Average Error Value Research Articles

Assessment of groundwater quality for agricultural purposes in Qazvin Province, northwestern Iran: A fuzzy inference and indicator Kriging approach

This study addresses the challenge of assessing groundwater quality for agriculture in Qazvin Province, northwestern Iran. In this region, over-extraction has led to significant degradation of groundwater resources. Traditional assessment methods often overlook uncertainties and spatial variability in groundwater quality. To address this, our study aimed to integrate fuzzy inference and geostatistical methods to assess groundwater quality under uncertain conditions. The research was conducted in two stages. First, a fuzzy inference system classified six key water quality parameters: Electrical Conductivity (EC), Sodium Adsorption Ratio (SAR), Residual Sodium Carbonate (RSC), Total Hardness (TH), sodium ion concentration (Na⁺), and chloride concentration (Cl⁻), into three categories: “desirable,” “acceptable,” and “unacceptable,” using 54 fuzzy rules. In the second stage, we applied ordinary Kriging and indicator Kriging to spatially interpolate these classifications and produce probabilistic maps of groundwater quality risk across the study area. In ordinary Kriging, the average Root Mean Square Error (RMSE) values for EC, SAR, RSC, TH, Na⁺, and Cl⁻ were 0.94 dS/m, 1.54, 1.42 meq/L, 3.13 mg/L, 3.03 mg/L, and 2.62 mg/L, respectively, indicating reliable assessments of groundwater quality parameters. Results also suggest that by 2023, areas classified as “unacceptable” increased by 142.0% since 2009, with an additional 25.2% of the region facing a 40–80% probability of further degradation. These findings highlight important trends in groundwater quality, assisting local authorities in prioritizing areas for preventive interventions. This supports sustainable agricultural practices and aligns with the United Nations Sustainable Development Goal 6 for water resource management.

The Characterization of Aquifer Parameters in Using Skimming Tubewells Through the Pumping Test Method: A Case Study of Tando Allahyar

Sindh is in the lower reaches of the Indus River; it is most vulnerable to a variety of upstream water development challenges. The aim of this research was to determine aquifer characteristics in the command area of Tando Allahyar-II distributary within the culmination of underground water potential. The hydraulic properties of the aquifer as well as the susceptibility of the formation to tedious extraction and saltwater upcoming were recognized. Three pumping tests were performed at head, middle, and tail reaches along the selected distributary. The drawdowns were measured at head reach (5.1667 h), at middle reach (6.0 h), and at tail reach (19.667 h) of the selected distributary by performing the pumping tests. Groundwater levels were lower at the tail reach compared to those at the head and middle reaches, likely due to a higher concentration of tubewells in the lower reach. The head and middle reaches showed higher groundwater levels, possibly due to constant head conditions promoting infiltration and recharge. The pumping test versus drawdown analysis revealed that the tubewells should be run with 7-h (on) and 4-h (off) operation. Further, the tubewells at all reaches (head, middle, and tail) should be closed for a minimum of 4 h between operations. This strategy would allow safe groundwater extraction, maintain water quality, and prevent water table depletion in the study area. The hydrodynamic and hydro-salinity behaviors were scrutinized in PWMIN 5.3 (version) by means of the MODFLOW mode. The results were estimated to compare the calibration and validation simulation outcomes using measured data. The model was successfully calibrated, and the root mean square (RMS) value of the head tubewell varied between 0.024 and 0.108, whereas it speckled between 0.0166 and 0.0349 for the middle tubewell and between 0.0659 and 0.0069 for the tail tubewell. The RMS values for hydrodynamic behavior for the head, middle, and tail reaches were less than 10%. These values represent a suitable match between the observed and simulated heads when a water table depletion of 1 to 2 m was observed due to extreme pumping. However, the average relative error values, for all validated procedures, were less than 10%.

Kinetic models for the methanation of COx gases to produce methane: A critical analysis

An exhaustive review of the available literature regarding the kinetic models for the methanation of COx gases to produce natural gas (CH4) is reported. Since the oldest and recent reported kinetic studies about this topic were discussed and analyzed. The characteristics of the experimental studies such as the reaction system, operating conditions, catalyst used, and the experimental procedure, as well as the derived reaction rate equations, reaction rate coefficients and activation energies are highlighted. The results of the various kinetic models are also discussed in order to identify appropriate approaches that can be used as reference to develop new kinetic models. Most of the works reported values of reaction order of 0.2–1 and activation energies of 75–110 kJ/mol in a temperature range of 220–450 °C. Average absolute error (ε) values were calculated to evaluate the accuracy of the predicted data, which resulted to be in the range of 0.15 %–8.08 % showing generally a good agreement.

