Search Results (703)

Accurate Remaining Useful Life (RUL) prediction of TFT-LCD modules is critical for industrial predictive maintenance, yet it remains heavily challenged by complex degradation mechanisms in different climates. Traditional purely data-driven models (SVR, LSTM) often lack physical interpretability, struggling to filter out environmental noise or predict irreversible failures. To address this, we propose a highly reliable prognostic tool based on a Physics-Informed Gaussian Process Regression (PI-GPR) framework, by embedding cumulative thermal load and thermo-mechanical stress into the model’s prior function. Evaluated using one-year field exposure data, the physical constraints empower the model to accurately predict device lifetime under highly variable environments, including luminance fluctuations in tropical hygrothermal conditions and device failures in cold environments. Quantitative results demonstrate that the unified PI-GPR framework achieves an outstanding coefficient of determination (R² = 0.93) and reduces the RUL prediction error to merely 7.5 days, significantly outperforming conventional shallow learning, deep sequence, and standard probabilistic baselines. Ultimately, this study provides a robust, physically grounded methodology for the health monitoring and life cycle management of display modules in practical industrial applications. Full article

Tungsten carbides are important materials for various application fields. Their unique combination of mechanical properties makes them a good choice for applications demanding high hardness and moderate fracture toughness, such as cutting tools, oil and gas, mining, or machining industries. The microstructure is composed of a hard phase embedded in a soft, ductile binder. Cobalt, which provides the best compatibility with the tungsten carbide phase, is the main binder. However, some issues have been addressed to cobalt during the last decades, including a classification as a critical raw material by the European Commission, a fluctuation of its price due to intense use in batteries, and health and ethical problems. Nickel-based binders are thus a good alternative to cobalt. Nevertheless, their processing requires a higher sintering temperature to achieve full density, which leads to abnormal grain growth and thus reduces mechanical properties. The proposed solution is to use a small amount of boron, which is added during the milling of the powders, to reduce the sintering temperature. After vacuum sintering, the results show that the sintering temperature can be decreased to reach full density. Mechanical properties show enhanced hardness with moderately decreased fracture toughness compared to the parts without boron additions (hardness around 1450 to 1515 HV₃₀ and fracture toughness around 10 to 12 MPa√m). Those results provide a good hardness-to-toughness ratio. Full article

Textile equipment monitoring and scheduling are constrained by device heterogeneity, stringent real-time requirements, and complex dynamic resource scheduling. To address these challenges, this study proposes a cloud-edge-end collaborative remote monitoring and scheduling system for textile equipment. The proposed system aims to overcome the limitations of traditional solutions in compatibility, real-time performance, and resource utilization. This work is positioned as an applied systems study, in which the scheduling modules are used as monitoring-driven service extensions rather than as standalone algorithmic contributions. We develop (i) an adaptive multi-protocol parsing mechanism, (ii) a collaborative hierarchical alerting framework, and (iii) monitoring-driven computing-resource and production-scheduling services. The system is implemented across the terminal device layer, edge computing layer, and central cloud layer. Embedded acquisition terminals were designed to support multiple industrial protocols, including Modbus RTU, OPC UA, and EtherCAT. Dynamic protocol adaptation was used to identify, parse, and map heterogeneous protocol frames into a unified information model at runtime. In the workshop deployment reported in this study, field validation was conducted on 120 air-jet looms connected through RS485-based Modbus RTU. Other interfaces were evaluated as prototype-supported communication options rather than as quantitatively validated workshop interfaces. A cloud-edge-end collaborative alerting framework is designed by combining an improved OPTICS algorithm with a graph neural network (GNN) model. It improves the redundant-alarm filtering rate by 42.1%, achieves 96.8% root-cause diagnosis accuracy, and keeps the end-to-end alert latency at or below 200 ms at the 99th percentile. A cross-layer resource scheduling strategy incorporating a fuzzy PID controller is proposed, accompanied by a weighted multi-criteria resource-optimization model. This strategy increases the average CPU utilization of edge nodes to 84.3 ± 3.6% and reduces burst-task response latency to 236 ± 48 ms. In addition, an adaptive particle-swarm optimization module based on a scalarized composite scheduling objective reduces the equipment idle rate to 6.5% and shortens the average order completion time by 28.4%. Overall, the proposed framework demonstrates the feasibility of cloud-edge-end collaborative monitoring and scheduling in the validated RS485/Modbus-RTU-based weaving-workshop scenario, while its application to other textile processes, machine types, and communication configurations requires further protocol-specific adaptation and field validation. Full article

