Optical Monitoring Techniques Research Articles

Research on the construction and verification of evaluation model for borehole collapse in gas extraction

To improve the precision prevention of gas extraction borehole deformation and collapse, it is an important prerequisite to achieve precise monitoring of boreholes. This work is based on distributed optical fiber monitoring and Brillouin Optical Time Domain Analysis Technique (BOTDA). We carried out simulation and validation experiments of borehole plugging with different particle size ratios, constructed a mathematical model for calculating the plugging rate (denoted by ω) of boreholes, and put forward a new methodology for accurate monitoring and evaluation of gas extraction boreholes. The experimental results show that there is a linear relationship between the strain of optical fiber (denoted by ε), the accumulation mass of the simulated coal samples (denoted by m), and the deformation collapse of the borehole. And a mathematical model for calculating the plugging rate of boreholes with more universality has been built by introducing the ratio, volumetric, and strain correction coefficients (denoted by φ, β, and γ). Based on the verification experiment, it was found that the change in the actual and theoretical plugging rates is consistent. With the increase in the ratio of medium coal (2.0–5.0 cm), the plugging rate error decreases first and then increases. The maximum errors are −18.36 % and 19.04 %, but the plugging rate results are still within the middle boundaries of the collapsed boreholes (29.31 % to 75.07 %) and have a neglected effect on the stage classification. Inevitable errors in model construction and actual accumulation errors are two important factors contributing to errors in mathematical models. Taking ω as 0 %, 27.71+40.05·rord.-2%, 74.50+14.13·rord.-2%, and ε limit value 143.77·φ·γ με as the critical value to distinguish the primary, middle, and late periods of borehole collapse. In this work, a new technology of borehole monitoring and evaluation with plugging rate and strain critical value as evaluation indexes is proposed, which can provide a reference for accurate monitoring of drainage boreholes and gas prevention.

Detection of cracks in concrete using near-IR fluorescence imaging

Structural health monitoring of civil infrastructure is a crucial component of assuring the serviceability and integrity of the built environment. A primary material used in the construction of civil infrastructure is concrete, a material that is susceptible to cracking due to a variety of causes, such as shrinkage, creep, overloading, and temperature change. Cracking reduces the durability of concrete structures, as it allows deleterious environmental agents to penetrate the surface, causing such damage as corrosion of steel reinforcement and delamination of the concrete itself. Conventional crack detection techniques are limited in scope due to issues relating to pre-planning, accessibility, and the need for close proximity to the test surface. Contactless optical image monitoring techniques offer the opportunity to overcome these limitations and have the potential to detect cracks at a distance. Concrete has been reported to have a near-infrared (Near-IR) fluorescence line at a wavelength of 1140 nm when excited with red light. This work investigates the use of fluorescence imaging for the detection of cracks in cementitious surfaces using shallow angle incidence excitation red light. Light oriented at a shallow angle does not excite interior surfaces of cracks, which appear as darker features in images of fluorescing concrete. Artificial cracks with widths of 0.2–1.5 mm were readily imaged using a near-IR camera at distances of 0.5 and 1.3 m. An additional concrete sample with a 0.08 mm wide crack was produced using a flexure apparatus and was also imaged. It is worth noting that the 0.08 mm crack was detected despite its width being below the 0.1 mm pixel resolution of the camera, with the aid of digital image enhancement algorithms.

Multi-parameter optical performance monitoring based on single-channel convolutional neural network

For the first time, a combination of grayscale maps and single-channel convolutional neural networks (CNN) is used in the direct detection optical performance monitoring (OPM) technique. Due to the faster monitoring speed of the direct detection OPM technique, it is very suitable for intermediate nodes of optical networks. The OPM technique proposed in this paper is an improvement based on the previous asynchronous delay-tap plot (ADTP) techniques. By reducing the redundant color channel information, the training process of the ADTP feature-based monitoring model is improved. In the simulation stage, three high-order modulated signals, 16quadrature amplitude modulation (QAM), 32QAM and 64QAM, are used as sample signals. The ADTP color maps, ADTP pseudo-color maps and ADTP grayscale maps generated by these three modulated signals with different signal-to-noise ratios and cumulative dispersion (CD) are collected. The accuracy of the monitoring model based on these three feature maps is close to 100% for both cumulative dispersion identification and signal modulation formats identification (MFI). However, the monitoring models based on grayscale maps and pseudo-color maps perform slightly better in estimating optical signal-to-noise ratio (OSNR) compared to the monitoring models based on color maps. And the grayscale map-based model slightly outperforms the other two models in terms of training complexity and monitoring speed. The superiority of grayscale maps and single-channel convolutional neural networks compared to previous ADTP-based OPM techniques is demonstrated.

