Optimal Window Research Articles

From ACEs to early relational health: Implications for clinical practice.

Children grow and develop in an environment of relationships. Safe, stable, nurturing relationships help build resilience and buffer the negative impact of adverse experiences. Promoting relational health in clinical practice shifts the focus from adverse childhood experiences (ACEs) to positive childhood experiences (PCEs). This approach evaluates a family's strengths and assets, and can be incorporated into both well-child and subspecialty care. While the optimal window for such interventions is in the prenatal period or as early as possible within the first 3 years of life, it is never too late to start. This statement describes how clinicians can bring a relational health approach to any medical encounter by understanding: what toxic stress is and how it can affect the developing brain, family relationships, and child development; how positive relationships, experiences, and behaviours can help buffer such effects and build resilience; observable signs of relational health and risk in parent-child interactions; the attributes of trustful, therapeutic relationships with families; and how to optimize these benefits through conversation and clinical practice.

Radiomic Assessment of Radiation-Induced Alterations of Skeletal Muscle Composition in Head and Neck Squamous Cell Carcinoma within the Currently Clinically Defined Optimal Time Window for Salvage Surgery-A Pilot Study.

Patients with locally advanced head and neck squamous cell carcinoma (HNSCC) frequently require primary radiochemotherapy (RCT). Despite intensity modulation, the desired radiation-induced effects observed in HNSCC may also be observed as side effects in healthy tissue, e.g., the sternocleidomastoid muscle (SCM). These side effects (e.g., tissue fibrosis) depend on the interval between the completion of RCT and restaging CT. For salvage surgery, the optimal time window for surgery is currently clinically postulated at between 6 and 12 weeks after completion of RCT. Thus, no extensive tissue fibrosis is to be expected. This interval is based on clinical studies exploring surgical complications. Studies directly exploring radiation-induced changes of the SCM in HNSCC patients are sparse. The present study quantified tissue alterations in the SCM and paravertebral musculature (PVM) after RCT, applying radiomics to determine the optimal time window for salvage surgery. Three radiomic key parameters, (1) volume, (2) mean positivity of pixels (MPP), and (3) uniformity, were extracted with mint LesionTM in the staging CTs and restaging CTs of 98 HNSCC patients. Of these, 25 were female, the mean age was 62 (±9.6) years, and 80.9% were UICC Stage IV. The mean restaging interval was 55 (±28; range 29-229) days. Only the mean volume significantly decreased after RCT, from 9.0 to 8.4 and 96.5 to 91.9 mL for the SCM and PVM, respectively (both p = 0.007, both Cohen's d = 0.28). In addition, the mean body mass index (BMI) decreased from 23.9 (±4.2) to 21.0 (±3.6) kg/m² (p < 0.001; Cohen's d = 0.9). The mean BMI decreased significantly and was correlated with the volume decrease for the SCM (r = 0.27; p = 0.007) and PVM (r = 0.41; p < 0.001). If t-test p-values were adjusted for the BMI decrease, no significant change in volumes for the SCM and PVM was observed (both p > 0.05). The present data support the clinically postulated optimal interval for salvage surgery of 6 to 12 weeks.

Blood Circulation Assessment by Steadily Fluorescent Near-Infrared-II Aggregation-Induced Emission Nano Contrast Agents.

The dysfunction of the blood circulation system typically induces acute or chronic ischemia in limbs and vital organs, with high disability and mortality. While conventional tomographic imaging modalities have shown good performance in the diagnosis of circulatory diseases, multiple limitations remain for real-time and precise hemodynamic evaluation. Recently, fluorescence imaging in the second region of the near-infrared (NIR-II, 1000-1700 nm) has garnered great attention in monitoring and tracing various biological processes in vivo due to its advantages of high spatial-temporal resolution and real-time feature. Herein, we employed NIR-II imaging to carry out a blood circulation assessment by aggregation-induced emission fluorescent aggregates (AIE nano contrast agent, AIE NPs). Thanks to the longer excited wavelength, enhanced absorptivity, higher brightness in the NIR-II region, and broader optimal imaging window of the AIE NPs, we have realized a multidirectional assessment for blood circulation in mice with a single NIR-II imaging modality. Thus, our work provides a fluorescence contrast agent platform for accurate hemodynamic assessment.

