What Contributes to Student Language Learning Satisfaction and Achievement with Learning Management Systems?

Learning management systems (LMSs) have received substantial global attention and have undergone extensive research, with most discussions focusing on users' acceptance and continuation of LMS use in the higher education sector. However, research is scarce in terms of identifying the factors that are advantageous to K-12 students' learning and satisfaction when using LMSs for language learning. This study aims to examine the impacts of internal and contextual factors on secondary students' learning satisfaction and English achievement when using LMSs. Data were collected from 289 students through an online survey. The results of the structural equation modeling showed that satisfaction had the most significant impact on English achievement. Furthermore, both internal and contextual factors, including technology self-efficacy, interest, task value, teacher support, and technology facilitation, positively impacted learning satisfaction with LMSs. In addition, teacher support exerted the strongest impact on satisfaction, followed by interest and technology self-efficacy. However, only internal factors, such as interest and task value, were positively associated with English achievement. Neither teacher support nor technology facilitation significantly impacted English performance. Given the increasing availability of LMS usage, the findings of this study can facilitate the more effective implementation of LMSs in China and globally. The study contributes to the theory and practice of LMSs use in K-12 English education. The limitations and implications of the study were discussed as well.

The use of energy management ISO 50001 to increase the effectiveness of water treatment plants: An application study on the Zai water treatment plant.

This study sought to determine the impact of implementing the energy management system ISO 50001 on the Zai Water Treatment Plant's energy efficiency performance and demonstrate how this implementation affected the cost and rate of energy consumption. The proposed study model contained three dependent variables-energy consumption, energy efficiency, and the cost of energy consumption. It also contained an independent variable-the energy management system ISO 50001. All these variables were used to develop various questions to help accomplish the study's goals. Planning was done by selecting pumping stations, selecting the most energy-consuming type of pump, and finally, choosing a pump maintenance project to improve energy performance. The researcher used the case of the Zai water pumping station as an example where the ISO 50001 energy management system was applied along with the stages of the Deming Cycle of management. Four pumping units from the Zai water pumping station served as the research sample for the study. •Find the impact of implementing the ISO 50001 energy management system on the energy efficiency performance of the Zai water treatment plant.•The effects of implementing ISO 50001 energy management system on cost and energy consumption at the Zai water treatment plant.•What effect does the ISO 50001 energy management system affect the Zai Water Treatment Plant's energy efficiency? After applying the ISO 50001 energy management system, several conclusions were drawn. Energy costs and consumption rates in the pumping units dropped while the energy efficiency in the chosen pumping units increased.

Development of dual polarization battery model with high accuracy for a lithium-ion battery cell under dynamic driving cycle conditions.

Lithium-ion batteries are a key technology for electric vehicles. They are suitable for use in electric vehicles as they provide long range and long life. However, Lithium-ion batteries need to be controlled by a Battery Management System (BMS) to operate safely and efficiently. The BMS continuously controls parameters, such as current, voltage, temperature, state of charge (SoC), and state of health (SoH), and protects the battery against overcharging and discharging, imbalances between cells, and thermal runaways. The battery models and several prediction algorithms that the BMS uses to carry out these checks are essential to the system's performance. This research assesses the Dual Polarization (DP) model's ability to mimic actual battery performance in different dynamic driving conditions. In the study, a battery model for a Lithium-Nickel-Manganese-Cobalt-Oxide (Li-NMC) cell with a nominal capacity of 2 Ah is developed. A DP model was used in the study. Modeling and parameter estimation were performed in MATLAB Simulink/Simscape. Firstly, the model parameters are estimated depending on the SoC using the current and voltage data obtained from the Hybrid Pulse Power Characterization (HPPC) test. A further validation study of the model for low dynamic and high dynamic driving cycles is then presented. Dynamic Stress Test (DST), the US06 Supplemental Federal Test Procedure (SFTP) and Worldwide harmonized Light vehicles Test Procedure (WLTP) cycles were used for model validation. As a result of the study, the model's Root Mean Square (RMS) error values were obtained as 0.0053 V for DST, 0.0059 V for US06, and 0.008 V for WLTP. The obtained model is particularly successful for simulating a battery under dynamic current conditions and for use in control and prediction algorithms.

Deep Reinforcement Learning-Empowered Cost-Effective Federated Video Surveillance Management Framework.

