Abnormal Fluctuations Research Articles

Adaptive Self‐Triggered Non‐Fragile Control for Nonhomogeneous Semi‐Markov Switching Systems Under Deception Attacks

ABSTRACTThe adaptive self‐triggered non‐fragile control is studied for nonhomogeneous semi‐Markov jump systems with deception attacks. Because network information transmission is vulnerable to cyber attacks, an adaptive self‐triggered strategy is proposed to significantly save network resources, in which the threshold can flexibly decide whether to carry out data transmission, thus showing a strong resource optimization ability. Owing to the fact that the actuator failure may lead to abnormal fluctuations or irregular changes in the output action, a non‐fragile controller is proposed under a certain degree of parameter perturbation. The main novelty is the design of an adaptive self‐triggered strategy to realize better dynamic performance, overcoming the difficulty caused by deception attacks. Furthermore, the desired adaptive self‐triggered non‐fragile controller is constructed to guarantee the stochastic stability for the corresponding system. Finally, the single‐link robotic arm system is verified to ensure the feasibility of the theoretical results presented.

An effective control method for silicon content in hot metal: Model predictive control (MPC) based on CNN–LSTM–Attention algorithm

As a key performance indicator in the smelting process of blast furnaces, silicon content in hot metal is used to characterise the thermal state and changing trend of the hearth. The silicon content in hot metal is closely related to the furnace condition and hot metal quality. Whether the silicon content in hot metal is too high or too low, it can cause accidents in the blast furnace. Predicting and controlling the silicon content in a hot metal can help avoid production accidents. Abnormal fluctuations in silicon content can be prevented. This ensures the safe and stable operation of the blast furnace smelting process. This study integrates the theory of the blast furnace smelting process and on-site data. It aims to predict and control the silicon content in hot metal by integrating process experience, big data technology and artificial intelligence. Firstly, data collected from the site was systematically integrated and processed. Subsequently, prediction models (XGBoost, LSTM, CNN–LSTM CNN–LSTM–Attention) were developed to predict the silicon content in hot metal for the next hour. Finally, with the help of MPC controller, an intelligent control model for the silicon content in hot metal was constructed to stabilise the silicon content in hot metal within the range of 0.25%–0.4%, providing a theoretical basis for achieving closed-loop control of the silicon content in hot metal.

Reliability analysis of gear-bearing drive systems considering gear manufacturing and installation errors

Gear-bearing drive systems often exhibit manufacturing and installation errors, which can significantly affect system performance, and longevity, and increase the probability of failures. This paper focuses on the reliability analysis of gear-bearing drive systems with uncertainties in system parameters such as gear backlash and bearing clearance, caused by gear and bearing manufacturing and installation errors. First, a dynamic model of the gear-bearing drive system, incorporating coupled dynamic meshing parameters, is established. Then, the deterministic dynamic model of the system is combined with the Chebyshev interval analysis method to develop a reliability analysis model for the gear-bearing drive system with uncertain parameters. The study analyzes the variations in system natural frequencies and vibration responses due to gear quality and initial gear and bearing clearances at different deviation rates. The results indicate that at the same rotational speed and deviation rate, the initial bearing clearance has a more significant impact on the system’s dynamic characteristics compared to the initial gear clearance. At different rotational speeds and the same deviation rate, system reliability decreases with increasing average initial interference of the bearing at low speeds. At high speeds, a large bearing clearance deviation may cause abnormal fluctuations in system vibration. This method provides a prioritization of parameter control for the structural optimization and design of gear-bearing systems.

Porous Microspheres Loading Small Molecule Targeting REV-ERBs Modulate Inflammatory Cytokines Fluctuations to Promote Periodontal Bone Regeneration.

