ASPECTS OF TEMPERATURE TAKING INTO ACCOUNT TO INCREASE THE ACCURACY OF SHORT-TERM FORECASTING OF NODE LOADS

The peculiarities of the influence of air temperature data on the accuracy of forecasting of nodal loads in power systems and how the accuracy of such forecasting changes depending on the training sample and its volume are considered. The application of the data analysis method to detect anomalous values ​​and omissions to reduce data distortion and improve forecasting results is considered. A neural network of deep learning of the LSTM type was used for multifactor prediction of nodal loads. To evaluate the effectiveness of the forecast accuracy, various variants of data samples for neural network training are considered.

Overallocation and Correction of Carbon Emissions in the Evaluation of Carbon Footprint

This paper points to several carbon footprint data distortions that overallocate carbon footprints to individual companies, and to several carbon data intricacies that lead to improved data integrity. Data distortion due to the same company being listed in multiple geographical jurisdictions or through different share classes overstates Emissions Scope 1 by 4.6%, Emissions Scope 2 by 5.5%, Emissions Scope 3 by 10.6% and Reserves by 6.0%. Data distortion due to index construction by having different market capitalization in representative indices overallocates Emissions Scope 1 by 33.9%, Emissions Scope 2 by 27.6%, Emissions Scope 3 by 21.3% and Reserves by 57.2%. A significant amount of carbon data is not precise but is estimated by third-party providers through proprietary techniques. The estimated data for Scope 1 Emissions is 46.4% for the companies in the index. In addition, carbon data is stale, resulting in 94.5% of data being two years old or more. Usage of carbon data in a present format may incorrectly remove some companies from portfolios (negative screen, complete removal) or incorrectly reduce some companies’ weight in a portfolio (partial screen, fractional removal).

An Open-Source Wireless Sensor Node Platform with Active Node-Level Reliability for Monitoring Applications

In wireless sensor networks, the quality of the provided data is influenced by the properties of the sensor nodes. Often deployed in large numbers, they usually consist of low-cost components where failures are the norm, even more so in harsh outdoor environments. Current fault detection techniques, however, consider the sensor data alone and neglect vital information from the nodes’ hard- and software. As a consequence, they can not distinguish between rare data anomalies caused by proper events in the sensed data on one side and fault-induced data distortion on the other side. In this paper, we contribute with a novel, open-source sensor node platform for monitoring applications such as environmental monitoring. For long battery life, it comprises mainly low-power components. In contrast to other sensor nodes, our platform provides self-diagnostic measures to enable active node-level reliability. The entire sensor node platform including the hardware and software components has been implemented and is publicly available and free to use for everyone. Based on an extensive and long-running practical experiment setup, we show that the detectability of node faults is improved and the distinction between rare but proper events and fault-induced data distortion is indeed possible. We also show that these measures have a negligible overhead on the node’s energy efficiency and hardware costs. This improves the overall reliability of wireless sensor networks with both, long battery life and high-quality data.

Component Contribution to the Total Reliability of the WAMS Network

Nowadays, the control of power systems relies on wide-area monitoring and control system (WAMS), which continuously measures and registers state vector values and is synchronized by signals from the uniform time system. A significant part of this system is the local information network, whose reliability largely determines the proper functioning of WAMS. One can assess the said reliability by dividing it into components. These are hardware or technical reliability associated with failure (destruction) of transmission channel elements or the integrity of communication lines, traffic reliability determined by time loss or data distortion without failure of a transmission channel element, software reliability related to errors in the development of exchange execution programs, and resilience against an external deliberate impact on the transmitted information. This paper addresses the assessment of the first three reliability components of the information network, shows its total value, and estimates the contribution of each component. The last component (resistance to an external deliberate action) is described in a huge number of works, which is why it is not considered in this paper.

Optimized Immunohistochemical Detection of Rat ESR2 Proteins Using the Specific Anti-ESR2 Monoclonal Antibody PPZ0506

