Accurate Monitoring Research Articles

Nondestructive detection of saline-alkali stress in wheat (Triticum aestivum L.) seedlings via fusion technology

BackgroundWheat (Triticum aestivum L.) is an important grain crops in the world, and its growth and development in different stages is seriously affected by saline-alkali stress, especially in seedling stage. Therefore, nondestructive detection of wheat seedlings under saline-alkali stress can provide more comprehensive technical support for wheat breeding, cultivation and management.ResultsThis research focused on moisture signal prediction and classification of saline-alkali stress in wheat seedlings using fusion techniques. After collecting and analyzing transverse relaxation time and Multispectral imaging (MSI) information of wheat seedlings, four regression models were used to predict the moisture signal. K-Nearest Neighbor (KNN) and Gaussian-Naïve Bayes (GNB) models were combined with fivefold cross validation to classify the prediction of wheat seedling stress. The results showed that wheat seedlings would increase the bound water content through a certain mechanism to enhance their saline-alkali stress. Under the same Na concentration, the effect of alkali stress on moisture, growth and spectrum of wheat seedlings is stronger than salt stress. The Gradient Boosting Decision Regression Tree model performs the best in predicting wheat moisture signals, with a coefficient of determination (R2P) of 0.98 and a root mean square error of 109.60. It also had a short training time (1.48 s) and an efficient prediction speed (1300 obs/s). The KNN and GNB demonstrated significantly enhanced predictive performance when classifying the fused dataset, compared to using single datasets individually. In particular, the GNB model performing best on the fused dataset, with Precision, Recall, Accuracy, and F1-score of 90.30, 88.89%, 88.90%, and 0.90, respectively.ConclusionsUnder the same Na concentration, the effects of alkali stress on water content, spectrum, and growth of wheat were stronger than that of salt stress, which was more unfavorable to the growth of wheat. The fusion of low-field nuclear magnetic resonance and MSI technology can improve the classification of wheat stress, and provide an effective technical method for rapid and accurate monitoring of wheat seedlings under saline-alkali stress.Graphical

Identifying PTSD sex-based patterns through explainable artificial intelligence in biometric data

Post-Traumatic Stress Disorder (PTSD) is a mental health condition that arises from exposure to traumatic events, affecting various aspects of human well-being. The complexity and variability of symptoms pose challenges for accurate diagnosis and monitoring, exacerbated by accessibility barriers. In response, alternative methodologies leveraging biometric data have emerged, such as facial movements, speech, or voice-to-text transcriptions, and analyzing them using Explainable Artificial Intelligence (XAI) techniques. Numerous studies have explored the presence or absence of PTSD, yet few have concentrated on either explicable indicators or distinctions among these indicators, such as the patient’s sex. This research used an XAI algorithm to identify patterns related to PTSD in three biometric data sets: facial movements, speech, and voice-to-text transcriptions. Such biometric data are grouped by sex. Utilizing the DAIC-WOZ database, this study involves feature selection and characterization. Experiment configurations addressed participant segmentation, feature reduction, and standardization. Training phases employed machine-learning classification algorithms with their corresponding performance evaluation. The interpretability stage explored the relationship between input features and class output. The findings reveal that among the three biometric data sets evaluated in this work (facial movements, speech, and voice-to-text transcriptions), speech characterization is the most effective in identifying PTSD indicators, suggesting a uniform speech pattern associated with breathy and tense voice and weak phonation in PTSD patients. Sex-specific analysis enhances prediction performance, revealing distinctions in the associated speech features. The Women’s model prioritizes tense voice and vocal volume variations, reduced glottal closure, and interrupted phonation. Conversely, the Speech-Men model reflects reduced resonance, making the voice thinner and weaker, indicating altered vocal quality. As for facial movements, sex-specific characteristics are not evident, but some features focused on lips are associated with PTSD. Similarly, PTSD is related to alertness, determination, and anxiety in both women and men. In conclusion, using an XAI algorithm to differentiate sex-based patterns in biometric data contributes to a better understanding of PTSD indicators, offering potential advancements in personalized diagnostic strategies.

