Management Applications Research Articles

Bridge Digital Twin for Practical Bridge Operation and Maintenance by Integrating GIS and BIM

As an emerging technology, digital twin (DT) is increasingly valued in bridge management for its potential to optimize asset operation and maintenance (O&M). However, traditional bridge management systems (BMS) and existing DT applications typically rely on standalone building information modeling (BIM) or geographic information system (GIS) platforms, with limited integration between BIM and GIS or consideration for their underlying graph structures. This study addresses these limitations by developing an integrated DT system that combines WebGIS, WebBIM, and graph algorithms within a three-layer architecture. The system design includes a common data environment (CDE) to address cross-platform compatibility, enabling real-time monitoring, drone-enabled inspection, maintenance planning, traffic diversion, and logistics optimization. Additionally, it features an adaptive data structure incorporating JSON-based bridge defect information modeling and triple-based roadmap graphs to streamline data management and decision-making. This comprehensive approach demonstrates the potential of DTs to enhance bridge O&M efficiency, safety, and decision-making. Future research will focus on further improving cross-platform interoperability to expand DT applications in infrastructure management.

Qingke Pingchuan granules alleviate airway inflammation in COPD exacerbation by inhibiting neutrophil extracellular traps in mice

BackgroundChronic obstructive pulmonary disease (COPD) imposes a significant global health and socioeconomic burden. Exacerbations of COPD (ECOPD), characterized by heightened airway inflammation and mucus hypersecretion, adversely affect patient health and accelerate disease progression. Qingke Pingchuan (QKPC) granules, a formulation from Traditional Chinese Medicine initially prescribed for acute bronchitis, have shown unexplored potential in ECOPD management, with mechanisms of action yet to be clarified. PurposeThis study investigates the therapeutic effects of QKPC in a mouse model of ECOPD, focusing on underlying molecular mechanisms. MethodsCOPD was induced in mice through chronic cigarette smoke (CS) exposure, followed by intratracheal administration of Pseudomonas aeruginosa lipopolysaccharide (LPS) to trigger exacerbation, after which mice were treated with QKPC granules. Major compounds in QKPC were identified via UHPLC-QE-MS, and high-throughput RNA sequencing of lung tissue samples identified differentially expressed genes. Transcriptomic data were integrated with network pharmacology analysis to pinpoint potential pathways, bioactive compounds, and target genes through which QKPC might attenuate ECOPD. Molecular docking, protein-small molecule binding assays, and in vitro analyses further validated interactions between key compounds and target genes, shedding light on plausible signaling pathways. ResultsQKPC treatment led to significant reductions in airway leukocyte infiltration and goblet cell metaplasia in CS- and LPS-exposed mice, accompanied by decreased levels of inflammatory cytokines (IL-6, IL-1β, CXCL1, and TNF-α) and mucin MUC5AC in bronchoalveolar lavage fluid. The integrative transcriptomic and network pharmacology analysis identified the neutrophil extracellular trap (NET) formation pathway as a key mechanism underlying QKPC's protective effect against ECOPD. In vitro assays demonstrated that epigallocatechin-3-gallate (EGCG) and quercetin, two important bioactive compounds in QKPC, significantly inhibited NETosis induced by cigarette smoke extract (CSE) plus LPS in human neutrophils. The two compounds were found to interact directly with the reactive oxidative species (ROS)-generating enzyme NOX2 and its regulatory subunit p47phox. Subsequent in vitro studies further confirmed EGCG and quercetin's capacity to reduce ROS production and downregulate NOX2 and p47phox protein levels in neutrophils stimulated with CSE and LPS. Additionally, in vivo studies confirmed QKPC's efficacy in reducing NET formation, oxidative stress, and NOX2/p47phox protein expression in the lung tissue of ECOPD mice. ConclusionThese findings suggest that QKPC granules alleviate airway inflammation in ECOPD, potentially through inhibition of pulmonary NET formation via the NOX2/p47phox-ROS pathway, underscoring their potential therapeutic application for ECOPD management in clinical settings.

