Original Variables Research Articles

Photovoltaic Power Generation Related Physical Quantity Mining Method Based On Historical Time Series Data Analysis

Abstract The impact of distributed photovoltaic grid-connected on distribution network security, power quality and system stability cannot be ignored. In order to better cope with the uncertainty and output of new energy output and understand the characteristics of distributed new energy power output, it is necessary to predict photovoltaic power. The historical time series data of photovoltaic power is large in dimension and quantity. If the data output is not carried out, a large amount of redundant data will affect the accuracy of photovoltaic prediction. Therefore, this paper proposes a feature selection method based on MIC most mutual information coefficient, which filters out the most relevant data of photovoltaic power generation from the original feature variables, and then performs the feature dimension reduction method of linear discriminant analysis (LDA) to map the high-dimensional data to a lower dimension space. Finally, using the prediction simulation example, using the long and short time neural network (LSTM) prediction comparison, the feature dimension reduction method of MIC feature selection and LDA effectively improves the accuracy of photovoltaic power generation prediction.

Detection of Anthocyanins in Potatoes Using Micro-Hyperspectral Images Based on Convolutional Neural Networks.

The color potato has the function of both a food and vegetable. The color potato not only contains various amino acids and trace elements needed by the human body but also contains anthocyanins. Anthocyanins have many functions, such as antioxidation, inflammation inhibition, vision improvement, and cancer prevention, so colored potatoes are deeply loved by consumers and have good market prospects. However, at present, the detection of anthocyanin content in color potatoes mainly depends on chemical methods, which are time-consuming and laborious, so it is necessary to study a fast and accurate detection method. In this study, microscopic hyperspectral equipment was used to collect the spectral information of the outer skin and inner skin of potatoes. The original spectrum, pretreatment spectrum, and characteristic spectrum variables of the outer skin and inner skin were predicted by the convolution neural network (CNN) algorithm and partial least squares regression (PLS) algorithm, respectively, and the performance of the model was evaluated by the prediction set correlation coefficient (Rp), prediction set root mean square error (RMSEP), correction set correlation coefficient (Rc), correction set root mean square error (RMSEC), and residual prediction deviation (RPD). The results revealed that the inner skin Raw + CNN model constructed under raw spectral data is optimal with Rc = 0.9508, RMSEC = 0.0374%, Rp = 0.9461, RMSEP = 0.2361% and RPD = 4.4933. The inner skin Savitzky-Golay (SG) + Detrend (DET) + CNN model constructed from pre-processed spectral data is optimal with Rc = 0.9499, RMSEC = 0.0359%, Rp = 0.9439, RMSEP = 0.2384%, RPD = 4.6516. The inner skin DET + competitive adaptive reweighted sampling (CARS) +CNN model constructed from the feature-based spectral data was optimal with Rc = 0.9527, RMSEC = 0.0708%, Rp = 0.9457, RMSEP = 0.2711%, and RPD = 4.1623. It can be seen that the Rp, RMSEP, Rc, RMSEC, and RPD values for modeling the spectral information of the inner skin were higher than those of the outer skin under the three different spectral data. The prediction accuracy of the model built by the CNN algorithm was better than the conventional algorithm PLS, the application of the CNN algorithm in inner skin can achieve accurate prediction of anthocyanin content in potato.

The Effect of Hotel Tax Contribution and Hotel Tax Effectiveness on Regional Original Income in Medan City

This study aims to determine the significant effect of Hotel Tax Contribution and Hotel Tax Effectiveness on Regional Original Income in Medan City. From analysis, results of regression analysis are obtained which give the equation Regional Original Income = 23.160 - 0.730 Hotel Tax Contribution + 0.824 Hotel Tax Effectiveness + e. Results of the research analysis show that the Hotel Tax Contribution does not have a significant effect on Regional Original Income partially, namely tcount < ttable, namely -10,621 < 2.00172 and a significance value of 0.000 <0.05. Results of the research analysis show Hotel Tax Effectiveness has significant effect on Regional Original Income partially, namely tcount > ttable, namely 9.744 > 2.00172 and a significance value of 0.000 <0.05. Simultaneous Regional Original Income is Fcount > Ftable, namely 58.020 > 3.16 and the significance value is 0.000 <0.05. Results of this study are supported by the value of R Square = 0.671 or 67.1% which means that 67.1% of the Regional Original Income variable can be explained by the Hotel Tax Contribution and Hotel Tax Effectiveness variables, while the remaining 32.9% is explained by the influence of other factors or variables outside the model such as Tax Efficiency.

