Research on cloud computing and services framework of marine environmental information management

Based on the features of marine environmental data and processing requirements, a cloud computing archi- tecture of marine environment information is proposed, which provides a new cloud technology framework for the integration and sharing of marine environmental information resources. A physical layer, software platform layer and an application layer are illustrated systematically, at the same time, a corresponding solu- tions for many difficult technical problems such as parallel query processing of multi-dimensional, spatio- temporal information, data slice storage, software service flow customization, analysis, reorganization and so on. A prototype system is developed and many different data-size experiments and a comparative analy- sis are done based on it. The experiment results show that the cloud platform based on this framework can achieve high performance and scalability when dealing with large-scale marine data.

Foundations and Applications of Information Systems Dynamics

Although numerous advances have been made in information technology in the past decades,there is still a lack of progress in information systems dynamics(ISD),owing to the lack of a mathematical foundation needed to describe information and the lack of an analytical framework to evaluate information systems.The value of ISD lies in its ability to guide the design,development,application,and evaluation of largescale information system-of-systems(So Ss),just as mechanical dynamics theories guide mechanical systems engineering.This paper reports on a breakthrough in these fundamental challenges by proposing a framework for information space,improving a mathematical theory for information measurement,and proposing a dynamic configuration model for information systems.In this way,it establishes a basic theoretical framework for ISD.The proposed theoretical methodologies have been successfully applied and verified in the Smart Court So Ss Engineering Project of China and have achieved significant improvements in the quality and efficiency of Chinese court informatization.The proposed ISD provides an innovative paradigm for the analysis,design,development,and evaluation of large-scale complex information systems,such as electronic government and smart cities.

New Record Ocean Temperatures and Related Climate Indicators in 2023

The global physical and biogeochemical environment has been substantially altered in response to increased atmospheric greenhouse gases from human activities.In 2023,the sea surface temperature(SST)and upper 2000 m ocean heat content(OHC)reached record highs.The 0–2000 m OHC in 2023 exceeded that of 2022 by 15±10 ZJ(1 Zetta Joules=1021 Joules)(updated IAP/CAS data);9±5 ZJ(NCEI/NOAA data).The Tropical Atlantic Ocean,the Mediterranean Sea,and southern oceans recorded their highest OHC observed since the 1950s.Associated with the onset of a strong El Niño,the global SST reached its record high in 2023 with an annual mean of~0.23℃ higher than 2022 and an astounding>0.3℃ above 2022 values for the second half of 2023.The density stratification and spatial temperature inhomogeneity indexes reached their highest values in 2023.

Extraction of Acoustic Normal Mode Depth Functions Using Range-Difference Method with Vertical Linear Array Data

Data-derived normal mode extraction is an effective method for extracting normal mode depth functions in the absence of marine environmental data.However,when the corresponding singular vectors become nonunique when two or more singular values obtained from the cross-spectral density matrix diagonalization are nearly equal,this results in unsatisfactory extraction outcomes for the normal mode depth functions.To address this issue,we introduced in this paper a range-difference singular value decomposition method for the extraction of normal mode depth functions.We performed the mode extraction by conducting singular value decomposition on the individual frequency components of the signal's cross-spectral density matrix.This was achieved by using pressure and its range-difference matrices constructed from vertical line array data.The proposed method was validated using simulated data.In addition,modes were successfully extracted from ambient noise.

Weber Law Based Approach for Multi-Class Image Forgery Detection

Today’s forensic science introduces a new research area for digital image analysis formultimedia security.So,Image authentication issues have been raised due to the wide use of image manipulation software to obtain an illegitimate benefit or createmisleading publicity by using tempered images.Exiting forgery detectionmethods can classify only one of the most widely used Copy-Move and splicing forgeries.However,an image can contain one or more types of forgeries.This study has proposed a hybridmethod for classifying Copy-Move and splicing images using texture information of images in the spatial domain.Firstly,images are divided into equal blocks to get scale-invariant features.Weber law has been used for getting texture features,and finally,XGBOOST is used to classify both Copy-Move and splicing forgery.The proposed method classified three types of forgeries,i.e.,splicing,Copy-Move,and healthy.Benchmarked(CASIA 2.0,MICCF200)and RCMFD datasets are used for training and testing.On average,the proposed method achieved 97.3% accuracy on benchmarked datasets and 98.3% on RCMFD datasets by applying 10-fold cross-validation,which is far better than existing methods.

