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.

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.

Recognizing Breast Cancer Using Edge-Weighted Texture Features of Histopathology Images

Around one in eight women will be diagnosed with breast cancer at some time.Improved patient outcomes necessitate both early detection and an accurate diagnosis.Histological images are routinely utilized in the process of diagnosing breast cancer.Methods proposed in recent research only focus on classifying breast cancer on specific magnification levels.No study has focused on using a combined dataset with multiple magnification levels to classify breast cancer.A strategy for detecting breast cancer is provided in the context of this investigation.Histopathology image texture data is used with the wavelet transform in this technique.The proposed method comprises converting histopathological images from Red Green Blue(RGB)to Chrominance of Blue and Chrominance of Red(YCBCR),utilizing a wavelet transform to extract texture information,and classifying the images with Extreme Gradient Boosting(XGBOOST).Furthermore,SMOTE has been used for resampling as the dataset has imbalanced samples.The suggested method is evaluated using 10-fold cross-validation and achieves an accuracy of 99.27%on the BreakHis 1.040X dataset,98.95%on the BreakHis 1.0100X dataset,98.92%on the BreakHis 1.0200X dataset,98.78%on the BreakHis 1.0400X dataset,and 98.80%on the combined dataset.The findings of this study imply that improved breast cancer detection rates and patient outcomes can be achieved by combining wavelet transformation with textural signals to detect breast cancer in histopathology images.

Analysis of Video Quality Variation with Different Bit Rates of H.264 Compression

The study applied a charge-coupled device (CCD) camera to send video signals to 4 DaVinciTM development boards (TMS320DM6446) of Texas Instruments (TI) to carry out H.264 Baseline Profile video coding. One of the development boards coded in the Variable Bit Rate (VBR) mode, and the other three development boards coded in the Constant Bit Rate (CBR) mode. In addition, the constant rates are 2 Mbps, 1.5 Mbps and 1 Mbps respectively. The H.264 video compression files produced by the boards were analyzed via video analysis software (CodecVisa) in the study. This software can analyze and present the compression data characteristics of the video files under each video frame, i.e., bits/MB, QP, and PSNR. In this research, the characteristics of data of each frame under four different compression conditions were compared. Their differences were calculated and averaged, and the standard deviation was evaluated. It was further connected with the values of quality characteristics and the peak signal to noise ratio (PSNR) of each frame to analyze the relation among the frame quality, the compression rate of CBR, as well as the quantitative granularity. The preliminary conclusion of the study is that the compression behaviors of CBRs in different coding sources are adjusted in a specific proportion in order to cope with the change in frame complexity. The frame will be severely damaged by a critical value during the process of network transmission while the source rate is less than the value of the characteristic.

An Online Outpatient Database System: A Case Study of General Hospital, Minna

Outpatients receive medical treatment without being admitted to a hospital. They are not hospitalized for 24 hours or more but visit hospital, clinic or associated facility for diagnosis or treatment [1]. But the problems of keeping their records for quick access by the management and provision of confidential, secure medical report that facilitates planning and decision making and hence improves medical service delivery are vital issues. This paper explores the challenges of manual outpatient records system for General Hospital, Minna and infers solutions to the current challenges by designing an online outpatient’s database system. The main method used for this research work is interview. Two (2) doctors, three (3) nurses on duty and two (2) staff at the record room were interviewed. Fifty (50) sampled outpatient records were collected. The combination of PHP, MYSQL and MACROMIDIA DREAMVEAVER was used to design the webpage and input data. The records were implemented on the designed outpatient management system and the outputs were produced. The finding shows these challenges facing the manual system of inventory management system. Distortion of patient’s folder and difficulty in searching a patient’s folder, difficulty in relating previous complaint with the new complains because of volume of the folder, slow access to patient diagnosis history during emergency, lack of back up when an information is lost, and preparation of accurate and prompt reports make it become a difficult task as information is difficult to collect from various register. Based on the findings, this paper highlights the possible solutions to the above problems. An online outpatient database system was designed to keep the outpatients records and improve medical service delivery.

