Management Information System for E-learning: A Case Study of Federal Polytechnic Bali

This research is aims at designing and implementing Management Information System for E-learning that will be dynamic and functional for any academic institution. The methodology used in developing the Website was Extreme Programming (XP) and the adopted approach is object oriented design. The design features of the website consist of some functionality that includes: First of all, the system will allow administrator to add new user (student/lecturer), view and edit record. Secondly, user will able to login after successful system validation. Thirdly, the system will allow lecturer to upload, download and update learning resources. Fourthly, students will have access to all learning materials and they will able to perform self-evaluation after reading available learning resources. This self-evaluation is one of the major contributions of the system towards teaching and learning activities. Furthermore, the new system will allow students to search different uploaded learning materials on the websites. In addition to that, only the administrator has the privilege to delete and edit user. Keywords: learning material/resources, e-learning, Management Information System (MIS), Website, coding, programming and script

Risk prediction for central lymph node metastasis in isolated isthmic papillary thyroid carcinoma by nomogram: A retrospective study from 2010 to 2021

BackgroundIsthmic papillary thyroid carcinoma (IPTC) is an aggressive thyroid cancer associated with a poor prognosis. Guidelines elaborating on the extent of surgery for IPTC are yet to be developed. This study aims to construct and validate a model to predict central lymph node metastasis (CLNM) in patients with IPTC, which could be used as a risk stratification tool to determine the best surgical approach for patients.MethodsElectronic medical records for patients diagnosed with isolated papillary thyroid carcinoma who underwent surgery at Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, from January 2010 to December 2021 were reviewed. All patients who underwent thyroidectomy with central neck dissection (CND) for isolated IPTC were included. We conducted univariate and multivariate logistic regression analyses to assess risk factors for ipsilateral and contralateral CLNM and the number of CLNM in IPTC patients. Based on the analysis, the nomogram construction and internal validations were performed.ResultsA total of 147 patients with isolated IPTC were included. The occurrence of CLNM was 53.7% in the patients. We identified three predictors of ipsilateral CLNM, including age, gender, and size. For contralateral CLNM, three identified predictors were age, gender, and capsular invasion. Predictors for the number of CLNM included age, gender, capsular invasion, tumor size, and chronic lymphocytic thyroiditis (CLT). The concordance index(C-index) of the models predicting ipsilateral CLNM, contralateral CLNM, 1-4 CLNM, and ≥5 CLNM was 0.779 (95%CI, 0.704, to 0.854), 0.779 (95%CI, 0.703 to 0.855), 0.724 (95%CI, 0.629 to 0.818), and 0.932 (95%CI, 0.884 to 0.980), respectively. The corresponding indices for the internal validation were 0.756 (95%CI, 0.753 to 0.758), 0.753 (95%CI, 0.750 to 0.756), 0.706 (95%CI, 0.702 to 0.708), and 0.920 (95%CI, 0.918 to 0.922). Receiver operating characteristic (ROC) curves, calibration, and decision curve analysis (DCA) results confirmed that the three nomograms could precisely predict CLNM in patients with isolated IPTC.ConclusionWe constructed predictive nomograms for CLNM in IPTC patients. A risk stratification scheme and corresponding surgical treatment recommendations were provided accordingly. Our predictive models can be used as a risk stratification tool to help clinicians make individualized surgical plans for their patients

Cloud computing for ECG analysis using mapreduce

Electrocardiograph (ECG) analysis brings a lot of technical concerns because ECG is one of the tools frequently used in the diagnosis of cardiovascular disease. According to World Health Organization (WHO) statistic in 2012, cardiovascular disease constitutes about 48% of non-communicable deaths worldwide. Although there are many ECG related researches, there is not much efforts in big data computing for ECG analysis which involves dataset more than one gigabyte. ECG files contain graphical data and the size grows as period of data recording gets longer. Big data computing for ECG analysis is critical when many patients are involved. Recently, the implementation of MapReduce in cloud computing becomes a new trend due to its parallel computing characteristic. Since large ECG dataset consume much time in analysis processes, this project will construct a cloud computing approach for ECG analysis using MapReduce in order to investigate the effect of MapReduce in enhancing ECG analysis efficiency in cloud computing. The project is expected to reduce ECG analysis process time for large ECG dataset