Alveolar bone loss, mainly due to periodontitis and local inflammation, poses a great challenge for current bone graft materials. To address this issue, we introduce PLGA-S@Gel-SeHA, a microsphere composed of Poly(lactic-co-glycolic acid) (PLGA), gelatin mineralized selenium-doped hydroxyapatite (Gel-SeHA) and STL1267 (S), which target circadian rhythm gene REV-ERBs (nuclear recerptor subfamily 1, group D, NR1D), to maintain inflammatory cytokine homeostasis for alveolar bone repair. Synthesized via iso-density emulsion and microfluidics, the PLGA-S@Gel-SeHA microspheres are of uniform size and porosity, greatly enhancing the cell adhesion and ingrowth. The combination of Gel-SeHA with PLGA provides abundant biomineralization sites and osteogenic activities. Incorporation of STL1267, currently the safest and most effective small molecule compound targeting REV-ERBs, enables sustained release that mitigates the severe fluctuations of inflammatory cytokines under LPS stimulation. Specifically, it reduces the levels of IL-6, TNF, and IL-1β by over 30% at critical circadian time points, thereby restoring their normal rhythmic expression. This promotes macrophage polarization toward anti-inflammatory M2 phenotypes favorable for osteogenesis. In rat alveolar bone defects, these microspheres demonstrate effective inflammation regulation and significant bone regeneration. By targeting circadian rhythm genes to redress the abnormal inflammatory cytokines fluctuations, this approach may provide a feasible anti-inflammatory strategy for bone repair.

A Novel Detection Algorithm for Anomalies and Attacks on Electric Vehicle Charging Stations Using Distributed Data Privacy Protection

Electric vehicles with charging stations are being rapidly deployed. Traditional methods for detecting anomalies and attacks on charging stations merely employ local data, resulting in lower detection accuracy. Intuitively, centralized federated learning approaches impose heavy computational loads on the central node. When the central node experiences abnormalities or failures, the charging station network may encounter data isolation. This paper starts from individual charging stations and utilizes a transformer for anomaly and attack detection. Based on this, a distributed privacy-preserving data aggregation scheme is proposed. More specifically, the transformer effectively eliminates the influence of abnormal data fluctuations on detection accuracy. The proposed scheme dynamically aggregates data through a distributed federated learning framework without requiring any prior knowledge or central node deployment. In addition, considering the statistical characteristics of the model parameters, an effective model parameter updating method is proposed to reduce the communication bandwidth requirement in distributed federation learning and enhance the algorithm’s robustness. According to the results, the suggested system may safely detect anomalous activity or possible threats at EV charging stations while safeguarding user privacy.

Immune dysregulation in ulcerative colitis: pathogenic mechanisms and therapeutic strategies of traditional Chinese medicine.

Ulcerative colitis (UC) is a chronic inflammatory bowel disease (IBD) characterized primarily by immune dysregulation. Its pathogenesis involves multiple factors, including dysregulation of T-cell subsets, hypersecretion of pro-inflammatory cytokines, imbalance in the gut microbiota, and disruption of the intestinal barrier. Among T-cell subsets, abnormal activation of Th1 and Th17 cells, in conjunction with Treg dysfunction, significantly amplifies local pro-inflammatory signals. Pro-inflammatory cytokines, such as TNF-α, IL-6, and IL-17, exacerbate apoptosis and disrupt tight junctions (TJs) in intestinal epithelial cells (IECs), thereby creating favorable conditions for invasion by pathogenic bacteria and their metabolites. Intestinal microecological imbalance not only leads to significant alterations in the structure of the bacterial flora but also involves abnormal fluctuations in its metabolites that directly regulate intestinal immune homeostasis, a factor closely associated with the severity of inflammation and prognosis of ulcerative colitis. Recent studies have demonstrated that in the treatment of UC, traditional Chinese medicine (TCM) achieves a multi-target, multi-pathway integrated intervention by regulating immune cell differentiation, balancing inflammatory factor levels, repairing the intestinal epithelial barrier, and remodeling the structure of the bacterial flora. This article reviews the pathogenic mechanisms underlying immune dysregulation in UC and the advances in research on TCM's role in immune regulation, anti-inflammatory repair, and flora modulation, encompassing the mechanisms of action of individual active ingredients and classic TCM compound formulas. Although some studies have preliminarily confirmed TCM's potential to modulate immunity and repair the intestinal barrier, breakthroughs in mechanism analysis, herb standardization, and large-scale validation remain forthcoming. It is anticipated that the unique advantages of TCM will be translated into a more precise therapeutic strategy for UC through modern molecular and systems biology approaches.

