Search Results (311)

Collaborative business ecosystems (CBEs) are increasingly exposed to disruptive events (e.g., pandemics, supply chain breakdowns, cyberattacks) that challenge organizational adaptability and value creation. Traditional approaches to resilience and robustness often fail to capture the full range of systemic responses. This study introduces a unified mathematical framework to evaluate four crisis response modes—plasticity, resilience, transformative resilience, and antifragility—within complex adaptive networks. Grounded in complex systems and collaborative network theory, our model formalizes both internal organizational capabilities (e.g., adaptability, learning, innovation, structural flexibility) and strategic interventions (e.g., optionality, buffering, information sharing, fault-injection protocols), linking them to pre- and post-crisis performance via dynamic adjustment functions. A composite performance score is defined across four dimensions (Innovation, Contribution, Prestige, and Responsiveness to Business Opportunities), using capability–strategy interaction matrices, weighted performance change functions, and structural transformation modifiers. The sensitivity analysis and scenario simulations enable a comparative evaluation of organizational configurations, strategy impacts, and phase-transition thresholds under crisis. This indicator-based formulation provides a quantitative bridge between resilience theory and practice, facilitating evidence-based crisis management in networked business environments. Full article

This study designed and validated a dual-component beat-frequency quartz-enhanced photoacoustic spectroscopy (BF-QEPAS) gas detection system utilizing time-division multiplexing (TDM). By applying TDM to drive distributed feedback lasers, the system achieved the simultaneous detection of acetylene and methane. Its key innovation lies in exploiting the transient response of the quartz tuning fork (QTF) to acquire gas concentrations while concurrently capturing the QTF resonant frequency and quality factor in real-time. Owing to the short beat period and rapid system response, this approach significantly reduces time-delay constraints in time-division measurements, eliminating the need for periodic calibration inherent in conventional methods and preventing detection interruptions. The experimental results demonstrate minimum detection limits of 5.69 ppm for methane and 0.60 ppm for acetylene. Both gases exhibited excellent linear responses over the concentration range of 200 ppm to 4000 ppm, with the R² value for methane being 0.996 and for acetylene being 0.997. The system presents a viable solution for the real-time, calibration-free monitoring of dissolved gases in transformer oil. Full article

We propose a hybrid Convolutional Graph Neural Network (C-GNN) for direction-of-arrival (DOA) estimation in sparse sensor arrays under low-snapshot conditions. The C-GNN architecture combines 1D convolutional layers for local spatial feature extraction with graph convolutional layers for global structural learning, effectively capturing both fine-grained and long-range array dependencies. Leveraging the difference coarray technique, the sparse array is transformed into a virtual uniform linear array (VULA) to enrich the spatial sampling; real-valued covariance matrices derived from the array measurements are used as the network’s input features. A final multi-layer perceptron (MLP) regression module then maps the learned representations to continuous DOA angle estimates. This approach capitalizes on the increased degrees of freedom offered by the virtual array while inherently incorporating the array’s geometric relationships via graph-based learning. The proposed C-GNN demonstrates robust performance in noisy, low-data scenarios, reliably estimating source angles even with very limited snapshots. By focusing on methodological innovation rather than bespoke architectural tuning, the framework shows promise for data-efficient DOA estimation in challenging practical conditions. Full article

