Academic Journal of Management Science and Engineering
Academic Journal of Management Science and Engineering (AJMSE), a peer-reviewed open access journal published in English-language, provides an international platform for the publication and dissemination of original work that contributes to the understanding of the main and related disciplines of management science and engineering either empirical or theoretical. The journal covers the whole spectrum of Business and management analytics, Complex management and economic systems, Data mining, Decision analysis, Decision support system, Financial engineering and risk management, Forecasting and analysis, Game theory, Industrial management, Information management, Information systems and e-Business, Logistics system management, Management engineering, Management information system, Mathematical modeling, Operations management, Optimization, Probability and statistics, Production system management, Project and program management, Quality control and reliability management, Research and Development Management, Risk Management, Scheduling and control, Simulation of management system, Social and economic modeling, Strategic management, Supply chain and logistics management, Transportation systems and management, Uncertain Decision Theory, and more.
A Review of Electricity-Gas-Hydrogen Integrated Energy Systems for Low-Carbon Energy Transition: Planning, Operation and Market Optimization
Electricity-gas-hydrogen integrated energy systems are becoming an important technical pathway for low-carbon energy transition, renewable energy accommodation and multi-energy coordination. With the increasing penetration of wind power and photovoltaic power, conventional energy systems face growing challenges related to intermittency, uncertainty, flexibility shortage and carbon emission reduction. Hydrogen, especially green hydrogen produced by renewable electricity through water electrolysis, can act as a flexible energy carrier, long-duration energy storage medium and cross-sector coupling resource. This paper provides a literature review of electricity-gas-hydrogen integrated energy systems from the perspectives of system architecture, hydrogen production, power-to-gas technology, hydrogen storage and transportation, coordinated planning, flexible operation, dynamic pricing and robust optimization. The review shows that power-to-gas, hydrogen storage and fuel cells can improve renewable energy utilization and enhance system flexibility. Meanwhile, market-oriented mechanisms such as dynamic pricing and Stackelberg game models can guide demand response and improve the economic performance of integrated energy systems. Robust optimization provides an effective tool for managing renewable generation uncertainty and load fluctuation. However, challenges remain in hydrogen infrastructure investment, multi-energy network modeling, hydrogen market design, carbon pricing coordination, safety standards and real-world demonstration. Finally, a technical route is proposed to summarize the future research framework for electricity-gas-hydrogen integrated energy systems.
Electricity-gas-hydrogen Integrated Energy System
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A Study on the Reconstruction of Tourism Scenes by Digital Intelligence Technology from the Perspective of Cultural Ecology: A Case Study of the “Chang’an Twelve Hours” Themed Block in Xi’an
As the experience economy deepens and new-generation information technologies achieve comprehensive penetration, the underlying business logic of the tourism industry is undergoing a fundamental shift from being "resource-oriented" to "scene-oriented." Constrained by physical space boundaries and homogeneous competition, the customer acquisition effectiveness of traditional cultural tourism projects has peaked, inevitably pushing the entire industry into a phase of stock-based competition. Against this macro-background, digital intelligence technologies (e.g., mobile internet, smart lighting and visual effects, big data) have broken the rigid constraints of physical space, leaping from auxiliary tools to the core driving force for revitalizing existing physical assets and unlocking commercial value.This study introduces the theory of cultural ecology and constructs a digital-intelligent interactive system of "environment-culture-behavior." Taking the "Chang'an Twelve Hours" themed block in Xi'an as a typical case, it analyzes the reconstruction logic of tourism scenes through digital intelligence technology. The essence of scene reconstruction lies in the deep reorganization of cultural tourism production factors by digital intelligence technology, aiming to achieve a leapfrog improvement in spatial efficiency (revenue per unit area) and commercial premium. Ultimately, this study yields efficiency-oriented operational strategies, providing a standardized reference framework for the digital-intelligent transformation of existing cultural tourism assets.
