Cameroon Climate Predictions Using the SARIMA-LSTM Machine Learning Model: Adjustment of a Climate Model for the Sudano-Sahelian Zone of Cameroon

It is acknowledged today within the scientific community that two types of actions must be considered to limit global warming: mitigation actions by reducing GHG emissions, to contain the rate of global warming, and adaptation actions to adapt societies to Climate Change, to limit losses and damages [1] [2]. As far as adaptation actions are concerned, numerical simulation, due to its results, its costs which require less investment than tests carried out on complex mechanical structures, and its implementation facilities, appears to be a major step in the design and prediction of complex mechanical systems. However, despite the quality of the results obtained, biases and inaccuracies related to the structure of the models do exist. Therefore, there is a need to validate the results of this SARIMA-LSTM-digital learning model adjusted by a matching approach, “calculating-test”, in order to assess the quality of the results and the performance of the model. The methodology consists of exploiting two climatic databases (temperature and precipitation), one of which is in-situ and the other spatial, all derived from grid points. Data from the dot grids are processed and stored in specific formats and, through machine learning approaches, complex mathematical equations are worked out and interconnections within the climate system established. Through this mathematical approach, it is possible to predict the future climate of the Sudano-Sahelian zone of Cameroon and to propose adaptation strategies.

Impact of an official accountability audit on industrial structure adjustment:a case study of environmental regulation in China

As a unique environmental regulation in China,the official accountability audit was piloted in 2014.With a focus on prioritizing the ecological environment,officials in pilot districts have implemented economic construction,adjusted industrial structures,and promoted coordinated development between the economy and environment.The effects of implementation have garnered widespread attention from society.However,there is limited research on the impact of an accountability audit on industrial structure adjustments.Using the“Accountability Audit of Officials for Natural Resource Assets(Trial)”released in 2015 as a quasi-natural experiment,this study collected panel data from 279 cities between 2013 and 2017.It then empirically analyzed the impact mechanism and effects of the accountability audit on industrial structure adjustment using the Propensity Score Matching and Difference-in-Differences model.The research findings indicate that the accountability audit directly impacted industrial structure adjustment,promoting the upgrading of the primary industry to the secondary industry and restricting the development of the tertiary industry.In addition,the audit is beneficial for enterprise entry,but not conducive to technological innovation,and has no significant impact on foreign direct investment.This conclusion fills a gap in the existing research and provides valuable insights for policymakers.

Construction of Psychological Adjustment Function Model of Music Education Based on Emotional Tendency Analysis

In the face offierce competition in the social environment,mental health problems gradually get the attention of the public,in order to achieve accurate mental health data analysis,the construction of music education is based on emotional tendency analysis of psychological adjustment function model.Design emotional tendency analysis of music education psychological adjustment function architecture,music teaching goal as psychological adjust-ment function architecture building orientation,music teaching content as a foundation for psychological adjust-ment function architecture and music teaching process as a psychological adjustment function architecture building,music teaching evaluation as the key of building key regulating function architecture,Establish a core literacy oriented evaluation system.Different evaluation methods were used to obtain the evaluation results.Four levels of psychological adjustment function model of music education are designed,and the psychological adjust-ment function of music education is put forward,thus completing the construction of psychological adjustment function model of music education.The experimental results show that the absolute value of the data acquisition error of the designed model is minimum,which is not more than 0.2.It is less affected by a bad coefficient and has good performance.It can quickly converge to the best state in the actual prediction process and has a strong con-vergence ability.

Controlling chaos using Takagi-Sugeno fuzzy model and adaptive adjustment

In this paper, an approach to the control of continuous-time chaotic systems is proposed using the Takagi-Sugeno （TS） fuzzy model and adaptive adjustment. Sufficient conditions are derived to guarantee chaos control from Lyapunov stability theory. The proposed approach offers a systematic design procedure for stabilizing a large class of chaotic systems in the literature about chaos research. The simulation results on Rossler＇s system verify the effectiveness of the proposed methods.

