Statistical Model Research Articles

Power quality validation in micro off-grid daily load using modular differential, LSTM deep, and probability statistics models processing NWP-data

Load corrections with respect to power quality (PQ) after the first pre-estimate of Renewable Energy (RE) power consumption must ensure system-tolerant performance without malfunctions. First, acceptable daily load sequences for the attached equipment are combined and determined according to the RE potential and charge states in accommodation to user needs and normal operation. The main motivation is a consequent day-to-day verification of algorithmically scheduled power consumption tasks in the proposed two-stage optimisation according to the system resources and user needs. Statistical artificial intelligence (AI) is employed, as local atmospheric turbulences with terrain obstacles and unexpected user activity result in various operational states in real microsystems. A new unconventional neurocomputing strategy, called Differential Learning (DfL), was applied in the modelling and prediction of the high dynamical PQ parameters in an experimental RE based system according to input-output training data, without an exact specification of its behaviour. The DfL models were compared with recent deep and machine learning techniques. Prediction models were formed after an initial detection of adequate daily training intervals. The AI models are finally tested to process the complete 24-hour forecast series of related input variables used in learning, to estimate the PQ target output at the corresponding times.

Vulnerability of barley yields in Québec.

Climate projections across Québec indicate increased water stress and recurrent vulnerability of cropping systems. In recent years, reports of droughts and water stress have been recorded across the province. Many parts of Québec have experienced droughts in the past few years, which have had uninvestigated impacts on crops. These droughts have been described as some of the most significant in the last 80 years. On the positive side, climate change is likely to trigger shorter winters and therefore longer growing seasons for several crops. However, for crops like maple syrup, the regions suitable for their cultivation will shift northwards. Despite these projections, studies monitoring the susceptibility of barley to environmental changes, climate variability, and adaptive capacity across Québec are still limited. This study aims to provide a provincial-scale portrait of vulnerability of barley in Québec based on historical growing season precipitation, barley yield, and socioeconomic data (literacy and poverty rates) using a composite statistical model. Growing season precipitation data were downloaded from Ouranos. Barley yield data were collected from the Institut de la Statistique du Québec, and the socio-demographic data were collected from the Advisory Council of Poverty and the Institut de la Statistique du Québec. A vulnerability index with sub-indices (sensitivity, exposure, and adaptive capacity) is deployed. It is hypothesised that (1) vulnerability is inversely associated with adaptive capacity, and (2) the peripheral regions of Québec are more vulnerable and less adaptive to climate stressors. Initial results show that when the vulnerability index for barley is more prominent, the associated index of adaptive capacity is relatively lower. A significant gradient between the peripheral and southern regions of Québec is observed, with vulnerability lowest around Montreal/Laval and gradually increasing towards the peripheral regions. A better understanding of vulnerability warrants a change in approach from focusing solely on climate-related variables to integrating socioeconomic proxies. The challenge, however, has been how to introduce these socioeconomic proxies into empirical and process-based crop models.

Predicting fish spawning phenology for adaptive management: Integrating thermal drivers and fishery constraints

Climate warming is causing shifts in reproductive phenology, a crucial life history trait determining offspring survival and population productivity. Evaluating these impacts on exploited marine resources is essential for implementing adaptive measures from an ecosystemic approach. This study introduces a statistical model designed to predict fish spawning phenology from sea surface temperature profiles, integrating mortality-corrected hatch-date distributions inferred from fishery-dependent samplings, along with the gonadosomatic index of adult individuals. When applied to different dolphinfish (Coryphaena hippurus) populations across a broad latitudinal range, the model reasonably predicts the spawning phenology across its extensive thermal ranges, elucidating a direct relationship between mean annual temperature and the breadth of the spawning season. Despite the varying thermal profiles, results show a consistent timing of spawning peaks approximately 49 days before the peak in temperature. Importantly, these findings account for the impact of fishery constraints, such as seasonal closures or different sampling schedules, offering a robust tool for adjusting management practices in response to inter-annual temperature variations. These insights are critical for both short-term fishery management, including the strategic planning of seasonal closures, and long-term projections of spawning phenology shifts under changing thermal regimes. By enhancing our ability to predict spawning times, this research contributes significantly to the sustainable management of fish populations and the adaptive response to environmental changes.

