Occupancy Rate Research Articles

Comparação do nível de dependência de Enfermagem e dimensionamento entre unidades de internação clínica e cirúrgica

Abstract Objective to compare the level of dependence on nursing care and staffing between clinical and surgical inpatient units. Method This is a descriptive, retrospective study carried out in four inpatient units of a large university hospital in southern Brazil. A total of 7,486 patient classification records were included between January and October 2022. Descriptive statistical analysis, demand for nursing hours, staff projections, and the chi-square test were used. Results Intermediate care (40.2%) and semi-intensive care (40.8%) patients prevailed, with a difference between the units (p-value<0.001). There was a shortage of nurses in all the units. In one clinical unit, the projected number of nursing technicians/assistants was the same as the number available (n=46). In two units, there was a slight to moderate surplus of mid-level staff. The surgical inpatient unit with the best classification rate (92.3%) showed the greatest discrepancy between the prescribed and actual sizing. Conclusion and implications for practice the units were highly dependent on nursing care. When also considering occupancy rates and adherence to patient classification, it is plausible that one of the clinical units has the highest demand for care.

The Economic Penalty of Terrorism: Increase in Discrimination against Arabs and Muslims after Paris Attacks

What repercussions does Islamist terrorism have for Arab/Muslim minorities in targeted countries? We highlight consequences in the domain of online real estate rental. Using high-frequency data on all transactions happening on the largest Parisian online rental market over the period of November 2014 to March 2018, we are able to trace the evolution of prices and occupancy rates over time. We demonstrate that discrimination against properties hosted by Arab/Muslims rises swiftly in the aftermath of the terror attacks in November 2015 and wanes at a much slower pace. We quantify the average financial loss per listing that results from increased discrimination to equal $178 per month. Our findings not only highlight the presence of discrimination in peer-to-peer transactions but also show how sharing economy data measuring these transactions can be used as a “seismograph” to track and measure the development of discriminatory attitudes in response to societal events.

Resources time footprint for assessment of human influence on ecosystem service from a sustainability standpoint

Nature provides diverse services to humanity, known as ecosystem services (ES), yet certain services, such as food and timber, are geographically distant from human settlements. This spatial separation of ES from supply area to demand site fosters transfer through human-made carrier as ES flow, resulting in social and ecological impacts. This study delves into this complex ES supply, flow, and demand relation. While the conventional ES assessment approach mainly quantifies ES supply potential, Resources Time Footprint (RTF) is introduced as a new indicator to evaluate human intervention part of ES supply and flow. RTF examines intergenerational sustainability of ES by evaluating material, land, labor, and pollutant resource utilization in relation to individual allocations. The efficacy of this integration is evaluated through a case study covering 17 ESs dynamics in central Bhutan for 2010 and 2020, and RTF is applied to potato-ES, given its higher human intervention in the area. This is finally validated against commonly used emergy analysis and its derivatives. The study observed a 3.5% increase in 17 ESs, with minor intergenerational implications associated with supply and flow of potato-ES. The average per capita RTF values were 0.81 and 0.52 years, or 2 and 1.3 years per 100 kg of potatoes for the 2010 and 2020 base case, respectively. This smaller RTF value for 2020 indicates reduced resource occupancy rates with higher intergenerational sustainability. This replicable indicator effectively evaluated human related impacts on ES supply and flow and identified land and labor as underperforming aspects with higher occupancy rates. The comparative validation showed inadequateness of emergy-derivatives in examining human intervention in ES flow, and limitations of ES-RTF in evaluating nature's contribution. This underscores the complementary nature of two methodologies. Overall, this study contributes to a telecoupling framework for a sustainable society, enhancing coherence and consistency in analyzing ES supply and flow.

COVID-19 Pandemic Impact on Nursing Homes Financial Performance.

