High stock price fluctuations pose a significant challenge for investors and analysts in determining investment strategies. Price dynamics influenced by economic, political, and psychological market factors require forecasting methods that can accommodate uncertainty and non-linear historical data patterns. This study applies Cheng's Fuzzy Time Series algorithm to predict the stock price of PT. ABCD by going through the stages of universe set formation, interval determination, fuzzification, fuzzy logic relationship formation, and defuzzification to obtain prediction results. The method implementation was carried out using two approaches: manual calculation using Microsoft Excel and automatic calculation using the Orange application. The results show that Cheng's method is able to produce predictions very close to the actual value, with an accuracy level measured using the Mean Absolute Percentage Error (MAPE) indicator of 0.058787% on both platforms. The consistency of the results between Excel and Orange proves the reliability of Cheng's method, so it can be used as a reference in supporting investment decision-making in the Indonesian capital market.

Drug-induced QT prolongation after successful inpatient loading of class III antiarrhythmics may occur during routine outpatient care. Insertable cardiac monitors offer continuous signals but are limited by single-lead configuration. We hypothesized that a spatially aware deep learning system (3DRECON-QT) can reconstruct spatial information from a single lead vector to quantify QT/QTc and identify high-risk prolongation. We developed 3DRECON-QT using a multitask encoder-decoder that ingests a 10-s single-lead signal, reconstructs 12 leads, and predicts QT/QTc. The model was developed using 12-lead ECGs with clinician-adjudicated QT/RR from a large health system and tested in an external center with different ECG hardware. Continuous monitoring performance was assessed in a public dofetilide-loading data set with serial ECGs. In a real-world cohort of outpatients on dofetilide or sotalol presenting to the hospital or emergency room for any reason, rates of ventricular arrhythmias and QT prolongation were assessed. Device validation was tested in patients with insertable cardiac monitor recordings paired with clinical 12-lead ECGs. 3DRECON-QT classified prolonged QTc from single-lead signals with area under the receiver operating characteristics curve, 0.942 (mean absolute error, 17.5 ms) in the internal test set and 0.943 (mean absolute error, 21.1 ms) externally. During continuous dofetilide monitoring, predictions correlated with ground truth (r, 0.851; mean absolute error, 17.8 ms; area under the receiver operating characteristics curve, 0.936 for prolonged QTc, 0.816 for ≥15% QTc rise). QTc prediction from true insertable cardiac monitor recordings showed r=0.824 and mean absolute error, 17.5 ms. In outpatients on class III antiarrhythmics (n=1676), 16.5% had high-risk QTc prolongation. Ventricular arrhythmia events were 3.97% versus 0.86% without prolongation (adjusted odds ratio, 4.24 [95% CI, 1.81-9.90]). 3DRECON-QT detected these events with area under the receiver operating characteristics curve 0.94 (F1 score, 0.60). A single-lead, deep-learning approach can achieve guideline-level measurement accuracy, enable continuous QTc surveillance from nonstandard ECG vectors, and identify clinically meaningful outpatient QTc prolongation associated with a >4-fold increase in serious ventricular arrhythmias. This strategy may enhance safety monitoring after class III antiarrhythmic initiation and support targeted intervention.

Enhancing Newborn Health Assessment: Ultrasound-based Body Composition Prediction Using Deep Learning Techniques.

A robust cross-tissue DNA methylation model for forensic age estimation from oral samples.

Customer-oriented knowledge management is a comprehensive approach aimed at developing a broad and integrated organizational vision, with its primary focus on achieving innovation and organizational effectiveness. This study examined customer-oriented knowledge management in technology-based companies located in Tehran using an artificial neural network approach. The research method was quantitative, survey-based, and applied in nature. Data were collected through a questionnaire administered to 386 managers and experts. To predict and evaluate patterns, a Multilayer Perceptron (MLP) neural network was utilized. The results indicated that input components such as customer-oriented knowledge management processes and behavioral data had strong correlations with output variables including customer satisfaction, innovation, and customer loyalty. The model demonstrated high predictive accuracy based on evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and the coefficient of determination (R²). The R² value of 0.83 reflected the model’s desirable performance. In the learning curve analysis, both training and testing errors decreased rapidly and stabilized, indicating optimal learning of the model and prevention of overfitting. The findings suggest that neural networks can serve as an effective tool for implementing customer-oriented knowledge management in technology-based companies, contributing to improved strategic decision-making processes and enhanced customer satisfaction.

Enhancing 5-Day Particulate Matter (PM10) Forecasts in Morocco Using U-Net: A Deep Learning Approach.

Accuracy of the O Formula Based on OCT and Ray-Tracing for Intraocular Lens Power Prediction.