Study on the tidal variability related to flooding and hydroelectric operations in the Qiantang river estuary

The Qiantang River estuary, located in southeastern China, displays abnormally large tidal ranges due to its funnel shape. Although numerous studies have discussed river-tide interplay in the Qiantang River estuary, these studies usually treat tidal waves with dramatically different periods as a whole and use ZhaKou as the upstream boundary. To date, the area upstream from ZhaKou to the Fuchun River Power Station (FRPS) remains unexplored, which motives the current study. This research systematically explores the spatiotemporal variations of major tidal constituents in the Qiantang River estuary using classical harmonic analysis (T_TIDE), non-stationary harmonic analysis (NS_TIDE), and variational mode decomposition (VMD). The hindcast of NS_TIDE explains 72.9%–93.9% of original signal variances, better than the T_TIDE. However, the improvement is less from ZhaKou to CangQian, with the hindcast explaining less than 80% of the original signal variance, possibly due to the assumption of a static riverbed topography in NS_TIDE misaligned with the Qiantang River's conditions. Long-period constituents such as MSf, Mf, and Mm have large amplitudes near FRPS. Furthermore, diurnal S1 and P1 tides show notable amplitude increases at TongLu due to hydroelectric operations. The VMD method successfully captured the seasonal changes of tidal parameters caused by nonlinear tide-river-morphology interplay. The amplitudes of semi-diurnal and quarter-diurnal tides increase significantly between July and September due to decreased bottom friction caused by runoff erosion; thereafter, they gradually decrease due to increased bottom friction induced by sediment accumulation. This study has implications for similar fluvial estuaries worldwide with significant variations in water depth.

A novel higher performance nomogram based on explainable machine learning for predicting mortality risk in stroke patients within 30 days based on clinical features on the first day ICU admission

BackgroundThis study aimed to develop a higher performance nomogram based on explainable machine learning methods, and to predict the risk of death of stroke patients within 30 days based on clinical characteristics on the first day of intensive care units (ICU) admission.MethodsData relating to stroke patients were extracted from the Medical Information Marketplace of the Intensive Care (MIMIC) IV and III database. The LightGBM machine learning approach together with Shapely additive explanations (termed as explain machine learning, EML) was used to select clinical features and define cut-off points for the selected features. These selected features and cut-off points were then evaluated using the Cox proportional hazards regression model and Kaplan-Meier survival curves. Finally, logistic regression-based nomograms for predicting 30-day mortality of stroke patients were constructed using original variables and variables dichotomized by cut-off points, respectively. The performance of two nomograms were evaluated in overall and individual dimension.ResultsA total of 2982 stroke patients and 64 clinical features were included, and the 30-day mortality rate was 23.6% in the MIMIC-IV datasets. 10 variables (“sofa (sepsis-related organ failure assessment)”, “minimum glucose”, “maximum sodium”, “age”, “mean spo2 (blood oxygen saturation)”, “maximum temperature”, “maximum heart rate”, “minimum bun (blood urea nitrogen)”, “minimum wbc (white blood cells)” and “charlson comorbidity index”) and respective cut-off points were defined from the EML. In the Cox proportional hazards regression model (Cox regression) and Kaplan-Meier survival curves, after grouping stroke patients according to the cut-off point of each variable, patients belonging to the high-risk subgroup were associated with higher 30-day mortality than those in the low-risk subgroup. The evaluation of nomograms found that the EML-based nomogram not only outperformed the conventional nomogram in NIR (net reclassification index), brier score and clinical net benefits in overall dimension, but also significant improved in individual dimension especially for low “maximum temperature” patients.ConclusionsThe 10 selected first-day ICU admission clinical features require greater attention for stroke patients. And the nomogram based on explainable machine learning will have greater clinical application.