The deep learning-based equipment health monitoring model adopting subject matter expert

ABSTRACT The emergence of Industry 4.0 has led to the development of modern production machines that are usually equipped with advanced sensors to collect data for further analysis. This study proposes a deep learning-based framework to perform equipment health monitoring (EHM) and further broadens its applicability through the integration of subject matter expert (SME) knowledge. A sliding window strategy was adopted to perform EHM in real time. Moreover, an autocorrelation function (ACF) and a partial autocorrelation function (PACF) were employed to determine the optimal window size based on the elbow method. An empirical study was conducted to demonstrate the effectiveness and practicality of the proposed framework. Furthermore, to provide better prediction results, an optimal combination of hyperparameters that minimized the loss function and further validated the window size obtained by the ACF and PACF was determined and used. The results showed that the proposed algorithm outperformed other representative machine learning models. Finally, a general framework was adopted to maintain equipment performance.

Efficient and accurate mapping method of underground metal mines using mobile mining equipment and solid-state lidar

Aiming at the problems of difficult sampling of environmental data, low efficiency, and accuracy of mapping in underground metal mines, this research uses remote control mobile mining equipment, solid-state lidar, and IMU as the hardware system to build an efficient mapping system for underground metal mines.A voxel grid-based point cloud processing method for solid-state lidar is proposed, which can extract planar features and segment ground point clouds more efficiently.An improved point-to-planematching method is proposed, which can improve the matching efficiency and obtain more accurate matching results. The ground point cloud isaddedto the sliding window optimization as a constraint, which overcomes the problem of large vertical error in the process of drawing. Through multiple sets of experiments and comparisons with multiple methods, the results show that the errors, verticaldrifts,and mapping efficiency of the proposed method are the best among the above methods.

Assessment of Carbon Sequestration Capacity of E. ulmoides in Ruyang County and Its Ecological Suitability Zoning Based on Satellite Images of GF-6.

Eucommia ulmoides Oliver. (E. ulmoides) is a species of small tree native to China. It is a valuable medicinal herb that can be used to treat Alzheimer's disease, diabetes, hypertension, and other diseases. In addition, E. ulmoides is a source of rubber. It has both medicinal and ecological value. As ecological problems become increasingly prominent, accurate information on the cultivated area of E. ulmoides is important for understanding the carbon sequestration capacity and ecological suitability zoning of E. ulmoides. In previous tree mapping studies, no studies on the spectral characteristics of E. ulmoides and its remote sensing mapping have been seen. We use Ruyang County, Henan Province, China, as the study area. Firstly, using the 2021 Gao Fen-6 (GF-6) Wide Field of View (WFV) time series images covering the different growth stages of E. ulmoides based on the participation of red-edge bands, several band combination schemes were constructed. The optimal time window to identify E. ulmoides was selected by calculating the separability of E. ulmoides from other land cover types for different schemes. Secondly, a random forest algorithm based on several band combination schemes was investigated to map the E. ulmoides planting areas in Ruyang County. Thirdly, the annual NPP values of E. ulmoides were estimated using an improved Carnegie Ames Stanford Approach (CASA) to a light energy utilization model, which, in turn, was used to assess the carbon sequestration capacity. Finally, the ecologically suitable distribution zone of E. ulmoides under near current and future (2041-2060) climatic conditions was predicted using the MaxEnt model. The results showed that the participation of the red-edge band of the GF-6 data in the classification could effectively improve the recognition accuracy of E. ulmoides, making its overall accuracy reach 96.62%; the high NPP value of E. ulmoides was mainly concentrated in the south of Ruyang County, with a total annual carbon sequestration of 540.104835 t CM-2·a-1. The ecological suitability zone of E. ulmoides can be divided into four classes: unsuitable area, low suitable area, medium suitable area, and high suitable area. The method proposed in this paper applies to the real-time monitoring of E. ulmoides, highlighting its potential ecological value and providing theoretical reference and data support for the reasonable layout of E. ulmoides.

