AI-Driven Sensing Technology: Review.

Machine learning and deep learning technologies are rapidly advancing the capabilities of sensing technologies, bringing about significant improvements in accuracy, sensitivity, and adaptability. These advancements are making a notable impact across a broad spectrum of fields, including industrial automation, robotics, biomedical engineering, and civil infrastructure monitoring. The core of this transformative shift lies in the integration of artificial intelligence (AI) with sensor technology, focusing on the development of efficient algorithms that drive both device performance enhancements and novel applications in various biomedical and engineering fields. This review delves into the fusion of ML/DL algorithms with sensor technologies, shedding light on their profound impact on sensor design, calibration and compensation, object recognition, and behavior prediction. Through a series of exemplary applications, the review showcases the potential of AI algorithms to significantly upgrade sensor functionalities and widen their application range. Moreover, it addresses the challenges encountered in exploiting these technologies for sensing applications and offers insights into future trends and potential advancements.

Study on the Spatial Damage Characteristics of Working Fluid in a Sandstone Reservoir and an Unplugged Fluid System.

Due to its good physical properties and low pressure coefficient, the K75 reservoir is prone to leakage and intrusion of drilling and completion fluids during well construction, resulting in plugging of the borehole throat, which will inevitably affect the injection and recovery capacity of the gas well. In order to ensure the strong injection and production capacity of the K75 reservoir, this paper clarifies the reservoir space damage characteristics under different pressure differences and different entry fluids by establishing physical simulation of the damage, selecting the optimal system of the decongestant, and conducting dynamic flow decongestion experiments, as well as combining with the characterization means of the continuous scanning system of the core and the permeability test of the gas measurement. The test results show that the degree of damage of the same working fluid increases with the increase of differential pressure of repulsion, and the degree of damage of sequential working fluids (drilling fluid, completion fluid, and perforating fluid) is greater than that of drilling fluid damage under the same differential pressure of repulsion. In order to relieve the damage of wellbore working fluids, a set of multifunctional, low-corrosion composite acid system formulas was selected through rock powder, mud cake dissolution experiments, and dynamic flow unblocking experiments; the permeability recovery rate of this acid system after unblocking was more than 90%, which is widely applicable, and it is useful for the acidizing and unblocking practice in similar reservoirs.

TP53R175H mutation promotes breast cancer cell proliferation through CORO1A-P38 MAPK pathway regulation.

Breast cancer is the most commonly diagnosed cancer in women. Among all types, triple-negative breast cancer is particularly challenging to cure because of its high recurrence rates and invasive and metastatic capacity. Although numerous studies have explored the role of TP53 mutations in cancer, there is a dearth of research regarding the correlation between TP53 mutations and breast cancer cell proliferation. In this study, our aim was to examine the impact of TP53 mutations on the prognosis of patients with breast cancer bioinformatics techniques. To detect cell proliferation, a CCK8 assay was performed, and western blotting was used to identify the expression of p53, p38, and p-p38 proteins. Cellular mRNA sequencing was used to screen target genes of TP53 mutations, and molecular docking was performed to identify the drugs that could hinder the proliferation of breast cancer cells.The results showed that the TP53 mutation rate is higher in patients with triple-negative breast cancer than non-triple-negative breast cancer, and those with TP53 mutations tended to have a poorer prognosis than those without. Patients with R175H site mutations also had shorter survival times than those without. Cytological experiments revealed that the TP53R175H mutation increases the rate of breast cancer cell proliferation. In conjunction with this, CORO1A was found to be a downstream target of TP53 mutations, and it was determined to promote breast cancer cell proliferation. Moreover, CORO1A overexpression resulted in the downregulation of p-p38 levels. Molecular docking studies further revealed that tea polyphenols can inhibit breast cancer proliferation by binding to p53.

Unblocking Process of Complex Sulfur-Iron Scale Blockage in Sulfur-Bearing Gas Wells and Its Mechanism.

In this paper, through the establishment of a blockage analysis process, multistage weight loss method, and FT-IR, GC-MS, SEM, EDS, XRD and other characterization test means, it is clear that the scale sample is mainly organic material covered with complex and diverse sulfur and iron compounds, and the mechanism of sulfur-containing gas well blockage is revealed by tracing into the well material, formation minerals, acid gas corrosion, and other substances. Through the selection of chelating agent, cleaning agent, and acid concentration, as well as the optimization of different process parameters, the "dispersion-chelation-dissolution" composite unblocking technology is proposed, and a set of unblocking formula systems for complex sulfur and iron scale is clarified. Finally, XRD and SEM characterization tests of the scale samples before and after dissolution were conducted to study the declogging mechanism and confirm the acid-insoluble nature of FeS2 and its stability in a weak acidic environment.