Real-time Fourier-domain optical vector oscilloscope

To meet the constant demands of high-capacity telecommunications infrastructure, data rates beyond 1 terabit per second per wavelength channel and optical multiplexing are widely applied. However, these features pose challenges for existing data acquisition and optical performance monitoring techniques because of bandwidth limitation and signal synchronization. We designed an approach that would address these limitations by optically converting the frequency limit to an unlimited time axis and combining this with a chirped coherent detection to innovatively obtain the full-field spectrum. With this approach, we demonstrated a real-time Fourier-domain optical vector oscilloscope, with a 3.4-terahertz bandwidth and a 280-femtosecond temporal resolution over a 520-picosecond record length. In addition to on-off keying and binary phase-shift keying signals (128 gigabits per second), quadrature phase-shift keying wavelength division-multiplexed signals (4 × 160 gigabits per second) are simultaneously observed. Moreover, we successfully demonstrate some high-precision measurements, which indicate them as a promising scientific and industrial tool in high-speed optical communication and ultrafast optical measurement.

Functional near infrared spectroscopy for brain functional connectivity analysis: A graph theoretic approach

BackgroundFunctional Near-Infrared Spectroscopy is an optical brain monitoring technique which uses NIRS to perform functional neuroimaging. It uses near-infrared light for measuring brain activity and to estimate the cortical hemodynamic activity in the brain due to motor activity. Functional NIRS measures the changes in oxygen levels in oxygenated and deoxygenated hemoglobin by optical absorption. One of the main challenges in the analysis of fNIRS signals is the signal degradation due to the interference from noise and artifacts from multiple sources. MethodsIn this context, this research aims to analyze the connectivity between different regions of the brain using graph theory and hence the geometrical association of brain networks in terms of functional parameters. In this study, the impact of two noise removal processes (CBSI and TDDR), along with two types of correlation fNIRS such as Pearson's Correlation (PC), and Cross Correlation (CC) and various whole-brain network architectures on the reproducibility of graph measurements for individual participants has been carefully examined for different densities ranging from 5% to 50%.The graph measures' repeatability at the individual level was studied using the test-retest variability (TRT) ResultsThe test-retest variability for global measurements in binary networks was substantially large at low densities, regardless of the noise removal method or the kind of correlation. Very low test –reset values are observed for weighted networks and great reproducibility for measures of the entire graph. When comparing the test-retest values for various methods, the kind of correlation, the absolute value of the correlation, and the weight calculation method on the raw correlation value all had significant major effects. ConclusionBased on a weighted network with the absolute cross correlation functioning as the weight, this study revealed that normalized global graph measurements were reliable. The node definition techniques that were utilized to remove noise were not essential for the normalized graph measures to be reproducible.

Strategies for in-situ thin film filter monitoring with a broadband spectrometer.

Optical monitoring of thin film interference filters is of primary importance for two main reasons: possible error compensation and greater thickness accuracy of the deposited layers compared to non-optical methods. For many designs, the latter reason is the most crucial, because for complex designs with a large number of layers, several witness glasses are used for monitoring and error compensation with a classical monitoring approach is no longer possible for the whole filter. One optical monitoring technique that seems to maintain some form of error compensation, even when changing witness glass, is broadband optical monitoring, as it is possible to record the determined thicknesses as the layers are deposited and re-refine the target curves for remaining layers or recalculate the thicknesses of remaining layers. In addition, this method, if used properly, can, in some cases, provide greater accuracy for the thickness of deposited layers than monochromatic monitoring. In this paper, we discuss the process of determining a strategy for broadband monitoring with the goal of minimizing thickness errors for each layer of a given thin film design.

