mine safety supervision system reform on three types of collieries in China

Coal mine safety supervision system plays an important role in the coal mine safety management in China.However,the current supervision system is established on the basis of learning the advanced experience from other developed countries.It needs to be further improved according to national conditions.Therefore,the effectiveness of coal mine safety supervision system reform on three types of collieries are assessed by using time series analysis method based on comparative analysis of the supervision system before and after the reform in this paper.The regression results show that the structural reform is not conductive to the improvement of coal mine safety situation in the short term,but conductive significantly in the long term.Specifically,the effects in township coal mines are more significant than stateowned key coal mines in the long run,but negative effects also exist in the short term.The negative effects in state-owned key coal mines are non-significant compared with township coal mines.Moreover,the regression results are analyzed from the aspects of the closure policy of illegal small township coal mines at the end of 1998 and shortage of the new supervision system.Finally,the suggestions on improving the new supervision system are put forward based on the above analysis.

Problems of Rural Food Safety and Strategies of Constructing Supervision System

This paper expounds the practical necessity of constructing diversified rural food safety supervision system as follows: it is the necessary requirements of guaranteeing people's health and life safety; it is an important component of governmental function of social management and the logical extension of administrative responsibilities; it is the basis of maintaining order of rural society and constructing harmonious society. The main problems existing in the supervision of rural food safety are analyzed as follows: first, the legislative work of rural food safety lags behind to some extent; second, the supervision of governmental departments on rural food safety is insufficient; third, the industrial supervision mechanism of rural food security is not perfect; fourth, the role of rural social organizations in supervising food safety is limited; fifth, the farmers' awareness of food safety supervision is not strong. Based on these problems, the targeted strategies of constructing diversified rural food safety supervision system are put forward as follows: accelerate the legislation of rural food safety, and ensure that there are laws to go by; give play to the dominant role of government, and strengthen administrative supervision on rural food safety; perfect industrial convention of rural food safety, and improve industrial supervision mechanism; actively support the fostering of social organizations, and give play to the role of supervision of organizations; cultivate correct concept of rights and obligations of farmers, and form awareness of food safety supervision.

Machine learning methods to assist energy system optimization

(1) Machine learning methods to assist energy system optimization,by A.T.D.Perera,P.U.Wickramasinghe,Vahid M.Nik,Jean-Louis Scartezzini,Pages 191-205 Abstract: This study evaluates the potential of supervised and transfer learning techniques to assist energy system optimization.A surrogate model is developed with the support of a supervised learning technique (by using artificial neural network) in order to bypass computationally intensive Actual Engineering Model (AEM).Eight different neural network architectures are considered in the process of developing the surrogate model.Subsequently,a hybrid optimization algorithm (HOA) is developed combining Surrogate and AEM in order to speed up the optimization process while maintaining the accuracy.Pareto optimization is conducted considering Net Present Value and Grid Integration level as the objective functions.Transfer learning is used to adapt the surrogate model (trained using supervised learning technique) for different scenarios where solar energy potential,wind speed and energy demand are notably different.Results reveal that the surrogate model can reach to Pareto solutions with a higher accuracy when grid interactions are above 10 %(with reasonable differences in the decision space variables).HOA can reach to Pareto solutions (similar to the solutions obtained using AEM) around 17 times faster than AEM.The Surrogate Models developed using Transfer Learning (SMTL) shows a similar capability.SMTL combined with the optimization algorithm can predict Pareto fronts efficiently even when there are significant changes in the initial conditions.Therefore,STML can be used along with the HOA,which reduces the computational time required for energy system optimization by 84 %.Such a significant reduction in computational time enables the approach to be used for energy system optimization at regional or national scale.

Survey on AI and Machine Learning Techniques for Microgrid Energy Management Systems

In the era of an energy revolution,grid decentralization has emerged as a viable solution to meet the increasing global energy demand by incorporating renewables at the distributed level.Microgrids are considered a driving component for accelerating grid decentralization.To optimally utilize the available resources and address potential challenges,there is a need to have an intelligent and reliable energy management system(EMS)for the microgrid.The artificial intelligence field has the potential to address the problems in EMS and can provide resilient,efficient,reliable,and scalable solutions.This paper presents an overview of existing conventional and AI-based techniques for energy management systems in microgrids.We analyze EMS methods for centralized,decentralized,and distributed microgrids separately.Then,we summarize machine learning techniques such as ANNs,federated learning,LSTMs,RNNs,and reinforcement learning for EMS objectives such as economic dispatch,optimal power flow,and scheduling.With the incorporation of AI,microgrids can achieve greater performance efficiency and more reliability for managing a large number of energy resources.However,challenges such as data privacy,security,scalability,explainability,etc.,need to be addressed.To conclude,the authors state the possible future research directions to explore AI-based EMS's potential in real-world applications.