A Preliminary Investigation into the Use of an Imagery Assisted Virtual Reality Intervention in Sport

This article explains an imagery assisted virtual reality psychological skills training program used with a NCAA Division I baseball team. This is the first time that imagery has been incorporated into a virtual reality program with the goal of increasing mental skills and strategies. Participants for this study were 27 NCAA baseball players. Each participant completed the Sport Imagery Ability Questionnaire and the Test of Performance Strategies Questionnaire at baseline and again after the winter season （2 months later）. Results indicated an increase in skill, goals and mastery imagery ability as well as increases in the use of several skills and strategies in both practice and competition. This manuscript focuses on both the development of an Imagery Assisted Virtual Reality program as well as the outcomes of the program.

Improved performance of back-gate MoS2 transistors by NH3-plasma treating high-k gate dielectrics

NH3-plasma treatment is used to improve the quality of the gate dielectric and interface. Al2O3 is adopted as a buffer layer between HfO2 and MoS2 to decrease the interface-state density. Four groups of MOS capacitors and back-gate transistors with different gate dielectrics are fabricated and their C–V and I–V characteristics are compared. It is found that the Al2O3/HfO2 back-gate transistor with NH3-plasma treatment shows the best electrical performance: high on–off current ratio of 1.53 × 107, higher field-effect mobility of 26.51 cm2/V·s, and lower subthreshold swing of 145 m V/dec.These are attributed to the improvements of the gate dielectric and interface qualities by the NH3-plasma treatment and the addition of Al2O3 as a buffer layer.

Personal Tacit Knowledge and Global Learning Professional Competencies—Multi-Dimensional Relationships

Global learning professional competencies (GLPCs) are essential for college students to be able to address the impact of globalization in the 21st century. Organizations and society-at-large look to higher education to prepare college students with GLPCs. In addition, there is a body of literature that suggest personal tacit knowledge enhance GLPCs. However, researchers have done little from an empirical perspective to determine the relationship between the use of P-T K and enhancement of GLPCs, hence the purpose of this study. The statistical results revealed significant correlations, p st century knowledge society through use of P-T K.

Binary Oriented Feature Selection for Valid Product Derivation in Software Product Line

Software Product Line(SPL)is a group of software-intensive systems that share common and variable resources for developing a particular system.The feature model is a tree-type structure used to manage SPL’s common and variable features with their different relations and problem of Crosstree Constraints(CTC).CTC problems exist in groups of common and variable features among the sub-tree of feature models more diverse in Internet of Things(IoT)devices because different Internet devices and protocols are communicated.Therefore,managing the CTC problem to achieve valid product configuration in IoT-based SPL is more complex,time-consuming,and hard.However,the CTC problem needs to be considered in previously proposed approaches such as Commonality VariabilityModeling of Features(COVAMOF)andGenarch+tool;therefore,invalid products are generated.This research has proposed a novel approach Binary Oriented Feature Selection Crosstree Constraints(BOFS-CTC),to find all possible valid products by selecting the features according to cardinality constraints and cross-tree constraint problems in the featuremodel of SPL.BOFS-CTC removes the invalid products at the early stage of feature selection for the product configuration.Furthermore,this research developed the BOFS-CTC algorithm and applied it to,IoT-based feature models.The findings of this research are that no relationship constraints and CTC violations occur and drive the valid feature product configurations for the application development by removing the invalid product configurations.The accuracy of BOFS-CTC is measured by the integration sampling technique,where different valid product configurations are compared with the product configurations derived by BOFS-CTC and found 100%correct.Using BOFS-CTC eliminates the testing cost and development effort of invalid SPL products.