Rank-Defect Adjustment Model for Survey-Line Systematic Errors in Marine Survey Net

In this paper,the structure of systematic and random errors in marine survey net are discussed in detail and the adjustment method for observations of marine survey net is studied,in which the rank_defect characteristic is discovered first up to now.On the basis of the survey_line systematic error model,the formulae of the rank_defect adjustment model are deduced according to modern adjustment theory.An example of calculations with really observed data is carried out to demonstrate the efficiency of this adjustment model.Moreover,it is proved that the semi_systematic error correction method used at present in marine gravimetry in China is a special case of the adjustment model presented in this paper.

Adjustment of Model Parameters to Estimate Distribution Transformers Remaining Lifespan

Currently, the electrical system in Argentina is working at its maximum capacity, decreasing the margin between the installed power and demanded consumption, and drastically reducing the service life of transformer substations due to overload (since the margin for summer peaks is small). The advent of the Smart Grids allows electricity distribution companies to apply data analysis techniques to manage resources more efficiently at different levels (avoiding damages, better contingency management, maintenance planning, etc.). The Smart Grids in Argentina progresses slowly due to the high costs involved. In this context, the estimation of the lifespan reduction of distribution transformers is a key tool to efficiently manage human and material resources, maximizing the lifetime of this equipment. Despite the current state of the smart grids, the electricity distribution companies can implement it using the available data. Thermal models provide guidelines for lifespan estimation, but the adjustment to particular conditions, brands, or material quality is done by adjusting parameters. In this work we propose a method to adjust the parameters of a thermal model using Genetic Algorithms, comparing the estimation values of top-oil temperature with measurements from 315 kVA distribution transformers, located in the province of Tucumán, Argentina. The results show that, despite limited data availability, the adjusted model is suitable to implement a transformer monitoring system.

Adjustment Model and Algorithm Based on Ellipsoid Uncertainty

In surveying adjustment models,there is usually some uncertain additional information or prior information on parameters,which can constrain the parameters,and guarantee the uniqueness and stability of parameter solution.In this paper,we firstly use ellipsoidal sets to describe uncertainty,and establish a new adjustment model with ellipsoidal uncertainty.Furthermore,we give a new adjustment criterion based on minimization trace of an outer ellipsoid with two ellipsoid intersections,and analyze the propagation law of uncertainty.Correspondingly,we give a new algorithm for the adjustment model with ellipsoid uncertainty.Finally,we give three examples to test and verify the effectiveness of our algorithm,and illustrate the relation between our result and the weighted mixed estimation.

Semi-parametric Adjustment Model Methods for Positioning of Seafloor Control Point

This paper focus on solving the problem of seafloor control point absolute positioning with low vertical accuracy based on the survey ship sailing circle. The method of dealing with the systematic error based on a semi-parametric adjustment model was proposed. Firstly, the influence of sound velocity change on ranging error is analyzed. Secondly, a semi-parametric adjustment model for determining three-dimensional coordinates of seafloor control points was established. And respectively proposed solutions under two different conditions, the observation duration is an integral multiple or non-integer multiple of the long-period term of the ranging error. The simulation experiment shows that this method can obviously improve the accuracy of vertical solution of seafloor control point compared with the difference technique and the least-squares method when internal waves exist and observation duration is less than an integer multiple of the long-period term of the ranging error.

Solution model of nonlinear integral adjustment including different kinds of observing data with different precisions

In order to process different kinds of observing data with different precisions, a new solution model of nonlinear dynamic integral least squares adjustment was put forward, which is not dependent on their derivatives. The partial derivative of each component in the target function is not computed while iteratively solving the problem. Especially when the nonlinear target function is more complex and very difficult to solve the problem, the method can greatly reduce the computing load.

A baroclinic typhoon model with a moving multi-nested grid andvariational adjustment initializationII. Forecast experiments

A 3-dimensional baroclinic typhoon model with a moving multi-nested grid and its initialization are described first. Prediction results are improved by using a simple but effective data assimilation method in which the initial field is adjusted by the sixth hour's typhoon report and the weak-constraint variational principle. Finally someforecast examples made by this typhoon model are given.

Processing Approach of Non-linear Adjustment Models in the Space of Non-linear Models

This paper investigates the mathematic features of non-linear models and discusses the processing way of non-linear factors which contributes to the non-linearity of a non-linear model. On the basis of the error definition,this paper puts forward a new adjustment criterion, SGPE.Last,this paper investigates the solution of a non-linear regression model in the non-linear model space and makes the comparison between the estimated values in non-linear model space and those in linear model space.

