Numerous Features Research Articles

Genitive Case in Kurdish and English Language

Linguistic and grammatical correctness, both in their production and when they are categorized under specialized grammar, involve numerous features unique to each language and the technical intricacies of its grammar. One such feature encompasses processes related to the genitive case, encompassing the nuanced details of genitive attribution, possession, indicators of possession, and the presence or absence of these indicators in their respective contexts. This research delves into the Genitive Case in Kurdish and English, elucidating their positions, commonalities, and distinctions. The methodology employed throughout this study is primarily analytical and comparative, wherein initial observations and insights about the phenomenon are presented, followed by a thorough analysis and demonstration of the genitive case in both Kurdish and English grammar.

Legal liability: seven doctrinal approaches to understanding its essence

The article provides a critical analysis of the civil law doctrine of understanding the “legal liability” category. Seven main approaches to the interpretation of the category under study are identified and their advantages and disadvantages are highlighted. The author concludes that neither current legislation nor legal doctrine is consistent on the issue under study.
 The author emphasises that the doctrine of legal theory distinguishes two types of legal liability: positive (relating to an offence not yet committed) and negative (relating to an offence already committed). The critical analysis of this approach is that such different legal phenomena as proper performance of duties and liability for their violation cannot be covered by the same legal concept – “liability”. As a result, the author makes a reservation that positive responsibility can be spoken of as a type of social liability, but not legal one. Thus, in the context of civil relations, legal liability should be referred to as negative liability. Positive liability is rather a moral phenomenon, which consists in the subject's conscientious (responsible) fulfilment of all legal principles.
 Based on the analysis of the doctrinal approaches to understanding the category of “legal liability” and their critical assessment in the legal literature, it has been noted that it is inappropriate to formulate a universal (generally accepted) definition of the concept under study today. This assertion is based on the fact that in many cases, experts seek to combine numerous and diverse features of legal liability into a definition which leaves out specific manifestations of various aspects of its phenomenon.
 It has been proved that the current doctrinal approaches to the understanding of legal liability can be reduced to two main areas: its study as a form of state coercion/influence or as a protective legal relationship.

Diverse Feature-Level Guidance Adjustments for Unsupervised Domain Adaptative Object Detection

Unsupervised Domain Adaptative Object Detection (UDAOD) aims to alleviate the gap between the source domain and the target domain. Previous methods sought to plainly align global and local features across domains but adapted numerous pooled features and overlooked contextual information, which caused incorrect perceptions of foreground information. To tackle these problems, we propose Diverse Feature-level Guidance Adjustments (DFGAs) for two-stage object detection frameworks, including Pixel-wise Multi-scale Alignment (PMA) and Adaptative Threshold Confidence Adjustment (ATCA). Specifically, PMA adapts features within diverse hierarchical levels to capture sufficient contextual information. Through a customized PMA loss, features from different stages of a network facilitate information interaction across domains. Training with this loss function contributes to the generation of more domain-agnostic features. To better recognize foreground and background samples, ATCA employs adaptative thresholds to divide the foreground and background samples. This strategy flexibly instructs the classifier to perceive the significance of box candidates. Comprehensive experiments are conducted on Cityscapes, Foggy Cityscapes, KITTI, and Sim10k datasets to further demonstrate the superior performance of our method compared to the baseline method.

Automatic and real-time tissue sensing for autonomous intestinal anastomosis using hybrid MLP-DC-CNN classifier-based optical coherence tomography.

