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As an important component of current power and energy storage systems, lithium-ion batteries have essential scientific significance and application value in terms of accurately and reliably diagnosing their aging to determine system performance, identify potential faults in modules, and prolong their service life. For this purpose, this paper first briefly describes the working principle of lithium-ion batteries and illustrates the possible impacts of various aging mechanisms on the state of battery capacity. Secondly, starting from both implementable and laboratory perspectives, it sorts out and summarizes the diagnostic mechanisms and applicable scenarios of current typical battery aging state assessment and diagnosis methods. Then, targeting the specific aging mechanisms involved in batteries, it elaborates on the targeted diagnosis processes for each aging mechanism. Finally, combined with implementable and laboratory diagnosis methods, it systematically summarizes a highly standardized and universal routine diagnosis process for battery aging. In addition, in combination with the latest development of aging diagnosis and related technologies, this paper reflects on and discusses the possible future development directions of battery diagnosis technologies.

Origami tessellations can be folded from a given planar pattern into a three-dimensional object with specific geometric properties, inspiring developments in various fields of science and engineering such as deployable structures, energy absorption devices, reconfigurable robots, and metamaterials. However, the range of existing origami patterns with functional properties such as flat-foldability is rather scant, as analytical solutions to constraint equations arising in the design process are generally highly complicated. In this paper, we tackle the challenging problem of automated design of scalene-faceted flat-foldable origami tessellations using an efficient metaheuristic algorithm. To this end, this study establishes constraint curves based on compatibility conditions for all six-fold (i.e., degree-6) vertices. Subsequently, a graphical method and a particle swarm optimization (PSO) method are adopted to produce optimal origami patterns. Moreover, mountain-valley assignments for the obtained geometric designs are determined using a computational approach based on mixed-integer linear programming. It turns out that the flat-foldable internal vertices of each C2-symmetric unit fragment (UF) exist as C2-symmetric pairs about the centroid of the UF. Furthermore, numerical experiments are carried out to examine the feasibility and compare the accuracy, computational efficiency, and global convergence of the proposed methods. The results of numerical experiments demonstrated that, in comparison with the graphical method, the proposed PSO method has not only a higher accuracy but also a significantly lower computational cost, enabling us to develop an intelligent computational platform to efficiently design scalene-faceted flat-foldable origami tessellations.

Stair slabs are some of the most common structural members with zigzag shapes in various buildings. However, the calculated deflection is usually overestimated during the design of stair slabs. In this article, the overestimated deflection is amended by utilizing step stiffness. To this end, it is assumed that the stress distribution of a unit cell in stair slabs is linear or bilinear under a constant bending moment. Stair slabs can be approximately regarded as flat slabs to derive the equivalent thickness based on the same bending strain energy. Subsequently, finite element (FE) models are established to verify that the obtained equivalent thickness can be applied to reinforced concrete (RC) stair slabs. To improve computational efficiency, the normalized models of stair slabs are adopted for further analysis. On this basis, a novel design method is proposed considering step stiffness for RC stair slabs. Furthermore, numerical examples are presented to compare the improved design method with the FE method and the conventional method. The results demonstrate that the design method considering step stiffness can not only ensure structural safety but also reduce concrete and steel consumption, making the design of stair slabs more economical and reasonable.

In the past several years, a few cervical Pap smear datasets have been published for use in clinical training. However, most publicly available datasets consist of pre-segmented single cell images, contain on-image annotations that must be manually edited out, or are prepared using the conventional Pap smear method. Multicellular liquid Pap image datasets are a more accurate reflection of current cervical screening techniques. While a multicellular liquid SurePath™ dataset has been created, machine learning models struggle to classify a test image set when it is prepared differently from the training set due to visual differences. Therefore, this dataset of multicellular Pap smear images prepared with the more common ThinPrep® protocol is presented as a helpful resource for training and testing artificial intelligence models, particularly for future application in cervical dysplasia diagnosis. The “Brown Multicellular ThinPrep” (BMT) dataset is the first publicly available multicellular ThinPrep® dataset, consisting of 600 clinically vetted images collected from 180 Pap smear slides from 180 patients, classified into three key diagnostic categories.

Fibre lithium-ion batteries are attractive as flexible power solutions because they can be woven into textiles, offering a convenient way to power future wearable electronics 1 – 4 . However, they are difficult to produce in lengths of more than a few centimetres, and longer fibres were thought to have higher internal resistances 3 , 5 that compromised electrochemical performance 6 , 7 . Here we show that the internal resistance of such fibres has a hyperbolic cotangent function relationship with fibre length, where it first decreases before levelling off as length increases. Systematic studies confirm that this unexpected result is true for different fibre batteries. We are able to produce metres of high-performing fibre lithium-ion batteries through an optimized scalable industrial process. Our mass-produced fibre batteries have an energy density of 85.69 watt hour per kilogram (typical values 8 are less than 1 watt hour per kilogram), based on the total weight of a lithium cobalt oxide / graphite full battery, including packaging. Its capacity retention reaches 90.5% after 500 charge–discharge cycles and 93% at 1C rate (compared with 0.1C rate capacity), which is comparable to commercial batteries such as pouch cells. Over 80 per cent capacity can be maintained after bending the fibre for 100,000 cycles. We show that fibre lithium-ion batteries woven into safe and washable textiles by industrial rapier loom can wirelessly charge a cell phone or power a health management jacket integrated with fibre sensors and a textile display. Rechargeable lithium-ion batteries produced in the form of metre-long fibres can be woven into sturdy, washable textiles on an industrial loom and used to power other fabric-based electronic components.