Machine learning-based identification of segment joint failure in underground tunnels.

Shield tunnels that reside deep within soft soil are subject to longitudinal differential settlement and structural deformation during long-term operation. Longitudinal deformation can be classified into two modes: bending and dislocation deformation. The failure of bolts and engineering treatment techniques differ between these two modes. Therefore, it is imperative to accurately identify the tunnel's longitudinal deformation mode to determine the validity of the segment joint and implement appropriate engineering treatment. Traditional methods for detecting dislocation or opening suffer from high labour costs. To address this issue, this study presents an innovative identification method using a back-propagation neural network (BPNN) to detect segment joint failure in underground tunnels. First, this study collects the tunnel settlement curves of various subways located in the East China soft soil area, and it calculates tunnel settlement-dislocation and settlement-opening datasets using the equivalent axial stiffness model. A corresponding BPNN regression model is subsequently established, and the new settlement curve is the input to this regression model to predict the dislocation and opening, thereby determining the validity of the segment joint. The efficiency of this method is demonstrated through its successful application to the Hangzhou Metro Tunnel. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.

Planting and mowing cover crops as livestock feed to synergistically optimize soil properties, economic profit, and environmental burden on pear orchards in the Yangtze River Basin.

BACKGROUND: Pears, as an important cash crop, are currently facing great issues due to unsustainable management practices. Cover cropping is a sustainable management strategy that can improve soil fertility and increase fruit yield, while it may also stimulate greenhouse gas emissions. Therefore, synergizing multiple indicators to achieve sustainable development is critical. This study introduces a new management system, namely the planting and mowing of ryegrass as a livestock feed system (PRSS), and analyzes its impact on soil quality, economic benefits, and environmental burdens. RESULTS: Our results indicated that PRSS could increase soil pH from 5.08 to 5.48 and decrease the content of soil alkali-hydrolyzable nitrogen, total phosphate, and available phosphate (26.96-59.89%) while also enhancing yield (+38.51%) compared with the traditional natural grass management system (TMS). The average soil methane fluxes in PRSS were 72.67 µg m-2 day-1 , higher than those of TMS (61.28 µg m-2 day-1 ). However, the gross primary production was lower than TMS (-37.24%), and no significant difference was observed in soil nitrous oxide fluxes. In different scenarios, the total profit of PRSS mode 1 (mowing ryegrass and selling to a livestock company) and PRSS mode 2 (mowing ryegrass and feeding own sheep) were 10 706.21 $ ha-1 and 26 592.87 $ ha-1 respectively. These values are respectively2.36 times and 5.85 times higher than that of TMS. The total global warming potential of TMS (18.19 t CO2 -eq ha-1 ) was 1.29 t CO2 -eq ha-1 higher and 2.89 t CO2 -eq ha-1 lower than that of PRSS mode 1 and mode 2 respectively. CONCLUSION: Compared with traditional natural grass, planting and mowing ryegrass in pear orchards can optimize soil properties, increase fruit yield, and reduce global warming potential. Different modes can greatly increase revenue but have varying impacts on environmental burdens. These findings can help rebuild the links between farmland and specialized livestock production, contributing to sustainable development in the pear industries. © 2023 Society of Chemical Industry.

A Deep Learning-Based Unbalanced Force Identification of the Hypergravity Centrifuge.

Accurate and quantitative identification of unbalanced force during operation is of utmost importance to reduce the impact of unbalanced force on a hypergravity centrifuge, guarantee the safe operation of a unit, and improve the accuracy of a hypergravity model test. Therefore, this paper proposes a deep learning-based unbalanced force identification model, then establishes a feature fusion framework incorporating the Residual Network (ResNet) with meaningful handcrafted features in this model, followed by loss function optimization for the imbalanced dataset. Finally, after an artificially added, unbalanced mass was used to build a shaft oscillation dataset based on the ZJU-400 hypergravity centrifuge, we used this dataset to train the unbalanced force identification model. The analysis showed that the proposed identification model performed considerably better than other benchmark models based on accuracy and stability, reducing the mean absolute error (MAE) by 15% to 51% and the root mean square error (RMSE) by 22% to 55% in the test dataset. Simultaneously, the proposed method showed high accuracy and strong stability in continuous identification during the speed-up process, surpassing the current traditional method by 75% in the MAE and by 85% in the median error, which provided guidance for counterweight and guaranteed the unit's stability.