Video surveillance systems are integral to bolstering safety and security across multiple settings. With the advent of deep learning (DL), a specialization within machine learning (ML), these systems have been significantly augmented to facilitate DL-based video surveillance services with notable precision. Nevertheless, DL-based video surveillance services, which necessitate the tracking of object movement and motion tracking (e.g., to identify unusual object behaviors), can demand a significant portion of computational and memory resources. This includes utilizing GPU computing power for model inference and allocating GPU memory for model loading. To tackle the computational demands inherent in DL-based video surveillance, this study introduces a novel video surveillance management system designed to optimize operational efficiency. At its core, the system is built on a two-tiered edge computing architecture (i.e., client and server through socket transmission). In this architecture, the primary edge (i.e., client side) handles the initial processing tasks, such as object detection, and is connected via a Universal Serial Bus (USB) cable to the Closed-Circuit Television (CCTV) camera, directly at the source of the video feed. This immediate processing reduces the latency of data transfer by detecting objects in real time. Meanwhile, the secondary edge (i.e., server side) plays a vital role by hosting a dynamically controlling threshold module targeted at releasing DL-based models, reducing needless GPU usage. This module is a novel addition that dynamically adjusts the threshold time value required to release DL models. By dynamically optimizing this threshold, the system can effectively manage GPU usage, ensuring resources are allocated efficiently. Moreover, we utilize federated learning (FL) to streamline the training of a Long Short-Term Memory (LSTM) network for predicting imminent object appearances by amalgamating data from diverse camera sources while ensuring data privacy and optimized resource allocation. Furthermore, in contrast to the static threshold values or moving average techniques used in previous approaches for the controlling threshold module, we employ a Deep Q-Network (DQN) methodology to manage threshold values dynamically. This approach efficiently balances the trade-off between GPU memory conservation and the reloading latency of the DL model, which is enabled by incorporating LSTM-derived predictions as inputs to determine the optimal timing for releasing the DL model. The results highlight the potential of our approach to significantly improve the efficiency and effective usage of computational resources in video surveillance systems, opening the door to enhanced security in various domains.

An Active Thermography approach for materials characterisation of thermal management systems for Lithium-ion batteries.

The aim of this work is an alternative non destructive technique for estimating the thermal properties of four different Thermal Management System (TMS) materials. More in detail, a thermographic setup realized with the Active Thermography approach (AT) is utilized for the purpose and the data elaboration follows the ISO 18755 Standard. As well known, Phase Changes Materials (PCMs) represent an innovative solution in the Thermal Management System (TMS) of Lithium-Ion batteries and, during the years, many solutions were developed to improve its thermal properties. As a matter of fact, parameters such as the internal temperature or heat exchanges impact on both efficiency and safety of the whole battery system. Consequently, the thermal conductivity was often chosen as a performance indicator of Thermal Management System (TMS) materials. In this work, both thermal diffusivity and thermal conductivity were estimated in two different testing conditions, respectively at room temperature and higher temperature conditions. The Active Thermography (AT) technique proposed in this activity has satisfactory estimated both thermal diffusivity and thermal conductivity of Thermal Management System (TMS) materials. An analytical model was also developed to reproduce the temperature experimental profiles. Finally, results obtained with AT approach were compared to those available from commercial datasheet and literature.

Dynamics of Carbon and Soil Enzyme Activities under Arabica Coffee Intercropped with Brachiaria decumbens in the Brazilian Cerrado.

The change in land use in the Brazilian Cerrado modifies the dynamics of soil organic matter (SOM) and, consequently, carbon (C) stocks and their fractions and soil enzyme activities. This study evaluated the effect of brachiaria (Brachiaria decumbens Stapf.) intercropped with Arabica coffee (Coffea arabica L.) on the stock and fractions of soil carbon and enzyme activities. The experiment was arranged in a completely randomized block design with three replications and treatments in a factorial design. The first factor consisted of coffee with or without intercropped brachiaria, the second of Arabica coffee cultivars ('I.P.R.103' and 'I.P.R.99') and the third factor of the point of soil sampling (under the canopy (UC) and in inter-rows (I)). Soil was sampled in layers of 0-10, 10-20, 20-30, 30-40, 40-60 and 60-80 cm. Soil from the 0-10 cm layer was also used to analyze enzymatic activity. Significant effects of coffee intercropped with brachiaria were confirmed for particulate organic carbon (POC), with highest contents in the 0-10 and 20-30 cm layers (9.62 and 6.48 g kg-1, respectively), and for soil enzymes (280.83 and 180.3 µg p-nitrophenol g-1 for arylsulfatase and ß-glucosidase, respectively).