Enhanced Steganalysis for Color Images Using Curvelet Features and Support Vector Machine

Algorithms for steganography are methods of hiding data transfers in media files.Several machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image analysis.Images with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for classification.To address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of images.Support Vector Machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or cover.The Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the proposedmethod.Using WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.

Microplastic Can Decrease Enzyme Activities and Microbes in Soil

An in vitro study was conducted to investigate the impacts of microplastics on enzyme activities and soil bacteria. The study included four different treatments of microplastics including a control. Different levels of microplastics were applied to the soil ranging from 0% to 5%, to assess the impacts of microplastics on soil enzymes and subsequent soil bacteria. After 30 days of incubation, the soil samples were collected and growth parameters of bacteria were assessed. Activities of β-glucosidase, urease and dehydrogenase enzymes were also determined. Our results showed that the presence of microplastics in the soil significantly reduced bacterial population together with bacterial strains. The activities of β-glucosidase, urease and dehydrogenase enzymes were reduced significantly to approximately 32%, 40% and 50% in microplastics treated soils respectively. Concentration of microplastic has a role to play towards this direction;the higher the concentration of microplastic the greater is the impact on enzymes and soil bacteria. The present study on the microbial soil health vis-à-vis microplastic application indicates that the material can have negative effect on the soil bacterial population of and thus ultimately may jeopardize soil health and crop production.

Is the Sundarbans of Bangladesh in a State of Pollution?

The Sundarbans is the world’s most extensive natural mangrove forest and home to various natural resources. The population in the vicinity has increased, causing more dependency on the resources of the Sundarbans. The increasing industrialization, urbanization, aquaculture, intensive agricultural practices, seaports, tourism facilities, and so on in the peripheral areas of the Sundarbans have made significant changes in the surrounding and upstream land uses of the Sundarbans. This situation may have detrimental influences on the ecosystem components of the Sundarbans. Therefore, it is highly demanded to prepare a piece of baseline information or database of different sources of pollution and their present status in the various components of the Sundarbans. This effort helps to identify issues and concerns, determine the key elements of the ecosystem to monitor the level or overall quality of the Sundarbans ecosystem. The present study systematically collects the potential sources of pollution, types, and current levels in the ecosystem components of the Sundarbans using academic databases, libraries, and online resources. Discharge of industrial waste into water, soil and air, heavy metal pollution, use of agrochemicals, oil (refined and crude) pollution, plastic materials from urban areas, and tourism are the major issues and concerns related to the sustainability of the Sundarbans ecosystem. The air quality of the Sundarbans is in good condition with 0 - 50 AQI of Bangladesh. While BOD, COD, TDS, TSS varied from 2.0 to 3.8 mg/L, 21.6 to 416 mg/L, 146.9 to 24,100 mg/L and 54 to 155 mg/L, respectively. Soil EC, organic carbon, total nitrogen, and total phosphorus ranged from 3.01 - 5.82 mS/cm, 1.41% - 2.69%, 0.51 - 1.05 mg/g, and 0.32 - 0.51 mg/g respectively. The air, water and soil quality parameters varied with the sites and seasons and not much at the state of contamination. Indeed, we must pay much attention to the Sundarbans’ air, water and soil quality with the massive and progressive change of the nearby land use pattern.

Conceptual Model for Automatic Early Warning Information System of Infectious Diseases Based on Internet Reporting Surveillance System

Objective To establish a conceptual model of automatic early warning of infectious diseases based on internet reporting surveillance system, with a view to realizing an automated warning system on a dally basis and timely identifying potential outbreaks of infectious diseases. Methods The statistic conceptual model was established using historic surveillance data with movable percentile method. Results Based on the infectious disease surveillance information platform, the conceptual model for early warning was established. The parameter, threshold, and revised sensitivity and specificity of early warning value were changed to realize dynamic alert of infectious diseases on a dally basis. Conclusion The instructive conceptual model of dynamic alert can be used as a validating tool in institutions of infectious disease surveillance in different districts.