Micro Hydro Power Site Characterization in Indonesia: Variable Optimization for Site Selection Using GeoDetector and RFE-Random Forest

Abstract Micro hydropower (MHP) systems are a promising alternative renewable and sustainable energy source to conventional fossil fuels, particularly in regions with abundant water resources like Indonesia. The success of MHP initiatives is contingent upon identifying suitable sites and remains challenging related to influencing parameters in site selection for the regional/national scale. Therefore, this study aimed to determine the essential influencing variables for MHP site selection by evaluating multiple variables related to the existing MHPs. The method used for analysis was the GeoDetector and Recursive Feature Elimination-Random Forest (RFE-RF) approach in the Geographic Information System (GIS) framework. Combining GeoDetector and RFE-RF models proves to be a potent tool for essential influencing variables screening in MHP site selection. The eight essential variables were obtained, down from nineteen original variables, with a better performance statistically. This hybrid approach considers spatial patterns in data for variable selection, ensuring alignment with the chosen machine learning method. This study result is expected to assist decision-makers in the preliminary evaluation stage of MHP site exploration and promote Indonesia’s transition to a cleaner, more renewable energy future and participatory forest conservation.

Modeling Cost-Estimation Factors for Public Building Projects with Hybrid Approach in Addis Ababa

Assessing the most important cost-influencing factors is essential for enhancing the predictive ability of cost estimation for building construction projects. The goal of this study is to examine and design a valid cost prediction model for assessing factors that impact the cost estimation of public buildings in Addis Ababa. This research solves these issues that typically arise in predictive cost estimation models in two major processes. First, the insights of 133 professionals gathered on the 38 cost-impacting elements, and 15 top factors design, time or cost, and parties’ experience were determined. The suggested hybrid approach is based on the Akaike information criterion (AIC) and principal component regression (PCR) employed, coupling a stepwise linear regression model. According to the findings of the study, principal component analysis reduced important factors to 14 and efficiently solved the problem of multicollinearity with a variance inflation factor of less than 2, while stepwise cross-validation solved the overfitting problem at the lowest AIC. The cost prediction model sorted out five factors: design completion by the public body when bids are invited; completion of the project scope definition when bids are invited; level of construction complexity; importance of project completion within budget; and subcontractor experience and capability have all been identified as the main cost-determining factors. The study’s contribution is the first approach (PCR–AIC) utilized in this work to explore numerous cost-estimating components, eliminate those that were related to one another, and identify the most crucial ones that consisted of the majority of the original variables’ attributes.

A retrospective view on non-linear methods in chemometrics, and future directions

This perspective article reviews how the chemometrics community approached non-linear methods in the early years. In addition to the basic chemometric methods, some methods that fall under the term “machine learning” are also mentioned. Thereafter, types of non-linearity are briefly presented, followed by discussions on important aspects of modeling related to non-linear data. Lastly, a simulated data set with non-linear properties is analyzed for quantitative prediction and batch monitoring. The conclusion is that the latent variable methods to a large extent handle non-linearities by adding more linear combinations of the original variables. Nevertheless, with strong non-linearities between the X and Y space, non-linear methods such as Support Vector Machines might improve prediction performance at the cost of interpretability into both the sample and variable space. Applying multiple local models can improve performance compared to a single global model, of both linear and non-linear nature. When non-linear methods are applied, the need for conservative model validation is even more important. Another approach is pre-processing of the data which can make the data more linear before the actual modeling and prediction phase.

A mixed-integer programming formulation for optimizing the double row layout problem

The Double Row Layout Problem (DRLP) asks for an arrangement of machines on both sides of a straight line corridor so as to minimize the total cost for transferring materials among machines. The DRLP is NP-Hard and has practical relevance, specially in manufacturing systems design. In this paper, we drastically reduce the time required to solve the problem by constructing a new and effective mixed-integer linear programming (MILP) model of the DRLP. The new model was obtained by reformulating an existing MILP model. This includes tightening some constraints, introducing new variables, implementing constraints to link the new and original variables; and adding valid inequalities and a valid system of equations. To reduce the size of the reformulated model, we eliminate several of the new introduced variables by a substitution using the system of equations. The computational results demonstrate that the proposed model requires considerably smaller computational times compared to the ones in the literature. As a consequence, optimal solutions can now be efficiently found for larger instances of the problem. Previous studies have been able to optimally solve, within reasonable time, instances with size up to 16 machines, while with the new model four instances with 20 machines could be optimally solved.