INDUSTRIAL ENVIRONMENT MONITORING AND ALERT SYSTEM USING IOT

ABSTRACT: The Industrial Environment Monitoring and Alert System using IoT is designed to enhance workplace safety and efficiency by integrating gas sensors, heat sensors, and an alarm system. This system continuously monitors industrial environments for potential hazards such as gas leaks and abnormal temperature fluctuations. The gas sensor detects harmful gases (e.g., methane, carbon monoxide), while the heat sensor monitors temperature levels to prevent overheating and fire hazards.If any parameter exceeds predefined safety limits, the system triggers an alarm and can send real-time alerts to concerned authorities via IoT-based communication. This project leverages IoT technology to enable remote monitoring and data logging, improving real- time decision-making and preventive maintenance. The collected data can be analyzed to predict hazardous conditions, ensuring a safer and more efficient industrial environment. Overall, this smart industrial safety system minimizes risks, enhances automation, and optimizes industrial operations by providing a proactive safety mechanism. Industries involving hazardous gases and high-temperature machinery pose significant safety challenges. Conventional safety measures rely heavily on human supervision, which can be prone to errors and inefficiencies. The integration of Internet of Things (IoT) technology in industrial safety systems offers a real-time, automated, and scalable solution for preventing accidents and ensuring a secure working environment. Several research studies highlight the role of IoT- based industrial monitoring systems in enhancing workplace safety. Smart sensors, cloud computing, and data analytics are commonly used to detect environmental anomalies and prevent accidents. Prior studies emphasize gas detection technologies, temperature monitoring, and wireless alert mechanisms for early hazard identification. However, existing systems often lack real-time remote monitoring capabilities and efficient data processing, which this study aims to improve.

The Analysis and Prediction Concerning The Impact of International Incidents on The Gold Price

Since 2020, the world has faced a series of significant events, including the COVID-19 pandemic, the Russia-Ukraine war, the Israeli-Palestinian conflict, the rise of right-wing politics in Europe, and the election of Donald Trump as President of the United States. These events have contributed to a period of global uncertainty, which is reflected in the price of gold, often seen as a safe haven during times of crisis. Beginning in early 2024, both international and domestic gold prices surged, with domestic gold prices increasing by 40%. This study compiles gold price data from 1984 onward, along with a selection of representative international incidents during this period. Using Students t-test and the 3-sigma rule, abnormal fluctuations in the data were identified. The Analytic Hierarchy Process (AHP) was then applied to classify the international incidents. The relationship between the severity of international incidents and abnormal fluctuations in gold prices was explored, and the correlation coefficient between these variables was calculated, revealing a positive correlation. Based on the findings, potential future impacts of international incidents on gold price fluctuations are predicted.

Analyzing Mechanisms of Business Fluctuations involving Time-Varying Structure in Japan: Methodological Proposition and Empirical Study

Abstract In this article, we present a comprehensive analysis of business cycles in Japan from 1975 to 2023, focusing on the mechanisms that underlie economic fluctuations. We develop a novel methodology to construct a comprehensive index of business cycles (CIBC) by integrating 30 indicators commonly used in business cycle analysis in Japan. Unlike traditional approaches, our method simultaneously incorporates all business cycle indicators with adjusted lags, which enhances its precision and scope. We decompose each indicator’s time series using an extended moving linear model, which ensures robustness amid abnormal fluctuations. We propose a two-lag time-varying loading factor model to analyze the lead-lag relationship between the CIBC and each variable using a distribution-free dynamic linear modeling method. In our analysis, we elucidate the complex interactions between key indicators and a common factor, which explains the dynamics of business fluctuations. We conducted a comprehensive empirical study using Japanese business cycle indicator data to demonstrate the performance of the proposed methodology. Despite its limitations, our study provides valuable insights that enable us to understand the mechanisms of business cycles in Japan from the perspective of empirical analysis. Our study lays the groundwork for future research in this area.

Smart Band Enabled AI-System for Mental Health Tracking

The paper presents the development of an AI- enabled smart band designed to continuously monitor mental wellness through the real-time analysis of three key physiological indicators: heart rate, body temperature, and movement patterns. These signals are captured by onboard sensors and transmitted wirelessly to a cloud-based platform using the ESP32/Node MCU module. The system applies AI algorithms to detect variations and correlations in the data that may indicate mental stress, fatigue, or anxiety. Elevated heart rate, abnormal temperature fluctuations, and irregular movement patterns—detected through the accelerometer—serve as critical markers for early- stage mental health concerns. The processed data is visualized on a user-friendly web and mobile dashboard, where users can monitor their mental state trends and receive real-time alerts. When stress thresholds are exceeded, the system delivers instant notifications via app alerts, SMS, or email, encouraging timely interventions. Personalized suggestions, such as light physical activities, deep breathing exercises, or short breaks, are generated based on individual user behavior. The smart band not only provides continuous, non-invasive mental health monitoring but also learns from user data over time using machine learning to improve prediction accuracy. This approach transforms traditional mental health assessment methods by introducing a proactive, AI-driven, and data- centric system for managing everyday stress and emotional well-being.