Background: Research on ESR2, also known as estrogen receptor β (ERβ), is a notorious example of data distortion due to the use of inadequately validated antibodies. Although the absence of reliable specific antibodies against ESR2 has severely hindered the promotion of ESR2 research, a specific anti-human ESR2 monoclonal antibody (PPZ0506) was identified in 2017 [1]. Our previous study confirmed its cross-reactivity and specificity against rodent ESR2 proteins, enabling the elucidation of the true ESR2 distribution in rodents [2]. Objective: We aimed to determine the optimized conditions for immunohistochemical detection of rat ESR2 proteins using PPZ0506. <Method> Several staining conditions using paraffin-embedded and frozen ovary sections were evaluated, and the distribution of rat ESR2 proteins was analyzed under optimal conditions. Result: Immunohistochemical staining with PPZ0506 required appropriate antigen retrieval and antibody dilution. Subsequent immunohistochemical analysis in multiple tissues under optimized conditions revealed that rat ESR2 proteins are expressed in a more localized manner than previously assumed. Our optimized immunohistochemical detection of rat ESR2 proteins, using a well-validated antibody, revealed their distribution in limited tissues and cell types. Conclusion: Our results suggest that previous immunohistochemical studies using inadequately validated antibodies against ESR2 proteins overestimated their distribution profiles. We expect that our optimized immunohistochemistry using the PPZ0506 antibody may solve conflicting problems in ESR2 research. References: 1. Andersson S, et al. Nat Commun 15;8:15840 (2017) 2. Ishii H, et al. Int J Mol Sci 20(24):6312 (2019)

A Novel Method to Estimate the Sea State for Recycling Work on the Sea Surface

The recycling of marine exploration equipment after it has surfaced is greatly affected by sea state. In order to estimate the sea state in real time, this paper proposes a method for measuring wave elevation, which modifies the integrated results of GNSS/SINS in the up direction by virtual horizontal lines to extract wave fluctuation information. From these wave information, the significant wave heights (SWH) can be calculated as the only input parameter of P-M spectrum, and a series of wave height data can be further simulated. When the GNSS is interrupted due to severe sea state, the simulated data can be integrated with the SINS to deal with the data distortion problem. The simulation results show that the application of wave spectrum in the GNSS intermittent situation has obvious improvement effect and important significance.

ABOUT INCORRECT AND UNREPRESENTATIVE CITATION IN THE REVIEW “HIGH THERMAL CONDUCTIVE COPPER/DIAMOND COMPOSITES: STATE OF THE ART” BY S.J. JIA, F. YANG AND IN OTHER PAPERS

Several papers published in Elsevier and Springer journals in 2020–2021, in which there were numerous violations of standards concerning citation practice in scientific papers, are analyzed. These papers deal with high-thermal-conductive diamond/metal composites. The examples under consideration mainly concern composites prepared via deposition of tungsten coatings onto diamond particles. The revealed errors and distortions in citations are indicated and classed. The examples given may be useful to a wide range of readers (authors, reviewers, editors) as illustrations of the data distortion encountered in scientific publications due to incorrect citation

A Robust and Reversible Watermarking Algorithm for a Relational Database Based on Continuous Columns in Histogram

Due to the discreteness of integer data, there are a large number of gaps and continuous columns in the histogram based on integer data. Aiming at the characteristics, this paper presents a robust and reversible watermarking algorithm for a relational database based on continuous columns in histogram. Firstly, it groups the database tuples according to the watermark length and the grouping key. Secondly, it calculates the prediction errors and uses the absolute values of the prediction errors to construct the histogram. Thirdly, it traverses the histogram to find all the continuous columns and in turn, computes the sum of the height of each continuous column and selects the group of continuous columns that has the largest sum as the positions to embed the watermarks. FCTD (Forest cover type data set) is utilized for experimental verification. A large amount of experimental data shows that the method is effective and robust. Not only does the data distortion caused by shifting histogram columns not exist, but the robustness of the watermark is also greatly improved.

A reversible database watermarking method non-redundancy shifting-based histogram gaps

In relational databases, embedding watermarks in integer data using traditional histogram shifting method has the problem of large data distortion. To solve this problem, a reversible database watermarking method without redundant shifting distortion is proposed, taking advantage of a large number of gaps in the integer histogram. This method embeds the watermark bit by bit on the basis of grouping. First, an integer data histogram is constructed with the absolute value of the prediction error of the data as a variable. Second, the positional relationship between each column and the gap in the histogram is analyzed to find out all the columns adjacent to the gap. Third, the highest column is selected as the embedded point. Finally, a watermark bit is embedded on the group by the histogram non-redundant shifting method. Experimental results show that compared with existing reversible database watermarking methods, such as genetic algorithm and histogram shift watermarking and histogram gap–based watermarking, the proposed method has no data distortion caused by the shifting redundant histogram columns after embedding watermarks on forest cover type data set and effectively reduces the data distortion rate after embedding watermarks.