Sustainable strategies related to soil fertility, economic benefit, and environmental impact on pear orchards at the farmer scale in the Yangtze River Basin, China.

Pears are an important income source in China, and unreasonable management practices have had a negative impact on the sustainability of pear orchards. However, multi-objective synergistic strategies are unclear on a farmer scale. In this study, we quantified indicators of soil fertility (soil organic matter (SOM)), environmental impact (global warming potentials (GWP)), and economic benefit (ratio of benefit and cost (BCR)) and analysed the synergetic strategies based on survey data from 230 smallholders in the Yangtze River Basin (Shanghai City, Chongqing City, Zhejiang province, and Jiangxi province). The average SOM, GWP, and BCR were 28.9 g kg-1, 17.3 t CO2-eq ha-1, and 3.63, respectively. Furthermore, optimised solutions using the Pareto multiple-objective optimisation model can reduce the GWP by 44.6% and improve the SOM and BCR by 34.4% and 43.9%, respectively, when fertiliser N rate and density are both decreased and the ratio of organic fertiliser application is increased compared to farmer management practices. The structural equation model indicated that planting density and fertiliser N rate can directly influence GWP and indirectly increase SOM and BCR; organic fertiliser application directly affects the GWP, SOM, and BCR. Our research provides a bottom-up approach based on the farmer scale, which can improve the sustainability of pear systems, and these findings can be used as guidelines for policymakers and pear orchard managers.

Forsythoside A Mitigates Alzheimer's-like Pathology by Inhibiting Ferroptosis-mediated Neuroinflammation via Nrf2/GPX4 Axis Activation.

Ferroptosis and neuroinflammation play crucial roles in Alzheimer's disease (AD) pathophysiology. Forsythoside A (FA), the main constituent of Forsythia suspensa (Thunb.) Vahl., possesses anti-inflammatory, antibacterial, antioxidant, and neuroprotective properties. The present study aimed to investigate the potential role of FA in AD neuropathology using male APP/PS1 double transgenic AD mice, Aß1-42-exposed N2a cells, erastin-stimulated HT22 cells, and LPS-induced BV2 cells. FA treatment significantly improved mitochondrial function and inhibited lipid peroxidation in Aß1-42-exposed N2a cells. In LPS-stimulated BV2 cells, FA treatment decreased the formation of the pro-inflammatory factors IL-6, IL-1ß, and NO. In male APP/PS1 mice, FA treatment ameliorated memory and cognitive impairments and suppressed Aß deposition and p-tau levels in the brain. Analyses using proteomics, immunohistochemistry, ELISA, and western blot revealed that FA treatment significantly augmented dopaminergic signaling, inhibited iron deposition and lipid peroxidation, prevented the activation of IKK/IκB/NF-κB signaling, reduced the secretion of pro-inflammatory factors, and promoted the production of anti-inflammatory factors in the brain. FA treatment exerted anti-ferroptosis and anti-neuroinflammatory effects in erastin-stimulated HT22 cells, and the Nrf2/GPX4 axis played a key role in these effects. Collectively, these results demonstrate the protective effects of FA and highlight its therapeutic potential as a drug component for AD treatment.

Optical fibre taper-enabled waveguide photoactuators.

Photoactuators have attracted significant interest for soft robot and gripper applications, yet most of them rely on free-space illumination, which requires a line-of-site low-loss optical path. While waveguide photoactuators can overcome this limitation, their actuating performances are fundamentally restricted by the nature of standard optical fibres. Herein, we demonstrated miniature photoactuators by embedding optical fibre taper in a polydimethylsiloxane/Au nanorod-graphene oxide photothermal film. The special geometric features of the taper endow the designed photoactuator with microscale active layer thickness, high energy density and optical coupling efficiency. Hence, our photoactuator show large bending angles (>270°), fast response (1.8 s for 180° bending), and low energy consumption (<0.55 mW/°), significantly exceeding the performance of state-of-the-art waveguide photoactuators. As a proof-of-concept study, one-arm and two-arm photoactuator-based soft grippers are demonstrated for capturing/moving small objects, which is challenging for free-space light-driven photoactuators.