Identifying vegetation patterns for a qualitative assessment of land degradation using a cellular automata model and satellite imagery.

We aim to identify the spatial distribution of vegetation and its growth dynamics with the purpose of obtaining a qualitative assessment of vegetation characteristics tied to its condition, productivity and health, and to land degradation. To do so, we compare a statistical model of vegetation growth and land surface imagery derived vegetation indices. Specifically, we analyze a stochastic cellular automata model and data obtained from satellite images, namely using the normalized difference vegetation index and the leaf area index. In the experimental data, we look for areas where vegetation is broken into small patches and qualitatively compare it to the percolating, fragmented, and degraded states that appear in the cellular automata model. We model the periodic effect of seasons, finding numerical evidence of a periodic fragmentation and recovery phenomenology if the model parameters are sufficiently close to the model's percolation transition. We qualitatively recognize these effects in real-world vegetation images and consider them a signal of increased environmental stress and vulnerability. Finally, we show an estimation of the environmental stress in land images by considering both the vegetation density and its clusterization.

A dataset of acoustic measurements from soundscapes collected worldwide during the COVID-19 pandemic

Political responses to the COVID-19 pandemic led to changes in city soundscapes around the globe. From March to October 2020, a consortium of 261 contributors from 35 countries brought together by the Silent Cities project built a unique soundscape recordings collection to report on local acoustic changes in urban areas. We present this collection here, along with metadata including observational descriptions of the local areas from the contributors, open-source environmental data, open-source confinement levels and calculation of acoustic descriptors. We performed a technical validation of the dataset using statistical models run on a subset of manually annotated soundscapes. Results confirmed the large-scale usability of ecoacoustic indices and automatic sound event recognition in the Silent Cities soundscape collection. We expect this dataset to be useful for research in the multidisciplinary field of environmental sciences.

Combined Influence of Multiple Factors on Lung Cancer and Anxiety: A Probit Model Analysis

Lung cancer remains a leading cause of cancer-related mortality worldwide, significantly impacting public health. Concurrently, anxiety is a prevalent psychological disorder known to influence the development and progression of various cancers. This research paper aims to examine the combined influence of various factors, including demographic characteristics, environmental exposures, and physical conditioning data, on the incidence of lung cancer and anxiety. Given the binary nature of both lung cancer and anxiety outcomes, the analyses will first employ separate binary probit regression models to identify significant predictors for each condition independently. Then, joint modeling techniques will be implemented with dual purposes. First, by comparing the results of the individual and joint models, the reliability and robustness of the findings will be enhanced through cross-validation. Second, joint models will enable an investigation into potential endogeneity between lung cancer and anxiety. Addressing this endogeneity is crucial as it can potentially improve model robustness and provide deeper insights into the interrelationship between these two health outcomes. For the study purpose, the demographic factors considered include age and gender. Environmental factors encompass smoking history, alcohol consumption, and peer pressure. Physical conditioning data includes pre-existing health conditions such as yellow fingers, anxiety, chronic disease, fatigue, allergy, wheezing, coughing, shortness of breath, swallowing difficulty, and chest pain. By leveraging advanced statistical modeling techniques, this research seeks to uncover nuanced relationships and potential causal pathways that may exist between lung cancer and anxiety. The findings from this study will contribute to the existing body of knowledge by providing additional case study showing the relationship among a multitude of factors on lung cancer and anxiety, respectively. Also, the endogeneity checking among lung cancer and anxiety may enhance the efficiency of models done by others in the future. The research paper's analysis on lung cancer incidences reveals that age and wheezing are significant predictors. In examining anxiety levels, smoking emerged as a significant predictor, indicating a higher likelihood of anxiety among smokers. In addition, the joint probit models confirmed these findings, with age and wheezing significantly predicting lung cancer incidence, and smoking significantly predicting anxiety levels. No significant endogeneity was observed between lung cancer and anxiety, suggesting that these health outcomes are influenced by different sets of factors. These findings underscore the importance of considering demographic, environmental, and physical conditioning data in understanding and addressing lung cancer and anxiety, and checking their potential endogeneity