Replacement of liquid electrolytes with polymer gel electrolytes is recognized as a general and effective way of solving safety problems and achieving high flexibility in wearable batteries 1 – 6 . However, the poor interface between polymer gel electrolyte and electrode, caused by insufficient wetting, produces much poorer electrochemical properties, especially during the deformation of the battery 7 – 9 . Here we report a strategy for designing channel structures in electrodes to incorporate polymer gel electrolytes and to form intimate and stable interfaces for high-performance wearable batteries. As a demonstration, multiple electrode fibres were rotated together to form aligned channels, while the surface of each electrode fibre was designed with networked channels. The monomer solution was effectively infiltrated first along the aligned channels and then into the networked channels. The monomers were then polymerized to produce a gel electrolyte and form intimate and stable interfaces with the electrodes. The resulting fibre lithium-ion battery (FLB) showed high electrochemical performances (for example, an energy density of about 128 Wh kg −1 ). This strategy also enabled the production of FLBs with a high rate of 3,600 m h −1 per winding unit. The continuous FLBs were woven into a 50 cm × 30 cm textile to provide an output capacity of 2,975 mAh. The FLB textiles worked safely under extreme conditions, such as temperatures of −40 °C and 80 °C and a vacuum of −0.08 MPa. The FLBs show promise for applications in firefighting and space exploration. A fibre lithium-ion battery that can potentially be woven into textiles shows enhanced battery performance and safety compared with liquid electrolytes.

Health-related quality of life (HRQOL) of caregivers of children with disabilities (CWD) is important for both children’s rehabilitation and caregivers’ life, but the corresponding attention is far from enough in mainland China. Thus, we investigated the HRQOL of 170 caregivers and related factors in Shanghai. The 12-item Short Form Health Survey (SF-12) was used to measure HRQOL. The potential factors were collected, including child characteristics, caregiver characteristics, and environmental factors. Univariate analysis and multiple linear regression were performed to identify the key factors that could be intervened. Compared with the general population, caregivers of CWD had a slightly higher score on the physical component summary (PCS, 52.57 ± 8.41), but the score of mental component summary (MCS, 31.58 ± 7.72) was extremely low. Caregiver’s illness condition, family size, and household income were significant factors of physical HRQOL. Caregivers with illness and caregivers living in an extended family were associated with higher mental HRQOL. Whereas these two factors had opposite effects on physical HRQOL. This finding indicated poor mental HRQOL among caregivers of CWD in Shanghai and thus requiring urgent attention and intervention. Improving physical fitness, maintaining family integration, and providing financial support should be considered when developing intervention for this population.

The fate of nanoparticles in the ecological chain of agriculture has been concerned as their potential pollution and biological effect to humans with rapid development and massive emission of nanomaterials. Here, we found that two rice cultivars (Oryza sativa L) have different heavy metal accumulation results in the roots and shoots after 15 days growth. Two rice cultivars (Oryza sativa L), grown in soil containing magnetite (Fe3O4@NH2) nanoparticles and heavy metal simultaneous, showed less Pb uptake in the roots and shoots, compared with that without Fe3O4@NH2 added. The shape and magnetic properties of Fe3O4@NH2 have no obvious change; however, the transmission electron microscope (TEM) results showed the shell of Fe3O4@NH2 could be broken in the process of interaction with soil. These results suggested that magnetite nanoparticles, such as Fe3O4@NH2, could potentially be used as the recyclable heavy metal fixation materials for alleviating heavy metal poisoning to plant. More Pb, Cd, and As accumulation in rice (Oryza sativa L) roots than shoots. The Si coating shell protected the nanoparticles shape from degradation in acid condition and soil environment. Pb taken up by rice (Oryza sativa L) from soil could be reduced based on the fixation effect of Fe3O4@NH2.

The fate of nanoparticles in the ecological chain of agriculture has been concerned as their potential pollution and biological effect to humans with rapid development and massive emission of nanomaterials. Here, we found that two rice cultivars ( Oryza sativa L ) have different heavy metal accumulation results in the roots and shoots after 15 days growth. Two rice cultivars ( Oryza sativa L ), grown in soil containing magnetite (Fe 3 O 4 @NH 2 ) nanoparticles and heavy metal simultaneous, showed less Pb uptake in the roots and shoots, compared with that without Fe 3 O 4 @NH 2 added. The shape and magnetic properties of Fe 3 O 4 @NH 2 have no obvious change; however, the transmission electron microscope (TEM) results showed the shell of Fe 3 O 4 @NH 2 could be broken in the process of interaction with soil. These results suggested that magnetite nanoparticles, such as Fe 3 O 4 @NH 2 , could potentially be used as the recyclable heavy metal fixation materials for alleviating heavy metal poisoning to plant.

Accurate registration of lung X-rays is an important task in medical image analysis. However, the conventional methods usually cost a lot in running time, and the existing deep learning methods are hard to deal with the large deformation caused by respiratory and cardiac motion. In this paper, we attempt to use deep learning methods to deal with large deformation and enable it to achieve the accuracy of conventional methods. We proposed the cascading affine and B-spline network (CABN), which consists of convolutional cross-stitch affine block (CCAB) and B-splines U-net-like block (BUB) for large lung motion. CCAB makes use of the convolutional cross-stitch model to learn global features among images. And BUB adopts the idea of cubic B-splines which is suitable for large deformation. We separately demonstrated CCAB, BUB, and CABN on two chest X-ray datasets. The experimental results indicate that our methods are highly competitive both in accuracy and runtime when compared to both other deep learning methods and iterative conventional approaches. Moreover, CCAB also can be used for the preprocessing of non-rigid registration methods, replacing affine in conventional methods.