Effect of perforation density distribution on production of perforated horizontal wellbore

To address the issue of horizontal well production affected by the distribution of perforation density in the wellbore,a numerical model for simulating two-phase flow in a horizontal well is established under two perforation density distribution conditions(i.e.increasing the perforation density at inlet and outlet sections respectively).The simulation results are compared with experimental results to verify the reliability of the numerical simulation method.The behaviors of the total pressure drop,superficial velocity of air-water two-phase flow,void fraction,liquid film thickness,air production and liquid production that occur with various flow patterns are investigated under two perforation density distribution conditions based on the numerical model.The total pressure drop,superficial velocity of the mixture and void fraction increase with the air flow rate when the water flow rate is constant.The liquid film thickness decreases when the air flow rate increases.The liquid and air productions increase when the perforation density increases at the inlet section compared with increasing the perforation density at the outlet section of the perforated horizontal wellbore.It is noted that the air production increases with the air flow rate.Liquid production increases with the bubble flow and begins to decrease at the transition point of the slug-stratified flow,then increases through the stratified wave flow.The normalized liquid flux is higher when the perforation density increases at the inlet section,and increases with the radial air flow rate.

Enhancing CNN for Forensics Age Estimation Using CGAN and Pseudo-Labelling

Age estimation using forensics odontology is an important process in identifying victims in criminal or mass disaster cases.Traditionally,this process is done manually by human expert.However,the speed and accuracy may vary depending on the expertise level of the human expert and other human factors such as level of fatigue and attentiveness.To improve the recognition speed and consistency,researchers have proposed automated age estimation using deep learning techniques such as Convolutional Neural Network(CNN).CNN requires many training images to obtain high percentage of recognition accuracy.Unfortunately,it is very difficult to get large number of samples of dental images for training the CNN due to the need to comply to privacy acts.A promising solution to this problem is a technique called Generative Adversarial Network(GAN).GAN is a technique that can generate synthetic images that has similar statistics as the training set.A variation of GAN called Conditional GAN(CGAN)enables the generation of the synthetic images to be controlled more precisely such that only the specified type of images will be generated.This paper proposes a CGAN for generating new dental images to increase the number of images available for training a CNN model to perform age estimation.We also propose a pseudolabelling technique to label the generated images with proper age and gender.We used the combination of real and generated images to trainDentalAge and Sex Net(DASNET),which is a CNN model for dental age estimation.Based on the experiment conducted,the accuracy,coefficient of determination(R2)and Absolute Error(AE)of DASNET have improved to 87%,0.85 and 1.18 years respectively as opposed to 74%,0.72 and 3.45 years when DASNET is trained using real,but smaller number of images.

地热能在预热中的应用综述

考虑到化石燃料的环境污染和成本波动等问题,发展可再生能源技术是未来可持续能源供应的必要和必然。与太阳能和风能相比,地热能在一天中的所有时间都是可利用的。这种可再生能源适用于供暖、制冷、淡水和电力的清洁供应。地热能除了作为主要能源直接用于发电外,还可以作为辅助热源用于预热,减少温室气体排放,节约燃料。在这些系统中,利用地热热源的热量进行蒸汽预热。此外,地热能可用于其他能源系统,如制氢装置,用于预热电解过程中消耗的水。针对利用地热能进行预热的优势,综述了地热能预热系统的应用研究。通过这一研究可以看出,利用地热能进行预热在节约燃料和提高性能方面具有很大的潜力。上述系统的性能取决于系统的配置、钻孔规格和地热源温度等因素。通过实施优化,可以进一步改进这些系统。

An overview of intelligent selection and prediction method in heterogeneous wireless networks

Heterogeneous wireless access technologies will coexist in next generation wireless networks.These technologies form integrated networks,and these networks support multiple services with high quality level.Various access technologies allow users to select the best available access network to meet the requirements of each type of communication service.Being always best connected anytime and anywhere is a major concern in a heterogeneous wireless networks environment.Always best connected enables network selection mechanisms to keep mobile users always connected to the best network.We present an overview of the network selection and prediction problems and challenges.In addition,we discuss a comprehensive classification of related theoretic approaches,and also study the integration between these methods,finding the best solution of network selection and prediction problems.The optimal solution can fulfill the requirements of the next generation wireless networks.