Neurodevelopmental Outcomes of Extremely Premature Infants with Extremely Low and Very Low Birth Weight at Three Years of Age: A Retrospective Study

Introduction: The purpose of this retrospective study is to identify medical conditions impacting neurodevelopmental outcomes of extremely low birth weight and very low birth weight preterm infants at three years of age. Methods: Infants born in Banner Diamond Children’s University Medical Center, receiving services in the Newborn Intensive Care Unit, and attending Neonatal Developmental Follow-Up Clinic were identified. Participants received developmental assessment and follow-up from August 2012 through December 2018. Relevant clinical conditions during initial hospital stay and up to three years of age were obtained by reviewing medical and developmental records. Bayley Scales of Infant Toddler Development (Bayley III) was used to evaluate skill development at 6, 9, 12, 18, 24, 30, 36 months. Results: Data analysis did not reveal significant p-values;it did demonstrate that some predictor variables impact neurodevelopmental outcomes in cognitive, language and motor skill development. Conclusion: This retrospective study reports significant association between birth weight and low cognitive scores. Correlations were also found between gestational age and Total Language, and the longer an infant stayed in the NICU, the poorer the Total Language Scaled Scores at 8 to 12 months, 15 to 18 months, and 24 to 36 months. Birth weight was found to be the greatest predictor of poor motor scores.

A Hybrid Deep Learning Approach for Green Energy Forecasting in Asian Countries

Electricity is essential for keeping power networks balanced between supply and demand,especially since it costs a lot to store.The article talks about different deep learning methods that are used to guess how much green energy different Asian countries will produce.The main goal is to make reliable and accurate predictions that can help with the planning of new power plants to meet rising demand.There is a new deep learning model called the Green-electrical Production Ensemble(GP-Ensemble).It combines three types of neural networks:convolutional neural networks(CNNs),gated recurrent units(GRUs),and feedforward neural networks(FNNs).The model promises to improve prediction accuracy.The 1965–2023 dataset covers green energy generation statistics from ten Asian countries.Due to the rising energy supply-demand mismatch,the primary goal is to develop the best model for predicting future power production.The GP-Ensemble deep learning model outperforms individual models(GRU,FNN,and CNN)and alternative approaches such as fully convolutional networks(FCN)and other ensemble models in mean squared error(MSE),mean absolute error(MAE)and root mean squared error(RMSE)metrics.This study enhances our ability to predict green electricity production over time,with MSE of 0.0631,MAE of 0.1754,and RMSE of 0.2383.It may influence laws and enhance energy management.

Optimizing ASReview Simulations:A generic Multiprocessing Solution for“Light-data'and“Heavy-data'Users

Active learning can be used for optimizing and speeding up the screening phase of systematic reviews.Running simulation studies mimicking the screening process can be used to test the performance of different machine-learning models or to study the impact of different training data.This paper presents an architecture design withamultiprocessing computational strategyforrunningmanysuch simulation studiesinparallel,using the ASReview Makita workflow generator and Kubernetes software for deployment with cloud technologies.We provide a technical explanation of the proposed cloud architecture and its usage.In addition to that,we conducted 1140 simulations investigating the computational time using various numbers of CPUs and RAM settings.Our analysis demonstrates the degree to which simulations can be accelerated with multiprocessing computing usage.The parallel computation strategy and the architecture design that was developed in the present paper can contribute to future research with more optimal simulation time and,at the same time,ensure the safe completion of the needed processes.

Assessing progression of keratoconus: novel tomographic determinants

Several methods have been described in the literature to both evaluate and document progression in keratoconus,but there is no consistent or clear definition of ectasia progression.The authors describe how modern corneal tomography,including both anterior and posterior elevation and pachymetric data can be used to screen for ectatic progression,and how software programs such as the Enhanced Reference Surface and the Belin-Ambrosio Enhanced Ectasia Display(BAD)can be employed to detect earlier changes.Additionally,in order to describe specific quantitative values that can be used as progression determinants,the normal noise measurement of the three parameters(corneal thickness at the thinnest point,anterior and posterior radius of curvature(ARC,PRC)taken from the 3.0 mm optical zone centered on the thinnest point),was assessed.These values were obtained by imaging five normal patients using three different technicians on three separate days.The 95%and 80%one-sided confidence intervals for all three parameters were surprisingly small(7.88/4.03μm for corneal thickness,0.024/0.012 mm for ARC,and 0.083/0.042 mm for PRC),suggesting that they may perform well as progression determinants.