The integration of Artificial Intelligence (AI) in turbomachinery and fan systems is transforming traditional design, diagnostics, and operational strategies. Artificial Intelligence allows for the efficient exploration of wide design space, easy and fast prediction of fan performance and improving existing system operation and maintenance. Nevertheless, this AI-driven revolution still raises concerns and diffidence in the community, as highlighted by the results of a survey delivered to over 100 fan experts and discussed in this paper. This manuscript aims to provide an overview of Fan-AI applications through a comprehensive literature review of notable use cases. The applications target different stages of the life cycle of fans, from ML-assisted three-dimensional design/optimization to data-driven performance prediction, AI-driven fan control and fault analysis/prognosis. For each of these categories, the relevant application are discussed, highlighting trends, adopted algorithms and strategies, as well as limiting factors. This study also shares the views of experts on both fan design, optimization and operations and AI methods in the upcoming challenges for fan industry. Starting from the need of high-quality data, the improvement of model generalization and the embedding of Fan-AI in the standard engineering practices. This paper concludes with a discussion on the future role of AI in fans, suggesting pathways for research and industrial adoption that balance technological innovation with domain-specific constraints. Full article

As a core nutritional component of milk, casein features excellent digestibility and biocompatibility, making it an ideal carrier for embedding natural bioactive substances in dairy product research. Luteolin, a typical flavonoid compound with superior antioxidant and anti-inflammatory bioactivities, is limited in industrial dairy applications due to poor environmental stability and low biological utilization. Moreover, the dynamic interplay mechanism between luteolin and casein throughout fermentation and cold storage remains unclear. This study hypothesized that luteolin could assemble with casein via non-covalent binding to form stable composite fermentation system, thereby optimizing the overall quality and functional attributes of fermented milk. This work aimed to explore the binding characteristics of luteolin of casein in fermented milk and its regulatory effects on products’ physicochemical properties, antioxidant capacity and nutritional digestibility. Experimental outcomes verified the hypothesis that luteolin bonded with casein through hydrogen bonding and hydrophobic interactions. With increased luteolin supplementation, the fermentation system presented lowered pH and elevated titratable acidity. Compared with control fermentation system without luteolin, the fermentatiuon system containing 0.06% luteolin achieved 31.31% higher DPPH radical scavenging rate, 27.02% higher ABTS clearance capacity, and 26.42% higher in vitro protein digestibility (p < 0.05). Dose-dependent increases in particle size and absolute zeta-potential enhanced system colloidal stability, while FTIR detection confirmed obvious variations in protein secondary structure in fermented milk. This study elucidates the distinctive structure–function correlation of the luteolin–casein fermentation system in fermented dairy matrices, providing original insights and reliable theoretical support for developing novel dairy products rich in functional nutritional factors. Full article

Using industrial by-product lithium slag (LS) as a raw material for ultra-high performance concrete (UHPC) is an important way to achieve low-carbon prefabricated structures. However, existing studies lack a constitutive model for LS-UHPC and its application in prefabricated beam connection nodes. To fill this gap, this paper first established a tensile-compressive constitutive model for LS-UHPC through mechanical tests; then it was embedded into the finite element model to simulate the bending performance of the connection nodes of the post-cast LS-UHPC prefabricated beams and verified by the test results. Finally, parameter analysis is carried out. The results show that moderately increasing the diameter of longitudinal reinforcement can significantly improve the flexural bearing capacity of the connection node, but when the diameter exceeds 18 mm and HRB500 high-strength steel bars are used, the node exhibits over-reinforced failure characteristics; increasing the strength grade of ordinary concrete has a limited effect on the improvement of flexural bearing capacity (<5%). This study clarified the mechanical constitutive relationship of LS-UHPC, revealed the failure mechanism and bearing capacity evolution law of its prefabricated connection nodes under parameter changes, and provided a theoretical basis and design suggestions for the application of low-carbon lithium slag UHPC in prefabricated assembly structures. Full article