Nursing homes expressed concern about potential severe adverse financial outcomes of COVID-19, with worries extending to the possibility of some facilities facing closure. Maintaining a strong financial well-being is crucial, and there were concerns that the pandemic might have significantly impacted both expenses and income. This longitudinal study aimed to analyze the financial performance of nursing homes during COVID-19 pandemic. Specifically, we examined the impact of the pandemic on nursing home operating margins, operating revenue per resident day, and operating cost per resident day. The study utilized secondary data from various sources, including CMS Medicare cost reports, Brown University's Long Term Care Focus (LTCFocus), CMS Payroll-Based Journal, CMS Care Compare, Area Health Resource File, Provider Relief Fund distribution data, and CDC's NH COVID-19 public file. The sample consisted of 45 833 nursing home-year observations from 2018 to 2021. Fixed-effects regression analysis was employed to assess the impact of the pandemic on financial performance while controlling for various organizational and market characteristics. The study found that nursing homes' financial performance deteriorated during the COVID-19 pandemic. Operating margins decreased by approximately 4.3%, while operating costs per resident day increased by $26.51, outweighing the increase in operating revenue per resident day by about $17. Occupancy rates, payer mix, and staffing intensity were found to impact financial performance. The study highlights the significant financial impact of the COVID-19 pandemic on nursing homes. While nursing homes faced substantial financial strains, the findings offered lessons for the future, underscoring the need for nursing homes to improve the accuracy of their cost reports and enhance financial transparency and accountability.

Development of lumped-parameters models for the thermal evaluation and air quality in aircrafts

Aircraft cabins are a challenging category when dealing with thermal comfort and air quality inside means of transport. Two simplified dynamic models are developed. The first one is a lumped resistance-capacitance model for assessing the cabin thermal behaviour during the cruise phase. The fuselage is discretised into several slices and each one is represented through an RC network consisting of eleven nodes, thirteen resistances and three capacities. A thermal balance equation is set for each node and the linear system is solved to calculate the air and surfaces’ temperatures. The model is validated by comparison with literature experimental data from ten flights, showing that the predicted temperatures agree well with the measured ones, presenting an RMSE of 1.5, 1.9 and 1.3 °C for cabin air, floor and cabin internal surface temperatures, respectively. A sensitivity analysis is conducted, for which the internal air temperature increases linearly with occupancy rate and decreases with cruising altitude. Secondly, an air quality model is proposed to evaluate the presence of pollutants inside the cabin, based on a simple concentration balance equation. Ventilation flow rates recommended from standards and a recirculation rate below 50-60% should be set to maintain acceptable CO2 levels.

Моделирование «ковидного провала» плановой госпитализации в период разгара пандемии COVID-19: анализ и оценка потерь использованной коечной мощности специализированного оториноларингологического стационара

Introduction. In the midst of the COVID-19 pandemic, healthcare organizations around the world were forced to change their operating hours, and some, especially those providing specialized medical care, were forced to stop operating completely. The purpose of the study is to develop a year-by-year model of the movement of patients before the pandemic and at its height, based on long-term data on the movement of inpatients. With its help, analyze possible losses in bed capacity (expressed in the number of idle bed days, or unserved patients) from the pandemic during 2020 — the year of the height of the pandemic. Materials and methods. The study was carried out according to the design of an observational retrospective analytical study. As research materials, we used anonymized data on the number of patients in the hospital at the Federal State Budgetary Institution «St. Petersburg Research Institute of ENT» of the Ministry of Health of Russia, obtained from the medical registration form N 007/u-02 „Sheet of daily records of the movement of patients and the bed capacity of a round-the-clock hospital, day hospital at a hospital facility“). The R and RStudio software environment was used as the main software product for solving statistical problems. Results. A load model has been created (the number of daily inpatients undergoing treatment) during the year. An analytical model of the “Covid failure” of bed capacity utilization during the height of the pandemic has been created. The coordinates of the dynamic points of bed release and return to typical bed occupancy values for this period of time were calculated (using linear regression forecasting). The share of hospital losses during the pandemic was calculated and assessed. Discussion. The use of “Covid failure” data superimposed on “pre-Covid” indicators allows not only to estimate annual losses, but also to predictively minimize losses during planned closures of hospital beds, for example, for repairs, given that the resumption of work of a medical institution’s beds will often be slower than the speed at which work stops. Analysis of linear regression coefficients of bed release and occupancy rates can give an idea of the speed of reaching standard values for the volume of medical care. As studies have shown during the COVID-19 pandemic, the minimum value of the quarantine period after the complete vacancy of beds is 14 days, and for high-risk scenarios 21 days. Based on this, for the conditions of organizing medical otorhinolaryngological care, the minimum predicted value of the time from the closure of hospitalization to its opening may be about 37 days after the start of patient discharge. The approach and mathematical functionality presented in the article can be used on the initial data of other institutions, as well as in preparation for the “Disease X” pandemic.