Monitoring the Dilution of Buffer Solutions with Different pH Values above and below Physiological pH in Very Small Volumes.

The accurate determination of the post-dilution concentration of biological buffers is essential for retaining the necessary properties and effectiveness of the buffer to maintain stable cellular environments and optimal conditions for biochemical reactions. In this work, we introduce a silicon-based impedance chip, which offers a rapid and reagent-free approach for monitoring the buffer concentrations after dilution with deionized (DI) water. The impedance of the impedance chip is measured, and the impedance data are modeled using a multiparameter equivalent circuit model. We investigated six aqueous biological buffers with pH values above and below the physiological pH for most tissues (pH ~ 7.2-7.4) following dilution with DI water by factors of 2.0, 10.0, 20.0, 100.0, and 200.0. The impedance measurement is then performed for the frequency spectrum of 40 Hz to 1 MHz. From the interpretation of the impedance measurement using the multiparameter equivalent circuit model, we report a buffer-sensitive equivalent circuit parameter RAu/Si of the silicon-based impedance chip showing a linear trend on a logarithmic scale with the buffer concentration change after dilution. The parameter RAu/Si is independent of the buffer pH and the added volume. The results demonstrate the efficacy of the silicon-based impedance chip as a versatile tool for precise post-dilution concentration determination of diverse biologically relevant buffers. The presented impedance chip offers rapid, accurate, and reliable monitoring, making it highly suitable for integration into automated liquid-handling systems to enhance the efficiency and precision of biological and chemical processes.

An efficient zero-labeling segmentation approach for pest monitoring on smartphone-based images

Timely and precise farm inspection, which involves the identification and recognition of harmful insects and diseases, is crucial for safeguarding crop production. Traditional vision-based pest recognition methods typically require extensive annotated data for each pest species and a lengthy training process. This approach is time-consuming, labor-intensive, and prone to human error. Zero-shot learning offers a potential solution by enabling pest segmentation and control without requiring explicit training data. This study supports farmers in automatically identifying ten common pests and their precise locations in real-world outdoor environments. The zero-shot pest segmentation is based on a hybrid approach combining Explainable Contrastive Language-Image Pre-training (ECLIP) and Segment-Anything (SAM). Moreover, an optimized super-resolution model and various data augmentation methods are implemented to improve the quality of the dataset. Lastly, a mask post-processing step is applied to remove highly overlapping segmented masks and noise blobs caused by the complex background. The mean Intersection over Union (mIoU) of 66.5 % on the validation set demonstrates the potential of zero-shot methods for automated pest segmentation during farm inspections. This research lays the foundation for accurate pest monitoring systems capable of adapting to new pests, ultimately improving agricultural productivity.

MpScope: Enabling multi-pipeline monitoring inside a switch

The core of programmable switches is the flexible data plane, composed of multiple programmable pipelines in existing programmable switches. These pipelines are isolated from each other and cannot share state and data. However, most of network monitoring systems ignore this condition and implicitly assume that the switch has only a single pipeline. This results in an inaccurate measurement and high communication overhead with the practical switch. To tackle this problem, we propose MpScope, a general multi-pipeline monitoring framework, which centers around the control plane, supporting accurate and efficient network monitoring. Specifically, MpScope combines the switch’s data plane and control plane to achieve comprehensive network monitoring of the whole switch scope. The control plane aggregates the statistical results from multiple pipelines and tunes the monitoring module residing in the different pipelines in the data plane dynamically. The data plane is responsible for real-time traffic measurement and statistic reports. Its behaviors can be adjusted periodically with the instructions from the control plane. Two typical monitoring applications, i.e., heavy hitter detection and distinct counting, are developed with MpScope to validate the effectiveness of multi-pipeline monitoring. Experiments show that MpScope significantly reduces communication overhead compared to the static threshold scheme while maintaining high detection accuracy over time.

Radiotracer Innovations in Breast Cancer Imaging: A Review of Recent Progress.