Amid global overcapacity and AI proliferation, equipment manufacturers confront a critical challenge: does digital transformation (DT) create competitive advantages or merely accelerate existing problems? This study reconceptualizes DT through a complex adaptive systems (CAS) perspective, where success depends on business units coordinating as adaptive agents rather than IT departments deploying technologies. We investigate how configurations of digital technologies and strategic orientations activate distinct multi-agent coordination mechanisms, producing differentiated performance outcomes and revealing why certain pathways demonstrate superior long-term stability through emergent organizational capabilities. Analyzing a panel dataset of 552 Chinese equipment manufacturers (2017–2022), this research employs a mixed-methods approach. It first uses fixed-effects regression to establish DT’s net effect on firm performance. Then, it applies natural language processing (NLP) to measure operational management and dynamic fuzzy-set Qualitative Comparative Analysis (fsQCA) to identify configurations of DT dimensions (foundation, boundary, application) and strategic orientations leading to high performance. Regression results confirm that business-embedded DT improves firm performance, but the net effects of digital foundation (positive), boundary (insignificant), and application (negative unless supported) diverge sharply. fsQCA identifies five equifinal pathways, highlighting that success depends on aligning DT investments with innovation focus and operational management. Operational-management-oriented configurations demonstrate greater long-term stability, with notable variations across regions and industries. This study emphasizes that effective digital transformation is not a technology-first unified deployment, but a business-driven adaptive evolution process, providing a new theoretical perspective for understanding performance variations in DT. Full article

Knowledge graph link prediction is a fundamental task for improving the completeness and reasoning capability of knowledge graphs. In industrial knowledge graph scenarios, missing relations may limit knowledge completion, relational reasoning, and downstream industrial applications. Fault diagnosis is a representative application scenario, where missing relations among fault phenomena, alarm information, fault locations, and fault causes may further affect fault analysis, maintenance decision-making, and industrial knowledge services. Industrial knowledge graphs usually suffer from sparse local structures, imbalanced relation distributions, explicit entity-type boundaries, and highly confusing candidate entities with similar structural or semantic contexts. These characteristics make it difficult for conventional embedding-based or graph neural network-based methods to achieve reliable candidate ranking by relying only on structural propagation or semantic matching. To address these challenges, this study proposes a type-constrained structural–semantic fusion framework with dynamic relation priors for industrial knowledge graph link prediction, and further investigates its application to fault diagnosis. The proposed framework extends a relation-centered graph neural reasoning backbone by generating dynamic relation priors through query-conditioned relation-level graph propagation over a predefined relation graph, thereby enhancing query-specific structural reasoning. It further introduces a semantic projection module to align textual representations of entities and relations with structural representations at the candidate-ranking stage. In addition, relation-category and hierarchy-aware signals are used to modulate relation representations during propagation, while entity-type constraints are incorporated into final scoring and type-constrained hard negative construction. In this way, structural evidence, textual semantic information, and entity-type validity constraints are jointly used for candidate ranking rather than being treated as isolated signals. Experiments are conducted on two public benchmark datasets, WN18RR and FB15k-237, and two industrial knowledge graph datasets in Chinese and English. The Chinese industrial knowledge graph is constructed from fault diagnosis knowledge and is used as a representative application dataset, while the English industrial knowledge graph is used to further evaluate the adaptability of the proposed framework in a related industrial production scenario. The proposed method achieves MRR scores of 0.599 and 0.446 on WN18RR and FB15k-237, respectively, and obtains MRR scores of 0.8532 and 0.7994 on the Chinese and English industrial knowledge graphs. The results demonstrate that the proposed framework improves both general link prediction performance and industrial-domain adaptability, especially in scenarios involving sparse structures, type-constrained candidate validity, and semantically confusing entities, and shows practical potential for fault diagnosis applications. Full article

Vent holes in tire molds typically exhibit large depth-to-diameter ratios (25–50) and variable drilling angles, both of which increase the risk of drill-bit breakage during automated drilling. To address this problem, this study develops a robotic drilling system consisting of a 6-DOF industrial robot and a dedicated end effector integrating a spindle unit, a linear feed unit, and a telescopic drill-bushing unit. A GRU-based feed-torque model predictive control method (GFTMPC) is proposed for robotic small-diameter deep-hole drilling, which achieves flexible control by integrating angle-aware feed-torque modeling with constrained MPC-based feed-rate optimization. The resulting GRU-based feed-torque model (GFTM) is embedded in the MPC framework for torque prediction and achieves an R² value of 0.9682. Under identical simulation conditions, GFTMPC reduces the RMSE of the feed-rate increment by 34.82% and the saturation ratio of the feed-rate increment by 90.78% relative to a PID baseline, indicating smoother feed regulation and fewer abrupt control actions in simulation. Comparative engineering experiments further suggest that, under the tested robotic configurations, adaptive feed-rate regulation by GFTMPC is an important contributor to improved tool life and drilling reliability. Hole-diameter measurements show deviations ranging from +0.03 mm to +0.11 mm, which were considered acceptable for the subsequent work steps in this application. Engineering application results show that robotic drilling increases daily throughput per worker by 71.38% and the average number of holes drilled per bit by 237%. Full article

