Input Variables Research Articles

Application of Machine Learning Predicting Injuries in Traffic Accidents through the Application of Random Forest

Background: The objective of this work is to analyze and predict the harmfulness in traffic accidents. Method: Several Random Forest statistical models are created, in which the predictable variable (response/ output variable) is the harmfulness of the accident, while the input variables are the various characteristics of the accident. In addition, these generated models will allow estimating the influence or importance of each of the factors studied (input variables) concerning the harmfulness of road accidents so that it is possible to know in which aspects it is more profitable to work with the objective of reducing mortality from traffic accidents [1]. Results: In this regard, the predictive algorithm has an out-of-bag error of 26.55% and an overall accuracy of 74.1%. Meanwhile, the local accuracy of the mildly wounded class is 66.1% compared to 81.4% of the dead and severely wounded class, which, as mentioned, has higher prediction reliability. Conclusion: Finally, it is worth noting the enormous usefulness of the Random Forest machine learning technique, which provides very useful information for possible research or studies that may be carried out. In the specific case of this work, through the use of the R programming language, which in turn presents a wide range of freely accessible utilities and functions with which it may be interesting working, it has generated results of great value for this area of activity, important to society as road safety.

Identifying bacterial fixation pathway of mediating soil carbon stock changes along tropical forest restoration

The mechanism mediating carbon accumulation changes along ropical forest restoration remains unclear. Here, we identified how functional bacteria, litter input, and abiotic variables control soil organic carbon stock changes along an age-chronosequence of tropical forest restoration. Over 51-yr recovery, significant increases in total organic carbon stocks (∼1.7 fold) were strongly associated with increases in copy number of carbon fixation bacterial genes (cbbL) (∼2.6 fold). The direct pathways of cbbL abundance, microbial and mineral-associated organic carbon explained 76 % of carbon stock variation. In contrast, litter carbon, soil water, and bulk density indirectly regulated carbon stocks through affecting cbbL abundance (68 %) and microbial carbon level (29 %). Furthermore, cbbL abundance had a higher contribution (71 %) to carbon fraction transformation than microbial carbon level (19 %). We suggest that tropical forest restoration controls carbon stocks primarily via direct bacterial fixation pathway mediated by litter carbon and physical soil variables. Our results are helpful to further understand the mechanism of tropical forest restoration regulating carbon transformation and accumulation.

Efficient proxy for time-lapse seismic forward modeling using a U-net encoder-decoder approach

The time-lapse seismic (4D seismic) forward modeling provides crucial data for calibrating reservoir models through different data assimilation algorithms. Unfortunately, the traditional 4D seismic forward-modeling methodology is time-expensive and entails significant computational resource consumption. To address these drawbacks, in this work, our goal is to develop a proxy model for the 4D seismic forward modeling using a class of machine learning algorithm named U-Net encoder–decoder. We applied the developed proxy model to a benchmark carbonate reservoir using an ensemble of reservoir simulation models from UNISIM IV dataset (a synthetic benchmark based on real data of a Brazilian pre-salt field). Moreover, we aim to introduce seminal strategies for interpreting the proposed proxy model operation, its outputs, and possible correlations between input and output variables. To achieve this, we trained and tested two versions of U-net-based models and applied methods for explainable artificial intelligence, such as Gradient-weighted Class Activation Mapping (Grad-CAM) and Forward Feature Selection. The experiments showed good results when applied to the test dataset. The correlation coefficient (R2) values were in the range of 0.7 to 0.9, showing the efficiency of the proxy model to replace the 4D seismic forward modeling. Through qualitative analysis, it was possible to identify which input properties and regions of the reservoir are more relevant for the model’s inference. These results are a step toward robust, explainable machine learning-based proxy forward modeling.

Prediction of ketosis using radial basis function neural network in dairy cattle farming.

