Conventional Monitoring Methods Research Articles

Emerging technologies revolutionising disease diagnosis and monitoring in aquatic animal health

AbstractIn recent years, aquaculture has seen tremendous growth worldwide due to technological advancements, leading to research and development of various innovations. Aquaculture farmers prioritise early diagnosis for timely treatment to achieve better productive and economic performance. Aquatic animal health experts still employ traditional diagnostic methods using visual diagnosis, cell culture, media culture, histopathology and serology. However, the developments of technologies in aquamedicine, such as sequencing, biosensors and CRISPR, have enabled rapid disease detection within minutes. Furthermore, integrating sensors, drones, artificial intelligence and the internet in aquaculture farm monitoring has helped farmers take decisive actions to improve production. Advancements in diagnostic techniques have significantly enhanced the efficient detection of bacterial, viral, parasitic and fungal diseases in aquatic animals. Moreover, monitoring water quality, aquatic animal health and animal behaviour on farms has become exceptionally streamlined with cutting‐edge tools like drones, sensors and artificial intelligence. Summarising research and development in aquatic animal health and monitoring aids efficient technology adoption in aquaculture. With these advanced technologies' continued development and adoption in developed countries, the aquaculture industry is experiencing growth and increased efficiency, benefiting farmers and consumers in these regions. However, farmers and educators in developing countries lack information about these technologies. Training of agricultural educators and efficient dissemination of knowledge and technologies through advertising and publication in collaboration with companies is essential. This review delves into emerging technologies capable of replacing the conventional diagnostic and monitoring methods utilised in aquaculture. We also explore their strengths, limitations and potential future applications within aquaculture settings.

Assessing the Water Status and Leaf Pigment Content of Olive Trees: Evaluating the Potential and Feasibility of Unmanned Aerial Vehicle Multispectral and Thermal Data for Estimation Purposes

Global warming presents a significant threat to the sustainability of agricultural systems, demanding increased irrigation to mitigate the impacts of prolonged dry seasons. Efficient water management strategies, including deficit irrigation, have thus become essential, requiring continuous crop monitoring. However, conventional monitoring methods are laborious and time-consuming. This study investigates the potential of aerial imagery captured by unmanned aerial vehicles (UAVs) to predict critical water stress indicators—relative water content (RWC), midday leaf water potential (ΨMD), stomatal conductance (gs)—as well as the pigment content (chlorophyll ab, chlorophyll a, chlorophyll b and carotenoids) of trees in an olive orchard. Both thermal and spectral vegetation indices are calculated and correlated using linear and exponential regression models. The results reveal that the thermal vegetation indices contrast in estimating the water stress indicators, with the Crop Water Stress Index (CWSI) demonstrating higher precision in predicting the RWC (R2 = 0.80), ΨMD (R2 = 0.61) and gs (R2 = 0.72). Additionally, the Triangular Vegetation Index (TVI) shows superior accuracy in predicting the chlorophyll ab (R2 = 0.64) and chlorophyll a (R2 = 0.61), while the Modified Chlorophyll Absorption in Reflectance Index (MCARI) proves most effective for estimating the chlorophyll b (R2 = 0.52). This study emphasizes the potential of UAV-based multispectral and thermal infrared imagery in precision agriculture, enabling assessments of the water status and pigment content. Moreover, these results highlight the vital importance of this technology in optimising resource allocation and enhancing olive production, critical steps towards sustainable agriculture in the face of global warming.

An Updated Systematic Review and Meta-Analysis of Tissue Oximetry Versus Conventional Methods for Postoperative Monitoring of Autologous Breast Reconstruction.

