Temporal Relationships Research Articles

Recognizing online video genres using ensemble deep convolutional learning for digital media service management

It's evident that streaming services increasingly seek to automate the generation of film genres, a factor profoundly shaping a film's structure and target audience. Integrating a hybrid convolutional network into service management emerges as a valuable technique for discerning various video formats. This innovative approach not only categorizes video content but also facilitates personalized recommendations, content filtering, and targeted advertising. Given the tendency of films to blend elements from multiple genres, there is a growing demand for a real-time video classification system integrated with social media networks. Leveraging deep learning, we introduce a novel architecture for identifying and categorizing video film genres. Our approach utilizes an ensemble gated recurrent unit (ensGRU) neural network, effectively analyzing motion, spatial information, and temporal relationships. Additionally,w we present a sophisticated deep neural network incorporating the recommended GRU for video genre classification. The adoption of a dual-model strategy allows the network to capture robust video representations, leading to exceptional performance in multi-class movie classification. Evaluations conducted on well-known datasets, such as the LMTD dataset, consistently demonstrate the high performance of the proposed GRU model. This integrated model effectively extracts and learns features related to motion, spatial location, and temporal dynamics. Furthermore, the effectiveness of the proposed technique is validated using an engine block assembly dataset. Following the implementation of the enhanced architecture, the movie genre categorization system exhibits substantial improvements on the LMTD dataset, outperforming advanced models while requiring less computing power. With an impressive F1 score of 0.9102 and an accuracy rate of 94.4%, the recommended model consistently delivers outstanding results. Comparative evaluations underscore the accuracy and effectiveness of our proposed model in accurately identifying and classifying video genres, effectively extracting contextual information from video descriptors. Additionally, by integrating edge processing capabilities, our system achieves optimal real-time video processing and analysis, further enhancing its performance and relevance in dynamic media environments.

Decoding the Environmental Synergy in BRI Nations: Analyzing the Influence of Renewable Energy Adoption, Financial Evolution, FDI, and Capital Resilience on Sustainability

In response to escalating global concerns over environmental degradation, this study explores the intricate relationships between financial development (FD), foreign direct investment (FDI), capital adequacy, renewable energy consumption (REC) and environmental sustainability in BRI. We aim to provide nuanced insights into how these economic variables impact environmental quality, contributing to a comprehensive understanding of the complex interplay between economic development and environmental conservation. We aim to explore the associations between FD, FDI, REC, and capital adequacy, and environmental quality, emphasizing short-term and long-term dynamics. We employ the Autoregressive Distributed Lag (ARDL) model and conduct D-H causality tests to discern these variables' temporal and causal relationships. This methodology allows us to capture the complexities of the relationships and provide a robust analysis of their impacts on environmental sustainability. The findings reveal a positive long-run association between FD and environmental quality, suggesting that a well-developed financial sector may contribute positively to environmental outcomes. However, the short-run dynamics introduce complexity, indicating a potential immediate positive impact and raising questions about contextual factors influencing FD's contribution to increased carbon emissions. Shifting the focus to FDI and REC, our research uncovers a potential positive association with environmental quality in the long run. The short-run analysis introduces nuances, suggesting a potential negative impact, reflecting the mixed effects observed in previous studies, which underscores the importance of considering temporal dimensions and policy interventions to enhance the positive contributions of FDI and REC to environmental sustainability. Further, our study delves into the impact of capital adequacy on environmental sustainability, revealing a positive long-run association, which challenges negative associations, underlining the need for tailored policies to balance economic growth and environmental conservation. As a whole, our findings contribute quantitative evidence to guide policymakers in fostering incremental improvements over time, acknowledging the multifaceted nature of the relationships under consideration.

