A hybrid MCDM approach based on combined weighting method, cloud model and COPRAS for assessing road construction workers' safety climate.

Purpose: The purpose of this paper is to propose a novel approach to assess the safety climate level of different groups of workers in a construction company and predict safety performance and implement targeted improvement measures. Design/methodology/approach: This paper utilizes the BP neural network and random forest algorithm to establish a weight learning mechanism for calculating the weights of safety climate evaluation criteria. The cloud model is employed to construct the decision matrix for different groups under the evaluation criteria. Meanwhile, the paper utilizes the COPRAS method to compare the safety climate of different groups. Findings: The findings show the accuracy of the CM-COPRAS model is assessed by comparing it with the other methods. The three models are almost consistent in assessing the safety climate for working age groups, accident experience groups, and work type groups, with slight differences in the evaluation results for the education groups. The consistency of the computational results of the CM-COPRAS model with the results of the existing research, i.e., that the education level is positively proportional to the safety climate supports the reasonableness and validity of the CM-COPRAS model. Originality: The paper proposes a hybrid MCDM method that integrates the Combined weighting method, Cloud model, and COPRAS for safety climate level evaluation in different construction worker groups. A case study is presented to demonstrate the applicability of the proposed method and to compare it with other methods to validate the effectiveness of the present method.

Target Detection of Diamond Nanostructures Based on Improved YOLOv8 Modeling.

Boron-doped diamond thin films exhibit extensive applications in chemical sensing, in which the performance could be further enhanced by nano-structuring of the surfaces. In order to discover the relationship between diamond nanostructures and properties, this paper is dedicated to deep learning target detection methods. However, great challenges, such as noise, unclear target boundaries, and mutual occlusion between targets, are inevitable during the target detection of nanostructures. To tackle these challenges, DWS-YOLOv8 (DCN + WIoU + SA + YOLOv8n) is introduced to optimize the YOLOv8n model for the detection of diamond nanostructures. A deformable convolutional C2f (DCN_C2f) module is integrated into the backbone network, as is a shuffling attention (SA) mechanism, for adaptively tuning the perceptual field of the network and reducing the effect of noise. Finally, Wise-IoU (WIoU)v3 is utilized as a bounding box regression loss to enhance the model's ability to localize diamond nanostructures. Compared to YOLOv8n, a 9.4% higher detection accuracy is achieved for the present model with reduced computational complexity. Additionally, the enhancement of precision (P), recall (R), mAP@0.5, and mAP@0.5:0.95 is demonstrated, which validates the effectiveness of the present DWS-YOLOv8 method. These methods provide effective support for the subsequent understanding and customization of the properties of surface nanostructures.

Research on driving behavior characteristics of older drivers based on drivers' behavior graphs analysis.

Considerable evidence suggests that the decline in physiological abilities prevalent in older drivers leads to a reduction in the visual and psychomotor functions required for safe driving. The purpose of this study is to further investigate the differences in driving behavior between older and younger drivers and to describe the change process of driving behavior. In this study, 19 younger and older drivers each were recruited for a driving simulation experiment that included five scenarios. Driving operation data, eye movement data, and physiological data of drivers in five conflict scenarios were collected. The differences in driving behaviors between the two groups were also compared and analyzed, on which the thresholds of different driving behavior nodes were determined and driving behavior graphs were established. The results show that the eye movement nodes of older drivers appear later in five scenarios, the operational nodes of older people appear later in two steering scenarios, and are closer to those of younger drivers in three straight ahead scenarios, indicating that older drivers were later in observing and collecting traffic information, and later in applying brakes and steering to avoid conflicts when steering. The study provides a reference for the analysis of driving behavior and driving safety of older people.

A Diamond/Graphene/Diamond Electrode for Waste Water Treatment.

Boron-doped diamond (BDD) thin film electrodes have great application potential in water treatment. However, the high electrode energy consumption due to high resistance directly limits the application range of existing BDD electrodes. In this paper, the BDD/graphene/BDD (DGD) sandwich structure electrode was prepared, which effectively improved the conductivity of the electrode. Meanwhile, the sandwich electrode can effectively avoid the degradation of electrode performance caused by the large amount of non-diamond carbon introduced by heavy doping, such as the reduction of the electrochemical window and the decrease of physical and chemical stability. The microstructure and composition of the film were characterized by scanning electron microscope (SEM), atomic force microscopy (AFM), Raman spectroscopy, and transmission electron microscopy (TEM). Then, the degradation performance of citric acid (CA), catechol, and tetracycline hydrochloride (TCH) by DGD electrodes was systematically studied by total organic carbon (TOC) and Energy consumption per unit TOC removal (ECTOC). Compared with the single BDD electrode, the new DGD electrode improves the mobility of the electrode and reduces the mass transfer resistance by 1/3, showing better water treatment performance. In the process of dealing with Citric acid, the step current of the DGD electrode was 1.35 times that of the BDD electrode, and the energy utilization ratio of the DGD electrode was 2.4 times that of the BDD electrode. The energy consumption per unit TOC removal (ECTOC) of the DGD electrode was lower than that of BDD, especially Catechol, which was reduced to 66.9% of BDD. The DGD sandwich electrode, as a new electrode material, has good electrochemical degradation performance and can be used for high-efficiency electrocatalytic degradation of organic pollutants.