The More Dazzling the Technology, the Thinner the Culture? A Flow Theory Reflection on the "Superficialization" Dilemma of AR Cultural Tourism and Industry Transformation Pathways
Driven by "technological showmanship," AR cultural tourism experiences exhibit a "superficialization" dilemma characterized by excessive sensory immersion yet insufficient cultural understanding, forming a paradox of "the more immersed in technology, the more alienated from culture." Based on the "challenge-skill balance" framework of Flow Theory and introducing Cognitive Load Theory as an analytical tool, this study diagnoses the flow disruption mechanism caused by cognitive overload and insufficient interaction depth in AR experiences. The findings reveal three interrelated problems in current AR cultural tourism projects: overdevelopment of sensory immersion, superficiality of interaction forms, and absence of meaning construction. The root causes lie in challenge-skill imbalance, alienation of immediate feedback, and the overshadowing of cultural goals by technological goals. Accordingly, this paper proposes three macro-level optimization pathways for the AR cultural tourism industry: constructing a hierarchical narrative system, establishing industry-wide cognitive load regulation standards, and advocating "blank space" design ethics, providing theoretical foundations and strategic references for the transformation of AR cultural tourism from an "attention economy" to an "experience economy."
Artificial Intelligence in Instrumentation: Applications, Methodologies, and Future Directions
The integration of artificial intelligence into instrumentation and measurement systems has emerged as a transformative force across industrial, environmental, and scientific domains. This article provides a systematic overview of AI applications in instrumentation, encompassing intelligent sensor data processing, predictive maintenance, automated meter reading, and fault diagnosis. Drawing upon recent advances documented in the literature, we examine the methodological landscape ranging from conventional machine learning to deep learning and large language models. Key benefits include enhanced measurement accuracy through intelligent compensation, reduced downtime through predictive maintenance, and improved operational efficiency through automation. However, significant challenges persist, including the blackbox nature of AI models, data scarcity, uncertainty quantification, and the gap between laboratory performance and realworld deployment. We argue that the future of intelligent instrumentation lies in hybrid approaches that integrate datadriven AI with conventional modeldriven methods, thereby combining the pattern recognition capabilities of AI with the interpretability and rigor of physicsbased models.
Thermal Management Challenges and the Role of Thermal Interface Materials in Advanced Packaging
As semiconductor devices scale and advanced packaging architectures such as 2.5D interposer-based integration, 3D chip stacking and heterogeneous chiplet assembly are adopted, local power densities have surpassed 600 W/cm2, which is beyond the capability of standard thermal management techniques. Thermal interface materials (TIMs) are strategically critical to the thermal path of packaged devices bridging the high resistance solid-solid contact surfaces between the die, heat spreader and heat sink. This review covers the physical mechanisms of the interfacial thermal resistance in advanced packaging, the classification and performance of the major classes of TIMs (carbon-based, metal-based and polymer composite systems), and the crucial reliability challenges (pump-out, thermal cycling degradation and 3D stacking heat path interruption) which prevent the widespread use of TIMs today. The review also examines the stimulus-responsive smart TIMs as the platform to meet the dynamic thermal management requirements of next-generation heterogeneous integration platforms.
An Adaptability Index and Key Obstacle Identification Model for SMEs’ Digital Transformation: Based on Perceived Utility Analytical Framework
To address the dilemma of "daring not to transform, unwilling to transform, and unable to transform" faced by small and medium-sized enterprises (SMEs) in digital transformation, this paper applies perceived utility theory to construct a digital adaptability index measurement model for SMEs. The model defines positive utility as the weighted sum of belief values and attribute weights of perceived attributes, and negative utility as the product of perceived cost and cost weight. The net utility is defined as the adaptability index. Hierarchical thresholds are set to classify SMEs into three levels: high, medium, and low adaptability. Furthermore, based on importance-performance analysis (IPA), an obstacle identification model is constructed, where attribute weights serve as importance indicators and belief values serve as performance indicators. Key obstacles are identified and improvement priority scores are calculated to achieve ordered ranking. This paper aims to provide an operable model tool for the diagnosis and tiered intervention of digital transformation in SMEs.
Study on Green Supply Chain Decisions under Different Power Structures
This paper investigates decision-making issues of green supply chains under alternative power structures. Assuming that the manufacturer and two retailers are fully rational and risk-neutral decision-makers, three game-theoretic models are established corresponding to manufacturer-led dominance, retailer-led dominance and balanced bilateral power, whose equilibrium solutions are derived analytically. Comparative analysis and numerical simulation of the three game models are implemented to identify the impacts of disparate power structures on the decision-making behaviors and profits of upstream and downstream enterprises within the supply chain. The results reveal that the effects of power structures on green technology investment, order quantity and supply chain members’ profits are predominantly contingent on consumers’ green preference coefficient and the impact coefficient of green technology investment on wholesale price.
