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Diabetes is a global chronic disease that seriously endangers human health and characterized by abnormally high blood glucose levels in the body. Diabetic wounds are common complications which associate with impaired healing process. Biomarkers monitoring of diabetic wounds is of great importance in the diabetes management. However, actual monitoring of biomarkers still largely relies on the complex process and additional sophisticated analytical instruments. In this work, we prepared hydrogels composed of different modules, which were designed to monitor different physiological indicators in diabetic wounds, including glucose levels, pH, and temperature. Glucose monitoring was achieved based on the combination of photonic crystal (PC) structure and glucose-responsive hydrogels. The obtained photonic crystal hydrogels (PCHs) allowed visual monitoring of glucose levels in physiological ranges by readout of intuitive structural color changes of PCHs during glucose-induced swelling and shrinkage. Interestingly, the glucose response of double network PCHs was completed in 15 min, which was twice as fast as single network PCHs, due to the higher volume fraction of glucose-responsive motifs. Moreover, pH sensing was achieved by incorporation of acid-base indicator dyes into hydrogels; and temperature monitoring was obtained by integration of thermochromic powders in hydrogels. These hydrogel modules effectively monitored the physiological levels and dynamic changes of three physiological biomarkers, both in vitro and in vivo during diabetic wound healing process. The multifunctional hydrogels with visual monitoring of biomarkers have great potential in wound-related monitoring and treatment. [ABSTRACT FROM AUTHOR]

The eradication of recalcitrant bacterial biofilm infections necessitates the development of speedy diagnostics and prompt therapeutics. However, constructing a portable versatile platform that enables in situ monitoring of biofilm infections accompanied by potent antibiofilm activity remains challenging. To address this conundrum, a microneedle theranostic patch (Mn: C/G@MN) is devised by incorporating an innovative biophotonic probe (manganese‐doped carbon dots, Mn: CDs) into methacrylated gelatin for visual monitoring of biofilm infection and on‐demand photo‐therapy. The Mn: C/G@MN patch penetrates the physical barrier of biofilms to track their acidic microenvironment, exhibiting a visualized fluorescence color change (from yellow to turquoise) to enable naked‐eye monitoring of biofilm infection. Furthermore, the Mn: C/G@MN patch can drastically eradicate biofilm on‐demand through the synergy of local hyperthermia and hydroxyl radical (•OH) storm under 808 nm near‐infrared light illumination, enabling the damaging of extracellular polymeric substances (EPS) matrix to disperse biofilms and subsequently kill detached bacteria. Both in vitro and in vivo findings authenticate that biofilm infection monitoring‐and‐treating can be achieved. Moreover, the versatile Mn: C/G@MN patch is conducive to suppressing inflammatory responses, expediting collagen deposition, stimulating angiogenesis, and accelerating biofilm‐infected wound healing. As envisaged, this work highlights the potential of such a versatile platform for application in integrated theranostics for biofilm infection. [ABSTRACT FROM AUTHOR]

Abstract Diabetes is a global chronic disease that seriously endangers human health and characterized by abnormally high blood glucose levels in the body. Diabetic wounds are common complications which associate with impaired healing process. Biomarkers monitoring of diabetic wounds is of great importance in the diabetes management. However, actual monitoring of biomarkers still largely relies on the complex process and additional sophisticated analytical instruments. In this work, we prepared hydrogels composed of different modules, which were designed to monitor different physiological indicators in diabetic wounds, including glucose levels, pH, and temperature. Glucose monitoring was achieved based on the combination of photonic crystal (PC) structure and glucose-responsive hydrogels. The obtained photonic crystal hydrogels (PCHs) allowed visual monitoring of glucose levels in physiological ranges by readout of intuitive structural color changes of PCHs during glucose-induced swelling and shrinkage. Interestingly, the glucose response of double network PCHs was completed in 15 min, which was twice as fast as single network PCHs, due to the higher volume fraction of glucose-responsive motifs. Moreover, pH sensing was achieved by incorporation of acid-base indicator dyes into hydrogels; and temperature monitoring was obtained by integration of thermochromic powders in hydrogels. These hydrogel modules effectively monitored the physiological levels and dynamic changes of three physiological biomarkers, both in vitro and in vivo during diabetic wound healing process. The multifunctional hydrogels with visual monitoring of biomarkers have great potential in wound-related monitoring and treatment. Graphical abstract

Protecting power infrastructure through visual surveillance can assure the safe operation of a power system, especially in unstructured environments where leaning utility poles are particularly inclined to cause large-area blackouts or even personal injury. Current methods place too much emphasis on detection and not enough on understanding leaning postures. However, due to the diversity and uncertainty of leaning utility poles, understanding them remains an urgent problem. Traditional posture estimation via three-dimensional (3D) point clouds is energy-intensive and costly, which limits its adoption in resource-constrained visual surveillance systems. In this study, we present a methodology to understand utility poles, and to estimate their leaning postures using a low-cost monocular camera. Edges and lines are extracted. Through their corresponding proximity and orientation, potential lines of utility poles are estimated. By analyzing relative geometric constraints between potential lines, utility poles are segmented and corresponding leaning angles are estimated, which is helpful to make risk-informed decisions to make leaning utility poles resilient. The approach requires neither prior training, nor calibration or adjustment of the camera's internal parameters. It is robust against color and illumination associated with severe weather conditions. The percentage of correctly segmented pixels was compared to the ground truth, demonstrating that the method can successfully understand utility poles, meeting safety monitoring requirements for power infrastructure. [ABSTRACT FROM AUTHOR]

ABSTRACT: Indicating hydrogels have shown the advantages of being fast, sensitive, and real-time in demonstrating the freshness of aquatic products due to their noticeable color change. In this work, an anti-freezing hydrogel embedded with pH-sensitive alizarin was developed as an original colorimetric indicator for monitoring the corruption of aquatic products at low temperatures. The anti-freezing hydrogel was prepared from 2-carboxyethyl acrylate, [2-(methacryloyloxy)ethyl]-trimethylammonium chloride, and 2-hydroxyethyl methacrylate monomers with triethylene glycol dimethacrylate as cross-linking agent. The hydrogel showed excellent anti-freezing capability, which was further incorporated with the pH-sensitive dye alizarin to gain the indicating capability. When applied in the indication of the freshness of salmon under low-temperature storage, it was found that the color changes of the indicator corresponded with the total volatile basic nitrogen contents of salmon, the correlation coefficient of which was 0.959 6. These results indicated the application potential of the hydrogels as freshness indicators during low-temperature storage or cold-chain transportation.

