Search Results (9,956)

Surface quality is a critical indicator of product performance, creating a growing demand for real-time surface inspection in industrial manufacturing. However, conventional surface normal measurement techniques require sequential measurements with varying illumination or observation angles, making them unsuitable for high-speed online inspection. To overcome this limitation, this paper proposes a novel imaging technique, termed normalography, for visualizing pixel-wise surface normal distributions. Analogous to thermography, normalography visualizes the spatial distribution of surface normal directions over a material surface. The proposed method targets highly glossy and smooth surfaces and is based on reflectance measurements. A multi-angle collimator was developed to simultaneously illuminate the target surface from multiple incident directions, while multispectral illumination was employed to distinguish the reflected light corresponding to each direction. An imaging system incorporating red, green, and blue illumination sources enables single-shot acquisition of surface normal information over a 1024 × 1024-pixel field of view. The proposed normalography enables camera-like real-time visualization of surface normal distributions without sequential image acquisition, demonstrating its potential for online surface inspection and quality monitoring in industrial manufacturing. Full article

Volatile organic compounds (VOCs) are the main precursors of ozone (O₃) and secondary organic aerosols (SOAs), posing a significant threat to the environment and human health. In this study, the characteristics, potential for the generation of O₃ and SOAs, sources and health risks of volatile organic compounds were investigated based on annual monitoring data for 2021 in Dezhou, which is located in the northwest of Shandong Province, China, and adjacent to the Beijing–Tianjin–Hebei region. The results showed that the ambient VOC concentrations were lower in spring and summer and higher in autumn and winter. The species contributing to the O₃ formation potential (OFP) and SOA formation potential (SOAFP) varied by season, but those with high contributions were all olefins and aromatic hydrocarbons. O₃ pollution occurred frequently in summer, with a proportion of 40.22%. Focusing on summer, six sources were identified through the positive matrix factorization (PMF) model, with the petrochemical industry (26.1%) and combustion (25.6%) being the primary sources. The non-carcinogenic risk values of the involved 18 toxic VOCs were all within the safety threshold, while the carcinogenic risk of benzene was regarded as low-probability under long-term exposure. This study provides significant support for targeted control of air pollutant emissions and improvement in regional air quality. Full article

Industrial maintenance and fault-diagnosis systems generate textual records, such as maintenance work orders, service requests, causal analyses, and troubleshooting solutions. These records contain domain-specific named entities that provide valuable knowledge for intelligent monitoring, fault diagnosis, maintenance decision support, and industrial knowledge graph construction. However, in practical industrial environments, maintenance records are strongly associated with specific equipment types, production processes, fault modes, and enterprise-specific terminology. As a result, entity schemas vary across systems, new entity types emerge with equipment updates, and high-quality annotation requires substantial domain expertise. These factors make it difficult to obtain sufficient labeled samples for each industrial entity type. Under such low-resource conditions, conventional supervised named entity recognition (NER) models tend to suffer from unstable entity boundary detection and biased entity representations. To address these challenges, this paper proposes a boundary-aware knowledge graph-guided adaptive prototype correction framework for few-shot NER in industrial maintenance information systems. The proposed framework first introduces a boundary-aware span detection mechanism to improve entity localization in noisy and irregular maintenance texts. A knowledge graph-guided adaptive prototype correction module is then designed to construct entity class prototypes from limited support examples, reducing prototype bias caused by sparse annotations. Experiments are conducted on two representative industrial datasets, MaintIE and CFDK, covering maintenance short texts and fault-diagnosis records. Experimental results show that the proposed framework achieves an average Micro-F1 improvement of 1.89 percentage points over the strongest compared baseline across 12 episodic settings on the two industrial datasets: three MaintIE coarse-grained settings, six MaintIE fine-grained settings, and three CFDK settings. The ablation and sensitivity analyses further indicate that boundary-aware span modeling and KG-guided prototype correction jointly contribute to low-resource entity classification. This study provides a data-efficient information extraction solution for AI-enabled industrial knowledge acquisition, fault diagnosis, and maintenance decision support. Full article