Rationale: Thermal gene switches (TGSs), engineered into cells, allow controlled gene expression upon heat stimulation, making it a promising tool for therapeutic applications. Their clinical translation, however, has been hindered by the lack of thermal activation platforms that can locally deliver heat and provide safe and accurate temperature control. Existing approaches are limited by poor delivery and localization of heat deep inside the body, reliance on exogenous agents, or the lack of integrated image guidance. To address these challenges, we developed a non-invasive system that combines real-time imaging with mild hyperthermia for reliable and localized activation of TGSs in deep tissue. Methods: We developed a dual-mode ultrasound-guided focused ultrasound (USgFUS) system using a single phased-array imaging transducer for both imaging and heating. The system integrates B-mode imaging and thermal strain imaging (TSI) for real-time anatomical guidance and temperature estimation. We validated the imaging performance both in vitro and in vivo settings and assessed focused ultrasound (FUS)-induced TGS activation of genetically engineered Jurkat T cells in vitro and in vivo. Results: The USgFUS system achieved high-resolution and high-contrast B-mode imaging, and it induced localized heating within temperature window of 39-43 °C, consistently within the mild hyperthermia range. TSI accurately estimated temperature elevation during FUS with 0.8 °C mean absolute error. In vitro, FUS heating increased transgene expression in TGS-engineered Jurkat T cells by ~150-fold compared to unheated controls, with negligible viability loss. In vivo, USgFUS selectively activated TGS in tumor-bearing mice, yielding a significant increase in transgene expression compared to unheated controls. Conclusion: This study introduces a dual-mode USgFUS system designed for non-invasive TGS activation. The system integrates local mild hyperthermia with real-time anatomical guidance and temperature monitoring using a standard clinical imaging probe. The results collectively demonstrate strong performance in preclinical models and engineered cells, enabling safe, spatiotemporally precise thermal gene regulation. Ultimately, our platform provides a foundation for future advancements in gene therapy, immunomodulation, and other biomedical applications.

Accelerating the application of lead-free inorganic halide perovskites in solar cells necessitates the development of novel perovskite materials with suitable bandgap widths, high stability, and environmental friendliness. This represents a crucial pathway for driving photovoltaic technology innovation and reducing reliance on conventional fossil fuels. However, traditional material development paradigms heavily depend on trial-and error experimental screening or pure density functional theory (DFT) calculations, which incur significant time and material costs.<br>To address these challenges, this study innovatively proposes and implements an efficient screening strategy based on the synergy between deep learning and DFT calculations. By constructing a database containing 1181 inorganic halide double perovskite materials, we systematically trained and compared the performance of five mainstream machine learning models for the bandgap prediction task: Random Forest Regression (RFR), Gradient Boosting Regression (GBR), Support Vector Regression (SVR), eXtreme Gradient Boosting Regression (XGBR), and a Deep Neural Network (DNN) model. Results demonstrate that the DNN model, leveraging its powerful nonlinear mapping capability and advantage in automatic high-dimensional feature extraction, achieved exceptional prediction accuracy on the test set, with the Mean Absolute Error (MAE) significantly reduced to 0.264 eV and the coefficient of determination (R<sup>2</sup>) reaching 0.925. Its performance was markedly superior to other compared models, highlighting the immense potential of deep learning in predicting complex material properties.<br>Using this optimized DNN model, this study successfully screened four promising inorganic double perovskite candidates from 55 potential materials: Cs<sub>2</sub>GaAgCl<sub>6</sub>, Cs<sub>2</sub>AgIrF<sub>6</sub>, Cs<sub>2</sub>InAgCl<sub>6</sub>, and Cs<sub>2</sub>AlAgBr<sub>6</sub>. Among them, Cs<sub>2</sub>AgIrF<sub>6</sub> and Cs<sub>2</sub>AlAgBr<sub>6</sub> performed particularly well, with predicted bandgaps of 1.36 eV and 1.20 eV, respectively. This range ideally matches the requirement for efficient light absorption in solar cells. Further device performance simulations revealed that the solar cell based on Cs<sub>2</sub>AgIrF<sub>6</sub> achieved a simulated power conversion efficiency (PCE) of 23.71%, with an open-circuit voltage (<i>V<sub>OC</sub></i>) of 0.94 V, a short-circuit current density (<i>J<sub>SC</sub></i>) of 31.19 mA/cm<sup>2</sup>, and a fill factor (FF) of 80.81%. Cs<sub>2</sub>AlAgBr<sub>6</sub> also exhibited a simulated efficiency of 22.37%, corresponding to <i>V<sub>OC</sub></i>=0.78 V, <i>J<sub>SC</sub></i>=36.73 mA/cm<sup>2</sup>, and FF=77.66%. Notably, both materials demonstrated high open-circuit voltages and fill factors, clearly indicating excellent carrier separation efficiency and significantly reduced nonradiative recombination losses within these materials.<br>In summary, this study successfully validates the significant efficacy of the deep learning-DFT synergistic strategy in accelerating the discovery of novel lead-free perovskite materials. This method not only substantially enhances the efficiency of DFT data analysis and the depth of pattern mining, overcoming some bottlenecks associated with traditional highthroughput calculations, but more importantly, it provides a practical and highly innovative approach for the rational design of high-performance, stable, and environmentally friendly lead-free perovskite solar cells, holding positive implications for advancing green, low-carbon energy technologies.

Refractive Outcomes Using Simulated Keratometry Versus Keratometry From an Optical Biometer.

The effect of inaccurate initial contact events on kinematics in healthy and pathological gait.

