Timing Information Research Articles

Computation of the expectation value of the spin operator S 2 for the Spin-Flip Bethe-Salpeter Equation

Abstract Spin-flip methods applied to excited-state approaches like the Bethe-Salpeter Equation allow access to the excitation energies of open-shell systems, such as molecules and defects in solids. The eigenstates of these solutions, however, are generally not eigenstates of the spin operator S 2. Even for simple cases where the excitation vector is expected to be, for example, a triplet state, the value of 〈S 2〉 may be found to differ from 2.00; this difference is called "spin contamination." The expectation values 〈S 2〉 must be computed for each excitation vector, to assist with the characterization of the particular excitation and to determine the amount of spin contamination of the state. Our aim is to provide for the first time in the spin-flip methods literature a comprehensive resource on the derivation of the formulas for 〈S 2〉 as well as its computational implementation. After a brief discussion of the theory of the Spin-Flip Bethe-Salpeter Equation and some examples further illustrating the need for calculating 〈S 2〉, we present the derivation for the general equation for computing 〈S 2〉 with the eigenvectors from an SF-BSE calculation, how it is implemented in a Python script, and timing information on how this calculation scales with the size of the SF-BSE Hamiltonian.

Prediction of mild cognitive impairment using EEG signal and BiLSTM network

Mild cognitive impairment (MCI) is a cognitive disease that primarily affects elderly persons. Patients with MCI have impairments in one or more cognitive areas, such as memory, attention, language, and problem-solving. The risk of Alzheimer’s disease development is 10 times higher among individuals who meet the MCI diagnosis than in those who do not have such a diagnosis. Identifying the primary neurophysiological variations between those who are suffering from cognitive impairment and those who are ageing normally may provide helpful techniques to assess the effectiveness of therapies. Event-related Potentials (ERPs) are utilized to investigate the processing of sensory, cognitive, and motor information in the brain. ERPs enable excellent temporal resolution of underlying brain activity. ERP data is complex due to the temporal variation that occurs in the time domain. It is actually a type of electroencephalography (EEG) signal that is time-locked to a specific event or behavior. To remove artifacts from the data, this work utilizes Independent component analysis, finite impulse response filter, and fast Fourier transformation as preprocessing techniques. The bidirectional long short-term memory network is utilized to retain the spatial relationships between the ERP data while learning changes in temporal information for a long time. This network performed well both in modeling and information extraction from the signals. To validate the model performance, the proposed framework is tested on two benchmark datasets. The proposed framework achieved a state-of-the-art accuracy of 96.03% on the SJTU Emotion EEG Dataset dataset and 97.31% on the Chung–Ang University Hospital EEG dataset for the classification tasks.

A Tightly Coupled GNSS RTK/IMU Integration with GA-BP Neural Network for Challenging Urban Navigation

Abstract Intelligent Transportation System (ITS) is increasing the importance of real-time acquisition of Positioning, Navigation, and Timing (PNT) information from high-accuracy Global Navigation Satellite Systems (GNSS) based on carrier phase observations. The complexity of urban environments, however, means that GNSS signals are prone to reflection, diffraction and blockage by tall buildings, causing a degraded positioning accuracy. To address this issue, we have proposed a tightly coupled single-frequency multi-system single-epoch Real-time Kinematic (RTK) GNSS/Inertial Measurement Unit (IMU) integration algorithm with the assistance of Genetic Algorithm Back Propagation (GA-BP) based on low-cost IMU equipment for challenging urban navigation. Unlike the existing methods, which only use IMU corrections predicted by machine learning as a direct replacement of filtering corrections during GNSS outages, this algorithm introduces a more accurate and efficient IMU corrections prediction model, and it is underpinned by a dual-check GNSS assessment where the weights of GNSS measurements and neural network predictions are adaptively adjusted based on duration of the integrated system GNSS failure, assisting RTK/IMU integration in GNSS outages or malfunction conditions. Field tests demonstrate that the proposed prediction model results in a 68.69% and 69.03% improvement in the Root Mean Square Error (RMSE) in the 2D and 3D component when the training and testing data are collected under 150s GNSS signal-blocked conditions. This corresponds to 52.43% and 51.27% for the signal discontinuously blocked with 500s.