Adsorption of cadmium ion from water by using non-toxic iron oxide adsorbent; experimental and statistical modelling analysis

Adsorption of cadmium ion from water by using non-toxic iron oxide adsorbent; experimental and statistical modelling analysis

The impact of intellectual property rights and the level of information sensitivity on information security in the United Arab Emirates

In the rapidly evolving landscape of technological innovation, the safeguarding of Intellectual Property Rights (IPR) emerges as a critical factor influencing economic growth and technological advancement. This study, conducted in the context of organizations operating in the United Arab Emirates (UAE), meticulously explores the intricate dynamics between IPR awareness, enforcement, and their implications for information security practices. The research undertakes a thorough investigation with three primary objectives: a comprehensive examination of IPR awareness, an exploration of the relationship between IPR enforcement and information security practices, and an assessment of the impact of information sensitivity. To achieve these objectives, a sample population of 150 respondents from various sectors was engaged, employing a combination of survey instruments and robust statistical analyses. The findings of the study illuminate a strong positive correlation between IPR awareness and information security practices, underscoring the pivotal role of cultivating IPR awareness among organizations. Furthermore, the enforcement of IPR, intricately connected with a resilient legal framework, regulatory authorities, international agreements, and effective customs and border control measures, is identified as a significant influencer of information security practices. The study employs a statistical model that exhibits a high explanatory power, elucidating approximately 85.9% of the variance in information security practices. In conclusion, the research offers profound implications for organizations, policymakers, and stakeholders in the UAE, advocating for strategies such as education, legal and regulatory support, international collaboration, and robust access control mechanisms to fortify IPR awareness, enforcement, and information security practices. The integration of advanced tools such as the smart PLS software adds depth and reliability to the study’s analytical framework, contributing to its comprehensive insights.

Current status and perspectives in environmental oncology

AbstractCancer stands as a leading global cause of death, with its etiology characterized by complexity and multifaceted factors. Growing research indicates a strong correlation between environmental factors and cancer incidence, underscoring the critical importance of intervening in environmental risk factors to mitigate cancer occurrence. Despite this, specialized research institutions focusing on the intersection of environment and cancer remain scarce, with global investment in cancer prevention significantly trailing behind efforts in diagnosis and treatment. Against the backdrop of rapid global climate change, industrialization, urbanization, aging populations, and the globalization of lifestyles, we proposed the concept of Environmental Oncology (EO) to address these challenges. We discussed the rationale and necessity of developing EO and presented a comprehensive research framework focusing on cancer prevention and treatment. Future EO research will aim to identify cancer causes and implement early prevention strategies using advanced scientific technologies and methods. By emphasizing multidisciplinary collaboration and integrating molecular biology at the micro level, EO will explore the relationship between external and internal environments and cancer. EO will identify potential therapeutic targets by studying the pathways through which environmental exposures lead to carcinogenesis. EO will develop early warning systems and disseminate research findings by collecting big data, employing robust statistical models, and establishing research centers.

Mandibular Gender Dimorphism: The Utility of Artificial Intelligence and Statistical Shape Modeling in Skeletal Facial Analysis.