Tissue oximetry monitoring has shown superior outcomes to conventional monitoring methods for autologous breast reconstruction in retrospective studies with consecutive cohorts. A recent study used consecutive cohorts with tissue oximetry as the earlier cohort and found that tissue oximetry was nonsuperior. We hypothesize that improvement in microsurgical outcomes with institutional experience confounds the superiority of tissue oximetry demonstrated in prior studies. This study aimed to perform a systematic review and meta-analysis of the outcomes of tissue oximetry monitoring compared with conventional monitoring. Relevant studies were found using PubMed, Embase, and Web of Science searches for keywords such as near-infrared spectroscopy or tissue oximetry and microsurgery. Studies included compared tissue oximetry and conventional monitoring in autologous breast reconstruction patients. Studies were excluded if they did not contain a comparison group. Random-effective models were used to analyze early returns to the operating room, the total number of partial or complete flap loss, and late fat necrosis. Six hundred sixty-nine studies were identified; 3 retrospective cohort studies met the inclusion criteria. A total of 1644 flaps were in the tissue oximetry cohort, and 1387 flaps were in the control cohort. One study contained tissue oximetry as the former cohort; 2 had tissue oximetry as the latter. Neither technique was superior for any measured outcomes. The estimated mean differences between tissue oximetry and conventional monitoring method were early returns, -0.06 (95% confidence interval [CI], -0.52 to 0.410; P = 0.82); partial flap loss, -0.04 (95% CI, -0.86 to 0.79; P = 0.93); complete flap loss, -1.29 (95% CI, -3.45 to 0.87; P = 0.24); and late fat necrosis -0.02 (95% CI, -0.42 to, 0.39; P = 0.94). In a systematic review and meta-analysis of mixed timeline retrospective cohort studies, tissue oximetry does not provide superior patient outcomes and shifts our current understanding of postoperative breast reconstruction monitoring. Prospective studies and randomized trials comparing monitoring methods need to be included in the existing literature.

Diesel operated combined heat and power SOFC system: SOFC characterization and proof of concept

The high volumetric energy density and easy storage capability of liquid hydrocarbons such as bio-diesel or diesel are needed for power generation in several applications such as ships and isolated or rural areas which only have limited access to other power grids. Solid oxide fuel cells (SOFCs) can in theory utilize such fuels in a high efficient manner, which could reduce greenhouse gas emissions and energy demand. However, SOFC systems able to directly utilize diesel are rather complex to design and monitor, leading to several possible causes of system lifetime reduction. To avoid an enhanced reduction of the system lifetime, the operating conditions of all components must be optimized and monitored in detail. However, detailed monitoring and identification of the cause of performance loss are challenging using conventional system monitoring methods. Therefore, here we (i) show the concept and characterization of a SOFC system operating under steam- and auto-thermal diesel reforming conditions, (ii) demonstrated the applicability of advanced methods to monitor complex SOFC systems, which are applied on the 30 cell SOFC stack during system operation and (iii) analyzed the efficiency potential of the SOFC within the developed system. The results present in this work are favorable to accelerate commercialization.

A Weak Electricigen-Based Bioelectrochemical Sensor for Real-Time Monitoring of Chemical Pollutants in Water.

Electroactive microorganisms are now understood to be abundant across nature, though many are categorized as "weak electricigens" not suitable for reasonable power generation. We report the use of weak electricigens from the natural environment for rapid, real-time water quality monitoring. Using a variety of pesticides as model chemical pollutants, the bioelectrochemical sensor was responsive within minutes at all concentrations tested (0.05-2 ppm) and could be repreatedly used long-term. Due to the prevalence of electroactive microorganisms in the natural environment, such sensors could work in tandem with conventional monitoring methods and may be useful for detecting emerging contaminants.

Combined prediction model of concrete arch dam displacement based on cluster analysis considering signal residual correction

Displacement is a crucial indicator reflecting the safety operational state of concrete arch dams. Consequently, the application of displacement monitoring models for arch dam structural health assessment has become widely prevalent. However, conventional singular displacement monitoring methods exhibit limited information extraction capabilities and lack precision, resulting in underexplored residual sequences from the monitoring model and unexcavated valuable information. To address this, this paper integrated cluster analysis with Long Short-Term Memory (LSTM), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Least Squares Support Vector Machine (LSSVM), and Particle Swarm Optimization (PSO) methods to establish a combined prediction model of concrete arch dam displacement based on cluster analysis considering signal residual correction. Firstly, a model combining cluster analysis k-means++ with LSTM (k-LSTM), was proposed for displacement prediction of arch dams. Secondly, CEEMDAN was employed to adaptively decompose the residual sequences, where Pearson correlation coefficient (PCC) was introduced to filter and reconstruct intrinsic mode components. Subsequently, PSO-LSSVM was utilized to predict the reconstructed sequences and obtain residual correction values. Finally, based on the summation of residual correction values and k-LSTM model predictions, the arch dam displacement combination prediction model was constructed. Engineering case demonstrates the efficiency of this model in arch dam displacement prediction. By effectively mining and utilizing valuable information from residual sequences, this combined model exhibits superior generalization capabilities over traditional singular models, thereby providing technical support and novel research perspectives for concrete arch dam health monitoring and operational management.