The purpose of the paper was to apply an Artificial Neural Networks with Radial Basis Function to develop an application model for diagnosing a subclinical ketosis type I and II in dairy cattle. While building the neural network model, applied methodology was compatible to the procedures used in Data Mining processes.The data set was created based on the composition of milk samples of 1520 Polish Holstein-Friesian cows. The milk samples were collected during test-day milkings and made available by Polish Federation of Cattle Breeders and Milk Producers. The milk composition parameters were used as the input variables for RBF network models. The value of the output variable was determined based on the content of β-hydroxybutyric acid in blood of cows. In the next stage of the work, the qualities of the pre-selected models were compared and the best ones were chosen. The sensitivity and specificity as well as the size of the AUC (Area Under the Curve) under the ROC (Receiver Operating Characteristic) were taken as the main criteria for network models evaluation. The model characterized by sensitivity of 0.86, specificity of 0.71 and AUC of 0.89 was selected for ketosis type I. The optimal for ketosis type II showed the sensitivity and specificity 0.81 and 0.75, respectively, and the size of AUC above 0.85. Chosen models were recorded using the predictive modelling markup language (PMML) for data mining models to be shared and used between the different applications.

Sediment flow connectivity index data for the Apulia region (Italy): An open-source geodatabase and the innovative CONNECTOSED WebGIS platform.

An open-source geodatabase and its associate WebGIS platform (CONNECTOSED) were developed to collect and utilize data for the Sediment Flow Connectivity Index (SfCI) for the Apulia region of southern Italy. Maps depicting sediment mobility and connectivity across the hydrographic basins of the Apulia region were generated and stored in the geodatabase. This geodatabase is organized into folders containing data in TIFF, shapefile, Jpeg and Pdf formats, including input variables (digital elevation model, land cover map, rainfall map, and soil units dataset for each hydrographic basin), classification graphs (ranking of variable values), dimensionless index maps (slope, ruggedness, rainfall, land cover, and soil stability) and key products (maps of sediment mobility, SfCI, and applied SfCI). The geodatabase maintains the mapping methodology underlying the SfCI algorithm by integrating various Earth datasets from multiple sources through ArcMap™, QGIS® and Matlab® software. This approach aligns surface characteristics with driving forces to describe the spatial variability of sediment pathways and identify hotspot areas. The availability of both input and processed data enables the computation and continuous updating of this applied geomorphological indicator, which is useful for assessing susceptibility to rapid Earth surface changes related to multi-hazard exposure. The geodatabase and the CONNECTOSED platform are valuable tools for researchers and stakeholders involved in land monitoring. The geodatabase and the CONNECTOSED platform are essential tools for researchers, policymakers, and stakeholders involved in land monitoring and environmental management. These tools provide open access to extensive datasets and detailed descriptions of surface dynamics, establishing connections between the causes and effects of extreme phenomena, such as floods, landslides, fires, soil pollution. This integration allows users to combine various forms of environmental data, a capability that is vital for enhancing scientific knowledge, supporting the development of insights, and fostering more informed, evidence-based decision-making in land use planning, conservation efforts, and sustainability initiatives.

Lithium and cobalt recovery from spent battery cathode by acid-reductive leaching using molasses: optimisation by response surface methodology

ABSTRACT The combination of molasses and sulphuric acid is reported for the first time as a leaching agent for lithium and cobalt recovery from spent lithium-ion batteries. The acid-reductive leaching process was optimised using a design of experiment with four input variables: sulphuric acid concentration, molasses concentration, leaching temperature, and leaching time. A response surface model with a central composite design determined the number of experiments, followed by quadratic regression modelling to analyze the relationship between input parameters and leaching recovery. ANOVA assessed the statistical significance of each variable, regression modelling, and optimal leaching conditions. The analysis showed temperature significantly affected lithium recovery, while both temperature and sulphuric acid concentration influenced cobalt recovery. The optimal conditions achieved 97.76% lithium and 99% cobalt recovery (2.5 M sulphuric acid, 60 g/L molasses, 54°C, 108 min). These findings highlight an eco-friendly and efficient approach to metal recovery.

In Situ Analysis of Li Plating and Stripping Behaviors Under Dynamic Current Conditions for Realistic Application Scenarios.