Prediction and Analysis of Transient Turbine Tip Clearance Using Long Short-Term Memory Neural Network

Abstract The transient turbine tip clearance (d) throughout the engine process is crucial to modern high-performance aero engines. However, there is still a lack of efficient and accurate transient prediction models of tip clearances with active thermal control (ATC) system, especially for the tip clearances of the complex turbine structures with various parameters. This study develops a transient prediction model for the tradeoff between computational efficiency and accuracy, which includes an offline dataset generation process and an online d prediction process. The offline dataset is first generated using an in-house finite element analysis code, which is validated against a transient tip clearance experiment, and data splicing and sensitivity analysis are applied to enrich the sample features and reduce the input parameters' dimensionality. Then, the long short-term memory neural network is employed to learn the transient tip clearances' timing information. The time consumption for the transient prediction model is significantly shorter than that for the tip clearance calculation method by three orders, and the maximum relative error is as low as 3.59%. In addition, the transient characteristics, including the overshoot value (s) and the response time (ts), are investigated with different jet Reynolds numbers (Rec) and temperatures (Tfc) of ATC cooling flow. The ts decreases with larger Rec and smaller Tfc due to a more significant cooling effect. However, the s increases with the increase of Rec and Tfc due to the different sensitivity of cooling parameters.

The time-dependent expression of FPR2 and ANXA1 in murine deep vein thrombosis model and its relation to thrombus age.

Thrombus age determination in fatal venous thromboembolism cases is an important task for forensic pathologists. In this study, we investigated the time-dependent expressions of formyl peptide receptor 2 (FPR2) and Annexin A1 (ANXA1) in a stasis-induced deep vein thrombosis (DVT) murine model, with the aim of obtaining useful information for thrombus age timing. A total of 75 ICR mice were randomly classified into thrombosis group and control group. In thrombosis group, a DVT model was established by ligating the inferior vena cava (IVC) of mice, and thrombosed IVCs were harvested at 1, 3, 5, 7, 10, 14, and 21days after modeling. In control group, IVCs without thrombosis were taken as control samples. The expressions of FPR2 and ANXA1 during thrombosis were detected using immunohistochemistry and double immunofluorescence staining. Their protein and mRNA levels in the samples were determined by Western blotting and quantitative real-time PCR. The results reveal that FPR2 was predominantly expressed by intrathrombotic neutrophils and macrophages. ANXA1 expression in the thrombi was mainly distributed in neutrophils, endothelial cells of neovessels, and fibroblastic cells. After thrombosis, the expressions of FPR2 and ANXA1 were time-dependently up-regulated. The percentage of FPR2-positive cells and the level of FPR2 protein significantly elevated at 1, 3, 5 and 7 days after IVC ligation as compared to those at 10, 14 and 21 days after ligation (p < 0.05). Moreover, the mRNA level of FPR2 were significantly higher at 5 days than that at the other post-ligation intervals (p < 0.05). Besides, the levels of ANXA1 mRNA and protein peaked at 10 and 14 days after ligation, respectively. A significant increase in the mRNA level of ANXA1 was found at 10 and 14 days as compared with that at the other post-ligation intervals (p < 0.01). Our findings suggest that FPR2 and ANXA1 are promising as useful markers for age estimation of venous thrombi.

A multi-step attack identification and correlation method based on multi-information fusion

With the continuous development of Internet technology, network security issues are becoming more and more complex. In order to obtain or damage user information, attackers usually need to use multiple attack steps to attack different network nodes to achieve the final goal. Currently, traditional intrusion detection mainly focuses on classifying attacks on a single piece of traffic information, and lacks the use of correlation and timing information between multi-step attack traffic to carry out multi-step attack identification and correlation research. To this end, we introduce an edge-based graph attention network to aggregate the neighbor information of flows and use a long short-term memory neural network to obtain time series information. By considering neighbor traffic and time information during the classification process, we are able to achieve accurate classification in multi-step attack scenarios. Then, based on the classification results, the sum of the number of attacks and the number of attacks is counted for each host node. Then standardize the statistical counts of internal network host nodes and external network host nodes and set thresholds to exclude low-risk nodes. Finally, a time-based depth-first traversal algorithm is used to obtain the key attack chain. However, this method may face some challenges, such as high computational complexity, the ability to handle large-scale data, and the generalization ability of the model. Experimental results show that the method we proposed is significantly better than the traditional method in terms of accuracy and recall rate for multi-step attacks, and it can effectively correlate attack steps and obtain multiple key attack chains.

A Method to Correct the Temporal Drift of Single-Photon Detectors Based on Asynchronous Quantum Ghost Imaging.