This review focuses on the pivotal role of radiotracers in breast cancer imaging, emphasizing their importance in accurate detection, staging, and treatment monitoring. Radiotracers, labeled with radioactive isotopes, are integral to various nuclear imaging techniques, including positron emission tomography (PET) and positron emission mammography (PEM). The most widely used radiotracer in breast cancer imaging is 18F-fluorodeoxyglucose (18F-FDG), which highlights areas of increased glucose metabolism, a hallmark of many cancer cells. This allows for the identification of primary tumors and metastatic sites and the assessment of tumor response to therapy. In addition to 18F-FDG, this review will explore newer radiotracers targeting specific receptors, such as estrogen receptors or HER2, which offer more personalized imaging options. These tracers provide valuable insights into the molecular characteristics of tumors, aiding in tailored treatment strategies. By integrating radiotracers into breast cancer management, clinicians can enhance early disease detection, monitor therapeutic efficacy, and guide interventions, ultimately improving patient outcomes. Ongoing research aimed at developing more specific and sensitive tracers will also be highlighted, underscoring their potential to advance precision medicine in breast cancer care.

Sea-fan retinal neovascularization associated with rickettsial retinitis: a case report

BackgroundRickettsial disease has been commonly associated with retinitis, retinal vasculitis, and optic nerve involvement, but the development of retinal neovascularization has been very rarely reported. We herein describe a case of rickettsial retinitis complicated with the development of sea-fan retinal neovascularization documented with multimodal imaging, including fundus photography, SS-OCT, fluorescein angiography, and SS-OCT angiography.Case presentationA 26-year-old female with a history of fever one week earlier presented with sudden decreased vision in the left eye. Best-corrected visual acuity (BCVA) was 20/2000 and the patient was diagnosed with rickettsial retinitis along the superotemporal retinal vascular arcade associated with serous retinal detachment and retinal hard exudates. The indirect immunofluorescence test was positive for Rickettsia conorii, and the patient was treated with oral doxycycline (200 mg/day) and oral prednisone (0.75 mg/kg/day, with gradual tapering). Four weeks after presentation, the retinal infiltrate and associated serous retinal detachment had resolved, but retinal hard exudates had increased. A large sea-fan preretinal fibrovascular neovascularization became apparent along the superotemporal retinal vascular arcade, but there was no associated retinal ischemia on fluorescein angiography. The patient received an adjunctive single intravitreal injection of 1.25 bevacizumab. Sequential follow-up examinations showed shrinking of sea-fan retinal neovascularization, a complete resolution of retinal hard exudates, and the development of a self-limited vitreous hemorrhage. On last follow-up, 30 months after intravitreal bevacizumab injection, BCVA was 20/25.ConclusionPatients with rickettsial retinitis may develop a sea-fan retinal neovascularization, with subsequent vitreous hemorrhage, putatively through inflammatory mechanisms. Multimodal imaging including OCT, fluorescein angiography, and OCT-angiography, is highly useful for accurate diagnosis and reliable monitoring of the evolution of retinitis, retinal neovascularization, and other retinal changes. The use of a combination therapy with oral doxycycline and corticosteroids and intravitreal anti-VEGF can improve outcomes.

A system to analyze the initiation of random X-chromosome inactivation using time-lapse imaging of single cells

In female eutherian mammal development, X-chromosome inactivation (XCI) of one of the two X chromosomes is initiated early. Understanding the relationship between the initiation of XCI and cell fate is critical for understanding early female development and requires a system that can monitor XCI in single living cells. Traditional embryonic stem cells (ESCs) used for XCI studies often lose X chromosomes spontaneously during culture and differentiation, making accurate monitoring difficult. Additionally, most XCI assessment methods necessitate cell disruption, hindering cell fate tracking. We developed the Momiji (version 2) ESC line to address these difficulties, enabling real-time monitoring of X-chromosome activity via fluorescence. We inserted green and red fluorescent reporter genes and neomycin and puromycin resistance genes into the two X chromosomes of PGK12.1 ESCs, creating a female ESC line that retains two X chromosomes more faithfully during differentiation. Momiji (version 2) ESCs exhibit a more stable XX karyotype than other ESC lines, including the parental PGK12.1 line. This new tool offers valuable insights into the relationship between XCI and cell fate, improving our understanding of early female development.

