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Shield performance prediction plays a critical role in construction decision-making. However, current models suffer from significant performance degradation in long-distance prediction. To address this gap, we propose a novel Long-Distance Shield Performance Prediction model (LSPP), which leverages the long-term prediction capabilities of Informer. The LSPP model incorporates conventional monitoring data, tunnelling parameters, and stratigraphic spatial information and is optimized using a ProbSparse self-attention mechanism and dynamic decoding techniques. A series of experiments demonstrate that LSPP significantly outperforms traditional models, such as LSTM and GRUs, particularly in long-distance predictions and under conditions of stratigraphic changes. Notably, the model achieves an R2 of 0.82 when predicting penetration after six rings, making it highly accurate and stable for engineering decision-making. [ABSTRACT FROM AUTHOR]

Wind power prediction is of significant value to the stability of the power grid. Employing the Informer model for wind power prediction yields better results than traditional neural networks, yet issues such as slow speed and insufficient accuracy persist. By utilizing a health assessment algorithm to optimize the Informer model, both prediction accuracy and speed can be concurrently enhanced. Initially, a health matrix is obtained by performing a health assessment of wind turbines based on operational data. Subsequently, this health matrix is used to optimize the encoding method of the Informer, improving prediction speed. Simultaneously, the decoding method, embedding vectors and prediction process of the Informer are refined to increase prediction accuracy. Finally, conventional Informer models and optimized Informer models are tested and compared using four distinct wind power datasets. The results indicate that the optimized Informer model achieves an approximately 15% increase in prediction accuracy and about a 100% increase in prediction speed. [ABSTRACT FROM AUTHOR]

In practical applications, purely periodic data is relatively rare, and most data often exhibit non-periodic characteristics, making it challenging to predict or describe through simple periodic changes. Single neural network frequently encounters issues such as overfitting, difficulties in capturing long-term dependencies, and limited ability to capture nonlinear relationships when dealing with non-periodic time series. To effectively forecast non-periodic time series, the ILTNet model was proposed based on the Informer model. The ILTNet model integrated linear prediction (AR model) and nonlinear prediction (Informer model and recurrent skip components), enabling effective capture of long-term dependencies. Experiments show that ILTNet model demonstrates significant advantages in non-periodic time series forecasting compared to the LSTNet, Informer, AR, and GRU models. For example, on the Exchange Rate dataset, ILTNet model reduces RSE by 0.0333 and 0.0277 compared to LSTNet model at horizons of 96 and 128, respectively. Compared to Informer model, ILTNet model achieves significant RSE reductions at all horizons, particularly reducing RSE by 0.2877 at a horizon of 96. [ABSTRACT FROM AUTHOR]

The accurate prediction of marine shaft centerline trajectories is essential for ensuring the operational performance and safety of ships. In this study, we propose a novel Transformer-based model to forecast the lateral and longitudinal displacements of ship main shafts. A key challenge in this prediction task is capturing both short-term fluctuations and long-term dependencies in shaft displacement data, which traditional models struggle to address. Our Transformer-based model integrates Bidirectional Splitting–Agg Attention and Sequence Progressive Split–Aggregation mechanisms to efficiently process bidirectional temporal dependencies, decompose seasonal and trend components, and handle the inherent symmetry of the shafting system. The symmetrical nature of the shafting system, with left and right shafts experiencing similar dynamic conditions, aligns with the bidirectional attention mechanism, enabling the model to better capture the symmetric relationships in displacement data. Experimental results demonstrate that the proposed model significantly outperforms traditional methods, such as Autoformer and Informer, in terms of prediction accuracy. Specifically, for 96 steps ahead, the mean absolute error (MAE) of our model is 0.232, compared to 0.235 for Autoformer and 0.264 for Informer, while the mean squared error (MSE) of our model is 0.209, compared to 0.242 for Autoformer and 0.286 for Informer. These results underscore the effectiveness of Transformer-based models in accurately predicting long-term marine shaft centerline trajectories, leveraging both temporal dependencies and structural symmetry, thus contributing to maritime monitoring and performance optimization. [ABSTRACT FROM AUTHOR]

Nigeria is one of the most populated countries in West Africa and is in seventh position globally. The issue of terrorism has become a common problem in Nigeria, and the government has been applying local strategies to address the situation but has yet to produce good results. The challenges necessitate the effort in this paper to develop a new deterministic model to curb terrorism and insurgency through technology intelligence in Nigeria. This analysis indicates that Unmanned Aerial Vehicles (UAV) and the transmission rate per capita are the most sensitive parameters. Also pictured from the graphs in Figures 2, 3, and 4 were drones used to reduce the number of informants of both the terrorist and kidnapper individuals in Nigeria. Finally, this paper recommended the model adopted for controlling terrorism in Nigeria. [ABSTRACT FROM AUTHOR]

