Reliable Applications Research Articles

The Performance and Clinical Applicability of HER2 Digital Image Analysis in Breast Cancer: A Systematic Review.

This systematic review aims to address the research gap in the performance of computational algorithms for the digital image analysis of HER2 images in clinical settings. While numerous studies have explored various aspects of these algorithms, there is a lack of comprehensive evaluation regarding their effectiveness in real-world clinical applications. We conducted a search of the Web of Science and PubMed databases for studies published from 31 December 2013 to 30 June 2024, focusing on performance effectiveness and components such as dataset size, diversity and source, ground truth, annotation, and validation methods. The study was registered with PROSPERO (CRD42024525404). Key questions guiding this review include the following: How effective are current computational algorithms at detecting HER2 status in digital images? What are the common validation methods and dataset characteristics used in these studies? Is there standardization of algorithm evaluations of clinical applications that can improve the clinical utility and reliability of computational tools for HER2 detection in digital image analysis? We identified 6833 publications, with 25 meeting the inclusion criteria. The accuracy rate with clinical datasets varied from 84.19% to 97.9%. The highest accuracy was achieved on the publicly available Warwick dataset at 98.8% in synthesized datasets. Only 12% of studies used separate datasets for external validation; 64% of studies used a combination of accuracy, precision, recall, and F1 as a set of performance measures. Despite the high accuracy rates reported in these studies, there is a notable absence of direct evidence supporting their clinical application. To facilitate the integration of these technologies into clinical practice, there is an urgent need to address real-world challenges and overreliance on internal validation. Standardizing study designs on real clinical datasets can enhance the reliability and clinical applicability of computational algorithms in improving the detection of HER2 cancer.

A hybrid physical and co-simulation modern adaptive power protection testbed for testing the resilience of smart grids under cyber-physical threats

Power protection systems play a critical role in ensuring the safe and reliable operation of modern power grids. With the increasing complexity of grid topologies and the integration of Distributed Energy Resources (DERs), traditional protection schemes face challenges in maintaining effective coordination among relay systems. This paper presents a new adaptive Overcurrent Relay (OCR) protection and investigates the resilience of different traditional and new adaptive OCR protection schemes against various cyber-physical threats in a smart grid environment. Key contributions include the implementation of a novel adaptive protection scheme designed to dynamically adjust OCR settings based on real-time grid conditions, improving flexibility and responsiveness in managing grid variations induced by DER integration and operational changes. The proposed scheme is validated and implemented using the Multifunction Protection Relay SIEMENS 7SJ62, following to the programming standards of IEC 61131–3, ensuring the reliability and practical applicability of the adaptive OCR scheme in real-world scenarios. Additionally, a dedicated testbed is developed to simulate real-time cyber-physical interactions, incorporating hardware components such as the SIEMENS 7SJ62 and OMICRON-256 test device, along with high-performance computers and communication networks to facilitate comprehensive evaluations of adaptive OCR schemes under varying operational conditions. The study also examines the implications of real-time cyber-attacks on power system operations and the effectiveness of adaptive OCR protection schemes, addressing cybersecurity concerns and proposing mitigation strategies to the system resilience.

Distributed new energy access grid load voltage deviation adaptive compensation method

Abstract Due to the sensitivity changes of compensation penalty parameters, there is a significant deviation in the load voltage of the distributed new energy access grid. Therefore, it is necessary to consider distributed energy access and design a new adaptive compensation method for grid load voltage deviation. Considering the integration of distributed new energy, an adaptive compensation model for grid load voltage deviation was constructed, and an adaptive compensation inverter for grid load pressure deviation was designed to adaptively compensate for grid load voltage deviation. The experimental results show that after adaptive compensation, the voltage deviation value is lower, which has a good compensation effect, reliability, and certain application values, reducing the operational risk of distributed new energy grids.