Incorporating the Third Law of Geography with Spatial Attention Module–Convolutional Neural Network–Transformer for Fine-Grained Non-Stationary Air Quality Predictive Learning

Accurate air quality prediction is paramount in safeguarding public health and addressing air pollution control. However, previous studies often ignore the geographic similarity among different monitoring stations and face challenges in dynamically capturing different spatial–temporal relationships between stations. To address this, an air quality predictive learning approach incorporating the Third Law of Geography with SAM–CNN–Transformer is proposed. Firstly, the Third Law of Geography is incorporated to fully consider the geographical similarity among stations via a variogram and spatial clustering. Subsequently, a spatial–temporal attention convolutional network that combines the spatial attention module (SAM) with the convolutional neural network (CNN) and Transformer is designed. The SAM is employed to extract spatial–temporal features from the input data. The CNN is utilized to capture local information and relationships among each input feature. The Transformer is applied to capture time dependencies across long-distance time series. Finally, Shapley’s analysis is employed to interpret the model factors. Numerous experiments with two typical air pollutants (PM2.5, PM10) in Haikou City show that the proposed approach has better comprehensive performance than baseline models. The proposed approach offers an effective and practical methodology for fine-grained non-stationary air quality predictive learning.

Cancer incidence and digital information seeking in Germany: a retrospective observational study

Awareness is vital for cancer prevention. US studies show a strong link between web searches and cancer incidence. In Europe, the relationship remains unclear. This study characterizes regional and temporal relationships between cancer incidence and web searches and investigates the content of searches related to breast, cervical, colorectal, lung, prostate, and testicular cancer, brain tumors, and melanoma in Germany (July 2018–December 2019). Aggregate data from Google Ads Keyword Planner and national cancer registry data were analyzed. Spearman’s correlation coefficient (rS) examined associations between cancer incidence and web search, repeated measures correlation (rrm) assessed time trends and searches were qualitatively categorized. The frequency of malignancy-related web searches correlated with cancer incidence (rS = 0.88, P = 0.007), e.g., breast cancer had more queries than the lower-incidence cervical cancer. Seasonally, incidence and searches followed similar patterns, peaking in spring and fall, except for melanoma. Correlations between entity incidence and searches (0.037 ≤ rrm ≤ 0.208) varied regionally. Keywords mainly focused on diagnosis, symptoms, and general information, with variations between entities. In Germany, web searches correlated with regional and seasonal incidence, revealing differences between North/East and South/West. These insights may help improve prevention strategies by identifying regional needs and assessing impact of awareness campaigns.

Efficient distributed association management method of data, model, and knowledge for digital twin railway

ABSTRACT Digital twin railway is a pivotal foundation for the intelligent construction and maintenance of railway engineering projects within extensive open spaces. Its essence is the integrated representation and association management of multi-granularity spatiotemporal data, executable analysis models, and professional knowledge. These elements are characterized by the prominent characteristics of multi-source, heterogeneity, and massive volume. However, current decentralized and independent management strategies often neglect the dynamic coupling relationships between them, and numerous multi-path joins and conversion aggregation operations exist across various spatial scale applications. Consequently, this results in challenges such as the inability to dynamically couple data-model-knowledge and conduct global association retrieval, thereby limiting the potential for real-time analysis and intelligent application capabilities. To address these problems, we first constructed a tripartite graph model ( DMK − Graph model ) that explicitly associates temporal, spatial, and interactive relationships. Subsequently, an association management architecture was proposed, accompanied by a global association graph index ( DMK − Graph index ) and a global-local indexing mechanism. Finally, a prototype system for railway data-model-knowledge association management was developed. The effectiveness of the distributed association management method was demonstrated by employing a case study of high-temperature safety risk analysis in railway tunnel engineering with multi-physics field coupling.