Machining error compensation of free-form surfaces by combination of ordinary kriging method and mirror compensation method

ABSTRACT Improving the accuracy and efficiency of free-form surface machining is a common goal of modern manufacturing. In this study, a compensation method combining the ordinary kriging method based on sequential quadratic programming method optimization (SQP-OK) and the mirror compensation method for machining errors of free-form workpieces was applied, with the aim of significantly improving the workpiece machining accuracy. Machining errors on the surface of the workpiece were predicted utilizing the SQP-OK after the workpieces were completed with semi-finishing machining based on the on-machine measurement (OMM) results of the workpiece. Based on the prediction result, the mirror compensation method was applied to calculate the compensation points. Free-form workpieces were compensated for by modifying the NC code for semi-finishing. To verify the effectiveness of the proposed method, two free-form workpieces were machined with computer numerical control (CNC) compensation, applying the proposed method and the method modifying the overall machining allowance. The experimental results revealed that the compensation method proposed in this paper reduced the average value of the machining errors decreased by 28.6% in absolute value, 31.1% in maximum undercut, and 28.8% in maximum overcut for the free-form workpiece, respectively, compared with the compensation method of modifying the overall machining allowance.

Enhancing transparency in data-driven urban pluvial flood prediction using an explainable CNN model

Mitigating severe losses caused by pluvial floods in urban areas with dense population and property requires effective flood prediction for emergency measures. Physics-based models face issues with low computational efficiency for real‐time flood prediction. An alternative approach for rapid prediction instead of physics-based models is to predict from a data-driven perspective. However, data-driven approaches for urban flood prediction are often perceived as “black box” and might raise concerns. In this study, we propose an explainable deep learning (DL) approach for rapid urban pluvial flood prediction with enhanced transparency using a convolutional neural network (CNN) and the explainable artificial intelligence (AI) framework Shapley additive explanation (SHAP). We process a systematic stepwise feature selection process and establish a CNN model considering topography, drainage networks and rainfall to predict maximum inundation depths. Then, SHAP is applied to provide trustworthy explanations for the decision making in model results. The results show that: 1) Forward selection can offer insights into selecting effective input variables for improved predictions and promote understanding of DL modelling. The spatial pattern of inundation depths predicted by the proposed CNN model shows good agreement with those predicted by the physics-based model, demonstrated by average correlation coefficient (CC) and mean absolute error (MAE) values of 0.982 and 0.021 m, respectively. 2) The CNN model substantially outperforms the physics-based model in computational speed when using the same hardware, achieving speedups of 210 times on GPU and 51 times on CPU in the case study (575167 grid cells, 14.38 km2). Particularly, it can still achieve good performance on a CPU-only standard laptop without high-performance hardware, with only a modest increase in computational time. 3) The SHAP explainable analysis confirms that the CNN model correctly captures the relationships between input variables and water depth, revealing a reasonable decision-making process, enhancing its transparency. The explainable DL approach incorporating SHAP for rapid urban pluvial flood prediction is promising to build trust among stakeholders and provide a trustworthy reference for prompt measures aiming at saving lives and protecting assets during flood emergencies. Additionally, the proposed DL approach can potentially be further expanded to analyze the causes of urban flooding events and serve as a foundation for exploring the transferability of data-driven urban flood prediction, providing benefits for better urban flood risk management.