Advancing healthcare through piezoresistive pressure sensors: a comprehensive review of biomedical applications and performance metrics

Abstract Piezoresistive pressure sensors have transformed biomedical applications, enabling precise diagnostics and monitoring. This concise review explores the fundamental principles, key components, and fabrication techniques of piezoresistive pressure sensors, focusing on critical performance metrics such as sensitivity, accuracy, and response time. Biomedical design challenges, including biocompatibility and long-term stability, are examined, offering insights into solutions for optimal sensor integration. In diverse biomedical applications, piezoresistive pressure sensors play pivotal roles, from blood pressure monitoring to implantable medical devices. The paper emphasizes their versatility in enhancing patient care through continuous and accurate monitoring. Looking forward, the review discusses emerging trends and potential research directions, positioning piezoresistive pressure sensors as central contributors to the future of biomedical technology, promising improved patient outcomes and advanced healthcare delivery through precise and continuous monitoring.

Analysis of home thermal energy conservation and green environment design based on intelligent devices and optical image monitoring

Global climate change and energy crisis are becoming increasingly serious, and household energy consumption occupies a certain proportion in the overall energy consumption. The purpose of this study is to analyze thermal energy use in home environment through intelligent devices and optical image monitoring technology, so as to achieve effective energy conservation and environmental optimization design. In this paper, intelligent sensors and optical image monitoring equipment are used to monitor and collect the thermal energy consumption of different households in real time. At the same time, data analysis technology is used to deeply analyze the heat flow, heat loss and equipment use efficiency in the home. The study compares the effect of traditional energy-saving methods and intelligent monitoring technology. After implementing targeted optimization design through intelligent monitoring equipment, the overall thermal energy saving rate is higher, and the comfort level and green index of the home environment are significantly improved. The results show that intelligent equipment and optical image monitoring technology show a good application prospect in home thermal energy saving. Through accurate monitoring and data analysis, heat energy waste can be effectively identified, so as to take corresponding measures to achieve energy conservation and emission reduction, and promote the landing of green environmental design.

Effectiveness of Baricitinib Risk Minimization Activities in Patients With Rheumatoid Arthritis—A Cohort Study in Four Nordic Countries

ABSTRACTIntroductionBaricitinib received European Medicines Agency (EMA) approval for the treatment of moderate‐to‐severe active rheumatoid arthritis (RA) in 2017. Due to risks including serious infections, increased blood lipid parameters, and missing information regarding safety in pregnancy, additional risk minimization measures (aRMM) including healthcare professional educational material and a patient alert card were implemented to inform on the risks and actions to minimize risk such as the need to screen for tuberculosis and active viral hepatitis before starting baricitinib, monitoring of blood lipids and against use during pregnancy. The aim of the study is to report occurrences of infections, lipid level monitoring and pregnancies, in real world data, to gain insights into whether baricitinib is used in accordance with the aRMM.MethodsA descriptive observational cohort study, utilizing routinely collected data from the Swedish, Norwegian, Finnish, and Danish national registries, was carried out. RA patients initiating baricitinib treatment were identified in the periods 2017–19 (Sweden and Finland), 2017–20 (Norway) and 2017–21 (Denmark). Monitored events included pregnancies, cases of tuberculosis, viral hepatitis, and hyperlipidemia, as well as changes in statin prescriptions (including any of initiation, escalation, reduction or change of statin). Hyperlipidemia and statin use served as proxies for lipid level assessment.ResultsAmong 3908 patients initiating baricitinib (3373 person‐years exposure), eight pregnancies (1% of 815 women aged 18–50 years) potentially overlapped with treatment. During baricitinib treatment, there was a small number of prevalent cases of tuberculosis (less than three events), and a low prevalence of hepatitis (0.2%), and hyperlipidemia (0.5%), whereas 9.3% had start of or changes in statin prescription, indicating lipid level monitoring.ConclusionThe study's findings suggest that patients with RA are treated with baricitinib in accordance with the risk minimization recommendations, particularly concerning pregnancy and infection risks. However, the methods used for assessing lipid levels warrant further refinement for more accurate monitoring.