Cloud-based typhoon-derived paddy rice flooding and lodging detection using multi-temporal Sentinel-1&2

Rice production in China’s coastal areas is frequently affected by typhoons,since the associated severe storms,with heavy rain and the strong winds,lead directly to the rice plants becoming flooded or lodged.Long-term flooding and lodging can cause a substantial reduction in rice yield or even destroy the harvest completely.It is therefore urgent to obtain accurate information about paddy rice flooding and lodging as soon as possible after the passing of the storm.This paper proposes a workflow in Google Earth Engine(GEE)for mapping the flooding and lodging area of paddy rice in Wenzhou City,Zhejiang,following super typhoon Maria(Typhoon No.8 in 2018).First,paddy rice in the study area was detected by multi-temporal Sentinel-1 backscatter data combined with Sentinel-2-derived Normalized Difference Vegetation Index(NDVI)using the Random Forests(RFs)algorithm.High classification accuracies were achieved,whereby rice detection accuracy was calculated at 95%(VH+NDVI-based)and 87%(VV+NDVI-bastd).Secondly,Change Detection(CD)based Rice Normalized Difference Flooded Index(RNDFI)and Rice Normalized Difference Lodged Index(RNDLI)were proposed to detect flooding and lodged paddy rice.Both RNDFI and RNDLI were tested based on four different remote sensing data sets,including the Sentinel-1-derived VV and VH backscattering coefficient,Sentinel-2-derived NDVI and Enhanced Vegetation Index(EVI).Overall agreement regarding detected area between the each two different data sets was obtained,with values of 79%to 93%in flood detection and 64%to 88%in lodging detection.The resulting flooded and lodged paddy rice maps have potential to reinforce disaster emergency assessment systems and provide an important resource for disaster reduction and emergency departments.

Rhizobacteria helps to explain the enhanced efficiency of phytoextraction strengthened by Streptomyces pactum

The ultimate purpose of phytoextraction is not only to remove heavy metals from soil but also to improve soil quality.Here, we evaluated how the joint effect of Streptomyces pactum(strain Act12) and inorganic(Hoagland’s solution) and organic(humic acid and peat) nutrients affected the phytoextraction practice of cadmium(Cd) and zinc(Zn) by potherb mustard, and the microbial community composition within rhizosphere was also investigated.The results indicated that the nutrients exerted synergistically with Act12, all increasing the plant biomass and Cd/Zn uptakes.The inoculation of Act12 alone significantly increased dehydrogenase activity of rhizosphere soil(P<0.05), while urease and alkaline phosphatase activities varied in different dosage of Act12.Combined application of microbial strain with nutrients increased enzymatic activities with the elevated dosage of Act12.16S ribosomal RNA high-throughput sequencing analysis revealed that Act12 inoculation reduced the diversity of rhizosphere bacteria.The Act12 and nutrients did not change dominant phyla i.e.,Proteobacteria, Bacteroidetes, Gemmatimonadetes, Actinobacteria and Acidobacteria, but their relative abundance differed among the treatments with: Peat>Act12>Humic acid >Hoagland’s solution.Comparatively, Sphingomonas replaced Thiobacillus as dominant genus after Act12 application.The increase in the Sphingomonas and Flavisolibacter abundances under Act12 and nutrients treatments gave rise to growth-promoting effect on plant.Our results revealed the important role for rhizosphere microbiota in mediating soil biochemical traits and plant growth, and our approach charted a path toward the development of Act12 combined with soil nutrients to enhance soil quality and phytoextraction efficiency in Cd/Zn-contaminated soils.