In gender-affirming surgery, facial skeletal dimorphism is an important topic for every craniofacial surgeon. Few cephalometric studies have assessed this topic; however, they fall short to provide skeletal contour insights that direct surgical planning. Herein, we propose statistical shape modeling (SSM) as a novel tool for investigating mandibular dimorphism for young white individuals. A single-center, retrospective study was performed using computed tomography (CT) scans of white individuals, aged 20 to 39 years old. AI-assisted, three-dimensional (3D) mandibles were reconstructed in Materialise Mimics v25.0. We used SSM to generate average 3D models for both genders. Relevant manual anthropometric measurements were taken for the SSMs and individual mandibles. Contour disparities were then represented using 3D overlays and heatmaps. Statistical analyses were performed using unpaired student t testing or Wilcoxon signed rank testing with 95% confidence interval as deemed appropriate by population-level normality assessment. Ninety-eight patients (53 females, 45 males) were included. Male mandibles showed greater bigonial width, intercondylar width, ramus height, and body length [p<0.005]. There was no statistically significant difference in the gonial angle measurements [p=0.62]. All relevant manual individual measurements demonstrated excellent concordance to their SSM counterparts. The 3D overlays of SSMs revealed squarer male chins with more lateral but less anterior projection than their female counterparts. Also, the female mandibles showed smoother transition at the gonial angle. SSM provides a novel tool to objectively evaluate volumetric and contour dimorphisms between genders. Moreover, this method can be automated, allowing for expedited comparisons between populations of interest compared to manual assessment. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 . Bullet points about the importance of this work: Advancing Anthropometric Assessment: Statistical shape modeling (SSM) offers a cutting-edge approach to visualizing gender-specific skeletal anatomic differences for aesthetic and gender-affirming facial surgery. Expediting Comparative Analysis: The workflow established in this paper streamlines the evaluative process, enabling rapid morphologic comparisons between populations. Patient-Centered Care: This study establishes a foundation for the development of SSMs in individualized operative planning.

Transformers for Molecular Property Prediction: Lessons Learned from the Past Five Years.

Molecular Property Prediction (MPP) is vital for drug discovery, crop protection, and environmental science. Over the last decades, diverse computational techniques have been developed, from using simple physical and chemical properties and molecular fingerprints in statistical models and classical machine learning to advanced deep learning approaches. In this review, we aim to distill insights from current research on employing transformer models for MPP. We analyze the currently available models and explore key questions that arise when training and fine-tuning a transformer model for MPP. These questions encompass the choice and scale of the pretraining data, optimal architecture selections, and promising pretraining objectives. Our analysis highlights areas not yet covered in current research, inviting further exploration to enhance the field's understanding. Additionally, we address the challenges in comparing different models, emphasizing the need for standardized data splitting and robust statistical analysis.

Heuristics for the Intelligent Prediction of Population Growth

Population growth is a phenomenon that is inevitable in a life course and the components of population growth are fertility, mortality and migration. Because of the impact population growth exact upon the socio- economic nature of a country, it will be wise to know the future size and distribution of it so that adequate measures can be made to forestall possible problems. There are different population models and algorithms implemented to project population growth, ranging from statistical to machine learning models; these model techniques were very suitable in their time, such as the Malthusian theory, which, first and foremost, gives deeper consideration to the exponential growth model, while many implemented classification and linear regression algorithm. Linear regression machine learning algorithms were considered the most effective algorithm for population growth projection. This study tends to develop a machine learning model that is data-driven mathematically, capable of implementing the data-independent prediction model equation that was used to predict the impact of the Non-pharmaceuticals approach and pharmaceuticals approach (NPA and PA) in mitigating the spread of the Covid-19 virus, and both models were re-modified to project Nigeria population growth. The data-independent prediction model (DIPM) utilized onset data from the NPA and PA interventions to predict the probability of mitigating the spread of the virus for a specific period. The DIPM has the properties of arithmetic and exponential methods. The fusion of these two properties with the aid of machine learning model has further reveal the data-independent prediction model as a conceptualization technique to reflect how data are processed in an algorithm setting in a concrete world, with the support of the Java array list algorithm, what all the statistical models and supervised machines learning model used in the past studies could not achieve, have been accomplished succinctly. The data independent prediction model is a robust technique for both projection and forecasting future population growth as well as proffer answers to historical issues in mathematical modeling of population expansion.