The impact of the 2022 spring COVID-19 booster vaccination programme on hospital occupancy in England: An interrupted time series analysis.

Regular booster vaccination programmes help protect the most vulnerable from COVID-19 and limit pressure on health systems. Existing studies find booster doses to be effective in preventing hospital admissions and deaths but focus on individual effects, failing to consider the population impact of incomplete vaccination coverage and seasonal patterns in disease transmission. We estimated the effectiveness of the 2022 spring booster vaccination programme, available for those aged 75 years and older, residents in care homes, and adults with weakened immune systems, on COVID-19 hospital bed occupancy in England. Booster vaccine coverage in the eligible population increased rapidly in the months after rollout (from 21st March 2022), flattening out just below 80% by July 2022. We used interrupted time series analysis to estimate a 23.7% overall reduction in the rate of hospital occupancy for COVID-19 following the programme, with a statistically significant benefit in the 6-12 weeks following rollout. In the absence of the programme, we calculate that a total of 380,104 additional hospital bed-days would have been occupied by patients with COVID-19 from 4th April to 31st August 2022 (95% CI: -122,842 to 1,034,590). The programme delayed and shortened the duration of the peak while not reducing its magnitude. In sensitivity analyses adjusting the start of the post-intervention period or removing the rate of COVID-19 infection in the over 60s from the model, the effect of the spring booster programme on hospital bed occupancy remained similar. Our findings suggest that timing is a critical consideration in the implementation of COVID-19 booster programmes and that policymakers cannot rely on intermittent booster vaccination of high-risk groups alone to mitigate anticipated peaks in hospital pressure due to COVID-19 epidemics.

Investigation and optimization the study of Hexavalent Chromium elimination efficiency by activated carbon prepared from modified rice husk with sorbic acid

The current work illustrates how successfully rice husk-activated carbon eliminates hexavalent chromium from aqueous solutions. The design of experiment (DOE) - response surface methodology (RSM) was utilized to build an appropriate experimental model. The experimental variables pH, contact length, and adsorbent dose were found, as well as their interactions and effects on Cr(VI) removal. The greatest result for Cr(VI) removal was obtained at pH = 2, agitation time of about 30.5 hours, and adsorbent dose = 4 g/l, with a starting hexavalent chromium content of 100 ppm. Hexavalent chromium removal is performed optimally under the parameters specified for these driving forces. ANOVA also demonstrated a strong coefficient of determination R2 > 99 %. Furthermore, Temkin isotherm and pseudosecond kinetic models with high R2 values were selected as useful for the inquiry. It illustrates that the removal of hexavalent chromium in studies is considered to follow a regular distribution of binding energies up to a given maximum binding energy. Furthermore, the rate of adsorption site occupancy is inversely proportional to the square of the number of unoccupied sites on the adsorbent.

Computer detection intelligent mining algorithm for software defined networks

The network security (NS) issues of software defined network (SDN) are becoming increasingly prominent. Traditional computer detection (for convenience, computer detection is abbreviated to CD) methods are usually based on rules and features, and cannot cope with new threats and unknown attacks. The purpose of this article is to analyze a CD intelligent mining algorithm (for convenience, intelligent mining algorithm is abbreviated to IMA) suitable for SDNs, and to optimize and improve the data acquisition difficulty, large data volume, dynamism, timeliness, and security issues of SDNs. On the basis of data preprocessing on the KDD (Data Mining and Knowledge Discovery) Cup 1999 dataset, this article constructed an architecture for CD IMAs. In the data intelligence layer, a combination of k-means algorithm and XGBoost (eXtreme Gradient Boosting) algorithm was used for computer network attack detection based on stream classification. Based on the preprocessed dataset, the k-means algorithm and its combination were trained and simulated. The effectiveness and performance optimization of the CD IMA analyzed in this article were verified by comparing it with traditional computer attack detection algorithms in terms of detection accuracy, CPU (Central Processing Unit) occupancy, detection time, and false detection rate. The experimental results showed that the accuracy of the CD IMAs analyzed in this article was over 80 %, and some even reached over 90 %. However, the accuracy of traditional computer attack detection algorithms has not reached 90 %. The detection time of traditional computer attack detection algorithms was all above 2 s, but some of the detection time of the algorithm in this article was within 2 s. The detection rate of the algorithms analyzed in this article was above 90 %, and the detection results were more accurate than traditional algorithms. This proved that the algorithm analyzed in this article can more effectively select optimized feature subsets, has good flow classification ability, and has high detection accuracy for different types of attacks. The CD IMA for SDNs has great potential and application prospects.

