Nonlinear Connection Research Articles

Nonlinear relationship between diabetes mellitus duration and diabetic retinopathy

To investigate the non-linear relationship between diabetes mellitus (DM) duration and the development of diabetic retinopathy (DR). By investigating the association between these variables, our goal is to contribute to the existing knowledge regarding the impact of DM duration on the development and severity of DR. A retrospective cross-sectional study was conducted on 420 patients in the Department of Endocrinology at Guangdong Provincial People’s Hospital, who had undergone ophthalmic consultations from December 2017 to November 2018. The analysis of DM duration and DR utilized a generalized additive model to identify both linear and non-linear connections. The threshold effect was determined using a two-piece regression model. The study included a total of 420 patients, with a mean age of 58.7 years. Of these, 56.9% (239/420) were male. The prevalence of DR was 38.33% (161/420). After adjusting for confounding factors, a nonlinear relationship between DM duration and DR was observed, with a turning point at 8 years. On the left side of the turning point, the prevalence increased by 24% per 1-year increase in DM duration (OR: 1.24; 95% CI: 1.11–1.38; P<0.0001). However, no statistically significant differences were found on the right side of the turning point (OR: 1.02; 95% CI: 0.97–1.08; P = 0.4987). Our study identified a non-linear relationship between DM duration and DR in patients. When the DM duration is less than 8 years, a positive correlation exists between DM duration and DR. However, once the DM duration exceeds 8 years, the effect reaches saturation, and no significant correlation is observed.

Enhancing aquifer protective capacity prediction over Ibeator and environ, Southeastern Nigeria using artificial neural networks and multivariate linear regression analysis

Aquifer protection is essential for securing a sustainable supply of clean water. This study integrates an artificial neural network (ANN) model, identifying non-linear connections, with multivariate linear regression (MLR) analysis to improve predictions of aquifer protective capacity and assess vulnerability. Twelve vertical electrical soundings (VES) were conducted with a maximum electrode spacing of 250 m. Aquifer parameters derived from the VES dataset were analyzed using ANN to capture complex patterns. The ANN model, trained on historical data, learned the relationship between input variables and protective capacity. MLR analysis identified influential factors affecting vulnerability. Results reveal varying aquifer depths, with Umudime being the deepest and western parts having the shallowest depths. The resistivity map shows high values around Okorobi and Uhuala and low values in eastern to northeastern parts. Hydraulic conductivity and 3D subsurface models exhibit an inverse relationship with resistivity. Transmissivity and storativity maps exhibit similar patterns. MLR outperforms ANN in predicting resistivity, transmissivity, and storability, indicating high forecasting accuracy for aquifer protective capacity. Input parameters' contribution levels follow a specific order for different aquifer properties. R2 Value 0.0869, indicating a weak correlation between the predicted and actual values in ANN model while R2 Value 0.9775 in MLR model shows a strong correlation and much better performance than the ANN model. The results of the modeling suggest that both the ANN and MLR models have shown promising effectiveness and accuracy in predicting aquifer parameters, aiding decision-makers in implementing targeted protection measures, predicting aquifer parameters, providing insights for effective management strategies.

The impact of income inequality on economic growth in MENA region: The role of energy poverty threshold effect

This research paper explores the impact of income inequality on economic growth under the influence of energy poverty threshold effect in 12 Middle East-North African (MENA) countries from 2000 to 2019. We examine the possibility of a non-linear connection between income inequality and growth below and above a specific energy poverty level using the fixed-effect threshold panel proposed by Hansen (1999) [107]. Our results suggest that income disparity adversely influences economic development, no matter the level of energy poverty. Moreover, our results indicate that the detrimental impact of inequality on economic growth diminishes as energy poverty decreases and reaches a certain threshold. Therefore, addressing energy poverty helps to minimize the negative economic consequences of inequality. Our research provides valuable insights for MENA. In fact, policymakers can mitigate these adverse effects by supporting and expanding the diffusion of clean fuels and modern cooking techniques. A comprehensive approach that combines expanding clean energy infrastructure, providing financial incentives, and conducting public awareness and training programs can ensure the availability, affordability while facilitating their efficient use.

