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As graphene-related technology advances, the benefits of graphene metamaterials become more apparent. In this study, a surface-isolated exciton-based absorber is built by running relevant simulations on graphene, which can achieve more than 98% perfect absorption at multiple frequencies in the MWIR (MediumWavelength Infra-Red (MWIR) band as compared to the typical absorber. The absorber consists of three layers: the bottom layer is gold, the middle layer is dielectric, and the top layer is patterned with graphene. Tunability was achieved by electrically altering graphene's Fermi energy, hence the position of the absorption peak. The influence of graphene's relaxation time on the sensor is discussed. Due to the symmetry of its structure, different angles of light source incidence have little effect on the absorption rate, leading to polarization insensitivity, especially for TE waves, and this absorber has polarization insensitivity at ultra-wide-angle degrees. The sensor is characterized by its tunability, polarisation insensitivity, and high sensitivity, with a sensitivity of up to 21.60 THz/refractive index unit (RIU). This paper demonstrates the feasibility of the multi-frequency sensor and provides a theoretical basis for the realization of the multi-frequency sensor. This makes it possible to apply it to high-sensitivity sensors.

This paper presents an indoor air quality (IAQ) monitoring system designed for a better end-user experience. The monitoring system consists of elements, from the monitoring sensor to the monitoring interface, designed and implemented by the research team, especially for the proposed monitoring system. The monitoring solution is intended for users who live in houses without automatic ventilation systems. The air quality sensor is designed at a minimum cost and complexity to allow multi-zone implementation without significant effort. The user interface uses a spatial graphic representation that facilitates understanding areas with different air quality levels. Presentation of the outdoor air quality level supports the user's decision to ventilate a space. An innovative element of the proposed monitoring interface is the real-time forecast of air quality evolution in each monitored space. The paper describes the implementation of an original monitoring solution (monitoring device, Edge/Cloud management system, innovative user monitoring interface) and presents the results of testing this system in a relevant environment. The research conclusions show the proposed solution's benefits in improving the end-user experience, justified both by the technical results obtained and by the opinion of the users who tested the monitoring system.

In this paper, we present a laser triangulation sensor to measure the distance between the sensor and an object without contact using a diffraction slit rather than a traditional lens. We show that by replacing the lens with a slit, we can exploit the resulting diffraction pattern to have finer and yet simpler image analysis, yielding better estimation of the distance to the object. To test our hypothesis, we build a precision position table and a laser triangulation sensor, generate large data sets to test different estimation algorithms on various materials, and compare data acquisition using a traditional lens versus using a slit. We show that position estimation when using a slit is both more precise and more accurate than comparable methods using a lens.

Adaptive cruise control (ACC) enables efficient, safe, and intelligent vehicle control by autonomously adjusting speed and ensuring a safe following distance from the vehicle in front. This paper proposes a novel adaptive cruise system, namely the Safety-First Reinforcement Learning Adaptive Cruise Control (SFRL-ACC). This system aims to leverage the model-free nature and high real-time inference efficiency of Deep Reinforcement Learning (DRL) to overcome the challenges of modeling difficulties and lower computational efficiency faced by current optimization control-based ACC methods while simultaneously maintaining safety advantages and optimizing ride comfort. Firstly, we transform the ACC problem into a safe DRL formulation Constrained Markov Decision Process (CMDP) by carefully designing state, action, reward, and cost functions. Subsequently, we propose the Projected Constrained Policy Optimization (PCPO)-based ACC Algorithm SFRL-ACC, which is specifically tailored to solve the CMDP problem. PCPO incorporates safety constraints that further restrict the trust region formed by the Kullback-Leibler (KL) divergence, facilitating DRL policy updates that maximize performance while keeping safety costs within their limit bounds. Finally, we train an SFRL-ACC policy and compare its computation time, traffic efficiency, ride comfort, and safety with state-of-the-art MPC-based ACC control methods. The experimental results prove the superiority of the proposed method in the aforementioned performance aspects.

This paper focuses on the preparation and evaluation of a novel humidity-control material, vermiculite/(sodium polyacrylate(AA)-acrylamide(AM)), using inverse suspension polymerization. Acrylic acid and acrylamide were introduced into the interlayer of modified vermiculite during the polymerization process, leading to the formation of a strong association with the modified vermiculite. The addition of vermiculite increased the specific surface area and pore volume of the composites. To investigate the moisture absorption and desorption properties of the composites, an orthogonal experiment and single-factor experiment were conducted to analyze the impacts of vermiculite content, neutralization degree, and the mass ratio of AA to AM. According to the control experiment, the addition of vermiculite was found to enhance the pore structure and surface morphology of the composite material, surpassing both vermiculite and PAA-AM copolymer in terms of humidity control capacity and rate. The optimal preparation conditions were identified as follows: vermiculite mass fraction of 4 wt%, a neutralization degree of 90%, and m AA :m AM = 4:1. The moisture absorption rate and moisture release rate of the composite material prepared under these conditions are 1.285 g/g and 1.172 g/g. The humidity control process of the composite material is governed by pseudo second-order kinetics, which encompasses the complete adsorption process. These results indicate that the vermiculite/PAA-AM composite humidity control material has excellent humidity control performance and is a simple and efficient humidity control method.