Variability of the treated biomedical waste disposal behaviours during the COVID lockdowns.

Literature review suggests that studies on biomedical waste generation and disposal behaviors in North America are limited. Given the infectious nature of the materials, effective biomedical waste management is vital to the public health and safety of the residents. This study explicitly examines seasonal variations of treated biomedical waste (TBMW) disposal rates in the City of Regina, Canada, from 2013 to 2022. Immediately before the onset of COVID-19, the City exhibited a steady pattern of TBMW disposal rate at about 6.6 kg∙capita-1∙year-1. However, the COVID-19 pandemic and its associated lockdowns brought about an abrupt and persistent decline in TBMW disposal rates. Inconsistent fluctuations in both magnitude and variability of the monthly TBMW load weights were also observed. The TBMW load weight became particularly variable in 2020, with an interquartile range 4 times higher than 2019. The average TBMW load weight was also the lowest (5.1 tonnes∙month-1∙truckload-1) in 2020, possibly due to an overall decline in non-COVID-19 medical emergencies, cancellation of elective surgeries, and availability of telehealth options to residents. In general, the TBMW disposal rates peaked during the summer and fall seasons. The day-to-day TBMW disposal contribution patterns between the pre-pandemic and post-pandemic are similar, with 97.5% of total TBMW being disposed of on fixed days. Results from this Canadian case study indicate that there were observable temporal changes in TBMW disposal behaviors during and after the COVID-19 lockdowns.

Using Surrogate Parameters to Enhance Monitoring of Community Wastewater Management System Performance for Sustainable Operations.

Community wastewater management systems (CWMS) are small-scale wastewater treatment systems typically in regional and rural areas with less sophisticated treatment processes and often managed by local governments or communities. Research and industrial applications have demonstrated that online UV-Vis sensors have great potential for improving wastewater monitoring and treatment processes. Existing studies on the development of surrogate parameters with models from spectral data for wastewater were largely limited to lab-based. In contrast, industrial applications of these sensors have primarily targeted large wastewater treatment plants (WWTPs), leaving a gap in research for small-scale WWTPs. This paper demonstrates the suitability of using a field-based online UV-Vis sensor combined with advanced data analytics for CWMSs as an early warning for process upset to support sustainable operations. An industry case study is provided to demonstrate the development of surrogate monitoring parameters for total suspended solids (TSSs) and chemical oxygen demand (COD) using the UV-Vis spectral data from an online UV-Vis sensor. Absorbances at a wavelength of 625 nm (UV625) and absorbances at a wavelength of 265 nm (UV265) were identified as surrogate parameters to measure TSSs and COD, respectively. This study contributes to the improvement of WWTP performance with a continuous monitoring system by developing a process monitoring framework and optimization strategy.

Eco-management of end-of-life tires: Advances and challenges for the Ecuadorian case.

The aim of this research is to analyse the performance of the extended producer responsibility model for the management of end-of-life tires (ELTs) in Ecuador that has been implemented since 2013. For this research, we conducted case studies of, and subsequently, a comparative analysis between, the ELT management system in Ecuador with respect to the ELT management models in Colombia and Brazil. Our findings show that although the programme implementation represented a significant advance in Ecuador's waste management system there are important challenges that should be considered in future adaptations of the programme. Among the measures that can be adopted to improve the ELT management system are the consolidation and stimulation of the market for products made from ELT waste; promotion of other productive sectors linked to the creation of new products and sectors that generate complementary products; enhancement of the generation, socialization and access to knowledge of the waste by-products for micro-, small- and medium-sized enterprises; increase the tire consumer fee, known as 'Ecovalor' and improvement of the quality and availability of information and indicators regarding ELT management. In this sense, the experiences of Colombia and Brazil show important lessons for the Ecuadorian case.

Optimal control of cooling management system for energy conservation in smart home with ANNs-PSO data analytics microservice platform.

