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PPG-CNTs-nZVI bead was synthesized by polyvinyl alcohol, pumice, carbon nanotube, and guar gum-nanoscale zero-valent iron to be applied on simultaneously removal of polycyclic aromatic hydrocarbons (PAHs; phenanthrene) and heavy metals (Pb2+) via adsorption. The individual and simultaneous removal efficiency of phenanthrene and Pb2+ using the PPG-CNTs-nZVI beads was evaluated with a range of initial concentrations of these two pollutants. The kinetics and isotherms of phenanthrene and Pb2+ adsorption by the PPG-CNTs-nZVI beads were also determined. The PPG-CNTs-nZVI beads show reasonably high phenanthrene adsorption capacities (up to 0.16 mg/g), and they absorbed 85% of the phenanthrene (initial concentration 0.5 mg/L) in 30 min. High Pb2+ adsorption capabilities were also demonstrated by the PPG-CNTs-nZVI beads (up to 11.6 mg/g). The adsorption fits the Langmuir model better than the Freundlich model. The adsorption still remained stable with various ionic strength circumstances and a wide pH range (2–5). Additionally, the co-adsorption of phenanthrene and Pb2+ by the PPG-CNTs-nZVI beads resulted in synergistic effects. Particularly, phenanthrene-Pb2+ complex formation via π–cation interactions demonstrated a greater affinity than phenanthrene or Pb2+ alone. The present findings suggest that PPG-CNTs-nZVI beads may be effective sorbents for the simultaneous removal of PAHs and heavy metals from contaminated waters. [ABSTRACT FROM AUTHOR]

Optical switches are desired in telecom and datacom as an upgrade to electrical ones for lower power consumption and expenses while improving bandwidth and network transparency. Compact, integrated optical switches are attractive thanks to their scalability, readiness for mass production, and robustness against mechanical disturbances. The basic unit relies mostly on a microring resonator or a Mach–Zehnder interferometer for binary "bar" and "cross" switching. Such single-mode structures are often wavelength / polarization dependent, sensitive to phase errors and loss-prone. Furthermore, when they are cascaded to a network, the number of control units grows quickly with the port count, causing high complexity in electronic wiring and drive circuit integration. Herein, we propose a new switching method by thermo-optic waveguide lens. Essentially, this multimode waveguide forms a square law medium by a pair of heater electrodes and focuses light within a chip by robust 1 × 1 imaging. A 1 × 24 basic switch is demonstrated with 32 electrodes and only two are biased at a time for a chosen output. By two-level cascading, the switch expands to 576 ports and only four electrodes are needed for one path. The chips are fabricated on wafer scale in a low-budget laboratory without resorting to foundries. Yet, the performance goes beyond state of the art for low insertion loss, low wavelength dependence and low polarization dependence. This work provides an original, alternative, and practical route to construct large-scale optical switches, enabling broad applications in telecom, datacom and photonic computing. [ABSTRACT FROM AUTHOR]

Sugar content is a critical factor in evaluating the quality of fresh peaches. This paper aims to develop a rapid and accurate prediction method for the sugar content of fresh peaches using Deep Learning combined with near-infrared spectroscopy (NIRS). The proposed model consists of three main steps. Firstly, the Multivariate Scattering Correction (MSC) is employed as a preprocessing technique to remove noise and redundant information from the raw NIR images of peaches which ensures the reliability of the subsequent analysis. Secondly, the Competitive Adaptive Reweighted Sampling (CARS) is utilized for bands selection which can effectively eliminate redundant information from the NIR spectral data and reduces computational complexity. Finally, a novel Linear Deep Belief Network (LDBN) model is constructed to predict the sugar content of fresh peaches. To validate the performance of the proposed method, a Fresh Peaches Near-infrared spectroscopy dataset (FPNIRS) containing sugar content information is constructed. The maximum value for the sugar content of fresh peaches was 13.4 and the minimum value was 7.5 in the collection of 180 fresh peach samples. The experimental results demonstrate that the proposed LDBN achieves the best prediction performance, with the determination coefficient of prediction set ( R P 2 ) value of 0.9346 and the root mean square error of prediction set (RMSEP) value of 0.4409. This study suggests that the combination of Near-infrared spectroscopy technology and the LDBN provides an effective method for accurately predicting the sugar content of fresh peaches. [ABSTRACT FROM AUTHOR]