Forensic isoscapes based on intra-individual temporal variation of δ^(18)O and ^(206)Pb/^(207)Pb in human teeth

Isotopic signatures used in the georeferencing of human remains are largely fixed by spatially distinct geologic and environmental processes.However,location-dependent temporal changes in these isotope ratios should also be considered when determining an individual’s provenance and/or trajectory.Distributions of the relevant isotopes can be impacted by predictable external factors such as climate change,delocalisation of food and water sources and changes in sources and uses of metals.Using Multi-Collector Inductively-Coupled Plasma Mass Spectrometer(MC-ICP-MS)analyses of ^(206)Pb/^(207)Pb in tooth enamel and dentin from a population of 21±1-year-old individuals born circa 1984 and isotope ratio mass spectrometry(IRMS)of δ^(18)O in their enamel,we examined the expected influence of some of these factors.The resulting adjustments to the geographic distribution of isotope ratios(isoscapes)found in tooth enamel and dentin may contain additional useful information for forensic identification,but the shifts in values can also impact the uncertainty and usefulness of identifications if they are not taken into account.

Decision guidance framework to support operations and analysis of a hybrid renewable energy system

In this paper we develop a framework to support the introduction of renewable energy generation and carbon emission constraints into a defined electric power network,and the key operational decisions regarding its configuration.We describe and model the major components of a hybrid renewable energy system(HRES),including renewable energy sources(solar and wind),fossil fuel generators,transmission/distribution,power storage,energy markets,and end-customer demand.Our methodology involves a conceptual diagram notation for power network topology,combined with a formal mathematical model that describes the HRES optimization framework.We introduce environmental goals as constraints to the model,based on emissions restrictions dictated by a policy-maker extraneous to the model.We then proceed to implement the HRES optimization problem solution through a mixed-integer linear programming(MILP)model by leveraging IBM Optimization Programming Language(OPL)CPLEX Studio.Lastly,we develop a proof-of-concept to demonstrate the feasibility of the model.

Taking the pulse of COVID-19:a spatiotemporal perspective

The sudden outbreak of the Coronavirus disease(COVID-19)swept across the world in early 2020,triggering the lockdowns of several billion people across many countries,including China,Spain,India,the U.K.,Italy,France,Germany,Brazil,Russia,and the U.S.The transmission of the virus accelerated rapidly with the most confirmed cases in the U.S.,India,Russia,and Brazil.In response to this national and global emergency,the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implementation strategies to rapidly respond to this crisis,for supporting research,saving lives,and protecting the health of global citizens.This perspective paper presents our collective view on the global health emergency and our effort in collecting,analyzing,and sharing relevant data on global policy and government responses,human mobility,environmental impact,socioeconomical impact;in developing research capabilities and mitigation measures with global scientists,promoting collaborative research on outbreak dynamics,and reflecting on the dynamic responses from human societies.

You Are How You Behave-Spatiotemporal Representation Learning for College Student Academic Achievement

Scholarships are a reflection of academic achievement for college students.The traditional scholarship assignment is strictly based on final grades and cannot recognize students whose performance trend improves or declines during the semester.This paper develops the Trajectory Mining on Clustering for Scholarship Assignment and Academic Warning(TMS)approach to identify the factors that affect the academic achievement of college students and to provide decision support to help low-performing students attain better performance.Specifically,we first conduct feature engineering to generate a set of features to characterize the lifestyles patterns,learning patterns,and Internet usage patterns of students.We then apply the objective and subjective combined weighted k-means(Wosk-means)algorithm to perform clustering analysis to identify the characteristics of different student groups.Considering the difficulty in obtaining the real global positioning system(GPS)records of students,we apply manually generated spatiotemporal trajectories data to quantify the direction of trajectory deviation with the assistance of the PrefixSpan algorithm to identify low-performing students.The experimental results show that the silhouette coefficient and Calinski-Harabasz index of the Wosk-means algorithm are both approximately 1.5 times to that of the best baseline algorithm,and the sum of the squared error of the Wosk-means algorithm is only the half of the best baseline algorithm.