To address the need for bubble detection in industrial silicone sealing rings, this paper presents a compact non-invasive ultrasonic monitoring system based on a capacitive micromachined ultrasonic transducer (CMUT) array, aiming to overcome the limitations of conventional X-ray inspection in terms of safety, portability, and real-time in situ monitoring. The system comprises two 8.8 mm × 8.8 mm CMUT arrays with associated transmitting and receiving circuitry. The silicone thickness is determined using the time-of-flight (TOF) method, while bubble size is quantitatively estimated by combining received signal amplitude analysis, which characterizes bubble-induced attenuation, with correlation function evaluation. Experimental measurements on industrial-grade silicone samples and finite element simulations demonstrate that the system achieves a spatial resolution of 0.5 mm and effectively captures attenuation variations caused by bubbles. The integrated strategy of TOF, amplitude analysis, and correlation assessment ensures reliable non-destructive evaluation. Compared with X-ray inspection, the proposed system is safer, more portable, and suitable for real-time on-site monitoring, thereby significantly improving quality control efficiency in silicone manufacturing. This study provides a novel CMUT-array-based solution for quantitative bubble detection in silicone media, offering both high resolution and practical application potential. Full article

With the rapid development of intelligent sensing systems, digital monitoring platforms, and multisource data acquisition technologies, accurate identification of operational states and potential risks from heterogeneous sensing signals has become an important research issue in artificial intelligence-driven sensing. Existing studies have primarily focused on either textual information understanding or behavioral data analysis, with limited attention paid to jointly modeling the consistency between textual declarations and executed behaviors. As a result, many potential risks that have not yet manifested as significant anomalies but already involve execution deviations are difficult to detect in a timely manner. To address this issue, a language–behavior consistency sensing framework for multisource sensing signals is proposed. Textual sensing signals and behavioral sensing signals are mapped into a shared state logic space, and intelligent perception and quantitative analysis of deviations between textual states and executed states are achieved through a textual state logic extraction module, an observed behavioral state modeling module, and a language–behavior consistency measurement module. Systematic experiments were conducted on a multisource sensing dataset containing public declaration texts, operation reports, behavioral logs, resource allocation records, and state-response information. The results show that the proposed method achieved the best performance in the baseline comparison experiment, with a language–behavior consistency score ($LCS$) of $0.742$, an $AUC$ of $0.846$, an $F1$-score of $0.811$, a Precision of $0.802$, and an explanation consistency score ($ECS$) of $0.821$, clearly outperforming advanced methods such as FinBERT, LSTM, Multimodal Transformer, and the Contrastive Multimodal Model. These results demonstrate that language–behavior consistency sensing can effectively fuse multisource sensing information and improve complex system state identification, anomaly early warning, and risk perception, providing an interpretable artificial intelligence-driven sensing framework with the potential to support industrial operation and maintenance, intelligent manufacturing, digital infrastructure management, and other intelligent monitoring scenarios. Full article

Pesticide and veterinary drug residues, heavy metals and other hazardous contaminants in agricultural products and food systems pose severe threats to food safety and agro-ecological security. Conventional detection techniques are plagued by complicated operations, long testing cycles and insufficient sensitivity, which fail to meet the practical requirements for rapid, accurate on-site detection and in situ remediation. This paper systematically introduces the fundamental physicochemical properties of typical carbon-based nanomaterials, including graphene, carbon nanotubes, carbon quantum dots and biomass-derived carbon. It comprehensively reviews the latest research advances of these materials in the detection of heavy metal ions, pesticide residues, mycotoxins and illegal additives, as well as in the non-destructive monitoring of food quality. Meanwhile, relevant applications of carbon-based nanomaterials in the adsorption, enrichment and catalytic remediation of heavy metals and organic pollutants in farmland soil and water environments are summarized. The intrinsic mechanisms underlying their performance in high-precision detection and environmental remediation are elaborated from the perspectives of optical sensing response and adsorption–separation effects. Furthermore, the current technical limitations and bottlenecks restricting the practical application of carbon-based nanomaterials are discussed. Combined with the industrial demands for rapid screening of agro-food safety risks and in situ treatment of farmland environments, the future development prospects of carbon-based nanomaterials in agriculture and food safety fields are outlined. This work aims to provide theoretical references for the development and industrialization of high-performance carbon-based sensing and remediation materials, and to facilitate the risk prevention and control of agro-food safety as well as the green and sustainable development of agricultural ecosystems. Full article