FOOD SAFETY CONTROL SYSTEM IN CHINA

Most countries have expended much effort to develop food safety control systems to ensure safe food supplies within their borders. China, as one of the world's largest food producers and consumers, pays a lot of attention to food safety issues. In recent years, China has taken actions and implemented a series of plans in respect to food safety. Food safety control systems including regulatory, supervisory, and science and technology systems, have begun to be established in China. Using, as a base, an analysis of the current Chinese food safety control system as measured against international standards, this paper discusses the need for China to standardize its food safety control system. We then suggest some policies and measures to improve the Chinese food safety control system.

The Technique of Building a Networked Manufacturing Process Monitoring System

This paper introduces the constitute,structure and the software model of a set of networked manufacturing process monitoring system,using JAVA network technique to realize a set of three layer distributed manufacturing process monitoring sys- tem which is comprised with remote manage center,manufacturing process supervision center and the units of measure and control layer such as displacement sensor,the device of temperature measure and alarm etc.The network integration of the production management layer,the process control layer and the hard ware control layer is realized via using this approach.The design using object-oriented technique based on JAVA can easily transport to different operation systems with high performance of the expansibili- ty.

Cooperative management of an emission trading system:a private governance and learned auction for a blockchain approach

Although blockchain technology has received a significant amount of cutting-edge research on constructing a novel carbon trade market in theory,there is little research on using blockchain in carbon emission trading schemes(ETS).This study intends to address existing gaps in the literature by creating and simulating an ETS system based on blockchain technology.Using the ciphertext-policy attributed-based encryption algorithm and the Fabric network to build a platform may optimize the amount of data available while maintaining privacy security.Considering the augmentation of information interaction during the auction process brought about by blockchain,the learning behavior of bidding firms is introduced to investigate the impact of blockchain on ETS auction.In particular,implementing smart contracts can provide a swift and automatic settlement.The simulation results of the proposed system demonstrate the following:(1)fine-grained access is possible with a second delay;(2)the average annual compliance levels increase by 2%when bidders’learning behavior is considered;and(3)the blockchain network can process more than 350 reading operations or 7 writing operations in a second.Novel cooperative management of an ETS platform based on blockchain is proposed.The data access control policy based on CP-ABE is used to solve the contradiction between data privacy on the firm chain and government supervision.A learned auction strategy is proposed to suit the enhancement of information interaction caused by blockchain technology.This study provides a new method for climate change policymakers to consider the blockchain application of the carbon market.

Differences between British and Japanese perspectives on forensic mental health systems:A preliminary study

AIM To clarify the differences in views on forensic mental health(FMH) systems between the United Kingdom and Japan.METHODS We conducted a series of semi-structured interviews with six leading forensic psychiatrists. Based on a discussion by the research team, we created an interview form. After we finished conducting all the interviews, we qualitatively analyzed their content. RESULTS In the United Kingdom the core domain of FMH was risk assessment and management; however, in Japan, the core domain of FMH was psychiatric testimony. In the United Kingdom, forensic psychiatrists were responsible for ensuring public safety, and psychopathy was identified as a disease but deemed as not suitable for medical treatment. On the other hand, in Japan, psychopathy was not considered a mental illness. CONCLUSION In conclusion, there are considerable differences between the United Kingdom and Japan with regard to the concepts of FMH. Some ideas taken from both cultures for better FMH practice were suggested.

Cryptanalysis on an organization-friendly blockchain system

To verify that an organization-friendly blockchain system may suffer from forgery and collusion attacks,forgery and collusion attacks were theoretically carried out according to the phase sequence of an organization-friendly blockchain system.Then,the organization-friendly blockchain system was improved and based on the phase sequence forgery and collusion attacks were conducted.The results show that the attacker can obtain illegal transaction data from forgery and collusion attacks on the organization-friendly blockchain system.However,for the improved organization-friendly blockchain,the attacker s forgery and collusion attacks cannot be completed.Therefore,the organization-friendly blockchain system may be subject to forgery and collusion attacks,but the improved organization-friendly blockchain system can prevent such attacks.

Augmenting IoT Intrusion Detection System Performance Using Deep Neural Network

Due to their low power consumption and limited computing power,Internet of Things(IoT)devices are difficult to secure.Moreover,the rapid growth of IoT devices in homes increases the risk of cyber-attacks.Intrusion detection systems(IDS)are commonly employed to prevent cyberattacks.These systems detect incoming attacks and instantly notify users to allow for the implementation of appropriate countermeasures.Attempts have been made in the past to detect new attacks using machine learning and deep learning techniques,however,these efforts have been unsuccessful.In this paper,we propose two deep learning models to automatically detect various types of intrusion attacks in IoT networks.Specifically,we experimentally evaluate the use of two Convolutional Neural Networks(CNN)to detect nine distinct types of attacks listed in the NF-UNSW-NB15-v2 dataset.To accomplish this goal,the network stream data were initially converted to twodimensional images,which were then used to train the neural network models.We also propose two baseline models to demonstrate the performance of the proposed models.Generally,both models achieve high accuracy in detecting the majority of these nine attacks.