A novel, portable deep learning-assisted smartphone-based electrochemiluminescence (ECL) cost-effective (~10$) sensing platform was developed and used for selective detection of lactate. Low-cost, fast prototyping screen printing and wax printing methods with paper-based substrate were used to fabricate miniaturized single-pair electrode ECL platforms. The lab-made 3D-printed portable black box served as a reaction chamber. This portable platform was integrated with a smartphone and a buck-boost converter, eliminating the need for expensive CCD cameras, photomultiplier tubes, and bulky power supplies. This advancement makes this platform ideal for point-of-care testing applications. Foremost, the integration of a deep learning approach served to enhance not just the accuracy of the ECL sensors, but also to expedite the diagnostic procedure. The deep learning models were trained (3600 ECL images) and tested (900 ECL images) using ECL images obtained from experimentation. Herein, for user convenience, an Android application with a graphical user interface was developed. This app performs several tasks, which include capturing real-time images, cropping them, and predicting the concentration of required bioanalytes through deep learning. The device's capability to work in a real environment was tested by performing lactate sensing. The fabricated ECL device shows a good liner range (from 50 µM to 2000 µM) with an acceptable limit of detection value of 5.14 µM. Finally, various rigorous analyses, including stability, reproducibility, and unknown sample analysis, were conducted to check device durability and stability. Therefore, the developed platform becomes versatile and applicable across various domains by harnessing deep learning as a cutting-edge technology and integrating it with a smartphone. [ABSTRACT FROM AUTHOR]

Mould bed is crucial for supporting the ship subsection and controlling its deformation during shipbuilding. However, its visualisation and control process intelligence are often inadequate, leading to reduced accuracy and efficiency in ship subsection construction. Aiming at the problem, a three-dimensional (3D) visual monitoring method for the marine intelligent mould bed is proposed based on digital twin, and the overall framework of the management and control system is designed. Meanwhile, a fuzzy PID control principle optimised by the genetic algorithm is proposed to better control the deformation of the ship subsection. On this basis, the verification of the developed system is carried out based on the physical intelligent mould bed, and the 3D visual management and control of the marine intelligent mould bed are realised. It provides a foundation for improving the accuracy and efficiency of ship subsection construction. [ABSTRACT FROM AUTHOR]

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In view of the issues such as opaque data and monitoring blind spots in industrial robots, study was conducted on physical industrial robots, leading to the development of a digital twin robot monitoring system. Firstly, modeling of virtual robots was accomplished from three dimensions: geometric, physical, and logical. Secondly, based on the open platform communications united architecture (OPC UA), data collection and communication for robots were implemented. User display interfaces were designed to achieve real-time visualization of data. Finally, through robot simulation experiments, the relative position errors of the robot end effector in the X, Y, and Z coordinates were determined to be 0.48%, 0.32%, and 0.27%, respectively. The maximum error in joint angles of the robot, verified through synchronized experiments using a digital twin robot monitoring system, was found to be 0.31°. The experimental results demonstrate that the method can achieve motion data monitoring for digital twin industrial robots, reducing the risk of accidents and failures in the production process. It provides insights and directions for the development of intelligent industrial robots. [ABSTRACT FROM AUTHOR]

This paper introduces a digital-twin-based system for foam cleaning robots in spent fuel pools, aiming to efficiently clean foam in spent fuel pools. The system adopts a four-layer architecture, including the physical entity layer, twin data layer, twin model layer, and application service layer. Initially, the robot was modeled in two dimensions, encompassing physical and kinematic aspects. Subsequently, data collection and fusion were carried out using laser radar and depth cameras, establishing a virtual model of the working scenario and mapping the physical entity to the digital twin model. Building upon this foundation, improvements were made in applying the full-coverage path planning algorithm by integrating a pure tracking algorithm, thereby enhancing the cleaning efficiency. Obstacle detection and localization were conducted using infrared and depth cameras positioned above the four corners of the spent fuel pool, with the digital twin platform transmitting coordinates to the robot for obstacle avoidance operations. Finally, comparative experiments were conducted on the robot's full-coverage algorithm, along with simulation experiments on the robot's position and motion direction. The experimental results indicated that this approach reduced the robot's overall cleaning time and energy consumption. Furthermore, it enabled motion data detection for the digital twin robot, reducing the risk of collisions during the cleaning process and providing insights and directions for the intelligent development of foam cleaning robots. [ABSTRACT FROM AUTHOR]

With the rapid development of aerospace, the large complex curved workpiece is widely used. However, the lack of digital monitoring and detection in the current manufacturing process leads to the low efficiency of the parts produced and processed, and quality consistency cannot be guaranteed. Aiming at the problems of low degree of virtual visualization and insufficient monitoring ability of large complex surface machining, a framework of large complex surface machining monitoring system based on digital twin technology was proposed. The digital research of intelligent processing monitoring system is carried out from six dimensions. By studying the key technologies of virtual twin model construction, multi-source data acquisition and transmission, and virtual-real mapping relationship construction, a digital twin monitoring system for large complex surface machining is developed. Finally, the feasibility and effectiveness of the twin system are verified by a real scene, and it provides a reference for monitoring the machining process of large complex curved workpieces. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Chronic wound management remains a challenge due to the epithelial barrier loss, drug‐resistant bacterial infections, inflammatory storm, and compromised angiogenesis. Herein, a boron‐based probe driven theranostic hydrogel dressing for visual monitoring and matching chronic wounds healing is developed to encourage chronic wound healing. The boron‐based (BT) probe is synthesized through the dynamic interaction between borax (B) and tannic acid (TA) and serves as a cross‐linker for constructing the guar gum/polyvinyl alcohol/BT (GPBT) hydrogels. Notably, the GPBT hydrogels offer visual monitoring through color change and remote diagnosis via smartphone. Meanwhile, they exhibit the ability to match the different healing stages of chronic wounds. During the early alkaline stage, the controlled release of TA accelerates healing by polarizing M1 macrophages to M2 macrophages and promoting angiogenesis. As the wound progresses into the acidic healing phase, the hydrogels undergo programmed disassembly to facilitate tissue regeneration. Remarkably, the hydrogel dressing demonstrates potent antibacterial activity by disrupting bacterial membrane potential. Furthermore, it has good self‐healing, self‐adaptive, and adhesion properties. This multifunctional theranostic hydrogel dressing, featuring visual monitoring and matching chronic wound healing, lays the foundation for the development of personalized and precise theranostic platforms. [ABSTRACT FROM AUTHOR]