Aiming at the problems of insufficient feature fusion, large number of network parameters and low target saliency in the current digital meter detection algorithm, a digital meter detection algorithm based on YOLOv5s is designed. First, a new feature fusion structure Bi-Directional Feature Pyramid Network Based on Multi-Scale Feature Fusion is designed to realize the full fusion between different scale feature maps and improve the detection accuracy; second, a new convolutional module Ghostconv Combined Channel Shuffle, is designed to realize the lightweight design of the network and meet the requirements of real-time substation detection tasks; finally, to improve the network’s ability to characterize the instrumented digits, the Convolutional Block Attention Module is introduced in the backbone network to further enhance the network performance. Experiments are carried out on the homemade dataset, and the experimental results show that the algorithm proposed in this paper improves the average accuracy by 2.84–98.58% compared with the original network; the amount of network parameters is reduced by 26.4%, and the detection speed is improved by 25 FPS, and the detection time for each image is only 0.012 s. Compared with other digital meter detection algorithms, the network performance and the number of parameters also have a great advantage. [ABSTRACT FROM AUTHOR]

As the most common filler in stormwater treatment, zeolite (NZ-Y) has good cation exchange capability and stabilization potential for the removal of heavy metal from aqueous solutions. In this study, sodium dodecyl sulfate (SDS) and NZ-Y were selected to preparing new adsorbent (SDS-NZ) by using a simple hydrothermal method. The sorption–desorption performance and mechanism of Cu(II) onto SDS-NZ were investigated. The results showed that the sorption of Cu(II) on SDS-NZ was in accordance with the pseudo-second-order kinetic model with an equilibrium time of 4 h. The sorption behavior fitted Langmuir isotherm with a saturation sorption capability of 9.03 mg/g, which was three times higher than that of NZ-Y. The modification of SDS increases the average pore size of NZ-Y by 3.96 nm, which results in a richer internal pore structure and more useful sorption sites for Cu(II) sorption. There was a positive correlation between solution pH values and sorption capability of Cu(II) in the range of 3.0–6.0. With the ionic strength increased, the sorption capability of Cu(II) onto SDS-NZ first decreased and then increased, which may be attributed to competitive sorption and compression of the electronic layer. The desorption of Cu(II) on SDS-NZ was favored by the increase in SDS concentration and ionic strength and decrease in solution pH values. The application of SDS-NZ in runoff improved the leaching risk of Cu(II). After several cycles, the ability of reused SDS-NZ to efficiently adsorb most heavy metals was verified with removal rates above 99%. [ABSTRACT FROM AUTHOR]

The anomalous Nernst effect (ANE), as a three-dimensional thermoelectric effect in ferromagnetic materials, is expected to break through the limitations of the traditional Seebeck effect, thereby improving thermoelectric conversion efficiency. This study reports an anisotropic anomalous Nernst effect in metallic nickel assembled by the alignment of nanowires. It is found that the ANE voltage varies with the angle to the alignment direction of nanowires within the in-plane of metallic nickel. The ANE voltage reaches 530 nV under a longitudinal temperature difference of 23 K when the magnetic field is applied parallel to the alignment direction of nanowires, which is 143% of its value in the vertical direction. This design concept for anisotropic materials may provide a novel path to enhanced thermoelectric efficiency in ANE-based thermoelectric devices. [ABSTRACT FROM AUTHOR]