Date palm is a staple food crop and crucial export agricultural commodity in Arab countries. Date palm diseases caused by phytoplasmas are devastating diseases that severely constrain the sustainable development of date palm cultivation and industry. The phytoplasmas associated with date palm diseases mainly consist of three taxa: ‘Candidatus Phytoplasma asteris’ of the 16SrI group, ‘Ca. Phytoplasma citri’ of the 16SrII group, and ‘Ca. Phytoplasma fraxini’ of the 16SrVII group. Based on the 16S rRNA gene sequences of the three groups of phytoplasmas, two sets of primer pairs (Da-1 and Da-5) enabling simultaneous visual detection of date palm phytoplasmas were designed and screened. Universal simultaneous visual detection methods were successfully established for different groups and candidate species of date palm pathogenic phytoplasmas. The whole detection procedure of this optimized method can be accomplished via isothermal amplification at 64 °C within 30–50 min, and the detection results can be directly and intuitively judged by the color variation of the reaction system. Sensitivity verification showed that for the three groups of phytoplasmas, the minimum detection limit of the Da-1 primer set for simultaneous visual detection ranged from 1 fg/μL to 10 fg/μL, while that of the Da-5 primer set ranged from 1 fg/μL to 100 pg/μL. The established LAMP assay demonstrated high sensitivity and specificity using synthetic DNA templates. This method provides a promising laboratory tool for the simultaneous visual detection of three major date palm phytoplasma groups. Further validation on naturally infected field samples is required before practical application in field diagnosis, epidemic monitoring, and port quarantine. Full article

Anticipating component degradation before failure occurs has become a cornerstone of intelligent condition monitoring in safety-critical engineering environments. This paper benchmarks three supervised learning algorithms—Linear Regression (LR), Random Forest (RF), and Gradient Boosting (GB)—against each other for the task of Remaining Useful Life (RUL) forecasting on turbofan engines, using the NASA CMAPSS FD001 simulation dataset as the evaluation testbed. The benchmark encompasses 100 run-to-failure training trajectories and 100 test sequences, each characterised by 21 on-board sensor channels recorded over successive flight cycles. Following a systematic preparation stage—which involved discarding uninformative constant-variance channels and applying a piecewise linear degradation labelling scheme capped at 125 cycles—all three algorithms were trained and scored on normalised feature vectors. Among the three candidates, Random Forest delivered the strongest results (RMSE = 17.09, MAE = 12.10, R² = 0.818), ahead of Gradient Boosting (RMSE = 17.42, R² = 0.811) and the linear baseline (RMSE = 20.60, R² = 0.736). These outcomes confirm that bagging-based ensemble regressors provide a compelling accuracy–deployability trade-off for degradation forecasting, with direct relevance to autonomous scheduling and condition surveillance in industrial automation contexts. Full article

A. kawachii is of considerable importance in the food industry due to its enzyme production. The objective was to evaluate the feasibility of coupling β-glucosidase production by A. kawachii, to partially purify the enzymatic extract by gel filtration chromatography, and to preliminarily apply it to the bioconversion of geniposide into genipin to form a blue chromophore in a simplified process. β-glucosidase production was carried out via liquid fermentation in a glucose-tryptone medium over 54 h, and fermentation process parameters were monitored. Subsequently, the enzyme extract was fractionated by gel filtration chromatography (Hi-Trap G-25 column). The simplified process for the formation of the blue chromophore was carried out by mixing the fraction with β-glucosidase activity, geniposide (10 g/L), and glycine at different concentrations (1.0, 2.5, 5.0, 7.5, and 10.0 g/L), then incubating it at 40 °C for 48 h. The fermentation time for maximum β-glucosidase production (0.059 U/mL) was 48 h. Residual fermentation compounds were removed by chromatography, thereby partially purifying the enzyme (purification fold = 3.88) to prevent it from reacting with genipin. At 7.5 g/L glycine, a yield of 0.96 g/L of the blue chromophore was obtained. In conclusion, the use of enzymes enables a sustainable process for producing a blue chromophore. This chromophore can be considered a potential functional ingredient for the development of nutraceuticals and cosmeceuticals. Full article