Respiratory rate detection of dairy cows based on infrared thermography in head movement scenarios.

Respiratory rate detection of dairy cows based on infrared thermography in head movement scenarios.

Continuous Analysis of Involuntary Nervous System Activity with Wearable Health Monitors in Space to Predict Pathological Changes in Cognitive Function

Long-term spaceflight missions are becoming a reality, where astronauts must overcome life-or-death obstacles with minimal resources while isolated from society. Isolation, a known stressor of space exploration, can lead to mental stress and abnormal cortisol fluctuations which disrupt functions including sleep, emotions, and cognitive abilities necessary for safe mission completion. Current testing by NASA uses Cognition, a series of cognitive performance assessments, for noncontinuous monitoring before, during, and after missions as it fits into busy schedules every week or two. The autonomic nervous system (ANS), regulated by the central nervous system, influences critical cognitive functions via its sympathetic "fight-or-flight" and parasympathetic "rest-and-digest" divisions. However, suboptimal CNS function can impair performance and in turn, alter ANS activity. The ANS regulates involuntary bodily functions, including cardiovascular, respiratory, or dermal activity, which serve as measurable indicators of autonomic states. Our study uses the Corsano TM CardioWatch 287-2B to continuously collect data – pulse rate, respiration, skin temperature, activity, photoplethysmography (PPG), and noninvasive blood pressure (NIBP) – from healthy volunteers (n=12, female=4). Baseline data collection is underway and will be collected for 58 to 87 days before two crews of 6 each undertake a two-week Mars analog mission in isolation will begin at the Mars Desert Research Station (MDRS). Cognitive performance will be measured between 12/4–1/12 through the Cognition Test Battery. Cognitive performance tests will be monitored with the administration of the Cognition Test Battery 6 times, every other day during the analog mission as the study volunteers live in simulated isolation. Cognition consists of 10 tests titled Motor Praxis Test (MP), Visual Object Learning (VOLT), Fractal 2-Back (F2B), Abstract Matching (AM), Line Orientation Test (LOT), Emotion Recognition Task (ERT), Matrix Reasoning Test (MRT), Digital Symbol Substitution Task (DSST), Balloon Analog Risk Test (BART), and the Psychomotor Vigilance Test (PVT) that combined test for sensory-motor speed, spatial learning and memory, working memory, abstract reasoning, spatial orientation, emotional identification, complex visual scanning, risk management and decision-making, attention, and reaction time. Each test provides the median, mean, and standard deviation of the reaction time as well as the accuracy scores and test-specific metrics such as the total money collected for BART. Reaction time, accuracy, and efficiency cognitive test results will be analyzed in a mixed linear model to determine the average relationship between them and the wearable sensor data for covariates of age, gender, and test timing. Preliminary baseline data (14 – 29 days) show diurnal patterns in NIBP, pulse, respiration, and heart rate variability (HRV) measures with profiles unique to each individual. Model results are expected to show ANS activity during Cognition testing differs from longitudinal patterns, serving as a predictor of cognitive states. The ability to predict cognitive performance levels in real-time will bring us a step closer to continuous cognitive performance monitoring for enhanced space mission readiness in extreme environments that allow for timely feedback, additional oversight, and appropriate intervention. This abstract was presented at the American Physiology Summit 2025 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.

Numerical simulation of abnormal response characteristics of hydraulic fracturing pumping pressure in strongly heterogeneous volcanic reservoirs

Volcanic reservoirs exhibit significant heterogeneity characteristics influenced by volcanic activity and tectonic evolution. The unclear mechanisms underlying abnormal pressure fluctuations during the pre-pad fluid injection stage in fracturing operations severely constrain reservoir stimulation effectiveness. This study establishes a hydraulic fracture propagation model for heterogeneous reservoirs using the three-dimensional discrete element method, analyzing fracture propagation behaviors and pressure response characteristics to identify dominant factors controlling abnormal pressure responses. Results indicate that bedding, high-angle natural fracture, and localized heterogeneous region with high mechanical strength all impede hydraulic fracture propagation, resulting in pressure surges with maximum increases of 20%, 25%, and 158%, respectively. Due to weak-plane normal stress constraints and additional tortuosity friction, pumping pressure exhibits a continuous rise without decline during hydraulic fracture penetration of weak plane. In contrast, variation in fracture propagation difficulty leads to a rise followed by an abrupt drop in pumping pressure when fracture penetrates localized heterogeneous region. Pumping pressure shows strong sensitivity to Young’s modulus and fracture toughness of localized heterogeneous region. Consequently, the interaction between the hydraulic fracture and localized heterogeneous region with high mechanical strength is the dominant controlling factor leading to abnormal pressure responses during the pre-pad fluid injection stage in volcanic reservoirs.