Market supervisor monetary penalties for non‐compliance with informational requirements: Do investors care?

AbstractThis study measures and differentiates investors' responses to market supervisors' monetary penalties for non‐compliance with informational requirements for the capital market in Poland, broken down by the type of distorted information and the form of a breach. An event study was conducted to measure the information content of monetary penalties. We used a market model and tested the significance of abnormal daily returns using both parametric and non‐parametric tests. Cross‐sectional analyses were conducted to measure the determinants of market reactions. We employed two novel classifications of non‐compliance: by the type of distorted information (financial reporting information and other information) and by the form of a breach (failing to provide information and providing erroneous information). We contribute to the literature by finding that investors react negatively to monetary penalties imposed on companies for non‐compliance with financial reporting information requirements, whereas they do not react to such penalties for non‐compliance with other information requirements. We incorporate original variables that explain the magnitude of the market reaction and find that (i) the longer the distance between the breach and penalty imposition, the weaker the market reaction, and (ii) the greater the monetary penalty, the stronger the market reaction. We also find that both forms of breach lead to similar negative market reactions.

Transfer learning via random forests: A one-shot federated approach

A one-shot federated transfer learning method using random forests (FTRF) is developed to improve the prediction accuracy at a target data site by leveraging information from auxiliary sites. Both theoretical and numerical results show that the proposed federated transfer learning approach is at least as accurate as the model trained on the target data alone regardless of possible data heterogeneity, which includes imbalanced and non-IID data distributions across sites and model mis-specification. FTRF has the ability to evaluate the similarity between the target and auxiliary sites, enabling the target site to autonomously select more similar site information to enhance its predictive performance. To ensure communication efficiency, FTRF adopts the model averaging idea that requires a single round of communication between the target and the auxiliary sites. Only fitted models from auxiliary sites are sent to the target site. Unlike traditional model averaging, FTRF incorporates predicted outcomes from other sites and the original variables when estimating model averaging weights, resulting in a variable-dependent weighting to better utilize models from auxiliary sites to improve prediction. Five real-world data examples show that FTRF reduces the prediction error by 2-40% compared to methods not utilizing auxiliary information.

Dealing with intervening factors in the patient engagement

PurposeThere is evidence that quality improvement projects developed with the participation of patients and family members are more likely to result in a sustainable change. To identify the intervening factors is an important step in promoting and supporting patient and family members’ engagement.Design/methodology/approachA survey was carried out with 90 hospitals. A total of 35 intervening factors were evaluated by the healthcare professionals from the quality area using a Likert scale. Factor analysis was applied to identify the relationship among the factors and cluster analysis and the standardized scores for each new latent variable were obtained to observe the association between them and hospitals profile. Cluster analysis allowed to group the hospitals with similar responses and to analyze whether there was any association with the profile of the institutions.FindingsA total of ten intervening factors are identified: two in the financial dimension, five in the structural and three in the personal and cultural. The standardized scores of latent variables suggest that the financial factors could be affected by the hospital capacity. The structural factors could be impacted by the accreditation status, location (region) and administrative control (ownership). And the personal and cultural factors could be by the location and dominant organizational culture. All of factors are influenced by the performed quality management activities. The cluster analysis allowed the identification of three groups in the financial dimension, and four in the other two dimensions. Except for the accreditation status in the personal and cultural dimension, no evidence of association between the groups and the variables raised to characterize the profile of the hospitals was found.Originality/valueThe study contributed to identify the relationship among the intervening factors turning possible to simplify and reduce them more comprehensively than those originally identified in the literature and at the same time maintaining the representativeness of the original variables.

The adverse inpatient medication event and frailty (AIME-frail) risk prediction model