Toward learning steelmaking—A review on machine learning for basic oxygen furnace process

AbstractBasic oxygen furnace (BOF) steelmaking is the most widely used process in global steel production today, accounting for around 70% of the industry's output. Due to the physical, mechanical, and chemical complexities involved in the process, conventional monitoring and control methods are often pushed to their limits. The increasing global competition has created a demand for new methods to monitor and control the BOF steelmaking process. Over the past decade, Machine Learning (ML) techniques have garnered substantial attention, offering a promising pathway to enhance efficiency and suitability of steel production. This paper presents the first comprehensive review of ML applications in the BOF steelmaking process. We provide an introduction to both fields: an overview of the BOF steelmaking process and ML. We analyze the existing work on ML applications in BOF steelmaking and synthesize common concepts into categories, supporting the identification of common use cases and approaches. This analysis concludes with the elaboration of challenges, potential solutions, and a future outlook for further research directions.

Transforming marine monitoring: Using eDNA metabarcoding to improve the monitoring of the Mediterranean Marine Protected Areas network

The creation of Marine Protected Areas (MPAs) is central to the European Union’s strategy for protecting and restoring marine environments. However, current research indicates that monitoring of MPAs is insufficient to ensure their protective role. In response, the European Commission encourages the development and adoption of more effective monitoring tools for MPAs. Environmental DNA (eDNA) methods may serve this function, but their use in MPA monitoring and management remains relatively unexplored. The current study addresses this gap by examining the potential for eDNA to enhance monitoring within Mediterranean MPAs, where monitoring challenges are especially severe. We addressed the gap by synthesizing the results of two analyses. First, a systematic literature review examined existing applications of eDNA metabarcoding to monitor marine habitats. From the experiences documented in the reviewed literature, we applied a SWOT analysis (a framework used to identify and analyze strengths, weaknesses, opportunities and threats) to assess the tool’s appropriateness for EU monitoring requirements. Second, we probed the tool’s suitability for Mediterranean MPAs with a questionnaire on monitoring and other challenges completed by the managers of 29 MPAs in the region. The findings of our SWOT analysis demonstrated that when compared to conventional monitoring methods, eDNA frequently outperformed them or acted as a crucial supplement. We also found that eDNA is suitable for most criteria set forth in EU requirements. Our questionnaire revealed monitoring strategy limitations that eDNA approaches may alleviate. Still, eDNA approaches do have drawbacks which may jeopardize its accuracy and sufficiency. Incorporating eDNA monitoring methods has great potential to enhance protection efforts in MPAs in the Mediterranean and elsewhere. Their successful adoption will require improved coordination among stakeholders and current, comprehensive, and customized recommendations for MPA managers. This will improve the overall functioning of MPA networks and thus support the role of MPAs in safeguarding vulnerable marine environments.

Addressing data-deficiency of threatened sharks and rays in a highly dynamic coastal ecosystem using environmental DNA

Marine biodiversity loss is accelerating, leading to the elevated extinction risks of many species, including sharks and rays. To mitigate these losses, information on their distribution and community composition is needed. Monitoring these (often) mobile species is challenging, especially in remote, highly dynamic and turbid coastal areas. Here, we use an environmental DNA (eDNA) approach to: (1) establish the presence and distribution of elasmobranch species, (2) compare this to a conventional fisheries-dependent approach, and (3) determine the influence of season, area-based protection and habitat on elasmobranch community composition in the highly dynamic Bijagós Archipelago in Guinea-Bissau (West Africa). We collected 127 seawater samples and detected elasmobranch DNA in 58 (45.7%) of these samples, confirming the presence of 13 different elasmobranch species (2 sharks, 11 rays), including seven threatened species. Eight of the species detected by the eDNA-approach were also recorded in a fisheries observer program, which recorded another eight species not detected by the eDNA approach. The most commonly occurring species, based on the number of eDNA sampling locations were the pearl whipray (Fontitrygon margaritella), smalltooth stingray (Hypanus rudis), scalloped hammerhead shark (Sphyrna lewini), and the blackchin guitarfish (Glaucostegus cemiculus). Species composition and richness differed significantly before (January-March) and after the rainy season (November-December). Furthermore, we showed that community composition and species richness did not differ between protected (MPA) and non-protected areas of the archipelago. Thus, we confirm that eDNA approaches are a valuable and non-invasive tool to study threatened shark and ray species in data-deficient and dynamic coastal areas, especially when combined with conventional monitoring methods such as fisheries-dependent information.