Efficient predictive modeling of resilient modulus in stabilized clayey soil using automated machine learning

Pavement subgrade design relies on the resilient modulus (Mr) to analyze structural response to vehicle-like loading. Adding stabilizers to the soil subgrade makes estimating Mr difficult and resource-intensive. This study uses an automated machine learning (ML) strategy to predict the Mr of stabilized clayey soil using recycled plastic waste. The proposed method automates model selection and hyperparameter tuning, making it a feasible alternative to tedious ML modeling and costly laboratory testing. From extensive laboratory investigation involving 3285 experimental data points, the automated ML model using Bayesian optimization evaluates ensembles, support vector machine (SVM), neural network (NET), decision trees, (TREE), and Gaussian process (GP) regression models, identifying the best model based on cross-validation mean squared error (MSE). Bayesian optimization explores hyperparameter spaces to find optimal configurations, enhancing the accuracy, scalability, and reliability of the prediction model. The optimization process yielded the best results for the ensemble least square boost (LSBoost) model with a cross-validation mean squared error (MSE) value of 6.723×10−29. The optimized ML model’s performance is measured using R2 and adjusted R2. The LSboost model’s R2 and adjusted R2 values of 0.9999 suggested overfitting, prompting further investigations using performance metrics like root mean squared error (RMSE), mean absolute error (MSE), and probability density function (PDF) for normalized absolute error (NAE) for training and testing datasets for the predictive ML model. The small RMSE and MAE (0.0049 and 0.0005) values and symmetrical NAE distribution of the proposed ML model demonstrate its high accuracy and generalization capabilities. The proposed model was subsequently tested on the new, unseen data and achieved predictions with an error rate of 0.24 %. This confirms the proposed ML model’s superiority over conventional deformation models, making it ideal for reliable geotechnical engineering applications.

A novel and effective oxidation-resistant approach in plasmonic MIM biosensors for real-time detection of urea and glucose in urine for monitoring diabetic and kidney disease severity

In this research article, we introduce a novel oxidation-resistant approach in a Metal–Insulator–Metal (MIM) plasmonic biosensor, specifically designed for the detection and diagnosis of diabetes and kidney disease severity using urea and glucose biomarkers in urine. The proposed biosensor features a four-hand-shaped fan resonator with integrated nanodots and stubs within its waveguide. We optimized its performance through Finite Element Method (FEM) simulations, achieving a maximum sensitivity of 2470.48 nm/RIU and a Figure of Merit (FOM) of 50.15. To ensure chemical stability, we utilized an oxidation-resistant plasmonic material (Au) in our sensor, a critical factor for reliable biosensing applications. Additionally, we focus on Diabetic Kidney Disease (DKD), a significant global health challenge, emphasizing the need for early detection and point-of-care treatment. By exploiting minute changes in resonant wavelength resulting from light–matter interactions through the properties of Surface Plasmon Polaritons (SPPs) and MIM structures, our biosensor demonstrates promising capabilities for real-time and quantitative analysis of biomarkers critical for monitoring the stages of DKD.

Unveiling the governing role of ‘remodeling triangle area’ in soft-hard tissue interface equilibrium for metal implants advancement

Metal implants holds significant promise for diverse fixed prostheses. However, their long-term reliability and broader application are hindered by challenges related to the disequilibrium at the soft-hard tissue interface. By using anti-inflammatory (PDA/IL4) and pro-inflammatory (PDA/LPS/IFNγ) coatings to modulate distinct immune characteristics, we discovered a dynamic bioactive structure at the soft-hard tissue interface around metal implant, which we have named the 'Remodeling Triangle Area' (RTA). We further demonstrate that the RTA can be influenced by the PDA/IL4 coating to favor a phenotype that enhances both innate and adaptive immunity. This leads to stronger epithelial adhesion, the formation of dense connective tissue via IGF1 secretion, and a more balanced soft-hard tissue interface through the OPG/RANKL axis. Conversely, the PDA/LPS/IFNγ coating shifts the RTA towards a phenotype that activates the innate immune response. This results in a less cohesive tissue structure and bone resorption, characterized by reduced IGF1 secretion and an imbalanced OPG/RANKL axis. Over all, our study introduces the novel concept termed the 'Remodeling Triangle Area' (RTA), an immune-rich anatomical region located at the nexus of the implant interface, epithelial, connective, and bone tissue, which becomes highly interactive post-implantation to modulate the soft-hard tissue interface equilibrium. We believe that an RTA-centric, immunomodulatory approach has the potential to revolutionize the design of next-generation metal implants, providing unparalleled soft-hard tissue interface equilibrium properties.