Medication harm affects between 5 and 15% of hospitalised patients, with approximately half of the harm events considered preventable through timely intervention. The Adverse Inpatient Medication Event (AIME) risk prediction model was previously developed to guide a systematic approach to patient prioritisation for targeted clinician review, but frailty was not tested as a candidate predictor variable. To evaluate the predictive performance of an updated AIME model, incorporating a measure of frailty, when applied to a new multisite cohort of hospitalised adult inpatients. A retrospective cohort study was conducted at two tertiary Australian hospitals on patients discharged between 1st January and April 31, 2020. Data were extracted from electronic medical records (EMRs) and clinical coding databases. Medication harm was identified using ICD-10 Y-codes and confirmed by senior pharmacist review of medical records. The Hospital Frailty Risk Score (HFRS) was calculated for each patient. Logistic regression analysis was used to construct a modified AIME model. Candidate variables of the original AIME model, together with new variables including HFRS were tested. Performance of the final model was reported using area under the curve (AUC) and decision curve analysis (DCA). A total of 4089 patient admissions were included, with a mean age±standard deviation (SD) of 64 years (±19 years), 2050 patients (50%) were males, and mean HFRS was 6.2 (±5.9). 184 patients (4.5%) experienced one or more medication harm events during hospitalisation. The new AIME-Frail risk model incorporated 5 of the original variables: length of stay (LOS), anti-psychotics, antiarrhythmics, immunosuppressants, and INR greater than 3, as well as 5 new variables: HFRS, anticoagulants, antibiotics, insulin, and opioid use. The AUC was 0.79 (95% CI: 0.76-0.83) which was superior to the original model (AUC=0.70, 95% CI: 0.65-0.74) with a sensitivity of 69%, specificity of 81%, positive predictive value of 0.14 (95% CI: 0.10-0.17) and negative predictive value of 0.98 (95% CI: 0.97-0.99). The DCA identified the model as having potential clinical utility between the probability thresholds of 0.05-0.4. The inclusion of a frailty measure improved the predictive performance of the AIME model. Screening inpatients using the AIME-Frail tool could identify more patients at high-risk of medication harm who warrant timely clinician review.

Causal rule ensemble method for estimating heterogeneous treatment effect with consideration of prognosticeffects.

We propose a novel framework based on the RuleFit method to estimate heterogeneous treatment effect in randomized clinical trials. The proposed method estimates a rule ensemble comprising a set of prognostic rules, a set of prescriptive rules, as well as the linear effects of the original predictor variables. The prescriptive rules provide an interpretable description of the heterogeneous treatment effect. By including a prognostic term in the proposed model, the selected rule is represented as an heterogeneous treatment effect that excludes other effects. We confirmed that the performance of the proposed method was equivalent to that of other ensemble learning methods through numerical simulations and demonstrated the interpretation of the proposed method using a real data application.

THE INFLUENCE OF REGIONAL TAXES, REGIONAL LEVIES AND REGIONAL COMPANY PROFITS ON ORIGINAL INCOME OF BOJONEGORO DISTRICT GOVERNMENT

This research was conducted with the aim of analyzing the influence of Regional Taxes, Regional Levies and Regional Company Profits on Regional Original Income. This type of research is quantitative research with data analysis techniques used, namely multiple linear regression analysis and hypothesis testing using the SPSS Version 26 software program. This research uses secondary data sourced from the District Regional Revenue Agency. Bojonegoro with time series data collection over a period of five years, namely 2018 to 2022. The method for determining the sample in this research is purposive sampling. The results of this research are that local taxes have no effect on local original income; regional levies influence regional original income; and the regional company profit variable has an effect on regional original income. It can be concluded that this research highlights that in the observed situation, the contribution of regional taxes, regional levies and regional company profits to the regional original income variable cannot be considered as the main factor, but further analysis is needed to understand the context and other variables that may influence the relationship between regional taxes, regional levies, regional company profits, and regional original income

Uniqueness of conservative solutions to the modified Camassa-Holm equation via characteristics

In this paper, for a given conservative solution, we introduce a set of auxiliary variables tailored to this particular solution, and prove that these variables satisfy a particular semilinear system having unique solutions. In turn, we get the uniqueness of the conservative solution in the original variables.

Steiner Cut Dominants

For a subset T of nodes of an undirected graph G, a T-Steiner cut is a cut [Formula: see text] with [Formula: see text] and [Formula: see text]. The T-Steiner cut dominant of G is the dominant [Formula: see text] of the convex hull of the incidence vectors of the T-Steiner cuts of G. For [Formula: see text], this is the well-understood s-t-cut dominant. Choosing T as the set of all nodes of G, we obtain the cut dominant for which an outer description in the space of the original variables is still not known. We prove that for each integer τ, there is a finite set of inequalities such that for every pair (G, T) with [Formula: see text], the nontrivial facet-defining inequalities of [Formula: see text] are the inequalities that can be obtained via iterated applications of two simple operations, starting from that set. In particular, the absolute values of the coefficients and of the right-hand sides in a description of [Formula: see text] by integral inequalities can be bounded from above by a function of [Formula: see text]. For all [Formula: see text], we provide descriptions of [Formula: see text] by facet-defining inequalities, extending the known descriptions of s-t-cut dominants.