Because the magnetic properties of an amorphous alloy (AA) obviously change with the change of temperature, a finite element simulation method for a motor, considering the effect of temperature, is proposed in this paper. In the early design stage of the high-speed permanent magnet synchronous motor (PMSM), the simulation of motor performance is mainly based on the magnetic performance test data at room temperature provided by the material's manufacturer. However, the influence of the temperature rise during the actual operation of the motor will lead to large errors between the simulation results and the measured results. Therefore, it is of great practical significance to measure the magnetic properties of the AA at different temperatures and use them for simulation purposes. In this paper, the magnetization characteristics and iron loss characteristics of the AA and silicon steel (ST100) used for comparison are measured at different temperatures, and the iron loss separation of the two materials at different temperatures is completed, and the hysteresis loss coefficient and eddy current loss coefficient at different temperatures are obtained. On this basis, the performance simulation of a motor model is carried out. The more accurate simulation method proposed in this paper can provide a reference for the design of AA motors in industry.

The United Nations World Drug Report published in 2022 alarmed that the global market of illicit drugs is steadily expanding in space and scale. Substances of abuse are usually perceived in the light of threats to human health and public security, while the environmental aspects of their use and subsequent emissions usually remain less explored. However, as with other human activities, drug production, trade, and consumption of drugs may leave their environmental mark. Therefore, this paper aims to review the occurrence of illicit drugs in surface waters and their bioaccumulation and toxicity in fish. Illicit drugs of different groups, i.e., psychostimulants (methamphetamines/amphetamines, cocaine, and its metabolite benzoylecgonine) and depressants (opioids: morphine, heroin, methadone, fentanyl), can reach the aquatic environment through wastewater discharge as they are often not entirely removed during wastewater treatment processes, resulting in their subsequent circulation in nanomolar concentrations, potentially affecting aquatic biota, including fish. Exposure to such xenobiotics can induce oxidative stress and dysfunction to mitochondrial and lysosomal function, distort locomotion activity by regulating the dopaminergic and glutamatergic systems, increase the predation risk, instigate neurological disorders, disbalance neurotransmission, and produce histopathological alterations in the brain and liver tissues, similar to those described in mammals. Hence, this drugs-related multidimensional harm to fish should be thoroughly investigated in line with environmental protection policies before it is too late. At the same time, selected fish species (e.g., Danio rerio , zebrafish) can be employed as models to study toxic and binge-like effects of psychoactive, illicit compounds.

The rapid increase in the number of vehicles has led to increasing traffic congestion, traffic accidents, and motor vehicle crime rates. The management of various parking lots has also become increasingly challenging. Vehicle-type recognition technology can reduce the workload of humans in vehicle management operations. Therefore, the application of image technology for vehicle-type recognition is of great significance for integrated traffic management. In this paper, an improved faster region with convolutional neural network features (Faster R-CNN) model was proposed for vehicle-type recognition. Firstly, the output features of different convolution layers were combined to improve the recognition accuracy. Then, the average precision (AP) of the recognition model was improved through the contextual features of the original image and the object bounding box optimization strategy. Finally, the comparison experiment used the vehicle image dataset of three vehicle types, including cars, sports utility vehicles (SUVs), and vans. The experimental results show that the improved recognition model can effectively identify vehicle types in the images. The AP of the three vehicle types is 83.2%, 79.2%, and 78.4%, respectively, and the mean average precision (mAP) is 1.7% higher than that of the traditional Faster R-CNN model.

Precision agriculture, driven by the convergence of smart sensors and advanced technologies, has emerged as a transformative force in modern farming practices. The present review synthesizes insights from a multitude of research papers, exploring the dynamic landscape of precision agriculture. The main focus is on the integration of smart sensors, coupled with technologies such as the Internet of Things (IoT), big data analytics, and Artificial Intelligence (AI). This analysis is set in the context of optimizing crop management, using resources wisely, and promoting sustainability in the agricultural sector. This review aims to provide an in-depth understanding of emerging trends and key developments in the field of precision agriculture. By highlighting the benefits of integrating smart sensors and innovative technologies, it aspires to enlighten farming practitioners, researchers, and policymakers on best practices, current challenges, and prospects. It aims to foster a transition towards more sustainable, efficient, and intelligent farming practices while encouraging the continued adoption and adaptation of new technologies.