RRWaveNet: A Compact End-to-End Multiscale Residual CNN for Robust PPG Respiratory Rate Estimation

Respiratory rate (RR) is an important biomarker as RR changes can reflect severe medical events such as heart disease, lung disease, and sleep disorders. Unfortunately, standard manual RR counting is prone to human error and cannot be performed continuously. This study proposes a method for continuously estimating RR, RRWaveNet. The method is a compact end-to-end deep learning model which does not require feature engineering and can use low-cost raw photoplethysmography (PPG) as input signal. RRWaveNet was tested subject-independently and compared to baseline in four datasets (BIDMC, CapnoBase, WESAD, and SensAI) and using three window sizes (16, 32, and 64 seconds). RRWaveNet outperformed current state-of-the-art methods with mean absolute errors at optimal window size of 1.66 1 1.01, 1.59 1 1.08, 1.92 1 0.96, and 1.23 1 0.61 breaths per minute for each dataset. In remote monitoring settings, such as in the WESAD and SensAI datasets, we apply transfer learning to improve the performance using two other ICU datasets as pretraining datasets, reducing the MAE by up to 21%. This shows that this model allows accurate and practical estimation of RR on affordable and wearable devices. Our study also shows feasibility of remote RR monitoring in the context of telemedicine and at home.

Optimal selection of time windows for preventive maintenance of offshore wind farms subject to wake losses

AbstractThe maintenance of wind farms is one of the major factors affecting their profitability. During preventive maintenance, the shutdown of wind turbines causes downtime energy losses. The selection of when and which turbines to maintain can significantly impact the overall downtime energy loss. This paper leverages a wind farm power generation model to calculate downtime energy losses during preventive maintenance for an offshore wind farm. Wake effects are considered to accurately evaluate power output under specific wind conditions. In addition to wind speed and direction, the influence of wake effects is an important factor in selecting time windows for maintenance. To minimize the overall downtime energy loss of an offshore wind farm caused by preventive maintenance, a mixed‐integer nonlinear optimization problem is formulated and solved by the genetic algorithm, which can select the optimal maintenance time windows of each turbine. Weather conditions are imposed as constraints to ensure the safety of maintenance personnel and transportation. Using the climatic data of Cape Cod, Massachusetts, the schedule of preventive maintenance is optimized for a simulated utility‐scale offshore wind farm. The optimized schedule not only reduces the annual downtime energy loss by selecting the maintenance dates when wind speed is low but also decreases the overall influence of wake effects within the farm. The portion of downtime energy loss reduced due to consideration of wake effects each year is up to approximately 0.2% of the annual wind farm energy generation across the case studies—with other stated opportunities for further profitability improvements.

Evaluation of hearing levels and vestibular function and the impact on cognitive performance in (pre)-symptomatic patients with DFNA9: protocol for a prospective longitudinal study (Rosetta study)

IntroductionUntreated hearing loss is the largest potentially modifiable risk factor for dementia. Additionally, vestibular dysfunction has been put forward as a potential risk factor for accelerated cognitive decline. Patients with...

Clocking in or Not? Optimal Design of a Novel Gamified Business Model in Online Learning

Clocking-in cash-back (CIC), an emerging gamified business model in online learning, has recently garnered significant attention. CIC allows users to secure a full refund of the course fee through consecutive completion of specific tasks within a required time window. These tasks, known as clocking in, encompass activities such as daily assignments and sharing progress updates on social media. By employing this gamification system, the firm effectively monitors user efforts, categorizing them as winners or quitters based on clocking-in completion. In this paper, we examine how a firm should set the optimal time window for its course and how the time window is affected by context-specific factors. We identify two opposing effects associated with extending the time window on users’ quitting time: the psychological disutility increasing effect (negative) and the effort cost decreasing effect (positive). Our results indicate that, as quitters’ positive word-of-mouth effects increase, there are cases in which the firm should opt for shortening the time window. Additionally, we find that, as the marginal content creation cost rises, the firm may find it more advantageous to raise the difficulty level by shortening the time window. Our findings provide valuable insights that online learning firms can utilize to enhance their design of the CIC mechanism.

EEG cortical network reveals the temporo-spatial mechanism of visual search

This study aims to explore a method based on brain networks for implicit attention by using wavelet coherence as feature to identify individual targets in the visual field, find the optimal classification rhythm and time window, and investigate the relationship between the optimal rhythm and N2pc event-related potential. The study uses a weighted minimum norm estimate to locate the sources of the scalp EEG and reconstructs the source time series. The functional connectivity between brain areas during the visual search process is evaluated using wavelet coherence analysis, and a lateral difference network is constructed based on the difference in coherence values between the left and right visual fields. A support vector machine classifier is trained based on the wavelet coherence network features to identify the target in the left or right visual field. We also extract N2pc from the source activity data of the parieto-occipital brain region and record the time period in which N2pc occurred. The study finds that the best classification performance is achieved in the theta rhythm from 200 to 400 ms and achieved an average classification accuracy of 87% (chance level: 51.07%) in a serial search task. And this time window corresponds to the time period when N2pc appeared. The results show that the use of wavelet coherence analysis to evaluate the functional connectivity between brain areas during the visual search process provides a new approach for analyzing brain activity. The study's findings regarding the relationship between the N2pc and theta rhythm and the effectiveness of using wavelet coherence network features based on the theta rhythm for visual search classification contribute to the understanding of the neural mechanisms underlying visual search.