Single-photon detection and timing has attracted increasing interest in recent years due to their necessity in the field of quantum sensing and the advantages of single-quanta detection in the field of low-level light imaging. While simple bucket detectors are mature enough for commercial applications, more complex imaging detectors are still a field of research comprising mostly prototype-level detectors. A major problem in these detectors is the implementation of in-pixel timing circuitry, especially for two-dimensional imagers. One of the most promising approaches is the use of voltage-controlled ring resonators in every pixel. Each of these runs independently based on a voltage supplied by a global reference. However, this yields the problem that the supply voltage can change across the chip which, in turn, changes the period of the ring resonator. Due to additional parasitic effects, this problem can worsen with increasing measurement time, leading to drift in the timing information. We present here a method to identify and correct such temporal drifts in single-photon detectors based on asynchronous quantum ghost imaging. We also show the effect of this correction on recent quantum ghost imaging (QGI) measurement from our group.

Neurological adverse effects of isoxazoline exposure in cats and dogs.

Isoxazolines are rarely reported to be associated with neurological adverse events in cats and dogs, but information about the onset and duration of neurological signs is lacking in the summary of product characteristics of these medicines. The Veterinary Poisons Information Service and the Dutch Poisons Information Center databases were searched using the Veterinary Dictionary for Drug-Related Affairs terms for ataxia, muscle tremor, convulsions or hyperesthesia in cats and dogs exposed to isoxazolines. There were 22 cases with and 57 cases without outcome information, mostly involving fluralaner or sarolaner. In both groups, muscle tremors and convulsions were the most common signs. In dogs, neurological signs occurred with oral therapeutic dose and overdosage. In cats, most fluralaner cases involved therapeutic topical exposure, and all sarolaner cases involved oral exposure. In all cases with outcome information, the animals recovered. Cases discussed with poison centres tend to involve more severe signs. The true incidence of neurological adverse effects from isoxazolines remains unclear.The delay between the administration and onset of signs can be long, and the association may be missed. A lack of timing information in the summary of product characteristics could also contribute to missed attribution of adverse effects.

Boosting Instagram Visibility with Machine Learning

Boosting Instagram Visibility project aims to enhance the visibility of Instagram posts by leveraging machine learning techniques. We follow a systematic process to analyze data gathered from Instagram posts, including details like post content, engagement metrics, and usage of hashtags. Firstly, we collect relevant data from Instagram through various means, ensuring we capture essential information such as post text, engagement metrics, and user demographics. This serves as the foundation for our analysis. Next, we preprocess the collected data to ensure its quality and consistency. We clean the data, handle missing values, and preprocess text to make it suitable for analysis. We then select or extract meaningful features from the data that are crucial for predicting post visibility. These features could include post content, engagement metrics, timing, and demographic information. With the features identified, we choose appropriate machine learning algorithms to analyze the data. These algorithms are selected based on the nature of the prediction task and the characteristics of the dataset. We train the selected machine learning model using the preprocessed data, splitting it into training and validation sets to evaluate the model's performance. After training, we evaluate the model's performance using metrics such as mean absolute error (MAE) or accuracy. This helps us understand how well the model predicts post reach based on the provided features. Using the trained model, we make predictions on new or unseen data, providing insights into the potential visibility of Instagram posts. Through interpretation and analysis of the model predictions, we gain insights into the factors that contribute most to post visibility, helping users optimize their Instagram strategies. Finally, we iterate on the model and process based on feedback and insights gained, continuously refining our approach to improve prediction accuracy. Key Words: Engagement metrics, Hashtags, Mean absolute error, Post visibility, Optimization strategies

Polarised cross sections for vector boson production with Sherpa

Measurements of vector boson polarisation in vector boson production processes offer a powerful probe of the electroweak symmetry breaking mechanism, scrutinising the Standard Model and new physics scenarios alike. Since massive vector bosons can only be observed as intermediate particles, polarised cross section templates from simulation are necessary to extract their polarisation from measurable unpolarised distributions. In this work we present an extension of the Sherpa Monte-Carlo event generator allowing the simulation of polarised cross sections for vector boson production processes. Based on the narrow-width approximation, polarised cross sections of all possible polarisation combinations for an arbitrary number of intermediate vector bosons can be simulated in a single simulation run. In addition, it is possible to directly predict the interference between different intermediate polarisation states, and various differing polarisation definitions can be studied simultaneously. Besides the simulation of polarised cross sections at fixed LO and LO+PS accuracy as well as in multijet-merged calculations, we also present parton-shower-matched polarised cross sections with approximate NLO QCD corrections in the vector boson production processes. We demonstrate that the differences of this approximation to full NLO QCD predictions are small and it thus opens up the possibility for fully-simulated calculations at the hadron level including polarisation information and higher-order QCD effects for the first time.