Design of Level Sensor Prototype for Fuel Tank in Base Transceiver Station Facilities

A monitoring system for fuel availability at Base Transceiver Stations (BTS) is an absolute necessity. The fuel availability in the generator set must be guaranteed because it ensures the energy supply and continuous operation. This research purpose is to create a robust and effective sensor that can be monitored in real-time so that industry players can estimate the consumption of fuel at the BTS in a certain period. The novelty of this research is that instead of looking at the amount of fuel consumption/reduction like the previous research, this study focuses on how much diesel availability is in the generator tank. So, calculating fuel requirements and predicting actual consumption are done directly. The sensor is a reed-switch oil level sensor and is connected to ESP32 as the microcontroller. It's installed horizontally and uses an optimum distance between sensors of 2.5 mm to improve the sensor reading's accuracy. The test results of converting voltage values into ADC signals on sensor readings produced a determination coefficient of 0.9987, which showed the reliability of the sensor-level design. The sensor precision test results produced an RSD value of 0.17% and the accuracy test produced an error range of -3% to 2% with an average error value of 1.59%. These results indicate that the designed level sensor has good precision and accuracy. Through this research, the provider can predict and plan their fuel consumption needs for BTS power supply and increase the Domestic Component Level in the sensor industry.

Improving prediction accuracy of open shop scheduling problems using hybrid artificial neural network and genetic algorithm

Scheduling issues are typically classified as constrained optimization problems that examine the allocation of machines and the sequence in which tasks are processed. Regarding the existence of one machine, identification of works processing sequence forms a complete time schedule. Therefore, following a review of previous works, the goal of the present study is designing a mathematical model for open shop scheduling (OSS) problems using different machines aiming at minimizing the maximum time required to complete the works using an artificial neural network (ANN) and genetic algorithm (GA). The research data were driven from a Shoe company carried out between the years 2019 and 2020. The GA and ANN methodologies were employed to analyze and forecast the scheduling of activities within the shoe manufacturing sector. The findings indicated that the probability associated with the third population of the GA was 0.15. Furthermore, an examination of the average values of standard error revealed that the neural network model outperformed in terms of predictive accuracy. The estimated minimum time necessary for task completion, as determined by the neural network, was calculated to be 0.96699, facilitating an optimal condition for meeting the established objectives.

Implementasi Data Mining Prediksi Penjualan Produk Semen Menggunakan Metode Linear Regression (Studi Kasus PT. Toyo Mortar Indonesia)

The cement industry in Indonesia plays a vital role in infrastructure development and the construction sector. PT. Toyo Mortar Indonesia, established in 2015 in the Tangerang industrial area, focuses on the production of instant mortar cement. However, the national cement industry faces the challenge of declining sales due to the pandemic, which has affected people's purchasing power and slowed down property and infrastructure projects. To achieve future growth potential, the right strategy is needed in sales planning. This study implements data mining using the linear regression method with the RapidMiner application to predict cement product sales. The data used includes historical sales data and product information from PT. Toyo Mortar Indonesia. In the tests conducted, the data was processed using 12 variables, of which 10 variables influenced the prediction results: TM-168, TM-178, TM-188, TM-189, TM-198, UM-100, UM-101, UM BIRU, raw materials, and other sales. The test results show that the Linear Regression method can predict cement product sales with an average Root Mean Square Error (RMSE) value of 493,125.701 with a standard deviation of 126,330.525. The average Absolute Error is 303,270.965 with a standard deviation of 46,207.586, and the average relative error is 1.89% with a standard deviation of 0.36%, indicating very good prediction accuracy. This can help the company in making decisions related to sales planning using the available sales data.

Experimental and numerical study on the effect of friction between crack faces on fracture parameters of hot mix asphalt concrete

This article investigates the effect of friction between crack faces in ASCB (asymmetric semi-circular bend) specimen on fracture parameters of hot mix asphalt concrete. To achieve this, fracture tests were conducted on ASCB specimens containing cracks with different angles (0, 10, 20, 30, and 40°). In addition to the fracture tests, friction tests were also performed by designing a simple fixture. Subsequently, finite element analyses were carried out, and friction coefficients between crack faces were calculated based on the results of fracture and friction tests. Furthermore, two fracture criteria, ASED (average minimum strain energy density) and MTS (maximum tangential stress), were examined under two different scenarios: assuming zero-friction and non-zero friction between crack faces to predict fracture load and fracture strength. The fracture tests showed that with an increase in the crack angle, the fracture load initially decreased up to the crack angle of 20°, and then increased. Furthermore, the friction tests revealed that an increase in the normal load led to an increase in the friction coefficient. As per finite element results, the crack face contact occurred at a crack angle of 13.1°. Hence, crack faces in the ASCB specimens with the crack angles of 20, 30, and 40° had friction, which significantly affected the fracture parameters. The results also indicated that the average error values for the MTS and ASED criteria compared to the experimental results were 22.8 % and 18.8 %, respectively, when the friction` between crack faces is ignored, and 12.4 % and 4.0 % for the scenario that the friction effect is taken into account. The ASED criterion demonstrated higher accuracy than MTS criterion. Additionally, the average error value of fracture strength predicted by the ASED criterion with assuming non-zero friction was 4 %, indicating good predictive accuracy for fracture strength.