Glycine-chelated zinc rather than glycine-mixed zinc has lower foliar phytotoxicity than zinc sulfate and enhances zinc biofortification in waxy corn.

To determine whether high spraying concentrations of Zn sources increase the Zn concentration in waxy corn (Zea mays L. var. ceratina Kulesh) seeds without compromising agronomic performance, field experiments were conducted between 2018 and 2020. Excess ZnSO4 application caused foliar burn, barren ear tip, and grain yield loss. ZnEDTA and Glycine-chelated Zn (ZnGly) caused less foliar burn, but Glycine-mixed Zn caused more foliar burn than ZnSO4. The seed Zn concentration increased with spraying Zn concentration. ZnEDTA (≤0.8%) had a higher threshold concentration than ZnGly (≤0.4%). Nevertheless, Zn biofortification efficacy did not significantly differ between 0.4% ZnGly and 0.8% ZnEDTA, and the grain Zn recovery rate of 0.4% ZnGly was much higher than that of 0.8% ZnEDTA. Additionally, dual-isotope labelling tests confirmed that 15N-glycine and 68Zn in ZnGly interacted. In the future, chelating technology is essential for developing new Zn fertilizers to optimize Zn biofortification efficacy.

Effect of planting and mowing cover crops as livestock feed on soil quality and pear production.

Introduction: The increasing demand for animal-products has led to an increasing demand for livestock feed. Using cover crop as green manure in orchards is an effective measure to improve fruit yield and quality. However, the effect of mowing cover forage crops as livestock feed on soil quality and crop production is unclear. Method: Therefore, a 4-year field experiment, which included two treatments, was conducted in pear orchards in Luniao County, China: natural grass (NG) and planting and mowing forage crop ryegrass as livestock feed (MF). Results: Under MF treatment, most soil nutrient content, especially alkalihydrolysable N (AN), total phosphate (TP), available phosphate (AP), and microbial biomass phosphate (MBP), had decreased significantly (P<0.05), while ß-D-glucosidase (BG, C-cycle enzyme) and soil C limitation at 10-20 cm depth and P limitation at subsoil (20-40 cm) was increased. In addition, the soil bacterial community component in topsoil (0-10 cm and 10-20 cm) and fungal community component in topsoil and subsoil were changed in the MF treatment. Network analysis showed that MF treatment had a lower edge number in topsoil but the community edge numbers increased from 12794 in NG to 13676 in MF in subsoil. The average weight degree of the three soil layers in MF treatment were reduced, but the modularity had increased than that in NG. For crop production, MF treatment was 1.39 times higher in pear yield and titratable acids (AC) reduced from 0.19% to 0.13% compared with NG. These changes were more associated with the indicators at the subsoil, especially for TP, AN, pH, and F-NMDS1 (non-metric multidimensional scaling (NMDS) axis 1 of fungi). Discussion: These results provide data support for the feasibility of planting and mowing forage crops as livestock feed on orchards as well as a new idea for the integration of crop and livestock.

Miniaturization of mechanical actuators in skin-integrated electronics for haptic interfaces.

Skin-integrated electronics, also known as electronic skin (e-skin), are rapidly developing and are gradually being adopted in biomedical fields as well as in our daily lives. E-skin capable of providing sensitive and high-resolution tactile sensations and haptic feedback to the human body would open a new e-skin paradigm for closed-loop human-machine interfaces. Here, we report a class of materials and mechanical designs for the miniaturization of mechanical actuators and strategies for their integration into thin, soft e-skin for haptic interfaces. The mechanical actuators exhibit small dimensions of 5 mm diameter and 1.45 mm thickness and work in an electromagnetically driven vibrotactile mode with resonance frequency overlapping the most sensitive frequency of human skin. Nine mini actuators can be integrated simultaneously in a small area of 2 cm × 2 cm to form a 3 × 3 haptic feedback array, which is small and compact enough to mount on a thumb tip. Furthermore, the thin, soft haptic interface exhibits good mechanical properties that work properly during stretching, bending, and twisting and therefore can conformally fit onto various parts of the human body to afford programmable tactile enhancement and Braille recognition with an accuracy rate over 85%.