Lithium metal batteries are considered the holy grail for next-generation high-energy systems. However, lithium anode faces poor reversibility, unsatisfying cyclability and rate capability due to its uncontrollable plating/stripping behavior. While galvanostatic conditions are extensively studied, the behavior under more realistic application scenarios with variable inputs are less explored. Here, an in situ imaging platform using in-plane microdevice configurations is developed to effectively investigate Li plating/stripping behavior under dynamic conditions. This platform offers high detectivity for analyzing the nuclei size, density, distribution, and growth location, rate, and mode. It is for the first time revealed that nuclei density and growth locations remain constant and are solely determined by the initial nucleation overpotentials during dynamic plating. A transition in growth modes from uniform granular growth to tip-induced dendrite growth, and finally to directional growth among the dendrites is also observed. Guided by these findings, a dynamic plating protocol is proposed, which can greatly improve the Li reversibility and cycling stability. This work not only provides a novel approach to visualize the evolution of key nucleation and growth parameters, especially under variable inputs, but also offers valuable guidance for the future industrialization of metal batteries and the rational design of charging facilities.

A Real‐Time Adaptive Machine Learning Charging and Neural Network Balancing Mechanism of Lithium‐Ion Battery Pack

ABSTRACTIn this article, a real‐time novel adaptive deep neural network (A‐DNN) charging scheme is proposed which increases the life of the batteries by controlling the heating impact inside the battery. The input variables used in the charging algorithm are state of charge (SoC), state of health (SoH), voltage (V), current (I), and temperature (T) which makes the algorithm adaptive toward the temperature deviation and reduces the peak overshoot of the temperature at different SoH of the batteries. The parameters of the battery 1‐RC model are estimated by the forgetting factor recursive least square (FF‐RLS) method. The SoC and SoH are estimated by the dual‐particle filter (D‐PF) algorithm. Furthermore, a DNN balancing mechanism sensitive to SoC and SoH is developed to avoid the fault in the battery during the charging process. The A‐DNN charging algorithm is compared with the constant current constant voltage (CC‐CV), constant current pulse charging (CC‐PC), and deep neural network (DNN) charging algorithms at 40°C, 45°C, and 50°C. The A‐DNN outperforms in terms of peak temperature, incremental life, and charging time of the batteries at 45°C. The proposed charging methodology reduces the economic cost of the EVs by increasing the life of the battery by 34.41% at 45°C as compared to the other algorithms.

Effect of Rice Input Variables and Climate Change Factors on Total Factor Productivity of Rice in Nigeria

This study evaluated the effect of rice production input variables and climate change variables on rice total factor productivity (TFP) in Nigeria. The research utilizes various data sets obtained from reputable organizations to analyze the period from 1961 to 2020. Descriptive and inferential statistics were employed to analyze the data. The study findings indicate an upward trajectory in rice TFP in Nigeria over the examined period. Regression analysis reveals that labor and capital have a significant positive effect on rice TFP, indicating that increased labor and capital investments can enhance productivity. Rainfall and sunlight duration also show a significant positive relationship with rice TFP, emphasizing their crucial role in rice farming. The study highlights the importance of addressing labor scarcity and promoting access to capital for farmers. Moreover, it emphasizes the quest for optimal rainfall and sunlight conditions throughout the rice cultivation process. The model's diagnostic tests confirm its reliability, and the findings demonstrate the statistical significance of the independent variables in explaining rice TFP. Overall, this research gives insightful information on the factors influencing rice TFP in Nigeria. It offers recommendations for stakeholders and decision-makers to enhance productivity by addressing labor scarcity, promoting capital access, and optimizing climatic conditions for rice cultivation.

Organizational, environmental, and sustainability of inputs to the implementation of a smoke-free area policy: A path analysis of Makassar's health facilities

The implementation of smoke-free policies in healthcare facilities represents a critical public health strategy; however, maintaining sustainable compliance remains a challenge globally. While healthcare institutions are expected to exemplify tobacco control measures, the effectiveness of smoke-free policies depends on multiple factors at the institutional and governmental levels. This study aimed to examine the influence of organizational, environmental, and sustainability factors on input indicators for smoke-free area policy implementation in healthcare facilities in Makassar, Indonesia. A quantitative correlational study was conducted across seven healthcare facilities that provide inpatient services. Using random cluster sampling, 232 healthcare workers were selected from a population of 736. Path analysis was employed to evaluate causal relationships between independent variables (organizational, environmental, and sustainability factors) and dependent variables (input indicators, including written policies, monitoring personnel, and promotional media). Data were collected through validated questionnaires and analyzed via structural equation modeling. Path analysis revealed that sustainability factors significantly influenced input indicators, particularly written policies (t statistic = 2.592, p<0.05, β=0.194). Key sustainability components, including funding stability, partnerships, organizational capacity, program evaluation, adaptation, and strategic planning, have demonstrated positive effects on policy implementation. Organizational and environmental factors had no significant impact on the input indicators. Sustainability factors emerged as crucial determinants for the successful implementation of smoke-free policies in healthcare facilities in Makassar city. Future research should investigate the barriers to policy implementation and explore additional input indicators to increase the effectiveness of smoke-free policies.

