VMD-WSLSTM Load Prediction Model Based on Shapley Values

Accurate short-term load forecasting can ensure the safe operation of the grid. Decomposing load data into smooth components by decomposition algorithms is a common approach to address data volatility. However, each component of the decomposition must be modeled separately for prediction, which leads to overly complex models. To solve this problem, a VMD-WSLSTM load prediction model based on Shapley values is proposed in this paper. First, the Shapley value is used to select the optimal set of special features, and then the VMD decomposition method is used to decompose the original load into several smooth components. Finally, WSLSTM is used to predict each component. Unlike the traditional LSTM model, WSLSTM can simplify the prediction model and extract common features among the components by sharing the parameters among the components. In order to verify the effectiveness of the proposed model, several control groups were used for experiments. The results show that the proposed method has higher prediction accuracy and training speed compared with traditional prediction methods.

Identification of Plasmodium secreted proteins based on monoDiKGap and distance-based Top-n-gram methods

Background: Many malaria infections are caused by Plasmodium falciparum. Accurate classification of the proteins secreted by the malaria parasite, which are essential for the development of anti-malarial drugs, is essential. Objective: To accurately classify the proteins secreted by the malaria parasite. Methods: Therefore, in order to improve the accuracy of the prediction of plasmodium secreted proteins, we established a classification model MGAP-SGD. MonodikGap features (k=7) of the secreted proteins were extracted, and then the optimal features were selected by the AdaBoost method. Finally, based on the optimal set of secreted proteins, the model was used to predict the secreted proteins using the stochastic gradient descent (SGD) algorithm. Results: Our model uses a 10-fold cross-validation set and independent test set in the stochastic gradient descent (SGD) classifier to validate the model, and the accuracy rates are 98.5859% and 97.973%, respectively. Conclusion: This also fully proves that the effectiveness and robustness of the prediction results of the MGAP-SGD model can meet the prediction needs of the secreted proteins of plasmodium.

Optimistic NAUTILUS navigator for multiobjective optimization with costly function evaluations

We introduce novel concepts to solve multiobjective optimization problems involving (computationally) expensive function evaluations and propose a new interactive method called O-NAUTILUS. It combines ideas of trade-off free search and navigation (where a decision maker sees changes in objective function values in real time) and extends the NAUTILUS Navigator method to surrogate-assisted optimization. Importantly, it utilizes uncertainty quantification from surrogate models like Kriging or properties like Lipschitz continuity to approximate a so-called optimistic Pareto optimal set. This enables the decision maker to search in unexplored parts of the Pareto optimal set and requires a small amount of expensive function evaluations. We share the implementation of O-NAUTILUS as open source code. Thanks to its graphical user interface, a decision maker can see in real time how the preferences provided affect the direction of the search. We demonstrate the potential and benefits of O-NAUTILUS with a problem related to the design of vehicles.

Understanding Population Dynamics in Multi- and Many-Objective Evolutionary Algorithms for High-Resolution Approximations

Achieving a high-resolution approximation and hitting the Pareto optimal set with some if not all members of the population is the goal for multi- and many-objective optimization problems, and more so in real-world applications where there is also the desire to extract knowledge about the problem from this set. The task requires not only to reach the Pareto optimal set but also to be able to continue discovering new solutions, even if the population is filled with them. Particularly in many-objective problems where the population may not be able to accommodate the full Pareto optimal set. In this work, our goal is to investigate some tools to understand the behavior of algorithms once they converge and how their population size and particularities of their selection mechanism aid or hinder their ability to keep finding optimal solutions. Through the use of features that look into the population composition during the search process, we will look into the algorithm’s behavior and dynamics and extract some insights. Features are defined in terms of dominance status, membership to the Pareto optimal set, recentness of discovery, and replacement of optimal solutions. Complementing the study with features, we also look at the approximation through the accumulated number of Pareto optimal solutions found and its relationship to a common metric, the hypervolume. To generate the data for analysis, the chosen problem is MNK-landscapes with settings that make it easy to converge, enumerable for instances with 3 to 6 objectives. Studied algorithms were selected from representative multi- and many-objective optimization approaches such as Pareto dominance, relaxation of Pareto dominance, indicator-based, and decomposition.

