Perceived innovativeness: A supervised latent-dirichlet-allocation-based approach

Rainfall is the height of rainwater that falls on a flat area, assuming it doesn't evaporate, doesn't seep, and doesn't flow. Rain levels are measured in mm (millimeters). The target of the research being conducted is in Badung Regency, Bali because Bali is a tourist area that is often visited by tourists and from Indonesian itself, so predictions of meteorology, such as rainfall will greatly impact tourism. In this test, predictions use the Long Short Term Memory (LSTM) method, using daily weather data from the BMKG from 2010 to 2020 as training data and daily weather data for 2021 as prediction data. Based on the test results above, the results show that the two LSTM tests with LSTM Model 128.64 and LSTM Model 64.32 have low MAE and MAPE error values. From First Scenario, the Mean Absolute Error (MAE) value is 8.97246598930908 and Mean Absolute Percentage Error (MAPE) value is 1.7657206683278308%. From Second Scenario, the Mean Absolute Error is 9.706669940783014 and Mean Absolute Percentage Error is 1.9028466692362323%. From the MAE and MAPE values obtained in these two scenarios, it can be proven that from the evaluation results of Rainfall predictions in Badung Regency, Bali, the predictions can be said to be very accurate because they have an error value of less than 10.

Tropical cyclones (TCs) are destructive meteorological phenomena, necessitating accurate predictions of TC track and intensity to reduce risks to human life. This study evaluates three Transformer-based models – vanilla Transformer (Transformer), inverted Transformer (iTransformer), and temporal-variate Transformer (TVFormer) – which are trained, validated, and tested on best track data from 1980 to 2021 from the China Meteorological Administration for TC prediction, integrating temporal, variate, and hybrid token embeddings to analyze temporal and variate correlations. Comparative analysis with four recurrent neural network (RNN) models demonstrates the superiority of the refined Transformer models over RNNs: iTransformer reduces mean absolute error (MAE) and root mean square error (RMSE) by 29.55% and 25.80% (latitude), 50.31% and 46.18% (longitude), 8.71% and 9.98% (pressure), and 8.68% and 9.45% (wind speed), while TVFormer achieves MAE and RMSE reductions of 13.98% and 13.84% (latitude), 39.11% and 38.02% (longitude), 13.69% and 14.02% (pressure), and 12.84% and 12.94% (wind speed) on average. Among Transformer variants, iTransformer excels in track prediction, outperforming Transformer with 21.74% lower MAE and 18.26% lower RMSE for latitude, and 32.73% lower MAE and 24.01% lower RMSE for longitude. TVFormer dominates intensity prediction, reducing pressure errors by 4.42% (MAE) and 3.92% (RMSE) and wind speed errors by 19.21% (MAE) and 14.79% (RMSE) compared to Transformer, while outperforming iTransformer with 4.59% lower MAE and 3.68% lower RMSE for pressure and 3.83% lower MAE and 3.18% lower RMSE for wind speed. Notably, TVFormer also enhances track prediction, with 7.10% reduction in MAE and 7.02% reduction in RMSE for latitude, and 22.84% reduction in MAE and 17.09% reduction in RMSE for longitude compared to Transformer. These results highlight the superiority of iTransformer in track prediction and the efficacy of TVFormer in intensity prediction, thanks to their ability to exploit temporal and variate dependencies, offering potential for TC disaster preparedness systems.