The variability and unpredictability of wind power generation present significant challenges for grid management and planning. Enhancing the accuracy of wind power forecasting is crucial for improving the reliability of renewable energy systems. To enhance the accuracy of temporal wind power predictions, the IVMD–DCInformer–HSSA framework has been introduced. Initially, the original wind power data is decomposed into multiple intrinsic mode function (IMF) components using the improved variational mode decomposition (IVMD) technique. Subsequently, the Sparrow search algorithm (HSSA) is employed to optimize the parameters of the enhanced Informer deep neural network, which are then integrated into the improved Informer model. The predictions of each IMF component resulting from the IVMD decomposition are then combined to generate the final prediction outcome. The experimental results show that the R‐squared value of the proposed combined model is increased to 0.9903, and the accuracy is increased by 1%–3% compared with other models, which has a good prediction effect. [ABSTRACT FROM AUTHOR]

The article discusses the concept of dirty hands in moral decision-making, particularly in politics, where individuals face choices between two evils that result in moral conflicts. Theoretical examples, such as the Loughinisland Massacre in Northern Ireland, are used to illustrate the complexities of dirty hands scenarios. The article explores the role of official apologies as a form of reparations for dirty hands, highlighting the importance of acknowledging harm, expressing remorse, and setting the record straight to restore dignity and moral standing to victims. The author argues that while dirty-handed agents may not be able to promise not to reoffend, their apologies can fulfill important social functions and contribute to reparative justice. [Extracted from the article]

As transitional justice becomes focused on transitions out of identity-based conflicts, the ability of interventions to transcend partisan approaches and be perceived as legitimate by all sides has become a key challenge. Meta-conflicts – conflicts over what the conflict was about – often obstruct efforts of dealing with the past. This note explores ways to eschew the common zero-sum game of condemning one party or the other, based on the 2024 report of the Kenova inquiry in Northern Ireland, which examined dozens of conflict-era murders relating to the operation of a British state informer in the Irish Republican Army's (IRA) counter-informing unit. Kenova's finding that ultimately both the British state and the IRA were responsible for killings of alleged informers is utilized here to draw broader implications regarding the possibility of bipartisan transitional justice, suggesting that the murky betrayals of 'dirty wars' could be an effective starting point of such efforts. [ABSTRACT FROM AUTHOR]

In order to solve the problem of complex coupling characteristics between multivariate load sequences and the difficulty in accurate multiple load forecasting for integrated renewable energy systems (IRESs), which include low-carbon emission renewable energy sources, in this paper, the TCN-FECAM-Informer multivariate load forecasting model is proposed. First, the maximum information coefficient (MIC) is used to correlate the multivariate loads with the weather factors to filter the appropriate features. Then, effective information of the screened features is extracted and the frequency sequence is constructed using the frequency-enhanced channel attention mechanism (FECAM)-improved temporal convolutional network (TCN). Finally, the processed feature sequences are sent to the Informer network for multivariate load forecasting. Experiments are conducted with measured load data from the IRES of Arizona State University, and the experimental results show that the TCN and FECAM can greatly improve the multivariate load prediction accuracy and, at the same time, demonstrate the superiority of the Informer network, which is dominated by the attentional mechanism, compared with recurrent neural networks in multivariate load prediction. [ABSTRACT FROM AUTHOR]

Objective] Because of the limited arrangement of NOx emission measurement points in CFB units, the reductant injection amount is often inaccurate. Moreover, given that the pollutant generation characteristics of the units are different under different loads, higher requirements are placed on the accurate measurement of NOx emissions. To analyze the NOx emission prediction of circulating fluidized bed units, the Informer neural network model is used to model the NOx emission of a 350 MW supercritical circulating fluidized bed unit. [Methods] First, the overview of the circulating fluidized bed unit denitration system and the Informer neural network model theory were introduced. On this basis, the data of a circulating fluidized bed unit running continuously for 50 h were obtained as sampling data, 20 parameters related to NOx emissions were determined as input characteristic parameters of the prediction model, and the relevant parameter data were standardized. Second, the simulation experiment platform and model evaluation indicators were determined, the simulation experiment steps were clarified, and the simulation experiment flowchart was drawn. On this basis, simulation experiments on 6 different NOx emission long-sequence time series predictions with prediction lengths of 12, 24, 36, 48, 72, and 96 were conducted, and regression and error analyses were performed on the simulation results of the 6 long-sequence predictions. Finally, according to the same experimental plan and the same operating data, the Transformer, RNN, and LSTM models were used to predict NOx emissions, and the prediction results of the Informer neural network model were compared with the prediction results of the three models in terms of evaluation indicators and time consumption. The comparison results of the evaluation indicators of the four models were presented in a table, and the comparison results of time consumption were presented in a bar chart. [Results] Results showed that the Informer neural network model has good feature extraction and long-sequence input abilities through the attention and distillation mechanisms. The NOx emission prediction effect of this model is significantly better than those of the Transformer, RNN, and LSTM models in terms of prediction accuracy and time consumption. When the prediction length is 24, 36, 48, 72, and 96, the evaluation index value of the Informer neural network model is the smallest, and its prediction accuracy is better than those of the three comparison models. When the prediction length is 12, the sparse attention mechanism of the Informer neural network model cannot effectively extract the periodic characteristics of the input data. The prediction accuracy of the Informer neural network model is slightly worse than that of the LSTM model but is significantly better than that of the Transformer and RNN models. The average time consumption of the Informer neural network model is lower than those of the three comparison models, and the average time consumption is reduced by 60% compared with the RNN model, 84% compared with the LSTM model, and 94% compared with the Transformer model. [Conclusions] The Informer neural network model can provide effective technical support for the prediction of NOx emissions of circulating fluidized bed units. This research serves the innovation and practical education of students majoring in energy and power at the China University of Mining and Technology, helps train students' scientific research thinking, and provides a certain reference for the development of practical teaching of energy and power majors. [ABSTRACT FROM AUTHOR]