A hybrid The Dwarf Mongoose Optimization Algorithm with Nelder- Mead method and its application for allocation reliability

The Dwarf Mongoose Optimization Algorithm (DMO) is inspired by the behaviour of Dwarf Mongoose which can strike the ideal balance throughout research between exploration and exploitation. In this article, we combine algorithms of the Dwarf Mongoose Optimization Algorithm and the Nelder-Mead Algorithm (DMONM). In addition, the statistically evaluated functions is utilized by calculating the average and the standard deviation values that are used to validate the suggested algorithm's performance. The experimental results are on high-efficiency optimization functions with various dimensions. The hybrid algorithm produces good, encouraging, and better outcomes than the original algorithms. The results show that the proposed algorithm could enhance the effects of DMO when it used to solve the optimization issues of the multi-objective reliability system

Judging Student Teacher Effectiveness: A Systematic Review of Literature

ABSTRACT This paper reports on a systematic literature review to understand better methodologies and data collection tools used to judge student teaching effectiveness, ways in which validity and reliability are considered, the processes involved in assessing new teaching effectiveness within teacher education programmes, and how evaluation and results are used to judge readiness to teach. The accurate and consistent judgement of teaching competence during and at completion of preparation continues to be an area of increasing interest and concern. The PRISMA review process identified 45 key papers. An in-depth analysis underscored several crucial factors, such as the challenge of ensuring the reliability of judgements within dynamic educational environments and the need for broader understanding and applications of reliability and dependability when making judgements. The findings of this systematic literature review hold implications that merit consideration by teacher education programmes for processes to judge teaching effectiveness. The analysis also highlighted the intricacies inherent in evaluating teaching effectiveness, alongside ongoing discourse regarding the criteria and measures for judging competence of student teachers.

Quantitative assessment of machine learning reliability and resilience.

Advances in machine learning (ML) have led to applications in safety-critical domains, including security, defense, and healthcare. These ML models are confronted with dynamically changing and actively hostile conditions characteristic of real-world applications, requiring systems incorporating ML to be reliable and resilient. Many studies propose techniques to improve the robustness of ML algorithms. However, fewer consider quantitative techniques to assess changes in the reliability and resilience of these systems over time. To address this gap, this study demonstrates how to collect relevant data during the training and testing of ML suitable for the application of software reliability, with and without covariates, and resilience models and the subsequent interpretation of these analyses. The proposed approach promotes quantitative risk assessment of ML technologies, providing the ability to track and predict degradation and improvement in the ML model performance and assisting ML and system engineers with an objective approach to compare the relative effectiveness of alternative training and testing methods. The approach is illustrated in the context of an image recognition model, which is subjected to two generative adversarial attacks and then iteratively retrained to improve the system's performance. Our results indicate that software reliability models incorporating covariates characterized the misclassification discovery process more accurately than models without covariates. Moreover, the resilience model based on multiple linear regression incorporating interactions between covariates tracks and predicts degradation and recovery of performance best. Thus, software reliability and resilience models offer rigorous quantitative assurance methods for ML-enabled systems andprocesses.

Projecting the long‐term effects of large‐scale human influence on the spatial and functional persistence of extant longleaf pine ecosystems in the Florida Flatwoods Pyrome

AbstractDecades of human activities and fire suppression have adversely affected longleaf pine (Pinus palustris) ecosystems, which are home to high levels of diversity and endemism. These iconic ecosystems also now face challenges from urbanization and climate change, which will alter conservation outcomes over the remainder of the 21st century. To explore how long‐term, large‐scale human influences could affect the spatial and functional persistence of extant longleaf pine ecosystems in the Florida Flatwoods Pyrome, we extracted a set of 2400 longleaf pine patches ≥40 ha in size from the Florida Longleaf Pine Ecosystem Geodatabase. Projections from the FUTURES urban growth model and the Florida 2070 project indicate that development will lead to losses of existing longleaf pine habitat, reductions in longleaf pine patch size, and patches that are predominantly located in close proximity to developed areas. Finer‐scale patterns of longleaf pine loss in three focal landscapes highlighted differences in land protection, ecological setting, and development pressure and the value of using of multiple urbanization iterations. The occurrence of suitable conditions to conduct prescribed fires, a crucial tool for maintaining, improving, and restoring longleaf pine ecosystems, is projected to decrease seasonally throughout the study area. As a result, the functional persistence of ecosystems is at risk due to climate changes that increase barriers to the safe and reliable application of intentional fire. The long‐term viability of this critical ecosystem will warrant the evaluation of adaptive strategies that explicitly account for the individual and compounding effects of urban development and changing fire management conditions when considering options for ecosystem protection, management, and restoration.