Integrating Policy Instruments for Enhanced Urban Resilience: A Machine Learning and IoT-Based Approach to Flood Mitigation

In the context of global urbanization, the interconnected architecture of economic, social, and administrative activities in modern cities cultivates a complex web of interdependencies. This intricacy amplifies the impacts of natural disasters such as urban flooding, presenting unprecedented challenges in risk management and disaster responsiveness. To address these challenges, this study defines the concept of urban flood resilience and outlines its practical applications in flood risk management, proposing an integrated resilience governance framework. The framework systematically enhances urban flood management by combining structural flood mitigation methods with advanced technologies, including the Internet of Things (IoT) and non-structural decision-support tools powered by Machine Learning Algorithms (MLAs). This integrated approach aims to improve early flood warning systems, optimize urban infrastructure planning, and reduce flood-related risks. The case study of the Cypress Creek watershed validates the framework’s effectiveness under specific scenarios, achieving reductions of 25% in inundation area, 30% in peak flow, and 20% in total flood volume. These results not only demonstrate the framework’s efficacy in mitigating flood impacts but also provide empirical support for developing resilient urban governance models, highlighting the essential role of adaptive policy instruments in urban flood management.

Fabrication of multifunctional cu-coated melamine foam by electroless chemical deposition technique for thermal management, EMI shielding, and antibacterial applications

Melamine foam (MF) inherently possesses excellent flame-retardant properties due to its ability to release nitrogen upon combustion. Its porous and loose structure also contributes to its high flexibility, breathability, and sound absorption capabilities. Incorporating copper nanoparticles (Cu NPs) significantly enhances the material's electrical conductivity, including its electromagnetic shielding effectiveness, thermal conductivity, and antibacterial properties. This, in turn, greatly broadens the material's potential applications. This research investigated the coating of Cu NPs on three-dimensional MF using an electroless chemical deposition technique to fabricate multifunctional materials. The electromagnetic shielding effectiveness (SET) increased to 85 dB as the Cu NPs content rose from 0 to 0.31 g. Similarly, the electrical conductivity increased from 0.19 to 357 S/m. These results indicate that the Cu-coated MF has high EMI shielding and conductivity properties. The strong adhesion of the Cu NPs to the MF surface created a coating with low infrared emissivity, contributing to the material's excellent thermal insulation properties. With a modest supply voltage of around 4 V, the material exhibited promising electrical conductivity, generating additional warmth of up to 74 °C through efficient joule heating. Furthermore, the Cu-coated MF demonstrated 100 % antibacterial activity, making it a promising candidate for a variety of applications.

Recent Progress and Opportunities of Wearable Non-Invasive Epidermal Sensors for Skin Disease Diagnosis.

With deteriorating environment and increased stress in modern life, skin diseases have become the fourth leading cause of nonfatal and chronic diseases. An early diagnosis might improve the chances of a successful treatment. Wearable epidermal sensors have been emerged as new non-invasive tools for clinical practice and research in dermatology, which can act as a complement to the otherwise mostly visual and tactile judgments. This review discusses the recent progress and opportunities of wearable epidermal sensors for skin disease diagnosis. The configuration, material choice, and fundamental platforms of wearable epidermal sensors are first summarized. Then, their emerging application in monitoring skin diseases is demonstrated by detecting skin hardness, skin hydration, and biomakers. With the advances highlighted here and the ongoing research efforts, the continuous breakthrough in wearable epidermal sensors and their attractive application in skin disease management is foreseeable in the future.