In the manufacturing process of optical lenses, the lack of monitoring and detection of the central processing process leads to low processing efficiency and difficulty in ensuring product consistency. We propose a digital twin system for alignment processing to address this issue. The system adopts a hierarchical architecture based on the digital twin five-dimensional model, aiming to improve the fidelity and interactivity of the virtual model of the centring lathe by combining dimension-driven virtual models with integrated data and physical models of the turning mechanism. We have successfully achieved the semantic and physical fusion of multi-source heterogeneous data during centring processing using information models and OPC UA-based data interaction methods. In addition, we adopted the VMD-GRU method based on feature fusion for real-time monitoring of critical components of the centring lathe. Finally, we validated the feasibility and effectiveness of the digital twin system for the central lathe through development examples. The application of this system is expected to promote the digital and intelligent development of high-precision optical component processing, providing references including references for related manufacturing fields. In summary, we propose a digital monitoring and detection system for the centring process of optical lens manufacturing. The application of this system will help improve product consistency and processing efficiency while reducing risks and costs in the manufacturing process. [ABSTRACT FROM AUTHOR]

In the process of copper alloy wire-arc additive manufacturing (WAAM), improper process parameters lead to an unstable additive manufacturing process or poor quality of formed parts. Copper alloys are sensitive to heat input and are prone to some welding defects: spatter, collapse, and poor overlapping during deposition. The state of the molten pool can reflect the quality of the WAAM. In this paper, an early-warning system for the molten pool is established using deep learning method to monitor the irregularity copper alloy WAAM process. We built the molten pool image classification model using different convolutional neural networks: Vgg16, ResNet50, EfficietNetb1, and ResNext50 and analyzed the model visually using Grad-CAM algorithm. We obtained the highest classification accuracy of 98.5612%. This model justified the state of the molten pool during WAAM process by accurately identifying the region of the liquid zone, the half-solidified zone, and the solidification zone of molten pool. [ABSTRACT FROM AUTHOR]

A novel, portable deep learning-assisted smartphone-based electrochemiluminescence (ECL) cost-effective (~10$) sensing platform was developed and used for selective detection of lactate. Low-cost, fast prototyping screen printing and wax printing methods with paper-based substrate were used to fabricate miniaturized single-pair electrode ECL platforms. The lab-made 3D-printed portable black box served as a reaction chamber. This portable platform was integrated with a smartphone and a buck-boost converter, eliminating the need for expensive CCD cameras, photomultiplier tubes, and bulky power supplies. This advancement makes this platform ideal for point-of-care testing applications. Foremost, the integration of a deep learning approach served to enhance not just the accuracy of the ECL sensors, but also to expedite the diagnostic procedure. The deep learning models were trained (3600 ECL images) and tested (900 ECL images) using ECL images obtained from experimentation. Herein, for user convenience, an Android application with a graphical user interface was developed. This app performs several tasks, which include capturing real-time images, cropping them, and predicting the concentration of required bioanalytes through deep learning. The device’s capability to work in a real environment was tested by performing lactate sensing. The fabricated ECL device shows a good liner range (from 50 µM to 2000 µM) with an acceptable limit of detection value of 5.14 µM. Finally, various rigorous analyses, including stability, reproducibility, and unknown sample analysis, were conducted to check device durability and stability. Therefore, the developed platform becomes versatile and applicable across various domains by harnessing deep learning as a cutting-edge technology and integrating it with a smartphone.

Transparent grid, as an advanced form of “Internet + smart energy”, is of great value in supporting energy transformation and promoting the development of new power systems. Based on the grid connection requirements of high-density distributed power supply, this paper provides specific grid connection methods and trend calculations and explores the impact of grid connection on high-density distributed power supply. Subsequently, the technical framework and blueprint of the transparent grid are proposed, combined with the grid visualization system to monitor the grid state visually, and based on the random matrix single-loop theory and ARIMA algorithm to realize fault identification and early warning of the transparent grid. The analysis of the algorithm shows that the fault identification has good rapidity, feasibility, and practicability, and while ensuring that no fault alarm is issued in the case of normal disturbance of distributed power supply, it is able to issue fault alarms for all locations and types of short-circuit faults in the system and determine the fault moment within 0.1s. It is “fast”, “accurate,” and “stable” in the research and judgment of active distribution network faults, and it is more intuitive in the assignment of risk levels, and it can identify the categories and levels of faults and realize the following Visualization of early warning.

Visualizing polymer chain growth is always a hot topic for tailoring structure‐function properties in polymer chemistry. However, current characterization methods are limited in their ability to differentiate the degree of polymerization in real‐time without isolating the samples from the reaction vessel, let alone to detect insoluble polymers. Herein, a reliable relationship is established between polymer chain growth and fluorescence properties through polymerization induced emission. (TPE‐C2)2‐Te is used to realize in situ oxidative polymerization, leading to the aggregation of fluorophores. The relationship between polymerization degree of growing polytelluoxane (PTeO) and fluorescence intensity is constructed, enabling real‐time monitoring of the polymerization reaction. More importantly, this novel method can be further applied to the observation of the polymerization process for growing insoluble polymer via surface polymerization. Therefore, the development of visualization technology will open a new avenue for visualizing polymer chain growth in real‐time, regardless of polymer solubility. [ABSTRACT FROM AUTHOR]

Abstract Visualizing polymer chain growth is always a hot topic for tailoring structure‐function properties in polymer chemistry. However, current characterization methods are limited in their ability to differentiate the degree of polymerization in real‐time without isolating the samples from the reaction vessel, let alone to detect insoluble polymers. Herein, a reliable relationship is established between polymer chain growth and fluorescence properties through polymerization induced emission. (TPE‐C2)2‐Te is used to realize in situ oxidative polymerization, leading to the aggregation of fluorophores. The relationship between polymerization degree of growing polytelluoxane (PTeO) and fluorescence intensity is constructed, enabling real‐time monitoring of the polymerization reaction. More importantly, this novel method can be further applied to the observation of the polymerization process for growing insoluble polymer via surface polymerization. Therefore, the development of visualization technology will open a new avenue for visualizing polymer chain growth in real‐time, regardless of polymer solubility.