A hybrid approach for modelling safety hazards in China subways: multi-stakeholders’ perspective

PurposeUnlike previous research that primarily utilized structural equation modelling (SEM) to evaluate safety hazards in subway projects, this research aims to utilize a hybrid approach to investigate and scrutinize the key indicators of safety hazards leading to accidents, thereby hindering the progress of subway projects in China, taking into cognizance the multiple stakeholder’s perspective.Design/methodology/approachBy administering a survey questionnaire to 373 highly involved stakeholders in subway projects spanning Changsha, Beijing and Qingdao, China, our approach incorporated a four-staged composite amalgamation of exploratory factor analysis (EFA), confirmatory factor analysis (CFA), covariance-based structural equation modelling (CB-SEM) and artificial neural network (ANN) to develop an optimized model that determines the causal relationships and interactions among safety hazards in subway construction projects.FindingsThe optimized model delineated the influence of individual safety hazards on subway projects. The feasibility and applicability of the model developed was demonstrated on an actual subway project under construction in Changsha city. The outcomes revealed that the progress of subway projects is significantly influenced by risks associated with project management, environmental factors, subterranean conditions and technical hazards. In contrast, risks related to construction and human factors did not exhibit a significant impact on subway construction progress.Research limitations/implicationsWhile our study provides valuable insights, it is important to acknowledge the limitation of relying on theoretical approaches without empirical validation from experiments or the field. In future research, we plan to address this limitation by assessing the SEM using empirical data. This will involve a comprehensive comparison of outcomes derived from CB-SEM with those obtained through SEM-ANN methods. Such an empirical validation process is crucial for enhancing the overall efficiency and robustness of the proposed methodologies.Originality/valueThe established hybrid model revealed complex non-linear connections among indicators in the intricate project, enabling the recognition of primary hazards and offering direction to improve management of safety in the construction of subways.

Green infrastructural development of tourism to mitigate the ecological footprint of economic growth: A comparative study across Asian nations

This research examines the intricate connection between tourism and environmental destruction in 28 Asian countries, concentrating on the non-linear impacts of tourism. Moreover, this study contemplates how tourism can mitigate the effects of economic growth on environmental decline. Westerlund, Johansen-Fisher, and Pedronico-integration tests are necessary to detect the co-integration connection between the proposed factors. The research also uses the Augmented Mean Group; the dynamic system generalized method of moments, and fully changed Ordinary Least Squares (OLS). These tools help address econometric and economic problems such as co-integration, dynamism, variation, inter-sectional dependence, and endogeneity. The results demonstrate a U-shaped non-linear connection between ecological footprint and Tourism in Asian nations. Primarily, the tourism industry can initially decrease environmental damage. However, as it increases in size, it can worsen the harm. Additionally, the study suggests that tourism negatively influences how economic growth affects ecological footprint. This research contributes to the existing literature on tourism’s effects on the environment. The research suggests that tourism significantly impacts the environment; therefore, initiatives to reduce damage should be aimed at tourism.

Association between blood lead levels and serum creatinine: a cross-sectional study.

The aim of this cross-sectional study was to investigate the association between blood lead levels and serum creatinine. In the present study, sample data were derived from the National Health and Nutrition Examination Survey (NHANES) 2015-2018 of participants with complete data of blood lead levels and serum creatinine. Multivariate linear regression models were used to examine the association between blood lead levels and serum creatinine. The nonlinear connection was described using fitted smoothing curves and threshold effect analysis. Furthermore, subgroup analyses and interaction tests were performed. A total of 9026 participants were enrolled in this study, and the mean serum creatinine for all subjects was 0.86 ± 0.34mg/dL. When blood lead levels were divided into quartiles, it was found that participants' serum creatinine levels gradually increased with increasing blood lead levels (Q1: 0.77 ± 0.19mg/dL, Q2: 0.84 ± 0.23mg/ dL, Q3: 0.89 ± 0.34mg/dL, Q4: 0.94 ± 0.50mg/dL). Compared to the lowest serum quartile, the highest quartile of blood lead levels had a positive correlation with serum creatinine in the fully adjusted model (β = 0.05 95% CI 0.01-0.08). Smooth curve fittings and threshold effect analysis showed an inverted J-shaped nonlinear relationship between blood lead levels and serum creatinine, with an inflection point of 3.10 (μg/dL). Subgroup analyses and interaction tests revealed that diabetes mellitus impacted the relationship between serum creatinine and blood lead levels. This study suggests a positive correlation between blood lead levels and serum creatinine.