The Need for Structural Adjustment: Was It Essential for African Countries over the Decade of the 80’s? An Econometric Analysis Using Count Data Models

Several economists agree to say that the need for adjustment was essential for African countries over the decade of the 80’s. The econometric analysis of a sample of 28 sub-Saharan African countries, from variables regarded as “representatives” for the adjustment objectives, proves that this assertion cannot be completely rejected.

Research on the adjustment model of ventilation characteristic parameters based on integrated method of circuit and path

Ventilation characteristic parameters are the base of ventilation network solution; however, they are apt to be affected by operating errors, reading errors, airflow stability, and other factors, and it is difficult to obtain accurate results. In order to check the ventilation characteristic parameters of mines more accurately, the integrated method of circuit and path is adopted to overcome the drawbacks caused by the traditional path method or circuit method in the digital debugging process of ventilation system, which can improve the large local error or the inconsistency between the airflow direction and the actual situation caused by inaccuracy of the ventilation characteristic parameters or checking in the ventilation network solution. The results show that this method can effectively reduce the local error and prevent the pseudo-airflow reversal phenomenon; in addition, the solution results are consistent with the actual situation of mines, and the effect is obvious.

Contribution of the MERISE-Type Conceptual Data Model to the Construction of Monitoring and Evaluation Indicators of the Effectiveness of Training in Relation to the Needs of the Labor Market in the Republic of Congo

This study proposes the use of the MERISE conceptual data model to create indicators for monitoring and evaluating the effectiveness of vocational training in the Republic of Congo. The importance of MERISE for structuring and analyzing data is underlined, as it enables the measurement of the adequacy between training and the needs of the labor market. The innovation of the study lies in the adaptation of the MERISE model to the local context, the development of innovative indicators, and the integration of a participatory approach including all relevant stakeholders. Contextual adaptation and local innovation: The study suggests adapting MERISE to the specific context of the Republic of Congo, considering the local particularities of the labor market. Development of innovative indicators and new measurement tools: It proposes creating indicators to assess skills matching and employer satisfaction, which are crucial for evaluating the effectiveness of vocational training. Participatory approach and inclusion of stakeholders: The study emphasizes actively involving training centers, employers, and recruitment agencies in the evaluation process. This participatory approach ensures that the perspectives of all stakeholders are considered, leading to more relevant and practical outcomes. Using the MERISE model allows for: • Rigorous data structuring, organization, and standardization: Clearly defining entities and relationships facilitates data organization and standardization, crucial for effective data analysis. • Facilitation of monitoring, analysis, and relevant indicators: Developing both quantitative and qualitative indicators helps measure the effectiveness of training in relation to the labor market, allowing for a comprehensive evaluation. • Improved communication and common language: By providing a common language for different stakeholders, MERISE enhances communication and collaboration, ensuring that all parties have a shared understanding. The study’s approach and contribution to existing research lie in: • Structured theoretical and practical framework and holistic approach: The study offers a structured framework for data collection and analysis, covering both quantitative and qualitative aspects, thus providing a comprehensive view of the training system. • Reproducible methodology and international comparison: The proposed methodology can be replicated in other contexts, facilitating international comparison and the adoption of best practices. • Extension of knowledge and new perspective: By integrating a participatory approach and developing indicators adapted to local needs, the study extends existing research and offers new perspectives on vocational training evaluation.

Mathematical model of the adjustment of the optimal price of the substituting commodity

In this paper, from the two economic concepts of the price elasticity of demand and the cross elasticity of demand, under the assumption that the operator＇s management goal is to obtain the profit maximization, the author established the mathematical model of the adjustment of the optimal price of the substituting commodity.

OUTLIER DETECTION AND RELIABILITY OF ADJUSTMENT MODELS WITH SINGULAR COVARIANCE MATRICES

Up to now,outlier detection and reliability theory are generallybased on the regular Gauss-Markov models,in which the covariance matrix of ob-servations is positively definite.For the adjustment models with singular covari-ance matrix,the statistics for outlier detection are derived by the authors.Thecorresponding reliability theory is developed.And the application of the theory isdemonstrated with a practical example.

