Reliable Forecasts Research Articles

Forecasting the Mass Activity of Platinum Anode Catalysts on Various Carbon Nanostructures in Direct Methanol Fuel Cells Using Machine Learning

Abstract Anode catalyst loading in direct methanol fuel cells (DMFC) is extremely high at around 4.5 mgPtRu/cm2, which increases cost and inhibits commercialization. Several carbon nanostructures, such as carbon nanotubes, graphene, mesoporous carbon, and carbon quantum dots, are used to support platinum as well as platinum with other metals and metal oxides to reduce the platinum content of catalysts. Optimizing the catalyst composition for DMFC requires extensive trial and error experiments due to the complex electrochemical and thermodynamic processes, which demands considerable time. We present here machine learning-aided models that correlate the composition of platinum-based catalysts on different carbon nanostructures with the mass activity of DMFC. Various machine learning techniques are employed to predict the mass activity of platinum-based catalysts using data from published literature. These models demonstrate a good level of predictive accuracy (R2>0.85) with the available datasets and show that even basic models can provide reliable forecasts. The SHapley Additive Explanations (SHAP) summary plot reveals that graphene's weight fraction is the most significant feature among all carbon nanostructures, followed by the weight fractions of cobalt and platinum. Hence, machine learning has demonstrated significant effectiveness in predicting platinum's mass activity based on catalyst composition and process parameters.

Empirical Comparison of Facebook Prophet and Traditional Models for Tomato Price Forecasting in Greece

Agricultural product prices are crucial to the income and livelihoods of millions worldwide, making accurate forecasting essential for market participants. The European Union regularly reports agricultural product prices through an online databank, ensuring up-to-date price information is widely available. This study evaluates the effectiveness of Facebook Prophet, a modern forecasting tool, in predicting tomato prices in Greece from 2013 to 2024.The results show that Facebook Prophet outperforms traditional forecasting models, providing more accurate predictions with lower Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) values. Notably, the model exhibited superior performance in forecasting tomato prices over a six-month horizon compared to conventional seasonally adjusted models. This demonstrates Facebook Prophet’s potential to significantly improve decision-making in agricultural markets, offering reliable price forecasts to stakeholders such as farmers, traders, and policymakers. Furthermore, the study incorporated feedback from market participants, which provided valuable insights into market practices and conditions. This integration of practical knowledge with advanced forecasting techniques enhanced the interpretation of the results, making them more applicable to real-world scenarios. Overall, the findings suggest that Facebook Prophet holds considerable promise for future agricultural price forecasting, with potential applications across various commodities. These insights pave the way for more precise agricultural forecasts, benefiting all market participants by supporting more informed and timely decision-making.

A Temporal Convolutional Neural Network Fusion Attention Mechanism Runoff Prediction Model Based on Dynamic Decomposition Reconstruction Integration Processing

Accurate and reliable runoff forecasting is of great significance for hydropower station operation and watershed water resource allocation. However, various complex factors, such as climate conditions and human activities, constantly affect the formation of runoff. Runoff data under changing environments exhibit highly nonlinear, time-varying, and stochastic characteristics, which undoubtedly pose great challenges to runoff prediction. Under this background, this study ingeniously merges reconstruction integration technology and dynamic decomposition technology to propose a Temporal Convolutional Network Fusion Attention Mechanism Runoff Prediction method based on dynamic decomposition reconstruction integration processing. This method uses the Temporal Convolutional Network to extract the cross-temporal nonlinear characteristics of longer runoff data, and introduces attention mechanisms to capture the importance distribution and duration relationship of historical temporal features in runoff prediction. It integrates a decomposition reconstruction process based on dynamic classification and filtering, fully utilizing decomposition techniques, reconstruction techniques, complexity analysis, dynamic decomposition techniques, and neural networks optimized by automatic hyperparameter optimization algorithms, effectively improving the model’s interpretability and precision of prediction accuracy. This study used historical monthly runoff datasets from the Pingshan Hydrological Station and Yichang Hydrological Station for validation, and selected eight models including the LSTM model, CEEMDAN-TCN-Attention model, and CEEMDAN-VMD-LSTM-Attention (DDRI) for comparative prediction experiments. The MAE, RMSE, MAPE, and NSE indicators of the proposed model showed the best performances, with test set values of 1007.93, 985.87, 16.47, and 0.922 for the Pingshan Hydrological Station and 1086.81, 1211.18, 17.20, and 0.919 for the Yichang Hydrological Station, respectively. The experimental results indicate that the fusion model generated through training has strong learning ability for runoff temporal features and the proposed model has obvious advantages in overall predictive performance, stability, correlation, comprehensive accuracy, and statistical testing.