A short video sentiment analysis model based on multimodal feature fusion

With the development of the internet, the number of short video platform users has increased quickly. People's social entertainment mode has gradually changed from text to short video, generating many multimodal data. Therefore, traditional single-modal sentiment analysis can no longer fully adapt to multimodal data. To address this issue, this study proposes a short video sentiment analysis model based on multimodal feature fusion. This model analyzes the text, speech, and visual content in the video. Meanwhile, the information of the three modalities is integrated through a multi-head attention mechanism to analyze and classify emotions. The experimental results showed that when the training set size was 500, the recognition accuracy of the multimodal sentiment analysis model based on modal contribution recognition and multi-task learning was 0.96. The F1 score was 98, and the average absolute error value was 0.21. When the validation set size was 400, the recognition time of the multimodal sentiment analysis model based on modal contribution recognition and multi-task learning was 2.1 s. When the iterations were 60, the recognition time of the multimodal sentiment analysis model based on modal contribution recognition and multi-task learning was 0.9 s. The experimental results show that the proposed multimodal sentiment analysis model based on modal contribution recognition and multi-task learning has good model performance and can accurately identify emotions in short videos.

A hybrid data-driven approach for state of health estimation in lithium-ion batteries

ABSTRACT The assessment of the state of health (SOH) in lithium-ion batteries is essential for ensuring safe operation and implementing scientific management practices. This paper proposes a novel hybrid data-driven approach for accurately estimating the SOH of lithium-ion cells. Initially, the sparrow search algorithm (SSA) was implemented to optimize the structural configuration of a hybrid long-short-term memory neural network and convolutional neural network model. Second, four health indicators, namely, constant current charging time (CCCT), constant current charging capacity (CCCC), discharging time (DT), and discharging capacity (DC), were identified as inputs to the estimation model by Pearson correlation analysis. The CALCE battery dataset is utilized for conducting experiments, yielding remarkably low average values of root mean square error (RMSE) and mean absolute error (MAE), standing at merely 0.6737% and 0.5286%, respectively. And comparing with GRU, TCN, CNN, LSTM, and PSO optimization models, the proposed method has higher SOH estimation accuracy, which is more than 11% better than that. The robustness of three additional kinds of batteries is tested to validate their performance under varying current rates and temperatures. The results demonstrate that the accurate estimation of SOH, and the estimated values’ relative errors for each cycle of the three different types of batteries are all basically within 1%, with their maximum average RMSE are only 0.4840%, 0.3609%, and 0.5681%, respectively, and their maximum average MAE are only 0.3900%, 0.2828%, and 0.4149%, respectively.

Impact of specimen size on mixed mode I and II fracture behavior of asphalt mixture using MMTS criterion

This study investigates how the diameter of specimens impacts the cracking behavior of asphalt mixtures. Testing was conducted on SCB specimens with diameters of 64, 86, and 150 mm under different fracture modes and temperatures. Fracture toughness and fracture energy, crucial fracture parameters, were determined for the tested specimens. The findings revealed that an increase in Me (mode mixity) values led to a decrease in fracture toughness but an increase in fracture energy. By considering all specimen sizes and test temperatures, the fracture toughness for mixed mode I/II and pure mode II was, on average, 13 % and 29 % lower than that for pure mode I loading, respectively. Conversely, the fracture energy for mixed mode I/II and pure mode II was, on average, 19 % and 38 % higher than that for pure mode I loading, respectively. Larger specimens exhibited enhancements in both fracture toughness and fracture energy. Considering all loading modes and test temperatures, the fracture toughness for specimen diameters of 86 mm and 150 mm was, on average, 9 % and 26 % higher than that for a diameter of 64 mm, respectively. The corresponding improvements in fracture energy were 8 % and 14 %, respectively. Additionally, it was observed that fracture toughness increased with decreasing temperature until −20 °C, after which it declined. On the other hand, fracture energy rose with increasing test temperature for all specimen sizes and mode mixities, with a notable increase at 10 °C due to the brittle nature of asphalt concrete. Moreover, the size of the fracture zone (rc) was influenced by temperature and specimen size (R), with larger R resulting in an increase in rc. However, a decrease in temperature initially caused a slight decrease in rc followed by an increase. The study concluded that the MMTS (modified maximum tangential stress) criterion effectively predicted the fracture behavior of SCB specimens across various sizes and loading modes, providing accurate estimations for asphalt mixture fracture behavior. By considering all loading modes and test temperatures, the average error values were 5.9 %, 6.2 %, and 6.5 % for specimen diameters of 64 mm, 86 mm, and 150 mm, respectively.