Recent advances in wearable ultrasound technologies have demonstrated the potential for hands-free data acquisition, but technical barriers remain as these probes require wire connections, can lose track of moving targets and create data-interpretation challenges. Here we report a fully integrated autonomous wearable ultrasonic-system-on-patch (USoP). A miniaturized flexible control circuit is designed to interface with an ultrasound transducer array for signal pre-conditioning and wireless data communication. Machine learning is used to track moving tissue targets and assist the data interpretation. We demonstrate that the USoP allows continuous tracking of physiological signals from tissues as deep as 164 mm. On mobile subjects, the USoP can continuously monitor physiological signals, including central blood pressure, heart rate and cardiac output, for as long as 12 h. This result enables continuous autonomous surveillance of deep tissue signals toward the internet-of-medical-things. A wearable ultrasound patch monitors subjects in motion using machine learning and wireless electronics. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of computation offloading in a high-mobility Internet of Vehicles (IoVs) environment. The goal is to address the challenges related to latency, energy consumption, and payment cost requirements. The approach considers both moving and parked vehicles as fog nodes, which can assist in offloading computational tasks. However, as the number of vehicles increases, the action space for each agent grows exponentially, posing a challenge for decentralised decision-making. The dynamic nature of vehicular mobility further complicates the network dynamics, requiring joint cooperative behaviour from the learning agents to achieve convergence. The traditional deep reinforcement learning (DRL) approach for offloading in IoVs treats each agent as an independent learner. It ignores the actions of other agents during the training process. This paper utilises a cooperative three-layer decentralised architecture called Vehicle-Assisted Multi-Access Edge Computing (VMEC) to overcome this limitation. The VMEC network consists of three layers: the fog, cloudlet, and cloud layers. In the fog layer, vehicles within associated Roadside Units (RSUs) and neighbouring RSUs participate as fog nodes. The middle layer comprises Mobile Edge Computing (MEC) servers, while the top layer represents the cloud infrastructure. To address the dynamic task offloading problem in VMEC, the paper proposes using a Decentralized Framework of Task and Computational Offloading (DFTCO), which utilises the strength of MADRL and NOMA techniques. This approach considers multiple agents making offloading decisions simultaneously and aims to find the optimal matching between tasks and available resources. [ABSTRACT FROM AUTHOR]

This study aimed to analyze the effects of rabbit bone marrow mesenchymal stem cell (BMSC) transplantation on anterior cruciate ligament (ACL) injury repair and serum urokinase-type plasminogen activator (u-PA), transforming growth factor-β (TGF-β), interleukin-1 (IL-1), and interleukin-4 (IL-4) levels. Sixty adult male New Zealand white rabbits were randomly classified into the model group (MG, n = 20), transplantation group (TG, n = 20), and control group (CG, n = 20). Rabbit models of right anterior forelimb ACL injury were injected with bioprotein gel (MG) or rabbit BMSC (TG) in the femoral and tibial tunnels, whereas rabbits in the CG were given no treatment. The IL-4, IL-1, TGF-β, and u-PA levels and tensile force required to break the cruciate ligaments were compared between the groups. After surgery, IL-4, IL-1, TGF-β, and u-PA levels gradually reduced in MG and TG. At the same time point, the highest IL-4 level was observed in MG and the lowest in CG. At 2, 4, 6, and 8 weeks after surgery, MG required the minimal and CG the maximal tensile forces to break the cruciate ligaments. Rabbit BMSC transplantation is an effective method to repair ACL injury and improve serum u-PA, TGF-β, IL-1, and IL-4 levels in rabbits. [ABSTRACT FROM AUTHOR]

In this paper, a single-phase five-level active neutral-point-clamped with coupled inductors (ANPC-CI) rectifier topology is studied to meet the needs of efficient and reliable power electronic converters. A sliding mode model predictive power control method is proposed to achieve high-efficiency energy conversion, precise voltage control, and low harmonic distortion. By analyzing the working states, establishing a mathematical model and introducing the theory of instantaneous power, the outer loop sliding mode power control and the inner loop model predictive power control algorithms are designed. When compared with traditional PI-DPC algorithm, this algorithm avoids the tedious setting of parameters, reduces input power fluctuations, and improves the system dynamic response speed. The superiority of the sliding mode model predictive power control is verified by simulation and experimental results. [ABSTRACT FROM AUTHOR]