Study of ultrasonic parameters for quality monitoring of plate heat exchanger

Abstract Heat exchangers are crucial heat transfer devices widely used in energy industries such as power generation, oil production, and engine cooling. The plate heat exchanger is one of the most economical and efficient types of exchangers. It transfers heat using metal plates and has a high heat transfer efficiency because the fluid is exposed to a higher plate surface area. However, due to the presence of dissolved or suspended particles in the heat-exchanging fluids, a layer of deposit (fouling layer) is formed on the heat exchanger plates. It results in reduced efficiency of the device. Thus, a common cleaning method, such as chemical-based or manual methods, is employed. However, it always poses a risk of over-cleaning or removal of the protective oxide layer. Thus, an efficient mechanism to monitor the onset of the fouling layer would greatly help to achieve increased efficiency and cost reduction in the cleaning of the plates. Recent advances show ultrasound as one of the promising non-destructive inspection techniques to monitor the growth of the fouling layer. However, the variation in heat-exchanging fluid temperature affects the investigated acoustical parameter measurement. Thus, this research aims to study the linear and nonlinear ultrasonic parameters incorporating temperature-dependent effects from the fluid to provide an accurate real-time monitoring tool. The nonlinear acoustics analysis is experimentally employed in the research using the Second harmonic generation method (SHGM). The proposed research will help design ultrasound-assisted plate heat exchangers for maximum heat transfer efficiency.

Advancements in Biotechnology and Stem Cell Therapies for Breast Cancer Patients.

This comprehensive review article examines the integration of biotechnology and stem cell therapy in breast cancer diagnosis and treatment. It discusses the use of biotechnological tools such as liquid biopsies, genomic profiling, and imaging technologies for accurate diagnosis and monitoring of treatment response. Stem cell-based approaches, their role in modeling breast cancer progression, and their potential for breast reconstruction post-mastectomy are explored. The review highlights the importance of personalized treatment strategies that combine biotechnological tools and stem cell therapies. Ethical considerations, challenges in clinical translation, and regulatory frameworks are also addressed. The article concludes by emphasizing the potential of integrating biotechnology and stem cell therapy to improve breast cancer outcomes, highlighting the need for continued research and collaboration in this field.

IoT based Smart Weather Monitoring with Flood and Earthquake Detection

Weather monitoring is essential for understanding the changing climatic conditions. Traditional weather monitoring system are slow, labor intensive, and outdated, often prone to error and inaccurate predictions. To overcome these challenges and provide an up-to date weather information, the IoT (Internet of Things) has been implemented in weather monitoring. This research presents an IoT-based weather monitoring system integrated with sensors to ensure accurate weather monitoring, including flood and earth quake detection. The collected data, such as temperature, humidity, atmospheric pressure, rainfall, flood levels, and seismic activity, is transmitted through IoT protocols to a centralized repository (Cloud) and visualized in the Blynk App. Flood sensors are strategically positioned in flood-prone areas, continuously monitoring water levels to detect abnormal rises indicative of potential floods. Similarly, seismic sensors detect ground vibrations and seismic activity, providing early alerts for earthquakes. The proposed system offers a comprehensive approach to monitor the weather, contributing to enhanced preparedness and mitigation strategies for natural disasters.