This study evaluated the influence of sucrose addition on kombucha fermentation and the use of agro-industrial fruit residues (cashew apple bagasse, mango peels, and grape pomace) as natural flavoring agents. Processing parameters, including pH, total soluble solids (TSS), total titratable acidity (TTA), sugar consumption, and the evolution of organic acids, ethanol, and Total Phenolic Compounds (TPC), were monitored during a 9-day fermentation period and subsequent 30-day refrigerated storage period. Fermentation without added sucrose caused a significant reduction in TPC (p < 0.05), suggesting microbial biotransformation and enzymatic cleavage of bonded polyphenols into smaller, more reactive phenolic monomers. During refrigerated storage, the chemical profiles of the developed beverages remained relatively stable, and ethanol concentrations remained below the regulatory limit for non-alcoholic beverages (0.5% v/v or 4.0 g/L). In addition, four commercial kombucha samples were evaluated over the same storage period to compare ethanol stability and regulatory compliance. Sensory analysis showed that visual appearance and complex flavor profiles were mainly driven by fruit matrices, confirming their effectiveness as natural functional and flavoring agents. These findings demonstrate that initial sucrose can be excluded from kombucha brewing, as fruit residues supported late fermentation dynamics without inducing non-compliant ethanol accumulation. Full article

Meat quality and safety are critical aspects of global food security. However, traditional evaluation techniques, including sensory analysis and chemical and instrumental tests, are constrained by subjectivity, high time consumption, their destructive character, and susceptibility to bias. With rapid advances in sensor technologies and computational methods, there is a growing demand for the nondestructive, accurate, and fast measurement of meat quality and safety attributes. In recent years, artificial intelligence (AI) integrated with nondestructive sensing has emerged as a transformative paradigm, offering unparalleled capabilities for extracting quality information from complex datasets generated by various nondestructive sensing technologies. This review provides a comprehensive analysis of AI-driven nondestructive technologies for meat quality and safety assessment, focusing on the integration of machine learning and deep learning with various sensing techniques. Additionally, the review evaluates state-of-the-art algorithms and their performances and identifies deployment barriers, particularly calibration transfer, environmental sensitivity, reproducibility issues, sensor fouling, and generalization challenges across batches and processing plants. Furthermore, economic and regulatory constraints, including high sensor costs, small and medium enterprise (SME) adoption challenges, and alignment with HACCP/ISO frameworks that further limit commercial scalability, are discussed. Unlike previous reviews that primarily focus on individual sensing techniques, this review emphasizes the practical challenges associated with industrial implementation and the development of scalable solutions for real-world deployment. Finally, strategic research priorities and recommendations are highlighted to accelerate the industrial adoption of intelligent meat quality monitoring systems across the global meat industry. Full article

Conventional DC control and protection (C&P) systems rely on single-modal electrical data and are susceptible to false tripping and missed detection under complex operating conditions or novel fault types. In high-voltage direct current (HVDC) transmission, multi-modal data encompass time-series electrical quantities, unstructured transient waveforms, condition monitoring measurements, and environmental variables, each reflecting the system operating state from a distinct dimension with significant inter-modal complementarity. Nevertheless, fusing these heterogeneous modalities poses three key challenges: feature conflicts arising from data heterogeneity, difficulty embedding domain-specific C&P knowledge into data-driven models, and degraded model robustness under data noise and missing data conditions. This paper proposes a three-layer hierarchical fusion model that integrates multimodal data preprocessing, a C&P phase-aware rule-guided feature weighting strategy, and a dual-path decision mechanism. Experiments conducted on a dataset covering normal operation, typical fault, and complex operating scenarios demonstrate that the proposed model achieves an overall fault identification accuracy of 96.2%, which is 13.9 and 6.5 percentage points higher than a single-modal baseline and a generic multimodal model, respectively. The average decision latency of 7.2 ms satisfies the millisecond-level real-time requirement of industrial C&P systems, confirming the engineering applicability of the proposed approach. Full article