An intelligent cooling management system with a smart home application is proposed to evaluate optimal target temperatures and air conditioner fan modes, thereby maximizing energy efficiency while ensuring residents' comfort. The proposed system integrates a home energy management system with a sophisticated backend infrastructure designed to enable seamless hardware connectivity for real-time data acquisition from various sensors, a gateway, internet of things (IoT) devices, and servers. Furthermore, it serves as a platform for implementing a software data analytics model, structured upon a microservice architecture, aimed at providing optimal feedback control. The data analytics platform utilized in this research integrates two sets of artificial neural networks (ANNs) and a particle swarm optimization (PSO) algorithm for computation and control. This platform is designed not only to gather real-time ambient data and air conditioner usage records but also to regulate the air conditioner's operation autonomously. By considering aprevailing ambient air condition, the ANNs accurately predict power consumption, indoor temperature, and indoor humidity following adjustments in target temperature and fan mode. The PSO-based data analytics model efficiently selects the most suitable target temperature and fan mode, thereby achieving a dual purpose of enhancing energy conservation while minimizing potential occupant discomfort. This optimization is driven by utilizing the predicted mean vote (PMV) calculated through the analysis performed by the ANNs. Validation of the developed intelligent cooling management system was conducted in a real smart home environment inside a single detached two-story house, using an 8,000 BTU air conditioner as the testbed within an 8 × 5 m2 space accommodating four occupants. The implementation results indicate that the proposed intelligent cooling management system can reliably predict the behavior and ambient data of the air conditioner and give the best-operating settings in any different environment scenarios and therefore shows potential for energy savings in smart home applications.

Normalizing Anti-Thrombin III for heparin management during routine cardiopulmonary bypass for congenital cardiothoracic surgery: A single institution practice review.

INTRODUCTION: Over the past decade, there has been an increase in the use of recombinant Anti-Thrombin III (AT-III) administration during neonatal and pediatric short- and long-term mechanical support for the replacement of acquired deficiencies. Recombinant AT-III (Thrombate) administration is an FDA licensed drug indicated primarily for patients with hereditary deficiency to treat and prevent thromboembolism and secondarily to prevent peri-operative and peri-partum thromboembolism. Herein we propose further use of Thrombate for primary AT-III deficiency of the newborn as well as for acquired dilution and consumption secondary to cardiopulmonary bypass (CPB). METHODOLOGY: All patients undergoing CPB obtain a preoperative AT-III level. Patients with identified deficiencies are normalized in the OR using recombinant AT-III as a patient load, in the CPB prime, or both. Patient baseline Heparin Dose Response (HDR) is assessed using the Heparin Management System (HMS) before being exposed to AT-III. If a patient load of AT-III is given, a second HDR is obtained and this AT-III Corrected HDR is used as the primary goal during CPB. Once CPB is initiated, an AT-III level is obtained with the first patient blood analysis. A subtherapeutic level results in an additional dose of AT-III. During the rewarm period, a final AT-III level is obtained and AT-III treated once again if subtherapeutic. A retrospective, matched analysis review of practice analyzing two groups, a Study Group (Repeat HDR, May 2022 onward) and Matched Group (Without Repeat HDR, July 2019 to April 2022), for age (D), weight (Kg) and operation was conducted. The focus of the study was to determine any change in heparin sensitivity identified post AT-III patient bolus load in the HDR (U/mL), Slope (U/mL/s), ACT (s), and total amount of heparin on CPB (U) and protamine (mg) used in each group. RESULTS: No significance was seen in Baseline AT-III (%), post heparin load HDR (U/mL), first CPB ACT (s), first CPB HDR (U/mL), or total CPB heparin (u/Kg) between the two groups. Statistical significance was seen in Baseline ACT (s), Baseline HDR (U/mL), Baseline Slope (U/mL/s), Post Heparin Load ACT (s), first CPB AT-III (%), and Protamine (mg/Kg) (p < .05). No statistical significance was seen in the Study Intragroup between pre versus post AT-III patient load baseline sample in ACT (s), however significance was seen in HDR (U/mL) and Slope (U/mL/s) (p < .05). CONCLUSION: Implementation of AT-III monitoring and therapy before and during CPB in conjunction with the HMS allows patients to maintain a steady state of anticoagulation with overall less need for excessive heparin replacement and potentially thrombin activation. The result is obtaining a steady state of anticoagulation, a reduced fluctuation in the heparin and ACT levels and a potential for lower co-morbidities associated with prolonged CPB times.

Optimization of hybrid renewable-diesel power plants considering operational cost, battery degradation, and emissions.

Renewable energy sources have immense potential for enhancing environmental sustainability; however, addressing their intermittency and irregularity is vital for optimizing economic benefits within microgrids. Integrating renewable energy systems with energy storage presents a promising solution. This study introduces an innovative energy management system designed for hybrid renewable power stations, incorporating battery energy storage systems and diesel generators. By accounting for battery degradation costs associated with charge depth and lifespan, the study transforms long-term battery expenses into real-time operational costs. The optimization problem is formulated as a mixed integer linear programming framework with the objectives of minimizing operating costs, battery degradation costs, and pollutant gas emissions. Through diverse case studies reflecting various market profiles. The proposed approach demonstrates reductions in overall system costs.

