BIG models or foundation models are rapidly emerging as a key force in advancing intelligent societies[1]–[3]Their significance stems not only from their exceptional ability to process complex data and simulate advan...BIG models or foundation models are rapidly emerging as a key force in advancing intelligent societies[1]–[3]Their significance stems not only from their exceptional ability to process complex data and simulate advanced cognitive functions,but also from their potential to drive innovation across various industries.展开更多
Experiments were conducted in this study to examine the thermal performance of a thermosyphon,made from Inconel alloy 625,could recover waste heat from automobile exhaust using a limited amount of fluid.The thermosyph...Experiments were conducted in this study to examine the thermal performance of a thermosyphon,made from Inconel alloy 625,could recover waste heat from automobile exhaust using a limited amount of fluid.The thermosyphon has an outer diameter of 27 mm,a thickness of 2.6 mm,and an overall length of 483 mm.The study involved directing exhaust gas onto the evaporator.This length includes a 180-mm evaporator,a 70-mm adiabatic section,a 223-mm condenser,and a 97-mm finned exchanger.The study examined the thermal performance of the thermosyphon under exhaust flow rates ranging from 0–10 g/sec and temperatures varying from 300℃–900℃.The influence of three parameters—inclination angle(5°–45°),water mass(2–5.3 g),and the quantity of non-condensable gas Argon(0–0.6 g)—was investigated to assess their impacts on the thermosyphon’s thermal efficiency.The experimental findings revealed that with 3 g of water and 0.0564 g of argon in the thermosyphon,the condenser reached its highest temperature at around 200℃.The ideal fuel loading rate for the thermosyphon falls between 0.2 and 0.7 g/s.Moreover,as inclination angles rise,outer wall temperatures of the thermosyphon increase.This is attributed to the explicit expansion of the effective heating area within the evaporation section,coupled with an amplified gravitational component of the water flux.Additionally,an upsurge in the quantity of non-condensable gas(NCG)can mitigate temperature gradients on the outer wall,resulting in a decline in the thermosyphon’s performance.The insulation applied to the adiabatic section demonstrated efficacy in augmenting temperature gradients on the outer wall,thereby improving the overall performance of the thermosyphon.As the water charge within the thermosyphon increases,there is a corresponding rise in heat transfer rates both from the exhaust to the thermosyphon and from the thermosyphon to the fuel.展开更多
On 14th September 2023,we were gathered in a Beijing conference hall to exchange views with the leaders of the Chinese Association for International Understanding.We had been introduced about the Association by resear...On 14th September 2023,we were gathered in a Beijing conference hall to exchange views with the leaders of the Chinese Association for International Understanding.We had been introduced about the Association by researcher Zhang Yaowu during our field trips.However,when we met Ai Ping,I had no idea that we were talking with the author of"A Tale of Two Continents:An Autobiography".In receiving his signed book,I realized that the speaker was the current Vice-President of the Chinese Association for International Understanding and a Deputy Minister and an Ambassador Extraordinary and Plenipotentiary of China to Ethiopia.展开更多
In this paper, the Automated Actuarial Loss Reserving Model is developed and extended using machine learning. The traditional actuarial reserving techniques are no longer compatible with the increase in technological ...In this paper, the Automated Actuarial Loss Reserving Model is developed and extended using machine learning. The traditional actuarial reserving techniques are no longer compatible with the increase in technological advancement currently at hand. As a result, the development of the alternative Artificial Intelligence Based Automated Actuarial Loss Reserving Methodology which captures diverse risk profiles for various policyholders through augmenting the Micro Finance services, Auto Insurance Services and Both Services lines of business on the same platform through the computation of the Comprehensive Automated Actuarial Loss Reserves (CAALR) has been implemented in this paper. The introduction of the four further types of actuarial loss reserves to those existing in the actuarial literature seems to significantly reduce lapse rates, reduce the reinsurance costs as well as expenses and outgo. As a matter of consequence, this helps to bring together a combination of new and existing policyholders in the insurance company. The frequency severity models have been extended in this paper using ten machine learning algorithms which ultimately leads to the derivation of the proposed machine learning-based actuarial loss reserving model which remarkably performed well when compared to the traditional chain ladder actuarial reserving method using simulated data.展开更多