PurposeThis paper aims to examine the effect of Web-based financial reporting and social media platforms on the proxies of information asymmetry in the Saudi Stock Exchange.Design/methodology/approachThe sample of this paper consists of 133 Saudi listed non-financial companies for the year 2019. Web-based disclosure level was measured using 25 items, and the social media platforms examined in this study are Facebook, Twitter and LinkedIn. The information asymmetry proxies are measured using the relative spread and the time-weighted average bid-ask spread.FindingsThe empirical results have shown that there is a negative and significant relation between Web-based financial reporting and the adoption of social media platforms and the proxies of information asymmetry. Indeed, the relative spread and the time-weighted average bid-ask spread decreased with increased Web-based reporting levels. Among three platforms (Facebook, Twitter and LinkedIn), the results show that only the use of Twitter as a channel for information disclosure has a negative and significant effect on information asymmetry proxies. Consequently, in the Saudi context, the authors demonstrate that the assumptions of the agency, stewardship and signaling theories are supported. Also, results reveal that the effect of information disclosure through websites and social media on reducing information asymmetry is stronger for large companies than small companies.Practical implicationsThe paper provides new insights into the role played by websites and social media platforms in the reduction of the information asymmetry in the stock market. Consequently, investors and regulatory authorities in the Saudi financial market must give great importance to online information disclosure and its implications for lowering information asymmetry. This empirical study informs regulators in Saudi Arabia to conduct the better practice of Web-based and social media financial reporting and to regulate the current practice of information disclosure. Besides, the obtained results have the potential to convince firms’ managers to improve online information disclosure to benefit from the reduction in information asymmetry.Originality/valueUnlike previous studies, this study investigates, simultaneously, the effect of Web-based and social media information disclosure on the proxies of information asymmetry in a developing economy. In addition, the hypotheses of this study are developed based on a set of theories (the agency, signaling and stewardship theories), to verify the applicability of these three theories in the Saudi context.

Patent indicator evaluation is a relatively objective patent quality evaluation method recognized by academia. Some subjective patent indicators are often related, which to some extent interferes with the objectivity of patent evaluation. This study selects five commonly used subjective patent indicators that are directly related to the compilers of patent documents, which includes patent forward citations, patent family, four digit international patent classifications, patentees, and patent inventors. Through the progressive limitation of the three constraints of patentees, patent priority countries, and patent disclosure years, the correlation between the subjective patent indicators is compared. The research results show that under the control of progressive constraint conditions, the positive correlation between subjective patent indicators is increasingly strengthened as a whole and has strong controllability. Further verification shows that the direct compilers of patent documents can affect the results of patent indicator evaluation by controlling the subjective patent indicator data, thus interfering with the evaluation of patent quality. It is recommended that researchers carefully select multiple subjective patent indicators with strong correlation before using patent indicators to evaluate patent quality, and complete the correlation analysis and screening of subjective patent indicators in advance.

Load forecasting is a key element in the performance of an electrical power system because of the associated economic constraints. The amount of load consumed depends on a number of factors including the environment, time of the day and day of week etc. Such complex features make the load forecasting task a tricky one, warranting efficient techniques for the problem. In this paper, four machine learning models named: K-Nearest Neighbors (KNN) based regression, Deep Neural Network (DNN) based regression, Random Forest (RF) based regression and Decision Tree (DT) based regression have been employed for the load forecasting purpose on an hourly historical load consumption dataset of a household in Islamabad, Pakistan, aggregated over a period of four years. The results show that the DT model gives the best forecasting results as compared to the RF, DNN and KNN algorithms, yielding a very low Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE).

Evaluation of large language models for carbohydrate counting in type 1 diabetes mellitus: a comparative study with patient estimations.

Summary Many methods have been developed to determine the solution gas/oil ratio (Rs), starting with experiments, followed by empirical correlations establishments, and recently with machine learning applications receiving much interest due to their ability to produce precise results compared with empirical correlations. In this paper, the group method of data handling (GMDH) and the enhanced GMDH based on discrete differential evolution (GMDH-DDE) are used for the first time to estimate the Rs and to provide a correlation to the laboratory measured Rs from bubblepoint pressure (Pb), oil API gravity (API), gas-specific gravity (γg), and reservoir temperature (T) without crude oil properties. These two methods are compared with backpropagation neural networks (BPNN). The reason for using the hybrid GMDH (GMDH-DDE) is to overcome the drawbacks of the GMDH, such as the method used to calculate neuron weights (i.e., quadratic polynomial transfer function), which seems to have inaccuracies. Also, in selecting model inputs, the GMDH tends to choose the most appropriate inputs for the model; however, the selection criteria are not straightforward and may affect the final results. Furthermore, the GMDH has a multicollinearity problem, affecting model coefficient stability and overfitting problems, etc. A total of 420 data sets from the Mpyo oil field were used, with 70% used for training and 30% used for testing. According to the findings, the GMDH-DDE outperformed both the GMDH and BPNN. In comparison with the GMDH and BPNN, the GMDH-DDE has a higher correlation coefficient (R), lower root-mean-square error (RMSE), and lower mean absolute error (MAE). During training, R, RMSE, and MAE were 0.9849, 0.090, and 0.010, respectively, and during testing, R = 0.9603, RMSE = 0.290, and MAE = 0.017. The second-best technique (GMDH) produces R, RMSE, and MAE values of 0.9611, 0.122, and 0.032 in training, and R = 0.9438, RMSE = 0.349, and MAE = 0.055 in testing. Furthermore, the GMDH-DDE used less computational time (1.32 seconds) compared with the GMDH (2.01 seconds) and BPNN (4.96 seconds), proving that the GMDH-DDE has accurate and fast convergence compared with the GMDH and BPNN. These findings show that the GMDH-DDE and GMDH can be adopted as alternative methods for predicting the Rs.