Temporal relationships between pain, functioning, and human assumed central sensitization in patients with chronic low back pain; a single-case design

BackgroundInterdisciplinary pain rehabilitation (IPR) aims to improve functioning in people with chronic low back pain (CLBP), and is not primary aimed at pain reduction. Many patients however also report a decrease in pain. An explanation could be that IPR results in a decrease in Central Sensitization (CS). As CS is not directly assessable in humans the term Human Assumed Central Sensitization (HACS) is used in this study. It is unknown whether a decrease in HACS precedes a decrease in pain and improved functioning or vice versa. ObjectivesThis study aimed to gain understanding into the temporal relationships between changes in pain, functioning, and HACS in patients with CLBP during IPR. DesignLongitudinal observational small-N-study. MethodTwelve patients filled in frequently repeated self-reports 1 week before, during the 12-week IPR program, and 2 weeks after IPR. Pain was assessed by Visual Analogue Scale for pain (daily), functioning by Pain Disability Index (weekly) and Work Ability Score (daily), and HACS by Central Sensitization Inventory part A (bi-weekly). Analyses were performed by visual inspection and time series cross-correlation analyses. ResultsVisual inspection showed large fluctuations within and between individual participants in patterns over time. Cross-correlation analyses showed that in most participants, relationships between pain, functioning, and HACS were strongest when analyzed at the same time (55% of comparisons). Strength and direction of (strongest) correlations showed high interindividual variability (neg: 0.33 – 0.97; pos: 0.22 – 0.99). ConclusionOverall, relationships between pain, functioning, and HACS did not show consistent temporality in patients with CLBP.

Prolonged Grief Symptoms Predict Social and Emotional Loneliness and Depression Symptoms

A minority of bereaved individuals develops severe, persistent, and disabling grief, termed prolonged grief. The International Classification of Diseases eleventh edition (ICD-11) and the Diagnostic and Statistical Manual of Mental Disorders fifth edition, Text Revision (DSM-5-TR) include such grief reactions as prolonged grief disorder (PGD). Loneliness is often experienced by bereaved persons and can have severe health consequences. Preliminary research suggests that loneliness may perpetuate grief, but prolonged grief may also aggravate loneliness. Since existing empirical research provides limited information on temporal relationships between both constructs, we aimed to fill this gap in knowledge. Bereaved adults (88% female, mean age 54 years) filled in questionnaires assessing general, social, and emotional loneliness and prolonged grief and depression symptoms across two time-points, six months apart. Cross-lagged panel model analyses showed that prolonged grief symptoms predicted more severe general, social, and emotional loneliness as well as more depression symptoms. Loneliness did not predict prolonged grief symptoms or depression symptoms. Depression symptoms did not predict prolonged grief symptoms or loneliness. Findings are inconsistent with the notion that loneliness causes prolonged grief and depression. Possibly, severe grief could lead to stigmatization, reduced social support, and feeling socially disconnected, perpetuating loneliness and depression symptoms.

Uncovering time series anomaly using deep learning technique

Time series data, characterized by its sequential and temporal nature, plays a crucial role in various domains such as finance, healthcare, and industrial processes. Identifying anomalies within time series data is a critical task with applications ranging from fault detection to fraud prevention. Traditional anomaly detection techniques often struggle to capture complex temporal patterns and dependencies in time series data. This study presents a novel time series anomaly detection method using long short-term memory (LSTM) neural networks. LSTMs are a type of recurrent neural networks (RNNs) designed to model long-term dependencies, making them suitable for capturing the complex temporal relationships present in time series data. Here we use a financial dataset featuring opening and closing time, we predict the volume of money and the current price of that money at a specific time. The results demonstrate the efficacy of the deep learning-based approach in detecting anomalies within time series data, outperforming traditional methods in terms of sensitivity and adaptability to complex temporal patterns. The proposed methodology presents a promising solution for real-world applications where early detection of anomalies is crucial for proactive decision-making and system integrity.

Research on wearable sensor gesture recognition based on CNN_GRU and attention mechanism

Gesture recognition based on wearable sensors has received extensive attention in recent years. This paper proposes a gesture recognition model (CGR_ATT) based on Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) fused attention mechanism to improve accuracy rate of wearable sensors. First, CNN serves as a feature extractor, learning features automatically from sensor data by performing multiple layers of convolution and pooling operations, capturing spatial features of gestures. Furthermore, a temporal modeling unit GRU is introduced to capture the temporal dynamics in gesture sequences. By controlling the information flow through gate mechanisms, it effectively handles the temporal relationships in sensor data. Finally, an attention mechanism is introduced to assign different weights to the hidden state of the GRU. By calculating the attention weights for each time period, the model automatically selects key time periods related to gesture movements. The GR-dataset proposed in this paper involves 910 sets of training parameters. The model achieves an ultimate accuracy of 97.57% . In compare with CLA-net, CLT-net, CGR, GRU, LSTM and CNN, the experimental results demonstrate that the proposed method has superior accuracy.

