Study on Phase Change Material in Grooved Bricks for Energy Efficiency of the Buildings

Phase change materials(PCMs)are an interesting technology due to their high density and isothermal behavior during phase change.Phase change material plays a major role in the energy saving of the buildings,which is greatly aided by the incorporation of phase change material into building products such as bricks,cement,gypsum board,etc.In this study,an experiment has been conducted with three identical small chambers made up of normal,grooved and PCM-treated grooved bricks.Before the inclusion of PCM in grooved bricks,PCM material behavior has been studied by different techniques such as DSC,TG/DTA,SEM,and XRD.Thermal properties and thermal stability were investigated by differential scanning calorimeter(DSC)and thermogravimetric analyzer(TGA)respectively.Scanning electron microscopy(SEM)and X-ray diffraction(XRD)were used to determine the microstructure and crystalloid phase of the PCM before and after the accelerated thermal cycling test(0,60,120).These three identical model rooms built were exposed at a temperature just above 40°C with a heater.When the maximum outdoor temperature was 40-41°C,then the temperature of the PCM-treated grooved chamber was 32-33°C.The PCM-treated wall was tested and compared with a conventional and grooved wall.The difference between the PCM-treated grooved chamber and the untreated one was 8-9°C.PCM-treated bricks provided more efficient internal heat retention in summer when the outside temperature increased.

Identifying malicious accounts in blockchains using domain names and associated temporal properties

The rise in the adoption of blockchain technology has led to increased illegal activities by cybercriminals costing billions of dollars.Many machine learning algorithms are applied to detect such illegal behavior.These algorithms are often trained on the transaction behavior and,in some cases,trained on the vulnerabilities that exist in the system.In our approach,we study the feasibility of using the Domain Name(DN)associated with the account in the blockchain and identify whether an account should be tagged malicious or not.Here,we leverage the temporal aspects attached to the DN.Our approach achieves 89.53%balanced-accuracy in detecting malicious blockchain DNs.While our results identify 73769 blockchain DNs that show malicious behavior at least once,out of these,34171 blockchain DNs show persistent malicious behavior,resulting in 2479 malicious blockchain DNs over time.Nonetheless,none of these identified malicious DNs were reported in new officially tagged malicious blockchain DNs.

EPASAD:ellipsoid decision boundary based Process-Aware Stealthy Attack Detector

Due to the importance of Critical Infrastructure(Cl)in a nation's economy,they have been lucrative targets for cyber attackers.These critical infrastructures are usually Cyber-Physical Systems such as power grids,water,and sewage treatment facilities,oil and gas pipelines,etc.In recent times,these systems have suffered from cyber attacks numer-ous times.Researchers have been developing cyber security solutions for Cls to avoid lasting damages.According to standard frameworks,cyber security based on identification,protection,detection,response,and recovery are at the core of these research.Detection of an ongoing attack that escapes standard protection such as firewall,anti-virus,and host/network intrusion detection has gained importance as such attacks eventually affect the physical dynamics of the system.Therefore,anomaly detection in physical dynamics proves an effective means to implement defense-in-depth.PASAD is one example of anomaly detection in the sensor/actuator data,representing such systems physical dynamics.We present EPASAD,which improves the detection technique used in PASAD to detect these micro-stealthy attacks,as our experiments show that PASAD's spherical boundary-based detection fails to detect.Our method EPASAD overcomes this by using Ellipsoid boundaries,thereby tightening the boundaries in various dimen-sions,whereas a spherical boundary treats all dimensions equally.We validate EPASAD using the dataset produced by the TE-process simulator and the C-town datasets.The results show that EPASAD improves PASAD's average recall by 5.8%and 9.5%for the two datasets,respectively.