Fault diagnosis of wind turbine blade icing based on feature engineering and the PSO-ConvLSTM-transformer

In winter, wind turbines often face the risk of blade icing, and traditional methods – which mostly rely on manual observation and sensors – are limited by the experience of testers and costs; therefore, they are not widely used in practice. With the development of artificial intelligence, data-driven methods have garnered increased attention. However, it is challenging to mine the effective hidden information within data. In this regard, this paper proposes a wind turbine blade icing detection method based on feature engineering and the PSO-ConvLSTM-Transformer. Firstly, feature engineering is carried out with the help of expert experience to construct the mechanism variables related to icing; secondly, the ConvLSTM-Transformer prediction model is constructed to mine the timing information between supervisory control and data acquisition (SCADA) data, and finally, the model hyper-parameters are optimized by using the particle swarm optimization (PSO) algorithm to improve the diagnostic performance and generalizability of the model. The proposed model is evaluated using two wind turbine blade icing datasets. The proposed PSO-ConvLSTM-Transformer has significant advantages over multiple baseline models. This study can inform wind farm operations in terms of optimizing maintenance strategies, ensuring the safe and efficient operation of wind turbines in cold weather conditions.

Jitter Measurements of 1 cm2 LGADs for Space Experiments

This work explores the possibility of using Low Gain Avalanche Diodes (LGADs) for tracker-based experiments studying Charged Cosmic Rays (CCRs) in space. While conventional silicon microstrip sensors provide only spatial information about the charged particle passing through the tracker, LGADs have the potential to provide additional timing information with a resolution in the order of tens of picoseconds. For the first time, it has been demonstrated that an LGAD with an active area of approximately 1 cm2 can achieve a jitter of less than 40 ps. A comparison of design and gain layers is carried out to understand which provides the best time resolution. For this purpose, laboratory measurements of sensors’ electrical properties and gain using LED and an Infrared laser, as well as their jitter, were performed.

Inorganic Halide Perovskite Nanowires/Conjugated Polymer Heterojunction-Based Optoelectronic Synaptic Transistors for Dynamic Machine Vision.

Inspired by the retina, artificial optoelectronic synapses have groundbreaking potential for machine vision. The field-effect transistor is a crucial platform for optoelectronic synapses that is highly sensitive to external stimuli and can modulate conductivity. On the basis of the decent optical absorption, perovskite materials have been widely employed for constructing optoelectronic synaptic transistors. However, the reported optoelectronic synaptic transistors focus on the static processing of independent stimuli at different moments, while the natural visual information consists of temporal signals. Here, we report CsPbBrI2 nanowire-based optoelectronic synaptic transistors to study the dynamic responses of artificial synaptic transistors to time-varying visual information for the first time. Moreover, on the basis of the dynamic synaptic behavior, a hardware system with an accuracy of 85% is built to the trajectory of moving objects. This work offers a new way to develop artificial optoelectronic synapses for the construction of dynamic machine vision systems.

A nearest neighbor query method for searching objects with time and location informations based on spatiotemporal similarity

A nearest neighbor query method for searching objects with time and location informations based on spatiotemporal similarity

Model-based meta-analysis to quantify the effects of short interfering RNA therapeutics on hepatitis B surface antigen turnover in hepatitis B-infected mice.