Survival analysis for lung cancer patients: A comparison of Cox regression and machine learning models

IntroductionSurvival analysis based on cancer registry data is of paramount importance for monitoring the effectiveness of health care. As new methods arise, the compendium of statistical tools applicable to cancer registry data grows. In recent years, machine learning approaches for survival analysis were developed. The aim of this study is to compare the model performance of the well established Cox regression and novel machine learning approaches on a previously unused dataset. Material and MethodsThe study is based on lung cancer data from the Schleswig-Holstein Cancer Registry. Four survival analysis models are compared: Cox Proportional Hazard Regression (CoxPH) as the most commonly used statistical model, as well as Random Survival Forests (RSF) and two neural network architectures based on the DeepSurv and TabNet approaches. The models are evaluated using the concordance index (C-I), the Brier score and the AUC-ROC score. In addition, to gain more insight in the decision process of the models, we identified the features that have an higher impact on patient survival using permutation feature importance scores and SHAP values. ResultsUsing a dataset including the cancer stage established by the Union for International Cancer Control (UICC), the best performing model is the CoxPH (C-I: 0.698±0.005), while using a dataset which includes the tumor size, lymph node and metastasis status (TNM) leads to the RSF as best performing model (C-I: 0.703±0.004). The explainability metrics show that the models rely on the combined UICC stage and the metastasis status in the first place, which corresponds to other studies. DiscussionThe studied methods are highly relevant for epidemiological researchers to create more accurate survival models, which can help physicians make informed decisions about appropriate therapies and management of patients with lung cancer, ultimately improving survival and quality of life.

Effects of vetiver root on cracking of expansive soils and its mechanistic analysis

The study investigated the reinforcing effect of vetiver root on soil by conducting outdoor planting tests and indoor root tests. The cracking indexes of soil specimens with varying root contents were analyzed, and a statistical model was established to determine the relationship between the cracking indexes, the number of dry and wet cycles, and the root content. The study revealed the crack evolution law of vetiver-reinforced expansive soil. The study explored the mechanism of the vegetation root in inhibiting the cracking of expansive soil and determined the optimal planting density of vetiver grass through outdoor planting tests. The results indicate that: The surface crack rate (CR), total crack length (CL), and crack number (CN) in the root-soil specimen exhibited exponential growth with an increase in the number of wet and dry cycles. This growth was more pronounced during the first and second cycles. The vetiver root could effectively reduce soil crack formation, and the specimen's cracking resistance is positively correlated with the root content. With the root content increased, the CR, CN, and CL decreased. The logistic model is suited to the CL of added root soil. The logistic model is more suitable for the growth model of the CR of the expansive soil with low root content, while the Boltzmann model is more suitable for the growth model of the CR of the expansive soil with high root content. Width of crack (CW) is better suited to the DoseResp growth model. The Boltzmann model is more applicable to the CN in expansive soils with low reinforcement, while the logistic growth model is more suitable for the development of CN above 0.21% root content. The development of the crack network was influenced by two key factors: the root content and the number of wet and dry cycles. Under the condition of planting roots, the development of crack networks in expansive soil differs from that of expansive soil with added roots, and there is no clear pattern to follow. The inhibitory effect of the vetiver root on cracking of expansive soil is related to the planting density of vetiver.

Teaching modeling in introductory statistics: A comparison of formula and tidyverse syntaxes

When incorporating programming into a statistics course, there are many pedagogical considerations. In R, one consideration is the particular R syntax used. This paper reports on a head-to-head comparison of a pair of introductory statistics labs, one conducted in the formula syntax, the other in tidyverse. Pre- and post-surveys show minimal differences between labs, with students reporting a positive experience regardless of section. Analysis of YouTube and RStudio Cloud data shows interesting distinctions. The formula section appeared to watch a larger proportion of pre-lab videos, but spend less time computing. Conversely, the tidyverse section watched a smaller proportion of videos and spent more time computing. The tidyverse labs were slightly longer in terms of lines of code and minutes of videos. The tidyverse labs exposed students to more distinct R functions, but reused functions more frequently. Both labs relied on a relatively small vocabulary of functions, which can provide a starting point for instructors interested in teaching introductory statistics in R. The instructor experience of teaching the two syntaxes diverged when discussing relationships between categorical variables, as well as working with numeric variable summary statistics. This work provides additional evidence for instructors looking to choose between syntaxes for introductory statistics.