A novel assessment method for COVID-19 humoral immunity duration using serial measurements in naturally-infected and vaccinated subjects.

Background: It is crucial for medical decision-making and vaccination strategies to collect information on sustainability of immune responses after infection or vaccination, and how long-lasting antibodies against SARS-COV-2 could provide a humoral and protective immunity, preventing reinfection with SARS-CoV-2 or its variants. The aim of this study is to present a novel method to quantitatively measure and monitor the diversity of SARS-CoV-2 specific antibody profiles over time. Methods: Two collections of serum samples were used in this study: A collection from 20 naturally-infected subjects (follow-ups to 1 year) and a collection from 83 subjects vaccinated with one or two doses of Pfizer BioNtech vaccine (BNT162b2/BNT162b2) (follow-ups to 6 months). The Multi-SARS-CoV-2 assay, a multiparameter serology test, developed for the serological confirmation of past-infections was used to determine the reactivity of six different SARS-CoV-2 antigens. For each patient sample, 3 dilutions (1/50, 1/400 and 1/3200) were defined as an optimal set over the six antigens and their respective linear ranges, allowing accurate quantitation of the corresponding six specific antibodies. Nonlinear mixed-effects modelling was applied to convert intensity readings from 3 determined dilutions to a single quantification value for each antibody. Results: Median half-life for the 20 naturally infected vs 74 vaccinated subjects (two doses) was respectively 120 vs 50 days for RBD, 127 vs 53 days for S1 and 187 vs 86 days for S2 antibodies. Respectively, 90% of the antibody concentration wanes after 158 vs 398 days for RBD, 171 vs 420 days for S1, and 225 vs 620 days for S2 after the second vaccine shot. Conclusion: The newly proposed method, based on a series of a limited number of dilutions, can convert a conventional qualitative assay into a quantitative assay. This conversion helps define the sustainability of specific immune responses against each relevant viral antigen and can help in defining the protection characteristics after an infection or a vaccination.

Dimension and Coloring alongside Domination in Neutrosophic Hypergraphs

New setting is introduced to study resolving number and chromatic number alongside dominating number. Different types of procedures including set, optimal set, and optimal number alongside study on the family of neutrosophic hypergraphs are proposed in this way, some results are obtained. General classes of neutrosophic hypergraphs are used to obtains these numbers and the representatives of the colors, dominating sets and resolving sets. Using colors to assign to the vertices of neutrosophic hypergraphs and characterizing resolving sets and dominating sets are applied. Some questions and problems are posed concerning ways to do further studies on this topic. Using different ways of study on neutrosophic hypergraphs to get new results about numbers and sets in the way that some numbers get understandable perspective. Family of neutrosophic hypergraphs are studied to investigate about the notions, dimension and coloring alongside domination in neutrosophic hypergraphs. In this way, sets of representatives of colors, resolving sets and dominating sets have key role. Optimal sets and optimal numbers have key points to get new results but in some cases, there are usages of sets and numbers instead of optimal ones. Simultaneously, three notions are applied into neutrosophic hypergraphs to get sensible results about their structures. Basic familiarities with neutrosophic hypergraphs theory and hypergraph theory are proposed for this article.