The objective of this study was to compare the efficacy of short interfering RNA therapeutics (siRNAs) inreducing hepatitis B surface antigen (HBsAg) levels in hepatitis B-infected (HBV) mice across multiple siRNA therapeutic classesusing model-based meta-analysis (MBMA) techniques. Literature data from 10 studies in HBV-infected mice were pooled, including 13 siRNAs, formulated as liposomal nanoparticles (LNPs) or conjugated to either cholesterol (chol) or N-acetylgalactosamine (GalNAc). Time course of the baseline- and placebo-corrected mean HBsAg profiles were modeled using kinetics of drug effect (KPD) model coupled to an indirect response model (IRM) within a longitudinal non-linear mixed-effects MBMA framework. Single and multiple dose simulations were performed exploring the role of dosing regimens across evaluated siRNA classes. The HBsAg degradation rate (0.72 day-1) was consistent across siRNAs but exhibited a large between-study variability of 31.4% (CV%). The siRNA biophase half-life was dependent on the siRNA class and was highest for GalNAc-siRNAs (21.06 days) and lowest for chol-siRNAs (2.89 days). ID50 estimates were compound-specific and were lowest for chol-siRNAs and highest for GalNAc-siRNAs. Multiple dose simulations suggest GalNAc-siRNAs may require between 4 and 7 times less frequent dosing at higher absolute dose levels compared to LNP-siRNAs and chol-siRNAs, respectively, to reach equipotent HBsAg-lowering effects in HBV mice. In conclusion, non-clinical HBsAg concentration-time data after siRNA administration can be described using the presented KPD-IRM MBMA framework. This framework allows to quantitatively compare the effects of siRNAs on the HBsAg time course and inform dose and regimen selection across siRNA classes. These results may support siRNA development, optimize preclinical study designs, and inform data analysis methodology of future anti-HBV siRNAs; and ultimately, support siRNA model-informed drug development (MIDD) strategies.

Ultra short term power load forecasting based on the fusion of Seq2Seq BiLSTM and multi head attention mechanism.

Ultra-short-term power load forecasting is beneficial to improve the economic efficiency of power systems and ensure the safe and stable operation of power grids. As the volatility and randomness of loads in power systems, make it difficult to achieve accurate and reliable power load forecasting, a sequence-to-sequence based learning framework is proposed to learn feature information in different dimensions synchronously. Convolutional Neural Networks(CNN) Combined with Bidirectional Long Short Term Memory(BiLSTM) Networks is constructed in the encoder to extract the correlated timing features embedded in external factors affecting power loads. The parallel BiLSTM network is constructed in the decoder to mine the power load timing information in different regions separately. The multi-headed attention mechanism is introduced to fuse the BiLSTM hidden layer state information in different components to further highlight the key information representation. The load forecastion results in different regions are output through the fully connected layer. The model proposed in this paper has the advantage of high forecastion accuracy through the example analysis of real power load data.

Construction and improvement of English vocabulary learning model integrating spiking neural network and convolutional long short-term memory algorithm.

To help non-native English speakers quickly master English vocabulary, and improve reading, writing, listening and speaking skills, and communication skills, this study designs, constructs, and improves an English vocabulary learning model that integrates Spiking Neural Network (SNN) and Convolutional Long Short-Term Memory (Conv LSTM) algorithms. The fusion of SNN and Conv LSTM algorithm can fully utilize the advantages of SNN in processing temporal information and Conv LSTM in sequence data modeling, and implement a fusion model that performs well in English vocabulary learning. By adding information transfer and interaction modules, the feature learning and the timing information processing are optimized to improve the vocabulary learning ability of the model in different text contents. The training set used in this study is an open data set from the WordNet and Oxford English Corpus data corpora. The model is presented as a computer program and applied to an English learning application program, an online vocabulary learning platform, or a language education software. The experiment will use the open data set to generate a test set with text volume ranging from 100 to 4000. The performance indicators of the proposed fusion model are compared with those of five traditional models and applied to the latest vocabulary exercises. From the perspective of learners, 10 kinds of model accuracy, loss, polysemy processing accuracy, training time, syntactic structure capturing accuracy, vocabulary coverage, F1-score, context understanding accuracy, word sense disambiguation accuracy, and word order relation processing accuracy are considered. The experimental results reveal that the performance of the fusion model is better under different text sizes. In the range of 100-400 text volume, the accuracy is 0.75-0.77, the loss is less than 0.45, the F1-score is greater than 0.75, the training time is within 300s, and the other performance indicators are more than 65%; In the range of 500-1000 text volume, the accuracy is 0.81-0.83, the loss is not more than 0.40, the F1-score is not less than 0.78, the training time is within 400s, and the other performance indicators are above 70%; In the range of 1500-3000 text volume, the accuracy is 0.82-0.84, the loss is less than 0.28, the F1-score is not less than 0.78, the training time is within 600s, and the remaining performance indicators are higher than 70%. The fusion model can adapt to various types of questions in practical application. After the evaluation of professional teachers, the average scores of the choice, filling-in-the-blank, spelling, matching, exercises, and synonyms are 85.72, 89.45, 80.31, 92.15, 87.62, and 78.94, which are much higher than other traditional models. This shows that as text volume increases, the performance of the fusion model is gradually improved, indicating higher accuracy and lower loss. At the same time, in practical application, the fusion model proposed in this study has a good effect on English learning tasks and offers greater benefits for people unfamiliar with English vocabulary structure, grammar, and question types. This study aims to provide efficient and accurate natural language processing tools to help non-native English speakers understand and apply language more easily, and improve English vocabulary learning and comprehension.