Impact of tent shade on heat exposures and simulated heat strain for people experiencing homelessness.

Concurrent increases in homelessness and heat intensity, duration, and frequency translate to an urban heat risk trap for the unsheltered population. Homelessness is both a driver and consequence of poor health, co-creating distinct geographies with various risk factors that exacerbate heat vulnerability. We tested the efficacy of different tent shadings over identical tents often observed in the Phoenix area (white bedsheet, mylar, tarp, and aluminum foil) and compared them to a control tent (uncovered) and ambient conditions. We monitored all meteorological variables at all six locations, notably Mean Radiant Temperature (MRT). The in-tent microclimate variability was applied to complete statistical and physiological modeling including substance use on heat strain. Findings indicate that tent shadings resulted in significantly lower in-tent MRT during the day (p < 0.05), but exacerbated in-tent thermal risk during the night compared to the control tent and ambient conditions. Furthermore, we found evidence that the temperature metric matters, and using only either MRT or air temperature (Tair) to assess "heat" could lead to inconsistent conclusions about in-tent microclimate. Interactions between shade types and time significantly amplified in-tent thermal risk. Physiological modeling indicates a higher risk of heat strain (core temperature beyond 40˚C) for people using substances. Decision makers should promote testing different heat intervening strategies toward realizing effective means of protecting human life and preventing heat illnesses. This study illuminates the need for an interdisciplinary approach to studying tents as shelters that considers the total heat load with heat strain modeling.

Electrocoagulation process for phosphate recovery of agricultural wastewater: effect of calcium adding, voltage, and time.

Recovery of valuable resources, such as phosphate recovery from wastewater, can help close the nutrient cycle and is interesting to investigate. This study aims to evaluate phosphate recovery and set aside TOC, OC, and IC in agricultural wastewater using electrocoagulation with a helix electrode configuration. This study employed the Response Surface Methodology (RSM) for statistical analysis and modeling, utilizing a central composite design (CCD). Variation of calcium concentration (2-7mg/L), voltage (15-45V), and electrocoagulation time (5-15min) was applied in an electrocoagulation reactor with a helix-shaped stainless steel cathode and a solid cylindrical Mg anode. Based on RSM analysis, electrocoagulation with a helical electrode configuration significantly affects phosphate recovery and the removal of TOC, OC, and IC when treating agricultural wastewater. Under operating conditions of 15V, 15min time, and 2mg/L calcium concentration, we achieved the lowest phosphate concentration of 0.003mg/L (99.74% reduction). The highest TOC allowance is > 100% of the initial concentration, the TC allowance is 55.79%, and the IC allowance is 30.91%. The formation of metal hydroxides affects the efficiency of TOC removal in the electrocoagulation process, and higher electrolysis times lead to higher TOC removal efficiency. Higher voltages also improve the coagulation and flotation processes in the reactor. Calcium concentration plays a role in enhancing the flocculation process and binding phosphonates from wastewater.