Eugenol targets laccase Cglac4 to subvert the pathogenicity of Colletotrichum gloeosporioides and control the postarvest fruit anthracnose

Eugenol has a high antifungal activity against filamentous fungi, so it is essential to elucidate the underlying mechanisms of eugenol controling disease. However, the mechanism underlying the target protein of eugenol is largely unclear. Here, we revealed that eugenol efficiently suppressed the growth and development of Colletotrichum gloeosporioides, the pathogen of postharvest fruit anthracnose, which caused significant economic losses. Moreover, we identified the target protein of eugenol in C. gloeosporioides-Cglac4, which positively regulated pathogenicity via inhibited the conidial germination and appressorium formation. Taken together, the identification of the eugenol-Cglac4 pair module not only illustrates a novel mechanism by which eugenol subvert the laccase activity of C. gloeosporioides to orchestrate the pathogenicity but also provides a potential application in the green management of postharvest anthracnose.

Global Assessment of Mesoscale Eddies with TOEddies: Comparison Between Multiple Datasets and Colocation with In Situ Measurements

The present study introduces a comprehensive, open-access atlas of mesoscale eddies in the global ocean, as identified and tracked by the TOEddies algorithm implemented on a global scale. Unlike existing atlases, TOEddies detects eddies directly from absolute dynamic topography (ADT) without spatial filtering, preserving the natural spatial variability and enabling precise, high-resolution tracking of eddy dynamics. This dataset provides daily information on eddy characteristics, such as size, intensity, and polarity, over a 30-year period (1993–2023), capturing complex eddy interactions, including splitting and merging events that often produce networks of interconnected eddies. This unique approach challenges the traditional single-trajectory perspective, offering a nuanced view of eddy life cycles as dynamically linked trajectories. In addition to traditional metrics, TOEddies identifies both the eddy core (characterized by maximum azimuthal velocity) and the outer boundary, offering a detailed representation of eddy structure and enabling precise comparisons with in situ data. To demonstrate its value, we present a statistical overview of eddy characteristics and spatial distributions, including generation, disappearance, and merging/splitting events, alongside a comparative analysis with existing global eddy datasets. Among the multi-year observations, TOEddies captures coherent, long-lived eddies with lifetimes exceeding 1.5 years, while highlighting significant differences in the dynamic properties and spatial patterns across datasets. Furthermore, this study integrates TOEddies with 23 years of colocalized Argo profile data (2000–2023), allowing for a novel examination of eddy-induced subsurface variability and the role of mesoscale eddies in the transport of global ocean heat and biogeochemical properties. This atlas aims to be a valuable resource for the oceanographic community, providing an open dataset that can support diverse applications in ocean dynamics, climate research, and marine resource management.

Community Based Management Of Sustainable Mangrove Ecosystems In Kendari City, Southeast Sulawesi

Mangrove ecosystems are a form of important role in maintaining the stability of the coastal environment that has ecological, physical and economic-social functions so as to provide interaction between people who live in mangrove areas. efforts in managing sustainable mangrove ecosystems in order to provide carrying capacity to all parties so as to identify, analyze, determine the needs and potential of natural resources in mangrove ecosystems. Mangrove ecosystem damage is caused by two things, namely community activities and natural factors so as to prevent degradation, the application of sustainable mangrove ecosystem management is carried out optimally to preserve mangroves. The study aims to determine the observation of ecosystems and community concepts in sustainable mangrove management in Kendari City. This research method uses descriptive quantitative with sampling techniques using cluster sampling and direct observation data collection. The results of this study indicate that the results of observations from two areas in Kendari City, namely mangroves in the Nambo area with observations of natural / bio and physical environment score range 23 is quite fulfilling for mangrove management while mangroves in the Bungkutoko area score range 37 is very fulfilling said to be sustainable for mangrove management and the results of the questionnaire statement regarding the concept of management descriptively shows community-based sustainable ecosystem management strongly agrees to be well implemented and can be said to be sustainable in mangrove ecosystem management Kendari City Southeast Sulawesi.