Summary of the results of field and laboratory studies of hydraulic resistance during channel bending

The reliability of channel forecasts, performed using mathematical modeling methods in the design of engineering activities on shipping rivers, largely depends on the accuracy of the assessment of hydraulic resistance and sediment transport parameters. Therefore, the hydraulic resistance of natural channels is one of the largest problems in the dynamics of channel flows and is the object of close attention of scientists. In the resistance of natural channels, roughness appears in three forms: the first of them is the roughness of the granular surface of the bottom; the second is the roughness created by boulders and the third type is the roughness of microforms: ripples and ridges. Channel mesoforms also contribute to the resistance to water movement: spits, side streams, middle streams, islands, bends of the channel and such complex formations as riffles. This type of resistance is usually called the resistance of the channel form. The resistance of the natural channel shape due to the movement and deformation of mesoforms can change significantly over time. The complexity and insufficient study of this issue significantly limit the possibilities of a theoretical approach to its solution. Therefore, the obtained results are mainly empirical or semiempirical in nature. The paper presents the results of experimental and field studies that allowed us to identify the main features of flow movement in winding sections of rivers. One of the features of flow movement at channel turns is the possibility of additional energy losses. The total resistance of a curved section of a channel is made up of three main parts: the resistance of the granular surface of the bottom, the resistance of the bottom ridges, and the resistance of the channel shape. The additional resistance created by the bend depends on the curvature of the channel, the rate of change in depth along the length of the bend, and also reflects the impact of side streams formed near the convex bank on the flow.

Evaluation, Calibration, and Application of Probabilistic 100-Meter Wind Speed Forecasts Produced by the WRF Ensemble Prediction System in Taiwan

Abstract The use of renewable energy is on the rise; however, it brings more challenges for grid operations and unit scheduling due to the intermittent nature and limitations in predicting renewable power generation. High-quality meteorological forecasts play a critical role in the effective application and management of renewable energy resources. This study evaluates the quality and economic benefits of probabilistic forecasts for 100-m wind speeds from the Weather Research and Forecasting model ensemble prediction system (WEPS). A multiple linear regression (MLR) method for calibration is also used to improve forecast quality. Additionally, this study explores the application of probabilistic hub-height wind speed forecasts in conjunction with an economic value analysis to determine whether wind turbines should be shut down during typhoon periods. The findings reveal that the forecast in the central offshore region of Taiwan exhibits the highest reliability and discrimination ability compared to those in the northern and southern regions. Additionally, employing the MLR calibration method significantly improved the reliability and discrimination ability of the probabilistic forecasts compared to the raw forecasts. Furthermore, during the typhoon seasons, almost all users, regardless of their cost-loss ratio, can benefit from basing their decisions on WEPS forecasts compared to using a single deterministic forecast. Importantly, decision-makers can benefit more from probabilistic than ensemble mean forecasts when both are derived from the same ensemble prediction system, since the former incorporates forecast uncertainty.

Climate-based modelling and forecasting of dengue in three endemic departments of Peru.

Amid profound climate change, incidence of dengue continues to rise and expand in distribution across the world. Here, we analysed dengue in three coastal departments of Peru which have recently experienced public health emergencies during the worst dengue crises in Latin American history. We developed a climate-based spatiotemporal modelling framework to model monthly incidence of new dengue cases in Piura, Tumbes, and Lambayeque over 140 months from 2010 to 2021. The framework enabled accurate description of in-sample and out-of-sample dengue incidence trends across the departments, as well as the characterisation of the timing, structure, and intensity of climatic relationships with human dengue incidence. In terms of dengue incidence rate (DIR) risk factors, we inferred non-linear and delayed effects of greater monthly mean maximum temperatures, extreme precipitation, sustained drought conditions, and extremes of a Peruvian-specific indicator of the El Niño Southern Oscillation. Building on our model-based understanding of climatic influences, we performed climate-model-based forecasting of dengue incidence across 2018 to 2021 with a forecast horizon of one month. Our framework enabled representative, reliable forecasts of future dengue outbreaks, including correct classification of 100% of all future outbreaks with DIR ≥ 50 (or 150) per 100,000, whilst retaining relatively low probability of 0.12 (0.05) for false alarms. Therefore, our model framework and analysis may be used by public health authorities to i) understand climatic drivers of dengue incidence, and ii) alongside our forecasts, to mitigate impacts of dengue outbreaks and potential public health emergencies by informing early warning systems and deployment of vector control resources.