A Comparative Study of Deep-Learning Autoencoders (DLAEs) for Vibration Anomaly Detection in Manufacturing Equipment

Speed reducers (SR) and electric motors are crucial in modern manufacturing, especially within adhesive coating equipment. The electric motor mainly transforms electrical power into mechanical force to propel most machinery. Conversely, speed reducers are vital elements that control the speed and torque of rotating machinery, ensuring optimal performance and efficiency. Interestingly, variations in chamber temperatures of adhesive coating machines and the use of specific adhesives can lead to defects in chains and jigs, causing possible breakdowns in the speed reducer and its surrounding components. This study introduces novel deep-learning autoencoder models to enhance production efficiency by presenting a comparative assessment for anomaly detection that would enable precise and predictive insights by modeling complex temporal relationships in the vibration data. The data acquisition framework facilitated adherence to data governance principles by maintaining data quality and consistency, data storage and processing operations, and aligning with data management standards. The study here would capture the attention of practitioners involved in data-centric processes, industrial engineering, and advanced manufacturing techniques.

A Survey on Prediction of Heart Functionality Sound by using Deep Learning Algorithm

The primary objective of this study is to enhance the precision of predicting cardiovascular illnesses by introducing a sophisticated system that leverages deep learning techniques. Traditional diagnostic methods have relied on the analysis of cardiac sounds, achieving an accuracy of approximately 87.5% through the use of machine learning algorithms such as Random Forest and Decision Trees. However, these conventional approaches have their limitations. In contrast, the proposed hybrid approach aims to surpass these limitations by focusing on capturing intricate patterns and temporal relationships within heart sound data using deep learning. This innovative approach seeks to demonstrate a substantial improvement in prediction accuracy when compared to existing methods. If proven effective, this deep learning-based diagnostic tool has the potential to provide a more nuanced understanding of heart function, enabling early identification of anomalies and ultimately leading to improved patient outcomes in cardiovascular health.

Association between epigenetic age and type 2 diabetes mellitus or glycemic traits: A longitudinal twin study.

Epigenetic clocks based on DNA methylation have been known as biomarkers of aging, including principal component (PC) clocks representing the degree of aging and DunedinPACE representing the pace of aging. Prior studies have shown the associations between epigenetic aging and T2DM, but the results vary by epigenetic age metrics and people. This study explored the associations between epigenetic age metrics and T2DM or glycemic traits, based on 1070 twins (535 twin pairs) from the Chinese National Twin Registry. It also explored the temporal relationships of epigenetic age metrics and glycemic traits in 314 twins (157 twin pairs) who participated in baseline and follow-up visits after a mean of 4.6 years. DNA methylation data were used to calculate epigenetic age metrics, including PCGrimAge acceleration (PCGrimAA), PCPhenoAge acceleration (PCPhenoAA), DunedinPACE, and the longitudinal change rate of PCGrimAge/PCPhenoAge. Mixed-effects and cross-lagged modelling assessed the cross-sectional and temporal relationships between epigenetic age metrics and T2DM or glycemic traits, respectively. In the cross-sectional analysis, positive associations were identified between DunedinPACE and glycemic traits, as well as between PCPhenoAA and fasting plasma glucose, which may be not confounded by shared genetic factors. Cross-lagged models revealed that glycemic traits (fasting plasma glucose, HbA1c, and TyG index) preceded DunedinPACE increases, and TyG index preceded PCGrimAA increases. Glycemic traits are positively associated with epigenetic age metrics, especially DunedinPACE. Glycemic traits preceded the increases in DunedinPACE and PCGrimAA. Lowering the levels of glycemic traits may reduce DunedinPACE and PCGrimAA, thereby mitigating age-related comorbidities.