A NARX Model to Predict Cabin Air Temperature to Ameliorate HVAC Functionality

Vehicular technology has integrated many features in the system, which enhances the safety and comfort of the user. Among these features, heating, ventilation, and air conditioning (HVAC) is the only feature that maintains the set cabin air temperature (CAT). The user’s command drives the set CAT, and the thermostat provides feedback to the HVAC to maintain the set CAT. The CAT is increased by extracting the heat from the engine surface produced by the fuel combustion, whereas the CAT is reduced by the known processes of the air conditioning system (ACS). Therefore, the CAT driven by the user’s command may not be optimal, and estimating the optimal CAT is still unsolved. In this work, we propose a new process where the user can input a range for CAT instead of a single value. Optimal HVAC criteria were defined, and the CAT was estimated by performing iterative analysis in the user-selected range satisfying the criteria. The HVAC criteria were defined based on two measurable parameters: air conditioning refrigerant fluid pressure (ACRFP) and engine surface temperature (EST) empirically defined as the vector CATOP. In this article, a NARX DL model was used by mapping the vehicle-level vectors (VLV) to predict the CATOP in real-time using field data obtained from a 2020 Cadillac CT5 test vehicle. Utilising the DL model, CATOP for future time steps was predicted by varying the CAT in the definite range and applying HVAC criteria. Thus, an optimal set CAT was estimated, corresponding to the optimal CATOP defined by the HVAC criteria. We performed the validation of the DL model for multiple datasets using traditional statistical techniques, namely, signal-to-noise ratio (SNR) values, first-order derivatives (FOD), and root-mean-square error (RMSE).

Delegation in Veto Bargaining

A proposer requires a veto player’s approval to change a status quo. Proposer is uncertain about Vetoer’s preferences. We show that Vetoer is typically given a non-singleton menu, or delegation set, of options to pick from. The optimal set balances the extent of compromise with the risk of a veto. We identify conditions for certain delegation sets to emerge, including “full delegation”: Vetoer can choose any action between the status quo and Proposer’s ideal action. By contrast to expertise-based delegation, Proposer gives less discretion to Vetoer when their preferences are more (likely to be) aligned. (JEL D72, D82)

Metric Dimension in fuzzy(neutrosophic) Graphs-VII

New notion of dimension as set, as two optimal numbers including metric number, dimension number and as optimal set are introduced in individual framework and in formation of family. Behaviors of twin and antipodal are explored in fuzzy(neutrosophic) graphs. Fuzzy(neutrosophic) graphs, under conditions, fixed-edges, fixed-vertex and strong fixed-vertex are studied. Some classes as path, cycle, complete, strong, t-partite, bipartite, star and wheel in the formation of individual case and in the case, they form a family are studied in the term of dimension. Fuzzification(neutrosofication) of twin vertices but using crisp concept of antipodal vertices are another approaches of this study. Thus defining two notions concerning vertices which one of them is fuzzy(neutrosophic) titled twin and another is crisp titled antipodal to study the behaviors of cycles which are partitioned into even and odd, are concluded. Classes of cycles according to antipodal vertices are divided into two classes as even and odd. Parity of the number of edges in cycle causes to have two subsections under the section is entitled to antipodal vertices. In this study, the term dimension is introduced on fuzzy(neutrosophic) graphs. The locations of objects by a set of some junctions which have distinct distance from any couple of objects out of the set, are determined. Thus it’s possible to have the locations of objects outside of this set by assigning partial number to any objects. The classes of these specific graphs are chosen to obtain some results based on dimension. The types of crisp notions and fuzzy(neutrosophic) notions are used to make sense about the material of this study and the outline of this study uses some new notions which are crisp and fuzzy(neutrosophic). Some questions and problems are posed concerning ways to do further studies on this topic. Basic familiarities with fuzzy(neutrosophic) graph theory and graph theory are proposed for this article.

Numerical Investigation on Vibration Performance of an Improved Switched Reluctance Machine with Double Auxiliary Slots

Considerable vibration and acoustic noise limit the further application of Switched Reluctance Machine (SRM) due to its structural characteristics and working principle. An improved SRM model with double auxiliary slots (DAS) was proposed, in which the direction of the magnetic line of force was adjusted, and the radial magnetic density in the air gap was reduced by changing the local tooth profiles of the stator and the rotor. The effects of initial rotor position and turn-on angle and turn-off angle on radial Electromagnetic Force (EMF) and maximum torque were investigated. The results indicate the radial EMF and torque increase significantly with the advancement of the turn-on angle or the delay of the turn-off angle. In the orthogonal experimental design, initial rotor position, turn-on angle, and turn-off angle were taken as the factors, and the optimal set of parameters that minimized radial EMF was determined according to a greater output torque. In contrast to conventional SRM, the radial EMF of the SRM with DAS significantly reduces when the optimal set is applied.