Calorific value and moisture content are the key indices to evaluate Caragana pellet fuel’s quality and combustion characteristics. Calorific value is the key index to measure the energy released by energy plants during combustion, which determines energy utilization efficiency. But at present, the determination of solid fuel is still carried out in the laboratory by oxygen bomb calorimetry. This has seriously hindered the ability of large-scale, rapid detection of fuel particles in industrial production lines. In response to this technical challenge, this study proposes using hyperspectral imaging technology combined with various chemometric methods to establish quantitative models for determining moisture content and calorific value in Caragana korshinskii fuel. A hyperspectral imaging system was used to capture the spectral data in the 935–1720 nm range of 152 samples from multiple regions in Inner Mongolia Autonomous Region. For water content and calorific value, three quantitative detection models, partial least squares regression (PLSR), random forest regression (RFR), and extreme learning machine (ELM), respectively, were established, and Monte Carlo cross-validation (MCCV) was chosen to remove outliers from the raw spectral data to improve the model accuracy. Four preprocessing methods were used to preprocess the spectral data, with standard normal variate (SNV) preprocessing performing best on the quantitative moisture content detection model and Savitzky–Golay (SG) preprocessing performing best on the calorific value detection method. Meanwhile, to improve the prediction accuracy of the model to reduce the redundant wavelength data, we chose four feature extraction methods, competitive adaptive reweighted sampling (CARS), successive pojections algorithm (SPA), genetic algorithm (GA), iteratively retains informative variables (IRIV), and combined the three models to build a quantitative detection model for the characteristic wavelengths of moisture content and calorific value of Caragana korshinskii fuel. Finally, a comprehensive comparison of the modeling effectiveness of all methods was carried out, and the SNV-IRIV-PLSR modeling combination was the best for water content prediction, with its prediction set determination coefficient $\left(R_P^2\right)$, root mean square error of prediction (RMSEP), and relative percentage deviation (RPD) of 0.9693, 0.2358, and 5.6792, respectively. At the same time, the moisture content distribution map of Caragana fuel particles is established by using this model. The SG-CARS-RFR modeling combination was the best for calorific value prediction, with its $R_P^2$, RMSEP, and RPD of 0.8037, 0.3219, and 2.2864, respectively. This study provides an innovative technical solution for Caragana fuel particles’ value and quality assessment. Full article

This paper presents platforms as a solution to rethink how we build, addressing the pressing paradox between meeting growing housing demands. The construction sector has not fully grasped the advantages of platforms beyond standardisation and efficiency. In contrast, other sectors have begun acknowledging that platforms can capture increased value through interactions among firms within a networked ecosystem. Learning from other sectors, this paper investigates platforms in the construction context, aiming to define, classify, and assess their impact on the construction value chain. The research approach was abductive, involving a cross-sectoral review of 190 platforms across 16 Australian and New Zealand Standard Industrial Classification (ANZSIC) industries and semi-structured interviews with stakeholder groups of the construction value chain in Australia. The findings categorise platforms as physical, digital, or hybrid, highlighting their potential to move value-added activities upstream, facilitate collaboration, and foster innovation through data-driven insights. The paper’s novelty lies in the exhaustive cross-sectoral review, the classification of platforms in the construction context, and the proposition of a platform approach as a versatile framework tailored to diverse needs and circumstances that offers a fresh perspective on sustainable building practices. The practical contribution of this study lies in offering guidelines for industry practitioners aiming to develop or refine a platform-based approach tailored to the construction context. Full article

In computer vision, the performance of detectors trained under optimal lighting conditions is significantly impaired when applied to infrared domains due to the scarcity of labeled infrared target domain data and the inherent degradation in infrared image quality. Progress in cross-domain pedestrian re-identification is hindered by the lack of labeled infrared image data. To address the degradation of pedestrian recognition in infrared environments, we propose a framework for zero-shot infrared domain adaptation. This integrated approach is designed to mitigate the challenges of pedestrian recognition in infrared domains while enabling zero-shot domain adaptation. Specifically, an advanced reflectance representation learning module and an exchange–re-decomposition–coherence process are employed to learn illumination invariance and to enhance the model’s effectiveness, respectively. Additionally, the CLIP (Contrastive Language–Image Pretraining) image encoder and DINO (Distillation with No Labels) are fused for feature extraction, improving model performance under infrared conditions and enhancing its generalization capability. To further improve model performance, we introduce the Non-Local Attention (NLA) module, the Instance-based Weighted Part Attention (IWPA) module, and the Multi-head Self-Attention module. The NLA module captures global feature dependencies, particularly long-range feature relationships, effectively mitigating issues such as blurred or missing image information in feature degradation scenarios. The IWPA module focuses on localized regions to enhance model accuracy in complex backgrounds and unevenly lit scenes. Meanwhile, the Multi-head Self-Attention module captures long-range dependencies between cross-modal features, further strengthening environmental understanding and scene modeling. The key innovation of this work lies in the skillful combination and application of existing technologies to new domains, overcoming the challenges posed by vision in infrared environments. Experimental results on the SYSU-MM01 dataset show that, under the single-shot setting, Rank-1 Accuracy (Rank-1) andmean Average Precision (mAP) values of 37.97% and 37.25%, respectively, were achieved, while in the multi-shot setting, values of 34.96% and 34.14% were attained. Full article