Sunflower-YOLO: Detection of sunflower capitula in UAV remote sensing images

Accurate identification and monitoring of sunflower capitula are crucial for field phenotypic analysis, cultivation management, phenological monitoring, and yield prediction. Manual observation, however, faces significant challenges due to the complexity of field environments and the morphological diversity of sunflower capitula. Unmanned Aerial Vehicles (UAVs) have emerged as an ideal platform for monitoring sunflower capitula due to their low cost and high spatiotemporal resolution. This study introduces Sunflower-YOLO, an enhanced model based on YOLOv7-tiny, designed for detecting sunflower capitula in UAV remote sensing images. The model effectively identifies sunflower capitula and distinguishes between three specific states: open, half-open, and bud. Sunflower-YOLO incorporates several key improvements: the SiLU activation function replaces the original LeakyReLU, enhancing the model’s nonlinear expression capability; a shallow high-resolution feature map and an additional detection head for small targets are introduced during the feature fusion stage to improve the detection performance of small capitula; and the integration of deformable convolution and the SimAM attention mechanism enhances the ELAN structure in the backbone, creating a new DeformAtt-ELAN structure that improves the model’s ability to capture morphological variations and reduces noise interference. Experimental results demonstrate that Sunflower-YOLO achieves precision, recall, and mAP@0.5 of 92.3 %, 89.7 %, and 93 %, respectively, marking improvements of 4.2 %, 4.2 %, and 3.7 % over the original YOLOv7-tiny model. The average precision (AP) for the three growth states is 98.7 %, 93.4 %, and 87 %, with AP for the half-open and bud states improving by 6.5 % and 4.7 %, respectively. The model’s FLOPs is 17.7 G, its size is 13.8MB, and it achieves an FPS of 188.52. Compared to current mainstream state-of-the-art (SOTA) models for object detection, Sunflower-YOLO achieves the highest mAP@0.5 in detecting multiple types of sunflower capitula. The constructed capitulum density map offers a practical view for observing sunflower growth status. This study highlights the immense potential of combining UAV remote sensing technology with YOLO object detection algorithms in monitoring sunflower capitula and their growth processes, providing an innovative and effective approach for precision agriculture practices.

In vivo electrical properties of the healthy liver and the hepatic tumor in a mouse model between 1 Hz and 1 MHz during a thermal treatment

Objective: Understansding the changing patterns of in vivo electrical properties for the target tissue is crucial for the accurate temperature monitoring and the treatment efficacy in thermal therapy. Our research aims to investigate the changing patterns and the reversibility of in vivo electrical properties for both healthy livers and liver tumors in a mouse model over a frequency range of 1 Hz to 1 MHz at temperatures between 30 °C to 90 °C. Methods and materials: The mice were anesthetized and the target organ was exposed. An 808-nm near-infrared laser was employed as the heating source to heat the organ in vivo. The four-needle electrode, connected to an impedance analyzer, was utilized to obtain the impedance at varying temperatures, which were monitored by a thermocouple. Results: The findings indicated a gradual decline in impedance with an increase in temperature. Furthermore, the impedance was normalized to that at 30 °C, and the real part of the normalized impedance was defined as the k-values, which range from 0 to 1. The results demonstrated a linear correlation between k-values and temperatures (R2 > 0.9 for livers and R2 > 0.8 for tumors). Significant differences were observed between livers and tumors at 1, 10 and 50 kHz (p < 0.05). Additionally, it was demonstrated that the electrical properties could be reversed when the temperature was below or equal to 45 °C. Conclusion: We believe that these results will contribute to the advancement of radiofrequency ablation systems and the development of techniques for temperature monitoring during liver thermal treatment.

Dual electrochemical detection of leucovorin and its metabolite 5-methyltetrahydrofolic acid

Current healthcare trends focus on personalised precision medicine, which customises treatment based on individual responses to both diseases and therapeutic interventions. This marks a departure from a “one size fits all” approach towards a more sophisticated strategy. However, notwithstanding progress in the theoretical knowledge for personalised precision approaches, resource constraints impede their implementation. Monitoring drug therapies is vital for the advancement of precision medicine, alongside drug development and targeted treatment strategies. This study presents the potential of electrochemical approaches including square wave voltammetry (SWV) as a proof-of-concept for the simultaneous monitoring of circulating concentrations of 5-methyltetrahydrofolate (5-MTHF) and leucovorin calcium (LV) within biological matrices. An easy-to-use and portable sensor has been developed for the detection of 5-MTHF and LV at therapeutically relevant concentrations without prior sample preparation. The sensor was successfully tested in artificial urine and human pooled serum, demonstrating its efficacy in diverse biological matrices. This approach successfully illustrated the dual detection of LV and 5-MTHF over the linear range 0 to 10 µM which is within the therapeutically relevant range for both within urine. The developed sensor exemplifies the potential of electrochemical sensors as point-of-care devices. Its rapid time to result and elimination of time-consuming sample preparation improve usability for clinicians, broadening the application of electrochemical sensors in clinical settings. This dual monitoring approach offers significant contributions to the evolution of precision medicine by providing real-time, accurate drug monitoring capabilities.