Deep learning models have made great accomplishments in space weather forecasting. The critical frequency of the ionospheric F2 layer (foF2) is a key ionospheric parameter, which can be understood and predicted by some advanced new deep learning technologies. In this paper, we utilized an Informer architecture model to predict foF2 for several hours up to 48 h and analyzed its variations during periods of quiet, moderate, and intense geomagnetic conditions. The Informer method forecasts the temporal variations of foF2 by processing and training the past and present foF2 data from the Haikou station, China, during 2006-2014. It is evident that the Informer-foF2 model achieves better prediction performance than the widely used long short-term memory model. The Informer-foF2 model captures the correlation features within the foF2 time series and better predicts the variations ranging for hours up to days during different geomagnetic activities. [ABSTRACT FROM AUTHOR]

Time series, a type of data that measures how things change over time, remains challenging to predict. In order to improve the accuracy of time series prediction, a deep learning model CL-Informer is proposed. In the Informer model, an embedding layer based on continuous wavelet transform is added so that the model can capture the characteristics of multi-scale data, and the LSTM layer is used to capture the data dependency further and process the redundant information in continuous wavelet transform. To demonstrate the reliability of the proposed CL-Informer model, it is compared with mainstream forecasting models such as Informer, Informer+, and Reformer on five datasets. Experimental results demonstrate that the CL-Informer model achieves an average reduction of 30.64% in MSE across various univariate prediction horizons and a reduction of 10.70% in MSE across different multivariate prediction horizons, thereby improving the accuracy of Informer in long sequence prediction and enhancing the model's precision. [ABSTRACT FROM AUTHOR]

The Konfrontasi (Indonesia-Malaysia Confrontation) signaled the first combat deployment of the Australian Special Air Service Regiment (SASR). Between 1965 and 1966, two SASR squadrons were deployed and involved in secretive Claret Operations. The SASR's operations were concentrated in the jungles of Borneo, along the porous and mountainous borders of Sarawak and Indonesia's Kalimantan. The SASR had conducted reconnaissance missions collecting intelligence and track-and-ambush missions inside Indonesia's territory. The SASR was also involved in 'hearts and minds' campaigns, building trust and partnerships with local aborigines in remote jungle areas to serve as friendly informers. This article assesses the tactical and strategic impact of SASR's operations on the campaign's overarching grand strategy. The SASR's strategic value in the Konfrontasi albeit limited at the tactical level, yielded handsome strategic utility with lasting political benefits for Australia and Malaysia. This article provides a balanced evaluation of SASR's performance and lessons for contemporary special forces operations. [ABSTRACT FROM AUTHOR]

This study investigates the effectiveness of Transformer-based models for retail demand forecasting. We evaluated vanilla Transformer, Informer, Autoformer, PatchTST, and temporal fusion Transformer (TFT) against traditional baselines like AutoARIMA and AutoETS. Model performance was assessed using mean absolute scaled error (MASE) and weighted quantile loss (WQL). The M5 competition dataset, comprising 30,490 time series from 10 stores, served as the evaluation benchmark. The results demonstrate that Transformer-based models significantly outperform traditional baselines, with Transformer, Informer, and TFT leading the performance metrics. These models achieved MASE improvements of 26% to 29% and WQL reductions of up to 34% compared to the seasonal Naïve method, particularly excelling in short-term forecasts. While Autoformer and PatchTST also surpassed traditional methods, their performance was slightly lower, indicating the potential for further tuning. Additionally, this study highlights a trade-off between model complexity and computational efficiency, with Transformer models, though computationally intensive, offering superior forecasting accuracy compared to the significantly slower traditional models like AutoARIMA. These findings underscore the potential of Transformer-based approaches for enhancing retail demand forecasting, provided the computational demands are managed effectively. [ABSTRACT FROM AUTHOR]