Risk based security assessment of power system using generalized regression neural network with feature extraction

A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle component analysis. In the risk based security assessment formulation, the failure rate associated to weather condition of each line was used to compute the probability of line outage for a given weather condition and the extent of security violation was represented by a severity function. For low voltage and line overload, continuous severity function was considered due to its ability to zoom in into the effect of near violating contingency. New severity function for voltage collapse using the voltage collapse prediction index was proposed. To reduce the computational burden, a new contingency screening method was proposed using the risk factor so as to select the critical line outages. The risk based security assessment method using GRNN was implemented on a large scale 87-bus power system and the results show that the risk prediction results obtained using GRNN with the incorporation of principal component analysis give better performance in terms of accuracy.

Cyclic stress-controlled tests on offshore clay

One of the main concerns in cyclic behavior of soft clay is gradual degradation with the progression of loading cycle.A series of cyclic constant-volume direct simple shear(CDSS) loading tests was performed on Malaysia offshore clay to study its undrained degradation.The testing program consists of stresscontrolled tests with cyclic shear ratio ranging from 0.34 to 0.83 at different overconsolidation ratios(OCRs).For a given cyclic stress ratio in stress-controlled tests,the accumulated cyclic strain and pore water pressure increase with elevated number of cycles.In heavily overconsolidated clay specimens,the negative cyclic pore water pressure is generated followed by positive cyclic pore water pressure as cyclic tests progress.The post-cyclic strength of offshore clay specimens is reduced by undrained cyclic stresscontrolled loading.

Visualization Techniques in Smart Grid

Visualization is an established methodology in scientific computing. It has been used in many fields because of its strong capability in large data management and information display. However, its applications in power systems, especially in Smart Grid are still in infancy stage. Besides, while there were a lot of researches working on visualizing data in transmission power system, the study on displaying distribution power system data was limited. Therefore, in this paper, author proposed some techniques to visualize the Smart Grid data at distribution. They are classified in three categories, which are low dimensional techniques, multivariate high dimensional techniques and Geographical Information System (GIS) techniques.

Laboratory Evaluation of Fiber-Modified Asphalt Mixtures Incorporating Steel Slag Aggregates

Vigorous and continued efforts by researchers and engineers have contributed towards maintaining environmental sustainability through the utilization of waste materials in civil engineering applications as an alternative to natural sources.In this study,granite aggregates in asphaltic mixes were replaced by electric arc furnace(EAF)steel slag aggregates with different proportions to identify the best combination in terms of superior performance.Asphalt mixtures showing the best performance were further reinforced with polyvinyl alcohol(PVA),acrylic,and polyester fibers at the dosages of 0.05%,0.15%,and 0.3%by weight of the aggregates.The performance tests of this study were resilient modulus,moisture susceptibility,and indirect tensile fatigue cracking test.The findings of this study revealed that the asphalt mixtures containing coarse steel slag aggregate exhibited the best performance in comparison with the other substitutions.Moreover,the reinforced asphalt mixtures with synthetic fibers at the content of 0.05%exhibited an almost comparable performance to the unreinforced asphalt mixtures.Modifying the asphalt mixtures with PVA,acrylic,and polyester fibers at the proportion of 0.15%have improved the fatigue cracking resistance by 41.13%,29.87%,and 18.97%,respectively.Also,the fiber-modified asphalt mixtures with PVA,acrylic,and polyester have enhanced the fatigue cracking resistance by about 57%,44%,and 39%,respectively.The results of the resilient modulus demonstrated that as the fiber content increase,the resilient modulus of the reinforced asphalt mixtures decreases.Therefore,introducing synthetic fibers at the content of 0.3%has slightly decreased the resilient modulus in comparison with unreinforced mixtures.On the other hand,the results of the mechanisticempirical pavement design showed that the reinforced asphalt mixes with a high content of synthetic fibers have shown lower service life than the control mixes due to the low resilient modulus.On the contrary,based on the laboratory results,the asphalt mixes incorporating PVA,acrylic,and polyester fibers at the proportion of 0.15%have shown the potential to reduce the thickness of the asphalt layer by about 14.9%,11.80%,and 8.70%,respectively.

