Modern Management Science & Engineering (MMSE) is an international, Refereed, double-blind peer-reviewed, open-access journal published by Scholink. The mission of MMSE is to provide a high-level platform for experts and scholars all over the world to share their latest ideas of management and foster the prosperity of the discipline. We would welcome scholars and researchers engaging in the related field to submit your manuscripts to MMSE. Both of online submission and E-mail submission (mmse@scholink.org) are acceptable. We would welcome scholars and researchers engaging in the related field but is not limited to the following fields: Public Management and Engineering Management Science Theories and Methods Business Management Engineering Management Loop Management Agricultural Engineering Management Industrial Engineering and Operations Management: Policy and Risk Management Operations and Supply Chain Management Sustainable Management and Green Engineering Strategic Management and Organizational Behavior Integration of Information Technology and Management Others Both of online submission and E-mail submission (mmse@scholink.org) are acceptable. ------------------------------------------------------------------------ Open access: Modern Management Science & Engineering is available online to the reader "without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself." Peer review: Modern Management Science & Engineering takes peer review policy. Peer review is the evaluation of work by one or more people of similar competence to the producers of the work (peers).
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A Study on the Integrated Application and Development Strategies of Low-Altitude Drones in the Field of Smart Transport and Logistics
Against the backdrop of the implementation of low-altitude airspace reforms and the comprehensive upgrading of smart logistics systems, low-altitude UAVs, with their advantages of manoeuvrability, minimal spatial constraints and rapid response times, are gradually being integrated into multiple sectors including urban delivery, traffic inspection, emergency supply support and urban-rural logistics, becoming a key enabler in the construction of an integrated air-ground smart transport and logistics system.Based on the economic environment of the low-altitude sector, this paper systematically analyses the current status of low-altitude UAVs’ practical applications in the field of smart transport and logistics. It objectively identifies the practical shortcomings in the industry’s development process and, by combining policy directions with industry realities, proposes optimisation strategies that are both feasible and implementable. The aim is to provide theoretical references and practical insights for the standardised development of UAV logistics and the enhancement of quality and efficiency in the smart transport industry.
Copyright (c) 2026 Haitao Yuan http://creativecommons.org/licenses/by/4.0
Research on the Information Literacy Needs of Chinese Economics and Management College Students under the Background of New Business
Under the context of deep integration of the digital economy and “New Business” construction, the cultivation model of economics and management talents in Chinese universities faces an urgent requirement for digital transformation. This study takes economics and management college students in Chinese universities as the research object to measure their current needs across four dimensions: information consciousness, information knowledge, information competence, and information ethics. A structural equation model (SEM) was constructed, and empirical analysis and model modification were executed using Amos 22.0. The empirical results demonstrate that: information consciousness needs have a significant positive impact on information knowledge needs; information knowledge needs positively promote information competence needs; and information competence needs positively promote information ethics needs. However, information consciousness needs exhibit a significant direct negative impact on information ethics needs, revealing that in the explosive digital environment, shallow information awareness may temporarily dilute ethics, which must instead be progressively cultivated through knowledge and competence. Based on these findings, targeted cultivation pathways are proposed at both the individual and institutional collaborative levels to provide scientific references for cultivating high-quality compound business talents.
Copyright (c) 2026 Dachao Pang, Yujie Li http://creativecommons.org/licenses/by/4.0
Influence of Involute and Cycloid-Pin Profile Modification on the Dynamic Transmission Characteristics of the RV Reducer
To clarify the combined influence of involute and cycloidal profile modifications on RV reducer dynamics, a coupled multibody model including the involute gear pair, cycloid-pin pair, and bearing-support system was developed. Composite meshing error, time-varying meshing stiffness, and external load were considered, while meshing stiffness and contact parameters from Romax were introduced to improve engineering fidelity; the dynamic response was solved using the Gill integration method. Comparative analyses were conducted for involute profile modification, cycloidal moved-distance equidistant modification, and cycloidal pressure-angle modification in terms of meshing stiffness, load distribution, contact stress, transmission error, and vibroacoustic behavior. Involute modification reduced the maximum meshing stiffness from to N/mm (17.3%) and suppressed stiffness fluctuation during the single-to-double tooth transition. For the cycloid-pin stage, pressure-angle modification lowered peak contact stress from about 1600 to 1100 MPa (31.3%). Although transmission error changed only slightly, the involute plus pressure-angle scheme achieved the best overall vibroacoustic performance, reducing the dominant low-frequency acoustic peak by about 11 dB(A) and equivalent acoustic radiation power by about 60%, providing guidance for RV reducer profile design and dynamic optimization.