Non-linear connection between the triglyceride–glucose index and prediabetes risk among Chinese adults: a secondary retrospective cohort study

BackgroundThe triglyceride–glucose (TyG) index has garnered recognition as a surrogate marker for insulin resistance, a pivotal factor in the pathogenesis of various metabolic disorders. Despite its emerging role, the empirical evidence delineating its association with prediabetes mellitus (Pre-DM) remains scant. This research aims to clarify the link between the TyG index and the likelihood of Pre-DM development within a Chinese demographic.MethodsThis investigation was structured as a retrospective cohort analysis, encompassing a sample of 179,177 Chinese adults. These individuals underwent medical examinations at the Rich Healthcare Group over a period spanning from 2010 to 2016. To ascertain the relationship between the TyG index and the incidence of Pre-DM, this study employed Cox regression analysis complemented by sensitivity and subgroup assessments. Furthermore, Cox proportional hazards regression with cubic spline functions and smooth curve fitting was incorporated to explore the existence of any non-linear connection within this association.ResultsUpon adjusting for a comprehensive array of confounding variables, a statistically significant positive correlation between the TyG index and the risk of Pre-DM was identified (HR: 1.60, 95%CI 1.56–1.65, P < 0.001). The analysis illuminated a non-linear relationship, with an inflection point at a TyG index value of 8.78. For TyG index values below and above this inflection point, the HR was calculated to be 1.94 (95%CI 1.86–2.03) and 1.26 (95%CI 1.20–1.33), respectively. Sensitivity analyses further fortified the reliability of these findings.ConclusionsThis comprehensive examination delineated a significantly positive, non-linear correlation between the TyG index and the risk of Pre-DM within a Chinese population. Individuals with TyG index values below 8.78 have a significantly increased risk of developing prediabetes. These findings underscore the TyG index’s potential efficacy as a predictive tool for assessing Pre-DM risk in clinical practice.

MS-CGAN: Fusion of conditional generative adversarial networks and multi-scale spatio-temporal features for lithology identification

Lithology identification constitutes a crucial undertaking in formation evaluation and reservoir characterization. However, the need for improved precision arises in conventional lithology identification models due to the difficulties presented by unequal distributions of small-sample logging data. An effective combination of domain expertise and data-driven models to predict lithology is essential due to the intricate and nonlinear connection between logging parameters and lithology, combined with the distinct characteristics of the oilfield environments. In this paper, we proposed a multi-scale conditional generative adversarial network(MS-CGAN) method, which combines conditional generative adversarial networks with multi-scale spatio-temporal features to address data imbalance issues and enhance the accuracy of lithology classification. Our approach, tested on two small datasets from the Hugoton and Panoma fields, USA, and the Daqing production wells, China, stands out as the optimal choice compared to other models. Comprehensive evaluation results indicate promising practical applications and potential benefits of the new model in enhancing lithology identification using limited data.