Integration of PSPU-Net GI Neural Network with Gravity Data for Enhanced 3D Basement Relief Estimation

Estimating the sediment-basement interface is critical to understanding basin evolution and its applications in energy, water resources, and seismic risk management. We develop PSPU-Net GI (gravity inversion) network, a deep-learning approach combining Pyramid Scene Parsing Network and U-Net, for gravity data to recover the sediment-basement interface. Training and validation involve smoothed basement models generated from random rectangles followed by filtering. We also incorporate uplifted basements and intrusions to enhance performance in complex geological contexts. Numerical results for synthetic models demonstrate PSPU-Net GI's effective recovery of sediment-basement interface relief. To improve field data predictions, we implement transfer learning and normalization strategies. Transfer learning constructs a small number of additional basement models based on the site-specific prior information and fine-tunes the neural network trained on the original general models. Normalization strategy provides a convenient way of harnessing depth information from seismic and wells. We apply our framework to the gravity data from the western margin of the Pannonian Basin (Austria). The predictions from the three implementations mentioned above (baseline PSPU-Net GI, PSPU-Net GI + transfer learning, PSPU-Net GI + normalization) successfully characterize the basement relief, and are consistent with results in previous publications. Compared with the prediction from baseline PSPU-Net GI, the prediction accuracies obtained from PSPU-Net GI implementations with the additional transfer learning and normalization components are notably improved.

Inter-species comparison of life traits related to amnesic shellfish toxin kinetic in five pectinid species

Pectinid species (scallops) hold significant economic value, but their filtration activity makes them vulnerable to harmful algal blooms, particularly Pseudo-nitzschia species producing domoic acid (DA). Domoic acid contamination can lead to amnesic shellfish poisoning in humans, causing prolonged fisheries closures and sales bans. This study aimed to compare several pectinid species to investigate if inter-specific differences in energetic traits could be linked to their ability to depurate the toxin. Using Dynamic Energy Budget (DEB) theory and the AmP multi-species estimation procedure, we analysed five species: two slow depurators (Pecten maximus and Placopecten magellanicus) and three hypothesised fast depurators (Argopecten purpuratus, Mimachlamys varia, and Nodipecten subnodosus). Results showed differences among species in assimilation rates, somatic maintenance rates, and reserve mobilisation rates but only the reserve mobilisation rates (i.e. the energy conductance parameter) consistently increased along the “slow-to-fast” depuration gradient. This research lays the groundwork for developing toxin kinetics models to quantify the processes affecting DA accumulation and depuration, and to assess the retention time of DA. Our approach and results will therefore not only be of interest to the DEB community in terms of multi-species approaches, but are likely to have applications in pectinid aquaculture and fisheries management.

Synthetic composite membranes and their manifold applications: A comprehensive review

Synthetic membranes play a crucial role in a wide range of separation processes, including dialysis, electrodialysis, ultrafiltration, and pervaporation, with growing interest in synthetic emulsion membranes due to their precision, versatility, and ion exchange capabilities. These membranes enable tailored solutions for specific applications, such as water and gas separation, wastewater treatment, and chemical purification, by leveraging their multi-layered structures and customizable properties. Emulsion membrane technology, particularly in pressure-driven methods like reverse osmosis (RO) and nanofiltration (NF), has shown great potential in overcoming traditional challenges, such as fouling and energy inefficiency, by improving filtration efficiency and selectivity. This review explores the latest advancements in emulsion membrane development, their adaptability to various industrial needs, and their contribution to addressing long-standing limitations in membrane separation technologies. The findings underscore the promise of emulsion membranes in advancing industrial processes and highlight their potential for broader applications in water treatment, environmental management, and other key sectors.