Laser-induced breakdown spectroscopy coupled with machine learning for rapid quantification of Escherichia coli concentration.

NAM-based development of a predictive test model for evaluating skin mildness potential of rinse-off products via integrated in vitro assays.

SurfGNN: A robust surface-based prediction model with interpretability for coactivation maps of spatial and cortical features.

Accident prevention in electric vehicles through battery state-of-health estimation based on GRU-HSIC.

Automatic prediction of depth of invasion in oral tongue squamous cell carcinoma using a multimodal regression network fusing prior text and anatomical knowledge.

A data-driven and expert flood knowledge model based on the development of the Huber loss function for flood forecasting.

This study aims to provide a comprehensive model to assist investors in strategic decision-making amid market uncertainty. Global economic uncertainty characterized by cycles of stagflation and recession has recurred in history and is expected to recur until 2025. This condition encourages the importance of investment strategies that can protect asset values from economic pressures. This study uses a quantitative approach with forecasting methods and risk analysis based on time series data. The data used are daily gold and silver prices from the London Bullion Market Association (LBMA) in USD, collected over a two-year period, namely from January 3, 2023 to January 4, 2025. The data is secondary and obtained from the official LBMA website. The research stages begin with a literature study to understand relevant concepts and methods, followed by data collection, and continued with data preprocessing. The preprocessing stages include checking for outliers, handling missing values using the series mean method, and merging data for temporal consistency. For the forecasting process, the Fuzzy Time Series–Markov Chain method is used, which consists of several steps: the formation of universe and interval sets using the Sturges formula, the definition of fuzzy sets, the fuzzification process, the formation of Fuzzy Logical Relationships (FLR) and Fuzzy Logical Relationship Groups (FLRG), and the preparation of transition probability matrices. The forecasting results are obtained through the defuzzification process, which are then evaluated using the Mean Absolute Percentage Error (MAPE) indicator to assess the accuracy of the model. Risk analysis is carried out using the Value at Risk (VaR) approach using the Extreme Value Theory (EVT) method and the Generalized Pareto Distribution (GPD). The entire analysis process is carried out using Microsoft Excel and RStudio software to ensure accuracy and efficiency in data processing and statistical modeling. This study has succeeded in developing a hybrid Fuzzy Time Series–Markov Chain model to forecast precious metal prices, especially gold and silver, with a very high level of accuracy. Based on an evaluation of various training and testing data proportions, the best model was obtained at a 95:5 ratio, with MAPE values of 0.66% for gold and 1.18% for silver in the training data, and 0.55% and 0.94% in the testing data. These results indicate that the model is able to effectively capture historical price patterns and provide predictions close to the actual value.

Integrated enviro-economic optimization of solar-powered electrocoagulation for sustainable nitrate removal from groundwater.

Proton radiography is an effective technique for diagnosing field distributions in plasmas. However, due to the complexity of electromagnetic field structures, reconstructing electromagnetic fields from proton radiographs is extremely challenging and often requires some simplified symmetry assumptions about the fields. Here, we present a machine learning approach to reconstruct three-dimensional (3D) magnetic field distributions from complex proton radiographs without relying on such assumptions.<br>To enable this, we construct the target 3D magnetic fields by linearly superposing multiple elementary magnetic structures generated from the Weibel instability. Each element is characterized by eight parameters—structural parameters (<i>a</i>, <i>b</i>, <i>B<sub>0</sub></i>), spatial coordinates (<i>x<sub>0</sub></i>, <i>y<sub>0</sub></i>, <i>z<sub>0</sub></i>), and rotation angles (<i>θ</i>, <i>ϕ</i>)—resulting in 80 degrees of freedom in total. Parameters were uniformly sampled within ±25% of their baseline values, and a dataset of 50,000 magnetic field–proton radiograph pairs was generated through forward simulation using GEANT4. All proton radiographs reside in the caustic regime, exhibiting multiple asymmetric caustics and significant flux concentrations.<br>A lightweight three-layer convolutional neural network (CNN) was designed for the reconstruction task. The network consists of an input layer, three convolutional modules (the first two following a ”convolution–batch normalization–max pooling” cascaded structure, and the third is simplified to a single convolutional layer), a flattening layer, a dropout layer, and an output layer. Bayesian optimization was applied to determine the optimal hyperparameters. The model was trained on 40000 samples, with 5000 samples for validation and 5000 for testing.<br>On the test set, the CNN achieves a mean absolute percentage error (MAPE) of 8.5% in predicting the 80 magnetic parameters, below the 12.9% random-guessing threshold. Prediction errors for most parameters follow near-zero-mean Gaussian distributions, with relative standard deviations under 6%. The reconstructed fields show high spatial agreement with the reference fields, and corresponding proton images match the originals with a cosine similarity of 0.89.<br>This study demonstrates that our CNN-based proton radiography reconstruction method can effectively reconstruct complex 3D magnetic fields without symmetry assumptions or manual parameter tuning, offering a novel tool for diagnosing electromagnetic fields in high-intensity laser-plasma interactions. Future work may incorporate multi-angle proton radiography and transfer learning from experimental data to enhance the method’s practicality and robustness.