A Dynamic Road Incident Information Delivery Strategy to Reduce Urban Traffic Congestion

Advanced information and communication technologies can be used to facilitate traffic incident management. If an incident is detected and blocks a road link, in order to reduce the incident-induced traffic congestion, a dynamic strategy to deliver incident information to selected drivers and help them make detours in urban areas is proposed by this work. Time-dependent shortest path algorithms are used to generate a subnetwork where vehicles should receive such information. A simulation approach based on an extended cell transmission model is used to describe traffic flow in urban networks where path information and traffic flow at downstream road links are well modeled.Simulation results reveal the influences of some major parameters of an incident-induced congestion dissipation process such as the ratio of route-changing vehicles to the total vehicles, operation time interval of the proposed strategy, traffic density in the traffic network, and the scope of the area where traffic incident information is delivered. The results can be used to improve the state of the art in preventing urban road traffic congestion caused by incidents.

So What’s the Big Deal With Information Literacy in the United States?

2010年7月13日,"国际大学生信息素养能力邀请赛"在武汉大学成功举办。本次邀请赛的主题是"信息沟通世界:培育未来信息职业的领导者"。"邀请赛"是武汉大学首届学生社团国际交流营(2010年7月11～18日)的活动之一,也是武汉大学信息管理学院信息职业领导力夏季研究班(2010年7月12～14日)的活动之一。活动期间特邀请了信息素养教育领域的专家授课;美洲、欧洲和亚洲不同国家和地区的多所大学共选派30名选手参加了比赛,并通过听课、撰写论文以及讨论等方式,围绕信息素养进行了学习和探讨。竞赛与课程的内容丰富了学员的信息素养知识,提升了学员的信息素养能力。现将其授课与讨论的主要内容刊出,以飨读者。

Research and application of real-time monitoring and early warning thresholds for multi-temporal agricultural products information

Monitoring and early warning is an important means to effectively prevent risks in agricultural production,consumption and price.In particular,with the change of modes of national administration against the background of big data,improving the capacity to monitor agricultural products is of great significance for macroeconomic decision-making.Agricultural product information early warning thresholds are the core of agricultural product monitoring and early warning.How to appropriately determine the early warning thresholds of multi-temporal agricultural product information is a key question to realize real-time and dynamic monitoring and early warning.Based on the theory of abnormal fluctuation of agricultural product information and the research of substantive impact on the society,this paper comprehensively discussed the methods to determine the thresholds of agricultural product information fluctuation in different time dimensions.Based on the data of the National Bureau of Statistics of China(NBSC)and survey data,this paper used a variety of statistical methods to determine the early warning thresholds of the production,consumption and prices of agricultural products.Combined with Delphi expert judgment correction method,it finally determined the early warning thresholds of agricultural product information in multiple time,and carried out early warning analysis on the fluctuation of agricultural product monitoring information in 2018.The results show that:(1)the daily,weekly and monthly monitoring and early warning thresholds of agricultural products play an important early warning role in monitoring abnormal fluctuations with agricultural products;(2)the multitemporal monitoring and early warning thresholds of agricultural product information identified by the research institute can provide effective early warning on current abnormal fluctuation of agricultural product information,provide a benchmarking standard for China's agricultural production,consumption and price monitoring and early warning at the national macro level,and further improve the application of China's agricultural product monitoring and early warning.

Research on data pre-deployment in information service flow of digital ocean cloud computing

Data pre-deployment in the HDFS （Hadoop distributed file systems） is more complicated than that in traditional file systems. There are many key issues need to be addressed, such as determining the target location of the data prefetching, the amount of data to be prefetched, the balance between data prefetching services and normal data accesses. Aiming to solve these problems, we employ the characteristics of digital ocean information service flows and propose a deployment scheme which combines input data prefetching with output data oriented storage strategies. The method achieves the parallelism of data preparation and data processing, thereby massively reducing I/O time cost of digital ocean cloud computing platforms when processing multi-source information synergistic tasks. The experimental results show that the scheme has a higher degree of parallelism than traditional Hadoop mechanisms, shortens the waiting time of a running service node, and significantly reduces data access conflicts.

Measuring a Quantum System’s Classical Information

In the governing thought, I find an equivalence between the classical information in a quantum system and the integral of that system’s energy and time, specifically , in natural units. I solve this relationship in four ways: the first approach starts with the Schrodinger Equation and applies the Minkowski transformation;the second uses the Canonical commutation relation;the third through Gabor’s analysis of the time-frequency plane and Heisenberg’s uncertainty principle;and lastly by quantizing Brownian motion within the Bernoulli process and applying the Gaussian channel capacity. In support I give two examples of quantum systems that follow the governing thought: namely the Gaussian wave packet and the electron spin. I conclude with comments on the discretization of space and the information content of a degree of freedom.