Improving speech depression detection using transfer learning with wav2vec 2.0 in low-resource environments

Depression, a pervasive global mental disorder, profoundly impacts daily lives. Despite numerous deep learning studies focused on depression detection through speech analysis, the shortage of annotated bulk samples hampers the development of effective models. In response to this challenge, our research introduces a transfer learning approach for detecting depression in speech, aiming to overcome constraints imposed by limited resources. In the context of feature representation, we obtain depression-related features by fine-tuning wav2vec 2.0. By integrating 1D-CNN and attention pooling structures, we generate advanced features at the segment level, thereby enhancing the model's capability to capture temporal relationships within audio frames. In the realm of prediction results, we integrate LSTM and self-attention mechanisms. This incorporation assigns greater weights to segments associated with depression, thereby augmenting the model's discernment of depression-related information. The experimental results indicate that our model has achieved impressive F1 scores, reaching 79% on the DAIC-WOZ dataset and 90.53% on the CMDC dataset. It outperforms recent baseline models in the field of speech-based depression detection. This provides a promising solution for effective depression detection in low-resource environments.

Temporal Relationship-Aware Treadmill Exercise Test Analysis Network for Coronary Artery Disease Diagnosis.

The treadmill exercise test (TET) serves as a non-invasive method for the diagnosis of coronary artery disease (CAD). Despite its widespread use, TET reports are susceptible to external influences, heightening the risk of misdiagnosis and underdiagnosis. In this paper, we propose a novel automatic CAD diagnosis approach. The proposed approach introduces a customized preprocessing method to obtain clear electrocardiograms (ECGs) from individual TET reports. Additionally, it presents TETDiaNet, a novel neural network designed to explore the temporal relationships within TET ECGs. Central to TETDiaNet is the TETDia block, which mimics clinicians' diagnostic processes to extract essential diagnostic information. This block encompasses an intra-state contextual learning module and an inter-state contextual learning module, modeling the temporal relationships within a single state and between states, respectively. These two modules help the TETDia block to capture effective diagnosis information by exploring the temporal relationships within TET ECGs. Furthermore, we establish a new TET dataset named TET4CAD for CAD diagnosis. It contains simplified TET reports for 192 CAD patients and 224 non-CAD patients, and each patient undergoes coronary angiography for labeling. Experimental results on TET4CAD underscore the superior performance of the proposed approach, highlighting the discriminative value of the temporal relationships within TET ECGs for CAD diagnosis.

Contributions of Synthetic Chemicals to Autoimmune Disease Development and Occurrence.

Exposure to many synthetic chemicals has been linked to a variety of adverse human health effects, including autoimmune diseases. In this scoping review, we summarize recent evidence detailing the effects of synthetic environmental chemicals on autoimmune diseases and highlight current research gaps and recommendations for future studies. We identified 68 recent publications related to environmental chemical exposures and autoimmune diseases. Most studies evaluated exposure to persistent environmental chemicals and autoimmune conditions including rheumatoid arthritis (RA), systemic lupus (SLE), systemic sclerosis (SSc), and ulcerative colitis (UC) and Crohn's disease. Results of recent original research studies were mixed, and available data for some exposure-outcome associations were particularly limited. PFAS and autoimmune inflammatory bowel diseases (UC and CD) and pesticides and RA appeared to be the most frequently studied exposure-outcome associations among recent publications, despite a historical research focus on solvents. Recent studies have provided additional evidence for the associations of exposure to synthetic chemicals with certain autoimmune conditions. However, impacts on other autoimmune outcomes, particularly less prevalent conditions, remain unclear. Owing to the ubiquitous nature of many of these exposures and their potential impacts on autoimmune risk, additional studies are needed to better evaluate these relationships, particularly for understudied autoimmune conditions. Future research should include larger longitudinal studies and studies among more diverse populations to elucidate the temporal relationships between exposure-outcome pairs and to identify potential population subgroups that may be more adversely impacted by immune modulation caused by exposure to these chemicals.