A Comparative Study on GPS Navigation Systems: Attacks, Applications and Challenges for Security and Reliability

A Comparative Study on GPS Navigation Systems: Attacks, Applications and Challenges for Security and Reliability

Biosilica-coated carbonic anhydrase displayed on Escherichia coli: A novel design approach for efficient and stable biocatalyst for CO2 sequestration

A robust and stable carbonic anhydrase (CA) system is indispensable for effectively sequestering carbon dioxide to mitigate climate change. While microbial surface display technology has been employed to construct an economically promising cell-displayed CO2-capturing biocatalyst, the displayed CA enzymes were prone to inactivation due to their low stability in harsh conditions. Herein, drawing inspiration from biomineralized diatom frustules, we artificially introduced biosilica shell materials to the CA macromolecules displayed on Escherichia coli surfaces. Specifically, we displayed a fusion of CA and the diatom-derived silica-forming Sil3K peptide (CA-Sil3K) on the E. coli surface using the membrane anchor protein Lpp-OmpA linker. The displayed CA-Sil3K (dCA-Sil3K) fusion protein underwent a biosilicification reaction under mild conditions, resulting in nanoscale self-encapsulation of the displayed enzyme in biosilica. The biosilicified dCA-Sil3K (BS-dCA-Sil3K) exhibited improved thermal, pH, and protease stability and retained 63 % of its initial activity after ten reuses. Additionally, the BS-dCA-Sil3K biocatalyst significantly accelerated the CaCO3 precipitation rate, reducing the time required for the onset of CaCO3 formation by 92 % compared to an uncatalyzed reaction. Sedimentation of BS-dCA-Sil3K on a membrane filter demonstrated a reliable CO2 hydration application with superior long-term stability under desiccation conditions. This study may open new avenues for the nanoscale-encapsulation of enzymes with biosilica, offering effective strategies to provide efficient, stable, and economic cell-displayed biocatalysts for practical applications.

Genetic and ecological inheritance of plant growth-promoting rhizobacteria

Abstract Background The utilization of beneficial (Rhizo) bacteria, as an alternative to traditional fertilizers, has emerged as an eco-friendly strategy for ameliorating sustainable agricultural production. This approach aims to reduce the use of agrochemicals and minimize environmental pollution. Scope This review provides an updated insight into the ecological impact of plant growth-promoting rhizobacteria (PGPR), focusing on the resident microbiome and its potential transferability to the next generation of plants. Conclusion In this context, PGPR are assumed to alter the rhizosphere microbiome by outcompeting the existing taxa through nutrient deprivation, acidification of the environment, metabolites production, and consequently, increasing the copiotrophic taxa. Such modifications can maximize the beneficial interactions of plant-PGPR by increasing the bioavailability of nutrients and handling diverse signaling pathways. The effects of interactions within the PGPR-root system can adjust the composition of root exudates and influence the release of bioactive molecules by the root, especially under stress conditions, which can act as signals to reactivate and recruit the beneficial microbes in the rhizosphere and endosphere in favor of the plants. Such changes in microbiome structure can occur gradually over time, even if the survival rate of PGPR in soil and their re-colonization efficiency inside plant tissue are limited. The aforementioned modifications in the rhizosphere and plant microbiome have the potential to increase the survival chances of the progeny plants growing under the same stress conditions. Establishing a comprehensive and robust knowledge framework that addresses all of these issues is critical for significantly advancing the field of microbe-plant interactions and for developing reliable applications of PGPR.

Production of Reverse Transcriptase and DNA Polymerase in Bacterial Expression Systems.

DNA amplification and reverse transcription enzymes have proven to be invaluable in fast and reliable diagnostics and research applications because of their processivity, specificity, and robustness. Our study focused on the production of mutant Taq DNA polymerase and mutant M-MLV reverse transcriptase in the expression hosts Vibrio natriegens and Escherichia coli under various expression conditions. We also examined nonspecific extracellular production in V. natriegens. Intracellularly, M-MLV was produced in V. natriegens at the level of 11% of the total cell proteins (TCPs) compared with 16% of TCPs in E. coli. We obtained a soluble protein that accounted for 11% of the enzyme produced in V. natriegens and 22% of the enzyme produced in E. coli. Taq pol was produced intracellularly in V. natriegens at the level of 30% of TCPs compared with 26% of TCPs in E. coli. However, Taq pol was almost non-soluble in E. coli, whereas in V. natriegens, we obtained a soluble protein that accounted for 23% of the produced enzyme. We detected substantial extracellular production of Taq pol. Thus, V. natriegens is a suitable alternative host with the potential for production of recombinant proteins.