The nonlinear connection between relative fat mass and non-alcoholic fatty liver disease in the Japanese population: an analysis based on data from a cross-sectional study

BackgroundRelative fat mass (RFM) is a newly developed, sex-specific anthropometric formula designed to estimate total body fat percentage. However, research investigating the correlation between RFM and the risk of non-alcoholic fatty liver disease (NAFLD) remains limited. This study evaluates the association between RFM and the risk of NAFLD within the Japanese population.MethodsThis study including 14,250 Japanese adults who underwent physical examinations at Murakami Memorial Hospital between 2004 and 2015. We employed binary logistic regression to elucidate the direct relationship between RFM levels and the incidence of NAFLD. Additionally, a generalized additive model (GAM) coupled with smooth curve fitting techniques was utilized to map the non-linear association.ResultsThe cohort had an average age of 43.53 ± 8.89 years, with a male majority of 52.00%. NAFLD was identified in 17.59% of the participants. After adjusting for confounding factors, a significant positive correlation between RFM and NAFLD risk was observed (OR: 1.15, 95%CI: 1.10–1.21, P < 0.0001 for females; OR: 1.15, 95%CI: 1.10–1.19, P < 0.0001 for males). Additionally, a non-linear relationship between RFM and the incidence of NAFLD was detected in both genders. The RFM threshold was identified as 34.95 for women and 23.40 for men. RFM was positively associated with the risk of NAFLD when RFM was below the respective threshold (OR: 1.29, 95%CI: 1.19–1.40, P < 0.0001 for females; OR: 1.23, 95%CI: 1.17–1.29, P < 0.0001 for males), whereas no significant association was found when RFM was above the threshold (OR: 1.05, 95%CI: 0.98–1.12, P = 0.1829 for females; OR: 1.01, 95%CI: 0.95–1.08, P = 0.7392 for males).ConclusionOur findings suggest a positive, nonlinear relationship between RFM and the risk of NAFLD, with a saturation effect. These results imply that maintaining RFM at a lower level may be advantageous in mitigating the risk of NAFLD.

Debt Risk Analysis of Automotive Enterprises

In order to maintain their financial stability, businesses must recognize and manage the financial risks that are necessarily involved in their operations and output. Businesses may better comprehend their financial status and undertake efficient risk early warning by examining financial indicators. Academic research on the detection and mitigation of financial risks is extensive, and in an effort to increase the accuracy of assessments, an increasing number of academics are using multi-indicator systems for thorough analysis. Furthermore, non-statistical techniques including hierarchical analysis, B-S option pricing models, and artificial neural networks have shown extremely effective at handling complicated data and non-linear connections. As the economy transitions from high to medium-high growth, traditional and new energy vehicles in the automobile sector confront significant possibilities and problems. Traditional automakers aggressively converting to new energy cars include SAIC Motor, BAIC Group, and GAC Group. Companies that produce new energy vehicles, such XPeng, BYD, Tesla, Li Auto, and NIO, are becoming more competitive by developing new technologies and tapping into new markets. To enhance market competitiveness and sustainability, businesses must address issues like excessive inventory and price volatility that have been brought about by the new energy vehicle market’s explosive expansion through efficient financial risk management. The short- and long-term debt repayment capacities of traditional and new energy vehicle (NEV) enterprises are the main subjects of this study’s analysis of their financial statements. According to the findings, traditional automakers have more stable financial circumstances whereas NEV enterprises exhibit stronger short-term solvency and lower long-term financial risk in specific years with superior liquidity and lower debt ratios. The study attempts to offer resources for the automobile industry’s deleveraging, sustainable development, and inventory reduction.

Nonlinear relationship between triglyceride-glucose index and the risk of prediabetes and diabetes: a secondary retrospective cohort study.

The triglyceride-glucose (TyG) index, recognized for its cost-efficiency and simplicity, serves as an accessible indicator of insulin resistance. Yet, its correlation with the risk of prediabetes and diabetes (Pre-DM/DM) in the Chinese demographic remains uncertain. Consequently, our study explored the association between the TyG index and the development of Pre-DM/DM within the Chinese population. The retrospective cohort study was carried out utilizing data from a health screening initiative. The study included 179541 adults over 20 who underwent medical examinations at the Rich Healthcare Group over a period spanning from 2010 to 2016. The correlation between the TyG index and Pre-DM/DM risk was investigated using Cox regression analysis. Furthermore, Cox proportional hazards regression with cubic spline functions and smooth curve fitting was incorporated to explore their non-linear connection. The mean age of study participants was 41.18 ± 12.20 years old, and 95255 (53.05%) were male. During a median follow-up of 3.01 years, 21281 (11.85%) participants were diagnosed with Pre-DM/DM. After adjusting the potential confounding factors, the results showed that the TyG index was positively correlated with incident Pre-DM/DM (HR: 1.67, 95%CI: 1.62-1.71, P< 0.001). Additionally, a non-linear association was observed between the TyG index and the onset of Pre-DM/DM, with an inflection point identified at 8.73. Hazard ratios (HR) to the left and right of this inflection point were 1.95 (95%CI: 1.86-2.04) and 1.34 (95%CI: 1.27-1.42), respectively. Furthermore, sensitivity analyses confirmed the stability of these findings. The TyG index exhibited a non-linear positive relationship with the risk of Pre-DM/DM. These findings imply that maintaining the TyG index at a lower, specified threshold may be beneficial in mitigating the onset of Pre-DM/DM.

