The critique of reform proposals for ISDS : solutions to existing and future problems

Addressing wide ranging dissatisfactions regarding the current Investor-State Dispute Settlement (ISDS) system, the UNCITRAL working group III (WGIII) members are working continuously to put forward necessary reforms to the system which may take couple of more years. Considering their works till to date, the proposals might bring great improvement to the current system and will also add significant qualities. However, still, they did not put forward elaborate proposals on some of the important matters for an effective and sustainable dispute settlement system. Moreover, they might follow little bit the similar track of the WTO which is in crisis itself

A new approach to seasonal energy consumption forecasting using temporal convolutional networks

There has been a significant increase in the attention paid to resource management in smart grids, and several energy forecasting models have been published in the literature. It is well known that energy forecasting plays a crucial role in several applications in smart grids, including demand-side management, optimum dispatch, and load shedding. A significant challenge in smart grid models is managing forecasts efficiently while ensuring the slightest feasible prediction error. A type of artificial neural networks such as recurrent neural networks, are frequently used to forecast time series data. However, due to certain limitations like vanishing gradients and lack of memory retention of recurrent neural networks, sequential data should be modeled using convolutional networks. The reason is that they have strong capabilities to solve complex problems better than recurrent neural networks. In this research, a temporal convolutional network is proposed to handle seasonal short-term energy forecasting. The proposed temporal convolutional network computes outputs in parallel, reducing the computation time compared to the recurrent neural networks. Further performance comparison with the traditional long short-term memory in terms of MAD and sMAPE has proved that the proposed model has outperformed the recurrent neural network

Forecasting Energy Consumption Demand of Customers in Smart Grid Using Temporal Fusion Transformer (TFT)

Energy consumption prediction has always remained a concern for researchers because of the rapid growth of the human population and customers joining smart grids network for smart home facilities. Recently, the spread of COVID-19 has dramatically increased energy consumption in the residential sector. Hence, it is essential to produce energy per the residential customers\u27 requirements, improve economic efficiency, and reduce production costs. The previously published papers in the literature have considered the overall energy consumption prediction, making it difficult for production companies to produce energy per customers\u27 future demand. Using the proposed study, production companies can accurately have energy per their customers\u27 needs by forecasting future energy consumption demands. Scientists and researchers are trying to minimize energy consumption by applying different optimization and prediction techniques; hence this study proposed a daily, weekly, and monthly energy consumption prediction model using Temporal Fusion Transformer (TFT). This study relies on a TFT model for energy forecasting, which considers both primary and valuable data sources and batch training techniques. The model\u27s performance has been related to the Long Short-Term Memory (LSTM), LSTM interpretable, and Temporal Convolutional Network (TCN) models. The model\u27s performance has remained better than the other algorithms, with mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) of 4.09, 2.02, and 1.50. Further, the overall symmetric mean absolute percentage error (sMAPE) of LSTM, LSTM interpretable, TCN, and proposed TFT remained at 29.78%, 31.10%, 36.42%, and 26.46%, respectively. The sMAPE of the TFT has proved that the model has performed better than the other deep learning models

Short term energy consumption forecasting using neural basis expansion analysis for interpretable time series

Smart grids and smart homes are getting people\u27s attention in the modern era of smart cities. The advancements of smart technologies and smart grids have created challenges related to energy efficiency and production according to the future demand of clients. Machine learning, specifically neural network-based methods, remained successful in energy consumption prediction, but still, there are gaps due to uncertainty in the data and limitations of the algorithms. Research published in the literature has used small datasets and profiles of primarily single users; therefore, models have difficulties when applied to large datasets with profiles of different customers. Thus, a smart grid environment requires a model that handles consumption data from thousands of customers. The proposed model enhances the newly introduced method of Neural Basis Expansion Analysis for interpretable Time Series (N-BEATS) with a big dataset of energy consumption of 169 customers. Further, to validate the results of the proposed model, a performance comparison has been carried out with the Long Short Term Memory (LSTM), Blocked LSTM, Gated Recurrent Units (GRU), Blocked GRU and Temporal Convolutional Network (TCN). The proposed interpretable model improves the prediction accuracy on the big dataset containing energy consumption profiles of multiple customers. Incorporating covariates into the model improved accuracy by learning past and future energy consumption patterns. Based on a large dataset, the proposed model performed better for daily, weekly, and monthly energy consumption predictions. The forecasting accuracy of the N-BEATS interpretable model for 1-day-ahead energy consumption with day as covariates remained better than the 1, 2, 3, and 4-week scenarios

On the Bayesian network based data mining framework for the choice of appropriate time scale for regional analysis of drought Hazard

Data mining has a significant role in hyrdrologic research. Among several methods of data mining, Bayesian network theory has great importance and wide applications as well. The drought indices are very useful tools for drought monitoring and forecasting. However, the multi-scaling nature of standardized type drought indices creates several problems in data analysis and reanalysis at regional level. This paper presents a novel framework of data mining for hydrological research-the Bayesian Integrated Regional Drought Time Scale (BIRDts). The mechanism of BIRDts gives effective and sufficient time scales by considering dependency/interdependency probabilities from Bayesian network algorithm. The resultant time scales are proposed for further investigation and research related to the hydrological process. Application of the proposed method consists of 46 meteorological stations of Pakistan. In this research, we have employed Standardized Precipitation Temperature Index (SPTI) drought index for 1-, 3-, 6-, 9-, 12-, 24-, and ()month time scales. Outcomes associated with this research show that the proposed method has rationale to aggregate time scales at regional level by configuring marginal posterior probability as weights in the selection process of effective drought time scales

Designing a relational model to identify relationships between suspicious customers in anti-money laundering (AML) using social network analysis (SNA)

The stability of the economy and political system of any country highly depends on the policy of anti-money laundering (AML). If government policies are incapable of handling money laundering activities in an appropriate way, the control of the economy can be transferred to criminals. The current literature provides various technical solutions, such as clustering-based anomaly detection techniques, rule-based systems, and a decision tree algorithm, to control such activities that can aid in identifying suspicious customers or transactions. However, the literature provides no effective and appropriate solutions that could aid in identifying relationships between suspicious customers or transactions. The current challenge in the field is to identify associated links between suspicious customers who are involved in money laundering. To consider this challenge, this paper discusses the challenges associated with identifying relationships such as business and family relationships and proposes a model to identify links between suspicious customers using social network analysis (SNA). The proposed model aims to identify various mafias and groups involved in money laundering activities, thereby aiding in preventing money laundering activities and potential terrorist financing. The proposed model is based on relational data of customer profiles and social networking functions metrics to identify suspicious customers and transactions. A series of experiments are conducted with financial data, and the results of these experiments show promising results for financial institutions who can gain real benefits from the proposed model

A New Two-Parametric ‘Useful’ Fuzzy Information Measure and its Properties

A ‘useful’ fuzzy measure of order α and type β is developed. Its validity established with a numerical example