A rapid estimation of intra-row weed density using an integrated CRM, BTSORT and HSV model across entire video stream of chilli crop canopies

Intra-row weeds have a significant impact on crop yield and health, competing with crops for nutrients, water, and sunlight. Traditional uniform herbicide applications in weed management not only risk harming the environment but can also compromise crop health. Precision spraying technology, guided by machine vision, offers a solution by accurately identifying and targeting weeds, thereby reducing overall herbicide use. Inter-row weed segmentation can be done easily with simple thresholding, but intra-row region weed cannot be segmented with simple thresholding due to many similarities between the intra-row weeds and plants. So, in this study, a novel methodology is introduced to dynamically estimate intra-row weed density for the entire crop row of chilli by integrating ByteTrack Simple Online and Real Time Tracker (BTSORT) with YOLOv7 crop recognition model to track the plant and Hue-Saturation-Value (HSV) color model with simple thresholding to segment weeds between tracks to avoid repetitive intra-row weed density estimation. The weed density between the plants in these regions is calculated and categorized into low, medium, and high levels based on the number of weed pixels in the intra-row region. The YOLOv7 Crop recognition model recognized the chilli plants with achieved a precision of 0.92 and a recall of 0.94 at 47.39 FPS. The BTSORT with YOLOv7 crop recognition model on a test video dataset performed well with MOTA and MOTP of 0.85 and 0.81, respectively. The developed dynamic intra-row weed density estimation method classifies it with an overall accuracy of 0.87. Additionally, the system processed 1280x720 frames 1.38 times faster than 1920x1080 frames, enabling efficient real-time intra-row weed density estimation across full crop rows.

Rotating micropolar hybrid nanofluid: Exothermic/endothermic effects and waste discharge on exponential sheet

This research offers a comprehensive investigation into the steady‐state, three‐dimensional flow of an incompressible hybrid nanofluid over a bi‐directionally stretching exponential sheet. A central aspect of the study is the behavior of rotating micropolar fluids within a Darcy‐Forchheimer porous medium, enhanced by the inclusion of electromagnetic forces. The effects of embedding nanoparticles and in a water‐based fluid are explored to reveal their influence on fluid motion and heat transfer. The thermal dynamics are scrutinized, with particular emphasis on the roles of heat absorption and viscous dissipation. Moreover, this study incorporates both endothermic and exothermic reactions in radiative flows, alongside the impact of Soret and Dufour diffusion effects. The discharge of wastewater is a critical issue in environmental management and industrial applications, underscoring the need for rigorous regulation to prevent water pollution and ensure adherence to environmental standards. This research addresses the non‐Newtonian flow resulting from wastewater discharge, incorporating Arrhenius activation energy in the analysis. Furthermore, the behavior of microorganisms within the flow is studied under velocity slip conditions and a range of convective boundary conditions. Through the application of similarity transformations, the governing differential equations are derived and solved numerically using MATLAB's bvp4c solver, with results depicted through detailed graphical analysis to illustrate the fluid's complex behavior. The heat transfer rate gets reduced by 1.96% with the enhancement in the Eckert number.

Effect of Intensive Nursing Management on Quality of Life and Mental Health in Elderly Gastric Cancer Patients Undergoing Chemotherapy: A Retrospective Study.

Aims/Background Implementing effective nursing management is particularly critical in the case of elderly gastric cancer (GC) patients receiving chemotherapy, who are more vulnerable to risk events. Therefore, this study explored the effect of intensive nursing management on quality of life (QoL) and mental health in elderly GC patients receiving chemotherapy. Methods A total of 155 elderly patients with GC undergoing chemotherapy in Central Hospital Affiliated to Shandong First Medical University from July 2021 to July 2023 were selected as the study subjects. The sample was divided into two groups according to different nursing methods: 74 patients who received intensive nursing management were classified in the observation group, while 81 patients treated with routine nursing management were classified under the reference group. The QoL, mental health and adverse reactions to chemotherapy were compared between the two groups. Results Before nursing management was implemented, there was no significant difference in the scores of the quality of life questionnaire-core 30 (QLQ-C30), patient health questionnaire-9 items (PHQ-9), and the Chinese version of the M.D. Anderson Symptom Inventory (MDASI-C) (p > 0.05). After management, in comparison with the reference group, the observation group showed significantly higher scores in each dimension of QLQ-C30, as well as significantly lower scores in PHQ-9 and MDASI-C (p < 0.001). The incidences of gastrointestinal reaction, myelosuppression, neurotoxicity, and hepatorenal damage were lower in the observation group than in the reference group (45.95% vs 70.37%, 40.54% vs 65.43%, 35.14% vs 53.09%, and 33.78% vs 51.25%, respectively; p < 0.05). Conclusion The application of intensive nursing management in treating elderly GC patients during chemotherapy effectively improves their physical and mental states, ameliorates clinical symptoms and enhances the QoL, showing certain clinical application value.