Organic farming promotes the abundance of fungi keystone taxa in bacteria-fungi interkingdom networks.

Soil bacteria-fungi interactions are essential in the biogeochemical cycles of several nutrients, making these microbes major players in agroecosystems. While the impact of the farming system on microbial community composition has been extensively reported in the literature, whether sustainable farming approaches can promote associations between bacteria and fungi is still unclear. To study this, we employed 16S, ITS, and 18S DNA sequencing to uncover how microbial interactions were affected by conventional and organic farming systems on maize crops. The Bray-Curtis index revealed that bacterial, fungal, and arbuscular mycorrhizal fungi communities were significantly different between the two farming systems. Several taxa known to thrive in healthy soils, such as Nitrosophaerales, Orbiliales, and Glomus were more abundant in the organic farming system. Constrained ordination revealed that the organic farming system microbial community was significantly correlated with the ß-glucosidase activity, whereas the conventional farming system microbial community significantly correlated with soil pH. Both conventional and organic co-occurrence interkingdom networks exhibited a parallel node count, however, the former had a higher number of edges, thus being denser than the latter. Despite the similar amount of fungal nodes in the co-occurrence networks, the organic farming system co-occurrence network exhibited more than 3-fold the proportion of fungal taxa as keystone nodes than the conventional co-occurrence network. The genera Bionectria, Cercophora, Geastrum, Penicillium, Preussia, Metarhizium, Myceliophthora, and Rhizophlyctis were among the fungal keystone nodes of the organic farming system network. Altogether, our results uncover that beyond differences in microbial community composition between the two farming systems, fungal keystone nodes are far more relevant in the organic farming system, thus suggesting that bacteria-fungi interactions are more frequent in organic farming systems, promoting a more functional microbial community.

ASEBIR Quality Special Interest Group guidance for quality in assisted reproduction technology.

Assisted human reproduction has undergone rapid advances since its inception 45 years ago. To keep pace with these advances, assisted reproduction laboratories should adhere to a quality management system that addresses staffing and training, physical space and air quality, equipment maintenance and other operational matters, and ensures gamete and embryo handling in accordance with the latest quality and safety standards. Accordingly, this review aims to provide a reference document that highlights the critical aspects to consider when establishing and operating an ART laboratory. The review collates and expands upon published national and international guidelines and consensus documents, providing easier access to this large body of important information.

A comprehensive review of recent developments in smart grid through renewable energy resources integration.

Energy generation and management are relevant for both utilities and electricity users, and they can be improved by incorporating sophisticated technology on smart grid. This opens up the possibility of transforming existing industries into a new era of enhanced networks that will give an intelligent, responsive, and bi-directional automatic management system for power generation, transmission, and distribution. The traditional grid is transitioning from a centralized generation structure to a more dispersed smart grid structure. In order to build this new decentralized structure, loads must be fully integrated into the grid and adequately separated from the main grid. The smart grid allows for the integration of loads that are clean, cost-effective, and efficient. The growing level of distributed generation (DG) integration puts the grid under strain, resulting in perturbations with dynamic responses. This paper discussed a detailed review of current developments in smart grid through the integration of renewable energy resources (RERs) into the grid. The purpose of this study is to present a comprehensive, up-to-date review of RERs integration on grid to evaluate research directions, progress, challenges, and potential solutions. It focuses on the concepts and structure of smart grids, followed by an in-depth examination of smart grid overview, energy sources, inertia issues, and applications. This evaluation will assist other researchers investigating smart grid energy resources in identifying research problems and gaps.

Maturity of Food Safety Management Systems in the Vietnamese Seafood Processing Industry.