Ultra-Short-Term Load Forecasting for Extreme Scenarios Based on DBSCAN-RSBO-BiGRU-KNN-Attention with Fine-Tuning Strategy

Extreme scenarios involving abnormal load fluctuations pose serious challenges to the safe and stable operation of power systems. To address these challenges, an ultra-short-term load forecasting model is proposed, specifically designed for extreme conditions. The model combines density-based spatial clustering of applications with noise (DBSCAN), random search Bayesian optimization (RSBO), bidirectional gated recurrent units (BiGRUs), k-nearest neighbor (KNN), and an attention mechanism, enhanced by a fine-tuning strategy to improve forecasting accuracy. Firstly, the original load data are reconstructed weekly, and extreme scenarios are identified using the DBSCAN. Secondly, the RSBO is employed to optimize model parameters within the high-dimensional search space. To further refine performance, the final fully connected layer is fine-tuned to adapt to extreme conditions. Finally, case studies demonstrate that the proposed approach reduces the root mean square error (RMSE) by 12.37% and the mean absolute error (MAE) by 6.73% compared to benchmark models, achieving superior accuracy under all tested extreme scenarios.

Parametric Subnational Population Volatility: The Importance of Abnormal Fluctuations

Parametric Subnational Population Volatility: The Importance of Abnormal Fluctuations

Anomalous pattern recognition based on image recognition in food safety detection

In food processing, foreign matter inevitably contaminates packaged food. To ensure food safety, ray-based detection is used; however, the original images suffer from aberrations and noise that degrade quality and hinder further processing. Thus, images are preprocessed to enhance quality by highlighting key features and suppressing irrelevant ones before abnormal pattern recognition. Following image segmentation, a BP neural network algorithm is applied for foreign object detection. In tests with contaminants such as metal wires, stones, and glass, the algorithm identified distinct abnormal fluctuations at gray levels of 132, 108, and 34, respectively, allowing it to reliably detect foreign objects. Although the practical detection rate reached 100%, occasional misjudgments suggest that further optimization is needed. Overall, this method introduces a novel approach to detecting foreign objects in food and offers promising new strategies for improving food safety monitoring.

Monitoring and Optimization of CFB Bed Temperature in the Flexible Process: A Hybrid Framework of Deep Learning Model and Mechanism Model.

In response to the issues of abnormal bed temperature fluctuations and inefficient combustion that occur during the flexible operation of the circulating fluidized bed (CFB) boiler combustion system, which is characterized by strong coupling, nonlinearity, and large inertia, this paper presents a novel monitoring and optimization approach that integrates a deep learning model with a mechanism model. Specifically, the Informer algorithm is utilized to construct a bed temperature range prediction model, thus enabling the real-time monitoring of the intricate change trends of the in-furnace bed temperature. Considering the poor reliability of combustion optimization target values derived from data model predictions and the constraints on the optimization upper limit, this article further combines the mechanism model to comprehensively determine the combustion optimization target values from the design perspective. The results indicated that the Informer prediction model exhibited a root-mean-square error (RMSE) of 3.385 °C, a mean absolute error (MAE) of 2.45 °C, and a mean absolute percentage error (MAPE) of 0.268% on the rolling test data set, demonstrating high overall prediction accuracy. The steady-state operational data of the generating unit at 300 and 200 MW were selected to validate the mechanism model, with particular emphasis on comparing the distribution trends of pressure and temperature along the height of the furnace. The outcomes revealed good consistency, indicating the model's high accuracy in performance simulation. By integrating the data model with the mechanism model, the target values for optimizing bed temperature performance under steady-state conditions were determined. In comparison to using the data model alone, during the 240 and 280 MW operating conditions, the average thermal efficiency of the boiler increased by 0.19 and 0.13%, respectively. Concurrently, the coal consumption rate for power generation decreased by 0.6707 and 0.4453 g/(kW·h), respectively, and the carbon emissions reduced per kilowatt-hour of electricity generated were 1.6738 and 1.1113 g, respectively.