Optimization of Fresh Inventory Management in Xinyue Laike Supermarket Based on ABC Classification and a Perishable EOQ Model
Fresh products are difficult to manage because they are perishable, demand changes quickly, and losses are strongly affected by storage and purchasing decisions. Taking Xinyue Laike Supermarket as a case, this paper rewrites the original thesis into a concise academic article. The study first identifies the main problems in the supermarket, including traditional inventory management, subjective ordering, high fruit loss rates, weak demand forecasting, and insufficient supplier cooperation. It then proposes an optimization framework based on ABC classification, a perishable-goods EOQ pricing model, particle swarm optimization, exponential smoothing, and supplier collaboration. The rewritten framework shows that high-value fruits should be controlled strictly, medium-value fruits should be supported by demand forecasting, and low-value fruits should be managed through stable cold-chain cooperation and simplified replenishment rules. The research provides a practical and operable method for reducing fruit inventory cost and improving fresh-food inventory efficiency in small supermarkets.
Port Throughput Prediction based on ARIMA Model and Holt-Winters Exponential Smoothing Method
Port throughput is a key indicator of port operation efficiency and economic contribution. Accurate forecasting helps improve port management and risk control. This paper uses monthly container throughput data of Shanghai Port from 2013 to 2023 to compare the ARIMA model and Holt-Winters exponential smoothing method. After trend and stationarity tests, ARIMA (1,1,2), Holt-Winters additive, and multiplicative models are constructed. Model performance is evaluated using MAE, RMSE, and MAPE. The results show that the Holt-Winters multiplicative model performs best and is used to forecast throughput from January to June 2025.
Resilient Supply Chain Network Design under Joint Facility-Road Disruptions
Industrial supply chain networks are increasingly exposed to compound disruptions caused by facility failures and road interruptions. These disruptions may reduce supply capacity, block transportation links, and lead to unmet demand. To address this problem, this paper studies the resilient design of a regional industrial supply chain network under joint facility-road disruptions. A two-stage robust optimization model is developed. In the first stage, facility location, facility protection, and road protection decisions are made before disruptions occur. In the second stage, transportation reallocation and shortage recourse decisions are optimized after the worst-case disruption scenario is realized. To solve the resulting min-max-min model, a column-and-constraint generation algorithm is used. The algorithm decomposes the original problem into a master problem and a subproblem. The master problem determines the first-stage network design, while the subproblem searches for the worst-case joint disruption scenario. A case study based on the electronic product supply chain in Guangdong Province is conducted to test the model. The results show that the proposed method can identify critical facilities and key roads, improve post-disruption service performance, and reduce shortage losses under extreme disruption scenarios. This study provides a practical decision-making framework for resilient supply chain network design under compound disruption risks.
Research on the Influencing Factors of Users' Intention to Use TikTok Short Video App
TikTok short videos are a product of advancements in internet technology and modern information technology. They provide users with a wide variety of content and knowledge. Due to their ease of use, convenience, entertainment value, and feedback interaction, among other advantages, the number of TikTok app users continues to grow. However, given the intense competition in the app market, it is particularly important to ensure user retention. Therefore, this study employs SPSS 16.0 and AMOS 17.0 software to empirically test the model of factors influencing users' willingness to use TikTok. The findings indicate: ease of use positively influences users' willingness to use TikTok, entertainment value positively influences users' willingness to use TikTok, and feedback interaction positively influences users' willingness to use TikTok. Among these, ease of use has the greatest impact on users' willingness, with a specific value of 0.33, followed by entertainment value with a value of 0.21, while feedback interaction has the smallest impact, with a value of 0.21.
Research Hotspots and Trends in International Public Panic Buying Behavior
This study employs bibliometric methods and knowledge mapping to analyze 164 papers on panic buying from the Science Citation Index Expanded database. It examines the research landscape, identifies key hotspots, traces evolutionary pathways, and proposes future directions. The findings reveal that: 1) Annual publication output has transitioned through three distinct phases: a low-frequency exploratory period, an explosive growth period, and a stable development period. 2) A novel method for identifying research hotspot clusters—based on core keywords, cluster labels, and central terms—is proposed, leading to the identification of four major knowledge clusters: triggering contexts, psychological drivers, behavioral manifestations, and coping strategies. 3) Research topics exhibit clear evolutionary phases: an early exploratory stage (pre-2019), a pandemic-driven systematic research stage (2020–2022), and a stage of theoretical model refinement and innovation (post-2023). 4) Future research should progress systematically along four dimensions: theoretical foundations, content expansion, methodological innovation, and practical applications.