Deformation serves as a key index to characterize the operational condition of dams. However, the prediction accuracy of deformation in dams remains limited due to the influence of multiple factors. Accordingly, this study innovatively combines the Informer with the segmented sequence decomposition and proposes a segmented sequence decomposition-Informer model (SD-Informer) for the deformation prediction of arch dams, which significantly improves the prediction accuracy and stability. The segmented sequence decomposition divides the predicted time series into annual segments and decomposes them in a segment-by-segment manner, thereby minimizing the reduction of prediction accuracy over long sequences and the boundary effects in decomposition. In addition, the Informer extracts macro- and micro-level information from deformation sequences using a multi-head attention mechanism, which significantly improves the prediction accuracy. LYX arch dam and XW arch dam, which have been in operation for more than 20 years, are taken as case studies. The results show that the performance of the SD-Informer surpasses that of wavelet neural networks, long short-term networks, and Informer, demonstrating that the SD-Informer is an accurate, robust, practical deformation prediction of arch dams for engineering applications. [ABSTRACT FROM AUTHOR]

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Accurate heat load prediction is the key to ensure the stable operation of thermal system and effective planning of thermal resources. However, current heat load forecasting methods tend to treat individual buildings as isolated entities, ignoring the temporal and spatial correlation between buildings. In this study, we propose a data-driven model based on spatiotemporal coupling to predict short-term heat load. Firstly, the spatial and temporal correlation and the correlation among features of various buildings are analyzed by using the autocorrelation function and Spearman correlation coefficient. Secondly, synchronous wavelet transform is used to eliminate high frequency noise of heat load. Secondly, the spatial and temporal characteristics of building heat load are extracted by using the improved Informer model which is concerned with multi-scale graphs. The experimental results show that the proposed model has better predictive performance than the baseline model. It provides a reference for the accurate regulation of thermal system. [ABSTRACT FROM AUTHOR]

Due to the potential for airborne radar to capture incomplete observational information regarding unmanned aerial vehicle (UAV) trajectories, this study introduces a novel approach called Node-former, which integrates neural ordinary differential equations (NODEs) and the Informer framework. The proposed method exhibits high accuracy in trajectory prediction, even in scenarios with prolonged data interruptions. Initially, data outside the acceptable error range are discarded to mitigate the impact of interruptions on prediction accuracy. Subsequently, to address the irregular sampling caused by data elimination, NODEs are utilized to transform computational interpolation into an initial value problem (IPV), thus preserving informative features. Furthermore, this study enhances the Informer's encoder through the utilization of time-series prior knowledge and introduces an ODE solver as the decoder to mitigate fluctuations in the original decoder's output. This approach not only accelerates feature extraction for long sequence data, but also ensures smooth and robust output values. Experimental results demonstrate the superior performance of Node-former in trajectory prediction with interrupted data compared to traditional algorithms. [ABSTRACT FROM AUTHOR]

This study focuses on the motor fault diagnosis facing the long-tailed distribution data, characterized by a multitude of fault types with limited data per category and the healthy state with massive data. This skewed distribution makes the traditional diagnostic models fail to identify less frequent faults. To this end, we introduce a novel fault diagnosis model, named Transformer- and gated-recurrent unit (GRU)-based network (TransGRU), to improve the diagnosis accuracy with the long-tailed distribution data. The TransGRU has two main modules, i.e. the feature extraction module and the correction module. The former is based on the Informer encoder with ProbSparse self-attention to extract features from the long-range multi-sensor data. The latter employs the GRU network addressing the long-tail effect by adjusting the diagnosis results via the gate mechanism. Besides, we informatively design an adaptive-conditional loss (ACL) function for the long-tailed fault diagnosis by integrating the properties of focal loss, class-tailored weights, and confusion weights. ACL concentrates on challenging classifications while balancing the representation and significance of various fault modes. Validation on experimental motor data confirms the capability of our TransGRU in identifying a wide range of fault types with limited fault data compared with the Transformer and state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Whistleblowers and leakers wield significant influence in technology law and policy. On topics ranging from cybersecurity to free speech, tech whistleblowers spur congressional hearings, motivate the introduction of legislation, and animate critical press coverage of tech firms. But while scholars and policymakers have long called for transparency and accountability in the tech sector, they have overlooked the significance of individual disclosures by industry insiders--workers, employees, and volunteers--who leak information that firms would prefer to keep private. This Article offers an account of the rise and influence of tech whistleblowing. Radical information asymmetries pervade tech law and policy. Firms exercise near-complete control over corporate information, shielding their activities from oversight and scrutiny by regulators and the public. Secrecy, however, begets leaks, and leaks have become the de facto source of crucial information for lawmakers, regulators, and the public. Today, whistleblowing is an important part of broader efforts to bring accountability and transparency to the tech industry. Yet existing frameworks for protecting whistleblowers are partial and haphazard. The law often permits firms to retaliate against internal critics, leakers, and organizers. The result is an informational environment shaped by selective disclosures on the part of tech whistleblowers and enormous discretion for tech firms that can choose whether and how to respond. Whistleblowing is therefore an incomplete, but still significant, source of information in the absence of meaningful, rigorous, and systematic transparency rules. In this Article, I make the case that broader protections for whistleblowing are a necessary component of systemic regulation of the tech sector. [ABSTRACT FROM AUTHOR]