Anastomosis is a common and critical part of reconstructive procedures within gastrointestinal, urologic, and gynecologic surgery. The use of autonomous surgical robots such as the smart tissue autonomous robot (STAR) system demonstrates an improved efficiency and consistency of the laparoscopic small bowel anastomosis over the current da Vinci surgical system. However, the STAR workflow requires auxiliary manual monitoring during the suturing procedure to avoid missed or wrong stitches. To eliminate this monitoring task from the operators, we integrated an optical coherence tomography (OCT) fiber sensor with the suture tool and developed an automatic tissue classification algorithm for detecting missed or wrong stitches in real time. The classification results were updated and sent to the control loop of STAR robot in real time. The suture tool was guided to approach the object by a dual-camera system. If the tissue inside the tool jaw was inconsistent with the desired suture pattern, a warning message would be generated. The proposed hybrid multilayer perceptron dual-channel convolutional neural network (MLP-DC-CNN) classification platform can automatically classify eight different abdominal tissue types that require different suture strategies for anastomosis. In MLP, numerous handcrafted features (∼1955) were utilized including optical properties and morphological features of one-dimensional (1D) OCT A-line signals. In DC-CNN, intensity-based features and depth-resolved tissues' attenuation coefficients were fully exploited. A decision fusion technique was applied to leverage the information collected from both classifiers to further increase the accuracy. The algorithm was evaluated on 69,773 testing A-line data. The results showed that our model can classify the 1D OCT signals of small bowels in real time with an accuracy of 90.06%, a precision of 88.34%, and a sensitivity of 87.29%, respectively. The refresh rate of the displayed A-line signals was set as 300 Hz, the maximum sensing depth of the fiber was 3.6 mm, and the running time of the image processing algorithm was ∼1.56 s for 1,024 A-lines. The proposed fully automated tissue sensing model outperformed the single classifier of CNN, MLP, or SVM with optimized architectures, showing the complementarity of different feature sets and network architectures in classifying intestinal OCT A-line signals. It can potentially reduce the manual involvement of robotic laparoscopic surgery, which is a crucial step towards a fully autonomous STAR system.

Learning Not to Regret

The literature on game-theoretic equilibrium finding predominantly focuses on single games or their repeated play. Nevertheless, numerous real-world scenarios feature playing a game sampled from a distribution of similar, but not identical games, such as playing poker with different public cards or trading correlated assets on the stock market. As these similar games feature similar equilibra, we investigate a way to accelerate equilibrium finding on such a distribution. We present a novel ``learning not to regret'' framework, enabling us to meta-learn a regret minimizer tailored to a specific distribution. Our key contribution, Neural Predictive Regret Matching, is uniquely meta-learned to converge rapidly for the chosen distribution of games, while having regret minimization guarantees on any game. We validated our algorithms' faster convergence on a distribution of river poker games. Our experiments show that the meta-learned algorithms outpace their non-meta-learned counterparts, achieving more than tenfold improvements.

Covering variable precision fuzzy rough sets based on overlap functions and the application to multi-label classification

Fuzzy rough sets (FRSs) play a significant role in the field of data analysis, and one of the common methods for constructing FRSs is the use of the fuzzy logic operators. To further extend FRSs theory to more diverse information backgrounds, this article proposes a covering variable precision fuzzy rough set model based on overlap functions and fuzzy β-neighbourhood operators (OCVPFRS). Some necessary properties of OCVPFRS have also been studied in this work. Furthermore, multi-label classification is a prevalent task in the realm of machine learning. Each object (sample or instance) in multi-label data is associated with various labels (classes), and there are numerous features or attributes that need to be taken into account within the attribute space. To enhance various performance metrics in the multi-label classification task, attribute reduction is an essential pre-processing step. Therefore, according to overlap functions and fuzzy rough sets’ excellent work on applications: such as image processing and multi-criteria decision-making, we establish an attribute reduction method suitable for multi-label data based on OCVPFRS. Through a series of experiments and comparative analysis with existing multi-label attribute reduction methods, the effectiveness and superiority of the proposed method have been verified.