Enhancing the Statistical Evaluation of Earthquake Forecasts—An Application to Italy

Abstract Testing earthquake forecasts is essential to obtain scientific information on forecasting models and sufficient credibility for societal usage. We aim to enhance the testing phase proposed by the Collaboratory for the Study of Earthquake Predictability (CSEP, Schorlemmer et al., 2018) with new statistical methods supported by mathematical theory. To demonstrate their applicability, we evaluate three short-term forecasting models that were submitted to the CSEP-Italy experiment, and two ensemble models thereof. The models produce weekly overlapping forecasts for the expected number of M4+ earthquakes in a collection of grid cells. We compare the models’ forecasts using consistent scoring functions for means or expectations, which are widely used and theoretically principled tools for forecast evaluation. We further discuss and demonstrate their connection to CSEP-style earthquake likelihood model testing, and specifically suggest an improvement of the T-test. Then, using tools from isotonic regression, we investigate forecast reliability and apply score decompositions in terms of calibration and discrimination. Our results show where and how models outperform their competitors and reveal a substantial lack of calibration for various models. The proposed methods also apply to full-distribution (e.g., catalog-based) forecasts, without requiring Poisson distributions or making any other type of parametric assumption.

Are more data always better? – Machine learning forecasting of algae based on long-term observations

Bloom-forming algae present a unique challenge to water managers as they can significantly impair provision of important ecosystem services and cause health risks to humans and animals. Consequently, effective short-term algae forecasts are important as they provide early warnings and enable implementation of mitigation strategies. In this context, machine learning (ML) emerges as a promising forecasting tool. However, the performance of ML models is heavily dependent on the availability of appropriate training data. Consequently, it is essential to determine the volume of data necessary to develop reliable ML forecasts. Understanding this will guide future monitoring strategies, optimize resource allocation, and set realistic expectations for management outcomes. In this study, we used 30 years of fortnightly measurements of 13 different parameters from a lake in the English Lake District (UK) to examine the impact of training data duration on the performance of ML models for forecasting chlorophyll-a two weeks in advance. Once training data availability exceeded four years, a Random Forest model was found to consistently outperform naive benchmarks (mean absolute percentage error 16.4 % lower than the best-performing benchmark). With more than 5 years of training data, model performance generally continued to improve, but with diminishing returns. Furthermore, it was found that equivalent and, in some cases, better performance could be achieved by only using a subset of the most important input features. Additionally, it was found that reducing the sampling frequency had negative impacts on performance, both due to the reduced number of training observations available, and increased forecast horizon. Our findings demonstrate that for lakes ecologically similar to the study site, a consistent and regular sampling programme focused on monitoring a limited number of key parameters can provide sufficient observations for generating short-term algae forecasts after approximately five years of data collection. Importantly, this result provides justification for the initiation of new monitoring programmes for sites where algal blooms are a concern, and suggests that there are likely many pre-existing monitoring datasets which would be suitable for training algae forecast models.

Machine learning and regression in the management of runoff in bauxite mines under rehabilitation.