Microwave-Assisted Alkaline Pretreatment and Microwave Assisted Enzymatic Saccharification of Oil Palm Empty Fruit Bunch Fiber for Enhanced Fermentable Sugar Yield

Lignocellulosic materials are promising alternative feedstocks for bioethanol production. However, the recalcitrant nature of lignocellulosic biomass necessitates an efficient pretreatment pretreatment step to improve the yield of fermentable sugars and maximizing the enzymatic hydrolysis efficiency. Microwave pretreatment may be a good alternative as it can reduce the pretreatment time and improve the enzymatic activity during hydrolysis. The overall goal of this paper is to expand the current state of knowledge on microwave-based pretreatment of lignocellulosic biomass and microwave assisted enzymatic reaction or Microwave Irradiation-Enzyme Coupling Catalysis (MIECC). In the present study, a comparison of microwave assisted alkali pretreatment was tried using Oil Palm empty fruit bunch. The microwave assisted alkali pretreatment of EFB using NaOH, significantly improved the enzymatic saccharification of EFB by removing more lignin and hemicellulose and increasing its accessibility to hydrolytic enzymes. The results showed that the optimum pretreatment condition was 3% (w/v) NaOH at 180 W for 12 minutes with the optimum component loss of lignin and holocellulose of about 74% and 24.5% respectively. The subsequent enzymatic saccharification of EFB pretreated by microwave assisted NaOH (3% w/v);resulted in 411 mg of reducing sugar per gram EFB at cellulose enzyme dosage of 20 FPU. The overall enhancement by the microwave treatment during the microwave assisted alkali pretreatment and microwave assisted enzymatic hydrolysis was 5.8 fold. The present study has highlighted the importance of well controlled microwave assisted enzymatic reaction to enhance the overall reaction rate of the process.

I-PRESENT^TM: An Involutive Lightweight Block Cipher

This paper proposes a new involutive light-weight block cipher for resource-constraint environments called I-PRESENTTM. The design is based on the Present block cipher which is included in the ISO/IEC 29192 standard on lightweight cryptography. The advantage of I-PRESENTTM is that the cipher is involutive such that the encryption circuit is identical to decryption. This is an advantage for environments which require the implementation of both circuits. The area requirement of I-PRESENTTM compares reasonably well with other similar ciphers such as PRINCE.

Experimental Study of Heat Transfer Enhancement in Solar Tower Receiver Using Internal Fins

The receiver is an important element in solar energy plants.The principal receiver’s tubes in power plants are devised to work under extremely severe conditions,including excessive heat fluxes.Half of the tube’s circumference is heated whilst the other half is insulated.This study aims to improve the heat transfer process and reinforce the tubes’structure by designing a new receiver;by including longitudinal fins of triangular,circular and square shapes.The research is conducted experimentally using Reynolds numbers ranging from 28,000 to 78,000.Triangular fins have demonstrated the best improvement for heat transfer.For Reynolds number value near 43,000 Nusselt number(Nu)is higher by 3.5%and 7.5%,sequentially,compared to circular and square tube fins,but varies up to 6.5%near Re=61000.The lowest friction factor is seen in a triangular fin receiver;where it deviates from circular fins by 4.6%,and square fin tubes by 3.2%.Adding fins makes the temperature decrease gradually,and in the case of no fins,the temperature gradient between the hot tube and water drops sharply in the planed tube by 7%.