Copyright (c) 2026 Wenwen Du, Yinghui Zhang http://creativecommons.org/licenses/by/4.0
Implementation Effects for Construction Ventilation in Extra-Long Tunnels under High-Cold and High-Altitude Conditions—A Case Study of Tianshan Shengli Tunnel
To address the technical challenges of low air pressure, low temperature, long distance, and multiple working faces in the construction ventilation of extra-long tunnels in high-cold and high-altitude areas, this study takes the Tianshan Shengli Tunnel project as the engineering case and systematically investigates the comparison, optimization, and implementation effect of ventilation schemes by combining theoretical calculation, comparative analysis, and on-site systematic monitoring. Based on a construction organization arranged in two major stages and sixteen sub-stages, two ventilation modes are compared and analyzed: full-face simultaneous construction (Scheme I) and process-alternating construction (Scheme II). A required-air-volume calculation system is proposed with blast-fume exhaust as the controlling factor, yielding design values of 1980m³/min for the main tunnel by drilling and blasting method, 1500m³/min for the central pilot tunnel by TBM method, and 855m³/min for the shaft by drilling and blasting method. Combined with on-site conditions, Scheme II is further optimized to establish a combined ventilation mode of “forced ventilation in the main tunnel + drift ventilation in the central pilot tunnel + centralized smoke exhaust through the shaft,” supplemented by refined on-site management. Compared with Scheme I, the total power is reduced by approximately 60% on average, with a maximum reduction of 74.9% during peak periods and the cumulative cost savings over the entire construction cycle amount to approximately 128.10 million RMB On-site monitoring results show that the wind speed at all sections meets the requirement of ≥0.3 m/s and the CO concentration is controlled below the allowable limit. In addition, a significant blocking effect of the secondary lining trolley on pollutant transport is discovered, with a CO concentration difference reaching 3.96 times. The research findings can provide technical reference for the construction ventilation of extra-long tunnels in high-cold and high-altitude areas.
Copyright (c) 2026 Cheng Xiaoqing, Huang Dengxia http://creativecommons.org/licenses/by/4.0
Generative AI Data Augmentation and Missing Value Completion Method for Multi-Source Heterogeneous Big Data
The proliferation of multi-source heterogeneous big data across healthcare, industrial Internet of Things (IIoT), and smart city domains has introduced two pervasive bottlenecks in data analytics: widespread missing values and insufficient high-quality training samples. Traditional missing value imputation methods fail to capture complex nonlinear correlations across heterogeneous data modalities, while existing data augmentation techniques are mostly designed for single-modal data and neglect intrinsic cross-source associations. To address these gaps, this paper proposes Hetero-GenAI, a unified generative artificial intelligence framework for joint missing value completion and data augmentation in multi-source heterogeneous big data. First, a heterogeneous data embedding module with cross-modal attention maps numerical, categorical, temporal, and textual features into a shared latent space, explicitly modeling inter-source and intra-source feature dependencies. Second, a missing-aware conditional diffusion model performs adaptive imputation by integrating missing masks as soft constraints, eliminating the need for pre-assumptions about missing mechanisms (MCAR, MAR, MNAR). Third, a distribution-aligned augmentation strategy generates diverse, realistic samples via latent space interpolation and semantic perturbation while preserving cross-modal semantic coherence and distribution consistency. Extensive experiments on two real-world datasets—the MIMIC-IV clinical dataset and NASA C-MAPSS industrial sensor dataset—demonstrate that the proposed method reduces numerical imputation RMSE by 12.3%–21.7% and improves categorical imputation F1-score by 8.5%–15.2% under 30% missing rates compared to state-of-the-art baselines. Furthermore, the augmentation pipeline improves downstream classification and prediction task performance by 9.2%–14.6%, verifying its dual effectiveness in data completion and quality enhancement.
Copyright (c) 2026 Linfeng Yang http://creativecommons.org/licenses/by/4.0
The Impact of Internet Penetration on Chinese Inbound Tourism Economy: An "Internet + Tourism" Perspective
Inbound tourism serves as a core carrier for cross-cultural communication and plays a pivotal role in promoting international cultural exchange. However, the development of Chinese inbound tourism market still possesses substantial potential for growth. Based on the panel data of 31 provinces in China from 2000 to 2019, this paper empirically analyzes the impact of internet penetration on the inbound tourism economy, and further examines regional heterogeneity across the eastern, central and western China. The empirical results demonstrate that internet penetration exerts a significant positive effect on the development of the inbound tourism economy. Furthermore, this promotion effect exhibits distinct regional disparities, characterized by a step-wise pattern where the impact in the central region is slightly higher than in the eastern region, and significantly higher than in the western region.