Prefabricated Wall Panels: A Review of Structural Systems, Mechanical Properties, and Evaluation Indices
With the rapid acceleration of prefabricated construction worldwide, new architectural wall panels have emerged as a critical vanguard of sustainable, high-efficiency engineering. Serving as a new generation of cement-based composites and lightweight structural assemblies, modern prefabricated panels feature ultra-high strength, exceptional toughness, superb thermal or acoustic insulation, and outstanding durability. This paper provides a comprehensive, deep-dive academic review synthesizing recent advancements across ultra-high space embedded wall panels, autoclaved lightweight aerated concrete (ALC) panels, light steel keel wallboards, and layered concrete-insulated composite systems. By aggregating recent empirical and numerical datasets, this review systematically dissects material composition optimization, joint connection mechanisms (rigid vs. flexible), structural reinforcement parameters, and multi-criteria lifecycle evaluation index systems. The findings demonstrate that while prefabricated wall technologies are maturing rapidly, critical challenges remain regarding the optimization of structural composite action, the elimination of localized thermal bridges, and the harmonization of objective-subjective lifecycle weighting. This review highlights prospective research paradigms to guide multi-scale testing, numerical standardization, and intelligent construction in green infrastructure.
Experimental Study on the Mechanical Response and Seepage Characteristics of Low-Permeability Reservoirs under Pressure-Driven Water Injection Conditions
To address the issues of insufficient injection capacity and slow energy replenishment associated with conventional waterflooding development in low-permeability oil reservoirs, this study takes typical low-permeability core samples from the X block in the eastern Yin'e Basin as the research subject. A systematic series of experiments on rock mechanics, single-phase and two-phase seepage, and imbibition is carried out to reveal the mechanical response of the reservoir, the evolution of seepage capacity, and the redistribution characteristics of residual oil under pressure-driven water injection conditions. The results indicate that pressure-driven water injection can significantly improve pore-throat connectivity and flow pathways, thereby enhancing the co-permeability of oil and water phases. In addition, imbibition oil displacement facilitates further mobilization of residual oil in medium and large pores.
Collaborative Dynamic Optimization of New Energy Vehicle Power Battery Recycling and Reuse Strategies Driven by Blockchain
Effective recycling and reuse of retired power batteries have become a critical issue for their sustainable development in the future. Currently, the market for retired power battery recycling is plagued by problems such as mixed recycling channels and lack of information transparency. Blockchain offers solutions for the full life-cycle management of power batteries. Thus, how to leverage blockchain to promote the efficient recycling and reuse of retired power batteries has emerged as a key issue. Considering two recycling channels and reuse modes empowered by blockchain, this paper establishes long-term dynamic decision-making models of the power battery closed-loop supply chain (PB-CLSC) using differential game, derives the feedback equilibrium strategies of all stakeholders. Furthermore, the impact of different recycling and reuse strategies with blockchain is thoroughly investigated, identifying the optimal recycling and reuse strategies.
Constructing and Validating a Performance Evaluation System for Highly Qualified Specialists: A Mixed-Methods Approach Based on Rough Set Theory and Analytic Hierarchy Process
The effective performance evaluation of highly qualified specialists (HQS) remains a persistent challenge for Chinese enterprises transitioning to knowledge-intensive operations. Traditional evaluation systems, designed for routine work, fail to capture the multidimensional nature of HQS contributions, which encompass cognitive engagement, value congruence, and affective attachment. This study addresses this gap by developing and validating a comprehensive performance evaluation system specifically tailored to HQS. Using a mixed-methods design, the study first identified 22 candidate indicators through literature review and expert interviews. Rough set theory was then applied to reduce redundant indicators, yielding a parsimonious 17-indicator system organized across four dimensions: work engagement (5 indicators), organizational identification (4 indicators), value identification (4 indicators), and emotional belonging (4 indicators). Analytic Hierarchy Process (AHP) was subsequently employed to determine indicator weights, revealing that value identification (weight = 0.4564) and emotional belonging (weight = 0.2219) are the most influential criterion-level dimensions. At the indicator level, company culture (0.1896) and management systems (0.1336) emerged as the most heavily weighted individual measures. Confirmatory factor analysis (CFA) of survey data from 562 HQS in Chinese enterprises demonstrated good model fit (χ²/df = 2.462, GFI = 0.940, CFI = 0.939, RMSEA = 0.051), providing empirical validation of the proposed system. This study contributes a theoretically grounded, empirically validated, and practically operational evaluation system that enables Chinese enterprises to assess HQS performance more comprehensively and systematically.