Energy efficiency, visual comfort, and thermal comfort of suspended particle device smart windows in a residential building: A full-scale experimental study

Smart window controls the visible light transmittance and solar heat gain coefficient through the transition to a clear or tint state. When applied in buildings, it can be an effective solution in terms of visual and thermal environment by controlling solar radiation. Various previous studies have provided smart windows’ effective performance results related to the visual or thermal environment, however, these studies were mostly based on numerical simulations or small-scale test cells. Few studies have comprehensively analyzed smart windows’ performance when applied to real buildings. Accordingly, this study aimed to analyze and evaluate the performance of smart windows applied in residential buildings related to the visual and thermal environments from various perspectives. For this purpose, we installed suspended particle device smart windows in an apartment building, i.e., a typical residential building type in South Korea, set up related sensors and equipment and collected data from July to September 2022. Based on this campaign, we evaluated visual and thermal comfort and lighting and cooling energy performance according to the sky condition (cloudy, partly cloudy, and clear days) and smart window tinting control method (always tinting, automatic control to maintain the indoor daylight illuminance at 200 lx, and automatic control to maintain the illuminance at 400 lx). In terms of glare, on partly cloudy and clear days, smart windows effectively controlled glare regardless of the tinting control method. In terms of the indoor daylight illuminance, automated control was more effective than always tinting in terms of providing useful daylight under all sky conditions. In terms of thermal comfort, tinting at all times was the most effective on partly cloudy and clear days. The lighting energy consumption increased by 3.9–17.9% when smart windows were used, while the cooling energy consumption was reduced by 0.0–20.5%. Overall, this study revealed that the appropriate smart window operation method varied depending on the sky condition. On cloudy day, maintenance of the indoor daylight illuminance at 200 lx using automated control was efficient. On partly cloudy and clear day, the automated control (200 lx or 400 lx) was efficient in terms of visual comfort, however, in terms of thermal comfort and energy consumption, tinting at all times was effective. This suggested that completely tinting smart window in conjunction with automatic artificial lighting control to ensure proper indoor illuminance could be the most effective on a partly cloudy day and a clear day. Based on this study, it is necessary to derive more detailed optimal smart window operation strategies in terms of glare, indoor daylight illuminance, thermal comfort, and energy consumption for all seasons.

Research on the Stability of Grid Connected Wind Turbine Combined with Energy Storage Power System Based on a New Wind Power Fluctuation Smoothing Strategy

Wind power equipped with an energy storage system (ESS) has been demonstrated as the best potential configuration for a rapid global energy transition in the future. In the traditional anti-pulse average filtering control strategy for controlling wind power fluctuations, the smoothing window interval is a constant value, when the forecast output power error is considerable, the fixed window interval width may not be able to meet the smoothing demand, so the traditional strategy has the problems of bad smoothing effect and high probability for power fluctuations exceeding the limit. We propose an optimization strategy for energy storage operation in this paper. Firstly, this strategy can dynamically adjust the window interval width according to the power prediction error, and find the optimal window interval width that makes the output power volatility satisfy requirements for grid connection, so as to achieve the effect of improving the smoothing effect. Secondly, by optimizing hydrogen storage systems operation to reduce the demand for storage system capacity, the odds of output power volatility exceeding the limits are reduced. The outcomes indicate that this new smoothing strategy is effective in improving the stability of the wind power grid connection.

GRU based optimal restricted access window mechanism for enhancing the performance of IEEE 802.11ah based IoT networks

GRU based optimal restricted access window mechanism for enhancing the performance of IEEE 802.11ah based IoT networks

Surgical reconstruction of arm and hand functions in tetraplegia : Current concepts

The reconstruction of arm and hand functions is of enormous importance for tetraplegic patients as it enables at least some degree of independence. Depending on the level of the spinal cord injury, certain residual functions are present in the arms which can be used for surgical reconstruction of upper extremity functions. By utilizing tendon and nerve transfers missing functions can at least be partially reconstructed. Tendon transfers are aproven technique with reliable results that can be performed at any time regardless of the type of accident. Due to the frequent presence of lower motor neuron damage, it is essential to consider the optimal time window for nerve transfer interventions. From the multitude of surgical options, an individual reconstruction plan must be created for each patient, which considers multiple factors. The combination of nerve transfers and later completing the functional reconstruction by tendon transfers is the preferred concept of the authors of this article.