The detection of brain disease is an essential issue in medical and research areas.Deep learning techniques have shown promising results in detecting and diagnosing brain diseases using magnetic resonance imaging(MRI)...The detection of brain disease is an essential issue in medical and research areas.Deep learning techniques have shown promising results in detecting and diagnosing brain diseases using magnetic resonance imaging(MRI)images.These techniques involve training neural networks on large datasets of MRI images,allowing the networks to learn patterns and features indicative of different brain diseases.However,several challenges and limitations still need to be addressed further to improve the accuracy and effectiveness of these techniques.This paper implements a Feature Enhanced Stacked Auto Encoder(FESAE)model to detect brain diseases.The standard stack auto encoder’s results are trivial and not robust enough to boost the system’s accuracy.Therefore,the standard Stack Auto Encoder(SAE)is replaced with a Stacked Feature Enhanced Auto Encoder with a feature enhancement function to efficiently and effectively get non-trivial features with less activation energy froman image.The proposed model consists of four stages.First,pre-processing is performed to remove noise,and the greyscale image is converted to Red,Green,and Blue(RGB)to enhance feature details for discriminative feature extraction.Second,feature Extraction is performed to extract significant features for classification using DiscreteWavelet Transform(DWT)and Channelization.Third,classification is performed to classify MRI images into four major classes:Normal,Tumor,Brain Stroke,and Alzheimer’s.Finally,the FESAE model outperforms the state-of-theart,machine learning,and deep learning methods such as Artificial Neural Network(ANN),SAE,Random Forest(RF),and Logistic Regression(LR)by achieving a high accuracy of 98.61% on a dataset of 2000 MRI images.The proposed model has significant potential for assisting radiologists in diagnosing brain diseases more accurately and improving patient outcomes.展开更多
Intrusion detection systems(IDS)are one of the most promising ways for securing data and networks;In recent decades,IDS has used a variety of categorization algorithms.These classifiers,on the other hand,do not work e...Intrusion detection systems(IDS)are one of the most promising ways for securing data and networks;In recent decades,IDS has used a variety of categorization algorithms.These classifiers,on the other hand,do not work effectively unless they are combined with additional algorithms that can alter the classifier’s parameters or select the optimal sub-set of features for the problem.Optimizers are used in tandem with classifiers to increase the stability and with efficiency of the classifiers in detecting invasion.These algorithms,on the other hand,have a number of limitations,particularly when used to detect new types of threats.In this paper,the NSL KDD dataset and KDD Cup 99 is used to find the performance of the proposed classifier model and compared;These two IDS dataset is preprocessed,then Auto Cryptographic Denoising(ACD)adopted to remove noise in the feature of the IDS dataset;the classifier algorithms,K-Means and Neural network classifies the dataset with adam optimizer.IDS classifier is evaluated by measuring performance measures like f-measure,recall,precision,detection rate and accuracy.The neural network obtained the highest classifying accuracy as 91.12%with drop-out function that shows the efficiency of the classifier model with drop-out function for KDD Cup99 dataset.Explaining their power and limitations in the proposed methodology that could be used in future works in the IDS area.展开更多
基金the National Natural Science Foundation of China(62103411)the Science and Technology Development Fund of Macao SAR(0093/2023/RIA2,0050/2020/A1)。
文摘BIG models or foundation models are rapidly emerging as a key force in advancing intelligent societies[1]–[3]Their significance stems not only from their exceptional ability to process complex data and simulate advanced cognitive functions,but also from their potential to drive innovation across various industries.
文摘Experiments were conducted in this study to examine the thermal performance of a thermosyphon,made from Inconel alloy 625,could recover waste heat from automobile exhaust using a limited amount of fluid.The thermosyphon has an outer diameter of 27 mm,a thickness of 2.6 mm,and an overall length of 483 mm.The study involved directing exhaust gas onto the evaporator.This length includes a 180-mm evaporator,a 70-mm adiabatic section,a 223-mm condenser,and a 97-mm finned exchanger.The study examined the thermal performance of the thermosyphon under exhaust flow rates ranging from 0–10 g/sec and temperatures varying from 300℃–900℃.The influence of three parameters—inclination angle(5°–45°),water mass(2–5.3 g),and the quantity of non-condensable gas Argon(0–0.6 g)—was investigated to assess their impacts on the thermosyphon’s thermal efficiency.The experimental findings revealed that with 3 g of water and 0.0564 g of argon in the thermosyphon,the condenser reached its highest temperature at around 200℃.The ideal fuel loading rate for the thermosyphon falls between 0.2 and 0.7 g/s.Moreover,as inclination angles rise,outer wall temperatures of the thermosyphon increase.This is attributed to the explicit expansion of the effective heating area within the evaporation section,coupled with an amplified gravitational component of the water flux.Additionally,an upsurge in the quantity of non-condensable gas(NCG)can mitigate temperature gradients on the outer wall,resulting in a decline in the thermosyphon’s performance.The insulation applied to the adiabatic section demonstrated efficacy in augmenting temperature gradients on the outer wall,thereby improving the overall performance of the thermosyphon.As the water charge within the thermosyphon increases,there is a corresponding rise in heat transfer rates both from the exhaust to the thermosyphon and from the thermosyphon to the fuel.