Abstract 7102: The central dogma of hepatocellular carcinoma: Genomic, transcriptomic, and proteomic changes

Abstract Cancer is a disease condition characterized by remarkable heterogeneity, wherein numerous molecular features display considerable disparities even with the same patient cohorts, leading to varied treatment outcomes. The primary factor contributing to the substantial differences observed across cancers stems from unique combinations of genetic mutations. Epigenetic alterations further contribute to differential gene expression and splicing, thereby enhancing transcriptomic diversity. The protein products originating from the cancer transcriptome constitute the specific intracellular and extracellular environment, consequently exerting supplementary effects on epigenetic, transcriptomic, and proteomic changes. The complex interplay and mutual influence between genetic aberrations, transcriptomes, and proteomes significantly contribute to shaping the extensive spectrum of molecular characteristics observed in cancer. With label-free mass spectrometry, RNA-sequencing, and low-pass whole genome sequencing, the quantification of the proteome, transcriptome, and genetic abbreviation of the cancer can be evaluated. With the mouse hydrodynamic tail vein injection (HDVI) models of hepatocellular carcinoma (HCC), different subtypes of HCC derived from different genetic abbreviations can be generated. With the proteome, transcriptome, and genetic abbreviation data of the better-defined HCC from HDVI models and HCC patients, the patient-derived HCC can be classified according to the origin of the genetic abbreviation used in the generation of the HDVI-derived HCC. Furthermore, the proteome of patient-derived plasma will be analyzed, and the signature change unique to HCC classified by different origins of genetic abbreviation will provide an abstract of the proteome, transcriptome, and genetic abbreviation of the HCC tissue. The transcriptomics and proteomics data of HDVI mice produced from five distinct sets of genetic abbreviations have been measured. The proteome and transcriptome of tissue, as well as the proteome of plasma, from a small cohort of HCC patients were studied and classified using this resource to illuminate the fundamental landscape of HCC. The final results will be presented and discussed at the meeting. Citation Format: Hoi Tang MA, Chun Yin YU, Lau Yan NG. The central dogma of hepatocellular carcinoma: Genomic, transcriptomic, and proteomic changes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7102.

Abstract 3894: The temporal influence of the tumor microenvironment in response to checkpoint blockade

Abstract Immune checkpoint inhibition (ICI) has produced a paradigm shift in treatment across a range of cancer subtypes. However, our understanding of why only certain patients respond, as well as our ability to predict this response, is still quite limited. This is exacerbated by the complexity of the tumor microenvironment (TME), which contains cancer cells, immune cells, and stromal cells. These differing cell types can have context-dependent roles in either promoting or inhibiting anti-tumor immune responses, making it challenging to identify features which reliably correlate with patient outcome. In addition, the roles of these diverse cell types are often influenced by their location within the TME, meaning that assays without spatial information cannot fully resolve this complexity. To address these gaps, we mapped the spatial distribution and phenotype of 21 cell populations across 117 patients with metastatic triple negative breast cancer that received nivolumab (anti-PD1) through the TONIC clinical trial (NCT02499367). We collected metastatic samples prior to and during treatment to understand how anti-PD1 reshaped the TME. We also collected archival material from the original primary tumor for each patient. We generated multiplexed imaging data from these samples, identified the location and phenotype of each cell, and then quantified the spatial distribution, diversity, and functional marker status of all cell populations in the TME. We detected numerous features indicative of a productive immune response that correlated positively with clinical benefit, whereas features indicative of a suppressive microenvironment correlated negatively with clinical benefit. We observed significant temporal effects, where many pre-treatment predictive features were not predictive on-treatment; similarly, many on-treatment predictive features had no predictive power prior to treatment. Intriguingly, we found features in the original primary tumors which predicted subsequent (metastatic) response to immunotherapy. We observed substantial differences in predictive accuracy of multivariate models based on timepoint, with on-treatment samples exhibiting the best performance and primary tumor samples exhibiting the worst. Of note, we observed substantially worse predictive performance when using bulk DNA sequencing data to predict outcome from the same patients, highlighting the importance of spatial information. Taken together, we show that the features associated with a productive anti-tumor immune response are temporally structured, and differ based on what phase of response is profiled. Furthermore, using the primary tumor to assess whether metastatic TNBC patients are good candidates for ICI may miss important predictive signals. These findings shed new light on the determinants of ICI outcome, and may shape the design of subsequent trials to better understand the temporal aspects of ICI. Citation Format: Noah F. Greenwald, Iris Nederlof, Hugo Horlings, Marleen Kok, Christina Curtis, Michael Angelo. The temporal influence of the tumor microenvironment in response to checkpoint blockade [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3894.

