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BackgroundAssessment of pain is critical to its optimal treatment. There is a high demand for accurate objective pain assessment for effectively optimizing pain management interventions. However, pain is a multivalent, dynamic, and ambiguous phenomenon that is difficult to quantify, particularly when the patient’s ability to communicate is limited. The criterion standard of pain intensity assessment is self-reporting. However, this unidimensional model is disparaged for its oversimplification and limited applicability in several vulnerable patient populations. Researchers have attempted to develop objective pain assessment tools through analysis of physiological pain indicators, such as electrocardiography, electromyography, photoplethysmography, and electrodermal activity. However, pain assessment by using only these signals can be unreliable, as various other factors alter these vital signs and the adaptation of vital signs to pain stimulation varies from person to person. Objective pain assessment using behavioral signs such as facial expressions has recently gained attention. ObjectiveOur objective is to further the development and research of a pain assessment tool for use with patients who are likely experiencing mild to moderate pain. We will collect observational data through wearable technologies, measuring facial electromyography, electrocardiography, photoplethysmography, and electrodermal activity. MethodsThis protocol focuses on the second phase of a larger study of multimodal signal acquisition through facial muscle electrical activity, cardiac electrical activity, and electrodermal activity as indicators of pain and for building predictive models. We used state-of-the-art standard sensors to measure bioelectrical electromyographic signals and changes in heart rate, respiratory rate, and oxygen saturation. Based on the results, we further developed the pain assessment tool and reconstituted it with modern wearable sensors, devices, and algorithms. In this second phase, we will test the smart pain assessment tool in communicative patients after elective surgery in the recovery room. ResultsOur human research protections application for institutional review board review was approved for this part of the study. We expect to have the pain assessment tool developed and available for further research in early 2021. Preliminary results will be ready for publication during fall 2020. ConclusionsThis study will help to further the development of and research on an objective pain assessment tool for monitoring patients likely experiencing mild to moderate pain. International Registered Report Identifier (IRRID)DERR1-10.2196/17783

The automatic detection of facial expressions of pain is needed to ensure accurate pain assessment of patients who are unable to self-report pain. To overcome the challenges of automatic systems for determining pain levels based on facial expressions in clinical patient monitoring, a surface electromyography method was tested for feasibility in healthy volunteers. In the current study, two types of experimental gradually increasing pain stimuli were induced in thirty-one healthy volunteers who attended the study. We used a surface electromyography method to measure the activity of five facial muscles to detect facial expressions during pain induction. Statistical tests were used to analyze the continuous electromyography data, and a supervised machine learning was applied for pain intensity prediction model. Muscle activation of corrugator supercilii was most strongly associated with self-reported pain, and the levator labii superioris and orbicularis oculi showed a statistically significant increase in muscle activation when the pain stimulus reached subjects' self -reported pain thresholds. The two strongest features associated with pain, the waveform length of the corrugator supercilii and levator labii superioris, were selected for a prediction model. The performance of the pain prediction model resulted in a c-index of 0.64. In the study results, the most detectable difference in muscle activity during the pain experience was connected to eyebrow lowering, nose wrinkling and upper lip raising. As the performance of the prediction model remains modest, yet with a statistically significant ordinal classification, we suggest testing with a larger sample size to further explore the variables that affect variation in expressiveness and subjective pain experience.

Current acute pain intensity assessment tools are mainly based on self-reporting by patients, which is impractical for non-communicative, sedated or critically ill patients. In previous studies, various physiological signals have been observed qualitatively as a potential pain intensity index. On the basis of that, this study aims at developing a continuous pain monitoring method with the classification of multiple physiological parameters. Heart rate (HR), breath rate (BR), galvanic skin response (GSR) and facial surface electromyogram were collected from 30 healthy volunteers under thermal and electrical pain stimuli. The collected samples were labelled as no pain, mild pain or moderate/severe pain based on a self-reported visual analogue scale. The patterns of these three classes were first observed from the distribution of the 13 processed physiological parameters. Then, artificial neural network classifiers were trained, validated and tested with the physiological parameters. The average classification accuracy was 70.6%. The same method was applied to the medians of each class in each test and accuracy was improved to 83.3%. With facial electromyogram, the adaptivity of this method to a new subject was improved as the recognition accuracy of moderate/severe pain in leave-one-subject-out cross-validation was promoted from 74.9 ± 21.0 to 76.3 ± 18.1%. Among healthy volunteers, GSR, HR and BR were better correlated to pain intensity variations than facial muscle activities. The classification of multiple accessible physiological parameters can potentially provide a way to differentiate among no, mild and moderate/severe acute experimental pain.