BackgroundGiven the increasing healthcare costs, there is an interest in developing machine learning (ML) prediction models for estimating hospitalization charges. We use ML algorithms to predict hospitalization charges for patients undergoing transfemoral transcatheter aortic valve replacement (TF-TAVR) utilizing the National Inpatient Sample (NIS) database.MethodsPatients who underwent TF-TAVR from 2012 to 2016 were included in the study. The primary outcome was total hospitalization charges. Study dataset was divided into 80% training and 20% testing sets. We used following ML regression algorithms: random forest, gradient boosting, k-nearest neighbors (KNN), multi-layer perceptron and linear regression. ML algorithms were built for for 3 stages: Stage 1, including variables that were known pre-procedurally (prior to TF-TAVR); Stage 2, including variables that were known post-procedurally; Stage 3, including length of stay (LOS) in addition to the stage 2 variables.ResultsA total of 18,793 hospitalization for TF-TAVR were analyzed. The mean and median adjusted hospitalization charges were $220,725.2 ($137,675.1) and $187,212.0 ($137,971.0–264,824.8) respectively. Random forest regression algorithm outperformed other ML algorithms at all stages with higher R2 score and lower mean absolute error (MAE), root mean squared area (RMSE) and root mean squared logarithmic error (RMSLE) (Stage 1: MAE 79,979.11, R2 0.157; Stage 2: MAE 76,200.09, R2 0.256; Stage 3: MAE 69,350.09, R2 0.453). LOS was the most important predictor of hospitalization charges.ConclusionsWe built ML algorithms that predict hospitalization charges with good accuracy in patients undergoing TF-TAVR at different stages of hospitalization and that can be used by healthcare providers to better understand the drivers of charges.

Signalling and agency theories appear in the accounting literature to be competing theories. This article demonstrates that they are actually consistent theories, in that one set of sufficient conditions of signalling theory is at least consistent with one set of sufficient conditions of agency theory. Indeed, a considerable overlap exists between the two theories: rational behaviour is common to both; information asymmetry in signalling theory is implied by positive monitoring costs in agency theory; ‘quality' in signalling theory can be defined in terms of agency theory variables; and signalling costs are implicit in some bonding devices of agency theory. Examples are given where both theories’ predictions about lobbying, accounting choices, and voluntary auditor selection are added together.

Background:Syphilis remains a major public health concern in China. We aimed to construct an optimum model to forecast syphilis epidemic trends and provide effective precautionary measures for prevention and control.Methods:Data on the incidence of syphilis between 1982 and 2020 were obtained from the China Health Statistics Yearbook. An exponential smoothing model (ES model) and a BP neural network model were constructed, and on this basis, the ES-BP combination model was created. The prediction performance was assessed to compare the MAE (Mean Absolute Error), MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Square Error).Results:The optimum ES model was Brown’s linear trend model, which had the lowest MAE and MAPE values, and its residual was a white noise sequence (P=0.359). The optimum BP neural network model had three layers with the number of nodes in the input, hidden, and output layers set to 5, 11, and 1, and the mean values of MAE, MSE, and RMSE by five-fold cross-validation were 1.519, 6.894, and 1.969, respectively. The ES-BP combination model had three layers, with model nodes 1, 4, and 1. The lowest mean values of MAE, MSE, and RMSE obtained by five-fold cross-validation were 1.265, 5.739, and 2.105, respectively.Conclusion:The ES, BP neural network, and ES-BP combination models can be used to predict syphilis incidence, but the prediction performance of the ES-BP combination model is better than that of a basic ES model and a basic BP neural network model.