SCADA Implementation on Solar Panel Electricity Consumption Monitoring and Regulation System

In an effort to motivate students and understand application of SCADA to the Solar Panel system which is one source potential non - fossil energy to be used as solution to meeting sustainable energy needs.​ Where in its operation , efficiency and reliability Solar Panel system is still a major problemthat must be overcome . Utilization of Supervisory Control and Data Acquisition (SCADA) in management Solar Panel Systems (PLTS) have become significant solution​ to improve loading efficiency . The purpose of this research is to create a prototypes solar panel practicum as a learning media to motivate student in understanding how solar panels work and can apply the process of monitoring and regulating solar panel system with SCADA in real-time. The equipment used​ includes Solar Panels, Solar Charger Controller (SCC), Inverter, Battery , PLC Omron CP1E-NA20DR-A and CP1W AD041, ACS712 Current Sensor , ZMPT101B Voltage Sensor, DHT22 Temperature and Humidity Sensor and Light Sensor (LDR) as well as CX- Programmer software Ver. 9.5 and CX -Supervidor Ver.4.0. The method used is Research and Development method as the basic for design and manufacture of solar panel practicum tools and the application of SCADA to the monitoring system and electricity usage of solar panels . From the results of tests conducted with the application of SCADA on solar panels can monitor Voltage , Current , Power, Temperature , Light and load usage regulator electric solar panels based on electrical energy consumption, as well as the condition of electrical equipment connected to the solar panel system with an average error values : Voltage 0.084%, Current 0.155%, Power 0.107% and Temperature 45 0C and Light 658.7 Lux.

Comparison of non-reactive solute transport models for the evaluation of fluid flow in packed beds with implications for heap leaching practice

This study investigated the effects of mean particle size fraction, bottom particle size, particle porosity and wettability on solution scale preferential flow behaviour via step input tracer tests in drip irrigated, narrowly and mixed-sized beds under steady state fluid flux. Nine solute transport models were used to quantify this behaviour reflected in the residence time distribution (RTD) profiles. Four were empirical models: three compartmental model configurations (CM-1, CM-2, CM-3) and tanks-in-series (TIS) model. The remainder five models were semi-empirical: advection dispersion (AD), piston exchange (PE), piston exchange - diffusion variant (PE-D), piston dispersion and exchange (PDE) and piston dispersion and exchange - diffusion variant (PDED). The model fit results showed that the mono-porosity TIS, AD and CM-2 models were the worst performers, while the dual porosity PDE and novel PDE-D models achieved the lowest average error values across the various systems. Higher levels of particle wettability coupled with capillary effects produced peculiar RTD curves that were relatively more difficult for the mono-porosity models to simulate. The model parameters investigated included the longitudinal dispersion coefficient (Dds), dead to total volume fraction (VD/VT), dynamic to total saturation fraction (βd/βT), overall mass transfer coefficient (Kma) and maximum diffusional pore length (X). The results showed that an increase in the average particle size within the beds led to higher VD/VT, Dds and X values, but lower βd/βT and Kma values. These indicate an overall increase in solution scale preferential flow behaviour. Decreased capillary suction and connectivity between particle pores and inter-particle voids were deemed responsible for the results. Higher levels of particle porosity acted as a buffer against these effects. Overall, the results highlight the benefit of the addition of fines (0.1–1 mm particles) during the agglomeration process in heaps to help reduce solution scale preferential flow behaviour and increase liquid hold-up. This is more necessary when the ore has low to moderate levels of porosity (surface area: <2 m2/g) and will also increase the modelling options available as most models performed better fitting data from such beds.