Research on the Transformation Path of Old Residential Communities in Xi’an from the Perspective of Complete Community
China’s urban development has entered a stage dominated by stock renewal, and the renovation of old residential communities has become an important starting point for high-quality urban development. The concept of complete community and the 15-minute living circle planning provide key guidance for the renewal of old communities. Based on the perspective of human geography, this paper takes old communities in Xi’an as the research object, constructs a three-dimensional analysis framework of “spatial facilities – service system – governance mechanism”, and systematically analyzes the practical dilemmas and explorations in the renovation of old communities. The study finds that the renovation of old communities in Xi’an is confronted with the coexistence of aging infrastructure and spatial resource constraints, mismatched supply and demand of public services and prominent shortcomings in full-age services, immature multi-stakeholder co-governance mechanism and lack of long-term management. Although distinctive experience has been formed in Party-building leading grassroots governance, there are still multiple tensions between patchwork renovation and systematic renewal demand, administrative leadership and resident participation. On this basis, it puts forward the optimization path of complete community construction from three dimensions: spatial reshaping, service embedding and governance restructuring, emphasizing solving spatial constraints through systematic renewal, filling service shortcomings through experience orientation, and building a long-term mechanism through multi-stakeholder co-governance. The study points out that the ultimate goal of old community renovation is to realize the leap from “living container” to “complete living community”. The construction of complete community is not only a realistic path for urban renewal in Xi’an, but also a livelihood practice to improve residents’ sense of happiness. It also reveals the internal connection between spatial and social dimensions in the renovation of old communities, providing a reference for the renewal of old communities in similar cities.
Optimization Design of Workshop Facility Layout for Company A
With the continuous growth of China’s economy and the improvement of living standards, consumers have increasingly higher expectations for product quality. To meet market demand and promote industrial upgrading, enterprises are accelerating product renewal and placing greater emphasis on product design and quality assurance. However, this has also led to rising operating costs and declining profit margins. Therefore, reducing unnecessary daily expenses has become an important issue for enterprises, and workshop layout optimization is one effective way to improve operational efficiency and reduce costs. FlexSim is a computer-based simulation software used to model discrete-event processes, and it is widely applied in manufacturing, material handling, and office workflow analysis. In this study, POSCO-OSTEM (Suzhou) Automotive Parts Co., Ltd. in Kunshan is selected as the research object. Based on its workshop layout and production process, a production flow simulation model is established. Following the Systematic Layout Planning method, the production process and workshop layout are analyzed and improved, with the aim of developing a practical layout optimization scheme to reduce on-site management costs.
Research on Chain Supermarket Inventory Optimization based on Multi-dimensional Spatio-temporal Data
With the increasing scale and complexity of chain supermarkets, inventory management in multi-dimensional spatio-temporal data environments becomes more challenging, as traditional methods fail to handle regional demand heterogeneity. This paper proposes an integrated inventory management approach that leverages spatio-temporal data by combining the DeepAR probabilistic forecasting model with the newsvendor model for region-specific optimization. Sales regions are divided into five areas, and four demand-influencing features are selected. The DeepAR model, with a Gaussian likelihood, estimates demand distributions for each region. Experimental results show stable training convergence and high prediction interval coverage (PICP above 0.85 for most regions), while adaptively widening intervals in data-scarce or highly seasonal regions. These forecasts are then used in the newsvendor model to determine optimal order quantities by minimizing overage and underage costs. The proposed framework enables differentiated inventory strategies, improving service levels and operational efficiency.
Research on the Profound Impact of Automobile Safety on Road Traffic and Social Stability
Automotive safety is the cornerstone of transformation in the transportation sector and a key pillar for safeguarding road traffic safety and sustaining the steady development of the industry. The advancement of automotive safety is not only pertinent to the lives, health and property security of road users, but also exerts a profound impact on the high-quality development of the automotive industry and the harmonious and stable operation of society. Continuously promoting the improvement of automotive safety levels is of great significance.