Integrated System of Spacecraft Impact Monitoring and Shape Reconstruction

When the spacecraft is in orbit, there is a risk of high-speed impact by space debris. After the spacecraft is impacted, it is of great significance to determine the exact location of the spacecraft and the structural changes caused by the impact. However, the conventional impact monitoring methods are mainly realized by sticking sensors on the surface of spacecraft structures. Such contact measurement methods generally have one or more of the problems of large size, heavy mass, complex system, easy to fall off, and do not have the function of reconstructing the shape of the impacted part of the spacecraft. This study proposes an integrated system of spacecraft impact monitoring and shape reconstruction, which realizes the organic integration of positioning and shape reconstruction and can help astronauts and ground technicians to timely know the impact location and shape changes after spacecraft damage.

Numerical and Experimental Investigation of Slope Deformation under Stepped Excavation Equipped with Fiber Optic Sensors

The real-time evaluation of slope stability is a crucial technical issue in foundation excavation and slope construction. However, conventional monitoring methods often fall short of achieving real-time and accurate measurements, which poses challenges to the timely assessment of slope stability. To address this problem, laboratory tests and numerical simulations were jointly used to evaluate slope stability in this paper. In numerical simulations, the finite element method (FEM) results clearly illustrate the distribution and evolution of internal strain during slope excavation, and the limit equilibrium method (LEM) calculates changes in the safety factor. In laboratory tests, the fiber Bragg grating (FBG) sensing technology was employed to monitor the internal strain of the slope in real time. The distribution characteristics of the slope internal strain field under the condition of stepped excavation were analyzed, and the feasibility of strain-based evaluation of slope stability was discussed. The measurements with FBG sensing technology agree well with the numerical simulation results, indicating that FBG can effectively monitor soil strain information. Of great significance is that the maximum horizontal strain of the slope is closely related to the safety factor and can be used to evaluate slope stability. Notably, the horizontal soil strain of the slope provides insight into both the formation and evolution of the critical sliding surface during excavation. The combination of numerical simulation and intelligent monitoring technology based on FBG proposed in this paper provides a reference for capturing strain information inside the slope and realizing real-time assessment and critical warning of slope stability.

Uncertainty Quantification of Satellite Soil Moisture Retrieved Precipitation in the Central Tibetan Plateau

SM2RAIN is a well-established methodology for estimating precipitation from satellite or observed soil moisture and it has been applied as a complementary approach to conventional precipitation monitoring methods. However, satellite soil moisture retrievals are usually subject to various biases and limited number of retrievals (and therefore large intervals) in remote areas, such as the Tibetan Plateau (TP), and little is known about their potential impacts on precipitation estimation. This study seeks to quantify the uncertainties in Soil Moisture Active and Passive (SMAP) soil moisture estimated precipitation through the commonly used SM2RAIN by referring to in situ soil moisture observations from the central Tibetan Plateau soil moisture network. The estimated precipitation is evaluated against rain gauge observations. Additional attention is paid to different orbits of the SMAP retrievals. Results show that the original SM2RAIN algorithm tends to underestimate the precipitation amount in the central TP when using SMAP soil moisture retrievals as input. The retrieval accuracy and sampling interval of SMAP soil moisture from ascending (descending) orbits each count for 1.04 mm/5 d (−0.18 mm/5 d) and 1.67 mm/5 d (0.72 mm/5 d) of estimated precipitation uncertainties as represented by root mean square error. Besides, the descending product of SMAP with a relatively less sampling interval and higher retrieval accuracy outperforms the ascending one in estimating precipitation, and the combination of both two orbits does add value to the overall SM2RAIN estimation. This study is expected to provide guidance for future applications of SM2RAIN-derived precipitation. Meanwhile, more reliable SM2RAIN precipitation estimations are desired when using higher quality satellite soil moisture products with better retrieval accuracy and smaller intervals.