In broadband terahertz waves generated by femtosecond lasers, spatial chirp will be simultaneously produced with the introduction of angular dispersion. The chirp characteristics of the terahertz wave will directly affect the frequency response, bandwidth response, and intensity response of the terahertz sensor. To enhance the capability of terahertz sensors, it is necessary to control and improve the chirped characteristics of broadband terahertz sources. We generate a chirped terahertz wave via optical rectification in a LiNbO3 prism using the technique of pulse front tilt. The effect of the pump-beam spot size on THz generation is systematically studied. The pump's spot size is manipulated using a telescope system. With a pump spot diameter of 1.8 mm, the scanning spectrum of the THz pulse is narrower and is divided into multiple distinct peaks. In contrast, using a pump spot diameter of 3.7 mm leads to increased efficiency in the generation of THz pulses. Also, we investigate the underlying properties governing the generation of chirped terahertz pulses using varying pump pulse spot diameters. [ABSTRACT FROM AUTHOR]

This study examines how different primary school campus layouts impact the wind environment and classroom ventilation in Xi'an, using simulations for winter and summer conditions. It evaluates four typical floorplans to find the best for outdoor wind quality and classroom ventilation. During winter, the outdoor wind speed at a height of 1.5 m remains below 5 m/s, adhering to the Green Building Evaluation Standard (GB/T50378-2019). Notably, Scenario 1 achieves higher wind speeds due to the canyon effect between buildings, facilitating effective air renewal. The wind speed amplification factors in all scenarios are within the permissible limit of 2, while Scenario 1 demonstrates superior outdoor wind performance. Wind pressure differences on building surfaces remain within the 5 Pa limit, with Scenario 3 exhibiting the lowest difference of 0.74 Pa, contributing to energy-efficient heating. In summer, Scenario 1 uniquely avoids vortex areas and windless zones, ensuring efficient airflow across the campus. Its open floor planning prevents the formation of stagnant air zones, in contrast to Scenarios 2, 3, and 4, which create enclosed or semi-enclosed spaces promoting vortex formation and windless areas. These findings underscore the benefits of Scenario 1's design in optimizing both winter and summer wind environments for energy efficiency and occupant comfort. This study recommends including adequately sized spaces in zigzag, branched, or enclosed floor plans to provide airflow and prevent high wind speeds. These results are crucial for shaping upcoming architectural plans to improve the environmental quality of school grounds, leading to improved health and comfort for students and teachers. [ABSTRACT FROM AUTHOR]

Monitoring sea ice in the Arctic region is crucial for polar maritime activities. The Canadian Ice Service (CIS) wants to augment its manual interpretation with machine learning-based approaches due to the increasing data volume received from newly launched synthetic aperture radar (SAR) satellites. However, fully supervised machine learning models require large training datasets, which are usually limited in the sea ice classification field. To address this issue, we propose a semi-supervised interactive system to classify sea ice in dual-pol RADARSAT-2 imagery using limited training samples. First, the SAR image is oversegmented into homogeneous regions. Then, a graph is constructed based on the segmentation results, and the feature set of each node is characterized by a convolutional neural network. Finally, a graph convolutional network (GCN) is employed to classify the whole graph using limited labeled nodes automatically. The proposed method is evaluated on a published dataset. Compared with referenced algorithms, this new method outperforms in both qualitative and quantitative aspects. [ABSTRACT FROM AUTHOR]

COVID-19 has been a massive trade shock that has disrupted global trade, making the last few years a special phase. Even during normal times, epidemic diseases have acted as trade shocks in specific countries, albeit not to the same extent as COVID-19. For some trade shocks, the situation normalizes after the disease transmission is over; for some, it does not. Thus, specific countries can sometimes lose their original trade ratio due to trade diversion; that is, an epidemic disease could lead to unexpected industry restructuring. To examine this, based on data on 110 WHO members from 1996 to 2018, we use a fixed-effect panel model supported by the Hausman Test to empirically identify whether epidemic diseases can cause trade shocks and trade diversion. We find: First, epidemic disease can lead to negative shocks to a country's trade growth and its ratio of worldwide trade. Second, with a longer epidemic, the probability of the trade diversion effect increases. Our results hold even after considering country heterogeneity. This presents a considerable concern about the shock of COVID-19 lasting further. Many countries are not just facing the problem of temporary trade shocks, but also the challenge of trade diversions. In particular, the probability of trade diversions is increasing rapidly, especially for late-developed countries due to their lack of epidemic containment and vaccine-producing capabilities. Even middle and high income countries cannot ignore global industry chain restructuring. Forward-looking policies should be implemented in advance; it may be too late when long-term trade damage is shown. [ABSTRACT FROM AUTHOR]

Adoptive cell immunotherapy (ACT) is an innovative promising treatment for tumors. ACT is characterized by the infusion of active anti-tumor immune cells (specific and non-specific) into patients to kill tumor cells either directly or indirectly by stimulating the body's immune system. The patient's (autologous) or a donor's (allogeneic) immune cells are used to improve immune function. Chimeric antigen receptor (CAR) T cells (CAR-T) is a type of ACT that has gained attention. T cells from the peripheral blood are genetically engineered to express CARs that rapidly proliferate and specifically recognize target antigens to exert its anti-tumor effects. Clinical application of CAR-T therapy for hematological tumors has shown good results, but adverse reactions and recurrence limit its applicability. Tumor infiltrating lymphocyte (TIL) therapy is effective for solid tumors. TIL therapy exhibits T cell receptor (TCR) clonality, superior tumor homing ability, and low targeted toxicity, but its successful application is limited to a number of tumors. Regardless, TIL and CAR-T therapies are effective for treating cancer. Additionally, CAR-natural killer (NK), CAR-macrophages (M), and TCR-T therapies are currently being researched. In this review, we highlight the current developments and limitations of several types of ACT. [ABSTRACT FROM AUTHOR]