A Brief Review on Hydrological Modelling

The interaction between water, climate, soil and land use is primary to the hydrological modelling concept. Hydrological models include spatial and temporal features. Hydrologists utilize hydrologic models as a primary tool for a variety of tasks including managing water resources, managing urban and rural areas, modelling ground water and more. In order to implement hydrologic models with ease, it is necessary to thoroughly comprehend their properties which have been developed and improved through the years. It is difficult to categorize hydrologic models precisely and various hydrologists may use different criteria. The reason is that while numerous models share common traits, the nature of the model is frequently the same. In this paper, the discussion starts with an introduction to ancient hydrology followed by the anthropogenic factors which directly or indirectly affect hydrological flows. Further the paper reviews hydrological modelling and the evolution of hydrologic models. The research aims to demonstrate classification of hydrologic models. The proper application of a model requires an in-depth understanding of it. In this research, eleven watershed hydrologic models were reviewed: ANN model, Unit Hydrograph, SCS-CN model, SVM model, HBV, TOPMODEL, PRMS, MIKESHE, VIC, HEC-HMS, MODFLOW and SWAT. SWAT model, one of the non-point source pollution models, is discussed along with its history and its major input variables. This literature also compiles and discusses applications of the SWAT model. SWAT was found to be promising models for long-term continuous simulations in agricultural watersheds. In agricultural watersheds, SWAT has been found to be the most assuring model for long-term continuous simulations.

Research on Real-Time Energy Consumption Prediction Method and Characteristics of Office Buildings Integrating Occupancy and Meteorological Data

A method based on Long Short-Term Memory (LSTM) networks is proposed to forecast hourly energy consumption. Using an office building in Shanghai as a case study, hourly data on occupancy, weather, and energy consumption were collected. Daily energy consumption was analyzed using single-link clustering, and days were classified into three types. The key input variables significantly influencing energy consumption, solar radiation, occupancy, and outdoor dry bulb temperature are identified by the Pearson correlation coefficient. By comparing five algorithms, it was found that the LSTM model performed the best. After considering the occupancy, the hourly MAPE was reduced from 11% to 9%. Accuracy improvements for each day type were noted as 1% for weekdays, 4% for Saturday, and 7% for Sunday. Further analysis indicated that the model started to predict the time (1:00) and commute time (7:00 and 17:00) with large errors. The model was optimized by varying the time step. For the times 1:00, 7:00, and 17:00, the best optimization of the model was achieved when the time step values were set to 6 h, 24 h, and 18 h with an MAPE of 3%, 6%, and 5%, respectively. As the model time step increased (≤2 weeks), the accuracy of the model decreased to 6%.

Agricultural Machinery Path Tracking with Varying Curvatures Based on an Improved Pure-Pursuit Method

The current research on path tracking primarily focuses on improving control algorithms, such as adaptive and predictive models, to enhance tracking accuracy and stability. To address the issue of low tracking accuracy caused by variable-curvature paths in automatic navigation within agricultural environments, this study proposes a fuzzy control-based path-tracking method. Firstly, a pure-pursuit model and a kinematic model were established based on a Four-Wheel Independent Steering and Four-Wheel Independent Driving (4WIS-4WID) structure. Secondly, a fuzzy controller with three inputs and one output was designed, using the lateral deviation, de; heading deviation, θe; and bending degree, c, of the look-ahead path as the input variables. Through multiple simulations and adjustments, 75 control rules were developed. The look-ahead distance, Ld, was obtained through fuzzification, fuzzy inference, and defuzzification processes. Next, a speed-control function was constructed based on the agricultural machinery’s pose deviations and the bending degree of the look-ahead path to achieve variable speed control. Finally, field tests were conducted to verify the effectiveness of the proposed path-tracking method. The tracking experiment results for the two types of paths indicate that under the variable-speed dynamic look-ahead distance strategy, the average lateral deviations for the variable-curvature paths were 1.8 cm and 3.3 cm while the maximum lateral deviations were 10.1 cm and 10.5 cm, respectively. Compared to the constant-speed fixed look-ahead pure-pursuit model, the average lateral deviation was reduced by 56.1% and the maximum lateral deviation by 50.4% on the U-shaped path. On the S-shaped path, the average lateral deviation was reduced by 56.0% and the maximum lateral deviation by 58.9%. The proposed method effectively improves the path-tracking accuracy of agricultural machinery on variable-curvature paths, meeting the production requirements for curved operations in agricultural environments.