Hazard identification and occupational risk management of a tractor driver at an agricultural production

The profession of an agricultural tractor driver is one of the most common professions in agriculture. There is a high level of occupational injury rates among machinists and drivers of vehicles, which is associated with dangerous and difficult working conditions and the specifics of the work. The purpose of the study is to identify hazards and assess occupational risks in the workplace. The scientific objective of the study is to improve the safety of tractor drivers based on the methodology of assessment and management of occupational risks. Based on the results of identification of risk-forming factors in the workplace, the work presents a register of hazards, the results of an assessment of occupational risks before and after measures to reduce the impact of hazards. The obtained data of the conducted research can be used for all similar jobs in agricultural production. The comprehensive approach proposed in the work to the assessment and management of occupational risks is aimed at improving the labor protection system, increasing the protection of employees, prevention and prevention of injuries at work.

Occupational diseases occurring in acceptable working conditions: Causes and features of development in the Russian Arctic

Introduction: Acceptable working conditions are essential but they do not ensure complete prevention of occupational diseases. The purpose of the study was to establish possible causes and features of development of occupational diseases in blue-collar employees having acceptable working conditions. Materials and methods: Data on working conditions and occupational disease rates collected within public health monitoring and information from the Register of extracts from occupational disease records in the Russian Arctic for 2007–2021 were analyzed. The relative risk (RR) and 95 % confidence interval (95 % CI), the goodness-of-fit criterion (χ2) for the analysis of four-field tables, and the approximation criterion (R2) to determine the measure of correlation between values were calculated. Results: It has been established that 74 of 10,343 occupational disease cases (0.72 %) were registered at enterprises of the Russian Arctic with acceptable working conditions. They were more often related to chemical exposure (40.5 %) and increased labor severity (28.4 %) mainly attributed to equipment design defects (58.1 %) and imperfect technological processes (33.8 %). This group of workers was noted for a younger mean age (48.1 ± 1.3 years), shorter duration of employment (18.9 ± 1.5 years), and a larger proportion of women (28.6 %) compared to those exposed to harmful and hazardous working conditions. It included employees of such jobs as a lorry driver (27.0 %) and painter (10.8 %) from such types of economic activity as mining (56.8 %) and construction (14.9 %). Poisoning (n = 17), radiculopathy (n = 12), mono(poly)neuropathy (n = 11), and bronchial asthma (n = 8) were found to be the most prevalent occupational disorders in them. Conclusion: Some circumstances of registration of health disorders in workers with low exposure to occupational risk factors (usually below occupational safety standards) remain unclear. It is critical to establish validity of such a diagnosis, specifics of drawing up a report on a case of occupational disease, and reporting forms of the Russian Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing (Rospotrebnadzor) describing occupational hazards.

SSIOE: Self-Supervised Indoor Occupancy Estimation for Intelligent Building Management