Accurate forecasting of carbon emissions at the county level is critical to support China’s dual-carbon goals. However, most current studies are limited to national or provincial scales, employing traditional statistical methods inadequate for capturing complex nonlinear interactions and spatiotemporal dynamics at finer resolutions. To overcome these limitations, this study develops and validates a high-resolution predictive model using advanced gradient boosting algorithms—Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM)—based on socioeconomic, industrial, and environmental data from 2732 Chinese counties during 2008–2017. Key variables were selected through correlation analysis, missing values were interpolated using K-means clustering, and model parameters were systematically optimized via grid search and cross-validation. Among the algorithms tested, LightGBM achieved the best performance (R² = 0.992, RMSE = 0.297), demonstrating both robustness and efficiency. Spatial–temporal analyses revealed that while national emissions are slowing, the eastern region is approaching stabilization, whereas emissions in central and western regions are projected to continue rising through 2027. Furthermore, SHapley Additive exPlanations (SHAP) were applied to interpret the marginal and interaction effects of key variables. The results indicate that GDP, energy intensity, and nighttime lights exert the greatest influence on model predictions, while ecological indicators such as NDVI exhibit negative associations. SHAP dependence plots further reveal nonlinear relationships and regional heterogeneity among factors. The key innovation of this study lies in constructing a scalable and interpretable county-level carbon emissions model that integrates gradient boosting with SHAP-based variable attribution, overcoming limitations in spatial resolution and model transparency. Full article

The last decade has seen a transformative advancement in computational technologies, enabling the precise creation, evaluation, visualization, and reproduction of high-fidelity three-dimensional (3D) models of archaeological sites and artefacts. With the advent of 3D printing, both small- and large-scale objects can now be reproduced with remarkable accuracy and at customizable scales. Artefacts composed of organic materials—such as wood—are inherently susceptible to biological degradation and thus require extensive, long-term conservation employing costly methodologies. These procedures often raise environmental concerns and lead to irreversible alterations in the wood’s chemical composition, dimensional properties, and the intangible essence of the original artefact. In the context of public education and the dissemination of knowledge about historical technologies and objects, 3D replicas can effectively fulfill the same purpose as original artefacts, without compromising interpretative value or cultural significance. Furthermore, the digital data embedded in 3D surface and object models provides a wealth of supplementary information that cannot be captured, preserved, or documented through conventional techniques. Waterlogged wooden objects can now be thoroughly documented in 3D, enabling ongoing, non-invasive scientific analysis. Given these capabilities, it is imperative to revisit the philosophical and ethical foundations of preserving waterlogged wood and to adopt innovative strategies for the conservation and presentation of wooden artefacts. These new paradigms can serve educational, research, and outreach purposes—core functions of contemporary museums. Full article

In the context of the rapid digital transformation of the catering industry, the design of ordering interfaces—key hubs of human–computer interaction—has become critical to user service quality and brand competitiveness, especially in terms of usability, visual appeal, and emotional resonance. Based on a human–computer interaction design framework, this study proposes a dual-path optimization model integrating user demand mapping and conflict resolution to synergize explicit need translation with innovative design problem solving. The model employs KE to capture implicit user needs, applies AHP to construct a weighted design element system, and uses QFD to establish a matrix linking user needs with technical attributes. To address contradictions among design elements, TRIZ is introduced to resolve conflicts between functional redundancy and interaction efficiency. Additionally, generative AI tools such as MidJourney are incorporated to accelerate concept generation and improve innovation. Based on user evaluations and controlled experiments, the optimized design demonstrates measurable improvements in task efficiency and visual appeal. Overall, the dual-path approach effectively bridges the gap between vague user needs and concrete design solutions, achieving a balanced integration of functionality, aesthetics, and interactivity. The proposed model overcomes the limitations of experience-driven design by offering a systematic methodology encompassing demand analysis, technological transformation, conflict resolution, and intelligent generation, with practical value for enhancing the user experience of digital service touchpoints in the catering sector. Full article