Informers in the service of state secret police collaborate with authorities and thus contribute to the power of repressive regimes. Through a case-study of Communist Czechoslovakia (1945–1989)—and drawing from secret police archives– this article presents selected stories of informers who in one way or another also 'resisted' collaboration with the Czechoslovak State Security (StB). By doing so, we try to further complexify the notions of 'everyday resistance', on the one hand, and 'collaboration' on the other. We demonstrate that resistant acts, similar to collaborative acts, can be apolitically devoid of ideology, highly idiosyncratic, and motivated by private drivers. Informing can be a tool of social navigation—namely, making the most out of one's circumstances—in repressive times. Hence, resisting while informing also can be approached as a method for an individual to maximize opportunities within the overlapping incentives—both public and private, personal and professional—that contour decision-making and social action in repressive regimes. [ABSTRACT FROM AUTHOR]

Copyright of Justicia (0124-7441) is the property of Universidad Simon Bolivar and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The output power of PV systems is influenced by various factors, resulting in strong volatility and randomness, which makes it difficult to forecast. Therefore, this paper proposes an Informer prediction model based on optimised VMD for predicting short-term PV power. Firstly, the temporal coding of the Informer model is improved and, secondly, the original sequence is decomposed into multiple modal components using VMD, and then optimisation of the results of VMD in conjunction with the optimisation strategy of SSA improves the characteristics of the time series data. Finally, the refined data are fed into the Informer framework for modelling and prediction, utilising the self-attention mechanism and multiscale feature fusion of Informer to precisely forecast PV power. The power of PV prediction data from the SSA-VMD-Informer model and four other commonly used models is compared. Experimental results indicate that the SSA-VMD-Informer model performs exceptionally well in short-term PV power prediction, achieving higher accuracy than traditional methods. As an example, the results of predicting the PV power on 24 April in a region of Xinjiang are 1.3882 for RMSE, 0.8310 for MSE, 1.14 for SDE, and 0.9944 for R2. [ABSTRACT FROM AUTHOR]

The increasing deployment of industrial robots in manufacturing requires accurate fault diagnosis. Online monitoring data typically consist of a large volume of unlabeled data and a small quantity of labeled data. Conventional intelligent diagnosis methods heavily rely on supervised learning with abundant labeled data. To address this issue, this paper presents a semi-supervised Informer algorithm for fault diagnosis modeling, leveraging the Informer model's long- and short-term memory capabilities and the benefits of semi-supervised learning to handle the diagnosis of a small amount of labeled data alongside a substantial amount of unlabeled data. An experimental study is conducted using real-world industrial robot monitoring data to assess the proposed algorithm's effectiveness, demonstrating its ability to deliver accurate fault diagnosis despite limited labeled samples. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of vehicle trajectories and ensure driving safety, and considering the differences in driver behavior and the impact of these differences on vehicle trajectories, a vehicle trajectory-prediction method (DBC-Informer) based on the categorization of driver behavior is proposed: firstly, the characteristic driver feature data are extracted through data preprocessing; secondly, descriptive statistical data are obtained through the classification of the driver's behavior into categories; finally, based on the Informer model, a two-layer driver category trajectory-prediction network architecture is established, which inputs the vehicle trajectories of different driving types into independent prediction sub-networks, respectively, to realize the accurate prediction of vehicle trajectories. The experimental results show that the MAE and MSE values of trajectory prediction of the DBC-Informer model in different time domains are much smaller than those of other comparative models, and the improvement of accuracy is more obvious in the long-term domain trajectory-prediction task scenario, and the increase in prediction error of the DBC-Informer model is significantly reduced after the prediction time exceeds 1 s. The on-line behavioral categorization is achieved by comparing different categorization models; it reaches 98% in classification accuracy and, according to the results of ablation experiments, the addition of the driver behavior-classification method to the prediction model improves the accuracy of prediction in longitudinal and lateral motion by 56% and 61%, respectively, which verifies the effectiveness of the driver behavior-classification method. It can be seen that the DBC-Informer model can more accurately portray the effects of different driving behaviors on vehicle trajectories and improve the accuracy of vehicle trajectory prediction, which provides important data support for vehicle warning systems. [ABSTRACT FROM AUTHOR]

Accurately assessing the state of health (SOH) of lithium batteries is of great significance for improving battery safety performance. However, the current assessment for SOH suffers from the difficulty of selecting health features and the lack of uncertainty using data-driven methods. To this end, this paper proposes a health state assessment method for lithium-ion batteries based on health feature extraction and an improved Informer model. First, multiple features that can reflect the SOH of lithium-ion batteries were extracted from the charging and discharging time, the peak value of incremental capacity curve (ICC), and the inflection point value of differential voltage curve, etc., and the correlation between multiple health features and the health state was evaluated by gray correlation analysis. Then, an improved Informer model is proposed to establish a health state estimation method for lithium-ion batteries. Finally, the proposed algorithm is tested and validated using publicly available battery charge/discharge datasets and compared with other algorithms. The results show that the method in this paper can realize high-precision SOH prediction with a root-mean-square error (RMSE) of 0.011, and the model fit reaches more than 98%. [ABSTRACT FROM AUTHOR]