Stochastic Processes in the Analysis of Electrical Load Forecasting

It is exceptionally vital to be able to precisely foresee power loads since that's a key portion of overseeing vitality well and building framework. The reason of this consider is to see into how irregular forms can be utilized, particularly in analyzing and anticipating changes in power stack designs over a run of time scales. Since power stack request changes over time, we require precise arranging strategies that can take into consideration the questions and complicated connections. The primary portion of the think about looks at well-known stochastic forms like autoregressive coordinates moving normal (ARIMA), Gaussian forms, and Markov models to see how well they can depict the irregular viewpoints of power stack information. It is checked to see how well each demonstrate can handle distinctive sorts of changes in time and whether it can be utilized in large-scale computing apps. The ponder too looks at how machine learning strategies can be combined with irregular forms to form estimates more precise. Profound learning and outfit strategies are tried to see in the event that they can learn complex designs and nonlinear connections from past stack information, which would make them valuable in expansion to irregular modeling strategies. A comparison of these strategies appears their aces and cons in terms of how well they make expectations, how quickly they work, and how well they hold up against demonstrate presumptions. The think about too talks approximately how imperative show determination components are for judging foreseeing victory. These incorporate demonstrate assessment strategies and measures like cruel outright rate mistake (MAPE) and root cruel square mistake (RMSE). It is talked around what precise stack estimating implies in genuine life, such as how to move forward vitality generation and delivery strategies, keep the lattice steady, and make beyond any doubt that assets are utilized proficiently.

Residual-enhanced graph convolutional networks with hypersphere mapping for anomaly detection in attributed networks

In the burgeoning field of anomaly detection within attributed networks, traditional methodologies often encounter the intricacies of network complexity, particularly in capturing nonlinearity and sparsity. This study introduces an innovative approach that synergizes the strengths of Graph Convolutional Networks (GCNs) with advanced deep residual learning and a unique residual-based attention mechanism, thereby creating a more nuanced and efficient method for anomaly detection in complex networks. The heart of our model lies in the integration of GCNs that capture complex structural relationships within the network data. This is further bolstered by deep residual learning, which is employed to model intricate nonlinear connections directly from input data. A pivotal innovation in our approach is the incorporation of a residual-based attention mechanism. This mechanism dynamically adjusts the importance of nodes based on their residual information, thereby significantly enhancing the sensitivity of the model to subtle anomalies. Furthermore, we introduce a novel hypersphere mapping technique in the latent space to distinctly separate normal and anomalous data. This mapping is the key to our model’s ability to pinpoint anomalies with greater precision. An extensive experimental setup was used to validate the efficacy of the proposed model. Using attributed social network datasets, we demonstrate that our model not only competes with but also surpasses existing state-of-the-art methods in anomaly detection. The results show the exceptional capability of our model to handle the multifaceted nature of real-world networks.

Collapse fragility analysis of historical masonry buildings considering in-plane and out-of-plane response of masonry walls.