Accurate and reliable forecasting of monthly runoff considering several years of rehabilitation helps in planning and managing the water resources system of bauxite mining areas. A combination of linear regression models and artificial intelligence was proposed and applied to predict runoff from mined areas. The ideal number of input variables was chosen considering a correction threshold (correlation above 95%), stepwise selection, and according to the recursive feature elimination (RFE). The linear regression equations were divided into explanatory and predictive, with the predictive ones being trained and validated with all possible combinations of annual data periods. Five machine learning models (linear model (LM), K-nearest neighbor regression (KNN), support vector machine (SVM), random forest (RF), and Cubist) were trained and used as a prediction instrument on the test set of each subsequent year. Precipitation, accumulated precipitation duration, maximum precipitation intensity, and number of events were the most explanatory variables in most linear regression and machine learning equations. The linear regression model approach presented satisfactory performance indices in predicting surface runoff in a mined bauxite area. The Cubist and RF models were the best predictors for the greatest number of years of rehabilitation, with a coefficient of determination varying with maximum values between 0.52 and 0.88 in validation. There was no best model that consistently presented the best result in all years of rehabilitation, and it is recommended to use the annual model for the respective year of rehabilitation. The linear regression equations and algorithms were useful tools in predicting runoff series and, therefore, promising monthly runoff prediction. The presented approach becomes a starting point in the search to optimize the hydrological planning of surface bauxite mines.

A New Approach to Assessing the Accuracy of Forecasting of Emergencies with Environmental Consequences Based on the Theory of Fuzzy Logic

Prevention of the occurrence and development of emergencies of a natural and man-made nature is one of the basic fundamental foundations of ensuring the national security of any state. The most important mechanism for preventing emergencies is an effective system of monitoring and forecasting emergencies established at the state level. In the process of functioning such a system, one of the main urgent problems requiring constant attention, continuous research, system analysis, and the search for solutions by scientific methods and methods is to increase the reliability of emergency forecasts. In this format, special attention is currently being paid worldwide to a comprehensive assessment of the adverse consequences of emergency situations, primarily related to the safety of the population, environmental conservation, and environmental safety. From the standpoint of solving this significant scientific and practical problem, the purpose of this work was to develop and justify a more advanced method for calculating the feasibility of forecasts of emergencies with environmental consequences as a tool for a reasonable detailed assessment of the quality, optimality of emergency forecasting processes and the reliability of the forecasts themselves.

A probabilistic machine learning framework for daily extreme events forecasting

Forecasting daily extreme events is crucial, particularly amidst the escalating severity of tropical storms and hurricanes in the East and Gulf coasts of the United States. The intensity of these hydrometeorological events, exacerbated by climate variability and change, has resulted in catastrophic floodings, posing significant risks to public safety and infrastructure. In response to the shortcomings associated with the deterministic rainfall-runoff models, specifically to account for uncertainties, we develop a probabilistic framework to enhance the accuracy and reliability of streamflow forecasting during extreme events. The core of our framework is a Monte Carlo-based deep learning model based on Long Short-Term Memory (LSTM) to account for model uncertainty. We employed the wavelet transform in our probabilistic framework to decompose observed discharge data, breaking down the data into its constituent components to identify trends. Furthermore, to account for the aleatory uncertainty in model input, we perturb the forcing datasets. Therefore, the proposed probabilistic framework accounts for both types of uncertainty, aleatory and epistemic. The model efficacy is tested across twenty-four basins in Southeast Texas, with a particular focus on the extreme conditions during Hurricane Harvey event. The results show that the proposed model outperforms the basic LSTM model by 45%, 40%, 9%, and 40% in NSE, KGE, PCC, and RMSE, respectively. This validation demonstrates the model’s robustness and applicability in real-world scenarios and underscores its potential as an effective tool for decision-makers in planning and risk management during extreme hydrological events.

Predicting Electricity Consumption using an Innovative Deep Energy Predictor Model for Enhanced Accuracy and Efficiency

Accurate forecasting of electricity consumption is crucial for efficient energy management and planning. Traditional models often struggle to capture the complex, nonlinear relationships inherent in energy consumption data, leading to less reliable predictions. In the quest to improve the accuracy and efficiency of electricity consumption predictions, we introduce the Deep Energy Predictor Model (DEPM). This innovative hybrid model combines the strengths of XGBoost for classification and Deep Neural Networks (DNN) for deep learning (DL) capabilities. The model leverages advanced feature extraction techniques using Cascaded ResNet, ensuring the capture of intricate patterns within the data. Implemented in Python, the DEPM highlights an impressive accuracy of 98%, with a precision of 0.97, recall of 0.99, and an F1-score of 0.98, setting a new standard for predictive models in the energy sector. This study elaborates on the model architecture, implementation details, and the evaluation metrics that underscore the model's superior performance. Our results demonstrate the potential of DEPM to significantly enhance the reliability of electricity consumption forecasts, providing a robust tool for energy management and planning.