Re-identification (ReID) has been widely applied in person and vehicle recognition tasks. This study extends its application to a novel domain: insect (silkworm) recognition. However, unlike person or vehicle ReID, silkworm ReID presents unique challenges, such as the high similarity between individuals, arbitrary poses, and significant background noise. To address these challenges, we propose a multi-view network for silkworm ReID, called WormNet, which is built upon an innovative strategy termed extraction purification extraction interaction. Specifically, we introduce a multi-order feature extraction module that captures a wide range of fine-grained features by utilizing convolutional kernels of varying sizes and parallel cardinality, effectively mitigating issues of high individual similarity and diverse poses. Next, a feature mask module (FMM) is employed to purify the features in the spatial domain, thereby reducing the impact of background interference. To further enhance the data representation capabilities of the network, we propose a channel interaction module (CIM), which combines an efficient channel attention network with global response normalization (GRN) in parallel to recalibrate features, enabling the network to learn crucial information at both the local and global scales. Additionally, we introduce a new silkworm ReID dataset for network training and evaluation. The experimental results demonstrate that WormNet achieves an mAP value of 54.8% and a rank-1 value of 91.4% on the dataset, surpassing both state-of-the-art and related networks. This study offers a valuable reference for ReID in insects and other organisms. Full article

As sustainability becomes increasingly central to organizational strategy, social economy organizations (SEOs) are rethinking their business models. This study employs stakeholder analysis using the value mapping (VM) tool developed by Short, Rana, Bocken, and Evans for the development of the VOLTO JÁ project. The objective of the VOLTO JÁ project is to operationalize a senior exchange programme between SEOs. The VM approach extends beyond conventional customer value propositions to prioritize sustainability for all stakeholders and identify key drivers of sustainable business model (SBM) innovation. The multi-stakeholder methodology comprises the following elements: (1) sequential focus groups aimed at enhancing sustainable business thinking; (2) semi-structured interviews; and (3) workshop to facilitate qualitative analysis and co-create the VM. The findings are then categorized into four value dimensions: (1) value captured—improved participant well-being, enhanced reputational capital, mitigation of social asymmetries, and affordable service experiences; (2) value lost—underused community assets; (3) value destroyed—institutional and systemic barriers to innovation; and (4) new value opportunities—knowledge sharing, service diversification, and open innovation to foster collaborative networks. The study demonstrates that the application of VM in SEOs supports SBM development by generating strategic insights, enhancing resource efficiency, and fostering the delivery of socially impactful services. Full article

Remaining Useful Life (RUL) prediction is a crucial task in predictive maintenance. Currently, gated recurrent networks, hybrid models, and attention-enhanced models used for predictive maintenance face the challenge of balancing prediction accuracy and model lightweighting when extracting complex degradation features. This limitation hinders their deployment on resource-constrained edge devices. To address this issue, we propose TBiGNet, a lightweight Transformer-based classification network model for RUL prediction. TBiGNet features an encoder–decoder architecture that outperforms traditional Transformer models by achieving over 15% higher accuracy while reducing computational load, memory access, and parameter size by more than 98%. In the encoder, we optimize the attention mechanism by integrating the individual linear mappings of queries, keys, and values into an efficient operation, reducing memory access overhead by 60%. Additionally, an adaptive feature pruning module is introduced to dynamically select critical features based on their importance, reducing redundancy and enhancing model accuracy by 6%. The decoder innovatively fuses two different types of features and leverages BiGRU to compensate for the limitations of the attention mechanism in capturing degradation features, resulting in a 7% accuracy improvement. Extensive experiments on the C-MAPSS dataset demonstrate that TBiGNet surpasses existing methods in terms of computational accuracy, model size, and memory access, showcasing significant technical advantages and application potential. Experiments on the C-MPASS dataset show that TBiGNet is superior to the existing methods in terms of calculation accuracy, model size and throughput, showing significant technical advantages and application potential. Full article