The Role of Urinary Extracellular Vesicles in Kidney Cancer: Diagnostic and Therapeutic Potential

Renal cancer ranks as the 14th most common cancer globally, with renal cell carcinoma (RCC) being the primary variant, arising from renal tubular epithelial cells; clear cell RCC constitutes about 80% of cases. Despite their limitations, surgery and targeted therapy remain the mainstays of RCC treatment. Regardless of advancements in RCC research, substantial obstacles continue to exist, such as delayed diagnosis, advanced distant metastasis, and drug resistance. As urine is an easily accessible biofluid, the identification of EVs has paved the way for novel biomarker research. Urinary extracellular vesicles (uEVs) are a novel source of biomarkers with potential applications in cancer detection and management, utilizing a less invasive approach. New data indicate that uEVs are crucial in several areas of RCC, containing tumor development, metastasis, immune evasion, and response to drugs. These vesicles facilitate intercellular communication by transporting a variety of bioactive substances, including RNA, DNA, proteins, and lipids, and are released into the extracellular space by the majority of cell types. uEVs RNAs and proteins are presently being investigated for their possible application as diagnostic biomarkers for different types of kidney cancer. This review summarizes the most recent research examining the potential of uEVs cargo as a biomarker for the diagnosis, prognosis, and treatment of renal cancer.

AI-Driven LOPCOW-AROMAN Framework and Topological Data Analysis Using Circular Intuitionistic Fuzzy Information: Healthcare Supply Chain Innovation

Artificial intelligence (AI) stands out as a significant technological innovation, driving progress in diverse areas such as big data analysis, supply chain management, energy efficiency, sustainable development, etc. The present study investigates how AI could contribute to the sustainability of the healthcare supply chain (HSC) and managing medical needs. Medical organizations can boost the logistics of their tasks, reduce pharmaceutical trash, and strengthen revenue projections through the adoption of AI tools. This study aims to provide a structured evaluation of AI-driven solutions for enhancing healthcare supply chain robustness, especially under conditions of uncertainty and complex logistics demands. To determine the investment value of AI applications in HSC management, the current research adopted a revolutionary multi-criteria decision-making (MCDM) methodology tailored to the healthcare sector’s unique demands, including six critical factors. In light of these criteria, six highly technologically advanced AI-based solutions are examined. The implementation of a circular intuitionistic fuzzy set (CIFS) in the instance discussed provides a versatile and expressive way to describe vague and uncertain information. This study leverages the CIF topology to address data complexities and uncover the underlying structural features of a large dataset. At the outset, we adopted the LOPCOW approach, which includes logarithmic variation to assign weights to criteria, whereas the AROMAN method utilizes a powerful two-step normalization technique to rank alternatives, hence guaranteeing a trustworthy and accurate appraisal. A substantial degree of robustness was confirmed by the technique following a comparison of the operators as well as sensitivity testing.

Efficient CO2 adsorption by deoiled flaxseed hydrochar

This study optimizes CO2 adsorption using hydrochar from de-oiled flaxseed (FDOP), a waste byproduct of the oil extraction industry, through hydrothermal carbonization (HTC). The aim is to enhance CO2 capture sustainably and cost-effectively. Using Response Surface Methodology (RSM), in optimal conditions achieved 1153.26 mg/g CO2 adsorption capacity under 215.15 °C synthesis temperature, 3.05 h synthesis time, 0.99 M acid concentration, 8.997 bar pressure, and 25.07 °C adsorption temperature. Statistical analysis (F-value: 44.48, R²: 0.9705) confirmed strong model reliability. Kinetic analysis showed both physical and chemical adsorption, while thermodynamic analysis revealed exothermic behavior (ΔH° values: -46.697 kJ/mol for M-225, -40.230 kJ/mol for MA-203). After 10 regeneration cycles, the adsorption capacity was reduced by only 2.8% and 3.1%, indicating excellent recyclability. This study highlights FDOP-derived hydrochar as a highly efficient, low-cost adsorbent, offering industrial applications for carbon capture and waste management.