An Effective Res-Progressive Growing Generative Adversarial Network-Based Cross-Platform Super-Resolution Reconstruction Method for Drone and Satellite Images

In recent years, accurate field monitoring has been a research hotspot in the domains of aerial remote sensing and satellite remote sensing. In view of this, this study proposes an innovative cross-platform super-resolution reconstruction method for remote sensing images for the first time, aiming to make medium-resolution satellites capable of field-level detection through a super-resolution reconstruction technique. The progressive growing generative adversarial network (PGGAN) model, which has excellent high-resolution generation and style transfer capabilities, is combined with a deep residual network, forming the Res-PGGAN model for cross-platform super-resolution reconstruction. The Res-PGGAN architecture is similar to that of the PGGAN, but includes a deep residual module. The proposed Res-PGGAN model has two main benefits. First, the residual module facilitates the training of deep networks, as well as the extraction of deep features. Second, the PGGAN structure performs well in cross-platform sensor style transfer, allowing for cross-platform high-magnification super-resolution tasks to be performed well. A large pre-training dataset and real data are used to train the Res-PGGAN to improve the resolution of Sentinel-2’s 10 m resolution satellite images to 0.625 m. Three evaluation metrics, including the structural similarity index metric (SSIM), the peak signal-to-noise ratio (PSNR), and the universal quality index (UQI), are used to evaluate the high-magnification images obtained by the proposed method. The images generated by the proposed method are also compared with those obtained by the traditional bicubic method and two deep learning super-resolution reconstruction methods: the enhanced super-resolution generative adversarial network (ESRGAN) and the PGGAN. The results indicate that the proposed method outperforms all the comparison methods and demonstrates an acceptable performance regarding all three metrics (SSIM/PSNR/UQI: 0.9726/44.7971/0.0417), proving the feasibility of cross-platform super-resolution image recovery.

Remote liquid level monitoring based on coda wave interferometry

Abstract Accurate monitoring of liquid level is important for both liquid stocking and related industrial procedures, it is however difficult under extreme circumstances where the inner space of the container is inaccessible or out of reach. Therefore, this paper proposes a remote liquid level monitoring method based on the use of ultrasonic coda wave interferometry. Coda waves stem from multiply scattering of elastic waves under the interactions with the boundary of propagation medium or scatters during their propagation, they are commonly observed in complex propagation medium. For having propagation trajectories much longer than the coherent waves, coda waves are capable of sampling the propagation medium repeatedly and more comprehensively, which in turn provides coda waves with higher sensitivity to its propagation medium. Letting ultrasonic waves propagate within the liquid container, which is often elastic medium, they are capable of probing the solid-liquid interface globally and repeatedly. As the changing in liquid level alters such solid-liquid interfaces inevitably, the propagation of coda wave is thus affected even without any contact with the liquid level. In this manuscript, experimental study is represented, of which the results show that waveform distortion of coda waves, estimated by remnant decorrelation coefficients, is quantitatively related with the change of liquid level, demonstrates the feasibility of remote liquid level measurement and continuous monitoring of liquid level with ultrasonic techniques.

Redesigning automated market power mitigation in electricity markets

Electricity markets are prone to the abuse of market power. Several U.S. markets employ algorithms to monitor and mitigate market power abuse in real-time. The performance of automated mitigation procedures is contingent on precise estimates of firms' marginal production costs. Currently, marginal cost is inferred from the past offers of a plant. We present new estimation approaches and compare them to the currently applied benchmark method. We test the performance of all approaches on auction data from the Iberian power market. The results show that our novel approaches outperform the benchmark approach significantly, reducing the mean (median) absolute estimation error from 11.53 (6.08) €/MWh in the benchmark to 4.03 (2.64) €/MWh for our preferred approach. This approach also performs best in our subsequent simulation of mitigation procedures. Here we find large welfare transfers from supplier to buyer surplus as well as a robust overall welfare gain, stemming from both productive and allocative efficiency gains. Our research contributes to accurate monitoring of market power and improved automated mitigation. Although we focus on power markets, our findings are applicable to monitoring of renewable energy tenders or market power surveillance in rail and air traffic.