Distributed clustering model for energy efficiency based topology control using game theory in wireless sensor networks

In this study, we use a topology control technique to tackle the issue of energy balance and consumption minimization in wireless sensor networks. By maintaining network connection while sensibly adjusting the transmission power level, such an algorithm may reduce and balance energy usage. This study provides an energy welfare topological control using a game-theoretic approach by calculating energy welfare as usefulness metric for energy populations using the welfare function from the social sciences. Energy balance occurs when every node works to improve its local society's energy situation to the best of its ability. We demonstrate that the consequence ant game is an intriguing game with a single Nash equilibrium that is Pareto optimum. According to economic theory, Pareto optimality is a situation in which improving one person's circumstances would always make another person's worse off. We demonstrate our suggested methodology's superiority in establishing energy balance and efficiency in wireless sensor networks by contrasting the simulation results of our algorithm with those of other approaches. Our approach surpasses existing methods by a wide margin. For reliable and long-lasting wireless sensor applications, this study offers insightful information about how to maximize network performance while preserving energy resources.

Establishing evidence criteria for implementation strategies in the US: a Delphi study for HIV services

BackgroundThere are no criteria specifically for evaluating the quality of implementation research and recommending implementation strategies likely to have impact to practitioners. We describe the development and application of the Best Practices Tool, a set of criteria to evaluate the evidence supporting HIV-specific implementation strategies.MethodsWe developed the Best Practices Tool from 2022–2023 in three phases. (1) We developed a draft tool and criteria based on a literature review and key informant interviews. We purposively selected and recruited by email interview participants representing a mix of expertise in HIV service delivery, quality improvement, and implementation science. (2) The tool was then informed and revised through two e-Delphi rounds using a survey delivered online through Qualtrics. The first and second round Delphi surveys consisted of 71 and 52 open and close-ended questions, respectively, asking participants to evaluate, confirm, and make suggestions on different aspects of the rubric. After each survey round, data were analyzed and synthesized as appropriate; and the tool and criteria were revised. (3) We then applied the tool to a set of research studies assessing implementation strategies designed to promote the adoption and uptake of evidence-based HIV interventions to assess reliable application of the tool and criteria.ResultsOur initial literature review yielded existing tools for evaluating intervention-level evidence. For a strategy-level tool, additions emerged from interviews, for example, a need to consider the context and specification of strategies. Revisions were made after both Delphi rounds resulting in the confirmation of five evaluation domains – research design, implementation outcomes, limitations and rigor, strategy specification, and equity – and four evidence levels – best, promising, more evidence needed, and harmful. For most domains, criteria were specified at each evidence level. After an initial pilot round to develop an application process and provide training, we achieved 98% reliability when applying the criteria to 18 implementation strategies.ConclusionsWe developed a tool to evaluate the evidence supporting implementation strategies for HIV services. Although specific to HIV in the US, this tool is adaptable for evaluating strategies in other health areas.

Multicenter external validation of prediction models for clinical outcomes after spinal fusion for lumbar degenerative disease

BackgroundClinical prediction models (CPM), such as the SCOAP-CERTAIN tool, can be utilized to enhance decision-making for lumbar spinal fusion surgery by providing quantitative estimates of outcomes, aiding surgeons in assessing potential benefits and risks for each individual patient. External validation is crucial in CPM to assess generalizability beyond the initial dataset. This ensures performance in diverse populations, reliability and real-world applicability of the results. Therefore, we externally validated the tool for predictability of improvement in oswestry disability index (ODI), back and leg pain (BP, LP).MethodsProspective and retrospective data from multicenter registry was obtained. As outcome measure minimum clinically important change was chosen for ODI with ≥ 15-point and ≥ 2-point reduction for numeric rating scales (NRS) for BP and LP 12 months after lumbar fusion for degenerative disease. We externally validate this tool by calculating discrimination and calibration metrics such as intercept, slope, Brier Score, expected/observed ratio, Hosmer–Lemeshow (HL), AUC, sensitivity and specificity.ResultsWe included 1115 patients, average age 60.8 ± 12.5 years. For 12-month ODI, area-under-the-curve (AUC) was 0.70, the calibration intercept and slope were 1.01 and 0.84, respectively. For NRS BP, AUC was 0.72, with calibration intercept of 0.97 and slope of 0.87. For NRS LP, AUC was 0.70, with calibration intercept of 0.04 and slope of 0.72. Sensitivity ranged from 0.63 to 0.96, while specificity ranged from 0.15 to 0.68. Lack of fit was found for all three models based on HL testing.ConclusionsUtilizing data from a multinational registry, we externally validate the SCOAP-CERTAIN prediction tool. The model demonstrated fair discrimination and calibration of predicted probabilities, necessitating caution in applying it in clinical practice. We suggest that future CPMs focus on predicting longer-term prognosis for this patient population, emphasizing the significance of robust calibration and thorough reporting.