Research Progress of Intelligent Detection Technology and Equipment for Diseases in Cable-Supported Bridge Systems
Bridges are important infrastructure for China's modernization. Currently, China's bridge construction scale, technical level, especially the construction level of long-span bridges, has leapt to the forefront of the world. Cable-supported bridges include cable-stayed bridges and suspension bridges. For bridge projects with a span exceeding 1000 m, cable-supported bridges are the only bridge type choice. Many early-built long-span cable-supported bridges at home and abroad have experienced service performance degradation during their service life. The actual service life of bridges is far shorter than the design life, one of the important reasons being the lack of management and maintenance. The domestic mainstream bridge maintenance management method is manual inspection, which is relatively passive, time-consuming, and has high repair costs. In recent years, UAV technology, robotics, machine learning algorithms, and LiDAR point cloud-based 3D reconstruction technology for buildings have made significant progress. Researchers have developed various bridge disease detection equipment and methods, some of which have been applied in practical engineering. This paper mainly introduces the main disease forms of cable-supported bridges and some research directions and achievements of intelligent detection technology, and analyzes the advantages and disadvantages of different intelligent detection technologies and equipment.
Geochemical Characteristics of Shale Desorbed Gas in the Shanxi Formation of the Ordos Basin
The Shanxi Formation in the Ordos Basin is an important strata for continental shale gas exploration in China. The study of the geochemical characteristics of its desorbed gas and the main controlling factors of reservoir formation is of great significance for resource potential assessment and development optimization. Based on the core samples from Well Chang96 and Well Yan2156 in the southeastern part of the basin, this paper systematically analyzes the characteristics of desorbed gas from shales in the Shanxi Formation. The research results show that: (1) The shale gas in the Shanxi Formation is mainly high-maturity thermogenic dry gas. The proportion of methane ranges from 80% to 93% (with an average of 80.05% in Member 1 and 84.02% in Member 2). The content of heavy hydrocarbons is extremely low (<0.3%), and the methane coefficient is >0.99. The non-hydrocarbon gases are mainly CO₂ and N₂ (with an average of 14% - 16%), and some are affected by air mixing. (2) The content of desorbed gas is closely related to lithology and organic matter abundance. Carbonaceous mudstone has the highest desorbed gas content, while fine-grained rocks such as mudstone and siltstone have significantly lower desorbed gas content, which is mainly controlled by differences in organic carbon content. (3) The desorbed gas content of shale gas in the Shanxi Formation of the Ordos Basin is low, which restricts resource potential assessment and favorable area prediction. This study provides a scientific basis for the efficient exploration and development of shale gas in the Ordos Basin and contributes to the improvement of the theoretical and technical systems for continental shale gas.
Study on the Bonding Performance and Reinforcement Mechanism of the Second Interface of Cement Sheath
To address the engineering challenge of debonding failure of the “second interface” (between the cement sheath and the formation rock) induced by alternating thermal-mechanical loads during geothermal well cementing, this study systematically investigated the reinforcement mechanism of a hybrid system of graphene and carbon nanotubes (CNTs) on the second-interface bonding performance of cement stone, using typical sandstone cores from the Ordos Basin as the substrate. The evolution of interfacial bond strength and failure mode under different CNTs contents (0%–0.12%) was evaluated by push-out shear tests. Combined with microstructural characterization techniques such as FE-SEM and EDS, the densification and toughening mechanisms of the interfacial transition zone (ITZ) were revealed from the perspectives of morphological evolution, element migration, and chemical bonding. The results showed that the 28-day second-interface bond strength reached 8.20 MPa, representing an approximately 15-fold increase compared to the baseline group (0.51 MPa). The shear stress–slip curve transitioned from a brittle “cliff-like” drop to a “pseudo-ductile” failure with serrated rebounds, indicating significantly enhanced fracture toughness. Microstructural analysis confirmed that graphene provided “template nucleation” and a two-dimensional skeleton, while CNTs played a “bridging–pull-out” energy dissipation role spanning cracks. Together, they constructed a “line-surface” three-dimensional interpenetrating network, reducing the interfacial Ca/Si atomic ratio from 0.59 to 0.43 and driving the interface bonding from physical van der Waals forces to chemical covalent bonds (Si–O–Si/Si–O–Ca). Excessive CNTs (≥0.12%) led to agglomeration, forming stress concentration sites and causing a decline in strength. This study provides a theoretical basis and material proportioning reference for the integrated design of “high thermal conductivity” and “strong interface” in geothermal well cementing.
