In order to achieve a sustainable mix design, this paper evaluates self-consolidating green concrete (SCGC) properties by experimental tests and then examines the design parameters with an artificial intelligence technique. In this regard, cement was partially replaced in different contents with granulated blast furnace slag (GBFS) powder, volcanic powder, fly ash, and micro-silica. Moreover, fresh and hardened properties tests were performed on the specimens. Finally, an adaptive neuro-fuzzy inference system (ANFIS) was developed to identify the influencing parameters on the compressive strength of the specimens. For this purpose, seven ANFIS models evaluated the input parameters separately, and in terms of optimization, twenty-one models were assigned to different combinations of inputs. Experimental results were reported and discussed completely, where furnace slag represented the most effect on the hardened properties in binary mixes, and volcanic powder played an effective role in slump retention among other cement replacements. However, the combination of micro-silica and volcanic powder as a ternary mix design successfully achieved the most improvement compared to other mix designs. Furthermore, ANFIS results showed that binder content has the highest governing parameters in terms of the strength of SCGC. Finally, when compared with other additive powders, the combination of micro-silica with volcanic powder provided the most strength, which has also been verified and reported by the test results.

The aim of this paper is to study the performance of a composite floor system at different heat stages using artificial intelligence to derive a sustainable design and to select the most critical factors for a sustainable floor system at elevated temperatures. In a composite floor system, load bearing is due to composite action between steel and concrete materials which is achieved by using shear connectors. Although shear connectors play an important role in the performance of a composite floor system by transferring shear force from the concrete to the steel profile, if the composite floor system is exposed to high temperature conditions excessive deformations may reduce the shear-bearing capacity of the composite floor system. Therefore, in this paper, the slip response of angle shear connectors is evaluated by using artificial intelligence techniques to determine the performance of a composite floor system during high temperatures. Accordingly, authenticated experimental data on monotonic loading of a composite steel-concrete floor system in different heat stages were employed for analytical assessment. Moreover, an artificial neural network was developed with a fuzzy system (ANFIS) optimized by using a genetic algorithm (GA) and particle swarm optimization (PSO), namely the ANFIS-PSO-GA (ANPG) method. In addition, the results of the ANPG method were compared with those of an extreme learning machine (ELM) method and a radial basis function network (RBFN) method. The mechanical and geometrical properties of the shear connectors and the temperatures were included in the dataset. Based on the results, although the behavior of the composite floor system was accurately predicted by the three methods, the RBFN and ANPG methods represented the most accurate values for split-tensile load and slip prediction, respectively. Based on the numerical results, since the slip response had a rational relationship with the load and geometrical parameters, it was dramatically predictable. In addition, slip response and temperature were determined as the most critical factors affecting the shear-bearing capacity of the composite floor system at elevated temperatures.

The application of artificial neural networks (ANNs) can be found in numerous fields, including image and speech recognition, natural language processing, and autonomous vehicles. As well, intrusion detection, the subject of this paper, relies heavily on it. Different intrusion detection models have been constructed using ANNs. While ANNs are relatively mature to construct intrusion detection models, some challenges remain. Among the most notorious of these are the bloated models caused by the large number of parameters, and the non-interpretability of the models. Our paper presents Convolutional Kolmogorov-Arnold Networks (CKANs), which are designed to overcome these difficulties and provide an interpretable and accurate intrusion detection model. Kolmogorov-Arnold Networks (KANs) are developed from the Kolmogorov-Arnold representation theorem. Meanwhile, CKAN incorporates a convolutional computational mechanism based on KAN. The model proposed in this paper is constructed by incorporating attention mechanisms into CKAN’s computational logic. The datasets CICIoT2023 and CICIoMT2024 were used for model training and validation. From the results of evaluating the performance indicators of the experiments, the intrusion detection model constructed based on CKANs has an attractive application prospect. As compared with other methods, the model can predict a much higher level of accuracy with significantly fewer parameters. However, it is not superior in terms of memory usage, execution speed and energy consumption.