Construction of a full color gamut color mixture model and a color prediction neural network model for rotor spinning melange yarn

This research is devoted to solving the bottleneck problem of spinning and color prediction of rotor spun melange yarn within the full color gamut range. Firstly, a full color gamut grid color mixture model with hue regulation range of 0–360°, saturation regulation range of 0-1 and lightness regulation range of 0-1 was constructed based on the preferred seven primary-color fibers through grid color mixing. Secondly, in the full color gamut grid color mixture model, 55 representative grid points were planned and corresponding melange yarns and fabrics were prepared based on grid point parameters, which served as the training set, testing set, and validation set for constructing neural network prediction models. Thirdly, based on the demand for color prediction of melange yarns, a color prediction model and a blending ratio prediction model with six sub-models are constructed by taking the reflectivity of the melange yarn fabric and the blending ratio of the primary-color fibers as input and output values. Finally, six prediction samples were produced to verify the predictive performance of the neural network prediction model. The results show that the average color difference between the predicted and measured colors is 1.06, and the average root mean square error value between the predicted and actual blending ratios is 0.48%. It indicates that the neural network prediction model based on the grid mixing of seven primary-color fibers can adapt to the prediction of the mixing color and blending ratio of primary-color fibers in the full color gamut. Due to the construction of six sub-models based on local hue regions, the prediction accuracy of the neural network prediction model has been greatly improved.

On soft measurement modeling for predicting photovoltaic power with uncertainty based on the Takagi–Sugeno model

Abstract Accurate solar power forecast is becoming more essential for safe and reliable power grid operation with the increasing number of grid-connected photovoltaic (PV) power production units. However, PV power exhibits significant output fluctuation due to both external inputs and intrinsic stochasticity in system dynamics. Therefore, an efficient and reliable soft measurement model of PV power with uncertainty is demanded in practice applications. The technique described in this paper captures the impacts produced by the fundamental uncertainty observed in the data, instead of relying on unrealistic assumptions about uncertainty. A soft measurement model using the Takagi-Sugeno (TS) Fuzzy Logic System (FLS) based on several input-output time series of PV plants is presented in this study. Chebyshev's inequality from probability theory and statistics is adopted to create the confidence interval-based response envelopes for these time series at each moment. An envelope-based measure of output uncertainty and a center-valued response forecasting model can be obtained by the proposed identification technique. PV datasets are employed to demonstrate the concept, which indicates the proposed soft measurement may outperform existing methods in terms of prediction accuracy. The average values of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Correlation Coefficient (R) are only 0.0787, 0.1113, and 0.9979. The average values of the prediction interval coverage probability (PICP) and the prediction interval normalized average width (PINAW) are 0.9806 and 0.1051.

Heatmap-Based Active Shape Model for Landmark Detection in Lumbar X-ray Images.

Medical staff inspect lumbar X-ray images to diagnose lumbar spine diseases, and the analysis process is currently automated using deep-learning techniques. The detection of landmarks is necessary in the automatic process of localizing the position and identifying the morphological features of the vertebrae. However, detection errors may occur owing to the noise and ambiguity of images, as well as individual variations in the shape of the lumbar vertebrae. This study proposes a method to improve the robustness of landmark detection results. This method assumes that landmarks are detected by a convolutional neural network-based two-step model consisting of Pose-Net and M-Net. The model generates a heatmap response to indicate the probable landmark positions. The proposed method then corrects the landmark positions using the heatmap response and active shape model, which employs statistical information on the landmark distribution. Experiments were conducted using 3600 lumbar X-ray images, and the results showed that the landmark detection error was reduced by the proposed method. The average value of maximum errors decreased by 5.58% after applying the proposed method, which combines the outstanding image analysis capabilities of deep learning with statistical shape constraints on landmark distribution. The proposed method could also be easily integrated with other techniques to increase the robustness of landmark detection results such as CoordConv layers and non-directional part affinity field. This resulted in a further enhancement in the landmark detection performance. These advantages can improve the reliability of automatic systems used to inspect lumbar X-ray images. This will benefit both patients and medical staff by reducing medical expenses and increasing diagnostic efficiency.