Log2Graph: A graph convolution neural network based method for insider threat detection

With the advancement of network security equipment, insider threats gradually replace external threats and become a critical contributing factor for cluster security threats. When detecting and combating insider threats, existing methods often concentrate on users’ behavior and analyze logs recording their operations in an information system. Traditional sequence-based method considers temporal relationships for user actions, but cannot represent complex logical relationships well between various entities and different behaviors. Current machine learning-based approaches, such as graph-based methods, can establish connections among log entries but have limitations in terms of complexity and identifying malicious behavior of user’s inherent intention. In this paper, we propose Log2Graph, a novel insider threat detection method based on graph convolution neural network. To achieve efficient anomaly detection, Log2Graph first retrieves logs and corresponding features from log files through feature extraction. Specifically, we use an auxiliary feature of anomaly index to describe the relationship between entities, such as users and hosts, instead of establishing complex connections between them. Second, these logs and features are augmented through a combination of oversampling and downsampling, to prepare for the next-stage supervised learning process. Third, we use three elaborated rules to construct the graph of each user by connecting the logs according to chronological and logical relationships. At last, the dedicated built graph convolution neural network is used to detect insider threats. Our validation and extensive evaluation results confirm that Log2Graph can greatly improve the performance of insider threat detection compared to existing state-of-the-art methods.

Screen time, sleep, brain structural neurobiology, and sequential associations with child and adolescent psychopathology: Insights from the ABCD study.

The precise roles of screen media activity (SMA) and sleep problems in relation to child/adolescent psychopathology remain ambiguous. We investigated temporal relationships among sleep problems, SMA, and psychopathology and potential involvement of thalamus-prefrontal-cortex (PFC)-brainstem structural covariation. This study utilized data from the Adolescent Brain Cognitive Development study (n = 4,641 ages 9-12) at baseline, Year1, and Year2 follow-up. Cross-Lagged Panel Models (CLPMs) investigated reciprocal predictive relationships between sleep duration/problems, SMA, and psychopathology symptoms. A potential mediating role of baseline Thalamus-PFC-brainstem covariation on SMA-externalizing relationships was examined. Participants were divided into discovery (n = 2,359, 1,054 girls) and replication (n = 2,282, 997 girls) sets. CLPMs showed 1) bidirectional associations between sleep duration and SMA in late childhood, with higher frequency SMA predicting shorter sleep duration (β = -0.10 [95%CI: -0.16, -0.03], p = 0.004) and vice versa (β= -0.11 [95%CI: -0.18, -0.05], p < 0.001); 2) externalizing symptoms at age 10-11 predicting sleep problems (β = 0.11 [95%CI: 0.04, 0.19], p = 0.002), SMA (β = 0.07 [95%CI: 0.01, 0.13], p = 0.014), and internalizing symptoms (β = 0.09 [95%CI: 0.05, 0.13], p < 0.001) at age 11-12; and 3) externalizing behavior at age 10-11 partially mediating the relationship between baseline thalamus-PFC-brainstem covariation and SMA at age 11-12 (indirect effect = 0.032 [95%CI: 0.003, 0.067], p-value = 0.030). Findings were replicable. We found bi-directional SMA-sleep-duration associations in late childhood. Externalizing symptoms preceded future SMA and sleep disturbances and partially mediated relationships between structural brain covariation and SMA. The findings emphasize the need for understanding individual differences and developing and implementing integrated strategies addressing both sleep concerns and screen time to mitigate potential impacts on psychopathology.

Single-channel EOG sleep staging on a heterogeneous cohort of subjects with sleep disorders.