BackgroundArtificial intelligence (AI)–driven pose estimation (PE) offers a scalable and cost-effective solution to track exercises in mobile health apps. However, occlusion, influenced by camera angle and distance, can reduce detection accuracy and repetition counting precision. The influence of smartphone positioning on these performance metrics remains underexplored in controlled studies.ObjectiveThe study aimed to examine how smartphone camera angle (front, side, and diagonal) and distance (90 cm, 180 cm, 200 cm, and 360 cm) affect detection performance and repetition counting accuracy during push-ups and squats using AI-based PE.MethodsIn this cross-sectional, within-subject study, 44 healthy university students (9 [20.5%] female participants; mean age 20.3 y, SD 0.4 y; mean BMI 23.2, SD 0.6 kg/m²) were assigned to perform either squats or push-ups. Each participant completed their assigned exercise across 12 predefined smartphone camera configurations, yielding approximately 264 squat trials (n=22) and 264 push-up trials (n=22). Each trial consisted of an average of 5 repetitions, totaling approximately 1320 repetitions per exercise. PE performance was assessed using binary classification accuracy, detection rate, and mean absolute error (MAE) for repetition counting. Generalized linear mixed-effects models evaluated classification odds, linear mixed-effects models analyzed MAE, and Tukey-adjusted post hoc tests followed significant effects.ResultsThe mean detection rate was 61.1% (SD 48.8%) for push-ups and 61.5% (SD 48.7%) for squats, with MAEs of 1.08 (SD 1.78) and 1.11 (SD 1.82) repetitions, respectively. Push-ups were most accurately detected from diagonal views at 90 to 180 cm (up to 85.7% detection; MAE=0.28) and least accurately from the front at 360 cm (20%; MAE=2.70). Squats performed best from a diagonal view at 200 cm (95.5%; MAE=0.05) and worst from the side at 90 cm (0%; MAE=5). Generalized linear mixed models showed that for push-ups, the front 90 cm and diagonal 360 cm views significantly reduced classification odds compared to the side 90 cm view (P=.03 and P=.04, respectively), whereas for squats, diagonal and front views significantly outperformed side views across all distances (P<.001). Post hoc tests confirmed that for push-ups, diagonal close or mid-range views had significantly lower MAEs than far front views, and for squats, diagonal and front views at 180 to 200 cm achieved the highest accuracy and lowest MAEs (P<.05).ConclusionsAI-based PE effectiveness for exercise tracking is significantly affected by smartphone positioning. Diagonal and frontal views at mid-range distances (180‐200 cm) provided the highest detection accuracy and counting precision. These findings offer actionable guidance for developers, clinicians, coaches, and users optimizing mobile health exercise monitoring.

This study showcased the Markov switching autoregressive model with time-varying parameters (MSAR-TVP) for modeling nonlinear time series with structural changes. This model enhances the MSAR framework by allowing dynamic parameter adjustments over time. Parameter estimation uses maximum likelihood estimation (MLE) enhanced by the Kim filter, which integrates the Kalman filter, Hamilton filter, and Kim collapsing, further refined by the Nelder–Mead optimization technique. The model was evaluated using U.S. real gross national product (GNP) data in both in-sample and out-of-sample contexts, as well as an extended dataset to demonstrate its forecasting effectiveness. The results show that the MSAR-TVP model improves forecasting accuracy, outperforming the traditional MSAR model for real GNP. It consistently excels in forecasting error metrics, achieving lower mean absolute percentage error (MAPE) and mean absolute error (MAE) values, indicating superior predictive precision. The model demonstrated robustness and accuracy in predicting future economic trends, confirming its utility in various forecasting applications. These findings have significant implications for sustainable economic growth, highlighting the importance of advanced forecasting models for informed economic policy and strategic planning.