• A location fusion method is proposed based on multiple visual cognition flows. • Several feature information flows are extracted from video frames. • A quality-based fusion combines target locations estimated from such flows. • A template memory strategy is used to recover the target after tracking loss. • A high tracking robustness and accuracy are achieved in complex scenes. In recent years, deep learning has revolutionized computer vision and has been widely used for monitoring in diverse visual scenes. However, in terms of some aspects such as complexity and explainability, deep learning is not always preferable over traditional machine-learning methods. Traditional visual tracking approaches have shown certain advantages in terms of data collection efficiency, computing requirements, and power consumption and are generally easier to understand and explain than deep neural networks. At present, traditional feature-based techniques relying on correlation filtering (CF) have become common for understanding complex visual scenes. However, current CF algorithms use a single feature to describe the information of the target and locate it accordingly. They cannot fully express changeable target appearances in a complex scene, which can easily lead to inaccurate target locations in time-varying visual scenes. Moreover, owing to the complexity of surveillance scenes, monitoring algorithms can lose their target. The original template update strategy uses each frame with a fixed interval length as a new template, which may lead to unreliable feature extraction and low tracking accuracy. To overcome these issues, in this work, we introduce an original location fusion mechanism based on multiple visual cognition processing streams to achieve real-time and efficient visual monitoring in complex scenes. First, we propose a process for extracting multiple forms of visual cognitive information, and it is periodically used to extract multiple feature information flows of a target of interest. Subsequently, a cognitive information fusion process is employed to fuse the positioning results of different visual cognitive information flows to achieve high-quality visual monitoring and positioning. Finally, a novel feature template memory storage and retrieval strategy is adopted. When the location result is unreliable, the target is retrieved from memory to ensure robust and accurate tracking. In addition, we provide an extensive set of performance results showing that our proposed approach exhibits more robust performance at a lower computational cost compared with 36 state-of-the-art algorithms for visual tracking in complex scenes. [Display omitted] Visual Tracking in Complex Scenes Using a Location Fusion Mechanism based on the Combination of Multiple Visual Cognition Flows [ABSTRACT FROM AUTHOR]

Hydrogen sulfide (H2S) is widely found in oil and natural gas wells and industrial wastewater tanks. Owing to its high toxicity, the monitoring and detection of H2S in the air is essential. However, recent techniques for the quantitative detection of H2S gas suffer from limitations such as high cost, complicated operation, and insufficient sensitivity, preventing their practical application in industry. Thus, we have developed a portable test paper for real-time and inexpensive monitoring of H2S gas by color changes. The test paper had a significantly low H2S detection limit of 200 ppb, which is considered safe for humans. Moreover, the color of the test paper did not change noticeably when exposed to CO2, N2, O2, and air environments, indicating that the test paper is selective for H2S gas and can be stored for a long time. In addition, we fitted a color difference linear model between the color difference values (ΔE) and the concentrations of H2S gas. The establishment of the linear model substantiates that the test paper can provide accurate intensity information when detecting H2S gas leakage. [ABSTRACT FROM AUTHOR]

Seafood products are prone to deterioration and, therefore, require a label that identifies quality with the naked eye. A visual freshness colour indicator membrane incorporated with poly‐L‐lactic acid and lead(II) acetate basic is designed via electrospinning technology for freshness monitoring. The colour indicator membrane had a uniform fibre thickness with an average diameter of 1.54 μm and a large specific surface area. In addition, the colour stability of the membrane did not vary with temperature and humidity. The hydrogen sulfide (H2S) responsivity results showed that the membrane gave a rapid and accurate response to the freshness of shrimp, and the detection limit of H2S is 0.45 ppm. Finally, in application, the colour change of the membrane correlated linearly with the freshness of the shrimp, allowing the quality of the shrimp to be identified with the naked eye. The experimental results showed that poly‐L‐lactic acid/lead(II) acetate basic colour indicator membrane had great potential in the detection of shrimp freshness. Highlights: The colour indicator membrane had a large specific surface area for more sensitive freshness detection.The colour indicator membrane was sensitive to H2S with a minimum detection limit of 0.45 ppm.The colour change of the membrane correlated with the freshness index of the shrimp. [ABSTRACT FROM AUTHOR]

With the development of intelligent factories, collaborative robotic arms are becoming the critical equipment for flexible production lines, and digital twin is the best way to realize intelligent and digitized collaborative robotic arm. This paper designs and develops a collaborative robotic arm monitoring system based on digital twin and proposes a six-dimensional system architecture. Key technologies such as equipment twin modeling for the assembly process, multi-source heterogeneous data acquisition, and digital twin–driven real-time monitoring are studied. The system is driven by twin models and real-time data, and based on the key physical characteristics of the collaborative robotic arm. The model and data are effectively integrated to realize real-time monitoring of the assembly process and provide support for intelligent decision-making. The effectiveness and feasibility of the system are verified by running the application on the UR5 and UR10 collaborative robotic arm assembly test benches. [ABSTRACT FROM AUTHOR]

Herein, we reported a class of well-fluorescence-stabilized rare-earth-free ratiometric fluorescent probe for highly sensitive visual detection of oxytetracycline (OTC), which was fabricated by encapsulation of blue-emitting dye 2-aminoterephthalic acid into nanopores of aluminum-based metal–organic framework MIL-53(Al) with microflower morphology (denoted as FMIL-53(Al)). The surface-distributed abundant open aluminum sites of FMIL-53(Al) can coordinate and fix the conformation of OTC molecules, which selectively increases the fluorescent quantum yield of OTC at ~ 520 nm. The dye in the probe is inert to the analyte, and the blue fluorescence intensity is constant. When the green fluorescence of OTC increases, a continuous color evolution from blue to green is observed. For sensing applications, NH2-BDC@FMIL-53(Al)-3 was selected as the sensor. The optimized conditions for the sensing process were as follows: sensing medium was HEPES buffer (10 mM, pH = 7.3), probe concentration: 0.1 mg/mL; incubation time: 45 min; optimal excitation wavelength: 383 nm. The detection limit was estimated to be 11.0 nM, which is considered to be one of the most sensitive ratiometric fluorescent probes for OTC detection. More importantly, a smartphone-based sensing method was developed, providing a promising non-instrument-based technique for the reliable, semi-quantitative assay of OTC in the field. [ABSTRACT FROM AUTHOR]