Instantaneous Frequency Analysis Based on High-Order Multisynchrosqueezing Transform on Motor Current and Application to RV Gearbox Fault Diagnosis

Motor current analysis is useful for ensuring the safety and reliability of electromechanical systems. However, for gearboxes, the commonly used methods of detecting faulty frequency sidebands are easily disturbed by installation errors, inherent harmonics, and fundamental frequency with high amplitude. Aiming at this problem, this study presents instantaneous frequency polarview (IFpolarview), which diagnoses faults based on motor angle and motor current frequency modulation (FM) features. Firstly, to address the problem of the limited analysis order of higher-order synchrosqueezing transform (HSST), the higher-order multisynchrosqueezing transform (HMSST) is introduced to improve the instantaneous frequency (IF) estimation accuracy and reveal the transient fault features from the motor current without further increasing the order and algorithm difficulty. Then, based on the motor angle and accurate motor current IF extracted from HMSST, the IFpolarview is proposed to visualize gear faults through detecting the FM of motor current synchronized with the faulty gear mesh. In the simulation, the IF estimation error of HMSST is 2.51%, which is smaller than other methods. The experimental results show that the HMSST has the smallest Rényi entropy value of 9.13, implying that the most aggregated time–frequency representation (TFR) of the energy is obtained. HMSST can enhance the resolution of fault characteristics, and IFpolarview concentrates the abnormal IF fluctuations with periodicity into a small angular interval, which highlights the fault features and demonstrates greater intuitiveness and reliability in comparison to the frequency sideband detection method.

Fuzzy Particle Filtering Based Approach for Battery RUL Prediction With Uncertainty Reduction Strategies

ABSTRACTThis paper proposes a two‐stage framework that combines uncertainty reduction and predictive modelling to enhance the accuracy of battery Remaining Useful Life (RUL) prediction. In the first stage, a simplified fuzzy optimization learning model is introduced to mitigate uncertainty caused by abnormal capacity fluctuations in battery data. The proposed fuzzy model reconstructs degradation data into a consistent downward trend based on mid‐ and short‐term tendencies of the battery, alleviating abnormal variability and improving suitability for predictive modelling. In the second stage, uncertainty arising during the recursive prediction process of a standalone Transformer model was mitigated through the integration of a particle filter. This approach dynamically manages prediction errors using particles, effectively controlling cumulative errors and enhancing the stability and reliability of long‐term predictions. This methodology can lead to extended battery life and increased operational reliability through accurate RUL prediction. The proposed methodology is validated through experiments using NASA and CALCE battery datasets, demonstrating superior prediction accuracy and stability compared to conventional approaches by systematically reducing uncertainties.

NAD+ Promotes Superovulation of Huaxi Cattle Through Regulation of Cumulus Cell Proliferation and Oocyte Maturation.

Superovulation and embryo transfer are key technologies to improve the reproductive ability of female animals and enhance the efficiency of livestock production. However, poor-quality oocytes or abnormal fluctuations of hormone levels caused by superovulation affect the embryonic development environment, which may lead to a significant decline in the number and quality of transferable embryos, thus reducing the efficiency of superovulation. In this study, nicotinamide adenine dinucleotide (NAD+) was injected into Huaxi cows during the superovulation period to observe the proliferation and apoptosis of transplanted embryos. We examined the proliferation, apoptosis, reactive oxygen species (ROS) and mitochondrial membrane potential of cumulus cells and oocytes directly treated with NAD+ and investigated the potential mechanism of NAD+ to improve the superovulation efficiency by serum metabolomics and single-cell RNA sequencing. The results show that the addition of NAD+ significantly increased the quantity and quality of transferable embryos after superovulation. Differential metabolites during estrus synchronization and embryo flushing are enriched in glycerophospholipid metabolic pathways, suggesting that NAD+ can regulate lipid metabolic pathways. We found that NAD+ optimized the secretion levels of the steroid hormone estradiol (E2) and progesterone (P4) during superovulation by regulating the activity of cumulus cells. In oocytes, we found that NAD+ can inhibit apoptosis, scavenge ROS, and enhance mitochondrial function, thereby promoting oocyte maturation and enhancing embryo developmental potential. In conclusion, NAD+ significantly improved the superovulation ability of Huaxi cattle and provides an effective way for animal husbandry to improve the yield of high-quality embryos.