IoT Technologies for Tackling COVID-19 in Malaysia and Worldwide:Challenges, Recommendations, and Proposed Framework

The Coronavirus(COVID-19)pandemic is considered as a global public health challenge.To contain this pandemic,different measures are being taken globally.The Internet of Things(IoT)has been represented as one of the most important schemes that has been considered to fight the spread of COVID-19 in the world,practically Malaysia.In fact,there are many sectors in Malaysia would be transformed into smart services by using IoT technologies,particularly energy,transportation,healthcare sectors.This manuscript presents a comprehensive review of the IoT technologies that are being used currently in Malaysia to accelerate the measures against COVID-19.These IoT technologies including smart thermal detection,surveillance network,and healthcare delivery technologies.In addition,the most potential IoT technologies developed by other nations to tackle COVID-19 are reviewed,including tracking and tracing systems based on IoT,adoption of IoT technologies in hospitals,and self-monitoring devices based on IoT.For these technologies to be incorporated in Malaysia,the main challenges with possible recommendations are discussed in this manuscript.Finally,this work proposes a conceptual framework to accelerate the battle pace with spread of COVID-19 in Malaysia by integrating IoT technologies used in fighting the outbreak of COVID-19 globally.

Medical Image Analysis Using Deep Learning and Distribution Pattern Matching Algorithm

Artificial intelligence plays an essential role in the medical and health industries.Deep convolution networks offer valuable services and help create automated systems to perform medical image analysis.However,convolution networks examine medical images effectively;such systems require high computational complexity when recognizing the same disease-affected region.Therefore,an optimized deep convolution network is utilized for analyzing disease-affected regions in this work.Different disease-relatedmedical images are selected and examined pixel by pixel;this analysis uses the gray wolf optimized deep learning network.This method identifies affected pixels by the gray wolf hunting process.The convolution network uses an automatic learning function that predicts the disease affected by previous imaging analysis.The optimized algorithm-based selected regions are further examined using the distribution pattern-matching rule.The pattern-matching process recognizes the disease effectively,and the system’s efficiency is evaluated using theMATLAB implementation process.This process ensures high accuracy of up to 99.02%to 99.37%and reduces computational complexity.

Mechanical and superconducting properties of nanosize MgO added dip-coated Bi₂Sr₂CaCu₂O₈superconducting tape

In this work, 3% to 8% (in weight) of nanosize MgO particles was added to Bi2Sr2CaCu2O8 (Bi- 2212) high-temperature superconductor to fab-ricate Bi-2212 superconducting material with superior mechanical properties. The mechanical strength of the samples was studied by con-ducting the compression test at room tempera-ture, and the addition of 5% nanosize MgO par-ticles produced the highest strength when com- pared with other samples. The sample with 5% MgO addition also exhibited superior super-conducting properties. The Bi-2212 powder with 5% nanosize MgO addition was used to fabricate Bi-2212 tapes through the dip-coating-then- stacking method. The fully processed tapes were investigated via dc electrical resistance measurements, XRD patterns, SEM micrographs, transport critical current density and tensile tests. The tensile tests were conducted at room and cryogenic (77 K) temperatures. Results of tensile tests and Young’s modulus for the tapes showed that the Bi-2212 tapes with nanosize MgO addition recorded better mechanical prop-erty compared to the non-added samples both at room and cryogenic temperatures. The double- core tape with 5% MgO addition recorded the highest failure point at 160 MPa. In addition to the strengthening effect that was observed in the nanosize MgO added Bi-2212 superconduc-tor tapes, the superconducting properties re-mained intact in the tapes.

An Investigation on the Effect of Migration Strategy on Parallel GA-Based Shortest Path Routing Algorithm

Genetic algorithm (GA) is one of the alternative approaches for solving the shortest path routing problem. In previous work, we have developed a coarse-grained parallel GA-based shortest path routing algorithm. With parallel GA, there is a GA operator called migration, where a chromosome is taken from one sub-population to replace a chromosome in another sub-population. Which chromosome to be taken and replaced is subjected to the migration strategy used. There are four different migration strategies that can be employed: best replace worst, best replace random, random replace worst, and random replace random. In this paper, we are going to evaluate the effect of different migration strategies on the parallel GA-based routing algorithm that has been developed in the previous work. Theoretically, the migration strategy best replace worst should perform better than the other strategies. However, result from simulation shows that even though the migration strategy best replace worst performs better most of the time, there are situations when one of the other strategies can perform just as well, or sometimes better.