Research on the effectiveness of a intersection risk warning system based on driving simulation experiment.

In order to improve the traffic safety condition of intersections, a real-time traffic conflict risk warning system (RTCRWS) is proposed for uncontrolled intersections. To evaluate the effectiveness of this system, a driving simulation experiment was designed and conducted. In this study, a virtual experimental scene including static road, traffic environment and dynamic traffic flow was constructed, and 45 drivers were recruited to complete the driving simulation experiment at 13 intersections. Three different data analysis methods were employed: (1) descriptive analysis of driving behavior characteristics; (2) descriptive analysis of physiological and psychological reactions of drivers; (3) Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) of RTCRWS. The results show that RTCRWS can effectively control the vehicle speed and reduce the driver's tension. In addition, the following conclusions are also drawn: (1) The early warning signs with better warning effect among the two types signs of RTCRWS were compared; (2) Among the elderly and young and middle-aged drivers, RTCRWS has a better warning effect on the elderly drivers. (3) Among the male and female drivers, RTCRWS has a better warning effect on female drivers.

Classification, Synthesis, and Application of Luminescent Silica Nanoparticles: a Review.

Luminescent materials are of worldwide interest because of their unique optical properties. Silica, which is transparent to light, is an ideal matrix for luminescent materials. Luminescent silica nanoparticles (LSNs) have broad applications because of their enhanced chemical and thermal stability. Silica spheres of various sizes could be synthesized by different methods to satisfy specific requirements. Diverse luminescent dyes have potential for different applications. Subject to many factors such as quenchers, their performance was not quite satisfying. This review thus discusses the development of LSNs including their classification, synthesis, and application. It is the highlight that how silica improves the properties of luminescent dye and what role silica plays in the system. Further, their applications in biology, display, and sensors are also described.

Speed and reaction behavior in different highway landscapes: A driving simulator study.

OBJECTIVE: Speed and reaction time have an important influence on traffic safety. The highway landscape can affect psychological and physiological characteristics, which are associated with the speed and reaction time of drivers. This study aimed to understand the association between the highway landscape and driving speed and reaction time. METHODS: Two simulation experiments were conducted, referred to as experiment 1 and experiment 2. Forty-four drivers were recruited to participate in the experiments. Based on the road alignment and enclosure degree, in experiment 1 and experiment 2 the highway landscape was divided into 24 and 8 types, respectively. VS-Design was used to establish the virtual scenes. A KMRTDS driving simulator was used to perform the experiments. RESULTS: Driver reaction time decreased with an increase in the enclosure degree of the road. Moreover, in straight sections, the speed decreased with an increase in the enclosure degree of the road, whereas in curved sections, the speed was mainly affected by the road geometric alignment. CONCLUSIONS: The driver's perceived ability was significantly affected by the highway landscape, and optimization of the highway landscape environment is necessary to promote transportation efficiency and improve the traffic safety situation.

The Formation of Nanoparticles and Their Competitive Interaction with Twins during Eutectic Si Growth.

In order to investigate the competitive interaction between nanoparticles and twin, the eutectic Si microstructures in Al⁻10Si (wt. %) base alloys with exclusive and combined addition of Sr and Sb are characterized by combined TEM and atom probe tomography (APT). The chemical short range order in Sb⁻Sb and Sb⁻Sr pairs is revealed by ab initio molecular dynamics simulation, which promotes the formation of clusters and nanoparticles. The coexistence of nanoparticles and twins is observed in Sb containing alloys, with a negative correlation in the corresponding number density, owing to the competitive stacking of precursors and individual atoms at the solid⁻liquid interface. Large size particles around 70 nm with a uniform distribution of Sr atoms are formed in Al⁻10Si⁻0.35Sb⁻0.015Sr (wt. %) alloys, due to the precursor aggregation and homogeneous nucleation in the droplets that nucleation are depressed. A model for the formation of nanoparticles and their interaction with twins is proposed.