Energy‐Aware Power Control Scheme For Water Quality Monitoring Systems

ABSTRACTThis research introduces an innovative power management approach for wireless sensor networks in aquatic environmental monitoring. The study presents a dynamic algorithm that optimizes energy consumption in long range (LoRa) communication nodes by adaptively adjusting transmission power based on sensor–gateway distances. Leveraging GPS data and a log‐normal shadowing model, the method enables efficient power allocation. Field testing in an aquaculture setting demonstrates the energy‐aware power control (EAPC) algorithm's efficacy. Nodes at 500 m from gateways achieve up to 39% reduction in power consumption compared to fixed‐power systems. At 1250 m, savings decrease to 22%, and at 1500 m, to 11%, while maintaining reliable communication. This research advances sustainable aquatic resource management, offering applications in aquaculture, environmental conservation, and water resource management. By optimizing power usage, the approach contributes to more effective long‐term monitoring of aquatic environments.

Leveraging Six Values for Company Performance: Adaptation of Sustainable Business Model Innovation Strategies in Chinese Electric Vehicle Brand Enterprises

Business model innovation is crucial for enhancing company performance. This study aims to investigate the relationship between the six dimensions of sustainable business model innovation and company performance among Chinese electric vehicle brands. A structural equation model is constructed based on a comprehensive literature review and hypothesis development. Using PLS-SEM, this study empirically analyzes questionnaire data collected from the top 12 electric vehicle brands in China to explore the relationship between these six core dimensions and company performance. The results indicate that innovation in “value proposition to customers”, value creation, value delivery, and “value of residual” have a significantly positive impact on the performance of Chinese electric vehicle brands. However, value capture innovation and “value of after-sales service” innovation were not found to be statistically significant. This paper provides an in-depth analysis of the mechanism through which sustainable business model innovation impacts company performance, enriching the theoretical foundation of academic research in this field and broadening its practical applications in management.

An innovative hollow-cable dome structure for indoor cooling

In the context of achieving low-carbon and environmentally friendly building solutions, structural design and internal comfort are equally critical considerations for modern buildings. To meet the spatial requirements of long-span structures and ensure internal comfort, this paper proposes an innovative hollow-cable dome structure. By substituting conventional cables with flexible pipes, the structure utilizes the continuous characteristic of cables to create a continuous internal volume. This design allows the hollow-cable dome to function as an air conditioning system, circulating cold air internally to adjust the indoor temperature. This paper first introduces a heat-fluid analysis framework for the hollow-cable dome. And then to verify the feasibility and practicality of the structure and its dual functions, simulations of the heat transfer and indoor cooling process are conducted using commercial software. The proposed structure efficiently realizes dual functions of structural support and temperature regulation, highlighting its promising potential for applications in both structural engineering and indoor environmental quality management. Additionally, the paper investigates the key factors affecting cooling efficiency, focusing on component parameters, structural complexity, and configuration. The comparative analysis reveals that component parameters and structural configuration are closely linked to the indoor cooling effect and temperature distribution. A larger outlet area results in greater cooling efficiency, while increased structural complexity promotes more uniform temperature changes, effectively minimizing hot and cold concentration zones. Therefore, the study provides a theoretical foundation for optimizing the design of the hollow-cable dome structure to enhance cooling performance. Future designs can leverage these insights to achieve better functionality and efficiency, further promoting environmentally friendly and sustainable building practices.