SUSCEPTIBLE VACCINATED INFECTED RECOVERED SUSCEPTIBLE MODEL: EQUILIBRIA POINTS AND APPLICATION ON COVID-19 CASE DATA IN INDONESIA

Severe Respiratory Syndrome Coronavirus-2 is the infectious agent that causes COVID-19. A vaccine program is an effort to stop the spread of COVID-19 infections in Indonesia. The susceptible vaccinated infected recovered susceptible (SVIRS) model can be used to represent the spread of infectious diseases. This study aims to construct the SVIRS model, identify the equilibria points thus apply it to COVID-19 case data in Indonesia, and determine transmission patterns, model accuracy, and interpretation. Literature and applications are the research methodologies employed. First-order nonlinear differential equations form the obtained SVIRS model. The model has two equilibrium points: a disease-free equilibrium point, and the other is endemic equilibrium point. The SVIRS model on the spread of COVID-19 in Indonesia was obtained using daily secondary data from January 11 to November 30, 2022. The model is solved by the fourth-order Runge-Kutta method. The model’s accuracy is accurate enough to explain the spread of COVID-19 in Indonesia with a mean average percentage error (MAPE) value of 43%. According to the transmission pattern, the number of COVID-19 cases in Indonesia peaked on July 27, 2022, then decreased to zero, obtaining an equilibrium point when no more cases of the disease were present.

Spectro-ViT: A vision transformer model for GABA-edited MEGA-PRESS reconstruction using spectrograms

This study investigated the use of a Vision Transformer (ViT) for reconstructing GABA-edited Magnetic Resonance Spectroscopy (MRS) data from a reduced number of transients. Transients refer to the samples collected during an MRS acquisition by repeating the experiment to generate a signal of sufficient quality. Specifically, 80 transients were used instead of the typical 320 transients, aiming to reduce scan time. The 80 transients were pre-processed and converted into a spectrogram image representation using the Short-Time Fourier Transform (STFT). A pre-trained ViT, named Spectro-ViT, was fine-tuned and then tested using in-vivo GABA-edited MEGA-PRESS data. Its performance was compared against other pipelines in the literature using quantitative quality metrics and estimated metabolite concentration values, with the typical 320-transient scans serving as the reference for comparison. The Spectro-ViT model exhibited the best overall quality metrics among all other pipelines against which it was compared. The metabolite concentrations from Spectro-ViT's reconstructions for GABA+ achieved the best average R2 value of 0.67 and the best average Mean Absolute Percentage Error (MAPE) value of 9.68%, with no significant statistical differences found compared to the 320-transient reference. The code to reproduce this research is available at https://github.com/MICLab-Unicamp/Spectro-ViT

Dynamic Prediction of Recidivism in Violence in Community-Based Schizophrenia Spectrum Disorder Patients: A Joint Model

To construct a model for predicting recidivism in violence in community-based schizophrenia spectrum disorder patients (SSDP) by adopting a joint modeling method. Based on the basic data on severe mental illness in Southwest China between January 2017 and June 2018, 4565 community-based SSDP with baseline violent behaviors were selected as the research subjects. We used a growth mixture model (GMM) to identify patterns of medication adherence and social functioning. We then fitted the joint model using a zero-inflated negative binomial regression model and compared it with traditional static models. Finally, we used a 10-fold training-test cross validation framework to evaluate the models' fitting and predictive performance. A total of 157 patients (3.44%) experienced recidivism in violence. Medication compliance and social functioning were fitted into four patterns. In the counting model, age, marital status, educational attainment, economic status, historical types of violence, and medication compliance patterns were predictive factors for the frequency of recidivism of violence (P<0.05). In the zero-inflated model, age, adverse drug reactions, historical types of violence, medication compliance patterns, and social functioning patterns were predictive factors for the recidivism in violence (P<0.05). For the joint model, the average value of Akaike information criterion (AIC) for the train set was 776.5±9.4, the average value of root mean squared error (RMSE) for the testing set was 0.168±0.013, and the average value of mean absolute error (MAE) for the testing set was 0.131±0.018, which were all lower than those of the traditional static models. Joint modeling is an effective statistical strategy for identifying and processing dynamic variables, exhibiting better predictive performance than that of the traditional static models. It can provide new ideas for promoting the construction of comprehensive intervention systems.