Automatic classification of heterogeneous slit-illumination images using an ensemble of cost-sensitive convolutional neural networks.

BACKGROUND: Lens opacity seriously affects the visual development of infants. Slit-illumination images play an irreplaceable role in lens opacity detection; however, these images exhibited varied phenotypes with severe heterogeneity and complexity, particularly among pediatric cataracts. Therefore, it is urgently needed to explore an effective computer-aided method to automatically diagnose heterogeneous lens opacity and to provide appropriate treatment recommendations in a timely manner. METHODS: We integrated three different deep learning networks and a cost-sensitive method into an ensemble learning architecture, and then proposed an effective model called CCNN-Ensemble [ensemble of cost-sensitive convolutional neural networks (CNNs)] for automatic lens opacity detection. A total of 470 slit-illumination images of pediatric cataracts were used for training and comparison between the CCNN-Ensemble model and conventional methods. Finally, we used two external datasets (132 independent test images and 79 Internet-based images) to further evaluate the model's generalizability and effectiveness. RESULTS: Experimental results and comparative analyses demonstrated that the proposed method was superior to conventional approaches and provided clinically meaningful performance in terms of three grading indices of lens opacity: area (specificity and sensitivity; 92.00% and 92.31%), density (93.85% and 91.43%) and opacity location (95.25% and 89.29%). Furthermore, the comparable performance on the independent testing dataset and the internet-based images verified the effectiveness and generalizability of the model. Finally, we developed and implemented a website-based automatic diagnosis software for pediatric cataract grading diagnosis in ophthalmology clinics. CONCLUSIONS: The CCNN-Ensemble method demonstrates higher specificity and sensitivity than conventional methods on multi-source datasets. This study provides a practical strategy for heterogeneous lens opacity diagnosis and has the potential to be applied to the analysis of other medical images.

Efficacy and safety of Qishen Yiqi dripping pills as a complementary treatment for Heart Failure: A protocol of updated systematic review and meta-analysis.

BACKGROUND: Heart failure (HF) has become a serious global public health issue due to its high incidence, high mortality and extremely low quality of life. According to several clinical trials, Qishen Yiqi Dripping pills (QSYQ) combined with routine western medicine treatment can further enhance the curative effect of HF patients. However, most of the trials are small in sample size and poor in quality, which can only provide limited evidence-based medicine. The existing systematic reviews of efficacy and safety has provided evidence for the clinical application of QSYQ to a certain extent, but there are still 3 major defects. Here, we will perform a systematic review and meta-analysis that include the randomized clinical trial (RCT) of CACT-IHF, apply meta-regression and subgroup analysis to cope with multiple confounding factors, and add the clinical efficacy standards of TCM, all-cause death and readmission rates as reliable efficacy evaluation indicators. The purpose of this study was to rigorously evaluate the clinical efficacy and safety of QSYQ in the complementary treatment of HF with a well-designed systematic review and meta-analysis. METHODS: Following the strict search strategy, 9 databases will be searched to ensure a comprehensive search. We search the database from the establishment until November 30, 2020. This study will include RCTs of QSYQ in HF patients' complementary treatment. Two searchers will independently draft and carry out the search strategy, and the third member will further complete it. Two members independently screen literature, extract data and cross-check, and solve different opinions through discussion or negotiation with the third member. The risk bias will be evaluated based on Cochrane tool of risk of bias. Meta-regression and subgroup analysis are used to check and deal with the heterogeneity. The data analysis will be conducted by the statistical software Stata 16.0. RESULTS: The results of this research will be delivered in a peer-reviewed journal. CONCLUSION: This study expects to provide credible and scientific evidence for the efficacy and safety of QSYQ in HF's complementary treatment, and at the same time provide a convenient and effective choice for decision-makers and patients. PROTOCOL REGISTRATION NUMBER: INPLASY 2020120106. ETHICAL APPROVAL: Since this study is on the basis of published or registered RCTs, ethical approval and informed consent of patients are not required.

Vibration Analysis of Post-Buckled Thin Film on Compliant Substrates.