Integrated energy systems (IES) may experience energy theft through disinformation injection attacks. With electricity as the primary target, the attacks are essentially two way. They gain additional benefits by manipulating meter readings on both the consumer and supply side. Regression based deep learning models with feature engineering can be used to detect these attacks. To detect energy theft more efficiently, this paper proposes an integrated energy systems theft detection (IESTD) based on variational modal decomposition‐maximal information coefficient (VMD‐MIC) and Informer. And using kernel density estimation (KDE), the error between the predicted and true values is fitted as a probability density function (PDF).The thresholds for the confidence intervals of the PDF and the thresholds for the number of outliers are set as the detection metrics. Simulations are performed on the three generated datasets. The results show that the model has good detection accuracy and strong robustness. [ABSTRACT FROM AUTHOR]

The daily 10.7-cm solar radio flux (F10.7) is one of the most important solar activity indices and has been widely applied in various space environment modeling as a crucial parameter. In this study, we adopt a deep-learning Informer model, based on the transformer architecture to predict the medium-term F10.7 index, which uses 48 historical daily F10.7 indices as input to directly forecast the following 1 – 27 days' F10.7 index. The model is demonstrated to be effective and to have superior performance compared with other widely-used forecasting techniques: two statistical methods provided by British Geological Survey (BGS), Space Weather Prediction Center (SWPC), and a multiflux neural network method provided by Collecte Localisation Satellites (CLS). In comparison, the Informer model significantly improves the forecast accuracy for the prediction horizon larger than 6 days, especially during the solar activity descending phase and at the solar activity minimum. For its effectiveness, accurate prediction capability and the advantage in F10.7 forecasting with longer horizon, the Informer could be potentially used as a candidate model for space weather operational forecasting. [ABSTRACT FROM AUTHOR]

In this study, a new short‐term wind power prediction model based on a temporal convolutional network (TCN) and the Informer model is proposed to solve the problem of low prediction accuracy caused by large wind speed fluctuations in short‐term prediction. First, an input feature selection method based on the maximum information coefficient is proposed after considering the problem of information interference caused by excessively large input features. A dynamic time planning method is used to select the optimal input step of historical power. Then, the combined forecasting model composed of TCN and the Informer is constructed in accordance with the numerical weather forecast and historical power data. Lastly, the pinball loss function is used to expand the prediction model into a quantile regression model, measure the effect of volatility, quantify the volatility range of prediction, and finally, obtain a deterministic prediction result. The actual measured data of wind farms in the Bohai Sea area are selected for analysis and calculation. The results show that the prediction model proposed in this study achieves better accuracy in deterministic prediction and interval prediction than the traditional model. [ABSTRACT FROM AUTHOR]

Time series anomaly detection is very important to ensure the security of industrial control systems (ICSs). Many algorithms have performed well in anomaly detection. However, the performance of most of these algorithms decreases sharply with the increase in feature dimension. This paper proposes an anomaly detection scheme based on Graph Attention Network (GAT) and Informer. GAT learns sequential characteristics effectively, and Informer performs excellently in long time series prediction. In addition, long-time forecasting loss and short-time forecasting loss are used to detect multivariate time series anomalies. Short-time forecasting is used to predict the next time value, and long-time forecasting is employed to assist the short-time prediction. We conduct a large number of experiments on industrial control system datasets SWaT and WADI. Compared with most advanced methods, we achieve competitive results, especially on higher-dimensional datasets. Moreover, the proposed method can accurately locate anomalies and realize interpretability. [ABSTRACT FROM AUTHOR]

Building on recent musicological research that emphasizes the mobility of songs, this article investigates Marseille as a site of cultural encounter and highlights the musical presence of enslaved galley rowers and translators in the city. It focuses on several descriptions of music-making in southern France by the French antiquarian Nicolas-Claude Fabri de Peiresc and his efforts to find materials on Ottoman and Persian music for the music theorist Marin Mersenne. Utilizing the constant influx of goods and people into the city, Peiresc relied on mercantile networks to obtain transcriptions of songs and music theoretical texts, engaging various informers and translators from the eastern Mediterranean. Focusing on a transcription of a single song that has been lost, the article traces the song's connections to cultures of collection, cultural translation, and transcription, with singing constituting a central site in which to negotiate and perform otherness. [ABSTRACT FROM AUTHOR]