Fifty-nine percent (59%) of the reported food safety issues in Vietnam are related to seafood products, mainly fish and fish products. The international export of seafood products continues to grow due to intensification of the production in the Vietnamese seafood processing industry. To ensure the production of safe food, a company-specific, effective food safety management system is essential. This research explores the maturity of food safety management systems in a convenience sample of the Vietnamese seafood processing industry to identify potential gaps and interventions for improvement. The food safety management system diagnostic instrument was used to assess the context riskiness, maturity of control and assurance activities and food safety performance of 11 companies. Maturity of their food safety management systems was further explored through hierarchical cluster analysis, and the differences in maturity between clusters were statistically tested through Mann-Whitney U tests (nonparametric). The influence of companies' organizational characteristics on the maturity of control and assurance activities was assessed through nonparametric K independent tests. A variability in the maturity of food safety management systems between the eleven Vietnamese companies was measured. Cluster analysis revealed two clusters, Cluster I (six companies) and Cluster II (five companies). The companies in both these clusters operate under a moderate level context riskiness and average to advanced level of food safety performance. However, control and assurance activities are at a lower maturity in Cluster I compared to Cluster II. None of the companies' organizational characteristics (i.e. certification level) have a statistically significant influence on the maturity of control and assurance activities. However, compliance with multiple food safety standards and the presence of physical intervention system(s) have a positive influence on food safety performance.

[Exploration of Application of Essential Principles of Safety and Performance of Medical Devices and IVD Medical Devices: Take Artificial Joint Products as Example].

Through the effective application of Essential Principles of Safety and Performance of Medical Devices and IVD Medical Devices (EP), to continuously improve the corresponding management tools to ensure the safety and effectiveness of medical device in the quality management system, risk management system, evaluation of safety and effectiveness for the supervision departments and manufacturers. The current status of the application of EP and the application issues are analyzed in the study. Take artificial joint products for example, the idea of using EP in quality management system, risk management system and evaluation of safety and effectiveness is investigated, and several thoughts are proposed. Supervision departments should strengthen the unified understanding of EP, develop requirements according to the classification of medical device，and refine specific execution requirements.

Implementation and participation of an online nutrition curriculum for pediatric gastroenterology fellows.

Our team of nutrition experts developed an online nutrition curriculum consisting of 21 modules to serve as a resource for a stand-alone nutrition curriculum or as a supplement to existing nutrition electives during the Pediatric Gastroenterology fellowship. From April 2020 through January 2023, 2090 modules were completed by 436 fellows from 75 different programs across North America. The program was accessed most during tight restrictions on in-person learning during the COVID-19 pandemic. Overall, participants posttest scores improved from baseline pretest scores indicating retention of information from the modules. The overall success of this program suggests that there should be continued efforts to develop and offer online learning opportunities in clinical nutrition. There is an opportunity to expand the audience for the curriculum to include pediatric gastroenterologists from across the globe.

Impact of the Narcotics Information Management System on Opioid Use Among Outpatients With Musculoskeletal and Connective Tissue Disorders: Quasi-Experimental Study Using Interrupted Time Series.

BACKGROUND: Opioids have traditionally been used to manage acute or terminal pain. However, their prolonged use has the potential for abuse, misuse, and addiction. South Korea introduced a new health care IT system named the Narcotics Information Management System (NIMS) with the objective of managing all aspects of opioid use, including manufacturing, distribution, sales, disposal, etc. OBJECTIVE: This study aimed to assess the impact of NIMS on opioid use. METHODS: We conducted an analysis using national claims data from 45,582 patients diagnosed with musculoskeletal and connective tissue disorders between 2016 and 2020. Our approach included using an interrupted time-series analysis and constructing segmented regression models. Within these models, we considered the primary intervention to be the implementation of NIMS, while we treated the COVID-19 outbreak as the secondary event. To comprehensively assess inappropriate opioid use, we examined 4 key indicators, as established in previous studies: (1) the proportion of patients on high-dose opioid treatment, (2) the proportion of patients receiving opioid prescriptions from multiple providers, (3) the overlap rate of opioid prescriptions per patient, and (4) the naloxone use rate among opioid users. RESULTS: During the study period, there was a general trend of increasing opioid use. After the implementation of NIMS, significant increases were observed in the trend of the proportion of patients on high-dose opioid treatment (coefficient=0.0271; P=.01) and in the level of the proportion of patients receiving opioid prescriptions from multiple providers (coefficient=0.6252; P=.004). An abrupt decline was seen in the level of the naloxone use rate among opioid users (coefficient=-0.2968; P=.04). While these changes were statistically significant, their clinical significance appears to be minor. No significant changes were observed after both the implementation of NIMS and the COVID-19 outbreak. CONCLUSIONS: This study suggests that, in its current form, the NIMS may not have brought significant improvements to the identified indicators of opioid overuse and misuse. Additionally, the COVID-19 outbreak exhibited no significant influence on opioid use patterns. The absence of real-time monitoring feature within the NIMS could be a key contributing factor. Further exploration and enhancements are needed to maximize the NIMS' impact on curbing inappropriate opioid use.