This study aims to evaluate how key financial, educational, technological, and institutional drivers shape resource efficiency (RCE), a critical pillar of sustainable development—across major economies. Enhancing RCE is vital for ensuring long-term ecological and economic stability while meeting global sustainability targets. Using panel data from 2000 to 2022 for G20 countries, this research examines the dynamic effects of natural resources (NRSs), educational quality (EDQ), financial inclusion (FIN), green innovation (GRI), and economic complexity (ECC) on RCE. The Cross-Sectional Autoregressive Distributed Lag (CS-ARDL) model is applied to capture both short- and long-term relationships and is validated by robustness checks using the Augmented Mean Group (AMG) and Common Correlated Effects Mean Group (CCEMG) estimators. The results show that EDQ and FIN exert a negative influence on RCE, suggesting that governance inefficiencies occur when aligning education systems and financial mechanisms with sustainability goals. In contrast, NRS, GRI, and ECC significantly enhance RCE, underscoring the value of resource stewardship, innovation-driven transitions, and complex economic structures in promoting efficiency. These findings have governance implications, emphasizing the need for institutional reforms that integrate sustainability into the education and financial sectors while supporting green innovation and economic diversification. Policymakers in G20 economies are urged to implement coherent strategies that redirect educational and financial frameworks toward inclusive, resilient, and resource-efficient development pathways, thereby advancing the United Nations Sustainable Development Goals (SDGs). Full article

Sustainable development remains a crucial global priority. Despite significant progress at both the policy and technical levels, disparities in urban development and the absence of a comprehensive evaluation framework impede practical outcomes in China. While previous research has established the value of multidimensional frameworks and international indices for assessing urban sustainability, existing studies often lack the integration of local dynamics and rely on linear methods that cannot fully capture the complex, nonlinear changes in Chinese cities. This study proposes a four-dimensional indicator system and employs the catastrophe progression method to evaluate sustainable development levels. This study used ten years of panel data (2012–2022) from eight representative Chinese cities and normalized and analyzed 38 sub-indicators to derive membership values for each city and dimension. The findings reveal substantial disparities in sustainable development across the cities, with notable improvements in environmental indicators but persistent volatility in social welfare and resource efficiency. Technological innovation and education resource allocation emerge as management priorities for most cities. This methodological innovation fills a critical gap, offering a replicable framework for other developing countries and supporting the localization of global sustainability agendas. The study’s findings directly inform policy, advancing the achievement of the UN Sustainable Development Goals. Full article

Mineral prospectivity mapping (MPM) is a pivotal technique in geoscientific mineral resource exploration. To address three critical challenges in current deep convolutional neural network applications for geoscientific mineral resource prediction—(1) model bias induced by imbalanced distribution of ore deposit samples, (2) deficiency in global feature extraction due to excessive reliance on local spatial correlations, and (3) diminished discriminative capability caused by feature smoothing in deep networks—this study innovatively proposes a T-GCN model integrating Transformer with graph convolutional neural networks (GCNs). The model achieves breakthrough performance through three key technological innovations: firstly, constructing a global perceptual field via Transformer’s self-attention mechanism to effectively capture long-range geological relationships; secondly, combining GCNs’ advantages in topological feature extraction to realize multi-scale feature fusion; and thirdly, designing a feature enhancement module to mitigate deep network degradation. In practical application to the PangXD ore district, the T-GCN model achieved a prediction accuracy of 97.27%, representing a 3.76 percentage point improvement over the best comparative model, and successfully identified five prospective mineralization zones, demonstrating its superior performance and application value under complex geological conditions. Full article