Electrochemical condition optimization and techno-economic analysis on the direct CO2 electroreduction of flue gas

Direct CO2 electroreduction of flue gas is a technology that simplifies CO2 capture and purification process and directly utilizes CO2 in flue gas. Low CO2 reduction reaction selectivity caused by the low CO2 partial pressure and side reactions competition is the challenge. In this study, a direct CO2 electroreduction system for multicomponent flue gases was established. A two-dimensional steady model was developed by considering major electrode reactions of flue gas in CuxO-based catalysts. General influence criteria and optimum operation window for the potential, temperature, and pressure were proposed. In addition, a techno-economic analysis of the flue gas electrolysis system was conducted. The results demonstrated that the CO2 electroreduction reaction performances are weak at atmospheric pressure owing to the side reaction competition and small reactant concentration. A method involving pressurization and catholyte inlet temperature declination is proposed to break the stalemate of tiny reactant concentrations and ensure the dominance of the CO2 electroreduction reaction. The selectivity and current density of the CO2 reduction reaction were 71% and − 148 mA/cm2 at −1.17 V vs. reversible hydrogen electrode potential (RHE), respectively, under the conditions of an inlet electrolyte temperature of 273 K and an operating pressure of 20 atm. C1 products with excellent yields are the dominant products. Furthermore, the techno-economic analysis showed that the flue gas electrolysis system has higher profitability and lower cost than the purified CO2 electrolysis system. The present results can be used to optimize the electrolyzer operating parameters and construct CO2 reduction systems.

Acute Effects of Back Squat Combined with Different Elastic Band Resistance on Vertical Jump Performance in Collegiate Basketball Players.

The purpose of this study was to compare the acute effects of back squat exercise with or without elastic band on countermovement jump performance. Thirteen collegiate male basketball players (age: 20.5 ± 0.9 years; height: 188.5 ± 8.5 cm; body mass: 82.8 ± 12.9 kg) completed 5 familiarization and 4 experimental sessions separated by at least 48 hours. In the experimental sessions, the order of the conditions was randomized so that the participants performed 1 set of 3 repetitions of barbell back squat at 85% of their one-repetition maximum (1-RM), 1 set of 3 repetitions of back squat at 85% 1-RM with 20% variable resistance training (VRT), 30%VRT, or 40%VRT of the total load coming from the elastic band. Countermovement jump performance was assessed before (baseline), 30 seconds, 3 minutes, 6 minutes, and 9 minutes following each condition. Jump height, rate of force development, peak power, and vastus lateralis, vastus medialis, and medial gastrocnemius electromyography data were collected. Compared with the baseline, 30%VRT significantly improved jump height at 3 minutes post-exercise by 1.3 cm (P < 0.001) and 6 minutes post-exercise by 1.2 cm (P = 0.005); 40%VRT significantly improved jump height from 30 seconds up to the 9th minute (1.2 to 1.9 cm, P ≤ 0.036). The superior jump height was also accompanied by improved kinetic and electromyography data. No significant changes were observed in the barbell back squat and 20%VRT conditions. In conclusion, back squat at 85% 1-RM with 40% elastic band resistance led to superior vertical jump performance with an optimal time window of 3 minutes.

Acute Effects of Back Squat Combined with Different Elastic Band Resistance on Vertical Jump Performance in Collegiate Basketball Players