The Particle Swarm Optimization Algorithm with Adaptive Chaos Perturbation

Aiming at the two characteristics of premature convergence of particle swarm optimization that the particle velocity approaches 0 and particle swarm congregate, this paper learns from the annealing function of the simulated annealing algorithm and adaptively and dynamically adjusts inertia weights according to the velocity information of particles to avoid approaching 0 untimely. This paper uses the good uniformity of Anderson chaotic mapping and performs chaos perturbation to part of particles based on the information of variance of the population’s fitness to avoid the untimely aggregation of particle swarm. The numerical simulations of five test functions are performed and the results are compared with several swarm intelligence heuristic algorithms. The results shows that the modified algorithm can keep the population diversity well in the middle stage of the iterative process and it can improve the mean best of the algorithm and the success rate of search

Lower data attacks on Advanced Encryption Standard

The Advanced Encryption Standard (AES) is one of the most commonly used and analyzed encryption algorithms. In this work, we present new combinations of some prominent attacks on AES, achieving new records in data requirements among attacks, utilizing only $2^4$ and $2^{16}$ chosen plaintexts (CP) for 6-round and 7-round AES-192/256 respectively. One of our attacks is a combination of a meet-in-the-middle (MiTM) attack with a square attack mounted on 6-round AES-192/256 while another attack combines an MiTM attack and an integral attack, utilizing key space partitioning technique, on 7-round AES-192/256. Moreover, we illustrate that impossible differential (ID) attacks can be viewed as the dual of MiTM attacks in certain aspects which enables us to recover the correct key using the meet-in-the-middle (MiTM) technique instead of sieving through all potential wrong keys in our ID attack. Furthermore, we introduce the constant guessing technique in the inner rounds which significantly reduces the number of key bytes to be searched. The time and memory complexities of our attacks remain marginal

Applications of mercury intrusion capillary pressure for pore structures: A review

The shape, size, and connectivity of porous structures control the overall storage capacity and flow in oil and gas reservoirs. The mercury intrusion capillary pressure (MICP) tech- nique is widely utilized to measure capillary pressure and calculate pore size distribution of core samples in the geo-energy industry. Combining the MICP capillary pressure data with parameters from other experimental methods (such as scanning electron microscopy, and nuclear magnetic resonance) or theoretical approaches (such as fractal theory) can more accurately describe the pore structure of reservoirs. In this paper, the latest advances on the application of primary drainage MICP curves from reservoir porous structures are reviewed in three main aspects: The measurement and calculation of MICP capillary pressure, estimation of pore size distributions making use of fractal characteristics, and determination of permeability. Experimental measurements and numerical simulation methods of MICP capillary pressure with its influencing factors are also discussed. MICP capillary pressure combined with other methods are argued to be one of the main directions for future research on reservoir pore structures.Cited as: Jiao, L., Andersen, P.Ø., Zhou, J., Cai, J. Applications of mercury intrusion capillary pressure for pore structures: A review. Capillarity, 2020, 3(4): 62-74, doi: 10.46690/capi.2020.04.0

The Role of School Management Towards Staff Motivation for Effective Performance in Nepal: During the Covid-19

This study attempts to investigates the role of motivation on academic staffs of private school in Nepal. To analyze the staff motivation, a descriptive cum exploratory research design along with structured questionnaire has been administered for data collection. Out of total 291 private school in Lalitpur metropolitan city 20 schools were selected for this study, purposive sampling method is used as per the convenient of the researchers. The result of the study clearly depict that the role of school management towards staff motivation for effective performance is found to be inferior in practice. Therefore, the study concludes that, to mitigate the impact of Covid-19 during and after the crisis, the ground reality of staff’s motivation must be taken into consideration. The present research recommended that there is a substantial room for motivation in private school, Nepal because role on motivation in school is far from satisfactory. Thus to improve the situation congenial climate should be provided and encouragement should be taken