This paper aims to estimate indoor occupancy given the real-time observed signals from the existing sensors; next, as a practical application, we build a dynamic control schedule for energy saving based on the estimated indoor occupancy. However, several issues need to be addressed. First, it is impossible to train the model with rich labels due to the expensive labeling cost. Second, manual annotation of the continuous occupancy rate is complex. Third, the mapping relationship between sensor data and occupancy will change in the long run. In this paper, we proposed a new algorithm named Self-Supervised Indoor Occupancy Estimation (SSIOE) to overcome the challenges. Specifically, our training scheme aims to (I) generate a set of pseudo labels in a simple way to mark the time periods believed to be either in a high or low occupancy state and (II) utilize these sparse labels for training a network to infer the continuous occupancy ratio. By reformulating the problem as a Wasserstein-distance-like estimation, SSIOE is a novel learning-based method that can rely only on the weak/sparse labels of either “high-occupancy” or “low-occupancy” and learn to estimate the continuous occupancy ratio. Furthermore, to deal with the scarce annotation problem, we proposed a novel physical constraint loss to model the physical prior. (III) Last but not least, to strengthen the adaptability, we integrated Model-Agnostic Meta-Learning (MAML) to train the model for dynamic model updating. Experimental results show that SSIOE can provide reliable occupancy estimation and flexibly adapt to various control modes without retraining the model. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the real-time occupancy estimation problem, which is crucial for building management systems to arrange lighting and air conditioning. A dynamic schedule adapted to the occupancy would help balance the user’s comfort and energy saving; however, installing the occupancy sensors raises additional costs. Therefore, this paper proposes SSIOE, a label-free machine learning algorithm that can learn to estimate the continuous occupancy ratio given the sensor status in real time. Prior works trained upon supervised machine learning methods suffer from heavy manual annotation workload, while the unsupervised machine learning approaches fail to provide continuous occupancy estimation values. This paper addresses the above challenges by proposing a novel self-supervised training scheme. Preliminary experiments suggest that this approach is feasible to work in real-world buildings. However, it should be noted that the occupancy estimated by SSIOE is a ratio (0.0-1.0), which is relative to the maximum and minimum occupancy of the training data (or fine-tuning data). For example, if the maximum number of occupants in the training data is 200 and the minimum number of occupants is 50, then the estimated occupancy value of 1.0 is about 200 occupants, and the occupancy value of 0.0 is about 50 occupants. When the maximum and minimum occupants of the space change, the model needs to be updated to capture the new mapping of the estimated occupancy ratio to the number of occupants. In future research, we plan to further improve and simplify the adaptation ability of SSIOE by continuously and automatically updating the system with the latest collected as the system runs.

Energy use and its contributors in hotel buildings: A systematic review and meta-analysis.

Energy use is the major source of carbon emissions in hotel buildings. Past studies presented contributors to energy use, most related to hotels' physical and economics characteristics. In search of effective variables affecting energy use in hotels, this systematic review and meta-analysis aims to synthesize empirical evidence. A descriptive picture of 28 previous studies, the arguments for the direction of effects in each variable, and a quantitative synthesis of the mean effect sizes were presented. Among 18 selected contributors from past studies, 15 were statistically significant (0.05 level). The analyses also revealed that the operationalization of the energy variable is important in evaluating the relationship with a contributor. Studies considering Energy Use Intensity (EUI) indicators presented weaker correlations with gross floor area (GFA) and number of guestrooms than those considering energy raw data, for example. The occupancy rate resulted in a non-significant outcome, but this result could be related to differences among the hotels categories, as identified in the subgroup and meta-regression analyses. Future research could help develop and investigate theories to sustain or deny the relationships found here, in addition to the assessment of the outcomes in other regions, bringing more variables related to sustainable management.

Marketing Strategy to Increase Room Occupancy Rates at Yama Resort

This research aims to identify marketing strategies to increase room occupancy rates at Yama Resort. The research method used is the Qualitative Method. Data was obtained through observation, interviews and literature study. This research shows that five marketing strategies can be implemented to increase room occupancy rates. Furthermore, eight strategies can be implemented based on the strength, weakness, opportunity and treatment (SWOT) analysis. Yama Resort management needs to focus on existing strengths and collaborate with business people to attract more guests. It will ultimately impact increasing the room occupancy rate at Yama Resort.

Analysis of Extraction Method for Maritime Traffic Intensity Zone Through Occupancy Rate of Automatic Identification System Data

Analysis of Extraction Method for Maritime Traffic Intensity Zone Through Occupancy Rate of Automatic Identification System Data

Optimizing Customized Bus Routing and Maximum Seat Occupancy Rate Under the Influence of Epidemic Outbreaks

Optimizing Customized Bus Routing and Maximum Seat Occupancy Rate Under the Influence of Epidemic Outbreaks

Dynamic vehicle parking pricing. A review

Dynamic parking pricing refers to the adjustment of the price of parking to achieve the required occupancy rates. It plays a significant role in parking management systems to minimize traffic congestion and cruising time, as well as to maximize revenue. The optimization of parking pricing and supply through a time-varying pricing strategy is a crucial issue. This paper reviews academic work on approaches to parking pricing, giving emphasis to time-varying pricing strategies. Approaches based on game theory, dynamic and stochastic control, multiobjective and multilevel programming, queuing theory, artificial intelligence, statistics, among others, are reviewed. We categorize these techniques to examine various issues of dynamic parking pricing. The main contributions and methods used are summarized. Furthermore, a brief discussion of the strengths, limitations, and possible future work is given.