Large‐scale eDNA monitoring of multiple aquatic pathogens as a tool to provide risk maps for wildlife diseases

AbstractMultiple parasites and pathogens cause disease in aquatic wildlife and in aquaculture species, generating a need for monitoring and management. Conventional disease monitoring methods involve laborious, costly, and invasive capture and examination of host species, and require specialized expertise for every host and pathogen of interest. Environmental DNA could provide simultaneous occurrence data for multiple pathogens across different host taxa, valuable for using parasite diversity as, for example, a bioindicator of ecosystem disturbance. Here, we tested the potential for simultaneous detection of four wildlife pathogens in water samples from 280, mainly riverine, sites across Switzerland. We targeted the crayfish pathogen Aphanomyces astaci, the amphibian pathogen Batrachochytrium dendrobatidis, and the fish pathogens Saprolegnia parasitica and Tetracapsuloides bryosalmonae. The eDNA detection showed a widespread distribution of A. astaci, S. parasitica, and T. bryosalmonae, although A. astaci and T. bryosalmonae were not detected in some alpine river catchments. B. dendrobatidis was detected only rarely, which was expected since the sampling did not target amphibian breeding sites. Co‐detection rates were higher in rivers than in lakes, likely reflecting the habitat preferences and distributions of the host species. We discuss the advantages and limitations of eDNA‐based pathogen monitoring and list a set of recommendations for managers. Our study illustrates how eDNA‐based techniques can monitor several pathogen species concurrently, thus facilitating more comprehensive disease monitoring schemes. Combined with metabarcoding approaches in the future, eDNA‐based sampling and detection can facilitate the incorporation of parasite and pathogen occurrence and diversity as an indicator for aquatic ecosystem health, and for revealing the hidden biodiversity and structure of parasite communities.

A Local Dynamic Broad Kernel Stationary Subspace Analysis for Monitoring Blast Furnace Ironmaking Process

For the actual blast furnace ironmaking process (BFIP), sophisticated dynamic, nonlinear and nonstationary characteristics make it hard to be modeled accurately with conventional monitoring methods. In this paper, local dynamic broad kernel stationary subspace analysis (Local-DBKSSA) is developed to improve the monitoring performance. Faced with complex dynamic nonlinear characteristics, a single model is considered to be unable for accurate representation. Thus, dynamic broad nonlinear features established by time shift and multi-kernel projection are adopted from more perspectives. Subsequently, the above features are integrated into stationary subspace analysis (SSA) to estimate stationary projections from time-varying data. In order to reduce the impact of large fluctuations and improve fault detection capability, a local statistic is further proposed. The effects of nonstationary characteristic on monitoring capability and the excellent performance of the local statistic are also theoretically analyzed. Finally, a case study based on actual BFIP data presents that the proposed method can discriminate between normal and sample faults more accurately and timely, and has better robustness to nonstationary perturbations under normal conditions by providing fewer false alarms.

An Old Theme and New Technology: Marked Glucocorticoid-Induced Hyperglycemia in a Hospitalized Patient Uncovered Retrospectively by Her Flash Glucose Monitoring

Abstract Background Glucocorticoid-induced hyperglycemia is a problem-facing endocrinologists and internal medicine specialists in hospital wards. Case History A 63-year-old woman with type 2 diabetes was admitted to the hospital with acute exacerbation of chronic obstructive airway disease. She was treated with a short course of intravenous hydrocortisone followed by oral prednisolone. On discharge, she attended her regular diabetes consultation. Throughout the period, she had her flash glucose monitoring (FGM) sensor in place, and she was monitoring her blood glucose regularly. As part of her diabetes clinic routine, the meter data uploaded and ambulatory glucose profiles were examined. These revealed three distinctly different blood glucose levels before, during, and after glucocorticoid therapy. Glucocorticoid therapy resulted in a marked rise in blood glucose that lasted for a further week before it returned to the pre-treatment levels. This old phenomenon has yet to be demonstrated using the new FGM technology. Conclusions The story (1) asserts the significant impact of glucocorticoid therapy on glycemic control, (2) demonstrates the prolonged impact on glycemic control following discontinuation of glucocorticoids, (3) suggests a lack of adequate monitoring and timely recognition of hyperglycemia in the hospital, and poor management glucocorticoid-induced hyperglycemia either due to failure of conventional monitoring methods or a degree of complacency to appreciate its magnitude.