Split-and-merge model selection of mixtures of Gaussian processes with RJMCMC

The mixture of Gaussian processes is a powerful statistical learning model that can be effectively applied to curve clustering and prediction. However, the corresponding model selection problem, that is, selecting an appropriate number of components in the mixture, is rather difficult to solve. In our previous work, we established the split-and-merge automatic model selection algorithm for mixtures of Gaussian processes along the output space under the framework of Reversible Jump Markov Chain Monte Carlo (RJMCMC), which can not only determine the number of actual Gaussian processes but also dynamically adjust the Gaussian process components to avoid dependence on parameter initialization and initial partitioning of the dataset during the parameter learning on a given dataset. In this study, we propose two algorithms: Penalized Likelihood RJMCMC and Penalized Prior RJMCMC. The former integrates a penalized term into the likelihood, while the latter incorporates a penalized term into the prior and operates within the full Bayesian inference framework, both aiming to focus more sharply on determining the number of components in the convergence process. Furthermore, we prove the geometric ergodicity of the RJMCMC algorithm for the mixture of Gaussian processes model, ensuring convergence of the posterior distribution with sufficient iterations. The experimental results further demonstrate the robustness of our PP-RJMCMC algorithm in model selection, showing superior performance compared to traditional approaches in curve classification and clustering. Additionally, the prediction performance is comparable to the EM algorithm. Although not directly explored in this study, the RJMCMC results can be used to initialize the EM algorithm, which could potentially improve prediction accuracy and accelerate computation.

Carbon emission prediction of 275 cities in China considering artificial intelligence effects and feature interaction: A heterogeneous deep learning modeling framework

High technology and artificial intelligence (AI) are crucial for achieving urban Dual Carbon Goals. This study proposes a heterogeneous deep learning framework with analysis and prediction phases to explore AI technology's impact on urban carbon emissions. In the analysis phase, fixed effect models address differences in AI development and time heterogeneity among cities. In the prediction phase, an Attention Deep & Cross Network (ADCN) model leveraging feature interactions is proposed to enhance prediction precision and robustness. The Shapley Additive Explanations (SHAP) method quantifies each feature's contribution to ADCN's predictions, elucidating factors' impacts on carbon emissions. This study investigates AI development levels and other variables across 275 Chinese cities to test model performance and uncover the AI-carbon emissions relationship. Results show that fixed effects models significantly improve prediction accuracy, with ADCN outperforming statistical and machine learning models (RMSE: 646.262, MAE: 474.818, R²: 0.993). SHAP analysis reveals that AI technology level (11.85 %), smart city (12.35 %), energy consumption (11.60 %), population (9.38 %), urbanization rate (8.89 %), and GDP (8.40 %) significantly influence carbon emissions. Especially, the interaction between AI technology and smart city or intelligent manufacturing proportion increases their carbon reduction by 1.059 × 1021 or 4.992 × 1019 tons. AI technology moderates the impact of increasing energy consumption and urbanization, reducing their potential emissions by 20 % and 1 %. The framework offers high accuracy and scalability, providing valuable insights for strategy development.

Predicting Economic Trends and Stock Market Prices with Deep Learning and Advanced Machine Learning Techniques

The volatile and non-linear nature of stock market data, particularly in the post-pandemic era, poses significant challenges for accurate financial forecasting. To address these challenges, this research develops advanced deep learning and machine learning algorithms to predict financial trends, quantify risks, and forecast stock prices, focusing on the technology sector. Our study seeks to answer the following question: “Which deep learning and supervised machine learning algorithms are the most accurate and efficient in predicting economic trends and stock market prices, and under what conditions do they perform best?” We focus on two advanced recurrent neural network (RNN) models, long short-term memory (LSTM) and Gated Recurrent Unit (GRU), to evaluate their efficiency in predicting technology industry stock prices. Additionally, we integrate statistical methods such as autoregressive integrated moving average (ARIMA) and Facebook Prophet and machine learning algorithms like Extreme Gradient Boosting (XGBoost) to enhance the robustness of our predictions. Unlike classical statistical algorithms, LSTM and GRU models can identify and retain important data sequences, enabling more accurate predictions. Our experimental results show that the GRU model outperforms the LSTM model in terms of prediction accuracy and training time across multiple metrics such as RMSE and MAE. This study offers crucial insights into the predictive capabilities of deep learning models and advanced machine learning techniques for financial forecasting, highlighting the potential of GRU and XGBoost for more accurate and efficient stock price prediction in the technology sector.