Objectives: The willingness of family members to take care of older relatives directly affects the quality of life of disabled older adults, so it is necessary to understand the status quo of willingness to care and its influencing factors. This has been extensively studied in other countries, but, it is rarely studied in China. Based on the theory of altruism, employing a unique sample from Shanghai, China in 2017 and 2022, we attempt to reveal the influencing factors of the care willingness of family caregivers during the transition period.Methods: To measure caregiver burden and functional disability of the care recipient, we employ the Zarit Burden Interview (ZBI) and the Barthel Index, respectively. Then we utilized the ordinary least squares (OLS) methodology and estimated four regression models. Models 1, 2, and 3 examined the impact of the variables of the caregiver burden, responsibility and love, and the quality of the caregiver-caregiver recipient relationship, respectively, on family caregivers’ willingness to care. Model 4 was the full model. To testify whether the caregiver burden is likely to act as a mediator, path analysis was used, and the path was adjusted and verified.Results: According to the survey, in Shanghai, only half of the caregivers had a very high care willingness to care for disabled older relatives, while nearly one-tenth of the caregivers had a low willingness. It was the caregiver burden rather than the functional disability of older adults that harms family caregivers’ willingness to care. Responsibility and caring out of love were positively related to care willingness. Relationship quality was the most important influencing factor, explaining 10.2% of the variance in care willingness. Path analysis demonstrated that responsibility, caring out of love, and relationship quality directly and through the mediation of caregiver burden indirectly affected care willingness.Conclusion: Our results revealed that reciprocal altruism presented by the quality of the caregiver-care recipient relationship had a significantly positive impact on family caregivers’ willingness to care. In addition, the caregiver burden was found not only directly affected care willingness, but also acted as a mediator. To promote the perfection of laws and policies, comprehensive samples of different types of cities should be included and the measurement of key variables could be further improved in future studies.

The quaternion cubature Kalman filter (QCKF) algorithm has emerged as a prominent nonlinear filter algorithm and has found extensive applications in the field of GNSS/SINS integrated attitude determination and positioning system (GNSS/SINS-IADPS) data processing for unmanned aerial vehicles (UAV). However, on one hand, the QCKF algorithm is predicated on the assumption that the random model of filter algorithm, which follows a white Gaussian noise distribution. The noise in actual GNSS/SINS-IADPS is not the white Gaussian noise but rather a ubiquitous non-Gaussian noise. On the other hand, the use of quaternions as state variables is bound by normalization constraints. When applied directly in nonlinear non-Gaussian system without considering normalization constraints, the QCKF algorithm may result in a mismatch phenomenon in the filtering random model, potentially resulting in a decline in estimation accuracy. To address this issue, we propose a novel Gaussian sum quaternion constrained cubature Kalman filter (GSQCCKF) algorithm. This algorithm refines the random model of the QCKF by approximating non-Gaussian noise with a Gaussian mixture model. Meanwhile, to account for quaternion normalization in attitude determination, a two-step projection method is employed to constrain the quaternion, which consequently enhances the filtering estimation accuracy. Simulation and experimental analyses demonstrate that the proposed GSQCCKF algorithm significantly improves accuracy and adaptability in GNSS/SINS-IADPS data processing under non-Gaussian noise conditions for Unmanned Aerial Vehicles (UAVs).

The Gaussian sum cubature Kalman filter (GSCKF) based on Gaussian mixture model (GMM) is a critical nonlinear non-Gaussian filter for data fusion of global navigation satellite system/strapdown inertial navigation systems (GNSS/SINS) tightly coupled integrated navigation system. However, the stochastic model of non-Gaussian noise in practical operating environments is not static, but rather time-varying. So if the GMM of GSCKF cannot be adjusted adaptively, it will lead to a decrease in estimation accuracy. To address this issue, we propose a novel adaptive GSCKF (AGSCKF) based on the dynamic adjustment of GMM. By analyzing the impact of GMM displacement parameter on the fitting accuracy of non-Gaussian noise, a novel algorithm for GMM displacement parameter adaptive adjustment is proposed using a cost function. Then this novel algorithm is applied to overcome the limitations of GSCKF under time-varying non-Gaussian noise environment, thereby improving the filtering performance. The simulation and experimental results indicate that the proposed AGSCKF exhibits significant advantage in changeable environments affected by time-varying non-Gaussian noise, which is applied to GNSS/SINS tightly coupled integrated navigation system data fusion can improve estimation accuracy and adaptability without sacrificing significant computational complexity.