To address issues in discrete manufacturing workshops, such as the difficulty for management personnel to coordinate workshop production and the challenge of visualizing and supervising a massive amount of temporary data, this paper proposes a service-oriented digital-twin-system framework for discrete workshops using the industrial IoT platform as the system-service platform to solve the problems of the opaque monitoring of operators in discrete workshops, the low interactivity of 2D monitoring systems, and the difficulty of the visual monitoring of workshop data. Firstly, the current situation of intelligent manufacturing workshop-monitoring demand in the context of new-generation information technology is analyzed, and a six-dimensional digital-twin-workshop-monitoring architecture is proposed, whereby a discrete workshop monitoring system based on the digital twin is constructed with IoT as the service platform. We will conduct research on the construction of virtual workshops for the system development process, twin data collection based on edge computing gateways, and dynamic monitoring of the production process. Finally, through the application of this system framework in a movable-arm-production workshop, the more intelligent human–machine interaction process of browsing and controlling workshop information, such as the equipment layout and production processes in the virtual workshop, has been realized. This includes data acquisition based on edge-computing gateways, dynamic real-time monitoring of the production process, etc., which provides a reference for realizing the visual monitoring of the discrete workshop. [ABSTRACT FROM AUTHOR]

This paper introduces a digital-twin-based system for foam cleaning robots in spent fuel pools, aiming to efficiently clean foam in spent fuel pools. The system adopts a four-layer architecture, including the physical entity layer, twin data layer, twin model layer, and application service layer. Initially, the robot was modeled in two dimensions, encompassing physical and kinematic aspects. Subsequently, data collection and fusion were carried out using laser radar and depth cameras, establishing a virtual model of the working scenario and mapping the physical entity to the digital twin model. Building upon this foundation, improvements were made in applying the full-coverage path planning algorithm by integrating a pure tracking algorithm, thereby enhancing the cleaning efficiency. Obstacle detection and localization were conducted using infrared and depth cameras positioned above the four corners of the spent fuel pool, with the digital twin platform transmitting coordinates to the robot for obstacle avoidance operations. Finally, comparative experiments were conducted on the robot’s full-coverage algorithm, along with simulation experiments on the robot’s position and motion direction. The experimental results indicated that this approach reduced the robot’s overall cleaning time and energy consumption. Furthermore, it enabled motion data detection for the digital twin robot, reducing the risk of collisions during the cleaning process and providing insights and directions for the intelligent development of foam cleaning robots.

In the manufacturing process of optical lenses, the lack of monitoring and detection of the central processing process leads to low processing efficiency and difficulty in ensuring product consistency. We propose a digital twin system for alignment processing to address this issue. The system adopts a hierarchical architecture based on the digital twin five-dimensional model, aiming to improve the fidelity and interactivity of the virtual model of the centring lathe by combining dimension-driven virtual models with integrated data and physical models of the turning mechanism. We have successfully achieved the semantic and physical fusion of multi-source heterogeneous data during centring processing using information models and OPC UA-based data interaction methods. In addition, we adopted the VMD-GRU method based on feature fusion for real-time monitoring of critical components of the centring lathe. Finally, we validated the feasibility and effectiveness of the digital twin system for the central lathe through development examples. The application of this system is expected to promote the digital and intelligent development of high-precision optical component processing, providing references including references for related manufacturing fields. In summary, we propose a digital monitoring and detection system for the centring process of optical lens manufacturing. The application of this system will help improve product consistency and processing efficiency while reducing risks and costs in the manufacturing process.

Rapid changes of the biosphere observed in recent years are caused by both small and large scale drivers, like shifts in temperature, transformations in land-use, or changes in the energy budget of systems. While the latter processes are easily quantifiable, documentation of the loss of biodiversity and community structure is more difficult. Changes in organismal abundance and diversity are barely documented. Censuses of species are usually fragmentary and inferred by often spatially, temporally and ecologically unsatisfactory simple species lists for individual study sites. Thus, detrimental global processes and their drivers often remain unrevealed. A major impediment to monitoring species diversity is the lack of human taxonomic expertise that is implicitly required for large-scale and fine-grained assessments. Another is the large amount of personnel and associated costs needed to cover large scales, or the inaccessibility of remote but nonetheless affected areas.To overcome these limitations we propose a network of Automated Multisensor stations for Monitoring of species Diversity (AMMODs) to pave the way for a new generation of biodiversity assessment centers. This network combines cutting-edge technologies with biodiversity informatics and expert systems that conserve expert knowledge. Each AMMOD station combines autonomous samplers for insects, pollen and spores, audio recorders for vocalizing animals, sensors for volatile organic compounds emitted by plants (pVOCs) and camera traps for mammals and small invertebrates. AMMODs are largely self-containing and have the ability to pre-process data (e.g. for noise filtering) prior to transmission to receiver stations for storage, integration and analyses. Installation on sites that are difficult to access require a sophisticated and challenging system design with optimum balance between power requirements, bandwidth for data transmission, required service, and operation under all environmental conditions for years. An important prerequisite for automated species identification are databases of DNA barcodes, animal sounds, for pVOCs, and images used as training data for automated species identification. AMMOD stations thus become a key component to advance the field of biodiversity monitoring for research and policy by delivering biodiversity data at an unprecedented spatial and temporal resolution.