Behaviour and design of extruded high-strength aluminium alloy SHS beam-columns

An extensive study of beam-columns made from 7A04-T6 aluminum alloy in a square hollow section (SHS) configuration is presented in this paper, integrating both experimental and numerical work to study their flexural buckling behaviour. Eight pin-ended SHS specimens with two extruded SHS profiles - 80×5 and 120 × 10 (in mm), were tested under eccentric compression, along with tests of material coupons and measurements of initial geometric imperfections. The experimental data were employed in the investigation to assess the validity of the numerical model, which was subsequently subjected to a series of parametric analyses aimed at expanding the existing results across a wider spectrum of slenderness ratios, cross-section dimensions, and load combinations. Both experimental and simulated datasets were employed to verify the precision of resistance forecasts for SHS beam-columns by design approaches outlined in European, Chinese and American standards. Findings indicated that both the European and Chinese standards tended to provide relatively conservative predictions for buckling resistances, while the American standard sometimes produced predictions leading to higher risk. Finally, a modification strategy for the design of AA7A04-T6 SHS beam-columns, utilizing modified interaction buckling factors that account for non-dimensional member slenderness and compression resistances, was suggested to enhance the precision and reliability of resistance forecasts.

Objective-based survival individual enhancement in the chimp optimization algorithm for the profit prediction using financial accounting information system

This paper develops an innovative Objective-based Survival Individual Enhancement approach for the Chimp Optimization Algorithm (OSIE-CHOA) designed to enhance financial accounting profit prediction using information systems. The OSIE-CHOA focuses on improving the search process by simultaneously elevating the fitness of under-performing individuals within a population and strengthening the diversity among the top-performing ones. Within the OSIE-CHOA, we identify the four most promising chimps during each iteration. Subsequently, half of the highest-performing chimps are selected for elimination and repositioning around these fortunate individuals, with an equal probability assigned to each chimp. According to the experimental findings, it is clearly seen that OSIE-CHOA considerably enhances prediction accuracy, allowing a decrease in the root mean square error (RMSE) by 15% and the mean absolute error (MAE) by 18% compared to the traditional CHOA. Moreover, OSIE-CHOA shows a convergence rate that is 20% higher, which makes it a good and efficient tool for financial analysts who require accurate and reliable profit forecasting. By facilitating the optimization of profit prediction models, OSIE-CHOA leads to the improvement of decision-making within the context of financial accounting information systems.

Data-driven forecasting framework for daily reservoir inflow time series considering the flood peaks based on multi-head attention mechanism

Accurate and reliable daily reservoir inflow forecast plays an essential role in several applications involving the management and planning of water resources, such as hydroelectric generation, flood control, water supply, and basin ecological dispatching. Runoff usually exhibits strong non-linearity, high uncertainty, and spatial and temporal variability. Existing techniques fail to capture complete dynamics change processes effectively. A data-driven forecasting framework for daily reservoir inflow time series considering the flood peaks based on a multi-head attention mechanism was developed, referred to as the GWOCS-VMD-CNN-Transformer (GCVCT). First, the model utilize Grey Wolf Optimizer coupled with Cuckoo Search (GWO-CS) algorithms to optimize parameters in variational mode decomposition model (VMD). This approach helps obtain highly correlated intrinsic mode function (IMF) components, enhancing the frequency resolution of the input dataset. The proposed method overcomes the bottleneck of other available methods by decomposing the time series to capture the main long-term and short-term properties of hydrological processes. Second, the convolution neural network and Transformer (CNN-Transformer) are based on a multi-head attention mechanism as the objective predictive method. Finally, six evaluation indicators verify the performance of the proposed approach. The approach’s reliability was evaluated using the historical daily reservoir inflow data from the Xiluodu (XLD) and Wudongde (WDD) reservoirs in the Jinsha River Basin, China. Several single and hybrid models were developed for comparative analysis. The results indicate that the proposed ensemble approach fits better than other developed model methods. The GCVCT model showed excellent performance in forecasting the inflows of XLD and WDD reservoirs, with NSE values of 0.985 and 0.984, respectively. Furthermore, the GCVCT framework forecast capacity for peak inflow was further verified through discussion and analysis of the 48 peak flows during the validation period, consistently outperforming other models in predicting peak flow for both study reservoirs. This framework provides an effective method for the scientific optimal scheduling of hydropower reservoirs, enabling more sustainable and efficient management practices. It also demonstrates the potential of powerful deep-learning models in intelligent hydrological forecasting.