Since the traditional transformer fault diagnosis method based on dissolved gas analysis (DGA) is challenging to meet today's engineering needs, this paper proposes a multi-model fusion transformer fault diagnosis method based on TimesNet and Informer. First, the original TimesNet structure is improved by adding the MCA module to the Inception structure of the original TimesBlock to reduce the model complexity and computational burden; second, the MUSE attention mechanism is introduced into the original TimesNet to act as a bridge, so that associations can be carried out effectively among the local features, thus enhancing the modeling capability of the model; finally, when constructing the feature module, the TimesNet and Informer multilevel parallel feature extraction modules are introduced, making full use of the local features of the convolution and the global correlation of the attention mechanism module for feature summarization, so that the model learns more time-series information. To verify the effectiveness of the proposed method, the model is trained and tested on the public DGA dataset, and the model is compared and experimented with classical models such as Informer and Transformer. The experimental results show that the model has a strong learning ability for transformer fault data and has an advantage in accuracy compared with other models, which can provide a reference for transformer fault diagnosis. [ABSTRACT FROM AUTHOR]

Sándor Joó (1910-1970) was a well-known pastor of the Reformed Church of Hungary. He managed to develop an affiliate into a full-right parish in one of the well-to-do districts of Budapest and built a new church for his parish as well. Reverend Joó up to date is one of the most read religious authors. He took some part in the anti-Nazi resistance, provided proofs of babtism for those who turned to his parish and offered shelter to some notable leftist politicians as well. Nonetheless, after the war he became a target of the Communist State Security. In my paper, I would like to focus on his case after the 1956 revolution. How should we approach the state security files? What were the methods of the state security to „manage reality"? What were their preconceptions? And finally, what is the meaning of „informer"?. [ABSTRACT FROM AUTHOR]

The IRA'S 'aspiration to act like a state shaped a particular form of underground penalty: emulating state criminal justice, relying on a discourse of treason, and the specific nature of punishment these entail' (p.110). However, in the pages that follow the author repeats the same quote from a senior IRA man 'I would say the IRA let go five times more[informers] than they shot' (p.15, p. 68). , Oxford: Oxford University Press, 2022; 239 pp.: ISBN 978-0-19-875940-9, £80.00 (Hardback) Ron Dudai presents us with a detailed study of the Provisional IRA's response to informers during, and after, the Northern Ireland Troubles. [Extracted from the article]

The phenomenon of transitional justice appears when authoritarian regimes transform into democracies—a topic that has been studied for many years. One main focus has been the question of the responsibility of the former ruling elite and their subordinates, and in particular, criminal responsibility. After the collapse of communism in Europe, secret police informers were perceived as sharing responsibility, alongside the regime's functionaries. The first steps toward bringing them to justice were taken in March 1990 in Czechoslovakia. The process was called "lustration," harking back to an ancient tradition of cleansing newborns of evil. In the jargon of the Czechoslovak security apparatus, "lustrace" meant checking an individual's secret police records. The best known example of lustration took place in Germany, but the basic idea was carried out in all post-communist Central European countries. This kind of transitional justice was not associated with criminal responsibility, and—except in Poland—the courts were not involved in lustration, which was conceived of as a purely administrative procedure. After the collapse of the Soviet Union, only the Baltic states adopted lustration legislation; in other post-Soviet states, such initiatives failed. A similar situation emerged when Yugoslavia disintegrated, where the ethnic wars overshadowed society's memories of communist wrongdoing. Lustration was unique to post-communist states and was not seen in any other transitional context. [ABSTRACT FROM AUTHOR]

For those who have speculated about the behaviour of the British people under Nazi rule, the Channel Islands have sometimes been used as a proxy, from which evidence can be selected to hypothesise about a Nazi-occupied Britain. In the area of collaboration, in particular, the historiography of the Channel Islands has been a victim of this flawed Anglo-centric approach. Looking at the evidence for the Channel Islands in their own right, this article looks at informing, as one of the most damaging and betterdocumented forms of collaboration, and asks: what kind of people were informers, what were their motives, and were they an integral part of a self-policing Nazi terror state? How did British Intelligence respond to informers on both professional and personal levels, and with what success did they investigate collaboration after the liberation of the Islands in May 1945? And how has the problematic evidence for informing, and by extension all collaboration, impacted perceptions of the Channel Islands under occupation? [ABSTRACT FROM AUTHOR]

Wind power energy is green, clean, and renewable, which is random and volatile. The integration of unstable wind energy severely threatens the security and constant operation of the power system. The need to enhance the reliability of wind power grid integration, mitigate the impact of wind power uncertainty, and develop a robust prediction model has become a pressing issue. However, only some people have considered the correlations among the power of multiple adjacent wind turbine arrays. In this paper, we propose GCNInformer to construct these relationships. Furthermore, we analyze the relationships among multiple features of individual wind turbines. GCNInformer is composed of two main components. The first component employs a graph convolutional network (GCN) to establish relationships among multiple wind turbine arrays, enhancing the correlation of the data. The second part employs Informer to extract the time information from the data and predict long‐term sequences. For training and testing, GCNInformer utilizes two data sets: Data_CQ and Data_DL. The evaluation of the model's performance is conducted using various metrics such as mean absolute percentage error, mean absolute error, root mean square error, and mean square error. Numerous experimental findings have validated the effectiveness of the GCNInformer. [ABSTRACT FROM AUTHOR]