Numerical Simulation Study on Methane Transport and Dispersion in Soil under Different Leakage Locations
To further investigate the influence of leakage direction in buried pipelines on gas diffusion within soil, this study develops a fluid–solid coupled numerical model based on the unified Darcy–Brinkman–Biot equations. The migration behavior of methane and crack evolution characteristics under single-side, adjacent double-side, and three-side leakage conditions are systematically simulated and compared. The results show that leakage direction directly determines the geometric configuration of soil cracks by altering the initial kinetic energy distribution and local gas pressure. As the number of leakage directions increases, the number of cracks increases significantly; however, due to energy dispersion, the length and width of individual cracks decrease. The three-side leakage condition exhibits the highest peak pressure in the initial stage (exceeding 3700 Pa), which drives the formation of a more complex and dispersed crack network. In addition, the evolution of methane concentration fields is highly consistent with crack development. As the main cracks penetrate the boundaries and form stable flow channels, the gas transport mechanism transitions from pore diffusion to rapid transport along crack pathways, exhibiting a pronounced channeling effect. These findings provide important numerical evidence for urban pipeline safety monitoring and risk assessment.
Selection and Application Research of LNG Vaporizer
In the context of global energy transition and sustainable development, liquefied natural gas (LNG) is becoming more and more prominent due to its clean and efficient advantages. As a key equipment in the LNG industry chain, gasifiers have a significant impact on their performance and efficiency, and different types of gasifiers have their own characteristics. This study aims to deeply explore the selection and application of LNG gasifiers, and establish scientific selection methods and application strategies through a comprehensive analysis of different types of gasifiers. From a theoretical point of view, it can fill the gaps in the comprehensive evaluation and systematic comparison of existing studies. In practice, it can ensure stable energy supply, reduce costs, and promote industrial technological progress. There has been progress in this field at home and abroad, but there are still problems such as imperfect multi-objective optimization and the need to strengthen reliability and safety. The innovation of this study lies in the multi-dimensional comprehensive analysis, the combination of qualitative and quantitative, and the focus on new technologies and application scenarios, providing strong support for LNG projects.
Multimodal Intelligence Improves Maritime Congestion Forecasting and Port Optimization
Maritime congestion at major global container ports represents one of the most pressing logistical bottlenecks of contemporary trade, with cascading consequences that permeate supply chains, freight economics, and carbon accounting simultaneously. This paper proposes a multimodal intelligence framework that integrates heterogeneous data streams — encompassing AIS positional records, meteorological observations, historical berth logs, and satellite-derived imagery — within a unified deep learning pipeline to forecast port congestion and optimize berth scheduling. A CNN-LSTM-GRU hybrid network with a cross-modal attention fusion layer is trained and evaluated on two-year real-world vessel traffic data from the Port of Shanghai and the Port of Singapore. Experimental results demonstrate that the proposed framework achieves a 23.7% reduction in MAE and a 19.4% reduction in RMSE relative to single-modality LSTM baselines. A downstream berth optimization module, driven by the congestion prediction output, reduces mean vessel waiting time by 17.6% under controlled simulation conditions. These findings confirm that multimodal data fusion constitutes a critical architectural principle for next-generation port intelligence systems.
Causal Transformers Reveal Disruption Cascades in Critical Mineral Supply Networks
Critical mineral supply chains—encompassing lithium, cobalt, and rare earth elements (REE)—underpin the global clean energy transition yet remain acutely vulnerable to multi-tier disruption cascades. Existing forecasting frameworks based on recurrent architectures or static Bayesian networks capture sequential dependencies but fail to distinguish genuine causal pathways from spurious temporal correlations, limiting their capacity to identify which network nodes amplify shocks across tiers. This paper introduces a causal Transformer (CT) architecture that integrates multi-head attention with a structural causal model (SCM) layer constrained by a directed acyclic graph (DAG) encoding domain knowledge. Applied to a four-tier network spanning extraction, processing, intermediate manufacturing, and end-use assembly for lithium-ion battery and permanent-magnet supply chains, the CT achieves F1 scores of 0.823–0.891 for cascade onset detection and a mean absolute error (MAE) of 0.97–1.31 tiers for propagation depth, outperforming long short-term memory (LSTM), gated recurrent unit (GRU), standard Transformer, and static Bayesian network baselines. Causal attribution identifies processing-tier geographic concentration as the dominant cascade amplifier, with Democratic Republic of Congo cobalt refining and Chinese REE separation collectively responsible for 63% of detected cascade events. Results carry direct policy implications, arguing for strategic stockpiling calibrated to processing-tier inventory compression cycles rather than extraction-tier geological events.