This review examines Bacillus thuringiensis subsp. israelensis (Bti) as both a highly selective microbial larvicide and a biological platform for protein storage and delivery, enabled by the structural features of its prokaryotic insect larvicidal organelle (PILO). Bti remains the most widely deployed biological agent for mosquito control. Decades of operational use demonstrate substantial public health benefits and only limited, manageable ecological tradeoffs within integrated vector management programs (IVMP). Its narrow host range underlies an excellent safety record for humans and other vertebrates. Moreover, laboratory and field studies consistently show that collateral effects are minimal, context dependent, reversible, and largely restricted to closely related non-target aquatic dipterans. These attributes have established Bti as a cornerstone of environmentally sustainable IVMP worldwide. Here, we synthesize current knowledge on Bti biology, ecological selectivity, field performance, and the resistance-management properties embedded in the molecular architecture of the PILO. Finally, we assess emerging opportunities and technical constraints in repurposing the PILO as an in vivo microbial factory for packaging heterologous proteins with potential pharmaceutical and industrial applications. Full article

While predictive maintenance increasingly relies on artificial intelligence, strict dependence on cloud computing introduces network latency and demands continuous connectivity, creating critical bottlenecks for time-sensitive industrial applications. To overcome this, we introduce a novel hybrid edge-cloud architecture, which allows deploying an ultra-low-power microcontroller (ESP32-S3) without dedicated AI acceleration hardware to perform complete, operational, predictive maintenance on ultra-constrained embedded hardware. The edge model is optimized to be very small to ensure that increasing model complexity does not cause inference latency to exceed 100 ms or make real-time operation infeasible. We created a very compact INT8-quantized neural network to perform the simultaneous classification of faults and estimation of Time-to-Failure (TTF) with a deterministic mean inference time of 42.3 ms. It dynamically estimates prediction confidence, processes high-confidence predictions locally, and offloads uncertain predictions to a higher-capacity cloud model, and recovers 97.3% of the cloud accuracy gain at 92% of the cloud latency budget. An asymmetric loss function penalizes over-prediction of the remaining useful life, and thus it provides conservative and safe warnings of fault. Operators’ interpretability is improved with Shapley Additive exPlanations (SHAP) and natural-language recommendations. Network outages of up to 50% have not influenced the safety-critical fault recall (above 0.924), so graceful degradation is reached when the network is used in real time in industrial applications. The edge-first with adaptive cloud fallback approach is demonstrated to be technically feasible for a full predictive maintenance workflow—including inference, confidence fusion, and explainability on a low-cost commercial microcontroller. Full article

This study investigates the role of the Massive Transformative Purpose (MTP) in shaping corporate brand identity and guiding brand management strategies in Exponential Organizations (ExOs). It examines how the MTP aligns internal and external brand dimensions, enhances stakeholder engagement, and drives societal impact, positioning it as a central element in ExO brand management. This study employs a qualitative multiple-case study methodology focusing on two ExOs: Airbnb, a digital-native hospitality company, and Mylia, a transformative learning enterprise. Semi-structured interviews with senior executives were triangulated with internal and external data to examine how the MTP drives strategy, culture, and stakeholder engagement. This allowed the application of the Corporate Brand Identity Matrix for exploring the different corporate brand identities and the relative nuances. The findings show that the MTP is essential to shaping ExOs’ corporate brand identity. It unifies organizational purpose, culture, and strategy, creating a cohesive identity that resonates both internally and externally. Embedding the MTP into daily practices fosters alignment, guides decision-making, strengthens stakeholder relationships, and shapes value propositions that distinguish ExOs while addressing stakeholder needs. The research bridges gaps in the literature on corporate brand identity, organizational purpose, and the unique characteristics of ExOs. It introduces the MTP Management Model, which integrates ExO-specific attributes to provide deeper insights into how these organizations align operational structures and brand identity with their transformative purpose. While the multiple-case study approach offers in-depth insights, the findings are context-specific and may not be fully generalizable across industries. The MTP Management Model provides a clear framework to integrate essential attributes, ensuring organizational coherence, effective communication, and enhanced competitiveness. Full article