Smart CardioWatch System for Patients with Cardiovascular Diseases Who Live Alone

The widespread use of smartwatches has increased their specific and complementary activities in the health sector for patient’s prognosis.In this study,we propose a framework referred to as smart forecasting CardioWatch(SCW)to measure the heart-rate variation(HRV)for patients with myocardial infarction(MI)who live alone or are outside their homes.In this study,HRV is used as a vital alarming sign for patients with MI.The performance of the proposed framework is measured using machine learning and deep learning techniques,namely,support vector machine,logistic regression,and decision-tree classification techniques.The results indicated that the analysis of heart rate can help health services that are located remotely from the patient to render timely emergency health care.Further,taking more cardiac parameters into account can lead to more accurate results.On the basis of our findings,we recommend the development of health-related software to aid researchers to develop frameworks,such as SCW,for effective provision of emergency health.

Research on Cooperation Mechanism of China's Financial Regulation and Supervision System

Financial regulation and supervision is an important subject in finance research. After the U. S. subprime mortgage crisis,the weakness in this field becomes the research focus. Every country attaches importance to strengthen the financial supervision and management. But the financial regulatory system is a large system,and the process of model building and index selection is very complicated. A model for China's financial regulation and supervision system was proposed and a system evaluation method was improved. A more exact result was drawn by choosing 2003- 2009 index data.

Comparison and Enlightenment of China's and EU's Food Safety Supervision Systems

Food is the paramount necessity of the people,and safety is most essential for food. Food safety is related to people's fundamental interests,affecting people's basic health. It is an important factor in promoting people's livelihood development and social progress. Compared with European countries and the United States in developed food safety supervision systems,the development of food safety supervision system in China is still relatively backward. As food safety supervision is not in place,food safety incidents have occurred in recent years,so that public's panic about food safety has intensified. It has a strong impact on social stability and the food economy. Thus,strengthening the construction of food safety supervision system is imperative. Since experiencing a series of food safety incidents,the EU has gradually improved its food safety supervision system and has developed the most comprehensive and mature food safety supervision system in the world. On the basis of sorting out the characteristics of EU's food safety supervision system,the development direction of China's food safety supervision system is proposed from four aspects: supervisor,legal system,law enforcement ability and safety standards.

Establishment of Supervision and Monitoring System for Taining Danxia World Natural Heritage Site

Taking Danxia World Natural Heritage Site in Taining for example, the way of establishing scientific and systematic supervision and monitoring system for the world natural heritage site was explored to ensure the scientific protection and sustainable development of world heritage site. Contents of supervising and monitoring heritage site were first elaborated, monitoring indexes given, and then contents, location, feedback and equipment requirements of heritage site monitoring proposed. Finally, the supervision and monitoring system for Danxia World Natural Heritage in Taining was established from three aspects: establishing the center of digital information monitoring and supervision center, adopting scientific monitoring means, improving distribution of supervision sites and long-range video monitoring system.

THE MICROCOMPUTER SUPERVISING DEVICE ON A KIND OF JIG BORING MACHINE

This paper describes the design principles and functionality of a MicrocomputerSupervising Devlce(MSD)developed by us on a kind of Jig BoringMachine(JBM) Some Interference-free methods both in software and hardwareare also presented.As our MSD implemented,machining the frames of raplerlooms on acommon JBM can meet the technological requlrements suceessfully.Thedesign ideas and the circuit principles of our MSD may also be applied to othersimllar machlnes.

A Systematic Literature Review of Deep Learning Algorithms for Segmentation of the COVID-19 Infection

Coronavirus has infected more than 753 million people,ranging in severity from one person to another,where more than six million infected people died worldwide.Computer-aided diagnostic(CAD)with artificial intelligence(AI)showed outstanding performance in effectively diagnosing this virus in real-time.Computed tomography is a complementary diagnostic tool to clarify the damage of COVID-19 in the lungs even before symptoms appear in patients.This paper conducts a systematic literature review of deep learning methods for classifying the segmentation of COVID-19 infection in the lungs.We used the methodology of systematic reviews and meta-analyses(PRISMA)flow method.This research aims to systematically analyze the supervised deep learning methods,open resource datasets,data augmentation methods,and loss functions used for various segment shapes of COVID-19 infection from computerized tomography(CT)chest images.We have selected 56 primary studies relevant to the topic of the paper.We have compared different aspects of the algorithms used to segment infected areas in the CT images.Limitations to deep learning in the segmentation of infected areas still need to be developed to predict smaller regions of infection at the beginning of their appearance.