DEVELOPMENT OF A LITHIUM-ION BATTERY CHARGING SYSTEM UNDER CONSTANT CURRENT AND VOLTAGE CONDITIONS USING STM-32 BASED ON FUZZY LOGIC CONTROL

Lithium-ion batteries are electrical energy storage devices often found in portable electronic equipment. Overcharging and discharging the battery will reduce its life and cause severe damage. Constant current and voltage control methods and control algorithms, such as fuzzy logic control, need to be added to avoid this. This research aims to develop a lithium-ion battery charging system using a constant current and voltage method based on fuzzy logic control. A constant current-constant voltage (cc-cv) charging system helps control the charging voltage and current by conditioning the initial charging to use a constant current so as not to overcharge. Constant current uses a buck converter circuit, while constant voltage uses a voltage regulator circuit. The charging system is equipped with a voltage sensor and a current sensor. System control uses fuzzy logic control methods with input variables as errors and delta errors while the output is a duty cycle. The overall system design was carried out at the Measurement, Reliability, Risk, and Safety Laboratory, ITS for 4 months. The test results show that charging the battery produces a voltage of 12.6 Volts and a current of 2.5 Amperes. The battery will be fully charged, and the charging system will stop when the flowing current decreases and the current is cut off at 100 mA.

Mapping National‐Scale Road Surface Types Using Multisource Open Data and Deep Learning Model

ABSTRACTIdentifying road surface type (e.g., paved and unpaved) is crucial for pavement maintenance, transportation management, and road network accessibility research. Existing approaches relying on vehicle‐mounted devices or remote sensing data are have limitation for large‐scale road networks. This study proposes a novel approach to identify national‐scale road surface type using multiple open geospatial datasets and machine learning models. Specifically, 16 input variables were designed based on these datasets (including OpenStreetMap, GDP, population, building height, and land cover). Nigeria and Cameroon were selected as study areas. A substantial dataset, auto‐extracting road surface tags from OpenSreetMap, was used to train a model. The trained model predicted road surface types across the two study areas. Result indicated: (1) Most of the input variables positively impact the output variable, with “road class” being the most influential; (2) The proposed approach with deep learning model‐TabNet performs the best, with an overall accuracy above 85%; and (3) More than 83% of roads in the two African countries are unpaved, with paved roads concentrated in backbone roads and southern provinces. This approach has been validated and offers valuable insights for local authorities aiming to enhance road infrastructure.

Implementation of Fuzzy Logic on Adding Sodium Bicarbonate and Carboxy Methyl Cellulose (CMC) Concentrations to the Texture of Wet Noodles

Abstract The purpose of this study was to identify appropriate concentrations of sodium bicarbonate and CMC in order to produce wet noodles with textures preferred by consumers. The steps used in this study are to conduct a literature study to obtain data in the form of input variables, namely sodium bicarbonate and Carboxy Methyl Cellulose (CMC), and output, namely texture. The input variables are fuzzified, and then the fuzzy rules and inference are determined by Mamdani and defuzzification methods. Data processing using the Mamdani method is carried out with the help of the Matlab application. The result of this study is the formulation of sample 1 with the addition of 0.6% sodium bicarbonate and 0.2% CMC and sample 2 with the addition of 0.6% sodium bicarbonate and 0% CMC, which are recommended to consumers because they produce the preferred texture of wet noodles.