In-process dimensional and geometrical characterization of laser-powder bed fusion lattice structures through high-resolution optical tomography

Additive Manufacturing (AM) technologies, i.e. Laser-Powder Bed Fusion (l-PBF), have made possible the fabrication of complex and lightweight components not feasible with conventional manufacturing processes. Lattice structures are one of the most representative examples and they have many advantages (high mechanical properties, thermal insulation and good energy absorption ability, etc.) which are limited by internal defects, poor surface quality and dimensional accuracy. In this context, X-ray-based Computed Tomography (XCT) is, currently, the only way for quality assessment of these structures, thanks to its capability to see also internal features, while optical scanning techniques can reconstruct only external surfaces. In this work, an innovative and cost-effective optical layerwise monitoring technique, for dimensional and geometrical characterization of lattice structures realized by l-PBF, was proposed. It exploited the high-resolution optical tomography (HR-OT) and it had a double purpose: it was used as a layerwise in-process monitoring method, able to detect timely and effectively the onset of geometric distortions and, in addition, it provided, at the end of the manufacturing process, a 3D reconstruction of the fabricated structure, comprising external and internal features, suitable for post-process quality assessment.This article is among the first to show a monitoring methodology suitable for lattice structures realized by l-PBF technologies.

Automating Optical Network Fault Management with Machine Learning

Effective fault management is essential for quality of service assurance in optical networks. Conventional fault management designs for optical networks mainly rely on threshold-based rules, which can hardly characterize the complex fault patterns therein. This article discusses the application of machine learning (ML) in actuating an automated optical network fault management architecture. The architecture is built on advanced optical performance monitoring (OPM) techniques and software-defined-networking-enabled programmable network control and management. With the viability of abundant OPM data, the architecture employs various ML models to automatically learn fault patterns and thereby realize data-driven cognitive fault management. We review the state of the art on fault detection, identification, and localization based on such an architecture, focusing specifically on soft failures. To overcome the applicability and scalability issues encountered by existing soft failure detection designs, we present a hybrid learning solution that combines the merits of supervised, unsupervised, and cooperative ML. In particular, we present a self-taught mechanism that self-learns fault patterns with unsupervised learning and then trains supervised learning classifiers with the learned patterns for online detection. We further introduce a broker-plane-aided federated learning framework to enable collaborative training of classifiers from multiple network domains while complying with domain privacy constraints. Performance evaluations show that the hybrid learning design can achieve high fault detection rates with negligible false alarms using less than 10 abnormal data samples (~ 0.1 percent of the scale of normal samples) for training.

Insights into Streptomyces coelicolor A3(2) growth and pigment formation with high-throughput online monitoring.

Streptomyces species are intensively studied for their ability to produce a variety of natural products. However, conditions influencing and leading to product formation are often not completely recognized. Therefore, in this study, high-throughput online monitoring is presented as a powerful tool to gain in-depth understanding of the cultivation of the model organism Streptomyces coelicolor A3(2). Through online measurements of oxygen transfer rate and autofluorescence, valuable information about availability of nutrients and product formation patterns of the pigments actinorhodin and undecylprodigiosin can be obtained and explained. Therefore, it is possible to determine the onset of pigmentation and to study in detail the influencing factors thereof. One factor identified in this study is the filling volume of the cultivation vessel. Slight variations led to varying pigmentation levels. By combining optical and metabolic online monitoring techniques, the correlation of the filling volume with pigmentation could be explained as a result of different growth trajectories caused by varying specific power inputs and their influence on the pellet formation of the filamentous system. Finally, experiments with the addition of supernatant from unpigmented and pigmented cultures could highlight the applicability of the presented approach to study quorum sensing and cell-cell interaction.

Machine Learning-Aided Optical Performance Monitoring Techniques: A Review

Future communication systems are faced with increased demand for high capacity, dynamic bandwidth, reliability and heterogeneous traffic. To meet these requirements, networks have become more complex and thus require new design methods and monitoring techniques, as they evolve towards becoming autonomous. Machine learning has come to the forefront in recent years as a promising technology to aid in this evolution. Optical fiber communications can already provide the high capacity required for most applications, however, there is a need for increased scalability and adaptability to changing user demands and link conditions. Accurate performance monitoring is an integral part of this transformation. In this paper, we review optical performance monitoring techniques where machine learning algorithms have been applied. Moreover, since many performance monitoring approaches in the optical domain depend on knowledge of the signal type, we also review work for modulation format recognition and bitrate identification. We additionally briefly introduce a neuromorphic approach as an emerging technique that has only recently been applied to this domain.