This paper explores the seismic fragility assessment of historical masonry buildings in the context of performance-based earthquake engineering, using multiple-record incremental dynamic analyses (MR-IDA). The methodology is applied to two case studies, the first being a stiff monumental structure, and the second a tall and slender masonry building. Both case studies are modelled in the OpenSees software using three-dimensional macroelements that account for the in-plane (IP) and out-of-plane (OOP) response of masonry walls. Simultaneously, the numerical models account for the influence of non-linear connections in floor-to-wall interfaces and wall-to-wall interlocking. The record-to-record variability induced by the random nature of the ground motion is addressed through earthquake selection consistent with the variability of European hazard. Ground motion records are normalised at a uniform level of seismic intensity to be sequentially increased for the computation of IDA curves. Once IDA curves and fragility functions are derived, the failure results are thoroughly discussed. The fragility functions accounting for the uncertainties arising from the record-to-record variability are contrasted against the results of the seismic assessments affected by multiple sources of uncertainty in material and modelling parameters from a previous study. Finally, the fragility curves derived from the methodology presented herein are directly compared with fragility functions from probabilistic seismic demand analysis (PSDA). Overall, IDA-based fragility functions are found to provide more conservative predictions than the ones obtained from the cloud-based approach using unscaled records.

Nonlinear Analysis of Machine Learning Applicability for Survival Analysis of Lung Cancer Patients

This study about looks into how nonlinear machine learning (ML) can be utilized to foresee the survival of lung cancer patients in arrange to create expectations more exact and make strides understanding comes about. Lung cancer is still one of the foremost deadly types of cancer within the world, so better approaches ought to be found to precisely analyze and arrange medicines. Whereas conventional measurements strategies have made a difference us get it survival examination, they aren't continuously great at catching the complicated, nonlinear connections that exist in organic information. The think about looks at how to combine diverse machine learning models, like Irregular Woodlands, Slope Boosting Machines, and Neural Systems, to see at nonlinear designs and connections in DNA and clinical datasets of lung cancer cases. A part of consideration is paid to methods for choosing highlights and utilizing progressed arrangement strategies to bargain with expansive sums of information and lower clamor. We utilize strict cross-validation strategies and comparison investigation to check how well ML models work compared to conventional factual strategies. To see how well a demonstrate can anticipate how long a understanding will live, measurements just like the Concordance File, Brier Score, and Log-Rank Test are utilized. The comes about appear that machine learning models, particularly those that are great at recognizing nonlinearities, do much way better than standard strategies, making gauges that are more exact and dependable. The comes about appear that machine learning has the ability to alter the way survival analysis is worn out cancer by giving personalized data around forecast and making it easier to select centered treatments. Within the future, analysts will center on combining information from numerous omics and making machine learning models that can be caught on. This will make these progressed examination devices indeed more valuable in clinical settings and boost believe in them. This work includes to the developing sum of information that machine learning is an vital device for moving forward cancer inquire about and quiet care.

On q-Painlevé VI and the geometry of Segre surfaces

In the context of q-Painlevé VI with generic parameter values, the Riemann–Hilbert correspondence induces a one-to-one mapping between solutions of the nonlinear equation and points on an affine Segre surface. Upon fixing a generic point on the surface, we give formulae for the function values of the corresponding solution near the critical points, in the form of complete, convergent, asymptotic expansions. These lead in particular to the solution of the nonlinear connection problem for the general solution of q-Painlevé VI. We further show that, when the point on the Segre surface is moved to one of the sixteen lines on the surface, one of the asymptotic expansions near the critical points truncates, under suitable parameter assumptions. At intersection points of lines, this then yields doubly truncated asymptotics at one of the critical points or simultaneous truncation at both.

Advanced Crop Recommendation Systems: Leveraging Random Forest and KNN Algorithms

In the time of precision agriculture, crop selection optimization is crucial to maximizing productivity and resource efficiency. This article explores the combination of Random Forest (RF) and K-Nearest Neighbors (KNN) algorithms to enhance crop recommendation systems. Crop performance and environmental characteristics have complex and non-linear connections that are captured by the reliable and accurate RF technique. Using a sizable dataset that includes crop yields, climate factors, and soil properties, the study assesses the efficacy of the integrated system in comparison to traditional recommendation methodologies. Preliminary we implemented KNN first and got 96% accuracy and then implemented on RF to get 99% accuracy and also created a GUI using tkinter to predict crops on random value.