Applying green learning to regional wind power prediction and fluctuation risk assessment

Deep Learning (DL) models, such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), have been widely used to predict the intermittency of wind power; however, the non-linear activation functions and backpropagation mechanisms in DL models increase computational complexity and energy consumption. This paper proposes a prediction model based on Green Learning (GL) to reduce energy consumption. The proposed GL model replaces the feature extraction of activation functions with a hybrid feature extraction approach combining categorical and numerical features. We also employ cluster centroids and quantile regression forest for classification/regression to eliminate the need for backpropagation in optimizing hyperparameters. Using Taiwan as a case study, this paper evaluates the risk of fluctuations in regional wind power generation in 2030. In simulations, the proposed GL model achieved excellent accuracy with energy consumption significantly lower than that of DL models. Our analysis also revealed that by 2030, fluctuations in wind power generation during the winter will exceed 40% of the peak supply capacity in the central region, indicating the need to enhance the resilience of regional power systems.

On relevant features for the recurrence prediction of urothelial carcinoma of the bladder

BackgroundUrothelial bladder cancer (UBC) is characterized by a high recurrence rate, which is predicted by scoring systems. However, recent studies show the superiority of Machine Learning (ML) models. Nevertheless, these ML approaches are rarely used in medical practice because most of them are black-box models, that cannot adequately explain how a prediction is made. ObjectiveWe investigate the global feature importance of different ML models. By providing information on the most relevant features, we can facilitate the use of ML in everyday medical practice. Design, setting, and participantsThe data is provided by the cancer registry Rhineland-Palatinate gGmbH, Germany. It consists of numerical and categorical features of 1,944 patients with UBC. We retrospectively predict 2-year recurrence through ML models using Support Vector Machine, Gradient Boosting, and Artificial Neural Network. We then determine the global feature importance using performance-based Permutation Feature Importance (PFI) and variance-based Feature Importance Ranking Measure (FIRM). ResultsWe show reliable recurrence prediction of UBC with 82.02% to 83.89% F1-Score, 83.95% to 84.49% Precision, and an overall performance of 69.20% to 70.82% AUC on testing data, depending on the model. Gradient Boosting performs best among all black-box models with an average F1-Score (83.89%), AUC (70.82%), and Precision (83.95%). Furthermore, we show consistency across PFI and FIRM by identifying the same features as relevant across the different models. These features are exclusively therapeutic measures and are consistent with findings from both medical research and clinical trials. ConclusionsWe confirm the superiority of ML black-box models in predicting UBC recurrence compared to more traditional logistic regression. In addition, we present an approach that increases the explanatory power of black-box models by identifying the underlying influence of input features, thus facilitating the use of ML in clinical practice and therefore providing improved recurrence prediction through the application of black-box models.

Geomorphology and mechanisms of subduction erosion in the sediment-starved Mariana convergent margin

