A dual fusion recognition model for sleep posture based on air mattress pressure detection.

In order to solve the difficult portability problem of traditional non-invasive sleeping posture recognition algorithms arising from the production cost and computational cost, this paper proposes a sleeping posture recognition model focusing on human body structural feature extraction and integration of feature space and algorithms based on a specific air-spring mattress structure, called SPR-DE (SPR-DE is the Sleep Posture Recognition-Data Ensemble acronym form). The model combines SMR (SMR stands for Principle of Spearman Maximal Relevance) with horizontal and vertical division based on the barometric pressure signals in the human body's backbone region to reconstruct the raw pressure data into strongly correlated non-image features of the sleep postures in different parts and directions and construct the feature set. Finally, the recognit-ion of the two sleep postures is accomplished using the AdaBoost-SVM integrated classifier. SPR-DE is compared with the base and integrated classifiers to verify its performance. The experimental results show that the amount of significant features helps the algorithm to classify different sleeping patterns more accurately, and the f1 score of the SPR-DE model determined by the comparison experiments is 0.998, and the accuracy can reach 99.9%. Compared with other models, the accuracy is improved by 2.9% ~ 7.7%, and the f1-score is improved by 0.029 ~ 0.076. Therefore, it is concluded that the SMR feature extraction strategy in the SPR-DE model and the AdaBoost-SVM can achieve high accuracy and strong robustness in the task of sleep posture recognition in a small area, low-density air-pressure mattress, taking into account the comfort of the mattress structural design and the sleep posture recognition, integrated with the mattress adaptive adjustment system.

A Green and Facile Microvia Filling Method via Printing and Sintering of Cu-Ag Core-Shell Nano-Microparticles.

In this work, we developed an eco-friendly and facile microvia filling method by using printing and sintering of Cu-Ag core-shell nano-microparticles (Cu@Ag NMPs). Through a chemical reduction reaction in a modified silver ammonia solution with L-His complexing agent, Cu@Ag NMPs with compact and uniform Ag shells, excellent sphericity and oxidation resistance were synthesized. The as-synthesized Cu@Ag NMPs show superior microvia filling properties to Cu nanoparticles (NPs), Ag NPs, and Cu NMPs. By developing a dense refill method, the porosity of the sintered particles within the microvias was significantly reduced from ~30% to ~10%, and the electrical conductivity is increased about twenty-fold. Combing the Cu@Ag NMPs and the dense refill method, the microvias could obtain resistivities as low as 7.0 and 6.3 µΩ·cm under the sintering temperatures of 220 °C and 260 °C, respectively. The material and method in this study possess great potentials in advanced electronic applications.

Highly efficient green synthesis and photodynamic therapeutic study of hypericin and its derivatives.

A highly efficient synthetic pathway for hypericin (7a) was achieved under mild conditions with an overall yield over two steps of 92% using emodinanthrone as a starting material, where protohypericin, a key precursor of hypericin, was synthesized in water with microwave assistance, which was then photocyclized to hypericin with a high yield via 1 h irradiation in a visible light reactor equipped with 575 nm monochromatic lamps. In addition, the method could be used to synthesize hypericin derivatives (7b-d) with similar overall yields. Furthermore, their effects of photodynamic therapy (PDT) were evaluated on A431, HepG-2, and MCF-7 cell lines. The PDT of 7b was better than that of 7a, whereas 7c and 7d were worse. Unlike other cell lines, MCF-7 was not sensitive to any of 7a-d at the same concentrations.

Kinetic Monte Carlo investigation of tetragonal strain on Onsager matrices.

We use three different methods to compute the derivatives of Onsager matrices with respect to strain for vacancy-mediated multicomponent diffusion from kinetic Monte Carlo simulations. We consider a finite difference method, a correlated finite difference method to reduce the relative statistical errors, and a perturbation theory approach to compute the derivatives. We investigate the statistical error behavior of the three methods for uncorrelated single vacancy diffusion in fcc Ni and for correlated vacancy-mediated diffusion of Si in Ni. While perturbation theory performs best for uncorrelated systems, the correlated finite difference method performs best for the vacancy-mediated Si diffusion in Ni, where longer trajectories are required.

Poly[µ(2)-aqua-diaqua-(µ(8)-3-nitro-benzene-1,2-dicarboxylato)(µ(6)-3-nitro-benzene-1,2-dicarboxylato)tetra-sodium].

In the title layered coordination polymer, [Na(4)(C(8)H(3)NO(6))(2)(H(2)O)(3)](n), the doubly deprotonated 3-nitro-benzene-1,2-dicarboxyl-ate ligands exhibit µ(8)- and µ(6)-coordination modes to the sodium ions, generating sheets lying parallel to (001). The coordination environments of the sodium ions are distorted octa-hedral, distorted trigonal-bipyramidal and moncapped trigonal-prismatic. One of the nitro groups is disordered over two sets of sites with site-occupancy factors 0.580 (8):0.419 (2). A network of O-H⋯O and O-H⋯N hydrogen bonds helps to establish the packing.