Based on the S–O–R theory adoption intention of blockchain technology in libraries: a two-stage analysis SEM–PLS and ANN

PurposeBlockchain, a groundbreaking technology that recently surfaced, is under thorough scrutiny due to its prospective utility across different sectors. This research aims to delve into and assess the cognitive elements that impact the integration of blockchain technology (BT) within library environments.Design/methodology/approachUtilizing the Stimulus–Organism–Response (SOR) theory, this research aims to facilitate the implementation of BT within academic institution libraries and provide valuable insights for managerial decision-making. A two-staged deep learning structural equation modelling artificial neural network (ANN) analysis was conducted on 583 computer experts affiliated with academic institutions across various countries to gather relevant information.FindingsThe research model can correspondingly expound 71% and 60% of the variance in trust and adoption intention of BT in libraries, where ANN results indicate that perceived possession is the primary predictor, with a technical capability factor that has a normalized significance of 84%. The study successfully identified the relationship of each variable of our conceptual model.Originality/valueUnlike the SOR theory framework that uses a linear model and theoretically assumes that all relationships are significant, to the best of the authors’ knowledge, it is the first study to validate ANN and SEM in a library context successfully. The results of the two-step PLS–SEM and ANN technique demonstrate that the usage of ANN validates the PLS–SEM analysis. ANN can represent complicated linear and nonlinear connections with higher prediction accuracy than SEM approaches. Also, an importance-performance Map analysis of the PLS–SEM data offers a more detailed insight into each factor's significance and performance.

Integration of LSTM networks with gradient boosting machines (GBM) for assessing heating and cooling load requirements in building energy efficiency

Due to rising demand for energy-efficient buildings, advanced predictive models are needed to evaluate heating and cooling load requirements. This research presents a unified strategy that blends LSTM networks and GBM to improve building energy load estimates’ precision and reliability. Data on energy usage, weather conditions, occupancy trends, and building features is collected and prepared to start the process. GBM model attributes are created using sequential relationships and initial load projections using LSTM networks. Combining LSTM with GBM takes advantage of each model's strengths: LSTM's sequential data processing and GBM's complex nonlinear connection capture. Performance measures like RMSE and MAE are used to evaluate the hybrid model's validity. Compared to individual models, the integrated LSTM-GBM method improves prediction accuracy. This higher predictive capacity allows real-time energy management systems, improving building operations and reducing energy use. Implementing this integrated model in Building Management Systems (BMS) shows its practicality in achieving sustainable building energy efficiency.

The Link Between Weight-Adjusted-Waist Index and Psoriasis in Adults: A Cross-Sectional Study Based on 2009-2014 and 2003-2006 Data.

The weight-adjusted-waist index (WWI) is a new indicator that may be used to assess obesity. However, there has never been any prior research indicating a link between psoriasis and WWI. Hence, the aim of this investigation is to search for the correlation between the two. Cross-sectional analyses used National Health and Nutrition Examination Survey (NHANES) data from 2003 to 2006 and 2019 to 2014. Through multivariate logistic regression, the connection between Psoriasis and WWI was explored, while subgroup analyses aimed to identify possible causes. Furthermore, Smoothed curve fitting was also performed to further understand the association. The non-linear connection was examined through the use of threshold effects. There were 21,916 participants over the age of 20 in the research. Psoriasis and WWI were revealed to be positively correlated [1.25 (1.14, 1.38)] using multivariate logistic regression analysis. This association was observed in both men [1.50(1.29,1.78)] and hypertensive patients [1.37(1.16,1.62)] in subgroup analysis. Furthermore, we noticed a curvilinear association between WWI and psoriasis, wherein the inflection point was identified as 10.15. To the left of the inflection point, there existed a negative correlation [0.12(0.02,0.91)] while to the right, a positive one [1.68(1.03,2.72)]. According to our findings, psoriasis and WWI are related, although more extensive prospective research is still required to support this conclusion.