Subduction erosion, commonly occurring in convergent margins, serves as a key mechanism for effective recycling of materials into the deep subduction. Subduction of bathymetric highs is widely recognized as a critical factor of subduction erosion of the forearc. The Mariana margin is an often-cited example of a tectonically erosive zone, with numerous topographic features such as seamounts, ridges, horsts, and grabens. However, few studies have attempted to determine the regional subduction erosion regime. By combining high-resolution bathymetric data and multichannel seismic profiles, this study analyzed the seafloor roughness, forearc taper angle, accretionary prism, and forearc topography to reveal evidence of subduction erosion at the Mariana margin. The subduction of rough seafloor (seamounts, horsts, and grabens) has accelerated the erosion process of the sediment-starved (<1 km) Mariana margin. In addition to causing depressions and landslides on the inner trench slope, this erosive process resulted in a flat forearc topography, which in turn may have induced a steeper forearc slope and taper angle. The arc-trench distance also shows a strong negative correlation with subduction erosion along the Mariana margin. The involvement of fluid inhibits the erosive process by favoring the development of a smooth surface along the plate interface. Our results suggest that tectonic erosion at the Mariana margin is jointly due to three mechanisms: seamount subduction, horst and graben subduction, and hydrofracturing. This integrated erosive process corresponds to a long-term subduction erosion rate of the submerged forearc of 7–8 km3/km/Myr, significantly lower than those calculated along the other global convergent margins.

Investigating boosting techniques’ efficacy in feature selection: A comparative analysis

Accurate Solar Irradiance (SI) forecasting is an important aspect of solar energy harvesting and it depends on various meteorological features. Numerous feature selection algorithms have been implemented for the selection of suitable meteorological parameters. However, boosting algorithms are not explored widely for feature selection applications. Therefore, in this study, a novel perspective is introduced by exploring the efficacy of boosting algorithms in feature selection applications. In the proposed study, we perform a comparative analysis of different boosting algorithms for feature selection applications including Extreme Gradient Boosting (XgBoost), Categorical Boosting (CatBoost), Random Forest (RF) and Light Gradient Boosting Machine (LGBM). The novelty of this approach is in utilizing these boosting techniques for the selection of the most appropriate features that improve the predictive performance of the model. The SI data of three different geographical locations: Islamabad, Pakistan, Basel, Switzerland and Golden, Colorado, USA are attained from the National Solar Radiation Database (NSRDB) and used in the proposed study. First, the appropriate features are selected by four boosting algorithms separately. The selected features are then fed to the Bidirectional Long-Short-Term Memory (BiLSTM) network for forecasting hour-ahead Global Horizontal Irradiance (GHI). The Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Scaled Error (MASE) and Normalized Root Mean Square Error (NRMSE) are used as performance indicators. Findings demonstrate that the BiLSTM network trained on selected features, proposed by the XgBoost model, produces better forecasting results. In the case of the Islamabad city dataset, the RMSE and MAE of BiLSTM trained with appropriate features, as compared to the conventional model, are improved by 29.92% and 14.03%, respectively. For the dataset of Basel, the RMSE and MAE of BiLSTM network improved by 14.43% and 28.72%, respectively. Moreover, for the Golden city dataset, the RMSE and MAE of the proposed approach are improved by 10.5% and 17.38%, respectively than the conventional model.

The ecological validity of picture SFON tasks

Research has identified that children differ in the extent to which they spontaneously focus on numerical aspects of the environment (Spontaneous Focusing on Numerosity, SFON) and that this correlates with their mathematics achievement. It is assumed that the mechanism underpinning this relationship is that children who spontaneously focus on numerical features of their environment will experience more self-initiated practice with number concepts. We explored this mechanism by investigating whether 4- to 5-year-old children’s verbal SFON scores on a picture description task related to their spontaneous focusing on number while engaged in play activities with their parent. We found that the scores derived from a picture description task were strongly correlated with the scores derived from the play sessions, rₛ = .638, 95% CI [.433, .781], providing evidence for this mechanism. We further investigated the role that verbal abilities may play in children’s performance on the picture description task, finding that general verbal abilities were not associated with verbal SFON scores. These results contribute to our understanding of the role played by verbal SFON tendencies in explaining differences in numerical development, and demonstrate the ecological validity of SFON picture tasks.