The purpose of this study was to compare the acute effects of back squat exercise with or without elastic band on countermovement jump performance. Thirteen collegiate male basketball players (age: 20.5 ± 0.9 years; height: 188.5 ± 8.5 cm; body mass: 82.8 ± 12.9 kg) completed 5 familiarization and 4 experimental sessions separated by at least 48 hours. In the experimental sessions, the order of the conditions was randomized so that the participants performed 1 set of 3 repetitions of barbell back squat at 85% of their one-repetition maximum (1-RM), 1 set of 3 repetitions of back squat at 85% 1-RM with 20% variable resistance training (VRT), 30%VRT, or 40%VRT of the total load coming from the elastic band. Countermovement jump performance was assessed before (baseline), 30 seconds, 3 minutes, 6 minutes, and 9 minutes following each condition. Jump height, rate of force development, peak power, and vastus lateralis, vastus medialis, and medial gastrocnemius electromyography data were collected. Compared with the baseline, 30%VRT significantly improved jump height at 3 minutes post-exercise by 1.3 cm (P &lt; 0.001) and 6 minutes post-exercise by 1.2 cm (P = 0.005); 40%VRT significantly improved jump height from 30 seconds up to the 9th minute (1.2 to 1.9 cm, P ≤ 0.036). The superior jump height was also accompanied by improved kinetic and electromyography data. No significant changes were observed in the barbell back squat and 20%VRT conditions. In conclusion, back squat at 85% 1-RM with 40% elastic band resistance led to superior vertical jump performance with an optimal time window of 3 minutes.

Transitional Dynamics of Sarcopenia and Associations of Nutritional Indices with State Transitions in Chinese aged ≥ 50.

Sarcopenia's temporal profile can be regarded as a dynamic process with distinct states, in which malnutrition plays an important role. This study aimed to address two research gaps: sarcopenia's transitional dynamics and associations of nutritional indices with state transitions in community-dwelling Chinese adults aged 50 and older. A prospective population-based cohort study. Community-based setting in western China. The analytic sample included data from 1910 participants aged ≥ 50 in the West China Health and Aging Trend study between 2018-2022. We defined three states: the initial normal state (normal muscle strength, physical performance and muscle mass), the worst sarcopenia state (low muscle mass plus low muscle strength and/or low physical performance) and the intermediate subclinical state (the other scenarios). The relevant measurement methods and cut-off points were based on the 2019 AWGS consensus. Using a continuous-time multistate Markov model, we calculated probabilities of transitions between different states over 1, 2 and 4 years; we also examined associations between nutritional indices and transitions, including body mass index (BMI), calf circumference (CC), mid-arm circumference (MAC), triceps skinfold thickness (TST), albumin (ALB), geriatric nutrition risk index (GNRI), vitamin D (VitD) and prealbumin (PA). For individuals in the normal state, their probabilities of remaining stable versus progressing to a subclinical state were 53.4% versus 42.1% at 2 years, and 40.6% versus 49.0% at 4 years. In the subclinical population, their 2- and 4-year chances were 60.2% and 51.2% for maintaining this state, 11.8% and 16.2% for developing sarcopenia, 28.0% and 32.6% for reverting to normal. For sarcopenic individuals, the likelihood of staying stable versus retrogressing to the subclinical state were 67.0% versus 26.3% at 2 years, and 48.3% versus 36.3% at 4 years. Increased BMI, CC, MAC, TST, ALB, GNRI and PA correlated with reversion from the subclinical state, among which increased TST, ALB and PA were also paralleled with reversion from sarcopenia, while decreased BMI, CC, MAC, TST and GNRI were associated with progression to sarcopenia. VitD was not significantly associated with any transitions. This study reveals how sarcopenia changes over time in a Chinese population. It also highlights the usefulness of simple and cost-effective nutritional status indices for indicating state transitions, which can help identify individuals at risk of sarcopenia and guide targeted interventions within the optimal time window.

A 5-year retrospective study of amisulpride steady-state plasma concentration in patients with schizophrenia in real-life settings based on therapeutic drug monitoring data.

Here, we present a retrospective analysis of the last 5 years' data collected in real-life settings as direct evidence to evaluate the optimal therapeutic window of amisulpride (AMI) for psychiatric patients. Retrospective analysis of the therapeutic drug monitoring (TDM) results of AMI in outpatients and inpatients were obtained from the Xi'an Mental Health Center from 2017 to 2021. The interquartile (P25-P75) AMI concentrations ranged 212.20-574.25ng/mL. The results showed that the proportion of outpatients who received TDM once was significantly higher than that of inpatients who received TDM once (P<0.001), whereas the reverse was true for those who experienced TDM more than twice (P<0.001). Higher estimated plasma concentrations were identified in inpatients, female patients, and patients over 59 years of age. Nearly 57.21% of the samples had high concentrations (>320ng/mL). The optimal therapeutic reference range for AMI may require reconstruction to guide the use of AMI for the treatment of schizophrenia.