An adaptive trust based service quality monitoring mechanism for cloud computing

Cloud computing is the newest paradigm in distributed computing that delivers computing resources over the Internet as services. Due to the attractiveness of cloud computing, the market is currently flooded with many service providers. This has necessitated the customers to identify the right one meeting their requirements in terms of service quality. The existing monitoring of service quality has been limited only to quantification in cloud computing. On the other hand, the continuous improvement and distribution of service quality scores have been implemented in other distributed computing paradigms but not specifically for cloud computing. This research investigates the methods and proposes mechanisms for quantifying and ranking the service quality of service providers. The solution proposed in this thesis consists of three mechanisms, namely service quality modeling mechanism, adaptive trust computing mechanism and trust distribution mechanism for cloud computing. The Design Research Methodology (DRM) has been modified by adding phases, means and methods, and probable outcomes. This modified DRM is used throughout this study. The mechanisms were developed and tested gradually until the expected outcome has been achieved. A comprehensive set of experiments were carried out in a simulated environment to validate their effectiveness. The evaluation has been carried out by comparing their performance against the combined trust model and QoS trust model for cloud computing along with the adapted fuzzy theory based trust computing mechanism and super-agent based trust distribution mechanism, which were developed for other distributed systems. The results show that the mechanisms are faster and more stable than the existing solutions in terms of reaching the final trust scores on all three parameters tested. The results presented in this thesis are significant in terms of making cloud computing acceptable to users in verifying the performance of the service providers before making the selection

Productivity prediction of fractured horizontal wells with low permeability flow characteristics

Horizontal well and large-scale fracturing are revolutionary technologies in  petroleum industry. The technologies bring obvious economic benefits to exploiting unconventional oil and gas reservoirs with low permeability, ultra-low permeability and shale gas. With the increasingly extensive application of these technologies, other correlated technologies have also gained great development. However, low-permeability reservoirs exhibit complicated features and horizontal well fractures have complex shape. The existing methods for the productivity prediction of fractured horizontal well in low-permeability reservoirs rarely consider the influencing factors in a comprehensive manner. In this paper, a horizontal well seepage model of casing fracturing completion was established according to the superposition principle of low-permeability reservoir and the relationship between potential and pressure, by which model the seepage characteristics of low-permeability reservoirs could be fully described. Based on the established new seepage model, a new targeted model with coupling seepage and wellbore flow was established for the productivity prediction of low-permeability fractured horizontal well. Finally, the new targeted model was verified through field experiment. The experimental results confirmed the reliability of productivity prediction by the proposed model. Sensitivity analysis was then performed on the parameters in the proposed model

Data-Driven Dynamic Inversion Method for Complex Fractures in Unconventional Reservoirs

Hydraulic fracturing is a crucial technology for enhancing the recovery of oil and gas from unconventional reservoirs. Accurately describing fracture morphology is essential for accurately predicting production dynamics. This article proposes a new fracture inversion model based on dynamic data-driven methods, which is different from the conventional linear elastic fracture mechanics model. This method eliminates the need to consider complex mechanical mechanisms, resulting in faster simulation speeds. In the model, the fracture morphology is constrained by combining microseismic data and fracturing construction data, and the fracture tip propagation domain is introduced to characterize the multi-directionality of fracture propagation. The simulated fracture exhibits a multi-branch fracture network morphology, aligning more closely with geological understanding. In addition, the influence of microseismic signal intensity on the direction of fracture propagation is considered in this study. The general stochastic approximation (GSA) algorithm is employed to optimize the direction of fracture propagation. The proposed method is applied to both the single-stage fracturing model and the whole well fracturing model. The research findings indicate that in the single-stage fracturing model, the inverted fracture morphology aligns closely with the microseismic data, with a fitting rate of the fracturing construction curve exceeding 95%, and a microseismic data fitting rate exceeding 93%. In the whole well fracturing model, a total of 18 sections were inverted. The fitting rate between the overall fracture morphology and the microseismic data reached 90%. The simulation only took 5 minutes, demonstrating high computational efficiency and meeting the needs of large-scale engineering fracture simulation. This method can effectively support geological modeling and production dynamic prediction