Deep-learning and data-resampling: A novel approach to predict cyanobacterial alert levels in a reservoir

The proliferation of harmful algal blooms results in adverse impacts on aquatic ecosystems and public health. Early warning system monitors algal bloom occurrences and provides management strategies for promptly addressing high-concentration algal blooms following their occurrence. In this study, we aimed to develop a proactive prediction model for cyanobacterial alert levels to enable efficient decision-making in management practices. We utilized 11 years of water quality, hydrodynamic, and meteorological data from a reservoir that experiences frequent harmful cyanobacterial blooms in summer. We used these data to construct a deep-learning model, specifically a 1D convolution neural network (1D-CNN) model, to predict cyanobacterial alert levels one week in advance. However, the collected distribution of algal alert levels was imbalanced, leading to the biased training of data-driven models and performance degradation in model predictions. Therefore, an adaptive synthetic sampling method was applied to address the imbalance in the minority class data and improve the predictive performance of the 1D-CNN. The adaptive synthetic sampling method resolved the imbalance in the data during the training phase by incorporating an additional 156 and 196 data points for the caution and warning levels, respectively. The selected optimal 1D-CNN model with a filter size of 5 and comprising 16 filters achieved training and testing prediction accuracies of 97.3% and 85.0%, respectively. During the test phase, the prediction accuracies for each algal alert level (L-0, L-1, and L-2) were 89.9%, 79.2%, and 71.4%, respectively, indicating reasonably consistent predictive results for all three alert levels. Therefore, the use of synthetic data addressed data imbalances and enhanced the predictive performance of the data-driven model. The reliable forecasts produced by the improved model can support the development of management strategies to mitigate harmful algal blooms in reservoirs and can aid in building an early warning system to facilitate effective responses.

WEATHERING THE STORM: A SYSTEMATIC LITERATURE REVIEW OF WEATHER STATIONS’ OBSERVATIONAL PRACTICES

This literature review examines the accuracy, error frequency, and the impact of workload and decision-making on the weather observations made by PAGASA synoptic station personnel in the Cordillera Administrative Region (CAR). The accuracy of weather data collected by PAGASA is critical for reliable forecasting and timely decision-making in various sectors, including agriculture, disaster management, and transportation. The review highlights key factors influencing data accuracy, such as instrument calibration and human error. Studies show that while the accuracy of weather observations is generally high, occasional discrepancies occur, often due to technical issues or human mistakes in data recording, especially during extreme weather events. The review also explores the frequency of errors in weather observation data. Research indicates that errors are infrequent, but they are more likely to occur during severe weather conditions or in cases of equipment malfunctions or power failures. Although external factors can occasionally disrupt the data collection process, the overall frequency of errors remains relatively low. Furthermore, the impact of workload and decision-making on the accuracy of observations is discussed. Heavy workloads during peak weather seasons and the pressure of rapid decision-making during extreme weather events were found to contribute to an increased likelihood of errors. Studies emphasize the importance of managing workload and decision-making stress to maintain the quality of data. The review concludes by suggesting that further research, training, and technological improvements are needed to enhance the accuracy and reliability of weather observations in PAGASA synoptic stations in CAR.