文摘On 14th September 2023,we were gathered in a Beijing conference hall to exchange views with the leaders of the Chinese Association for International Understanding.We had been introduced about the Association by researcher Zhang Yaowu during our field trips.However,when we met Ai Ping,I had no idea that we were talking with the author of"A Tale of Two Continents:An Autobiography".In receiving his signed book,I realized that the speaker was the current Vice-President of the Chinese Association for International Understanding and a Deputy Minister and an Ambassador Extraordinary and Plenipotentiary of China to Ethiopia.
文摘In this paper, the Automated Actuarial Loss Reserving Model is developed and extended using machine learning. The traditional actuarial reserving techniques are no longer compatible with the increase in technological advancement currently at hand. As a result, the development of the alternative Artificial Intelligence Based Automated Actuarial Loss Reserving Methodology which captures diverse risk profiles for various policyholders through augmenting the Micro Finance services, Auto Insurance Services and Both Services lines of business on the same platform through the computation of the Comprehensive Automated Actuarial Loss Reserves (CAALR) has been implemented in this paper. The introduction of the four further types of actuarial loss reserves to those existing in the actuarial literature seems to significantly reduce lapse rates, reduce the reinsurance costs as well as expenses and outgo. As a matter of consequence, this helps to bring together a combination of new and existing policyholders in the insurance company. The frequency severity models have been extended in this paper using ten machine learning algorithms which ultimately leads to the derivation of the proposed machine learning-based actuarial loss reserving model which remarkably performed well when compared to the traditional chain ladder actuarial reserving method using simulated data.
基金supported by financial support from Universiti Sains Malaysia(USM)under FRGS Grant Number FRGS/1/2020/TK03/USM/02/1the School of Computer Sciences USM for their support.
文摘The detection of brain disease is an essential issue in medical and research areas.Deep learning techniques have shown promising results in detecting and diagnosing brain diseases using magnetic resonance imaging(MRI)images.These techniques involve training neural networks on large datasets of MRI images,allowing the networks to learn patterns and features indicative of different brain diseases.However,several challenges and limitations still need to be addressed further to improve the accuracy and effectiveness of these techniques.This paper implements a Feature Enhanced Stacked Auto Encoder(FESAE)model to detect brain diseases.The standard stack auto encoder’s results are trivial and not robust enough to boost the system’s accuracy.Therefore,the standard Stack Auto Encoder(SAE)is replaced with a Stacked Feature Enhanced Auto Encoder with a feature enhancement function to efficiently and effectively get non-trivial features with less activation energy froman image.The proposed model consists of four stages.First,pre-processing is performed to remove noise,and the greyscale image is converted to Red,Green,and Blue(RGB)to enhance feature details for discriminative feature extraction.Second,feature Extraction is performed to extract significant features for classification using DiscreteWavelet Transform(DWT)and Channelization.Third,classification is performed to classify MRI images into four major classes:Normal,Tumor,Brain Stroke,and Alzheimer’s.Finally,the FESAE model outperforms the state-of-theart,machine learning,and deep learning methods such as Artificial Neural Network(ANN),SAE,Random Forest(RF),and Logistic Regression(LR)by achieving a high accuracy of 98.61% on a dataset of 2000 MRI images.The proposed model has significant potential for assisting radiologists in diagnosing brain diseases more accurately and improving patient outcomes.
文摘Intrusion detection systems(IDS)are one of the most promising ways for securing data and networks;In recent decades,IDS has used a variety of categorization algorithms.These classifiers,on the other hand,do not work effectively unless they are combined with additional algorithms that can alter the classifier’s parameters or select the optimal sub-set of features for the problem.Optimizers are used in tandem with classifiers to increase the stability and with efficiency of the classifiers in detecting invasion.These algorithms,on the other hand,have a number of limitations,particularly when used to detect new types of threats.In this paper,the NSL KDD dataset and KDD Cup 99 is used to find the performance of the proposed classifier model and compared;These two IDS dataset is preprocessed,then Auto Cryptographic Denoising(ACD)adopted to remove noise in the feature of the IDS dataset;the classifier algorithms,K-Means and Neural network classifies the dataset with adam optimizer.IDS classifier is evaluated by measuring performance measures like f-measure,recall,precision,detection rate and accuracy.The neural network obtained the highest classifying accuracy as 91.12%with drop-out function that shows the efficiency of the classifier model with drop-out function for KDD Cup99 dataset.Explaining their power and limitations in the proposed methodology that could be used in future works in the IDS area.