Tribological and mechanical properties improvement of G20CrNiMo alloy via fabricating gradient composite layer through ultrasonic strengthening grinding process

Fretting wear is one of the main causes of damage failure at the contact interface, which significantly limits the reliable application of mechanical parts. To address this issue, a composite microstructure comprising a gradient structure, α-Al2O3 particles, and micro-dimples was fabricated on the G20CrNiMo alloy by an improved method of ultrasonic strengthening grinding process (USGP). Subsequently, the fretting resistance and mechanical properties of the treated G20CrNiMo alloy were investigated. The results indicated that USGP could significantly enhance mechanical strength and wear resistance properties. The USGP-treated sample demonstrated an increment of approximately 25.32 % in tensile strength compared to the untreated sample. It also exhibited reductions of about 19.61 % and 49.56 % in friction coefficient and wear volume, respectively. After USGP treatment, the gradient structure incorporating dense dislocation and high-angle grain boundaries (HAGBs) limited the shear motion of the G20CrNiMo alloy, thereby enhancing its mechanical properties. Furthermore, the synergistic effect of gradient structure, Al2O3 particles, and micro-dimples effectively restricted shear deformation and formed a supplementary glaze to further improve its fretting wear resistance.

Hierarchical flower-like ytterbium benzene-1,3,5-tricarboxylate metal organic framework for simultaneous sensitive electrochemical detection of Cd2+, Pb2+ and Cu2+

Hierarchical electrochemical sensors with specific recognition function have been widely applied for trace amounts detection of metal ions, showing great significance for monitoring environmental quality. Herein, a novel flower-like ytterbium benzene-1,3,5-tricarboxylate metal-organic framework (Yb-MOF-1) was prepared by a one-step solvothermal method, which was then cast on a glassy carbon electrode to construct a sensing platform. Compared to the traditional rod-like Yb-MOF-2 prepared at a lower pH (pH = 4.26), the Yb-MOF-1 assembled from ultrathin nanosheets has a large surface area (462.356 m²/g), showing simultaneous electrochemical detection performance towards Cd2+, Pb2+ and Cu2+ in aqueous solution with detection limits of 9.22, 1.69, 15.15 nM, respectively. The experimental results demonstrate its good anti-interference and repeatability. The reliable practical application of Yb-MOF-1 for Cd2+, Pb2+ and Cu2+ detection in real water samples shows good recovery rates ranging from 95.8 % to 103.5 %, proving the effective strategy in exploring efficient electrochemical sensing materials.

Bidirectional Zero Poisson's Ratio Elastomers with Self‐Deformable Soft Mechanical Metamaterials for Stretchable Displays

AbstractStretchable displays, capable of reversible expansion, represent significant advancements in free‐form display technologies. However, the high Poisson's ratio (ν) of elastomer substrates leads to unintended deformation under tensile strain, resulting in image warping. To address this, a meta‐elastomer (ME) substrate with a bidirectional zero ν, incorporating a self‐deformable soft mechanical metamaterial (MM) frame, is introduced. The ν of the ME is precisely programmed by the interaction between the deformation of the MM frame, which exhibits a negative ν, and the elastomer matrix, which has a positive ν. The soft MM frame, stiffer than the elastomer matrix, undergoes both structural deformation and length alteration during substrate tensile strain. This synergistic effect enables achieving a nearly bidirectional zero ν, thus overcoming the limitations of conventional tessellated rigid MM composites. Furthermore, the ME substrate, which is chemically cross‐linked at the junction interface, demonstrates exceptional mechanical robustness, enduring over 180% stretching and more than 10 000 cycles. By counteracting the Poisson's effect, the ME substrate with an integrated pixel array ensures translational pixel movement along the tensile axis during bidirectional stretching, minimizing undesired pixel movement in other directions. The stretchable ME presents key advancements for implementing more stable and reliable stretchable display applications.