Tetracycline (TC) and copper ion (Cu2+), as important additives in animal feed, play a crucial role in disease prevention and growth regulation. However, the abuse leads to concentration accumulation, which seriously threatens human health and the ecological environment. There is an urgent need to develop a detection method to achieve fast and synchronous detection of these pollutants without cross-interference. Here, a carbon dots-doped lanthanide-based fluorescent nanosensor (CDs@Tb-MOFs@SiO 2 –NH 2 -Eu) was synthesized, which can detect TC in the 380 nm channel by "antenna effect" and internal filtering effects (IFE), and identify Cu2+ in the 320 nm channel. The sensor was highly sensitive to TC within 0–4 μM with a detection limit as low as 3.64 nM, and Cu2+ could be detected within 0–40 μM with a detection limit of 38 nM. A portable dual-channel visual fluorescence sensor was obtained by loading the probes onto test paper and cotton swabs in food samples, which indicates the practicability of this sensing strategy. [Display omitted] • Meticulously designed dual-channel fluorescent nanosensors. • Visual monitoring of tetracycline and copper ions. • Portable nanosensors based on test paper and cotton swabs. • A logic gate promoted the development of TC and Cu2+ intelligent devices. [ABSTRACT FROM AUTHOR]

Ventilator-associated pneumonia (VAP) is a common healthcare-acquired infection often arising during artificial ventilation using endotracheal intubation (ETT), which offers a platform for bacterial colonization and biofilm development. In particular, the effects of prolonged COVID-19 on the respiratory system. Herein, we developed an antimicrobial coating (FK-MEM@CMCO-CS) capable of visualizing pH changes based on bacterial infection and releasing meropenem (MEM) and FK13-a1 in a controlled manner. Using a simple dip-coating process with controlled loading, chitosan was cross-linked with sodium carboxymethyl cellulose oxidation (CMCO) and coated onto PVC-based ETT to form a hydrogel coating. Subsequently, the coated segments were immersed in an indicator solution containing bromothymol blue (BTB), MEM, and FK13-a1 to fabricate the FK-MEM@CMCO-CS coating. In vitro studies have shown that MEM and FK13-a1 can be released from coatings in a pH-responsive manner. Moreover, anti-biofilm and antibacterial adhesion results showed that FK-MEM@CMCO-CS coating significantly inhibited biofilm formation and prevented their colonization of the coating surface. In the VAP rat model, the coating inhibited bacterial growth, reduced lung inflammation, and had good biocompatibility. The coating can be applied to the entire ETT and has the potential for industrial production. [Display omitted] • Visual pH-sensitive antimicrobial coating inhibits bacterial colonization. • FK13-a1 peptide combined with MEM reduces antibiotic usage in VAP treatment. • Hydrogel coating enables pH-responsive drug release and infection monitoring. [ABSTRACT FROM AUTHOR]

The escalating global health concern arises from chronic wounds induced by bacterial infections, posing a significant threat to individuals. Consequently, an imperative exist for the development of hydrogel dressings to facilitate prompt wound monitoring and efficacious wound management. To this end, pH-sensitive bromothymol blue (BTB) and pH-responsive drug tetracycline hydrochloride (TH) were introduced into the polysaccharide-based hydrogel to realize the integration of wound monitoring and controlled treatment. Polysaccharide-based hydrogels were formed via a Schiff base reaction by cross-linking carboxymethyl chitosan (CMCS) on an oxidized sodium alginate (OSA) skeleton. BTB was used as a pH indicator to monitor wound infection through visual color changes visually. TH could be dynamically released through the pH response of the Schiff base bond to provide effective treatment and long-term antibacterial activity for chronically infected wounds. In addition, introducing polylactic acid nanofibers (PLA) enhanced the mechanical properties of hydrogels. The multifunctional hydrogel has excellent mechanical, self-healing, injectable, antibacterial properties and biocompatibility. Furthermore, the multifaceted hydrogel dressing under consideration exhibits noteworthy capabilities in fostering the healing process of chronically infected wounds. Consequently, the research contributes novel perspectives towards the advancement of intelligent and expeditious bacterial infection monitoring and dynamic treatment platforms. [Display omitted] [ABSTRACT FROM AUTHOR]

In this study, we propose a multi-thread artificial intelligence (AI) camera system that can simultaneously recognize remote objects in desired multiple areas of interest (AOIs), which are distributed in a wide field of view (FOV) by using single image sensor. The proposed multi-thread AI camera consists of an ultrafast active vision system and a convolutional neural network (CNN)-based ultrafast object recognition system. The ultrafast active vision system can function as multiple virtual cameras with high spatial resolution by synchronizing exposure of a high-speed camera and movement of an ultrafast two-axis mirror device at hundreds of hertz, and the CNN-based ultrafast object recognition system simultaneously recognizes the acquired high-frame-rate images in real time. The desired AOIs for monitoring can be automatically determined after rapidly scanning pre-placed visual anchors in the wide FOV at hundreds of fps with object recognition. The effectiveness of the proposed multi-thread AI camera system was demonstrated by conducting several wide area monitoring experiments on quick response (QR) codes and persons in nature spacious scene such as meeting room, which was formerly too wide for a single still camera with wide angle lens to simultaneously acquire clear images. [ABSTRACT FROM AUTHOR]

Sensing biogenic amine (BAs) content is very important for assessing food freshness. To address the limitations such as small color difference values (ΔE) and complex preparation of probes for visualizing the freshness of seafood, a pH-responsive ratiometric fluorescent probe (EnEB) was prepared by Eu(NO 3 ) 3 , trimeric acid (BTC), and hydrochloric acid norepinephrine (Enr). EnEB emitted blue (446 nm) and red fluorescence (616 nm) originating from Enr and Eu 3+ , respectively, and exhibiting a fluorescence wavelength difference up to 170 nm. The ratiometric fluorescent signals of EnEB showed a linear correlation with pH in the range of 5.5-8.0. Thus, EnEB can rapidly and precisely detect BAs, such as histamine, tyramine, and spermine, with detection limits and response times of 1.14 μmol/L (3 s), 1.04 μmol/L (8 s), and 0.41 μmol/L (2 s), respectively. Furthermore, an EnEB aerogel was prepared by loading EnEB in a matrix formed by polyvinyl alcohol (PVA) and agarose (AG). EnEB aerogel exhibited excellent acid-base gas-sensing properties. The fluorescence color of EnEB aerogel can change significantly with the deterioration of seafood. When seafood changed from fresh to decayed, the ΔE value of EnEB aerogel was as high as 80.9. Importantly, the results of seafood freshness by naked eye using EnEB aerogel was consistent well with the TVB-N content and the freshness standard stipulated by national food standard, indicating EnEB aerogel can accurately visually and real-time monitor seafood freshness. Furthermore, the strategy for sensing food freshness based on EnEB aerogel also offered multiple color variations to indicate fine freshness levels of seafood. This work provided a convenient, efficient, and accurate approach to assessing the freshness of seafood. Additionally, EnEB also has promising applications in security and anti-counterfeiting., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Taking the inspection robot of underground substation as the basic service platform, using the illusory engine UE4 as the development tool, and with the help of UE4 programming language blueprint, a digital twin system of inspection robot of underground substation of coal mine based on UE4 is designed. The system can remotely control the inspection robot to complete fixed-point and automatic inspection, display the inspection environment in three dimensions, receive the inspection data collected, analyze and display the data. The whole system architecture and each functional module are described in detail. The operation results show that the digital twin system of inspection robot realizes the inspection maintenance cooperative operation control of intelligent inspection robot; the system runs stably, can replace manual to complete the efficient inspection and management of underground substation, improve the production efficiency of coal mine.