Optimization of Pricing for Priority Use of Port Berths
With the growth of container shipping demand and the increasing size of vessels, the scarcity of frontline port resources, such as berths and quay cranes, has become increasingly prominent. Traditional port operations usually treat berth allocation, quay crane assignment and charging mechanisms as separate decisions, making it difficult to fully reflect the internal relationship among resource scarcity, differentiated service demand and port revenue management. As a differentiated service with service commitment attributes, berth priority usage rights can guide vessels’ choices through pricing mechanisms and provide a new approach for optimizing port resource allocation and improving revenue. Therefore, this thesis studies the pricing problem of berth priority usage rights, focusing on the coordinated optimization relationship among priority pricing, berth planning and quay crane allocation. This thesis first defines the service connotation of berth priority usage rights, regarding them as differentiated services in which the port provides vessels with earlier latest service completion commitments. On the demand side, reservation price and consumer utility theory are introduced to describe vessels’ willingness to pay for different priority levels. Vessels make choices according to the net utility formed by the difference between the reservation price and the actual price of each level. When positive net utility exists, a vessel chooses the priority level with the highest utility; when all paid priority levels fail to provide positive net utility, the vessel chooses the non-priority option. In this way, the priority price is not only a charging parameter, but also an important decision variable that affects vessels’ priority-level choices, demand structure and resource allocation results.
Research on Vehicle Scheduling Method in Logistics Distribution
Vehicle distribution is a critical link in logistics activities that directly connects with customers, and distribution costs account for a significant proportion of overall logistics operations. This paper aims to solve the vehicle scheduling problem faced by logistics enterprises in practice and proposes an optimization model for the Capacitated Vehicle Routing Problem based on the Ant Colony Algorithm. The main purpose of the study is to improve vehicle utilization efficiency and reduce operating costs by optimizing distribution paths. The experimental results show that the Ant Colony Algorithm has high efficiency and feasibility in solving the vehicle scheduling problem with capacity constraints, effectively reducing logistics distribution costs and providing a viable solution for practical logistics vehicle scheduling.
Suitability Site Selection of LNG Refueling Stations along Pinglu Canal Based on GIS and Combination Weighting Method
With the official opening of the Pinglu Canal, the core backbone project of the New Western Land-Sea Corridor, the demand for clean energy refueling infrastructure for inland shipping has increased sharply. Liquefied Natural Gas (LNG), as a low-carbon and efficient ship fuel, has become the main direction for the green transformation of inland shipping. However, the current site selection of LNG refueling stations along inland rivers faces problems such as single evaluation dimension, strong subjectivity in weight determination, and lack of spatial quantitative analysis. To address these issues, this paper constructs a suitability evaluation index system for LNG refueling stations along the Pinglu Canal from four dimensions: waterway and navigation conditions, market demand conditions, resource and infrastructure conditions, and safety and environmental conditions. A combination weighting model integrating Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) is established to determine the index weights, which effectively balances the subjectivity of expert judgment and the objectivity of data information. Combined with Geographic Information System (GIS) spatial analysis technology, the single-factor raster layers are superimposed and analyzed to obtain the comprehensive suitability distribution map of LNG refueling stations. The results show that the highly suitable areas for LNG refueling stations along the Pinglu Canal are mainly concentrated in the Qinzhou Port section, Luwu Town section and Xijin Reservoir area of Hengzhou, accounting for 4.2%, 2.5% and 2.0% of the total study area respectively (totaling 8.7% of the total region). Finally, 5 optimal alternative sites are proposed, which can provide scientific decision-making basis for the planning and construction of LNG refueling infrastructure along the Pinglu Canal.