Wind energy, as a kind of environmentally friendly renewable energy, has attracted a lot of attention in recent decades. However, the security and stability of the power system is potentially affected by large-scale wind power grid due to the randomness and intermittence of wind speed. Therefore, accurate wind speed prediction is conductive to power system operation. A hybrid wind speed prediction model based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Multiscale Fuzzy Entropy (MFE), Long short-term memory (LSTM) and INFORMER is proposed in this paper. Firstly, the wind speed data are decomposed into multiple intrinsic mode functions (IMFs) by ICEEMDAN. Then, the MFE values of each mode are calculated, and the modes with similar MFE values are aggregated to obtain new subsequences. Finally, each subsequence is predicted by informer and LSTM, each sequence selects the one with better performance than the two predictors, and the prediction results of each subsequence are superimposed to obtain the final prediction results. The proposed hybrid model is also compared with other seven related models based on four evaluation metrics under different prediction periods to verify its validity and applicability. The experimental results indicate that the proposed hybrid model based on ICEEMDAN, MFE, LSTM and INFORMER exhibits higher accuracy and greater applicability. [ABSTRACT FROM AUTHOR]

It was aimed to investigate the prevalence of intestinal parasites in tumbler pigeons reared in Kırıkkale. One hundred and five stool samples were obtained as one sample from each cage reached. The fresh stool samples collected were taken separately in containers with caps and delivered to laboratory within approximately 2 hours. Stool samples were analyzed by Carbolfuchsin, Native-Lugol staining and Fulleborn flotation techniques. Eimeria spp. oocysts were sporulated in 2.5% potassium dichromate and the species were identified. Parasite species were found in 82.9% of the examined pigeon stool. In the study, Heterakis spp., Ascaridia spp., Capillaria spp., Strongyle type eggs and Eimeria spp., and Cryptosporidium spp. oocysts were detected. The number of feces infected with one parasite (32.4%) species was higher than the number of feces infected with two (22.9%), three (19.0%) and four species (8.6%). While helminth+protozoan mixed infections were detected in 40.9%, helminth parasite eggs in 13.3% and protozoan oocysts were found alone in 28.6% of the stools examined. All of the sporulated Eimeria oocysts were identified as E. labbeana. In this study, intestinal parasites were detected at a high rate in pigeons fed for hobby purposes. It has been revealed that animal owners should be informed about the issue, attention should be paid to the cleanliness and hygiene of pigeon cages, and more importance should be given to the diagnosis and treatment of intestinal parasites in these animals in order to reduce the prevalence of parasitic infections in pigeons in the region. [ABSTRACT FROM AUTHOR]

Tool wear (TW) is the gradual deterioration and loss of cutting edges due to continuous cutting operations in real production scenarios. This wear can affect the quality of the cut, increase production costs, reduce workpiece accuracy, and lead to sudden tool breakage, affecting productivity and safety. Nevertheless, since conventional tool wear monitoring (TWM) approaches often employ complex physical models and empirical rules, their application to complex and non-linear manufacturing processes is challenging. As a result, this study presents a TWM model using a convolutional neural network (CNN), an Informer encoder, and bidirectional long short-term memory (BiLSTM). First, local feature extraction is performed on the input multi-sensor signals using CNN. Then, the Informer encoder deals with long-term time dependencies and captures global time features. Finally, BiLSTM captures the time dependency in the data and outputs the predicted tool wear state through the fully connected layer. The experimental results show that the proposed TWM model achieves a prediction accuracy of 99%. It is able to meet the TWM accuracy requirements of real production needs. Moreover, this method also has good interpretability, which can help to understand the critical tool wear factors. [ABSTRACT FROM AUTHOR]

4 Thus, Lewis' book is enthralling, not just for analyzing the STASI, but for the questions and perspectives it allows the readership to generate and use when thinking about and analyzing other state security agencies and other forms of collaboration. Lewis discusses how STASI informants had to develop "usable selves" by "cobbling together makeshift identities" simply to survive in the GDR - and in the STASI. Even though it has been over 30 years since the end of the German Democratic Republic (GDR), it continues to exert fascination and importance in various areas of scholarship. [Extracted from the article]

This article deals with the communicative interactions between actors as a crucial epistemological moment. In particular, it analyses the information exchanges and negotiations between the Polish section of the US-American Cold War broadcaster Radio Free Europe (RFE) and its Polish informants as go-betweens in the 1950s to the early 1970s. Framing these interactions as intimate epistemic economies, the author pays special attention to questions of intimacy, confidentiality, testimony, and trust as well as epistemic uncertainty regarding the identity of actors and the content of messages. Furthermore, following recent scholarship, I investigate the boundaries between the history of science and the history of knowledge by interrogating about the role of subjectivity, ignorance, error, and failure in knowledge making. Finally, two main case studies are used to exemplify the tension between different types of knowledge including rumour and gossip, in the light of the epistemic realm of un/knowledgeability in which the US and Polish secret services operated. [ABSTRACT FROM AUTHOR]