Hydrogen sulfide (H2S) is widely found in oil and natural gas wells and industrial wastewater tanks. Owing to its high toxicity, the monitoring and detection of H2S in the air is essential. However, recent techniques for the quantitative detection of H2S gas suffer from limitations such as high cost, complicated operation, and insufficient sensitivity, preventing their practical application in industry. Thus, we have developed a portable test paper for real-time and inexpensive monitoring of H2S gas by color changes. The test paper had a significantly low H2S detection limit of 200 ppb, which is considered safe for humans. Moreover, the color of the test paper did not change noticeably when exposed to CO2, N2, O2, and air environments, indicating that the test paper is selective for H2S gas and can be stored for a long time. In addition, we fitted a color difference linear model between the color difference values (ΔE) and the concentrations of H2S gas. The establishment of the linear model substantiates that the test paper can provide accurate intensity information when detecting H2S gas leakage.

The human health is still threatened by refractory keratitis and diabetic foot ulcers caused by bacterial infections, hypoxia, and chronic inflammation, so that patients are exposed to the risk of amputation, vision loss, and even death. Herein, an oxygen‐producing double‐layered hydrogel is developed that can visualize bacterial infections and supply oxygen to enhance antimicrobial photodynamic therapy (PDT) and inflammation alleviation for diabetic wounds healing. The inner layer hydrogel (containing oxidized sodium alginate/carboxymethyl chitosan [CMCS] via Schiff‐base) is incorporated with a photodynamic metal–organic framework (PCN‐224) and a pH indicator (bromothymol blue). The outer layer hydrogel (containing agarose and CMCS) loads photosynthetic cyanobacteria that continuously generate oxygen to relieve hypoxia of tissue and enhance antimicrobial PDT efficiency. Meanwhile, some unique advantages are reflected by continuous oxygen supply under natural light, such as cell migration acceleration, inflammation relief, promotion of skin capillary formation, and wound tissue recovery. Therefore, the self‐oxygenated double‐layered hydrogel offers tremendous benefits in the synergistic treatment of refractory anaerobe wounds from timely infection monitoring to tissue repair. [ABSTRACT FROM AUTHOR]

The advance of technologies in several areas has allowed the development of smart city applications, which can improve the way of life in modern cities. When employing visual sensors in that scenario, still images and video streams may be retrieved from monitored areas, potentially providing valuable data for many applications. Actually, visual sensor networks may need to be highly dynamic, reflecting the changing of parameters in smart cities. In this context, characteristics of visual sensors and conditions of the monitored environment, as well as the status of other concurrent monitoring systems, may affect how visual sensors collect, encode and transmit information. This paper proposes a fuzzy-based approach to dynamically configure the way visual sensors will operate concerning sensing, coding and transmission patterns, exploiting different types of reference parameters. This innovative approach can be considered as the basis for multi-systems smart city applications based on visual monitoring, potentially bringing significant results for this research field.

The deterioration of food freshness and occurrence of spoilage is attributed to adverse external conditions during storage, leading to unnecessary waste and posing food safety concerns. Therefore, it is imperative to develop packaging that monitors and maintains food freshness. Intelligent and active packaging precisely meets these desired requirements. Intelligent packaging has the potential to offer real-time feedback on environmental changes within the packaging, providing valuable information about food quality and safety. On the other hand, active packaging represents a novel approach that incorporates active ingredients, such as antimicrobials into packages to enhance food quality. This review highlights recent advancements in dual-functional packaging utilizing various plant essential oils and pH-sensitive natural pigments. It explores different methods, with a focus on electrospinning, and also discusses existing challenges and future trends. This information aims to assist in the development of packaging systems, offering a new direction for the development of antibacterial and pH-sensitive packaging. [Display omitted] [ABSTRACT FROM AUTHOR]

To address issues in discrete manufacturing workshops, such as the difficulty for management personnel to coordinate workshop production and the challenge of visualizing and supervising a massive amount of temporary data, this paper proposes a service-oriented digital-twin-system framework for discrete workshops using the industrial IoT platform as the system-service platform to solve the problems of the opaque monitoring of operators in discrete workshops, the low interactivity of 2D monitoring systems, and the difficulty of the visual monitoring of workshop data. Firstly, the current situation of intelligent manufacturing workshop-monitoring demand in the context of new-generation information technology is analyzed, and a six-dimensional digital-twin-workshop-monitoring architecture is proposed, whereby a discrete workshop monitoring system based on the digital twin is constructed with IoT as the service platform. We will conduct research on the construction of virtual workshops for the system development process, twin data collection based on edge computing gateways, and dynamic monitoring of the production process. Finally, through the application of this system framework in a movable-arm-production workshop, the more intelligent human–machine interaction process of browsing and controlling workshop information, such as the equipment layout and production processes in the virtual workshop, has been realized. This includes data acquisition based on edge-computing gateways, dynamic real-time monitoring of the production process, etc., which provides a reference for realizing the visual monitoring of the discrete workshop.