Buckling stability of thin films on compliant substrates is universal and essential in stretchable electronics. The dynamic behaviors of this special system are unavoidable when the stretchable electronics are in real applications. In this paper, an analytical model is established to investigate the vibration of post-buckled thin films on a compliant substrate by accounting for the substrate as an elastic foundation. The analytical predictions of natural frequencies and vibration modes of the system are systematically investigated. The results may serve as guidance for the dynamic design of the thin film on compliant substrates to avoid resonance in the noise environment.

Morphable 3D mesostructures and microelectronic devices by multistable buckling mechanics.

Three-dimensional (3D) structures capable of reversible transformations in their geometrical layouts have important applications across a broad range of areas. Most morphable 3D systems rely on concepts inspired by origami/kirigami or techniques of 3D printing with responsive materials. The development of schemes that can simultaneously apply across a wide range of size scales and with classes of advanced materials found in state-of-the-art microsystem technologies remains challenging. Here, we introduce a set of concepts for morphable 3D mesostructures in diverse materials and fully formed planar devices spanning length scales from micrometres to millimetres. The approaches rely on elastomer platforms deformed in different time sequences to elastically alter the 3D geometries of supported mesostructures via nonlinear mechanical buckling. Over 20 examples have been experimentally and theoretically investigated, including mesostructures that can be reshaped between different geometries as well as those that can morph into three or more distinct states. An adaptive radiofrequency circuit and a concealable electromagnetic device provide examples of functionally reconfigurable microelectronic devices.

Mechanically-Guided Deterministic Assembly of 3D Mesostructures Assisted by Residual Stresses.

Formation of 3D mesostructures in advanced functional materials is of growing interest due to the widespread envisioned applications of devices that exploit 3D architectures. Mechanically guided assembly based on compressive buckling of 2D precursors represents a promising method, with applicability to a diverse set of geometries and materials, including inorganic semiconductors, metals, polymers, and their heterogeneous combinations. This paper introduces ideas that extend the levels of control and the range of 3D layouts that are achievable in this manner. Here, thin, patterned layers with well-defined residual stresses influence the process of 2D to 3D geometric transformation. Systematic studies through combined analytical modeling, numerical simulations, and experimental observations demonstrate the effectiveness of the proposed strategy through ≈20 example cases with a broad range of complex 3D topologies. The results elucidate the ability of these stressed layers to alter the energy landscape associated with the transformation process and, specifically, the energy barriers that separate different stable modes in the final 3D configurations. A demonstration in a mechanically tunable microbalance illustrates the utility of these ideas in a simple structure designed for mass measurement.

Renewable-emodin-based wearable supercapacitors.

With the increasing dependency of human life on wearable electronics, the development of corresponding energy-storage devices is being insensitively pursued. Considering the special usage locations of wearable energy-storage devices, the safety and non-toxicity of electrode materials adopted should be of concern. In this work, a novel all-solid-state wearable supercapacitor based on the renewable-biomolecule emodin, naturally derivable from traditional Chinese herbal rhubarb or Polygonum cuspidatum, was successfully fabricated. Such supercapacitors exhibited excellent charge storage and rate capability with great flexibility and could be integrated into wearable electronics. As a proof of concept, a strap-shaped supercapacitor was fabricated, and it was capable of powering an electronic watch. Our work will promote the development of safe wearable electronics.

A finite deformation model of planar serpentine interconnects for stretchable electronics.

Lithographically defined interconnects with filamentary, serpentine configurations have been widely used in various forms of stretchable electronic devices, owing to the ultra-high stretchability that can be achieved and the relative simple geometry that facilitates the design and fabrication. Theoretical models of serpentine interconnects developed previously for predicting the performance of stretchability were mainly based on the theory of infinitesimal deformation. This assumption, however, does not hold for the interconnects that undergo large levels of deformations before the structural failure. Here, an analytic model of serpentine interconnects is developed starting from the finite deformation theory of planar, curved beams. Finite element analyses (FEA) of the serpentine interconnects with a wide range of geometric parameters were performed to validate the developed model. Comparisons of the predicted stretchability to the estimations of linear models provide quantitative insights into the effect of finite deformation. Both the theoretical and numerical results indicate that a considerable overestimation (e.g., > 50% relatively) of the stretchability can be induced by the linear model for many representative shapes of serpentine interconnects. Furthermore, a simplified analytic solution of the stretchability is obtained by using an approximate model to characterize the nonlinear effect. The developed models can be used to facilitate the designs of serpentine interconnects in future applications.