The application of digital twin (DT) technology to intelligent machining shows promise, but its effectiveness in predicting vibration and assessing surface quality has not been thoroughly validated for widespread industrial use. This study presents a physics-informed predictive digital twin framework operating in an offline or near-real-time predictive configuration for vibration prediction and surface roughness modeling in milling processes. Impact hammer testing was conducted to extract the dominant modal properties of the spindle–tool assembly, which were embedded into a Simulink-based dynamic framework to predict tool vibration under varying cutting conditions. Full-immersion slot milling experiments on AL6061 were performed for validation. Within all datasets, including training phase and validation phase, the predicted vibration amplitudes exhibit a coefficient of determination $R^2=0.94$ with measured values. The overall MAPE and RMSE are about 10.39% and 0.234, respectively. Power-law regression-based surface roughness prediction models were subsequently established using cutting parameters and both measured and DT-predicted vibration features through logarithmic transformation and least-squares fitting. The results show that the roughness prediction model using vibration features predicted by the digital twin model achieved a correlation coefficient of approximately $R^2=0.84$, with MAPE = 9.57% and RMSE = 0.16 μm, which is comparable to the predictive model based on experimentally measured vibration. These results indicate that, within the investigated machining conditions, the digital twin can provide vibration features suitable for surface roughness prediction, demonstrating its potential as a virtual sensing approach. This work advances digital twin applications from process monitoring toward predictive, quality-oriented machining systems and provides a foundation for adaptive parameter updating in intelligent manufacturing environments. Full article

To address the challenges of scarce anomaly samples, inadequate modeling of temporal dynamic features, and limited feature selection capability of lightweight models in industrial anomalous sound detection, this paper proposes a method under an unsupervised framework. In the time-domain feature extraction branch, a Lightweight Temporal Pyramid Module (LTPM) is introduced to enhance the multi-scale temporal modeling capability of TgramNet, capturing both short-term and long-term temporal dependencies. In the classification network, the Efficient Channel Attention (ECA) mechanism is embedded into the bottleneck structure of MobileFaceNet to enable adaptive channel recalibration. Furthermore, three waveform-level data augmentation strategies—noise perturbation, time shifting, and amplitude scaling—are adopted. Experimental results on the DCASE 2020 Task 2 dataset demonstrate that the proposed method achieves competitive performance compared with existing approaches, attaining optimal or highly competitive results across multiple machine types. The minimum Area Under the Curve (mAUC) across different machine IDs, along with ROC curve analysis, verifies the stability and generalization capability of the model. This method offers a promising lightweight approach for industrial anomalous sound detection and condition monitoring applications. Full article

As a form of living heritage, Historic Urban Landscapes (HULs) have long been limited by the static perspectives and reductionist tendencies of conventional conservation and research approaches. Although the geological and archaeological concept of “stratification” offers a methodological basis for understanding the diachronic evolution of heritage, its unidimensional temporal lens fails to capture the inherent complexity and systemic nature of historic urban landscapes. To address this gap, this study proposes a “multidimensional stratification” theoretical framework through theoretical critique and paradigm reconstruction. The framework introduces innovations at the ontological, epistemological, and methodological levels, positing that the evolution of historic urban landscapes emerges from the nonlinear interaction and dynamic interweaving of four core dimensions: time, space, society, and value. It further systematizes five intrinsic attributes of such landscapes: authenticity, integrity, continuity, adaptability, and dynamism. Building on this foundation, the paper constructs a systematic analytical pathway—elements–processes–patterns–modes–drivers–characteristics—that enables dynamic analysis from micro-level identification to macro-level generalization, offering a scalable tool for HUL conservation and regeneration. To demonstrate the framework’s applicability, the historic urban area of Shenyang—a nationally designated historical and cultural city—is selected as a case study. Its urban landscape comprises four core districts: the Shengjing City District, the South Manchuria Railway Concession District, the Commercial Port District, and the Tiexi Industrial District, representing historical strata from the Qing dynasty capital, modern colonial planning, commercial opening, to industrial heritage. Using the multidimensional stratification approach, this study elucidates the spatial complexity, temporal nonlinearity, social dynamism, and value pluralism embedded in Shenyang’s historic urban area. Corresponding conservation strategies grounded in holism, dynamism, and differentiation are proposed. The research not only advances the theoretical understanding of HUL but also provides a novel paradigm—integrating holistic, dynamic, and operational perspectives—for the conservation, renewal, and regenerative practice of historic urban landscapes worldwide. Full article