A STUDY AND DEVELOPMENT OF APPLICATION ON SENTIMENT ANALYSIS

A subfield of natural language processing called sentiment analysis is concerned with locating and obtaining subjective data from text. Analysing and categorising the emotional tone or polarity indicated in text—such as reviews, social media postings, news stories, and consumer feedback—is its primary goal. A subfield of natural language processing called sentiment analysis is concerned with locating and obtaining subjective data from text. Analysing and categorising the emotional tone or polarity indicated in text—such as reviews, social media postings, news stories, and consumer feedback—is its primary goal. Conversely, machine learning-based techniques employ algorithms to learn from a labelled dataset and categorise fresh data according to patterns seen in the training data. Hybrid methods combine both approaches to achieve better accuracy and coverage. Machine learning models, particularly Support Vector Machines (SVM), were utilized for sentiment analysis, involving the conversion of text data into numerical feature vectors and learning a hyperplane to classify sentiments accurately. Keywords: Sentiment Analysis, Machine Learning, HTML, Support Vector Machines, Bootstrap, Visual Studio Code.

Robust Drug Use Detection on X: Ensemble Method with a Transformer Approach

There is a growing trend for groups associated with drug use to exploit social media platforms to propagate content that poses a risk to the population, especially those susceptible to drug use and addiction. Detecting drug-related social media content has become important for governments, technology companies, and those responsible for enforcing laws against proscribed drugs. Their efforts have led to the development of various techniques for identifying and efficiently removing drug-related content, as well as for blocking network access for those who create it. This study introduces a manually annotated Twitter dataset consisting of 112,057 tweets from 2008 to 2022, compiled for use in detecting associations connected with drug use. Working in groups, expert annotators classified tweets as either related or unrelated to drug use. The dataset was subjected to exploratory data analysis to identify its defining features. Several classification algorithms, including support vector machines, XGBoost, random forest, Naive Bayes, LSTM, and BERT, were used in experiments with this dataset. Among the baseline models, BERT with textual features achieved the highest F1-score, at 0.9044. However, this performance was surpassed when the BERT base model and its textual features were concatenated with a deep neural network model, incorporating numerical and categorical features in the ensemble method, achieving an F1-score of 0.9112. The Twitter dataset used in this study was made publicly available to promote further research and enhance the accuracy of the online classification of English-language drug-related content.

Hybrid whale algorithm with evolutionary strategies and filtering for high-dimensional optimization: Application to microarray cancer data.

The standard whale algorithm is prone to suboptimal results and inefficiencies in high-dimensional search spaces. Therefore, examining the whale optimization algorithm components is critical. The computer-generated initial populations often exhibit an uneven distribution in the solution space, leading to low diversity. We propose a fusion of this algorithm with a discrete recombinant evolutionary strategy to enhance initialization diversity. We conduct simulation experiments and compare the proposed algorithm with the original WOA on thirteen benchmark test functions. Simulation experiments on unimodal or multimodal benchmarks verified the better performance of the proposed RESHWOA, such as accuracy, minimum mean, and low standard deviation rate. Furthermore, we performed two data reduction techniques, Bhattacharya distance and signal-to-noise ratio. Support Vector Machine (SVM) excels in dealing with high-dimensional datasets and numerical features. When users optimize the parameters, they can significantly improve the SVM's performance, even though it already works well with its default settings. We applied RESHWOA and WOA methods on six microarray cancer datasets to optimize the SVM parameters. The exhaustive examination and detailed results demonstrate that the new structure has addressed WOA's main shortcomings. We conclude that the proposed RESHWOA performed significantly better than the WOA.