Stock Price Forecasting Based on Dynamic Factor Augmented Model Averaging Approach

Accurate forecasting of stock prices not only guides investor behavior but also assesses financial risk and promotes balanced economic and social development. This paper uses a dynamic factor-enhanced model averaging method to forecast the daily closing price of the Shanghai Composite Index, maximizing the use of valid information by weighting the forecast values of different models. Firstly, the common factor is extracted from the smoothed original explanatory variables; then the dynamic factor augmented model selection method and the model averaging method based on different criteria are used to predict different lag orders of the common factor and the explanatory variables, and the effectiveness of the dynamic factor augmented censored group cross-validation model averaging method is verified using multiple predictor error indicators as well as the DM test. The experimental results show that the dynamic factor augmented censored group cross-validation model averaging method has better prediction results and is more robust.

CARACTERIZACIÓN DEL AGROECOSISTEMA CON MAÍZ NATIVO EN TEZONAPA, VERACRUZ, MÉXICO

<p><strong>Background:</strong> Agroecosystems (AE) have different technological, productive, economic and socio-cultural characteristics; therefore, it is important to characterize them to offer information for the design of focused agricultural policies, and that promote the development of the region. <strong>Objective:</strong> Characterize the AE with native maize in Tezonapa, Veracruz, based on socio-cultural, technical-productive, economic and environmental factors. <strong>Methodology:</strong> The study was carried out using the survey technique by the non-probabilistic snowball method. Ninety-seven native maize producers were surveyed in 29 Tezonapa communities in 2020. The questionnaire was semi-structured with 53 questions in five sections, including producer data, as well as socio-cultural, technical-productive, environmental and economic. The data obtained from the questionnaires were captured in the Microsoft Office Excel<sup>®</sup> 2019 computer program, subsequently, a principal component analysis (PCA), descriptive statistics and a dendrogram were executed. For the typology of maize production systems, the 53 original variables were used, generating a dendrogram. <strong>Results:</strong> Two types of AE were found: Traditional (T) and Traditional Subsistence (TS) with differentiated technical-productive, socio-cultural, economic and environmental characteristics. According to the PCA, the most significant variables in the description of the AEs were two sociodemographic, five technical-productive, and three economic. <strong>Implications:</strong> The producers of Tezonapa must organize themselves for the management of innovations that impact the yield of native maize crop, but taking into account their knowledge about sustainable agricultural practices. <strong>Conclusions:</strong> The AEs studied, although they present technical-productive and economic limitations, also have benefits when providing food, generating and exchanging empirical knowledge, which has allowed the conservation of native maize, and said valuable knowledge must be rescued and promoted. </p>

A novel implicit decision variable classification approach for high-dimensional robust multi-objective optimization in order scheduling

This paper efficiently addresses the high-dimensional robust order scheduling problem. A novel algorithm named dynamic cooperative coevolution based on an implicit decision variable classification approach (DCC/IDVCA) is developed to search for robust order schedules. To significantly reduce the computational resources required for solving the high-dimensional robust order scheduling problem, we propose decomposing the original decision variables through implicit classification methods. First, a novel estimation method is introduced to evaluate the weighted contribution of variables to robustness. This method utilizes historical information, including the variation of the overall mean effective fitness and the frequency of variables being classified into highly robustness-related subcomponents in previous cycles, for evaluating their weighted contribution to robustness. Then, based on the corresponding weighted robustness contributions, the original variables are classified into highly and weakly robustness-related variables. Finally, these two types of variables are decomposed into highly and weakly robustness-related subgroups within a dynamic cooperative coevolution framework and optimized separately. In the experimental section, the proposed algorithm is applied to two practical order scheduling problems in discrete manufacturing industry. The experimental results demonstrate that the proposed algorithm achieves competitive outcomes compared to state-of-the-art high-dimensional robust multi-objective optimization algorithms.