Predicting Future Citations from A.I. Publication Trends: A Comparative Analysis of Forecasting Models

ABSTRACT Forecasting future citations based on trends in A.I. publications in libraries is essential for understanding the long-term impact of academic research. This study evaluates how well historical citation data from 2003 to 2023 can predict future citation patterns and compares the effectiveness of various forecasting models. The analysis includes traditional time series models like ARIMA and Exponential Smoothing, as well as machine learning techniques such as Linear Regression, Decision Tree Regression, Random Forest Regression, and Gradient Boosting Regression. ARIMA and Exponential Smoothing are chosen for their robustness in handling historical trends, while machine learning models are applied to capture complex, nonlinear relationships. The study assesses model performance using metrics such as Mean Squared Error (MSE) and R-squared values, identifying the most accurate models for forecasting. The findings reveal that Exponential Smoothing provides reliable forecasts of future citations, while Random Forest Regression demonstrates superior performance among machine learning techniques. Additionally, linear regression models predict citation and publication trends effectively, though they may benefit from more sophisticated approaches for greater accuracy. This research offers valuable insights for researchers, institutions, and policymakers, helping them anticipate the future impact of academic publications and guiding strategic decisions in research and funding.

Modelling Seasonal Variation and Lassa Fever Outbreak in Nigeria: A Predictive Approach

<i>Background</i>: Lassa fever, a severe viral hemorrhagic fever caused by the Lassa virus, is a significant public health concern in West Africa, particularly in Nigeria. First identified in the 1950s, Lassa fever has been a persistent threat, causing outbreaks annually. This study investigates the temporal patterns and trends of Lassa fever outbreaks in Nigeria between 2017 and 2023, leveraging a comprehensive dataset from the Nigerian Centre for Disease Control (NCDC). <i>Objective</i>: The goal of this study is to analyze the seasonal variations and predict future occurrences of Lassa fever outbreaks in Nigeria. By employing the Box-Jenkins time series analysis and geo-spatial analysis, we aim to: Identify temporal patterns by Examining monthly and annual trends in Lassa fever case numbers, Forecast future outbreaks by utilizing an ARIMA model to predict future incidence rates and inform public health strategies by providing evidence-based recommendations to improve Lassa fever prevention and control efforts. <i>Methods</i>: This study utilized a secondary dataset comprising over 60 data points collected from the NCDC portal between 2017 and 2023. The Box-Jenkins time series analysis, specifically the ARIMA model, was employed to analyze the temporal patterns and forecast future trends. The model's adequacy was assessed using the Ljung-Box test. Additionally, geo-spatial analysis was conducted to visualize the spatial distribution of Lassa fever cases. <i>Results:</i> The analysis revealed distinct seasonal patterns in Lassa fever incidence, influenced by Nigeria's climatic and environmental conditions. Monthly fluctuations in confirmed cases were observed, with peak periods aligning with specific seasons. The ARIMA (0, 1, 1)(0, 1, 1)<sub>12</sub> model demonstrated a strong fit to the data, providing reliable forecasts for future outbreaks. <i>Conclusion:</i> This study underscores the importance of strengthening surveillance systems for early detection and rapid response to Lassa fever outbreaks, particularly during peak seasons. Implementing effective rodent control measures, promoting good hygiene practices, and improving environmental sanitation are crucial for reducing the risk of Lassa fever transmission. Furthermore, enhancing collaboration between government agencies, healthcare providers, and research institutions is essential for optimizing Lassa fever prevention and control efforts.

Computational intelligence analysis on drug solubility using thermodynamics and interaction mechanism via models comparison and validation

This study investigates the application of various regression models for predicting drug solubility in polymer and API-polymer interactions in complex datasets. Four models—Gaussian Process Regression (GPR), Support Vector Regression (SVR), Bayesian Ridge Regression (BRR), and Kernel Ridge Regression (KRR)—are evaluated. Preprocessing the dataset using the Z-score approach helped to detect outliers, further improving the accuracy and dependability of the analysis. Also, Fireworks Algorithm (FWA) is employed for hyper-parameter tuning in this work. The GPR model demonstrated superior performance, achieving the lowest MSE and MAE for both drug solubility and gamma predictions, with R2 scores of 0.9980 and 0.9950 for training and test data, respectively. The results of this study show the robustness of GPR in generating reliable and precise forecasts, thus providing a strong method for intricate regression tasks in pharmaceutical and other scientific fields. In addition, the Fireworks Algorithm (FWA) is presented as an optimization method, demonstrating its potential in improving the model’s predictive abilities by effectively exploring and exploiting the search space. The results emphasize the significance of choosing suitable regression models and optimization techniques to attain dependable and superior predictive analytics.