Switchable instantaneous frequency measurement by optical power monitoring based on DP-QPSK modulator

We propose and analyze an instantaneous frequency measurement system by using optical power monitoring technique with improved resolution. The primary component adopted in the proposal is a dual-polarization quadrature phase shift keying (DP-QPSK) modulator which is used to modulate the microwave signal that has a designed time delay and phase shifting. The generated optical signal is sent to polarization beam splitter (PBS) in DP-QPSK modulator. Owing to the complementary transmission nature of polarization interference introduced by PBS, the frequency information is converted into the optical power and the relationship between the amplitude comparison function (ACF) and microwave frequency to be measured is established. Thus, the frequency of the microwave signal can be easily measured through monitoring the optical powers of the two output ports of the PBS. Furthermore, by adjusting the direct current (DC) biases of the DP-QPSK modulator instead of changing the electrical delay, the measurement range and resolution can be switched. In this paper, the basic principle of the instantaneous frequency measurement system is derived in detail, and simulation has been performed to investigate the resolution, the measurement range, and the influence of imperfection devices. The proposed scheme is wavelength-independent and its measurement range is switchable, which can avoid the laser wavelength drifting problem and thus greatly increasing the system flexibility.

Electrical monitoring approaches in 3-dimensional cell culture systems: Toward label-free, high spatiotemporal resolution, and high-content data collection in vitro

3-dimensional (3D) cell cultures better mimic natural environment of cells than 2-dimensional (2D) cell cultures to obtain in vivo like inter and intracellular responses. However, third dimension brings complexity to cell culture. Therefore, high-resolution/high-content screening in 3D is one of the most important challenges with this type of cell cultures. Although optical monitoring techniques, well-established in 2D area, are enhanced to monitor 3D cell cultures, they are generally endpoint, static, time inefficient, and labor intensive. Alternatively, electrical sensing can become a solution to achieve dynamic, real-time, and label-free monitoring of cells in both 2D and 3D cell cultures. Developments in electrical monitoring of cell culture have led to novel approaches, proposed by adapting fundamentals of 2D electrical techniques to 3D to obtain high spatiotemporal systems. In this review, we classified these approaches into five main groups: (i) 3D impedance measurement approach (ii) electrical impedance tomography, (iii) 3D microelectrode array approach, (iv) 3D nanoelectronics scaffold approach, and (v) microphysiometry. We also defined the challenges in the adaptation of electrical monitoring techniques to 3D cultures and explained possible solutions in terms of specific applications and technical point of views, including methods particular to our group. In conclusion, 3D electrical monitoring in cell cultures is considerably challenging but highly accelerated recently by significant advances of microfabrication technology, bioengineering, and material science. Novel approaches reviewed here have a lot of potential and offer opportunities for further developments to find solutions, fit to serve the (bio)medical needs.

Low-margin efficient power and spectrum assignment in elastic optical networks

In this work the spectrum and power allocation (SPA) trade-off in elastic optical network (EON) is discussed in terms of the residual margin and residual spectrum in real-time application, both terms refer to the normalization of the sum-power and sum-spectrum in the resource allocation, respectively. Realistic scenarios have been investigated using optical performance monitoring techniques to measure the quality of transmission (QoT). The SPA-EON problem is formulated and three algorithms finding improved performance-complexity trade-offs are proposed to solve it: i) an analytical method based on combinatorial optimization, namely SPA-CO algorithm, ensuring the optimal solution but with a high computational cost; ii) a sub-optimum low-complexity method based on distance adaptive transmission (DAT), namely SPA-DAT, and iii) an SPA algorithm based on the distributed Verhulst algorithm, namely SPA-V, which achieves good solutions under acceptable computational time. A bunch of metrics, including probability of success, sum-power, and allocated spectrum are evaluated for the three SPA algorithms. The SPA-V was proved to be promising in EON operation, achieving the best performance-complexity trade-off.