Understanding Factors of Bias in the Way Information Is Processed Through a Metamemetic and Multilogical Approach

Training in complex thinking is required in fields like computer science and discussing sensitive topics that can easily polarize internet users’ propensities. Multilogicality and Metamemetic reasoning are strongly suggested as an approach to identifying and analyzing factors related to AI Bias and human biases. This approach entails identifying problems and deducting invalid premises, distinguishing them from valid premises or those we are uncertain about. The theme of this paper focuses on four groups of people: curators, developers, businesses, and users (the fourth group being the main focus). This approach offers a new way to apply critical thinking strategies in the context of living in a digital age.

Spatiotemporal changes of biogenic elements in the Changjiang River Estuary and adjacent waters in summer over the last decade

The long-term spatiotemporal changes of surface biogenic elements in the Changjiang River Estuary and adjacent waters during the summer of 2008–2016 were analyzed in this study.The concentrations of dissolved inorganic nitrogen(DIN),soluble reactive phosphate(PO_(4)^(3−))and silicate(SiO_(3)^(2−))were generally stable,with a slight decrease of DIN and PO_(4)^(3−),and a slight increase of SiO_(3)^(2−),which mainly occurred in the estuarine waters.The grey correlation analysis was carried out between biogenic elements and chlorophyll a(Chl-a).Results showed that compared with the absolute values of biogenic elements,the correlations between the concentration ratio of nitrogen to phosphorus(N/P),ratio of silicon to nitrogen(Si/N)and Chl-a were closer,indicating the important influence on phytoplankton by the structure of biogenic elements.The study area was generally in a state of potential P limitation,and could have potential impact on the phytoplankton community,triggering the shift of red tide dominant species from diatoms to dinoflagellates.

Time Series Forecasting Fusion Network Model Based on Prophet and Improved LSTM

Time series forecasting and analysis are widely used in many fields and application scenarios.Time series historical data reflects the change pattern and trend,which can serve the application and decision in each application scenario to a certain extent.In this paper,we select the time series prediction problem in the atmospheric environment scenario to start the application research.In terms of data support,we obtain the data of nearly 3500 vehicles in some cities in China fromRunwoda Research Institute,focusing on the major pollutant emission data of non-road mobile machinery and high emission vehicles in Beijing and Bozhou,Anhui Province to build the dataset and conduct the time series prediction analysis experiments on them.This paper proposes a P-gLSTNet model,and uses Autoregressive Integrated Moving Average model(ARIMA),long and short-term memory(LSTM),and Prophet to predict and compare the emissions in the future period.The experiments are validated on four public data sets and one self-collected data set,and the mean absolute error(MAE),root mean square error(RMSE),and mean absolute percentage error(MAPE)are selected as the evaluationmetrics.The experimental results show that the proposed P-gLSTNet fusion model predicts less error,outperforms the backbone method,and is more suitable for the prediction of time-series data in this scenario.

Accurate recognition of the reproductive development status and prediction of oviposition fecundity in Spodoptera frugiperda(Lepidoptera:Noctuidae)based on computer vision

Spodoptera frugiperda(Lepidoptera:Noctuidae)is an important migratory agricultural pest worldwide,which has invaded many countries in the Old World since 2016 and now poses a serious threat to world food security.The present monitoring and early warning strategies for the fall army worm(FAW)mainly focus on adult population density,but lack an information technology platform for precisely forecasting the reproductive dynamics of the adults.In this study,to identify the developmental status of the adults,we first utilized female ovarian images to extract and screen five features combined with the support vector machine(SVM)classifier and employed male testes images to obtain the testis circular features.Then,we established models for the relationship between oviposition dynamics and the developmental time of adult reproductive organs using laboratory tests.The results show that the accuracy of female ovary development stage determination reached 91%.The mean standard error(MSE)between the actual and predicted values of the ovarian developmental time was 0.2431,and the mean error rate between the actual and predicted values of the daily oviposition quantity was 12.38%.The error rate for the recognition of testis diameter was 3.25%,and the predicted and actual values of the testis developmental time in males had an MSE of 0.7734.A WeChat applet for identifying the reproductive developmental state and predicting reproduction of S.frugiperda was developed by integrating the above research results,and it is now available for use by anyone involved in plant protection.This study developed an automated method for accurately forecasting the reproductive dynamics of S.frugiperda populations,which can be helpful for the construction of a population monitoring and early warning system for use by both professional experts and local people at the county level.