Robust least squares projection twin SVM and its sparse solution

Least squares projection twin support vector machine(LSPTSVM)has faster computing speed than classical least squares support vector machine(LSSVM).However,LSPTSVM is sensitive to outliers and its solution lacks sparsity.Therefore,it is difficult for LSPTSVM to process large-scale datasets with outliers.In this paper,we propose a robust LSPTSVM model(called R-LSPTSVM)by applying truncated least squares loss function.The robustness of R-LSPTSVM is proved from a weighted perspective.Furthermore,we obtain the sparse solution of R-LSPTSVM by using the pivoting Cholesky factorization method in primal space.Finally,the sparse R-LSPTSVM algorithm(SR-LSPTSVM)is proposed.Experimental results show that SR-LSPTSVM is insensitive to outliers and can deal with large-scale datasets fastly.

Consistent and Accurate TDOA Localization in Dynamic NLOS Environments via Prior Information Independent Estimation

In the time-difference-of-arrival(TDOA)localization,robust least squares(LS)problems solved by mathematical programming were proven to be superior in mitigating the effects of non-line-of-sight(NLOS)propagation.However,the existing algorithms still suffer from two disadvantages:1)The algorithms strongly depend on prior information;2)The approaches do not satisfy the mean square error(MSE)optimal criterion of the measurement noise.To tackle the troubles,we first formulate an MSE minimization model for measurement noise by taking the source and the NLOS biases as variables.To obtain stable solutions,we introduce a penalty function to avoid abnormal estimates.We further tackle the nonconvex locating problem with semidefinite relaxation techniques.Finally,we incorporate mixed constraints and variable information to improve the estimation accuracy.Simulations and experiments show that the proposed method achieves consistent performance and good accuracy in dynamic NLOS environments.

The role of green industrial transformation in mitigating carbon emissions:Exploring the channels of technological innovation and environmental regulation

The industrial sector is vital to economic progress,yet industrial pollution poses environmental and economic concerns.The purpose of the study was to investigate the influence of green industrial transformation in re-ducing Pakistan’s carbon intensity between 1975 and 2020.Carbon emissions are considered an endogenous construct,while foreign direct investment(FDI)inflows,technological innovation,green industrial transforma-tion,environmental legislation,and research and development(R&D)investment are possible mediators.The association between variables is assessed using the robust least-squares approach.Green industrial transforma-tion is connected with lower carbon emissions,yet technical innovation,R&D investment,and inbound FDI raise a country’s carbon emissions.The findings support the pollution haven hypothesis in a country.The causality esti-mates indicate that inward FDI contributes to environmental regulations;green industrial transformation directly relates to inbound FDI and R&D expenditures;and technological innovations correspond to inbound FDI,R&D ex-penditures,industrial ecofriendly progression,and environmental standards.According to the impulse response function,environmental policies are anticipated to have a differential effect on carbon emissions in 2023,2024,2028-2030,while they are likely to decrease in the years 2025-2027 and 2031 forward.Additionally,inward FDI and technology advancements would almost certainly result in a rise in carbon emissions over time.Green industrial transitions are projected to result in a ten-year reduction in carbon emissions.The variance decomposi-tion analysis indicates that eco-friendly industrial adaptations would likely have the largest variance error shock on carbon emissions(11.747%),followed by inbound FDI,technological advancements,and regulatory changes,with R&D spending having a minimal impact over time.Pakistan’s economy should foster a green industrial revolution to avoid pollution and increase environmental sustainability to meet its environmental goals.