Research on the Innovation of University English Teaching Mode Driven by Artificial Intelligence

Abstract With the continuous deepening and development of information technology in the field of education, the combination of artificial intelligence technology and university English teaching has become a trend. This paper constructs a human-machine symbiotic English teaching model based on intelligent speech recognition, which takes into account university English learning abilities and learning objectives. Specifically, the multi-scale attention mechanism is used instead of the Conformer model to extract the global information of speech features and the local information of different scales and fuse them to learn more speech representations of different scales so as to better recognize the teacher and student behaviors in the English classroom. Meanwhile, combining the teaching mechanism of human-computer symbiosis, we construct a human-computer symbiosis English teaching model for speech recognition in an intelligent classroom and receive feedback after teaching experiments. The occupancy rate of teacher and student behaviors in the English classroom is 50.86% and 49.14%, respectively, and the conversion rate of teacher and student behaviors is 29.78%, which achieves a high level of teacher-student interaction in this model. In addition, the class utilizing the English teaching model of this paper was 5.02 points higher than the traditional class after one semester of teaching, P=0.038&lt;0.05, and the results showed a significant difference. Through the comparison of cases and teaching experiments, the reasonableness of the English teaching model based on speech recognition is demonstrated, and the research content and problem areas of this paper are summarized.

Digital twins, the journey of an operational weather prediction system into the heart of Destination Earth

Moving a world leading numerical weather prediction system of the European Center for Medium-Range Weather Forecasts (ECMWF), that runs on a dedicated, bespoke, high performance computing cluster and supporting infrastructure, into the heart of the digital twin for climate change adaptation and extreme weather events, developed in the Destination Earth (DestinE) initiative of the European Commission, has been a challenging and exciting journey. In this paper we describe this journey with a focus on those aspects required to leverage the pre-exascale EuroHPC systems that have been made available to DestinE to run its computational representation [1]. EuroHPC systems can be effectively used for DestinE and are in fact key assets to deliver the computational power required for Earth system digital twins at global km-scale resolution. Each of these systems is operated by a national hosting entity that implements its own procedures, e.g. for identity and access management, specific system configuration like schedulers, filesystems, software management systems, and specific, sometimes vendor associated, toolchains, tooling, and container runtimes. In particular, the different scheduling policies encountered required us to adapt our workflows for each site. We found that having dedicated resources available, which was trialed in a period from 16th February to 14th April on LUMI, allowed to achieve high occupation rates, with 92% on the reserved GPU allocation and greater than 97% efficiency on the CPU reservation. Also, a stronger focus on federation of these systems, with a focus not only on federation of identities and accounts, but also in the areas of data ownership/transfer, observability, services and service accounts, maintenance coordination and performance portability could benefit DestinE. In general, it would be beneficial to make it easier to transfer a workload, or a digital twin system, from one EuroHPC site to the next and run and maintain them across several sites concurrently.

Construction of a Database for Vocal Music Teaching in Colleges and Universities Based on the Inheritance and Protection of the Central Plains Culture

Abstract Multi-cultural integration is an inevitable path to enhance the inheritance and protection of the Central Plains culture and to innovate the teaching mode of vocal music in colleges and universities. In this paper, we constructed a university vocal music teaching database system based on distributed database, and used semi-connection algorithm to query and optimize the data related to the integration of Central Plains culture into vocal music teaching resources. In order to make up for the defects of the semi-connection algorithm when facing a large number of tuples, the semi-connection algorithm is improved by introducing the idea of symmetric homology to realize the massive data scheduling of the vocal music teaching resources database. For the college vocal teaching database constructed in this paper, the query optimization test and performance test are carried out, and the teaching practice application is carried out with the vocal teaching database. The results show that the query throughput rate of vocal music teaching resources reaches 3.18*103MB/s under Read-only load when the number of running rounds reaches 16 rounds, and when the number of concurrent users of the vocal music teaching database is 1000, the CPU occupancy rate and memory utilization rate are 1.37% and 1.06%, respectively. By integrating Zhongyuan culture into the vocal music teaching database, more than 70% of the students indicated that it could enhance their interest in learning about Zhongyuan culture. The use of technology to realize the integration of vocal music teaching and the culture of the Central Plains provides a new reference for the inheritance and protection of the culture of the Central Plains.