Theoretical Study on the Generation of Switching Stress Waves in Power Semiconductor Devices

Appropriate condition monitoring methods can evaluate the status of power semiconductor devices in time. The traditional methods of condition monitoring are based primarily on the identification of electrical, magnetic, and thermal parameters. In recent years, studies have found that power semiconductor devices can generate stress waves when they are turned <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> and <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> , which can be detected by acoustic emission sensors. Acoustic emission detection technology has the characteristics of rapidity, noninvasiveness, and real-time performance. It is a supplement to conventional condition monitoring methods and is anticipated to be used for the online monitoring of power semiconductor devices. However, the source mechanism of the stress waves that power semiconductor devices produce when they turn <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> and <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> has not yet been identified, which is not conducive to the further application of acoustic emission detection technology in power semiconductor devices. Therefore, taking an insulated-gate bipolar transistor as an example, this article proposes new theoretical models to explain the reason for the stress wave generated by the device at the time of turn- <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> , and verifies the rationality of the models through formula derivations, finite-element simulations, and experimental results.

Convolutional Neural Network-Based Machine Vision for Non-Destructive Detection of Flooding in Packed Columns.

In chemical processes, packed columns are frequently employed in various unit operations. However, the flow rates of gas and liquid in these columns are often constrained by the risk of flooding. To ensure the safe and efficient operation of packed columns, it is crucial to detect flooding in real time. Conventional flooding monitoring methods rely heavily on manual visual inspections or indirect information from process variables, which limit the real-time accuracy of results. To address this challenge, we proposed a convolutional neural network (CNN)-based machine vision approach for non-destructive detection of flooding in packed columns. Real-time images of the packed column were captured using a digital camera and analyzed with a CNN model, which was been trained on a dataset of recorded images to identify flooding. The proposed approach was compared with deep belief networks and an integrated approach of principal component analysis and support vector machines. The feasibility and advantages of the proposed method were demonstrated through experiments on a real packed column. The results showed that the proposed method provides a real-time pre-alarm approach for detecting flooding, enabling process engineers to quickly respond to potential flooding events.

Fuzzy modeling to define corrosivity potential in oil pipelines

In this work, a Fuzzy logic model was developed using the Fuzzy Logic Toolbox™ of the MATLAB® software, for monitoring the corrosivity potential in oil pipelines whose corrosion mechanism is predominantly by microbiological action. With the use of operational parameters, the model presents itself as an alternative to conventional monitoring methods, allowing to infer the corrosion rate in the pipeline, and therefore, the corrosivity potential. The model was applied to an oil pipeline and its results were compared with conventional monitoring methods. The analysis of the results concluded that the model can be used as a monitoring method for pipelines with those predominant corrosion mechanisms, helping to manage the integrity of oil pipelines.

Clinical applications of contactless photoplethysmography for monitoring in adults: A systematic review and meta-analysis.

Contactless photoplethysmography (cPPG) is a method of physiological monitoring. It differs from conventional monitoring methods (e.g., saturation probe) by ensuring no contact with the subject by use of a camera. The majority of research on cPPG is conducted in a laboratory setting or in healthy populations. This review aims to evaluate the current literature on monitoring using cPPG in adults within a clinical setting. Adhering to the Preferred Items for Systematic Reviews and Meta-analysis (PRISMA, 2020) guidelines, OVID, Webofscience, Cochrane library, and clinicaltrials.org were systematically searched by two researchers. Research articles using cPPG for monitoring purposes in adults within a clinical setting were selected. Twelve studies with a total of 654 individuals were included. Heart rate (HR) was the most investigated vital sign (n = 8) followed by respiratory rate ((n = 2), Sp02 (n = 2), and HR variability (n = 2). Four studies were included in a meta-analysis of HR compared to ECG data which demonstrated a mean bias of -0.13 (95% CI, -1.22-0.96). This study demonstrates cPPG can be a useful tool in the remote monitoring of patients and has demonstrated accuracy for HR. However, further research is needed into the clinical applications of this method.

Study on the Application of Real-Time Drone Monitoring in Ordos Open-Pit Coal Mine

In the process of intelligent mine construction in open-pit mine, in order to improve the safety monitoring ability of mine transportation system, solve the problems of large human interference and blind Angle detection by existing conventional monitoring methods, this paper establishes an open-pit mine monitoring data set, and proposes a real-time intelligent monitoring model based on UAV. The reasoning component with strong computing power and low power consumption is selected, and the lightweight object detection model is selected for the experiment. A quantitative standard of dynamic energy consumption detection by evaluation algorithm is proposed. Through experimental comparison, it is found that YOLOv4-tiny has the highest comprehensive grade in detection accuracy, speed, energy consumption and other aspects, which is suitable for application in the above model.