Implementation of Fuzzy Logic in Temperature and Fermentation Time Control Systemon Alcohol Content of Cassava Tape Product

The fermentation process of cassava tape involves complex biochemical reactions and requires careful attention to various factors. Temperature and fermentation time are crucial parameters that significantly affect the final quality of cassava tape. Along with the development of artificial intelligence (AI), the fermentation process can be controlled and monitored more precisely, thereby increasing the efficiency and consistency of the final product. This study aims to determine the Mamdani fuzzy logic approach in the temperature control system and fermentation duration in the cassava tape production process to optimally regulate the alcohol content of cassava tape. The research method is a literature study, testing Mamdani fuzzy logic using Matlab software, and analyzing input variables manually. The results of the study showed that the optimal temperature for cassava tape fermentation was between 25°C - 30°C and the optimal time for cassava tape fermentation was with a "long" duration. From the defuzzification results, the final results showed an alcohol content of 15.9% at a temperature of 29°C and a fermentation time of 80 hours so that the alcohol content of cassava tape was in accordance with the specifications.

Design of Temperature and Humidity Control System for Cocoa Bean Dryer Using Fuzzy Mamdani Method

This research aims to develop a fuzzy logic control system for optimizing temperature and humidity during cocoa bean fermentation. The study employs a literature review method and data analysis using MATLAB. A Mamdani-type fuzzy inference system is designed to control the temperature and humidity of a cocoa bean dryer. Input variables include temperature (0-65°C) and humidity (0-100%), while output variables are heater and fan settings. The system's performance is evaluated using a case study, demonstrating its ability to adjust heater and fan operations based on environmental conditions. Results show that for an input of 65°C temperature and 50% humidity, the system recommends turning off the heater (membership value 79.8) and running the fan at high speed (membership value 84.7). This fuzzy logic approach offers a promising solution for maintaining optimal conditions during cocoa bean fermentation, potentially improving the quality and consistency of the final product. Keywords: cocoa fermentation, fuzzy logic control, temperature regulation

A fuzzy logic approach to improve the sensitivity of the rapid entire body assessment method

Conventional ergonomic observation methods, such as rapid entire body assessment (REBA), are limited in their sensitivity and reliability, particularly in detecting changes in input variables. This study integrates fuzzy logic with the REBA method, utilizing trapezoidal membership functions to fuzzify the input variables. The center of gravity method was employed for defuzzification, and if–then rules were formulated to enhance the REBA method. The findings revealed that the fuzzy REBA method is more sensitive to changes in the risk of work-related musculoskeletal disorders (WMSDs) than conventional REBA. Furthermore, the intraclass correlation coefficient (ICC) of the risk scores obtained with the fuzzy REBA method ranged from 0.76 to 0.89, indicating reliable outcomes. The proposed fuzzy REBA method offers a more reliable approach for on-site ergonomic assessments, thereby reducing observational errors and potentially reducing the incidence of WMSDs in the workplace.

Incidental nanoparticle characterisation in industrial settings to support risk assessment modelling.

Research on nanoparticle (NP) release and potential exposure can be assessed through experimental field campaigns, laboratory simulations, and prediction models. However, risk assessment models are typically designed for manufactured NP (MNP) and have not been adapted for incidental NP (INP) properties. A notable research gap is identifying NP sources and their chemical, physical, and toxicological properties, especially in real-world settings. This work aims to provide insights into the release and physico-chemical properties of INP while contributing to improving models for INP release. INP release was evaluated through a case study in a ceramic tile firing facility, where aerosol (10nm - 10μm) properties were determined. The Control Banding (CB) Nanotool model was applied to test outputs based on provided input parameters. RESULTS: demonstrate the constant generation and release of INP during tile firing, with NP concentrations up to 68711/cm³ and mean diameters of 37nm, with 95% smaller than 100nm. Particle morphology was mostly spherical, suggesting nucleation from precursor gases as the main formation mechanism. INP chemical composition was driven by primary ceramic components, while trace elements like Ni and Ti exhibited size-dependent patterns. In vitro cell viability tests indicated low to medium cytotoxicity of PM2 aerosols, decreasing human alveolar epithelial cell viability in a concentration-dependent manner. Applying the risk model with varying input parameters revealed that the risk level (RL) based on severity scores decreased when aerosol size distribution data were used, illustrating the model's sensitivity to input variables. We conclude on the need for comprehensive experimental datasets to support risk assessment models and achieve effective risk management strategies in real-world scenarios.