Copyright (c) 2026 Yiting Jiang, Bing Li http://creativecommons.org/licenses/by/4.0
Research on All-Domain Situation Awareness Technology for Video Surveillance Based on Artificial Intelligence
In the context of the continuous deepening of smart city and safe city construction, the urban video surveillance system has achieved all-round three-dimensional coverage, and the traditional monitoring operation mode relying on manual duty and post-event review has become difficult to adapt to the current dynamic and complex public security control requirements. As a core research direction in the field of intelligent security, global situation awareness, relying on computer vision, artificial intelligence and spatio-temporal big data analysis technology, breaks through the functional limitations of traditional video surveillance and can conduct intelligent perception, analysis and early warning of various scene elements in the monitored area at all times and in all directions, It effectively addresses industry pain points such as fragmented vision, low data utilization, lagging event response, and frequent false and missed detections in traditional surveillance. This paper, in light of the current development status of intelligent surveillance technology, systematically explores the integrated application system of artificial intelligence and video global situation awareness, comparatively analyzes the inherent flaws of traditional surveillance models and the technical advantages of AI intelligent perception, and focuses on dissecting core key technologies such as lightweight target detection, multi-target trajectory tracking, abnormal behavior recognition, and multi-source data fusion. Build a five-layer modular global situation awareness system architecture. By setting up a multi-scenario experimental environment for comparative testing, verify the practicality and accuracy of the technical solution proposed in this paper, sort out the shortcomings and bottlenecks of the current technology in combination with the current application status, propose targeted optimization and improvement plans, and predict the future development trend of the industry. The experimental and research results show that artificial intelligence technology can effectively enhance the all-domain scene perception ability of video surveillance, the accuracy of complex event judgment and the efficiency of emergency response, provide technical support for the intelligent upgrade of scenarios such as urban traffic governance, public security prevention and control, and emergency management and control of sudden incidents, and provide theoretical reference for the standardized and large-scale construction of the all-domain intelligent security system.
Copyright (c) 2026 Zheyuan Wang, Buwei Zhang, Linjun Li http://creativecommons.org/licenses/by/4.0
The Impact of Enterprise Innovation Investment on Labor Cost Stickiness—Evidence from Listed Manufacturing Companies
In recent years, China’s manufacturing industry has been in a critical period of transition towards “innovation-driven,” and enterprises generally face the dual challenges of rising labor costs and the urgent need to increase innovation investment. This paper aims to deeply explore how innovation investment affects enterprise labor cost stickiness. In terms of research methodology, this paper selects the financial data of China’s A-share listed manufacturing enterprises from 2021 to 2025 as the research sample, and draws on the ABJ model to construct a fixed-effects regression model containing an interaction term between innovation investment and changes in operating revenue. The empirical research results show that there is indeed a significant phenomenon of labor cost stickiness in the sample manufacturing enterprises, and confirm that the greater the innovation investment, the higher the labor cost stickiness of manufacturing enterprises. The conclusion shows that with the increase of innovation investment, enterprises’ dependence on core technical personnel with high asset specificity deepens. In order to avoid high adjustment costs and prevent the risk of technology leakage, enterprises are often reluctant to lay off employees easily when performance declines, thereby exacerbating the downward rigidity of labor costs.
Copyright (c) 2026 Menglin Fan http://creativecommons.org/licenses/by/4.0
Design and Practice of a BOPPPS-Based Teaching Model for Junior High School Artificial Intelligence Courses
Artificial intelligence (AI) has been incorporated into China's compulsory education curriculum; however, junior high school AI classrooms frequently overemphasize operational demonstration at the expense of inquiry, thinking, and process-oriented evaluation. This study constructed a six-link, dual closed-loop teaching model based on the BOPPPS (Bridge-in, Objective, Pre-test, Participatory Learning, Post-test, Summary) framework and verified its effectiveness through a quasi-experiment. Two parallel seventh-grade classes (60 students) were taught for six weeks, with the experimental group receiving the BOPPPS-based model and the control group receiving conventional lecture-based instruction. Data collected from pre- and post-tests, a core literacy scale, questionnaires, and classroom observation were analyzed with SPSS. The results show no significant between-group difference at baseline, whereas after the intervention the experimental group significantly outperformed the control group in academic achievement (p < 0.01) and across all dimensions of core literacy. The findings indicate that the BOPPPS-based model effectively enhances learning outcomes, classroom engagement, and information technology core literacy in junior high school AI courses, providing a replicable paradigm for frontline teaching.
Copyright (c) 2026 Songyan Shi, Yanqi Li http://creativecommons.org/licenses/by/4.0
Research on College Students' Usage Intention and Influencing Factors Regarding Generative Artificial Intelligence
This study aims to explore college students' willingness to use generative artificial intelligence (AI) and its influencing factors. It adopts a mixed-methods approach, including a questionnaire survey covering students' usage status, perceived barriers like operational complexity and academic integrity concerns, and in-depth interviews focusing on specific usage scenarios, prompt optimization experiences, and attitudes towards independent thinking impacts. The results show that while most students recognize generative AI's value in learning and daily tasks, factors such as unclear usage norms, content accuracy doubts, and concerns about reduced independent thinking significantly hinder their willingness to use it. The study concludes that colleges should provide targeted training and clear usage guidelines to help students use generative AI rationally and effectively.
Copyright (c) 2026 Guohang Sun, Xiujuan Wang, Guohang Sun http://creativecommons.org/licenses/by/4.0