Functional magnetic resonance imaging signal has sub-second temporal accuracy

Neuronal activation sequence information is essential for understanding brain functions. Extracting such timing information from blood-oxygenation-level-dependent functional magnetic resonance imaging (fMRI) signals is confounded by local cerebral vascular reactivity (CVR), which varies across brain locations. Thus, detecting neuronal synchrony as well as inferring inter-regional causal modulation using fMRI signals can be biased. Here we used fast fMRI measurements sampled at 10 Hz to measure the fMRI latency difference between visual and sensorimotor areas when participants engaged in a visuomotor task. The regional fMRI timing was calibrated by subtracting the CVR latency measured by a breath-holding task. After CVR calibration, the fMRI signal at the lateral geniculate nucleus (LGN) preceded that at the visual cortex by 496 ms, followed by the fMRI signal at the sensorimotor cortex with a latency of 464 ms. Sequential LGN, visual, and sensorimotor cortex activations were found in each participant after the CVR calibration. These inter-regional fMRI timing differences across and within participants were more closely related to the reaction time after the CVR calibration. Our results suggested the feasibility of mapping brain activity using fMRI with accuracy in hundreds of milliseconds.

A short-term forecasting method for photovoltaic power generation based on the TCN-ECANet-GRU hybrid model

Due to the uncertainty of weather conditions and the nonlinearity of high-dimensional data, as well as the need for a continuous and stable power supply to the power system, traditional regression analysis and time series forecasting methods are no longer able to meet the high accuracy requirements of today's PV power forecasting. To significantly improve the prediction accuracy of short-term PV output power, this paper proposes a short-term PV power forecasting method based on a hybrid model of temporal convolutional networks and gated recurrent units with an efficient channel attention network (TCN-ECANet-GRU) using the generated data of an Australian PV power station as the research object. First, temporal convolutional networks (TCNs) are used as spatial feature extraction layers, and an efficient channel attention network (ECANet) is embedded to enhance the feature capture capability of the convolutional network. Then, the GRU is used to extract the timing information for the final prediction. Finally, based on the experimental validation, the TCN-ECANet-GRU method generally outperformed the other baseline models in all four seasons of the year according to three performance assessment metrics: the normalized root mean square error (RMSE), normalized mean absolute error (MAE) and coefficient of determination (R2). The best RMSE, MAE and R2 reached 0.0195, 0.0128 and 99.72%, respectively, with maximum improvements of 11.32%, 8.57% and 0.38%, respectively, over those of the suboptimal model. Therefore, the model proposed in this paper is effective at improving prediction accuracy. Using the proposed method, this paper concludes with multistep predictions of 3, 6, and 9 steps, which also indicates that the proposed method significantly outperforms the other models.

Profiling Social Sentiment in Times of Health Emergencies with Information from Social Networks and Official Statistics

Social networks and official statistics have become vital sources of information in times of health emergencies. The ability to monitor and profile social sentiment is essential for understanding public perception and response in the context of public health crises, such as the one resulting from the COVID-19 pandemic. This study will explore how social sentiment monitoring and profiling can be conducted using information from social networks and official statistics, and how this combination of data can offer a more complete picture of social dynamics in times of emergency, providing a valuable tool for understanding public perception and guiding a public health response. To this end, a three-layer architecture based on Big Data and Artificial Intelligence is presented: the first layer focuses mainly on collecting, storing, and governing the necessary data such as social media and official statistics; in the second layer, the representation models and machine learning necessary for knowledge generation are built, and in the third layer the previously generated knowledge is adapted for better understanding by crisis managers through visualization techniques among others. Based on this architecture, a KDD (Knowledge Discovery in Databases) framework is implemented using methodological tools such as sentiment analysis, fuzzy 2-tuple linguistic models and time series prediction with the Prophet model. As a practical demonstration of the proposed model, we use tweets as data source (from the social network X, formerly known as Twitter) generated during the COVID-19 pandemic lockdown period in Spain, which are processed to identify the overall sentiment using sentiment analysis techniques and fuzzy linguistic variables, and combined with official statistical indicators for prediction, visualizing the results through dashboards.