The origin, development, and application of fractal geometry theory in architecture

Fractal geometry plays a crucial role in the study of fractals, with wide-ranging applications and it is closely related to architectural design creation, notably in architecture. Fractals are becoming more prevalent in todays society and can be used to describe a variety of extremely irregular items in the actual world. This article aims to provide an overview and analysis of the significant implications of fractal geometry in architecture, exploring its relationship with elements in nature, such as trees. By searching, organizing data, and elucidating examples, the article delves into the origin and basic background of fractal geometry. The use of fractal geometry in architecture demonstrates the perfect symbiosis between fractal and nature through analysis of TOD, the ancient gardens in Suzhou, and other practical applications. One notable fractal algorithm is the L-system, a modeling approach expressing fractal concepts, enabling parameterization and architectural design. Fractal geometry successfully expands possibilities for the harmonious coexistence of architecture and nature, embodying numerous natural features and human-centric ideas.

Model-driven multimodal LSTM-CNN for unbiased structural forecasting of European Union allowances open-high-low-close price

European Union allowances (EUAs), the “currency in circulation” of the EU Emissions Trading Scheme (ETS), have spawned a great deal of speculative trading. This study proposes a model-driven long short-term memory network (LSTM)-convolutional neural network (CNN) hybrid model that integrates numerical data features and candlestick features to achieve accurate and unbiased structural prediction of EUA futures open-high-low-close (OHLC) prices during the four phases of the EU ETS. During EU ETS Phase IV, the out-of-sample prediction outcomes of the LSTM-CNN model exhibited a mean absolute percentage error (MAPE) of 0.942%, a mean absolute error (MAE) of 0.877, a root mean squared error (RMSE) of 1.157, a goodness-of-fit (R2) of 0.953, an accuracy ratio (AR) of 0.544, and a forecast correct rate of ups and downs (UP) of 0.579. In comparison to Naive methods, vector autoregression (VAR) combined with vector error correction model (VECM), multiple linear regression (MLR), partial least squares (PLS), support vector regression (SVR), and standalone LSTM, the LSTM-CNN approach demonstrated a notable enhancement in the average MAPE across the four stages of the EU ETS—specifically, an improvement of 21.66%, 43.15%, 15.73%, 15.72%, 10.45%, and 5.91%, respectively. Drawing from the unbiased structural forecasts of OHLC data, this study proposes fruitful intraday trading strategies that attain substantive investment returns in the realm of EUA futures trading. The multimodal forecasting methodology and intraday trading strategies advanced in this study hold considerable promise within the domain of energy finance.

Parity-time imbalance induced by balanced gain and loss medium in non-Hermitian photonic crystals

Photonic crystals, including gain and loss materials, show numerous intriguing features than the ordinary photonic crystals. In this work, using the full wave simulation, we numerically investigate the photonic crystals with gain and loss which are satisfying the parity-time (PT) symmetry condition to reveal the effect of PT symmetry on the optical properties of photonic crystals, including the exceptional point, distribution of electromagnetic fields and edge states. This work reveals a novel mechanism of coexistence and competition between topological states and non-Hermiticity in all-dielectric photonic crystals and provides an innovative understanding of non-Hermiticity in topological photonic systems.

Robust Constrained Model Predictive Voltage Control Scheme and its Classification

In this study, a prospective power system is suggested (PPS), predictive current control (PCC), enhanced voltage control (EVC), model predictive control (MPC), induction motor (IM) circuit. Instead of monitoring the torque and speed as in conventional MP DTC, the stator voltage is directly regulated in the suggested PVC model thanks to the predictive control theory. A voltage source inverter's use of a model predictive control approach. For all potential voltage vectors produced by the inverter, this method forecasting value of the load using a discrete model of the system. "Voltage-source converter based high voltage direct current (VSC-HVDC)" connected offshore wind farms using a "model predictive control (MPC)" based Enhanced Voltage Control Strategy (EVCS) (OWFs). All wind farm generator (WTGs) and wind farm side VSCs are effectively synchronized in the proposed Midi controller EVCS to maintain voltages within a practical range and reduce system power loss. Review of induction motor defects, including mechanical and insulating issues. The summary of static current signature of machine failures. Numerous useful feature extraction techniques have been introduced for induction motor problem detection.