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Objective To establish an acute low back pain (ALBP) model by simulating low-frequency vibration of helicopters and explore the causes of ALBP in army aviation pilots in order to provide a reliable animal model and evaluation method for its pathogenesis and protection. Methods Forty-eight male SD rats (8 weeks old, weighing 200±20 g) were selected and randomly divided into groups A, B and C, with vibration for 1, 3 and 6 h, respectively, and group D as blank control, with 12 rats in each group. The rats were fixed in sitting posture on a vibrating table (10 Hz, with 6 degree vibration) for corresponding durations during 5 consecutive days. Animal behavioral tests were performed before and on days 1, 4, 7 and 14 after modelling, including paw withdrawal threshold, open field test, rotarod fatigue test, gait analysis, and 24-hour food intake assessment. Finally, light microscopy was used to observe the morphological structure of the multifidus muscle. Results Behavioral examinations revealed that persistent low-frequency vibration resulted in decreased foot-contraction reflex thresholds (P < 0.01), amount of 24-hour food consumption (P < 0.01), count of upright standing (P < 0.05), rotarod velocity at rat falling off (P < 0.05), duty cycle (P < 0.05), footprint surface area (P < 0.05), and walking speed (P < 0.05). Histological observation for the multifidus muscle demonstrated cellular edema and myocyte disorganization accompanied by inflammatory cell infiltration and aggregation. Conclusion Continuous exposure to a low-frequency vibration leads to significant low back pain-related behaviors and histological changes in the lumbar multifidus muscle of rats.

Self‐recoverable mechanoluminescence (ML) is becoming a novel technology widely used in the fields of sensing, display, and artificial intelligence. The dominant ML material, ZnS: Mn2+, is reported to solely present a yellow emission color, which limits the applications of self‐recoverable ML materials to a large extent. Herein, an effective strategy to extend the ML emission range of ZnS: Mn2+ by the ferromagnetic coupling of Mn2+ ions are reported. Under the thermal carbon‐reduction atmosphere (TCRA), the emission ranges of ML, photoluminescence (PL), and persistent luminescence spectra of ZnS: Mn2+ phosphors are all successfully broadened from yellow to red. Furthermore, as for the PL and ML intensities of ZnS: Mn2+, they are intensified to 1.76 and 3.23 folds larger under the TCRA treatment than those in pure nitrogen, respectively. Various spectra and magnetic test results reveal that the red emission bands of ZnS: Mn2+ @TCRA phosphors originate from the ferromagnetic coupling of Mn2+ ions. This study is the first to realize strong red emission and tunable multicolor luminescence in the conventional ZnS‐based phosphors, which introduces opportunities for discovering the multiband emissions of Mn2+ ion in other compounds. Brightly multicolored ZnS: xMn2+ @TCRA elastic films have been fabricated to demonstrate their anti‐counterfeiting and security applications. [ABSTRACT FROM AUTHOR]

Precision matrices can efficiently exhibit the correlation between variables and they have received much attention in recent years. When one encounters large datasets stored in different locations and when data sharing is not allowed, the implementation of high-dimensional precision matrix estimation can be numerically challenging or even infeasible. In this work, we studied distributed sparse precision matrix estimation via an alternating block-based gradient descent method. We obtained a global model by aggregating each machine's information via a communication-efficient surrogate penalized likelihood. The procedure chooses the block coordinates using the local gradient, to guide the global gradient updates, which can efficiently accelerate precision estimation and lessen communication loads on sensors. The proposed method can efficiently achieve the correct selection of non-zero elements of a sparse precision matrix. Under mild conditions, we show that the proposed estimator achieved a near-oracle convergence rate, as if the estimation had been conducted with a consolidated dataset on a single computer. The promising performance of the method was supported by both simulated and real data examples. [ABSTRACT FROM AUTHOR]

The capability of healing structural damage is highly desired for triboelectric nanogenerators (TENGs), which are subject to repeated mechanical loading. However, it remains difficult to balance the intrinsic mechanical strength and healing capability of the materials used in TENGs. Herein, we exploit a mechanically strong and mildly healable polymer (THP) as the key material of TENGs. THP is based on polyurethane–urea containing coordinate bonds, and its healing can be triggered by simply wetting the breakage area with water or alcoholic drinks. The healing ratio and the tensile strength of THP can reach ∼90% and ∼11.6 MPa, respectively. Correspondingly, the prepared THP-based TENGs (THP-TENGs) exhibit high enough robustness to withstand mechanical loading as well as excellent healability for recovering device functionalities after mechanical damage. In addition, disused devices can be recycled to produce new ones based on the solution processibility of THP. The fabricated THP-TENG could serve as a mechanical energy harvester as well as a self-powered sensor for detecting tactile and electrophysiological signals, suggesting utilities in advanced wearable systems. [ABSTRACT FROM AUTHOR]