Emerging contaminants (ECs) represent a growing environmental problem due to their presence in various environmental matrices. In Mexico, research on ECs remains limited. This scoping review, conducted in accordance with the PRISMA-ScR guidelines, analyzes their environmental fate during the period 2004–2025, considering their distribution in surface and groundwater, sediments, and influents and effluents from wastewater treatment plants (WWTPs). The reviewed studies demonstrate the presence of pharmaceutically active compounds (PhACs), endocrine-disrupting compounds (EDCs), personal care products, phthalates, bisphenols, pesticides, illicit drugs, xanthines, perfluoroalkyl and polyfluoroalkyl substances (PFAS), metabolites, and industrial compounds. The Apatlaco, Cuautla, and Santa Catarina rivers exhibit the greatest diversity of ECs, while groundwater shows evidence of infiltration of PhACs, phthalates, bisphenols, and triclosan, associated with urban, industrial, and tourist discharges. In sediments, PhACs and EDCs accumulate at concentrations in the ng/g range, while phthalates reach concentrations of thousands of ng/g, indicating their affinity for the solid phase. At Mexican wastewater treatment plants, conventional treatment methods do not completely remove ECs, allowing residual amounts to be discharged into receiving bodies of water. Overall, national monitoring should be expanded to include metabolites, transformation products, and advanced treatment assessment. Full article

Oil-well anomaly monitoring supports safe and efficient oil-and-gas production, but delayed recognition of abnormal operating states can reduce lifting efficiency, trigger costly interventions, and increase operational risk. Existing data-driven detectors are also vulnerable to optimistic estimates when segmentation, normalization, and train–test splitting leak information across source files. This study presents ALSTMResNet, an application-oriented active-learning workflow built on an LSTMResNet backbone for one-step-ahead anomaly screening under partial labels. The workflow converts real 3W well records into leakage-aware file-wise splits, train-only standardized sliding windows, and a fixed 15-channel representation that combines process measurements with temporal covariates; active learning is used only during training to select additional labels while leaving the deployed backbone unchanged. Experiments on the retained benchmark split obtain an $F_1$-score of 0.9354, and five repeated file-wise trials give an average $F_1$-score of $0.8661\pm 0.0666$ while reducing retraining time relative to the full-data backbone. Label-budget and acquisition-policy analyses show that the workflow remains competitive under constrained labels, although uncertainty, entropy, margin, least-confidence, and random querying have limited separation in the present binary setting. These results indicate that ALSTMResNet can support cost-aware oil-well anomaly monitoring when labels are partially available and retraining resources are constrained. Full article

Rotating machines in industrial environments often generate fundamental-frequency noise and its harmonics, which may affect workers’ physical and mental health. This study investigates the active control of single-frequency noise in an open sound field. A multi-harmonic active noise control (ANC) algorithm based on local secondary path (LSP) equalization is adopted to reduce computational complexity. A semi-anechoic chamber test platform is built to experimentally investigate the influence of the distance between the error microphone and the secondary source (Des), as well as collinear and non-collinear arrangements, on noise-reduction level, spatial attenuation range, and the upper frequency limit of noise control. The experimental results show that, under a 100 Hz tonal noise condition, the noise-reduction level first increases and then decreases as Des increases. This phenomenon can be explained by the phase relationship between the primary and secondary sound fields. As Des changes, the propagation delay of the secondary sound field also changes, thereby altering the interference pattern at the monitoring positions. When the phase difference approaches destructive interference, higher noise reduction is achieved, whereas further increases in Des gradually reduce the cancellation effectiveness because of phase mismatch. A relatively high noise-reduction performance is maintained when Des ranges from 200 cm to 500 cm. Secondary destructive interference may also appear at certain distances. In addition, the collinear arrangement shows a significantly higher upper frequency limit for noise reduction in comparison with the non-collinear arrangement. The results provide useful experimental evidence and practical guidance for optimizing the layout of ANC systems in complex acoustic environments such as vehicles and industrial plants. Full article