Intercomparison of IBBCEAS, NitroMAC and FTIR analyses for HONO, NO<sub>2</sub> and CH<sub>2</sub>O measurements during the reaction of NO<sub>2</sub> with H<sub>2</sub>O vapour in the simulation chamber CESAM

Abstract. We report on applications of the ultraviolet-light-emitting-diode-based incoherent broadband cavity-enhanced absorption spectroscopy (UV-LED-IBBCEAS) technique for optical monitoring of HONO, NO2 and CH2O in a simulation chamber. Performance intercomparison of UV-LED-IBBCEAS with a wet chemistry-based NitroMAC sensor and a Fourier transform infrared (FTIR) spectrometer has been carried out on real-time simultaneous measurement of HONO, NO2 and CH2O concentrations during the reaction of NO2 with H2O vapour in CESAM (French acronym for Experimental Multiphasic Atmospheric Simulation Chamber). The 1σ (signal-to-noise ratio (SNR) = 1) detection limits of 112 pptv for NO2, 56 pptv for HONO and 41 ppbv for CH2O over 120 s were found for the UV-LED-IBBCEAS measurement. On the contrary to many set-ups where cavities are installed outside the simulation chamber, we describe here an original in situ permanent installation. The intercomparison results demonstrate that IBBCEAS is a very well suitable technique for in situ simultaneous measurements of multiple chemically reactive species with high sensitivity and high precision even if the absorption bands of these species are overlapped. It offers excellent capacity for non-invasive optical monitoring of chemical reactions without any perturbation. For the application to simulation chambers, it has the advantage to provide a spatially integrated measurement across the reactor and hence to avoid point-sampling-related artefacts.

Artificial intelligence based optical performance monitoring

Abstract In this paper, a technique for optical performance monitoring (OPM) using deep learning-based artificial neural network (ANN) is implemented. ANN is trained with parameters derived from eye-diagram for the identification of optical signal to noise ratio (OSNR), chromatic dispersion (CD) and polarisation mode dispersion (PMD) simultaneously and independently in a 10 Gb/s system with non-return-to-zero (NRZ) on-off keying (OOK) data signal. ANN-based OPM confirms that the proposed approach can provide reliable estimated results. The mean squared errors for OSNR, CD and differential group delay (DGD) are found to be 4.6071 dB, 0.0417 ps/nm/km and 0.0016 ps/km, respectively. The proposed technique may be utilized in analyzing the signals of future heterogeneous optical communication networks intelligently.

Parametric handheld optical probe (HOPE) for biological tissue characterization in the near-infrared spectral range

Various optical monitoring techniques across the visible and near-infrared spectral regions have been developed to assess tissue characteristics for a wide range of biomedical and health science applications. In this work, we designed a multimodal non-invasive optical probe that can provide information regarding live tissue metrics. The probe, 12 mm in diameter, consists of three separate small probes with different diameters. Each of the probes is sensitive to a different intrinsic tissue feature for diagnostic purposes. These features include diffuse light reflectance, scattering coefficient, refractive index, blood flow, and temperature. The probe apparatus is based on wideband light source and spectrometer, a laser Doppler flowmetry instrument, and a temperature-sensing device. The advantage of combining three different approaches in a single probe create a sensor able to characterize broad tissue metrics with high temporal resolution to better understand disease mechanisms. The performance of this hybrid handheld optical probe (HOPE) was tested in two models of tissue injury: (1) arterial occlusion of a human hand (n=3); and (2) brain hypoxia in mice (n=2). Following data analysis, differences in tissue parameters were observed compared to baseline measures. Experimental results illustrate the ability of the HOPE setup to monitor variations in different tissue parameters. This probe may serve as a sensor device for characterization of tissue features in a medical environment during clinical diagnosis and therapeutic procedures.

Modulation format-independent optical performance monitoring technique insensitive to chromatic dispersion and polarization mode dispersion using a multi-task artificial neural network.

We propose and experimentally demonstrate modulation format-independent optical performance monitoring (OPM) based on a multi-task artificial neural network (MT-ANN). Optical power measurements at a series of center wavelengths adjusted using a widely tunable optical bandpass filter (OBPF) are used as the input features for a MT-ANN to simultaneously realize high-precision optical signal-to-noise ratio (OSNR) and launch power monitoring and baud rate identification (BRI). This technique is insensitive to chromatic dispersion (CD) and polarization mode dispersion (PMD). The experimental verification in a 9-channel WDM system shows that for 10 Gbaud QPSK and 32 Gbaud PDM-16QAM signals with OSNR in the range of 1-30 dB, the OSNR mean absolute error (MAE) and root mean square error (RMSE) are 0.28 dB and 0.48 dB, respectively. For launch power in the range of 0-8 dBm, the MAE and RMSE of the launch power monitoring are 0.034 dB and 0.066 dB, respectively, and the identification accuracy for both baud rates is 100%. Furthermore, this technique utilizes a single MT-ANN instead of three ANNs to realize the simultaneous monitoring of three OPM parameters, which greatly reduces the cost and complexity.