This paper studies the importance of gold's effect on investment and the fact that gold is often seen as a safe-haven asset during economic uncertainty. When inflation rates rise, investors may turn to gold to preserve their wealth; the government will reserve gold to reduce the exchange rate risk. To provide a comprehensive analysis, the study incorporates forecasting the price of gold using both the Autoregressive Integrated Moving Average (ARIMA) and ARIMA-Generalized Autoregressive Conditional Heteroskedasticity (ARIMA-GARCH) models. The gold price data is daily from 1/01/2021 to 3/01/2024. We perform model comparisons that the ARIMA (2,1,3) and the ARIMA (2,1,3)-GARCH (1,1), which model gives lower mean absolute error (MAE) and root mean squared error (RMSE) values. The results show that the MAE and RMSE predictions of the ARIMA (2,1,3)-GARCH (1,1) model are 80.1371 and 96.8299, better than those of the other model. Therefore, the ARIMA (2,1,3)-GARCH (1,1) model forecast results are better precise. It gives a forecast value for gold prices in the world market at the end of 2024 of 1942.094 USD per troy ounce. Hence, the recommendation for investors and policymakers is that if the price is higher than 1942.094 USD per troy ounce in 2024, investors and policymakers should slow down to buy and wait for it to adjust first, or investors and policymakers with gold should gradually sell to make some profit. Moreover, good portfolio management will reduce the exchange rate risk by including an optimized amount of gold in currency portfolios. However, holding gold is risky; its prices may fluctuate due to factors beyond our control, such as war, uncertainty about world economic growth, and inflation. Therefore, investors and policymakers should consider the abovementioned factors and be careful when hedging in gold.

Geophysical data sets are growing at an ever-increasing rate, requiring computationally efficient data selection (thinning) methods to preserve essential information. Satellites, such as WindSat, provide large data sets for assessing the accuracy and computational efficiency of data selection techniques. A new data thinning technique, based on support vector regression (SVR), is developed and tested. To manage large on-line satellite data streams, observations from WindSat are formed into subsets by Voronoi tessellation and then each is thinned by SVR (TSVR). Three experiments are performed. The first confirms the viability of TSVR for a relatively small sample, comparing it to several commonly used data thinning methods (random selection, averaging and Barnes filtering), producing a 10% thinning rate (90% data reduction), low mean absolute errors (MAE) and large correlations with the original data. A second experiment, using a larger dataset, shows TSVR retrievals with MAE < 1 m s−1 and correlations ⩽ 0.98. TSVR was an order of magnitude faster than the commonly used thinning methods. A third experiment applies a two-stage pipeline to TSVR, to accommodate online data. The pipeline subsets reconstruct the wind field with the same accuracy as the second experiment, is an order of magnitude faster than the nonpipeline TSVR. Therefore, pipeline TSVR is two orders of magnitude faster than commonly used thinning methods that ingest the entire data set. This study demonstrates that TSVR pipeline thinning is an accurate and computationally efficient alternative to commonly used data selection techniques.

Rating is one of the most frequently used metrics in the television industry to evaluate television programs or channels. This research is an attempt to develop a prediction model of television program ratings using rating data gathered from UseeTV (interned-based television service from Telkom Indonesia). The machine learning methods (Random Forest and Extreme Gradient Boosting) were tried out utilizing a set of rating data from 20 television programs collected from January 2018 to August 2019 (train dataset) and evaluated using September 2019 rating data (test dataset). Research results show that Random Forest gives a better result than Extreme Gradient Boosting based on evaluation metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). On the training dataset, prediction using Random Forest produced lower RMSE and MAE scores than Extreme Gradient Boosting in all programs, while on the testing dataset, Random Forest produced lower RMSE and MAE scores in 16 programs compared with Extreme Gradient Boosting. According to MAPE score, Random Forest produced more good quality prediction (4 programs in the training dataset, 16 programs in the testing dataset) than Extreme Gradient Boosting method (1 program in the training dataset, 12 programs in the testing dataset) both in training and testing dataset.