Efficient Sampling for Big Provenance

Largani, Sara Moshtaghi; Lee, Seokki

doi:10.1145/3543873.3587556

Cited by 7 publications

(1 citation statement)

References 13 publications

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“…In addition to requiring that electronic contracts be preserved in a readable, saveable, and retrievable manner, the aforementioned law also specifies that certain additional conditions must be met in electronic contracts, such as consumer notices in certain instances. Last but not least, the expansion and development of alternative electronic signature and registration regulations, such as the Electronic Transactions Standardization Law (Wright & Serguieva, 2017;Hoorfar et al, 2023;Moshtaghi Largani & Lee, 2023), have been made possible by the electronic signature legislation.…”

Section: 3mentioning

confidence: 99%

Towards Standardized Regulations for Block Chain Smart Contracts: Insights From Delphi and Swara Analysis

2024

AJM

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The rise of digital currency and the public ledger Block Chain has led to the development of a new type of electronic contract known as "smart contracts." For these contracts to be considered valid, they must adhere to traditional contract rules and be concluded without any impediments. Once written, encrypted, and signed, smart contracts are recorded in the Block Chain Ledger, providing transparent and secure record-keeping. Smart contracts offer several benefits, including their ability to execute automatically without requiring human intervention, their provision of public visibility of contract provisions on the Block Chain, their avoidance of financial crimes like Money Laundering, and their prevention of contract abuses. However, disputes arising from smart contracts still require human intervention, presenting unique challenges in enforcing these contracts, such as evidentiary issues, enforceability of waivers of defenses, and jurisdictional and choice-of-law considerations. Due to the novel nature of smart contracts, there are currently no standardized regulations that apply to them. Countries that have approved them have turned to customary law to legitimize their use. The Delphi method was used to identify critical success factors for applying blockchain transactions in a manufacturing company. Stepwise Weight Assessment Ratio Analysis (SWARA) was then utilized to determine the most influential factors. The proposed methodology was implemented, and results show that the most influential factors for the successful application of blockchain transactions as smart contracts in a manufacturing company are: turnover, the counter argument, vision, components for building, and system outcome quality. Conversely, connections with government entities and subcontractors, and the guarantee of quality have the least influence on successful implementation. These findings can contribute to the development of a legal framework for smart contracts in a manufacturing company.

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Section: 3mentioning

confidence: 99%

Towards Standardized Regulations for Block Chain Smart Contracts: Insights From Delphi and Swara Analysis

2024

AJM

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Forecasting heating and cooling loads in residential buildings using machine learning: a comparative study of techniques and influential indicators

Mehdizadeh Khorrami,

Soleimani,

Pinnarelli

et al. 2023

Asian J Civ Eng

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Residential buildings are a significant source of energy consumption and greenhouse gas emissions, making it crucial to accurately predict their energy demand for reducing their environmental impact. In this study, machine-learning techniques such as linear regression, decision tree classification, logistic regression, and neural networks were applied to forecast the heating and cooling loads of 12 different building types using their area and height attributes. The correlation coefficient was utilized to assign weights to the predictors in linear regression, and the models’ performance was evaluated using metrics such as equations of R2, MAE, and RMSE. The decision tree technique demonstrated the highest accuracy of 98.96% and 93.24% for predicting heating and cooling loads, respectively, among the classification methods. Notably, the cooling load prediction was more accurate than the heating load prediction. The height and area of the roof and floor, along with the relative compactness of the building, were identified as the most influential factors in the heating and cooling loads. These findings have significant implications for optimizing energy efficiency in residential buildings and mitigating their impact on climate change.

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Correction: Forecasting heating and cooling loads in residential buildings using machine learning: a comparative study of techniques and influential indicators

Mehdizadeh Khorrami,

Soleimani,

Pinnarelli

et al. 2023

Asian J Civ Eng

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Efficient Sampling for Big Provenance

Cited by 7 publications

References 13 publications

Towards Standardized Regulations for Block Chain Smart Contracts: Insights From Delphi and Swara Analysis

Towards Standardized Regulations for Block Chain Smart Contracts: Insights From Delphi and Swara Analysis

Forecasting heating and cooling loads in residential buildings using machine learning: a comparative study of techniques and influential indicators

Correction: Forecasting heating and cooling loads in residential buildings using machine learning: a comparative study of techniques and influential indicators

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