Reliability of the whole plant can be predicted by the condition of system machines and components. For this purpose, various condition monitoring methods like visual monitoring, performance monitoring, wear particle analysis by ferrography, etc., can be used effectively based on the applications. By these methods, information about forthcoming failures can be available well in advance which can reduce chances of failure as well as hazardous effects on the plant and on society also. For this purpose, reliability study of vacuum drying plant was done. It includes subsystems which are vacuum pumping, heating, autoclave, scavenging and air circulation. Real site data have been used for this purpose. The present study involves understanding the functions of overall systems in plant and equipment having interdependent relationship in the manner of specific configuration and draws the reliability block diagram down to the component level based on testing or field data. Basic faults were found out by using failure mode effect analysis (FMEA) for calculation of reliability. Analyzing causes and effects of failure in each subsystem and making critical analysis of the subsystem ranking of the critical components have been carried out by risk priority number (RPN) and by using analytical hierarchical process (AHP). [ABSTRACT FROM AUTHOR]

Rapid changes of the biosphere observed in recent years are caused by both small and large scale drivers, like shifts in temperature, transformations in land-use, or changes in the energy budget of systems. While the latter processes are easily quantifiable, documentation of the loss of biodiversity and community structure is more difficult. Changes in organismal abundance and diversity are barely documented. Censuses of species are usually fragmentary and inferred by often spatially, temporally and ecologically unsatisfactory simple species lists for individual study sites. Thus, detrimental global processes and their drivers often remain unrevealed. A major impediment to monitoring species diversity is the lack of human taxonomic expertise that is implicitly required for large-scale and fine-grained assessments. Another is the large amount of personnel and associated costs needed to cover large scales, or the inaccessibility of remote but nonetheless affected areas. To overcome these limitations we propose a network of A utomated M ultisensor stations for Mo nitoring of species D iversity (AMMODs) to pave the way for a new generation of biodiversity assessment centers. This network combines cutting-edge technologies with biodiversity informatics and expert systems that conserve expert knowledge. Each AMMOD station combines autonomous samplers for insects, pollen and spores, audio recorders for vocalizing animals, sensors for volatile organic compounds emitted by plants (pVOCs) and camera traps for mammals and small invertebrates. AMMODs are largely self-containing and have the ability to pre-process data (e.g. for noise filtering) prior to transmission to receiver stations for storage, integration and analyses. Installation on sites that are difficult to access require a sophisticated and challenging system design with optimum balance between power requirements, bandwidth for data transmission, required service, and operation under all environmental conditions for years. An important prerequisite for automated species identification are databases of DNA barcodes, animal sounds, for pVOCs, and images used as training data for automated species identification. AMMOD stations thus become a key component to advance the field of biodiversity monitoring for research and policy by delivering biodiversity data at an unprecedented spatial and temporal resolution. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low monitoring accuracy and large memory consumption of traditional monitoring software, a visual monitoring software for MEMS micro-drive debugging is designed in this paper. According to the characteristics of MEMS system and the driving principle of micro actuator, the functional module of visual monitoring software for micro actuator debugging is designed. It includes monitoring pause button module program, visual display module program, monitoring quadrature signal module program and monitoring signal communication module program. Improve the monitoring accuracy through the connection between various modules. Select the material of the micro-drive and set its structural parameters, connect and debug the hardware of the drive, and test the performance of the designed software. The experimental results show that the software designed in this paper has high accuracy and low memory loss, the average accuracy is more than 90%, and the memory consumption is only 156 kb. It shows that this method can effectively monitor MEMS micro actuator, and the monitoring results are reliable. [ABSTRACT FROM AUTHOR]

Aiming at problem that opening of water plug-in gate valve in coal preparation plant needs manual adjustment and cannot achieve centralized control, a line laser assisted visual monitoring method for opening of water plug-in gate valve in coal preparation plant was proposed. Line laser is used as an auxiliary detection means to increase target detection characteristics and avoid influence of light changes on target detection, and video monitoring system is used to collect plug-in gate valve image. Template matching method is used to locate target in the image, adaptive threshold method is used to detect line laser projected on the gate, and then adjacent disconnected areas of the line laser on the gate are connected through morphological operations such as corrosion, expansion and so on. Meanwhile, Gaussian filtering method is adopted to remove image noise, so position and length of the line laser in the image are obtained to detect opening of plug-in gate valve. PLC centralized control system realizes closed-loop control of opening of plug-in gate valve by comparing the detected opening value and the set one. The experimental results show that the method can accurately locate plug-in gate valve under different background brightness conditions, and improve detection accuracy of opening of plug-in gate valve.

Introduction. When operating on the face and neck, a technology for intraoperatuve neurophysiological monitoring is used to ensure the right nerve function. Aim. Development of a technology for the intraoperatuve neuromonitoring and a practical estimation of its efficiency in the reconstructive neuroplasty surgery. Methods. In 120 patients with the paralysis of mimic muscles after the removal of a cerebellopontine angle neuroma and a facial nerve damage, neuroplasty was performed to restore the innervation. An original technique was developed for identification and monitoring of the functional state of the nerves and muscles intraoperative visual neuromonitoring (IOVNM). Its concept consists in electric stimulation of the nerves within the surgical field and visual observation of the response muscular contractions. A special ESVM-1 electric stimulator with a set of built-in test programs has been created. The control of the instrument, selection of tests, their launch, visual observation of the reactions and the intraoperative monitoring itself are performed by the operating surgeon. Results. The presented technology eliminates the need for a complex equipment for neurophysiological monitoring and does not require the participation of a specialist in neurophysiology. Conclusion. The use of IOVNM during the operation provides an opportunity to assess the functional state of the facial nerve and mimic muscles, to optimize the operation scheme and accelerate its implementation.

The urban street lighting system is an important part of modern city. A visual intelli¬gent monitoring platform is designed for street lamps to realize real-time monitoring, which can ensure its security and obtain high energy efficiency ratio. This paper presents a monitoring and control scheme of urban street lamp based on LoRa communication. The system is composed of a remote monitoring center, a LoRa gateway and a street lamp terminal. It uses a combination of LoRa and 4G network to achieve long-distance communication and network data transmission. Introduce the Alabama Cloud Internet of Things platform based on the mqtt protocol to realize lighting terminal status display on aicloud platform and store information; use ARM to develop an embedded control system to realize the upload of street lamp information and the issuance of instructions, and to each street lamp terminal Carry out real-time control. The actual measure¬ment results of the system prove that the street lamp terminal responds to the commands issued by the host computer, collects and reports street lamp information, and greatly reduces the light¬ing power consumption as well as the cost of manual inspections. [ABSTRACT FROM AUTHOR]