Sleep staging based on full polysomnography is the gold standard in the diagnosis of many sleep disorders. It is however costly, complex, and obtrusive due to the use of multiple electrodes. Automatic sleep staging based on single-channel electro-oculography (EOG) is a promising alternative, requiring fewer electrodes which could be self-applied below the hairline. EOG sleep staging algorithms are however yet to be validated in clinical populations with sleep disorders.&#xD;Approach. We utilized the SOMNIA dataset, comprising 774 recordings from subjects with various sleep disorders, including insomnia, sleep-disordered breathing, hypersomnolence, circadian rhythm disorders, parasomnias, and movement disorders. The recordings were divided into train (574), validation (100), and test (100) groups. We trained a neural network that integrated transformers within a U-Net backbone. This design facilitated learning of arbitrary-distance temporal relationships within and between the EOG and hypnogram.&#xD;Main results. For 5-class sleep staging, we achieved median accuracies of 85.0% and 85.2% and Cohen's kappas of 0.781 and 0.796 for left and right EOG, respectively. The performance using the right EOG was significantly better than using the left EOG, possibly because in the recommended AASM setup, this electrode is located closer to the scalp. The proposed model is robust to the presence of a variety of sleep disorders, displaying no significant difference in performance for subjects with a certain sleep disorder compared to those without.&#xD;Significance. The results show that accurate sleep staging using single-channel EOG can be done reliably for subjects with a variety of sleep disorders.

Multi-temporal Scale Wind Power Forecasting Based on Lasso-CNN-LSTM-LightGBM

Due to the increasingly severe climate problems, wind energy has received widespread attention as the most abundant energy on Earth. However, due to the uncertainty of wind energy, a large amount of wind energy is wasted, so accurate wind power prediction can greatly improve the utilization of wind energy. To increase the forecast for wind energy accuracy across a range of time scales, this paper presents a multi-time scale wind power prediction by constructing an ICEEMDAN-CNN-LSTM-LightGBM model. Initially, feature selection is performed using Lasso regression to identify the most significant variables affecting the forecast for wind energy across distinct time intervals. Subsequently, the ICEEMDAN is utilized to break down the wind power data into various scales to capture its nonlinear and non-stationary characteristics. Following this, a deep learning model based on CNN and LSTM networks is developed, with the CNN responsible for extracting spatial features from the time series data, and the LSTM designed to capture the temporal relationships. Finally, the outputs of the deep learning model are fed into the LightGBM model to leverage its superior learning capabilities for the ultimate prediction of wind power. Simulation experiments demonstrate that the proposed ICEEMDAN-CNN-LSTM-LightGBM model achieves higher accuracy in multi-time scale wind power prediction, providing more reliable decision assistance with the management and operation of wind farms.

Does wealth equate to happiness? an 11-year panel data analysis exploring socio-economic indicators and social media metrics.

The Easterlin paradox questions the link between economic growth and national well-being, emphasizing the necessity to explore the impact of economic elasticity, income inequality, and their temporal and spatial heterogeneity on subjective happiness. Despite the importance of these factors, few studies have examined them together, thus ongoing debates about the impact of economics on well-being persist. To fill this gap, our analysis utilizes 11 years of panel data from 31 provinces in China, integrating macroeconomic indicators and social media content to reassess the Easterlin paradox. We use GDP per capita and the Gini coefficient as proxies for economic growth and income inequality, respectively, to study their effects on the subjective well-being expressed by citizens on social media in mainland China. Our approach combines machine learning and fixed effects models to evaluate these relationships. Key findings include: (1) In temporal relationships, a 46.70% increase in GDP per capita implies a 0.38 increase in subjective well-being, while a 0.09 increase in the Gini coefficient means a 1.47 decrease in subjective well-being. (2) In spatial relationships, for every 46.70% increase in GDP per capita, subjective well-being rises by 0.51; however, this relationship is buffered by unfair distribution, and GDP per capita no longer significantly affects subjective well-being when the Gini index exceeds 0.609. This study makes a synthetic contribution to the debate on the Easterlin paradox, indicating that economic growth can enhance well-being if income inequality is kept below a certain level. Although these results are theoretically enlightening for the relationship between economics and national well-being globally, this study's sample comes from mainland China. Due to differences in cultural, economic, and political factors, further research is suggested to explore these dynamics globally.