Study of the mechanisms of streamline-adjustment-assisted heterogeneous combination flooding for enhanced oil recovery for post-polymer-flooded reservoirs

Streamline-adjustment-assisted heterogeneous combination flooding is a new technology for enhanced oil recovery for post-polymer-flooded reservoirs.In this work,we first carried out a series of 2D visualization experiments for different chemical flooding scenarios after polymer flooding.Then,we explored the synergistic mechanisms of streamline-adjustment-assisted heterogeneous combination flooding for enhanced oil recovery and the contribution of each component.Test results show that for single heterogeneous combination flooding,the residual oil in the main streamline area after polymer flooding is ready to be driven,but it is difficult to be recovered in the non-main streamline area.Due to the effect of drainage and synergism,the streamline-adjustment-assisted heterogeneous combination flooding diverts the injected chemical agent from the main streamline area to the non-main streamline area,which consequently expands the active area of chemical flooding.Based on the results from the single-factor contribution of the quantitative analysis,the contribution of temporary plugging and profile control of branched preformed particle gels ranks in the first place and followed by the polymer profile control and the effect of streamline adjustment.On the contrary,the surfactant contributes the least to enhance the efficiency of oil displacement.

A New Method for Identifying the Model Error of Adjust ment System

Some theory problems affecting parameter estimation are discussed in this paper. Influence and transformation between errors of stochastic and functional models is pointed out as well. For choosing the best adjustment model, a formula, which is different from the literatures existing methods, for estimating and identifying the model error, is proposed. On the basis of the proposed formula, an effective approach of selecting the best model of adjustment system is given.

Risk-adjustment in hepatobiliarypancreatic surgery

AIM: The present study evaluates the performance of the POSSUM, the American Society of Anesthetists (ASA), APACHE and Childs classification in predicting mortality and morbidity in hepatopancreaticobiliary (HPB) surgery. We describe especially the limitations and advantages of risk in stratifying the patients. METHODS: We investigated 177 randomly chosen patients undergoing elective complex HPB surgery in a single institution with a total of 71 pre-operative and intra-operative risk factors. Primary endpoint was in-hospital mortality and morbidity. Ordered logistic regression analysis was used to identify individual predictors of operative morbidity and mortality. RESULTS: The operative mortality in the series was 3.95%. This compared well with the p-POSSUM and APACHE predicted mortality of 4.31% and 4.29% respectively. Postoperative complications amounted to 45% with 24 (13.6%) patients having a major adverse event. On multrvariate analysis the pre-operative POSSUM physiological score (OR = 1.18, P = 0.009) was superior in predicting complications compared to the ASA (P= 0.108), APACHE (P= 0.117) or Childs classification (P= 0.136). In addition, serum sodium, creatinine, international normalized ratio (INR), pulse rate, and intra-operative blood loss were independent risk factors. A combination of the POSSUM variables and INR offered the optimal combination of risk factors for risk prognostication in HPB surgery. CONCLUSION: Morbidity for elective HPB surgery can be accurately predicted and applied in everyday surgical practice as an adjunct in the process of informed consent and for effective allocation of resources for intensive and high-dependency care facilities.

Control algorithm for vehicle height adjustable system of hydro-pneumatic suspension based on LuGre model

In order to control the vehicle body position precisely,1/4 nonlinear mathematical model of hydro-pneumatic suspension is established,and the influence of the frictional force in a hydraulic cylinder is analyzed.The friction characteristics are described based on the LuGre model when the piston of a hydraulic actuator is operated at a low speed.Due to the fact parameters of the friction model are effected by the system condition,an adaptive friction compensation（AFC）controller is designed through the Backstepping method,and a dual-observer has been implemented to estimate the friction state.The global asymptotic convergence of a closed-loop system is proven by the Lyapunov theorem.The simulation results show that the positional accuracy of the adaptive friction compensation yiedls a significant improvement in the vehicle height adjustment as compared to the PID control,demonstrating the effectiveness of the adaptive fiction compensation method in the vehicle height adjustable system of the hydro-pneumatic suspension.