Remote and optical monitoring techniques applied to the maritime sector

A rising global attention to the environmental impact of anthropogenic activities pushes towards a continuous reduction of such impact and a control, through monitoring techniques, of the main sources of pollution. The transport sector is involved, as all other field of anthropogenic activities in the efforts towards a drastic abatement of emissions. Shipping activities generate greenhouse effect gases (GHGs), affecting the environment on a global scale, and other pollutants harmful for human health and the ecosystem on a local scale. These latter aspects are particularly relevant in ports where berths are close to densely inhabited areas. Many efforts have been and will be spent to predict and quantify these emissions with the aim of controlling them but direct measurements aimed at the identification and quantification of particularly polluting substances are to be considered a key point to achieve an effective control of emissions. In the context of ports, as in any transportation infrastructure, an identification of polluting vehicles and a quantification in an objective way of their emissions is crucial to implement any control activity of the polluting emissions. The development of optical remote sensing techniques appears to be particularly promising. This work provides a state of the art of the main techniques based on optical sensors (LIDAR, LIDAR DIAL, DOAS, camera UV) possibly applied in the maritime sector, highlighting advantages and potentialities. Results of a preliminary measurement campaign are reported to show the first encouraging feedback for the feasibility of the application.

On optical sensing of surface roughness of flat and curved microplastics in water

The growth of microplastic (MP) pollution is of increasing concern and represents a global challenge. In situ detection of these small particles is difficult because of their sizes, shapes, transparency or translucency, surface texture and ambient conditions. We propose and demonstrate the use of a prototype optical sensor to detect flat, nearly flat, curved and rough MPs prepared from commercial polyethylene terephthalate (PET) plastics and PET bottles in water. The prototype measures the specular reflection of a laser radiation incident on MPs, with a photodiode, and the transmitted laser speckle pattern, with a charge-coupled device (CCD) camera. The presence of the MPs as well as the optical surface roughness are determined from the specular reflection. Additionally, the so-called speckle contrast calculated from the speckle pattern, as a promising tool, is used to rank the rough MPs according to the different average surface roughness, to approximately twice the wavelength of the probing light. The novel application of laser speckle contrast and the optical roughness estimation allows the description of MP surface roughness in water. Moreover, in combination with earlier studies, these results, therefore, pave a way for the complete and a relatively easier description of MPs properties optical and also advances our step towards the development of simple and robust optical monitoring techniques for micro and nanoplastics in open and wastewater.

Machine Learning Techniques for Optical Performance Monitoring and Modulation Format Identification: A Survey

The trade-off between more user bandwidth and quality of service requirements introduces unprecedented challenges to the next generation smart optical networks. In this regard, the use of optical performance monitoring (OPM) and modulation format identification (MFI) techniques becomes a common need to enable the development of next-generation autonomous optical networks, with ultra-low latency and self-adaptability. Recently, machine learning (ML)-based techniques have emerged as a vital solution to many challenging aspects of OPM and MFI in terms of reliability, quality, and implementation efficiency. This article surveys ML-based OPM and MFI techniques proposed in the literature. First, we address the key advantages of employing ML algorithms in optical networks. Then, we review the main optical impairments and modulation formats being monitored and classified, respectively, using ML algorithms. Additionally, we discuss the current status of optical networks in terms of MFI and OPM. This includes standards, monitoring parameters, and the available commercial products with their limitations. Second, we provide a comprehensive review of the available ML-based techniques for MFI, OPM, and joint MFI/OPM, describing their performance, advantages, and limitations. Third, we give an overview of the exiting ML-based OPM and MFI techniques for the emerging optical networks such as the new fiber-based networks that use future space division multiplexing techniques (e.g., few-mode fiber), the hybrid radio-over-fiber networks, and the free space optical networks. Finally, we discuss the open issues, potential future research directions, and recommendations for the potential implementation of ML-based OPM and MFI techniques. Some lessons learned are presented after each section throughout the paper to help the reader identifying the gaps, weaknesses, and strengths in this field.